diff --git a/.circleci/config.yml b/.circleci/config.yml index 4fad4111d..854bb40f7 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -47,8 +47,8 @@ jobs: pip install opentelemetry-api==1.25.0 pip install opentelemetry-sdk==1.25.0 pip install opentelemetry-exporter-otlp==1.25.0 - pip install openai - pip install prisma + pip install openai==1.40.0 + pip install prisma==0.11.0 pip install "detect_secrets==1.5.0" pip install "httpx==0.24.1" pip install fastapi @@ -125,6 +125,7 @@ jobs: pip install tiktoken pip install aiohttp pip install click + pip install "boto3==1.34.34" pip install jinja2 pip install tokenizers pip install openai @@ -165,7 +166,6 @@ jobs: pip install "pytest==7.3.1" pip install "pytest-asyncio==0.21.1" pip install aiohttp - pip install openai python -m pip install --upgrade pip python -m pip install -r .circleci/requirements.txt pip install "pytest==7.3.1" @@ -190,6 +190,7 @@ jobs: pip install "aiodynamo==23.10.1" pip install "asyncio==3.4.3" pip install "PyGithub==1.59.1" + pip install "openai==1.40.0" # Run pytest and generate JUnit XML report - run: name: Build Docker image @@ -208,6 +209,9 @@ jobs: -e AZURE_EUROPE_API_KEY=$AZURE_EUROPE_API_KEY \ -e MISTRAL_API_KEY=$MISTRAL_API_KEY \ -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ + -e GROQ_API_KEY=$GROQ_API_KEY \ + -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \ + -e COHERE_API_KEY=$COHERE_API_KEY \ -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \ -e AWS_REGION_NAME=$AWS_REGION_NAME \ -e AUTO_INFER_REGION=True \ @@ -280,10 +284,11 @@ jobs: pip install aiohttp pip install openai python -m pip install --upgrade pip - python -m pip install -r .circleci/requirements.txt + pip install "pydantic==2.7.1" pip install "pytest==7.3.1" pip install "pytest-mock==3.12.0" pip install "pytest-asyncio==0.21.1" + pip install "boto3==1.34.34" pip install mypy pip install pyarrow pip install numpydoc @@ -312,6 +317,10 @@ jobs: -e OPENAI_API_KEY=$OPENAI_API_KEY \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ -e OTEL_EXPORTER="in_memory" \ + -e APORIA_API_BASE_2=$APORIA_API_BASE_2 \ + -e APORIA_API_KEY_2=$APORIA_API_KEY_2 \ + -e APORIA_API_BASE_1=$APORIA_API_BASE_1 \ + -e APORIA_API_KEY_1=$APORIA_API_KEY_1 \ --name my-app \ -v $(pwd)/litellm/proxy/example_config_yaml/otel_test_config.yaml:/app/config.yaml \ my-app:latest \ @@ -404,7 +413,7 @@ jobs: circleci step halt fi - run: - name: Trigger Github Action for new Docker Container + name: Trigger Github Action for new Docker Container + Trigger Stable Release Testing command: | echo "Install TOML package." python3 -m pip install toml @@ -415,7 +424,8 @@ jobs: -H "Authorization: Bearer $GITHUB_TOKEN" \ "https://api.github.com/repos/BerriAI/litellm/actions/workflows/ghcr_deploy.yml/dispatches" \ -d "{\"ref\":\"main\", \"inputs\":{\"tag\":\"v${VERSION}\", \"commit_hash\":\"$CIRCLE_SHA1\"}}" - + echo "triggering stable release server for version ${VERSION} and commit ${CIRCLE_SHA1}" + curl -X POST "https://proxyloadtester-production.up.railway.app/start/load/test?version=${VERSION}&commit_hash=${CIRCLE_SHA1}" workflows: version: 2 build_and_test: diff --git a/.circleci/requirements.txt b/.circleci/requirements.txt index c4225a9aa..93f234ad5 100644 --- a/.circleci/requirements.txt +++ b/.circleci/requirements.txt @@ -1,11 +1,11 @@ # used by CI/CD testing -openai +openai==1.34.0 python-dotenv tiktoken importlib_metadata cohere redis anthropic -orjson +orjson==3.9.15 pydantic==2.7.1 google-cloud-aiplatform==1.43.0 diff --git a/.github/workflows/ghcr_deploy.yml b/.github/workflows/ghcr_deploy.yml index 51e24f856..8c8060a92 100644 --- a/.github/workflows/ghcr_deploy.yml +++ b/.github/workflows/ghcr_deploy.yml @@ -21,6 +21,14 @@ env: # There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu. jobs: + # print commit hash, tag, and release type + print: + runs-on: ubuntu-latest + steps: + - run: | + echo "Commit hash: ${{ github.event.inputs.commit_hash }}" + echo "Tag: ${{ github.event.inputs.tag }}" + echo "Release type: ${{ github.event.inputs.release_type }}" docker-hub-deploy: if: github.repository == 'BerriAI/litellm' runs-on: ubuntu-latest @@ -186,6 +194,8 @@ jobs: platforms: local,linux/amd64,linux/arm64,linux/arm64/v8 build-and-push-helm-chart: + if: github.event.inputs.release_type != 'dev' + needs: [docker-hub-deploy, build-and-push-image, build-and-push-image-database] runs-on: ubuntu-latest steps: - name: Checkout repository @@ -203,9 +213,17 @@ jobs: - name: lowercase github.repository_owner run: | echo "REPO_OWNER=`echo ${{github.repository_owner}} | tr '[:upper:]' '[:lower:]'`" >>${GITHUB_ENV} + - name: Get LiteLLM Latest Tag id: current_app_tag - uses: WyriHaximus/github-action-get-previous-tag@v1.3.0 + shell: bash + run: | + LATEST_TAG=$(git describe --tags --exclude "*dev*" --abbrev=0) + if [ -z "${LATEST_TAG}" ]; then + echo "latest_tag=latest" | tee -a $GITHUB_OUTPUT + else + echo "latest_tag=${LATEST_TAG}" | tee -a $GITHUB_OUTPUT + fi - name: Get last published chart version id: current_version @@ -233,7 +251,7 @@ jobs: name: ${{ env.CHART_NAME }} repository: ${{ env.REPO_OWNER }} tag: ${{ github.event.inputs.chartVersion || steps.bump_version.outputs.next-version || '0.1.0' }} - app_version: ${{ steps.current_app_tag.outputs.tag || 'latest' }} + app_version: ${{ steps.current_app_tag.outputs.latest_tag }} path: deploy/charts/${{ env.CHART_NAME }} registry: ${{ env.REGISTRY }} registry_username: ${{ github.actor }} diff --git a/.gitignore b/.gitignore index 8a9095b84..67aa57bb3 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,7 @@ .venv .env +.newenv +newenv/* litellm/proxy/myenv/* litellm_uuid.txt __pycache__/ diff --git a/Dockerfile b/Dockerfile index c8e9956b2..bd840eaf5 100644 --- a/Dockerfile +++ b/Dockerfile @@ -62,6 +62,11 @@ COPY --from=builder /wheels/ /wheels/ RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ && rm -f *.whl && rm -rf /wheels # Generate prisma client +ENV PRISMA_BINARY_CACHE_DIR=/app/prisma +RUN mkdir -p /.cache +RUN chmod -R 777 /.cache +RUN pip install nodejs-bin +RUN pip install prisma RUN prisma generate RUN chmod +x entrypoint.sh diff --git a/Dockerfile.custom_ui b/Dockerfile.custom_ui new file mode 100644 index 000000000..1bd28f650 --- /dev/null +++ b/Dockerfile.custom_ui @@ -0,0 +1,41 @@ +# Use the provided base image +FROM ghcr.io/berriai/litellm:litellm_fwd_server_root_path-dev + +# Set the working directory to /app +WORKDIR /app + +# Install Node.js and npm (adjust version as needed) +RUN apt-get update && apt-get install -y nodejs npm + +# Copy the UI source into the container +COPY ./ui/litellm-dashboard /app/ui/litellm-dashboard + +# Set an environment variable for UI_BASE_PATH +# This can be overridden at build time +# set UI_BASE_PATH to "/ui" +ENV UI_BASE_PATH="/prod/ui" + +# Build the UI with the specified UI_BASE_PATH +WORKDIR /app/ui/litellm-dashboard +RUN npm install +RUN UI_BASE_PATH=$UI_BASE_PATH npm run build + +# Create the destination directory +RUN mkdir -p /app/litellm/proxy/_experimental/out + +# Move the built files to the appropriate location +# Assuming the build output is in ./out directory +RUN rm -rf /app/litellm/proxy/_experimental/out/* && \ + mv ./out/* /app/litellm/proxy/_experimental/out/ + +# Switch back to the main app directory +WORKDIR /app + +# Make sure your entrypoint.sh is executable +RUN chmod +x entrypoint.sh + +# Expose the necessary port +EXPOSE 4000/tcp + +# Override the CMD instruction with your desired command and arguments +CMD ["--port", "4000", "--config", "config.yaml", "--detailed_debug"] \ No newline at end of file diff --git a/Dockerfile.database b/Dockerfile.database index 22084bab8..c995939e5 100644 --- a/Dockerfile.database +++ b/Dockerfile.database @@ -62,6 +62,11 @@ RUN pip install PyJWT --no-cache-dir RUN chmod +x build_admin_ui.sh && ./build_admin_ui.sh # Generate prisma client +ENV PRISMA_BINARY_CACHE_DIR=/app/prisma +RUN mkdir -p /.cache +RUN chmod -R 777 /.cache +RUN pip install nodejs-bin +RUN pip install prisma RUN prisma generate RUN chmod +x entrypoint.sh diff --git a/README.md b/README.md index 7df894ea1..e13732000 100644 --- a/README.md +++ b/README.md @@ -8,10 +8,10 @@ Deploy on Railway

-

Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, etc.] +

Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]

-

OpenAI Proxy Server | Hosted Proxy (Preview) | Enterprise Tier

+

LiteLLM Proxy Server (LLM Gateway) | Hosted Proxy (Preview) | Enterprise Tier

PyPI Version @@ -35,9 +35,9 @@ LiteLLM manages: - Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints - [Consistent output](https://docs.litellm.ai/docs/completion/output), text responses will always be available at `['choices'][0]['message']['content']` - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing) -- Set Budgets & Rate limits per project, api key, model [OpenAI Proxy Server](https://docs.litellm.ai/docs/simple_proxy) +- Set Budgets & Rate limits per project, api key, model [LiteLLM Proxy Server (LLM Gateway)](https://docs.litellm.ai/docs/simple_proxy) -[**Jump to OpenAI Proxy Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#openai-proxy---docs)
+[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#openai-proxy---docs)
[**Jump to Supported LLM Providers**](https://github.com/BerriAI/litellm?tab=readme-ov-file#supported-providers-docs) 🚨 **Stable Release:** Use docker images with the `-stable` tag. These have undergone 12 hour load tests, before being published. @@ -120,6 +120,7 @@ from litellm import completion ## set env variables for logging tools os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key" +os.environ["HELICONE_API_KEY"] = "your-helicone-auth-key" os.environ["LANGFUSE_PUBLIC_KEY"] = "" os.environ["LANGFUSE_SECRET_KEY"] = "" os.environ["ATHINA_API_KEY"] = "your-athina-api-key" @@ -127,13 +128,13 @@ os.environ["ATHINA_API_KEY"] = "your-athina-api-key" os.environ["OPENAI_API_KEY"] # set callbacks -litellm.success_callback = ["lunary", "langfuse", "athina"] # log input/output to lunary, langfuse, supabase, athina etc +litellm.success_callback = ["lunary", "langfuse", "athina", "helicone"] # log input/output to lunary, langfuse, supabase, athina, helicone etc #openai call response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) ``` -# OpenAI Proxy - ([Docs](https://docs.litellm.ai/docs/simple_proxy)) +# LiteLLM Proxy Server (LLM Gateway) - ([Docs](https://docs.litellm.ai/docs/simple_proxy)) Track spend + Load Balance across multiple projects @@ -165,6 +166,10 @@ $ litellm --model huggingface/bigcode/starcoder ### Step 2: Make ChatCompletions Request to Proxy + +> [!IMPORTANT] +> 💡 [Use LiteLLM Proxy with Langchain (Python, JS), OpenAI SDK (Python, JS) Anthropic SDK, Mistral SDK, LlamaIndex, Instructor, Curl](https://docs.litellm.ai/docs/proxy/user_keys) + ```python import openai # openai v1.0.0+ client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000") # set proxy to base_url @@ -190,8 +195,15 @@ git clone https://github.com/BerriAI/litellm # Go to folder cd litellm -# Add the master key +# Add the master key - you can change this after setup echo 'LITELLM_MASTER_KEY="sk-1234"' > .env + +# Add the litellm salt key - you cannot change this after adding a model +# It is used to encrypt / decrypt your LLM API Key credentials +# We recommned - https://1password.com/password-generator/ +# password generator to get a random hash for litellm salt key +echo 'LITELLM_SALT_KEY="sk-1234"' > .env + source .env # Start diff --git a/cookbook/Migrating_to_LiteLLM_Proxy_from_OpenAI_Azure_OpenAI.ipynb b/cookbook/Migrating_to_LiteLLM_Proxy_from_OpenAI_Azure_OpenAI.ipynb new file mode 100644 index 000000000..39677ed2a --- /dev/null +++ b/cookbook/Migrating_to_LiteLLM_Proxy_from_OpenAI_Azure_OpenAI.ipynb @@ -0,0 +1,565 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Migrating to LiteLLM Proxy from OpenAI/Azure OpenAI\n", + "\n", + "Covers:\n", + "\n", + "* /chat/completion\n", + "* /embedding\n", + "\n", + "\n", + "These are **selected examples**. LiteLLM Proxy is **OpenAI-Compatible**, it works with any project that calls OpenAI. Just change the `base_url`, `api_key` and `model`.\n", + "\n", + "For more examples, [go here](https://docs.litellm.ai/docs/proxy/user_keys)\n", + "\n", + "To pass provider-specific args, [go here](https://docs.litellm.ai/docs/completion/provider_specific_params#proxy-usage)\n", + "\n", + "To drop unsupported params (E.g. frequency_penalty for bedrock with librechat), [go here](https://docs.litellm.ai/docs/completion/drop_params#openai-proxy-usage)\n" + ], + "metadata": { + "id": "kccfk0mHZ4Ad" + } + }, + { + "cell_type": "markdown", + "source": [ + "## /chat/completion\n", + "\n" + ], + "metadata": { + "id": "nmSClzCPaGH6" + } + }, + { + "cell_type": "markdown", + "source": [ + "### OpenAI Python SDK" + ], + "metadata": { + "id": "_vqcjwOVaKpO" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "x1e_Ok3KZzeP" + }, + "outputs": [], + "source": [ + "import openai\n", + "client = openai.OpenAI(\n", + " api_key=\"anything\",\n", + " base_url=\"http://0.0.0.0:4000\"\n", + ")\n", + "\n", + "# request sent to model set on litellm proxy, `litellm --model`\n", + "response = client.chat.completions.create(\n", + " model=\"gpt-3.5-turbo\",\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"this is a test request, write a short poem\"\n", + " }\n", + " ],\n", + " extra_body={ # pass in any provider-specific param, if not supported by openai, https://docs.litellm.ai/docs/completion/input#provider-specific-params\n", + " \"metadata\": { # 👈 use for logging additional params (e.g. to langfuse)\n", + " \"generation_name\": \"ishaan-generation-openai-client\",\n", + " \"generation_id\": \"openai-client-gen-id22\",\n", + " \"trace_id\": \"openai-client-trace-id22\",\n", + " \"trace_user_id\": \"openai-client-user-id2\"\n", + " }\n", + " }\n", + ")\n", + "\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Function Calling" + ], + "metadata": { + "id": "AqkyKk9Scxgj" + } + }, + { + "cell_type": "code", + "source": [ + "from openai import OpenAI\n", + "client = OpenAI(\n", + " api_key=\"sk-1234\", # [OPTIONAL] set if you set one on proxy, else set \"\"\n", + " base_url=\"http://0.0.0.0:4000\",\n", + ")\n", + "\n", + "tools = [\n", + " {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"get_current_weather\",\n", + " \"description\": \"Get the current weather in a given location\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"location\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The city and state, e.g. San Francisco, CA\",\n", + " },\n", + " \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n", + " },\n", + " \"required\": [\"location\"],\n", + " },\n", + " }\n", + " }\n", + "]\n", + "messages = [{\"role\": \"user\", \"content\": \"What's the weather like in Boston today?\"}]\n", + "completion = client.chat.completions.create(\n", + " model=\"gpt-4o\", # use 'model_name' from config.yaml\n", + " messages=messages,\n", + " tools=tools,\n", + " tool_choice=\"auto\"\n", + ")\n", + "\n", + "print(completion)\n" + ], + "metadata": { + "id": "wDg10VqLczE1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Azure OpenAI Python SDK" + ], + "metadata": { + "id": "YYoxLloSaNWW" + } + }, + { + "cell_type": "code", + "source": [ + "import openai\n", + "client = openai.AzureOpenAI(\n", + " api_key=\"anything\",\n", + " base_url=\"http://0.0.0.0:4000\"\n", + ")\n", + "\n", + "# request sent to model set on litellm proxy, `litellm --model`\n", + "response = client.chat.completions.create(\n", + " model=\"gpt-3.5-turbo\",\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"this is a test request, write a short poem\"\n", + " }\n", + " ],\n", + " extra_body={ # pass in any provider-specific param, if not supported by openai, https://docs.litellm.ai/docs/completion/input#provider-specific-params\n", + " \"metadata\": { # 👈 use for logging additional params (e.g. to langfuse)\n", + " \"generation_name\": \"ishaan-generation-openai-client\",\n", + " \"generation_id\": \"openai-client-gen-id22\",\n", + " \"trace_id\": \"openai-client-trace-id22\",\n", + " \"trace_user_id\": \"openai-client-user-id2\"\n", + " }\n", + " }\n", + ")\n", + "\n", + "print(response)" + ], + "metadata": { + "id": "yA1XcgowaSRy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Langchain Python" + ], + "metadata": { + "id": "yl9qhDvnaTpL" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.prompts.chat import (\n", + " ChatPromptTemplate,\n", + " HumanMessagePromptTemplate,\n", + " SystemMessagePromptTemplate,\n", + ")\n", + "from langchain.schema import HumanMessage, SystemMessage\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"anything\"\n", + "\n", + "chat = ChatOpenAI(\n", + " openai_api_base=\"http://0.0.0.0:4000\",\n", + " model = \"gpt-3.5-turbo\",\n", + " temperature=0.1,\n", + " extra_body={\n", + " \"metadata\": {\n", + " \"generation_name\": \"ishaan-generation-langchain-client\",\n", + " \"generation_id\": \"langchain-client-gen-id22\",\n", + " \"trace_id\": \"langchain-client-trace-id22\",\n", + " \"trace_user_id\": \"langchain-client-user-id2\"\n", + " }\n", + " }\n", + ")\n", + "\n", + "messages = [\n", + " SystemMessage(\n", + " content=\"You are a helpful assistant that im using to make a test request to.\"\n", + " ),\n", + " HumanMessage(\n", + " content=\"test from litellm. tell me why it's amazing in 1 sentence\"\n", + " ),\n", + "]\n", + "response = chat(messages)\n", + "\n", + "print(response)" + ], + "metadata": { + "id": "5MUZgSquaW5t" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Curl" + ], + "metadata": { + "id": "B9eMgnULbRaz" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "```\n", + "curl -X POST 'http://0.0.0.0:4000/chat/completions' \\\n", + " -H 'Content-Type: application/json' \\\n", + " -d '{\n", + " \"model\": \"gpt-3.5-turbo\",\n", + " \"messages\": [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"what llm are you\"\n", + " }\n", + " ],\n", + " \"metadata\": {\n", + " \"generation_name\": \"ishaan-test-generation\",\n", + " \"generation_id\": \"gen-id22\",\n", + " \"trace_id\": \"trace-id22\",\n", + " \"trace_user_id\": \"user-id2\"\n", + " }\n", + "}'\n", + "```\n", + "\n" + ], + "metadata": { + "id": "VWCCk5PFcmhS" + } + }, + { + "cell_type": "markdown", + "source": [ + "### LlamaIndex" + ], + "metadata": { + "id": "drBAm2e1b6xe" + } + }, + { + "cell_type": "code", + "source": [ + "import os, dotenv\n", + "\n", + "from llama_index.llms import AzureOpenAI\n", + "from llama_index.embeddings import AzureOpenAIEmbedding\n", + "from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext\n", + "\n", + "llm = AzureOpenAI(\n", + " engine=\"azure-gpt-3.5\", # model_name on litellm proxy\n", + " temperature=0.0,\n", + " azure_endpoint=\"http://0.0.0.0:4000\", # litellm proxy endpoint\n", + " api_key=\"sk-1234\", # litellm proxy API Key\n", + " api_version=\"2023-07-01-preview\",\n", + ")\n", + "\n", + "embed_model = AzureOpenAIEmbedding(\n", + " deployment_name=\"azure-embedding-model\",\n", + " azure_endpoint=\"http://0.0.0.0:4000\",\n", + " api_key=\"sk-1234\",\n", + " api_version=\"2023-07-01-preview\",\n", + ")\n", + "\n", + "\n", + "documents = SimpleDirectoryReader(\"llama_index_data\").load_data()\n", + "service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)\n", + "index = VectorStoreIndex.from_documents(documents, service_context=service_context)\n", + "\n", + "query_engine = index.as_query_engine()\n", + "response = query_engine.query(\"What did the author do growing up?\")\n", + "print(response)\n" + ], + "metadata": { + "id": "d0bZcv8fb9mL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Langchain JS" + ], + "metadata": { + "id": "xypvNdHnb-Yy" + } + }, + { + "cell_type": "code", + "source": [ + "import { ChatOpenAI } from \"@langchain/openai\";\n", + "\n", + "\n", + "const model = new ChatOpenAI({\n", + " modelName: \"gpt-4\",\n", + " openAIApiKey: \"sk-1234\",\n", + " modelKwargs: {\"metadata\": \"hello world\"} // 👈 PASS Additional params here\n", + "}, {\n", + " basePath: \"http://0.0.0.0:4000\",\n", + "});\n", + "\n", + "const message = await model.invoke(\"Hi there!\");\n", + "\n", + "console.log(message);\n" + ], + "metadata": { + "id": "R55mK2vCcBN2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### OpenAI JS" + ], + "metadata": { + "id": "nC4bLifCcCiW" + } + }, + { + "cell_type": "code", + "source": [ + "const { OpenAI } = require('openai');\n", + "\n", + "const openai = new OpenAI({\n", + " apiKey: \"sk-1234\", // This is the default and can be omitted\n", + " baseURL: \"http://0.0.0.0:4000\"\n", + "});\n", + "\n", + "async function main() {\n", + " const chatCompletion = await openai.chat.completions.create({\n", + " messages: [{ role: 'user', content: 'Say this is a test' }],\n", + " model: 'gpt-3.5-turbo',\n", + " }, {\"metadata\": {\n", + " \"generation_name\": \"ishaan-generation-openaijs-client\",\n", + " \"generation_id\": \"openaijs-client-gen-id22\",\n", + " \"trace_id\": \"openaijs-client-trace-id22\",\n", + " \"trace_user_id\": \"openaijs-client-user-id2\"\n", + " }});\n", + "}\n", + "\n", + "main();\n" + ], + "metadata": { + "id": "MICH8kIMcFpg" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Anthropic SDK" + ], + "metadata": { + "id": "D1Q07pEAcGTb" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "\n", + "from anthropic import Anthropic\n", + "\n", + "client = Anthropic(\n", + " base_url=\"http://localhost:4000\", # proxy endpoint\n", + " api_key=\"sk-s4xN1IiLTCytwtZFJaYQrA\", # litellm proxy virtual key\n", + ")\n", + "\n", + "message = client.messages.create(\n", + " max_tokens=1024,\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"Hello, Claude\",\n", + " }\n", + " ],\n", + " model=\"claude-3-opus-20240229\",\n", + ")\n", + "print(message.content)" + ], + "metadata": { + "id": "qBjFcAvgcI3t" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## /embeddings" + ], + "metadata": { + "id": "dFAR4AJGcONI" + } + }, + { + "cell_type": "markdown", + "source": [ + "### OpenAI Python SDK" + ], + "metadata": { + "id": "lgNoM281cRzR" + } + }, + { + "cell_type": "code", + "source": [ + "import openai\n", + "from openai import OpenAI\n", + "\n", + "# set base_url to your proxy server\n", + "# set api_key to send to proxy server\n", + "client = OpenAI(api_key=\"\", base_url=\"http://0.0.0.0:4000\")\n", + "\n", + "response = client.embeddings.create(\n", + " input=[\"hello from litellm\"],\n", + " model=\"text-embedding-ada-002\"\n", + ")\n", + "\n", + "print(response)\n" + ], + "metadata": { + "id": "NY3DJhPfcQhA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Langchain Embeddings" + ], + "metadata": { + "id": "hmbg-DW6cUZs" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.embeddings import OpenAIEmbeddings\n", + "\n", + "embeddings = OpenAIEmbeddings(model=\"sagemaker-embeddings\", openai_api_base=\"http://0.0.0.0:4000\", openai_api_key=\"temp-key\")\n", + "\n", + "\n", + "text = \"This is a test document.\"\n", + "\n", + "query_result = embeddings.embed_query(text)\n", + "\n", + "print(f\"SAGEMAKER EMBEDDINGS\")\n", + "print(query_result[:5])\n", + "\n", + "embeddings = OpenAIEmbeddings(model=\"bedrock-embeddings\", openai_api_base=\"http://0.0.0.0:4000\", openai_api_key=\"temp-key\")\n", + "\n", + "text = \"This is a test document.\"\n", + "\n", + "query_result = embeddings.embed_query(text)\n", + "\n", + "print(f\"BEDROCK EMBEDDINGS\")\n", + "print(query_result[:5])\n", + "\n", + "embeddings = OpenAIEmbeddings(model=\"bedrock-titan-embeddings\", openai_api_base=\"http://0.0.0.0:4000\", openai_api_key=\"temp-key\")\n", + "\n", + "text = \"This is a test document.\"\n", + "\n", + "query_result = embeddings.embed_query(text)\n", + "\n", + "print(f\"TITAN EMBEDDINGS\")\n", + "print(query_result[:5])" + ], + "metadata": { + "id": "lX2S8Nl1cWVP" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Curl Request" + ], + "metadata": { + "id": "oqGbWBCQcYfd" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "```curl\n", + "curl -X POST 'http://0.0.0.0:4000/embeddings' \\\n", + " -H 'Content-Type: application/json' \\\n", + " -d ' {\n", + " \"model\": \"text-embedding-ada-002\",\n", + " \"input\": [\"write a litellm poem\"]\n", + " }'\n", + "```\n", + "\n" + ], + "metadata": { + "id": "7rkIMV9LcdwQ" + } + } + ] +} \ No newline at end of file diff --git a/cookbook/litellm_router/error_log.txt b/cookbook/litellm_router/error_log.txt index 6853ef465..983b47cbb 100644 --- a/cookbook/litellm_router/error_log.txt +++ b/cookbook/litellm_router/error_log.txt @@ -1,10 +1,10 @@ -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -21,13 +21,13 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -49,7 +49,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -61,7 +61,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -70,7 +70,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -79,7 +79,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -109,7 +109,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -128,7 +128,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -148,7 +148,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -162,7 +162,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -174,7 +174,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -184,7 +184,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -193,19 +193,19 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -214,7 +214,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -234,7 +234,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -244,7 +244,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -253,7 +253,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -267,31 +267,31 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -305,7 +305,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -330,7 +330,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -339,7 +339,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -360,7 +360,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -369,7 +369,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -378,7 +378,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -388,7 +388,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -409,7 +409,7 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -422,13 +422,13 @@ Exception: Expecting value: line 1 column 1 (char 0) Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -438,7 +438,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: Expecting value: line 1 column 1 (char 0) -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -462,7 +462,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -482,7 +482,7 @@ Exception: 'Response' object has no attribute 'get' Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -492,7 +492,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -516,7 +516,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -529,7 +529,7 @@ Exception: 'Response' object has no attribute 'get' Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -546,13 +546,13 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -580,13 +580,13 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -624,7 +624,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -638,13 +638,13 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -660,7 +660,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -681,7 +681,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -691,31 +691,31 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -771,7 +771,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -780,7 +780,7 @@ Exception: 'Response' object has no attribute 'get' Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -800,7 +800,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -820,7 +820,7 @@ Exception: 'Response' object has no attribute 'get' Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -830,7 +830,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -840,7 +840,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -850,7 +850,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -862,13 +862,13 @@ Exception: 'Response' object has no attribute 'get' Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -877,7 +877,7 @@ Exception: 'Response' object has no attribute 'get' Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -898,7 +898,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -919,7 +919,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -936,19 +936,19 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -961,25 +961,25 @@ Exception: 'Response' object has no attribute 'get' Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. Call all LLM APIs using the Ope Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -993,7 +993,7 @@ Question: Given this context, what is litellm? LiteLLM about: About Call all LLM APIs using the OpenAI format. Exception: 'Response' object has no attribute 'get' -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 diff --git a/cookbook/litellm_router/request_log.txt b/cookbook/litellm_router/request_log.txt index 0aed74904..821d87ab5 100644 --- a/cookbook/litellm_router/request_log.txt +++ b/cookbook/litellm_router/request_log.txt @@ -20,7 +20,7 @@ Call all LLM APIs using the OpenAI format. Response ID: 52dbbd49-eedb-4c11-8382-3ca7deb1af35 Url: /queue/response/52dbbd49-eedb-4c11-8382-3ca7deb1af35 Time: 3.50 seconds -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 @@ -35,7 +35,7 @@ Question: Does litellm support ooobagooba llms? how can i call oobagooba llms. C Response ID: ae1e2b71-d711-456d-8df0-13ce0709eb04 Url: /queue/response/ae1e2b71-d711-456d-8df0-13ce0709eb04 Time: 5.60 seconds -Question: What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +Question: What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 10 diff --git a/cookbook/litellm_router/test_questions/question3.txt b/cookbook/litellm_router/test_questions/question3.txt index a12278750..d6006f9c7 100644 --- a/cookbook/litellm_router/test_questions/question3.txt +++ b/cookbook/litellm_router/test_questions/question3.txt @@ -1,4 +1,4 @@ -What endpoints does the litellm proxy have 💥 OpenAI Proxy Server +What endpoints does the litellm proxy have 💥 LiteLLM Proxy Server LiteLLM Server manages: Calling 100+ LLMs Huggingface/Bedrock/TogetherAI/etc. in the OpenAI ChatCompletions & Completions format diff --git a/deploy/charts/litellm-helm/Chart.yaml b/deploy/charts/litellm-helm/Chart.yaml index fcd2e83cc..af578ee17 100644 --- a/deploy/charts/litellm-helm/Chart.yaml +++ b/deploy/charts/litellm-helm/Chart.yaml @@ -18,13 +18,13 @@ type: application # This is the chart version. This version number should be incremented each time you make changes # to the chart and its templates, including the app version. # Versions are expected to follow Semantic Versioning (https://semver.org/) -version: 0.2.1 +version: 0.2.3 # This is the version number of the application being deployed. This version number should be # incremented each time you make changes to the application. Versions are not expected to # follow Semantic Versioning. They should reflect the version the application is using. # It is recommended to use it with quotes. -appVersion: v1.41.8 +appVersion: v1.43.18 dependencies: - name: "postgresql" diff --git a/deploy/charts/litellm-helm/README.md b/deploy/charts/litellm-helm/README.md index e005280b8..d355558e3 100644 --- a/deploy/charts/litellm-helm/README.md +++ b/deploy/charts/litellm-helm/README.md @@ -1,5 +1,9 @@ # Helm Chart for LiteLLM +> [!IMPORTANT] +> This is community maintained, Please make an issue if you run into a bug +> We recommend using [Docker or Kubernetes for production deployments](https://docs.litellm.ai/docs/proxy/prod) + ## Prerequisites - Kubernetes 1.21+ diff --git a/docker-compose.yml b/docker-compose.yml index ca98ec784..6991bf7eb 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -9,13 +9,11 @@ services: ######################################### ## Uncomment these lines to start proxy with a config.yaml file ## # volumes: - # - ./proxy_server_config.yaml:/app/config.yaml - # command: [ "--config", "./config.yaml", "--port", "4000"] ############################################### ports: - "4000:4000" # Map the container port to the host, change the host port if necessary environment: - DATABASE_URL: "postgresql://postgres:example@db:5432/postgres" + DATABASE_URL: "postgresql://llmproxy:dbpassword9090@db:5432/litellm" STORE_MODEL_IN_DB: "True" # allows adding models to proxy via UI env_file: - .env # Load local .env file @@ -25,11 +23,31 @@ services: image: postgres restart: always environment: - POSTGRES_PASSWORD: example + POSTGRES_DB: litellm + POSTGRES_USER: llmproxy + POSTGRES_PASSWORD: dbpassword9090 healthcheck: - test: ["CMD-SHELL", "pg_isready"] + test: ["CMD-SHELL", "pg_isready -d litellm -U llmproxy"] interval: 1s timeout: 5s retries: 10 + + prometheus: + image: prom/prometheus + volumes: + - prometheus_data:/prometheus + - ./prometheus.yml:/etc/prometheus/prometheus.yml + ports: + - "9090:9090" + command: + - '--config.file=/etc/prometheus/prometheus.yml' + - '--storage.tsdb.path=/prometheus' + - '--storage.tsdb.retention.time=15d' + restart: always -# ...rest of your docker-compose config if any \ No newline at end of file +volumes: + prometheus_data: + driver: local + + +# ...rest of your docker-compose config if any diff --git a/docs/my-website/docs/batches.md b/docs/my-website/docs/batches.md index 51f3bb5ca..101d1e505 100644 --- a/docs/my-website/docs/batches.md +++ b/docs/my-website/docs/batches.md @@ -1,23 +1,73 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# Batches API +# [BETA] Batches API Covers Batches, Files ## Quick Start -Call an existing Assistant. - - Create File for Batch Completion - Create Batch Request +- List Batches + - Retrieve the Specific Batch and File Content + + +```bash +$ export OPENAI_API_KEY="sk-..." + +$ litellm + +# RUNNING on http://0.0.0.0:4000 +``` + +**Create File for Batch Completion** + +```shell +curl http://localhost:4000/v1/files \ + -H "Authorization: Bearer sk-1234" \ + -F purpose="batch" \ + -F file="@mydata.jsonl" +``` + +**Create Batch Request** + +```bash +curl http://localhost:4000/v1/batches \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "input_file_id": "file-abc123", + "endpoint": "/v1/chat/completions", + "completion_window": "24h" + }' +``` + +**Retrieve the Specific Batch** + +```bash +curl http://localhost:4000/v1/batches/batch_abc123 \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ +``` + + +**List Batches** + +```bash +curl http://localhost:4000/v1/batches \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ +``` + + **Create File for Batch Completion** @@ -77,48 +127,15 @@ file_content = await litellm.afile_content( print("file content = ", file_content) ``` - - +**List Batches** -```bash -$ export OPENAI_API_KEY="sk-..." - -$ litellm - -# RUNNING on http://0.0.0.0:4000 -``` - -**Create File for Batch Completion** - -```shell -curl https://api.openai.com/v1/files \ - -H "Authorization: Bearer sk-1234" \ - -F purpose="batch" \ - -F file="@mydata.jsonl" -``` - -**Create Batch Request** - -```bash -curl http://localhost:4000/v1/batches \ - -H "Authorization: Bearer sk-1234" \ - -H "Content-Type: application/json" \ - -d '{ - "input_file_id": "file-abc123", - "endpoint": "/v1/chat/completions", - "completion_window": "24h" - }' -``` - -**Retrieve the Specific Batch** - -```bash -curl http://localhost:4000/v1/batches/batch_abc123 \ - -H "Authorization: Bearer sk-1234" \ - -H "Content-Type: application/json" \ +```python +list_batches_response = litellm.list_batches(custom_llm_provider="openai", limit=2) +print("list_batches_response=", list_batches_response) ``` + ## [👉 Proxy API Reference](https://litellm-api.up.railway.app/#/batch) diff --git a/docs/my-website/docs/budget_manager.md b/docs/my-website/docs/budget_manager.md index 1a2c7e7ee..6bea96ef9 100644 --- a/docs/my-website/docs/budget_manager.md +++ b/docs/my-website/docs/budget_manager.md @@ -7,14 +7,14 @@ Don't want to get crazy bills because either while you're calling LLM APIs **or* :::info -If you want a server to manage user keys, budgets, etc. use our [OpenAI Proxy Server](./proxy/virtual_keys.md) +If you want a server to manage user keys, budgets, etc. use our [LiteLLM Proxy Server](./proxy/virtual_keys.md) ::: LiteLLM exposes: * `litellm.max_budget`: a global variable you can use to set the max budget (in USD) across all your litellm calls. If this budget is exceeded, it will raise a BudgetExceededError * `BudgetManager`: A class to help set budgets per user. BudgetManager creates a dictionary to manage the user budgets, where the key is user and the object is their current cost + model-specific costs. -* `OpenAI Proxy Server`: A server to call 100+ LLMs with an openai-compatible endpoint. Manages user budgets, spend tracking, load balancing etc. +* `LiteLLM Proxy Server`: A server to call 100+ LLMs with an openai-compatible endpoint. Manages user budgets, spend tracking, load balancing etc. ## quick start diff --git a/docs/my-website/docs/completion/input.md b/docs/my-website/docs/completion/input.md index c5988940d..ab35abfa7 100644 --- a/docs/my-website/docs/completion/input.md +++ b/docs/my-website/docs/completion/input.md @@ -48,19 +48,20 @@ Use `litellm.get_supported_openai_params()` for an updated list of params for ea |Anyscale | ✅ | ✅ | ✅ | ✅ | ✅ | |Cohere| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | |Huggingface| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | -|Openrouter| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ | | | | | +|Openrouter| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ |✅ | | | | |AI21| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | |VertexAI| ✅ | ✅ | | ✅ | ✅ | | | | | | | | | ✅ | ✅ | | | -|Bedrock| ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | ✅ (for anthropic) | | +|Bedrock| ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | ✅ (model dependent) | | |Sagemaker| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | |TogetherAI| ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | ✅ | |AlephAlpha| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | |Palm| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | |NLP Cloud| ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | |Petals| ✅ | ✅ | | ✅ | ✅ | | | | | | -|Ollama| ✅ | ✅ | ✅ | ✅ | ✅ | | | ✅ | | | | | ✅ | | | +|Ollama| ✅ | ✅ | ✅ | ✅ | ✅ | | | ✅ | | | | | ✅ | | |✅| | | | | | | |Databricks| ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | | |ClarifAI| ✅ | ✅ | |✅ | ✅ | | | | | | | | | | | +|Github| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | ✅ |✅ (model dependent)|✅ (model dependent)| | | :::note By default, LiteLLM raises an exception if the openai param being passed in isn't supported. diff --git a/docs/my-website/docs/completion/json_mode.md b/docs/my-website/docs/completion/json_mode.md new file mode 100644 index 000000000..6c325cd8d --- /dev/null +++ b/docs/my-website/docs/completion/json_mode.md @@ -0,0 +1,321 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Structured Outputs (JSON Mode) + +## Quick Start + + + + +```python +from litellm import completion +import os + +os.environ["OPENAI_API_KEY"] = "" + +response = completion( + model="gpt-4o-mini", + response_format={ "type": "json_object" }, + messages=[ + {"role": "system", "content": "You are a helpful assistant designed to output JSON."}, + {"role": "user", "content": "Who won the world series in 2020?"} + ] +) +print(response.choices[0].message.content) +``` + + + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "gpt-4o-mini", + "response_format": { "type": "json_object" }, + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant designed to output JSON." + }, + { + "role": "user", + "content": "Who won the world series in 2020?" + } + ] + }' +``` + + + +## Check Model Support + +Call `litellm.get_supported_openai_params` to check if a model/provider supports `response_format`. + +```python +from litellm import get_supported_openai_params + +params = get_supported_openai_params(model="anthropic.claude-3", custom_llm_provider="bedrock") + +assert "response_format" in params +``` + +## Pass in 'json_schema' + +To use Structured Outputs, simply specify + +``` +response_format: { "type": "json_schema", "json_schema": … , "strict": true } +``` + +Works for: +- OpenAI models +- Azure OpenAI models +- Google AI Studio - Gemini models +- Vertex AI models (Gemini + Anthropic) + + + + +```python +import os +from litellm import completion + +# add to env var +os.environ["OPENAI_API_KEY"] = "" + +messages = [{"role": "user", "content": "List 5 important events in the XIX century"}] + +class CalendarEvent(BaseModel): + name: str + date: str + participants: list[str] + +class EventsList(BaseModel): + events: list[CalendarEvent] + +resp = completion( + model="gpt-4o-2024-08-06", + messages=messages, + response_format=EventsList +) + +print("Received={}".format(resp)) +``` + + + +1. Add openai model to config.yaml + +```yaml +model_list: + - model_name: "gpt-4o" + litellm_params: + model: "gpt-4o-2024-08-06" +``` + +2. Start proxy with config.yaml + +```bash +litellm --config /path/to/config.yaml +``` + +3. Call with OpenAI SDK / Curl! + +Just replace the 'base_url' in the openai sdk, to call the proxy with 'json_schema' for openai models + +**OpenAI SDK** +```python +from pydantic import BaseModel +from openai import OpenAI + +client = OpenAI( + api_key="anything", # 👈 PROXY KEY (can be anything, if master_key not set) + base_url="http://0.0.0.0:4000" # 👈 PROXY BASE URL +) + +class Step(BaseModel): + explanation: str + output: str + +class MathReasoning(BaseModel): + steps: list[Step] + final_answer: str + +completion = client.beta.chat.completions.parse( + model="gpt-4o", + messages=[ + {"role": "system", "content": "You are a helpful math tutor. Guide the user through the solution step by step."}, + {"role": "user", "content": "how can I solve 8x + 7 = -23"} + ], + response_format=MathReasoning, +) + +math_reasoning = completion.choices[0].message.parsed +``` + +**Curl** + +```bash +curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "gpt-4o", + "messages": [ + { + "role": "system", + "content": "You are a helpful math tutor. Guide the user through the solution step by step." + }, + { + "role": "user", + "content": "how can I solve 8x + 7 = -23" + } + ], + "response_format": { + "type": "json_schema", + "json_schema": { + "name": "math_reasoning", + "schema": { + "type": "object", + "properties": { + "steps": { + "type": "array", + "items": { + "type": "object", + "properties": { + "explanation": { "type": "string" }, + "output": { "type": "string" } + }, + "required": ["explanation", "output"], + "additionalProperties": false + } + }, + "final_answer": { "type": "string" } + }, + "required": ["steps", "final_answer"], + "additionalProperties": false + }, + "strict": true + } + } + }' +``` + + + + + +## Validate JSON Schema + + +Not all vertex models support passing the json_schema to them (e.g. `gemini-1.5-flash`). To solve this, LiteLLM supports client-side validation of the json schema. + +``` +litellm.enable_json_schema_validation=True +``` +If `litellm.enable_json_schema_validation=True` is set, LiteLLM will validate the json response using `jsonvalidator`. + +[**See Code**](https://github.com/BerriAI/litellm/blob/671d8ac496b6229970c7f2a3bdedd6cb84f0746b/litellm/litellm_core_utils/json_validation_rule.py#L4) + + + + + +```python +# !gcloud auth application-default login - run this to add vertex credentials to your env +import litellm, os +from litellm import completion +from pydantic import BaseModel + + +messages=[ + {"role": "system", "content": "Extract the event information."}, + {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."}, + ] + +litellm.enable_json_schema_validation = True +litellm.set_verbose = True # see the raw request made by litellm + +class CalendarEvent(BaseModel): + name: str + date: str + participants: list[str] + +resp = completion( + model="gemini/gemini-1.5-pro", + messages=messages, + response_format=CalendarEvent, +) + +print("Received={}".format(resp)) +``` + + + +1. Create config.yaml +```yaml +model_list: + - model_name: "gemini-1.5-flash" + litellm_params: + model: "gemini/gemini-1.5-flash" + api_key: os.environ/GEMINI_API_KEY + +litellm_settings: + enable_json_schema_validation: True +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_API_KEY" \ + -d '{ + "model": "gemini-1.5-flash", + "messages": [ + {"role": "system", "content": "Extract the event information."}, + {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."}, + ], + "response_format": { + "type": "json_object", + "response_schema": { + "type": "json_schema", + "json_schema": { + "name": "math_reasoning", + "schema": { + "type": "object", + "properties": { + "steps": { + "type": "array", + "items": { + "type": "object", + "properties": { + "explanation": { "type": "string" }, + "output": { "type": "string" } + }, + "required": ["explanation", "output"], + "additionalProperties": false + } + }, + "final_answer": { "type": "string" } + }, + "required": ["steps", "final_answer"], + "additionalProperties": false + }, + "strict": true + }, + } + }, + }' +``` + + + \ No newline at end of file diff --git a/docs/my-website/docs/completion/prefix.md b/docs/my-website/docs/completion/prefix.md new file mode 100644 index 000000000..e3619a2a0 --- /dev/null +++ b/docs/my-website/docs/completion/prefix.md @@ -0,0 +1,119 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Pre-fix Assistant Messages + +Supported by: +- Deepseek +- Mistral +- Anthropic + +```python +{ + "role": "assistant", + "content": "..", + ... + "prefix": true # 👈 KEY CHANGE +} +``` + +## Quick Start + + + + +```python +from litellm import completion +import os + +os.environ["DEEPSEEK_API_KEY"] = "" + +response = completion( + model="deepseek/deepseek-chat", + messages=[ + {"role": "user", "content": "Who won the world cup in 2022?"}, + {"role": "assistant", "content": "Argentina", "prefix": True} + ] +) +print(response.choices[0].message.content) +``` + + + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "deepseek/deepseek-chat", + "messages": [ + { + "role": "user", + "content": "Who won the world cup in 2022?" + }, + { + "role": "assistant", + "content": "Argentina", "prefix": true + } + ] +}' +``` + + + +**Expected Response** + +```bash +{ + "id": "3b66124d79a708e10c603496b363574c", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": " won the FIFA World Cup in 2022.", + "role": "assistant", + "tool_calls": null, + "function_call": null + } + } + ], + "created": 1723323084, + "model": "deepseek/deepseek-chat", + "object": "chat.completion", + "system_fingerprint": "fp_7e0991cad4", + "usage": { + "completion_tokens": 12, + "prompt_tokens": 16, + "total_tokens": 28, + }, + "service_tier": null +} +``` + +## Check Model Support + +Call `litellm.get_model_info` to check if a model/provider supports `response_format`. + + + + +```python +from litellm import get_model_info + +params = get_model_info(model="deepseek/deepseek-chat") + +assert params["supports_assistant_prefill"] is True +``` + + + + +Call the `/model/info` endpoint to get a list of models + their supported params. + +```bash +curl -X GET 'http://0.0.0.0:4000/v1/model/info' \ +-H 'Authorization: Bearer $LITELLM_KEY' \ +``` + + \ No newline at end of file diff --git a/docs/my-website/docs/completion/stream.md b/docs/my-website/docs/completion/stream.md index f563c987b..491a97ca5 100644 --- a/docs/my-website/docs/completion/stream.md +++ b/docs/my-website/docs/completion/stream.md @@ -1,3 +1,6 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Streaming + Async - [Streaming Responses](#streaming-responses) @@ -73,4 +76,73 @@ async def completion_call(): pass asyncio.run(completion_call()) -``` \ No newline at end of file +``` + +## Error Handling - Infinite Loops + +Sometimes a model might enter an infinite loop, and keep repeating the same chunks - [e.g. issue](https://github.com/BerriAI/litellm/issues/5158) + +Break out of it with: + +```python +litellm.REPEATED_STREAMING_CHUNK_LIMIT = 100 # # catch if model starts looping the same chunk while streaming. Uses high default to prevent false positives. +``` + +LiteLLM provides error handling for this, by checking if a chunk is repeated 'n' times (Default is 100). If it exceeds that limit, it will raise a `litellm.InternalServerError`, to allow retry logic to happen. + + + + +```python +import litellm +import os + +litellm.set_verbose = False +loop_amount = litellm.REPEATED_STREAMING_CHUNK_LIMIT + 1 +chunks = [ + litellm.ModelResponse(**{ + "id": "chatcmpl-123", + "object": "chat.completion.chunk", + "created": 1694268190, + "model": "gpt-3.5-turbo-0125", + "system_fingerprint": "fp_44709d6fcb", + "choices": [ + {"index": 0, "delta": {"content": "How are you?"}, "finish_reason": "stop"} + ], +}, stream=True) +] * loop_amount +completion_stream = litellm.ModelResponseListIterator(model_responses=chunks) + +response = litellm.CustomStreamWrapper( + completion_stream=completion_stream, + model="gpt-3.5-turbo", + custom_llm_provider="cached_response", + logging_obj=litellm.Logging( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hey"}], + stream=True, + call_type="completion", + start_time=time.time(), + litellm_call_id="12345", + function_id="1245", + ), +) + +for chunk in response: + continue # expect to raise InternalServerError +``` + + + + +Define this on your config.yaml on the proxy. + +```yaml +litellm_settings: + REPEATED_STREAMING_CHUNK_LIMIT: 100 # this overrides the litellm default +``` + +The proxy uses the litellm SDK. To validate this works, try the 'SDK' code snippet. + + + \ No newline at end of file diff --git a/docs/my-website/docs/data_security.md b/docs/my-website/docs/data_security.md index b2d32b6e5..9572a9597 100644 --- a/docs/my-website/docs/data_security.md +++ b/docs/my-website/docs/data_security.md @@ -14,6 +14,14 @@ For security inquiries, please contact us at support@berri.ai +## Self-hosted Instances LiteLLM + +- ** No data or telemetry is stored on LiteLLM Servers when you self host ** +- For installation and configuration, see: [Self-hosting guided](../docs/proxy/deploy.md) +- **Telemetry** We run no telemetry when you self host LiteLLM + +For security inquiries, please contact us at support@berri.ai + ### Supported data regions for LiteLLM Cloud LiteLLM supports the following data regions: diff --git a/docs/my-website/docs/embedding/supported_embedding.md b/docs/my-website/docs/embedding/supported_embedding.md index 73ac47755..aa3c2c4c5 100644 --- a/docs/my-website/docs/embedding/supported_embedding.md +++ b/docs/my-website/docs/embedding/supported_embedding.md @@ -270,7 +270,7 @@ response = embedding( | embed-multilingual-v2.0 | `embedding(model="embed-multilingual-v2.0", input=["good morning from litellm", "this is another item"])` | ## HuggingFace Embedding Models -LiteLLM supports all Feature-Extraction Embedding models: https://huggingface.co/models?pipeline_tag=feature-extraction +LiteLLM supports all Feature-Extraction + Sentence Similarity Embedding models: https://huggingface.co/models?pipeline_tag=feature-extraction ### Usage ```python @@ -282,6 +282,25 @@ response = embedding( input=["good morning from litellm"] ) ``` + +### Usage - Set input_type + +LiteLLM infers input type (feature-extraction or sentence-similarity) by making a GET request to the api base. + +Override this, by setting the `input_type` yourself. + +```python +from litellm import embedding +import os +os.environ['HUGGINGFACE_API_KEY'] = "" +response = embedding( + model='huggingface/microsoft/codebert-base', + input=["good morning from litellm", "you are a good bot"], + api_base = "https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud", + input_type="sentence-similarity" +) +``` + ### Usage - Custom API Base ```python from litellm import embedding diff --git a/docs/my-website/docs/enterprise.md b/docs/my-website/docs/enterprise.md index f33e2dda9..19e45bebf 100644 --- a/docs/my-website/docs/enterprise.md +++ b/docs/my-website/docs/enterprise.md @@ -27,11 +27,17 @@ This covers: - ✅ IP address‑based access control lists - ✅ Track Request IP Address - ✅ [Use LiteLLM keys/authentication on Pass Through Endpoints](./proxy/pass_through#✨-enterprise---use-litellm-keysauthentication-on-pass-through-endpoints) + - ✅ Set Max Request / File Size on Requests - ✅ [Enforce Required Params for LLM Requests (ex. Reject requests missing ["metadata"]["generation_name"])](./proxy/enterprise#enforce-required-params-for-llm-requests) - - **Spend Tracking** + - **Customize Logging, Guardrails, Caching per project** + - ✅ [Team Based Logging](./proxy/team_logging.md) - Allow each team to use their own Langfuse Project / custom callbacks + - ✅ [Disable Logging for a Team](./proxy/team_logging.md#disable-logging-for-a-team) - Switch off all logging for a team/project (GDPR Compliance) + - **Spend Tracking & Data Exports** - ✅ [Tracking Spend for Custom Tags](./proxy/enterprise#tracking-spend-for-custom-tags) + - ✅ [Exporting LLM Logs to GCS Bucket](./proxy/bucket#🪣-logging-gcs-s3-buckets) - ✅ [API Endpoints to get Spend Reports per Team, API Key, Customer](./proxy/cost_tracking.md#✨-enterprise-api-endpoints-to-get-spend) - - **Advanced Metrics** + - **Prometheus Metrics** + - ✅ [Prometheus Metrics - Num Requests, failures, LLM Provider Outages](./proxy/prometheus) - ✅ [`x-ratelimit-remaining-requests`, `x-ratelimit-remaining-tokens` for LLM APIs on Prometheus](./proxy/prometheus#✨-enterprise-llm-remaining-requests-and-remaining-tokens) - **Guardrails, PII Masking, Content Moderation** - ✅ [Content Moderation with LLM Guard, LlamaGuard, Secret Detection, Google Text Moderations](./proxy/enterprise#content-moderation) diff --git a/docs/my-website/docs/fine_tuning.md b/docs/my-website/docs/fine_tuning.md new file mode 100644 index 000000000..fd3cbc792 --- /dev/null +++ b/docs/my-website/docs/fine_tuning.md @@ -0,0 +1,313 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# [Beta] Fine-tuning API + + +:::info + +This is an Enterprise only endpoint [Get Started with Enterprise here](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat) + +::: + +## Supported Providers +- Azure OpenAI +- OpenAI +- Vertex AI + +Add `finetune_settings` and `files_settings` to your litellm config.yaml to use the fine-tuning endpoints. +## Example config.yaml for `finetune_settings` and `files_settings` +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + +# For /fine_tuning/jobs endpoints +finetune_settings: + - custom_llm_provider: azure + api_base: https://exampleopenaiendpoint-production.up.railway.app + api_key: os.environ/AZURE_API_KEY + api_version: "2023-03-15-preview" + - custom_llm_provider: openai + api_key: os.environ/OPENAI_API_KEY + - custom_llm_provider: "vertex_ai" + vertex_project: "adroit-crow-413218" + vertex_location: "us-central1" + vertex_credentials: "/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json" + +# for /files endpoints +files_settings: + - custom_llm_provider: azure + api_base: https://exampleopenaiendpoint-production.up.railway.app + api_key: fake-key + api_version: "2023-03-15-preview" + - custom_llm_provider: openai + api_key: os.environ/OPENAI_API_KEY +``` + +## Create File for fine-tuning + + + + +```python +client = AsyncOpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") # base_url is your litellm proxy url + +file_name = "openai_batch_completions.jsonl" +response = await client.files.create( + extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use + file=open(file_name, "rb"), + purpose="fine-tune", +) +``` + + + +```shell +curl http://localhost:4000/v1/files \ + -H "Authorization: Bearer sk-1234" \ + -F purpose="batch" \ + -F custom_llm_provider="azure"\ + -F file="@mydata.jsonl" +``` + + + +## Create fine-tuning job + + + + + + + +```python +ft_job = await client.fine_tuning.jobs.create( + model="gpt-35-turbo-1106", # Azure OpenAI model you want to fine-tune + training_file="file-abc123", # file_id from create file response + extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use +) +``` + + + + +```shell +curl http://localhost:4000/v1/fine_tuning/jobs \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "custom_llm_provider": "azure", + "model": "gpt-35-turbo-1106", + "training_file": "file-abc123" + }' +``` + + + + + + + + + + +```python +ft_job = await client.fine_tuning.jobs.create( + model="gemini-1.0-pro-002", # Vertex model you want to fine-tune + training_file="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl", # file_id from create file response + extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm proxy which provider to use +) +``` + + + + +```shell +curl http://localhost:4000/v1/fine_tuning/jobs \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "custom_llm_provider": "vertex_ai", + "model": "gemini-1.0-pro-002", + "training_file": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl" + }' +``` + + + + +:::info + +Use this to create Fine tuning Jobs in [the Vertex AI API Format](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#create-tuning) + +::: + +```shell +curl http://localhost:4000/v1/projects/tuningJobs \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "baseModel": "gemini-1.0-pro-002", + "supervisedTuningSpec" : { + "training_dataset_uri": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl" + } +}' +``` + + + + + + + +### Request Body + + + + +* `model` + + **Type:** string + **Required:** Yes + The name of the model to fine-tune + +* `custom_llm_provider` + + **Type:** `Literal["azure", "openai", "vertex_ai"]` + + **Required:** Yes + The name of the model to fine-tune. You can select one of the [**supported providers**](#supported-providers) + +* `training_file` + + **Type:** string + **Required:** Yes + The ID of an uploaded file that contains training data. + - See **upload file** for how to upload a file. + - Your dataset must be formatted as a JSONL file. + +* `hyperparameters` + + **Type:** object + **Required:** No + The hyperparameters used for the fine-tuning job. + > #### Supported `hyperparameters` + > #### batch_size + **Type:** string or integer + **Required:** No + Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. + > #### learning_rate_multiplier + **Type:** string or number + **Required:** No + Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting. + + > #### n_epochs + **Type:** string or integer + **Required:** No + The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. + +* `suffix` + **Type:** string or null + **Required:** No + **Default:** null + A string of up to 18 characters that will be added to your fine-tuned model name. + Example: A `suffix` of "custom-model-name" would produce a model name like `ft:gpt-4o-mini:openai:custom-model-name:7p4lURel`. + +* `validation_file` + **Type:** string or null + **Required:** No + The ID of an uploaded file that contains validation data. + - If provided, this data is used to generate validation metrics periodically during fine-tuning. + + +* `integrations` + **Type:** array or null + **Required:** No + A list of integrations to enable for your fine-tuning job. + +* `seed` + **Type:** integer or null + **Required:** No + The seed controls the reproducibility of the job. Passing in the same seed and job parameters should produce the same results, but may differ in rare cases. If a seed is not specified, one will be generated for you. + + + + +```json +{ + "model": "gpt-4o-mini", + "training_file": "file-abcde12345", + "hyperparameters": { + "batch_size": 4, + "learning_rate_multiplier": 0.1, + "n_epochs": 3 + }, + "suffix": "custom-model-v1", + "validation_file": "file-fghij67890", + "seed": 42 +} +``` + + + +## Cancel fine-tuning job + + + + +```python +# cancel specific fine tuning job +cancel_ft_job = await client.fine_tuning.jobs.cancel( + fine_tuning_job_id="123", # fine tuning job id + extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use +) + +print("response from cancel ft job={}".format(cancel_ft_job)) +``` + + + + +```shell +curl -X POST http://localhost:4000/v1/fine_tuning/jobs/ftjob-abc123/cancel \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{"custom_llm_provider": "azure"}' +``` + + + + +## List fine-tuning jobs + + + + + +```python +list_ft_jobs = await client.fine_tuning.jobs.list( + extra_query={"custom_llm_provider": "azure"} # tell litellm proxy which provider to use +) + +print("list of ft jobs={}".format(list_ft_jobs)) +``` + + + + +```shell +curl -X GET 'http://localhost:4000/v1/fine_tuning/jobs?custom_llm_provider=azure' \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" +``` + + + + + + +## [👉 Proxy API Reference](https://litellm-api.up.railway.app/#/fine-tuning) \ No newline at end of file diff --git a/docs/my-website/docs/getting_started.md b/docs/my-website/docs/getting_started.md index edbdf3c00..e9b2a0db6 100644 --- a/docs/my-website/docs/getting_started.md +++ b/docs/my-website/docs/getting_started.md @@ -87,13 +87,14 @@ from litellm import completion ## set env variables for logging tools os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key" +os.environ["HELICONE_API_KEY"] = "your-helicone-key" os.environ["LANGFUSE_PUBLIC_KEY"] = "" os.environ["LANGFUSE_SECRET_KEY"] = "" os.environ["OPENAI_API_KEY"] # set callbacks -litellm.success_callback = ["lunary", "langfuse"] # log input/output to langfuse, lunary, supabase +litellm.success_callback = ["lunary", "langfuse", "helicone"] # log input/output to langfuse, lunary, supabase, helicone #openai call response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) diff --git a/docs/my-website/docs/index.md b/docs/my-website/docs/index.md index 762156f46..0712c3034 100644 --- a/docs/my-website/docs/index.md +++ b/docs/my-website/docs/index.md @@ -10,14 +10,41 @@ https://github.com/BerriAI/litellm - Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints - [Consistent output](https://docs.litellm.ai/docs/completion/output), text responses will always be available at `['choices'][0]['message']['content']` - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing) -- Track spend & set budgets per project [OpenAI Proxy Server](https://docs.litellm.ai/docs/simple_proxy) +- Track spend & set budgets per project [LiteLLM Proxy Server](https://docs.litellm.ai/docs/simple_proxy) ## How to use LiteLLM You can use litellm through either: -1. [OpenAI proxy Server](#openai-proxy) - Server to call 100+ LLMs, load balance, cost tracking across projects +1. [LiteLLM Proxy Server](#openai-proxy) - Server (LLM Gateway) to call 100+ LLMs, load balance, cost tracking across projects 2. [LiteLLM python SDK](#basic-usage) - Python Client to call 100+ LLMs, load balance, cost tracking -## LiteLLM Python SDK +### **When to use LiteLLM Proxy Server (LLM Gateway)** + +:::tip + +Use LiteLLM Proxy Server if you want a **central service (LLM Gateway) to access multiple LLMs** + +Typically used by Gen AI Enablement / ML PLatform Teams + +::: + + - LiteLLM Proxy gives you a unified interface to access multiple LLMs (100+ LLMs) + - Track LLM Usage and setup guardrails + - Customize Logging, Guardrails, Caching per project + +### **When to use LiteLLM Python SDK** + +:::tip + + Use LiteLLM Python SDK if you want to use LiteLLM in your **python code** + +Typically used by developers building llm projects + +::: + + - LiteLLM SDK gives you a unified interface to access multiple LLMs (100+ LLMs) + - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing) + +## **LiteLLM Python SDK** ### Basic usage @@ -310,6 +337,7 @@ LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, Helicone from litellm import completion ## set env variables for logging tools +os.environ["HELICONE_API_KEY"] = "your-helicone-key" os.environ["LANGFUSE_PUBLIC_KEY"] = "" os.environ["LANGFUSE_SECRET_KEY"] = "" os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key" @@ -317,7 +345,7 @@ os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key" os.environ["OPENAI_API_KEY"] # set callbacks -litellm.success_callback = ["lunary", "langfuse"] # log input/output to lunary, langfuse, supabase +litellm.success_callback = ["lunary", "langfuse", "helicone"] # log input/output to lunary, langfuse, supabase, helicone #openai call response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) @@ -356,7 +384,7 @@ response = completion( ) ``` -## OpenAI Proxy +## **LiteLLM Proxy Server (LLM Gateway)** Track spend across multiple projects/people diff --git a/docs/my-website/docs/load_test.md b/docs/my-website/docs/load_test.md index 754db4b8f..ce528746d 100644 --- a/docs/my-website/docs/load_test.md +++ b/docs/my-website/docs/load_test.md @@ -1,6 +1,6 @@ import Image from '@theme/IdealImage'; -# 🔥 Load Test LiteLLM +# Load Test LiteLLM ## How to run a locust load test on LiteLLM Proxy diff --git a/docs/my-website/docs/migration_policy.md b/docs/my-website/docs/migration_policy.md new file mode 100644 index 000000000..2685a7d48 --- /dev/null +++ b/docs/my-website/docs/migration_policy.md @@ -0,0 +1,20 @@ +# Migration Policy + +## New Beta Feature Introduction + +- If we introduce a new feature that may move to the Enterprise Tier it will be clearly labeled as **Beta**. With the following example disclaimer +**Example Disclaimer** + +:::info + +Beta Feature - This feature might move to LiteLLM Enterprise + +::: + + +## Policy if a Beta Feature moves to Enterprise + +If we decide to move a beta feature to the paid Enterprise version we will: +- Provide **at least 30 days** notice to all users of the beta feature +- Provide **a free 3 month License to prevent any disruptions to production** +- Provide a **dedicated slack, discord, microsoft teams support channel** to help your team during this transition \ No newline at end of file diff --git a/docs/my-website/docs/observability/arize_integration.md b/docs/my-website/docs/observability/arize_integration.md new file mode 100644 index 000000000..652d79af5 --- /dev/null +++ b/docs/my-website/docs/observability/arize_integration.md @@ -0,0 +1,72 @@ +import Image from '@theme/IdealImage'; + +# Arize AI + +AI Observability and Evaluation Platform + +:::tip + +This is community maintained, Please make an issue if you run into a bug +https://github.com/BerriAI/litellm + +::: + + + +## Pre-Requisites +Make an account on [Arize AI](https://app.arize.com/auth/login) + +## Quick Start +Use just 2 lines of code, to instantly log your responses **across all providers** with arize + + +```python +litellm.callbacks = ["arize"] +``` +```python +import litellm +import os + +os.environ["ARIZE_SPACE_KEY"] = "" +os.environ["ARIZE_API_KEY"] = "" # defaults to litellm-completion + +# LLM API Keys +os.environ['OPENAI_API_KEY']="" + +# set arize as a callback, litellm will send the data to arize +litellm.callbacks = ["arize"] + +# openai call +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hi 👋 - i'm openai"} + ] +) +``` + +### Using with LiteLLM Proxy + + +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + +litellm_settings: + callbacks: ["arize"] + +environment_variables: + ARIZE_SPACE_KEY: "d0*****" + ARIZE_API_KEY: "141a****" +``` + +## Support & Talk to Founders + +- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +- [Community Discord 💭](https://discord.gg/wuPM9dRgDw) +- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ +- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/braintrust.md b/docs/my-website/docs/observability/braintrust.md new file mode 100644 index 000000000..02a9ba5cb --- /dev/null +++ b/docs/my-website/docs/observability/braintrust.md @@ -0,0 +1,147 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Braintrust - Evals + Logging + +[Braintrust](https://www.braintrust.dev/) manages evaluations, logging, prompt playground, to data management for AI products. + + +## Quick Start + +```python +# pip install langfuse +import litellm +import os + +# set env +os.environ["BRAINTRUST_API_KEY"] = "" +os.environ['OPENAI_API_KEY']="" + +# set braintrust as a callback, litellm will send the data to braintrust +litellm.callbacks = ["braintrust"] + +# openai call +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hi 👋 - i'm openai"} + ] +) +``` + + + +## OpenAI Proxy Usage + +1. Add keys to env +```env +BRAINTRUST_API_KEY="" +``` + +2. Add braintrust to callbacks +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY + + +litellm_settings: + callbacks: ["braintrust"] +``` + +3. Test it! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-D '{ + "model": "groq-llama3", + "messages": [ + { "role": "system", "content": "Use your tools smartly"}, + { "role": "user", "content": "What time is it now? Use your tool"} + ] +}' +``` + +## Advanced - pass Project ID + + + + +```python +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hi 👋 - i'm openai"} + ], + metadata={ + "project_id": "my-special-project" + } +) +``` + + + + +**Curl** + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-D '{ + "model": "groq-llama3", + "messages": [ + { "role": "system", "content": "Use your tools smartly"}, + { "role": "user", "content": "What time is it now? Use your tool"} + ], + "metadata": { + "project_id": "my-special-project" + } +}' +``` + +**OpenAI SDK** + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } + ], + extra_body={ # pass in any provider-specific param, if not supported by openai, https://docs.litellm.ai/docs/completion/input#provider-specific-params + "metadata": { # 👈 use for logging additional params (e.g. to langfuse) + "project_id": "my-special-project" + } + } +) + +print(response) +``` + +For more examples, [**Click Here**](../proxy/user_keys.md#chatcompletions) + + + + +## Full API Spec + +Here's everything you can pass in metadata for a braintrust request + +`braintrust_*` - any metadata field starting with `braintrust_` will be passed as metadata to the logging request + +`project_id` - set the project id for a braintrust call. Default is `litellm`. \ No newline at end of file diff --git a/docs/my-website/docs/observability/gcs_bucket_integration.md b/docs/my-website/docs/observability/gcs_bucket_integration.md new file mode 100644 index 000000000..509929b3a --- /dev/null +++ b/docs/my-website/docs/observability/gcs_bucket_integration.md @@ -0,0 +1,127 @@ +import Image from '@theme/IdealImage'; + +# Google Cloud Storage Buckets + +Log LLM Logs to [Google Cloud Storage Buckets](https://cloud.google.com/storage?hl=en) + +:::info + +✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat) + +::: + + +### Usage + +1. Add `gcs_bucket` to LiteLLM Config.yaml +```yaml +model_list: +- litellm_params: + api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/ + api_key: my-fake-key + model: openai/my-fake-model + model_name: fake-openai-endpoint + +litellm_settings: + callbacks: ["gcs_bucket"] # 👈 KEY CHANGE # 👈 KEY CHANGE +``` + +2. Set required env variables + +```shell +GCS_BUCKET_NAME="" +GCS_PATH_SERVICE_ACCOUNT="/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json +``` + +3. Start Proxy + +``` +litellm --config /path/to/config.yaml +``` + +4. Test it! + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "fake-openai-endpoint", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + } +' +``` + + +## Expected Logs on GCS Buckets + + + +### Fields Logged on GCS Buckets + +Example payload of a `/chat/completion` request logged on GCS +```json +{ + "request_kwargs": { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "This is a test" + } + ], + "optional_params": { + "temperature": 0.7, + "max_tokens": 10, + "user": "ishaan-2", + "extra_body": {} + } + }, + "response_obj": { + "id": "chatcmpl-bd836a8c-89bc-4abd-bee5-e3f1ebfdb541", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Hi!", + "role": "assistant", + "tool_calls": null, + "function_call": null + } + } + ], + "created": 1722868456, + "model": "gpt-3.5-turbo", + "object": "chat.completion", + "system_fingerprint": null, + "usage": { + "prompt_tokens": 10, + "completion_tokens": 20, + "total_tokens": 30 + } + }, + "start_time": "2024-08-05 07:34:16", + "end_time": "2024-08-05 07:34:16" +} +``` + +## Getting `service_account.json` from Google Cloud Console + +1. Go to [Google Cloud Console](https://console.cloud.google.com/) +2. Search for IAM & Admin +3. Click on Service Accounts +4. Select a Service Account +5. Click on 'Keys' -> Add Key -> Create New Key -> JSON +6. Save the JSON file and add the path to `GCS_PATH_SERVICE_ACCOUNT` + +## Support & Talk to Founders + +- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +- [Community Discord 💭](https://discord.gg/wuPM9dRgDw) +- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ +- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/helicone_integration.md b/docs/my-website/docs/observability/helicone_integration.md index f7fd330c3..80935c1cc 100644 --- a/docs/my-website/docs/observability/helicone_integration.md +++ b/docs/my-website/docs/observability/helicone_integration.md @@ -1,64 +1,170 @@ -# Helicone Tutorial +# Helicone - OSS LLM Observability Platform :::tip -This is community maintained, Please make an issue if you run into a bug +This is community maintained. Please make an issue if you run into a bug: https://github.com/BerriAI/litellm ::: +[Helicone](https://helicone.ai/) is an open source observability platform that proxies your LLM requests and provides key insights into your usage, spend, latency and more. -[Helicone](https://helicone.ai/) is an open source observability platform that proxies your OpenAI traffic and provides you key insights into your spend, latency and usage. +## Using Helicone with LiteLLM -## Use Helicone to log requests across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM) -liteLLM provides `success_callbacks` and `failure_callbacks`, making it easy for you to send data to a particular provider depending on the status of your responses. +LiteLLM provides `success_callbacks` and `failure_callbacks`, allowing you to easily log data to Helicone based on the status of your responses. -In this case, we want to log requests to Helicone when a request succeeds. +### Supported LLM Providers + +Helicone can log requests across [various LLM providers](https://docs.helicone.ai/getting-started/quick-start), including: + +- OpenAI +- Azure +- Anthropic +- Gemini +- Groq +- Cohere +- Replicate +- And more + +### Integration Methods + +There are two main approaches to integrate Helicone with LiteLLM: + +1. Using callbacks +2. Using Helicone as a proxy + +Let's explore each method in detail. + +### Approach 1: Use Callbacks + +Use just 1 line of code to instantly log your responses **across all providers** with Helicone: -### Approach 1: Use Callbacks -Use just 1 line of code, to instantly log your responses **across all providers** with helicone: ```python -litellm.success_callback=["helicone"] +litellm.success_callback = ["helicone"] ``` -Complete code -```python -from litellm import completion - -## set env variables -os.environ["HELICONE_API_KEY"] = "your-helicone-key" -os.environ["OPENAI_API_KEY"], os.environ["COHERE_API_KEY"] = "", "" - -# set callbacks -litellm.success_callback=["helicone"] - -#openai call -response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) - -#cohere call -response = completion(model="command-nightly", messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}]) -``` - -### Approach 2: [OpenAI + Azure only] Use Helicone as a proxy -Helicone provides advanced functionality like caching, etc. Helicone currently supports this for Azure and OpenAI. - -If you want to use Helicone to proxy your OpenAI/Azure requests, then you can - - -- Set helicone as your base url via: `litellm.api_url` -- Pass in helicone request headers via: `litellm.headers` - Complete Code + ```python -import litellm +import os from litellm import completion -litellm.api_base = "https://oai.hconeai.com/v1" -litellm.headers = {"Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}"} +## Set env variables +os.environ["HELICONE_API_KEY"] = "your-helicone-key" +os.environ["OPENAI_API_KEY"] = "your-openai-key" -response = litellm.completion( - model="gpt-3.5-turbo", - messages=[{"role": "user", "content": "how does a court case get to the Supreme Court?"}] +# Set callbacks +litellm.success_callback = ["helicone"] + +# OpenAI call +response = completion( + model="gpt-4o", + messages=[{"role": "user", "content": "Hi 👋 - I'm OpenAI"}], ) print(response) ``` + +### Approach 2: Use Helicone as a proxy + +Helicone's proxy provides [advanced functionality](https://docs.helicone.ai/getting-started/proxy-vs-async) like caching, rate limiting, LLM security through [PromptArmor](https://promptarmor.com/) and more. + +To use Helicone as a proxy for your LLM requests: + +1. Set Helicone as your base URL via: litellm.api_base +2. Pass in Helicone request headers via: litellm.metadata + +Complete Code: + +```python +import os +import litellm +from litellm import completion + +litellm.api_base = "https://oai.hconeai.com/v1" +litellm.headers = { + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API +} + +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "How does a court case get to the Supreme Court?"}] +) + +print(response) +``` + +### Advanced Usage + +You can add custom metadata and properties to your requests using Helicone headers. Here are some examples: + +```python +litellm.metadata = { + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API + "Helicone-User-Id": "user-abc", # Specify the user making the request + "Helicone-Property-App": "web", # Custom property to add additional information + "Helicone-Property-Custom": "any-value", # Add any custom property + "Helicone-Prompt-Id": "prompt-supreme-court", # Assign an ID to associate this prompt with future versions + "Helicone-Cache-Enabled": "true", # Enable caching of responses + "Cache-Control": "max-age=3600", # Set cache limit to 1 hour + "Helicone-RateLimit-Policy": "10;w=60;s=user", # Set rate limit policy + "Helicone-Retry-Enabled": "true", # Enable retry mechanism + "helicone-retry-num": "3", # Set number of retries + "helicone-retry-factor": "2", # Set exponential backoff factor + "Helicone-Model-Override": "gpt-3.5-turbo-0613", # Override the model used for cost calculation + "Helicone-Session-Id": "session-abc-123", # Set session ID for tracking + "Helicone-Session-Path": "parent-trace/child-trace", # Set session path for hierarchical tracking + "Helicone-Omit-Response": "false", # Include response in logging (default behavior) + "Helicone-Omit-Request": "false", # Include request in logging (default behavior) + "Helicone-LLM-Security-Enabled": "true", # Enable LLM security features + "Helicone-Moderations-Enabled": "true", # Enable content moderation + "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]', # Set fallback models +} +``` + +### Caching and Rate Limiting + +Enable caching and set up rate limiting policies: + +```python +litellm.metadata = { + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API + "Helicone-Cache-Enabled": "true", # Enable caching of responses + "Cache-Control": "max-age=3600", # Set cache limit to 1 hour + "Helicone-RateLimit-Policy": "100;w=3600;s=user", # Set rate limit policy +} +``` + +### Session Tracking and Tracing + +Track multi-step and agentic LLM interactions using session IDs and paths: + +```python +litellm.metadata = { + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API + "Helicone-Session-Id": "session-abc-123", # The session ID you want to track + "Helicone-Session-Path": "parent-trace/child-trace", # The path of the session +} +``` + +- `Helicone-Session-Id`: Use this to specify the unique identifier for the session you want to track. This allows you to group related requests together. +- `Helicone-Session-Path`: This header defines the path of the session, allowing you to represent parent and child traces. For example, "parent/child" represents a child trace of a parent trace. + +By using these two headers, you can effectively group and visualize multi-step LLM interactions, gaining insights into complex AI workflows. + +### Retry and Fallback Mechanisms + +Set up retry mechanisms and fallback options: + +```python +litellm.metadata = { + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", # Authenticate to send requests to Helicone API + "Helicone-Retry-Enabled": "true", # Enable retry mechanism + "helicone-retry-num": "3", # Set number of retries + "helicone-retry-factor": "2", # Set exponential backoff factor + "Helicone-Fallbacks": '["gpt-3.5-turbo", "gpt-4"]', # Set fallback models +} +``` + +> **Supported Headers** - For a full list of supported Helicone headers and their descriptions, please refer to the [Helicone documentation](https://docs.helicone.ai/getting-started/quick-start). +> By utilizing these headers and metadata options, you can gain deeper insights into your LLM usage, optimize performance, and better manage your AI workflows with Helicone and LiteLLM. diff --git a/docs/my-website/docs/observability/langfuse_integration.md b/docs/my-website/docs/observability/langfuse_integration.md index 9703d38a0..32322181b 100644 --- a/docs/my-website/docs/observability/langfuse_integration.md +++ b/docs/my-website/docs/observability/langfuse_integration.md @@ -1,6 +1,6 @@ import Image from '@theme/IdealImage'; -# 🔥 Langfuse - Logging LLM Input/Output +# 🪢 Langfuse - Logging LLM Input/Output LangFuse is open Source Observability & Analytics for LLM Apps Detailed production traces and a granular view on quality, cost and latency @@ -200,6 +200,13 @@ The following parameters can be updated on a continuation of a trace by passing Any other key value pairs passed into the metadata not listed in the above spec for a `litellm` completion will be added as a metadata key value pair for the generation. +#### Disable Logging - Specific Calls + +To disable logging for specific calls use the `no-log` flag. + +`completion(messages = ..., model = ..., **{"no-log": True})` + + ### Use LangChain ChatLiteLLM + Langfuse Pass `trace_user_id`, `session_id` in model_kwargs ```python diff --git a/docs/my-website/docs/observability/langsmith_integration.md b/docs/my-website/docs/observability/langsmith_integration.md index c038abd82..88818e653 100644 --- a/docs/my-website/docs/observability/langsmith_integration.md +++ b/docs/my-website/docs/observability/langsmith_integration.md @@ -14,7 +14,7 @@ https://github.com/BerriAI/litellm An all-in-one developer platform for every step of the application lifecycle https://smith.langchain.com/ - + :::info We want to learn how we can make the callbacks better! Meet the LiteLLM [founders](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) or @@ -56,7 +56,7 @@ response = litellm.completion( ``` ## Advanced -### Set Custom Project & Run names +### Set Langsmith fields - Custom Projec, Run names, tags ```python import litellm @@ -77,6 +77,7 @@ response = litellm.completion( metadata={ "run_name": "litellmRUN", # langsmith run name "project_name": "litellm-completion", # langsmith project name + "tags": ["model1", "prod-2"] # tags to log on langsmith } ) print(response) diff --git a/docs/my-website/docs/observability/logfire_integration.md b/docs/my-website/docs/observability/logfire_integration.md index a2d406f9c..b75c5bfd4 100644 --- a/docs/my-website/docs/observability/logfire_integration.md +++ b/docs/my-website/docs/observability/logfire_integration.md @@ -1,6 +1,6 @@ import Image from '@theme/IdealImage'; -# 🔥 Logfire - Logging LLM Input/Output +# Logfire Logfire is open Source Observability & Analytics for LLM Apps Detailed production traces and a granular view on quality, cost and latency diff --git a/docs/my-website/docs/observability/raw_request_response.md b/docs/my-website/docs/observability/raw_request_response.md index dddf75e98..71305dae6 100644 --- a/docs/my-website/docs/observability/raw_request_response.md +++ b/docs/my-website/docs/observability/raw_request_response.md @@ -1,10 +1,16 @@ import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; # Raw Request/Response Logging + +## Logging See the raw request/response sent by LiteLLM in your logging provider (OTEL/Langfuse/etc.). -**on SDK** + + + ```python # pip install langfuse import litellm @@ -34,13 +40,85 @@ response = litellm.completion( ) ``` -**on Proxy** + + + + ```yaml litellm_settings: log_raw_request_response: True ``` + + + + **Expected Log** - \ No newline at end of file + + + +## Return Raw Response Headers + +Return raw response headers from llm provider. + +Currently only supported for openai. + + + + +```python +import litellm +import os + +litellm.return_response_headers = True + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "your-api-key" + +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) + +print(response._hidden_params) +``` + + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + api_key: os.environ/GROQ_API_KEY + +litellm_settings: + return_response_headers: true +``` + +2. Test it! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-D '{ + "model": "gpt-3.5-turbo", + "messages": [ + { "role": "system", "content": "Use your tools smartly"}, + { "role": "user", "content": "What time is it now? Use your tool"} + ] +}' +``` + + + + +**Expected Response** + + \ No newline at end of file diff --git a/docs/my-website/docs/observability/scrub_data.md b/docs/my-website/docs/observability/scrub_data.md new file mode 100644 index 000000000..f8bb4d556 --- /dev/null +++ b/docs/my-website/docs/observability/scrub_data.md @@ -0,0 +1,97 @@ +# Scrub Logged Data + +Redact messages / mask PII before sending data to logging integrations (langfuse/etc.). + +See our [**Presidio PII Masking**](https://github.com/BerriAI/litellm/blob/a176feeacc5fdf504747978d82056eb84679c4be/litellm/proxy/hooks/presidio_pii_masking.py#L286) for reference. + +1. Setup a custom callback + +```python +from litellm.integrations.custom_logger import CustomLogger + +class MyCustomHandler(CustomLogger): + async def async_logging_hook( + self, kwargs: dict, result: Any, call_type: str + ) -> Tuple[dict, Any]: + """ + For masking logged request/response. Return a modified version of the request/result. + + Called before `async_log_success_event`. + """ + if ( + call_type == "completion" or call_type == "acompletion" + ): # /chat/completions requests + messages: Optional[List] = kwargs.get("messages", None) + + kwargs["messages"] = [{"role": "user", "content": "MASK_THIS_ASYNC_VALUE"}] + + return kwargs, responses + + def logging_hook( + self, kwargs: dict, result: Any, call_type: str + ) -> Tuple[dict, Any]: + """ + For masking logged request/response. Return a modified version of the request/result. + + Called before `log_success_event`. + """ + if ( + call_type == "completion" or call_type == "acompletion" + ): # /chat/completions requests + messages: Optional[List] = kwargs.get("messages", None) + + kwargs["messages"] = [{"role": "user", "content": "MASK_THIS_SYNC_VALUE"}] + + return kwargs, responses + + +customHandler = MyCustomHandler() +``` + + +2. Connect custom handler to LiteLLM + +```python +import litellm + +litellm.callbacks = [customHandler] +``` + +3. Test it! + +```python +# pip install langfuse + +import os +import litellm +from litellm import completion + +os.environ["LANGFUSE_PUBLIC_KEY"] = "" +os.environ["LANGFUSE_SECRET_KEY"] = "" +# Optional, defaults to https://cloud.langfuse.com +os.environ["LANGFUSE_HOST"] # optional +# LLM API Keys +os.environ['OPENAI_API_KEY']="" + +litellm.callbacks = [customHandler] +litellm.success_callback = ["langfuse"] + + + +## sync +response = completion(model="gpt-3.5-turbo", messages=[{ "role": "user", "content": "Hi 👋 - i'm openai"}], + stream=True) +for chunk in response: + continue + + +## async +import asyncio + +def async completion(): + response = await acompletion(model="gpt-3.5-turbo", messages=[{ "role": "user", "content": "Hi 👋 - i'm openai"}], + stream=True) + async for chunk in response: + continue +asyncio.run(completion()) +``` \ No newline at end of file diff --git a/docs/my-website/docs/observability/sentry.md b/docs/my-website/docs/observability/sentry.md index 59e43b8f7..5b1770fba 100644 --- a/docs/my-website/docs/observability/sentry.md +++ b/docs/my-website/docs/observability/sentry.md @@ -1,3 +1,4 @@ +# Sentry - Log LLM Exceptions import Image from '@theme/IdealImage'; @@ -9,7 +10,6 @@ https://github.com/BerriAI/litellm ::: -# Sentry - Log LLM Exceptions [Sentry](https://sentry.io/) provides error monitoring for production. LiteLLM can add breadcrumbs and send exceptions to Sentry with this integration Track exceptions for: diff --git a/docs/my-website/docs/oidc.md b/docs/my-website/docs/oidc.md new file mode 100644 index 000000000..f30edf504 --- /dev/null +++ b/docs/my-website/docs/oidc.md @@ -0,0 +1,263 @@ +# [BETA] OpenID Connect (OIDC) +LiteLLM supports using OpenID Connect (OIDC) for authentication to upstream services . This allows you to avoid storing sensitive credentials in your configuration files. + +:::info + +This feature is in Beta + +::: + + +## OIDC Identity Provider (IdP) + +LiteLLM supports the following OIDC identity providers: + +| Provider | Config Name | Custom Audiences | +| -------------------------| ------------ | ---------------- | +| Google Cloud Run | `google` | Yes | +| CircleCI v1 | `circleci` | No | +| CircleCI v2 | `circleci_v2`| No | +| GitHub Actions | `github` | Yes | +| Azure Kubernetes Service | `azure` | No | +| File | `file` | No | +| Environment Variable | `env` | No | +| Environment Path | `env_path` | No | + +If you would like to use a different OIDC provider, please open an issue on GitHub. + +:::tip + +Do not use the `file`, `env`, or `env_path` providers unless you know what you're doing, and you are sure none of the other providers will work for your use-case. Hint: they probably will. + +::: + +## OIDC Connect Relying Party (RP) + +LiteLLM supports the following OIDC relying parties / clients: + +- Amazon Bedrock +- Azure OpenAI +- _(Coming soon) Google Cloud Vertex AI_ + + +### Configuring OIDC + +Wherever a secret key can be used, OIDC can be used in-place. The general format is: + +``` +oidc/config_name_here/audience_here +``` + +For providers that do not use the `audience` parameter, you can (and should) omit it: + +``` +oidc/config_name_here/ +``` + +#### Unofficial Providers (not recommended) + +For the unofficial `file` provider, you can use the following format: + +``` +oidc/file/home/user/dave/this_is_a_file_with_a_token.txt +``` + +For the unofficial `env`, use the following format, where `SECRET_TOKEN` is the name of the environment variable that contains the token: + +``` +oidc/env/SECRET_TOKEN +``` + +For the unofficial `env_path`, use the following format, where `SECRET_TOKEN` is the name of the environment variable that contains the path to the file with the token: + +``` +oidc/env_path/SECRET_TOKEN +``` + +:::tip + +If you are tempted to use oidc/env_path/AZURE_FEDERATED_TOKEN_FILE, don't do that. Instead, use `oidc/azure/`, as this will ensure continued support from LiteLLM if Azure changes their OIDC configuration and/or adds new features. + +::: + +## Examples + +### Google Cloud Run -> Amazon Bedrock + +```yaml +model_list: + - model_name: claude-3-haiku-20240307 + litellm_params: + model: bedrock/anthropic.claude-3-haiku-20240307-v1:0 + aws_region_name: us-west-2 + aws_session_name: "litellm" + aws_role_name: "arn:aws:iam::YOUR_THING_HERE:role/litellm-google-demo" + aws_web_identity_token: "oidc/google/https://example.com" +``` + +### CircleCI v2 -> Amazon Bedrock + +```yaml +model_list: + - model_name: command-r + litellm_params: + model: bedrock/cohere.command-r-v1:0 + aws_region_name: us-west-2 + aws_session_name: "my-test-session" + aws_role_name: "arn:aws:iam::335785316107:role/litellm-github-unit-tests-circleci" + aws_web_identity_token: "oidc/circleci_v2/" +``` + +#### Amazon IAM Role Configuration for CircleCI v2 -> Bedrock + +The configuration below is only an example. You should adjust the permissions and trust relationship to match your specific use case. + +Permissions: + +```json +{ + "Version": "2012-10-17", + "Statement": [ + { + "Sid": "VisualEditor0", + "Effect": "Allow", + "Action": [ + "bedrock:InvokeModel", + "bedrock:InvokeModelWithResponseStream" + ], + "Resource": [ + "arn:aws:bedrock:*::foundation-model/anthropic.claude-3-haiku-20240307-v1:0", + "arn:aws:bedrock:*::foundation-model/cohere.command-r-v1:0" + ] + } + ] +} +``` + +See https://docs.aws.amazon.com/bedrock/latest/userguide/security_iam_id-based-policy-examples.html for more examples. + +Trust Relationship: + +```json +{ + "Version": "2012-10-17", + "Statement": [ + { + "Effect": "Allow", + "Principal": { + "Federated": "arn:aws:iam::335785316107:oidc-provider/oidc.circleci.com/org/c5a99188-154f-4f69-8da2-b442b1bf78dd" + }, + "Action": "sts:AssumeRoleWithWebIdentity", + "Condition": { + "StringEquals": { + "oidc.circleci.com/org/c5a99188-154f-4f69-8da2-b442b1bf78dd:aud": "c5a99188-154f-4f69-8da2-b442b1bf78dd" + }, + "ForAnyValue:StringLike": { + "oidc.circleci.com/org/c5a99188-154f-4f69-8da2-b442b1bf78dd:sub": [ + "org/c5a99188-154f-4f69-8da2-b442b1bf78dd/project/*/user/*/vcs-origin/github.com/BerriAI/litellm/vcs-ref/refs/heads/main", + "org/c5a99188-154f-4f69-8da2-b442b1bf78dd/project/*/user/*/vcs-origin/github.com/BerriAI/litellm/vcs-ref/refs/heads/litellm_*" + ] + } + } + } + ] +} +``` + +This trust relationship restricts CircleCI to only assume the role on the main branch and branches that start with `litellm_`. + +For CircleCI (v1 and v2), you also need to add your organization's OIDC provider in your AWS IAM settings. See https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles_create_for-idp_oidc.html for more information. + +:::tip + +You should _never_ need to create an IAM user. If you did, you're not using OIDC correctly. You should only be creating a role with permissions and a trust relationship to your OIDC provider. + +::: + + +### Google Cloud Run -> Azure OpenAI + +```yaml +model_list: + - model_name: gpt-4o-2024-05-13 + litellm_params: + model: azure/gpt-4o-2024-05-13 + azure_ad_token: "oidc/google/https://example.com" + api_version: "2024-06-01" + api_base: "https://demo-here.openai.azure.com" + model_info: + base_model: azure/gpt-4o-2024-05-13 +``` + +For Azure OpenAI, you need to define `AZURE_CLIENT_ID`, `AZURE_TENANT_ID`, and optionally `AZURE_AUTHORITY_HOST` in your environment. + +```bash +export AZURE_CLIENT_ID="91a43c21-cf21-4f34-9085-331015ea4f91" # Azure AD Application (Client) ID +export AZURE_TENANT_ID="f3b1cf79-eba8-40c3-8120-cb26aca169c2" # Will be the same across of all your Azure AD applications +export AZURE_AUTHORITY_HOST="https://login.microsoftonline.com" # 👈 Optional, defaults to "https://login.microsoftonline.com" +``` + +:::tip + +You can find `AZURE_CLIENT_ID` by visiting `https://login.microsoftonline.com/YOUR_DOMAIN_HERE/v2.0/.well-known/openid-configuration` and looking for the UUID in the `issuer` field. + +::: + + +:::tip + +Don't set `AZURE_AUTHORITY_HOST` in your environment unless you need to override the default value. This way, if the default value changes in the future, you won't need to update your environment. + +::: + + +:::tip + +By default, Azure AD applications use the audience `api://AzureADTokenExchange`. We recommend setting the audience to something more specific to your application. + +::: + + +#### Azure AD Application Configuration + +Unfortunately, Azure is bit more complicated to set up than other OIDC relying parties like AWS. Basically, you have to: + +1. Create an Azure application. +2. Add a federated credential for the OIDC IdP you're using (e.g. Google Cloud Run). +3. Add the Azure application to resource group that contains the Azure OpenAI resource(s). +4. Give the Azure application the necessary role to access the Azure OpenAI resource(s). + +The custom role below is the recommended minimum permissions for the Azure application to access Azure OpenAI resources. You should adjust the permissions to match your specific use case. + +```json +{ + "id": "/subscriptions/24ebb700-ec2f-417f-afad-78fe15dcc91f/providers/Microsoft.Authorization/roleDefinitions/baf42808-99ff-466d-b9da-f95bb0422c5f", + "properties": { + "roleName": "invoke-only", + "description": "", + "assignableScopes": [ + "/subscriptions/24ebb700-ec2f-417f-afad-78fe15dcc91f/resourceGroups/your-openai-group-name" + ], + "permissions": [ + { + "actions": [], + "notActions": [], + "dataActions": [ + "Microsoft.CognitiveServices/accounts/OpenAI/deployments/audio/action", + "Microsoft.CognitiveServices/accounts/OpenAI/deployments/search/action", + "Microsoft.CognitiveServices/accounts/OpenAI/deployments/completions/action", + "Microsoft.CognitiveServices/accounts/OpenAI/deployments/chat/completions/action", + "Microsoft.CognitiveServices/accounts/OpenAI/deployments/extensions/chat/completions/action", + "Microsoft.CognitiveServices/accounts/OpenAI/deployments/embeddings/action", + "Microsoft.CognitiveServices/accounts/OpenAI/images/generations/action" + ], + "notDataActions": [] + } + ] + } +} +``` + +_Note: Your UUIDs will be different._ + +Please contact us for paid enterprise support if you need help setting up Azure AD applications. diff --git a/docs/my-website/docs/pass_through/bedrock.md b/docs/my-website/docs/pass_through/bedrock.md new file mode 100644 index 000000000..2fba346a3 --- /dev/null +++ b/docs/my-website/docs/pass_through/bedrock.md @@ -0,0 +1,236 @@ +# Bedrock (Pass-Through) + +Pass-through endpoints for Bedrock - call provider-specific endpoint, in native format (no translation). + +Just replace `https://bedrock-runtime.{aws_region_name}.amazonaws.com` with `LITELLM_PROXY_BASE_URL/bedrock` 🚀 + +#### **Example Usage** +```bash +curl -X POST 'http://0.0.0.0:4000/bedrock/model/cohere.command-r-v1:0/converse' \ +-H 'Authorization: Bearer anything' \ +-H 'Content-Type: application/json' \ +-d '{ + "messages": [ + {"role": "user", + "content": [{"text": "Hello"}] + } + ] +}' +``` + +Supports **ALL** Bedrock Endpoints (including streaming). + +[**See All Bedrock Endpoints**](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html) + +## Quick Start + +Let's call the Bedrock [`/converse` endpoint](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html) + +1. Add AWS Keyss to your environment + +```bash +export AWS_ACCESS_KEY_ID="" # Access key +export AWS_SECRET_ACCESS_KEY="" # Secret access key +export AWS_REGION_NAME="" # us-east-1, us-east-2, us-west-1, us-west-2 +``` + +2. Start LiteLLM Proxy + +```bash +litellm + +# RUNNING on http://0.0.0.0:4000 +``` + +3. Test it! + +Let's call the Bedrock converse endpoint + +```bash +curl -X POST 'http://0.0.0.0:4000/bedrock/model/cohere.command-r-v1:0/converse' \ +-H 'Authorization: Bearer anything' \ +-H 'Content-Type: application/json' \ +-d '{ + "messages": [ + {"role": "user", + "content": [{"text": "Hello"}] + } + ] +}' +``` + + +## Examples + +Anything after `http://0.0.0.0:4000/bedrock` is treated as a provider-specific route, and handled accordingly. + +Key Changes: + +| **Original Endpoint** | **Replace With** | +|------------------------------------------------------|-----------------------------------| +| `https://bedrock-runtime.{aws_region_name}.amazonaws.com` | `http://0.0.0.0:4000/bedrock` (LITELLM_PROXY_BASE_URL="http://0.0.0.0:4000") | +| `AWS4-HMAC-SHA256..` | `Bearer anything` (use `Bearer LITELLM_VIRTUAL_KEY` if Virtual Keys are setup on proxy) | + + + +### **Example 1: Converse API** + +#### LiteLLM Proxy Call + +```bash +curl -X POST 'http://0.0.0.0:4000/bedrock/model/cohere.command-r-v1:0/converse' \ +-H 'Authorization: Bearer sk-anything' \ +-H 'Content-Type: application/json' \ +-d '{ + "messages": [ + {"role": "user", + "content": [{"text": "Hello"}] + } + ] +}' +``` + +#### Direct Bedrock API Call + +```bash +curl -X POST 'https://bedrock-runtime.us-west-2.amazonaws.com/model/cohere.command-r-v1:0/converse' \ +-H 'Authorization: AWS4-HMAC-SHA256..' \ +-H 'Content-Type: application/json' \ +-d '{ + "messages": [ + {"role": "user", + "content": [{"text": "Hello"}] + } + ] +}' +``` + +### **Example 2: Apply Guardrail** + +#### LiteLLM Proxy Call + +```bash +curl "http://0.0.0.0:4000/bedrock/guardrail/guardrailIdentifier/version/guardrailVersion/apply" \ + -H 'Authorization: Bearer sk-anything' \ + -H 'Content-Type: application/json' \ + -X POST \ + -d '{ + "contents": [{"text": {"text": "Hello world"}}], + "source": "INPUT" + }' +``` + +#### Direct Bedrock API Call + +```bash +curl "https://bedrock-runtime.us-west-2.amazonaws.com/guardrail/guardrailIdentifier/version/guardrailVersion/apply" \ + -H 'Authorization: AWS4-HMAC-SHA256..' \ + -H 'Content-Type: application/json' \ + -X POST \ + -d '{ + "contents": [{"text": {"text": "Hello world"}}], + "source": "INPUT" + }' +``` + +### **Example 3: Query Knowledge Base** + +```bash +curl -X POST "http://0.0.0.0:4000/bedrock/knowledgebases/{knowledgeBaseId}/retrieve" \ +-H 'Authorization: Bearer sk-anything' \ +-H 'Content-Type: application/json' \ +-d '{ + "nextToken": "string", + "retrievalConfiguration": { + "vectorSearchConfiguration": { + "filter": { ... }, + "numberOfResults": number, + "overrideSearchType": "string" + } + }, + "retrievalQuery": { + "text": "string" + } +}' +``` + +#### Direct Bedrock API Call + +```bash +curl -X POST "https://bedrock-runtime.us-west-2.amazonaws.com/knowledgebases/{knowledgeBaseId}/retrieve" \ +-H 'Authorization: AWS4-HMAC-SHA256..' \ +-H 'Content-Type: application/json' \ +-d '{ + "nextToken": "string", + "retrievalConfiguration": { + "vectorSearchConfiguration": { + "filter": { ... }, + "numberOfResults": number, + "overrideSearchType": "string" + } + }, + "retrievalQuery": { + "text": "string" + } +}' +``` + + +## Advanced - Use with Virtual Keys + +Pre-requisites +- [Setup proxy with DB](../proxy/virtual_keys.md#setup) + +Use this, to avoid giving developers the raw AWS Keys, but still letting them use AWS Bedrock endpoints. + +### Usage + +1. Setup environment + +```bash +export DATABASE_URL="" +export LITELLM_MASTER_KEY="" +export AWS_ACCESS_KEY_ID="" # Access key +export AWS_SECRET_ACCESS_KEY="" # Secret access key +export AWS_REGION_NAME="" # us-east-1, us-east-2, us-west-1, us-west-2 +``` + +```bash +litellm + +# RUNNING on http://0.0.0.0:4000 +``` + +2. Generate virtual key + +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{}' +``` + +Expected Response + +```bash +{ + ... + "key": "sk-1234ewknldferwedojwojw" +} +``` + +3. Test it! + + +```bash +curl -X POST 'http://0.0.0.0:4000/bedrock/model/cohere.command-r-v1:0/converse' \ +-H 'Authorization: Bearer sk-1234ewknldferwedojwojw' \ +-H 'Content-Type: application/json' \ +-d '{ + "messages": [ + {"role": "user", + "content": [{"text": "Hello"}] + } + ] +}' +``` \ No newline at end of file diff --git a/docs/my-website/docs/pass_through/cohere.md b/docs/my-website/docs/pass_through/cohere.md new file mode 100644 index 000000000..c7313f9cc --- /dev/null +++ b/docs/my-website/docs/pass_through/cohere.md @@ -0,0 +1,253 @@ +# Cohere API (Pass-Through) + +Pass-through endpoints for Cohere - call provider-specific endpoint, in native format (no translation). + +Just replace `https://api.cohere.com` with `LITELLM_PROXY_BASE_URL/cohere` 🚀 + +#### **Example Usage** +```bash +curl --request POST \ + --url http://0.0.0.0:4000/cohere/v1/chat \ + --header 'accept: application/json' \ + --header 'content-type: application/json' \ + --header "Authorization: bearer sk-anything" \ + --data '{ + "chat_history": [ + {"role": "USER", "message": "Who discovered gravity?"}, + {"role": "CHATBOT", "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton"} + ], + "message": "What year was he born?", + "connectors": [{"id": "web-search"}] + }' +``` + +Supports **ALL** Cohere Endpoints (including streaming). + +[**See All Cohere Endpoints**](https://docs.cohere.com/reference/chat) + +## Quick Start + +Let's call the Cohere [`/rerank` endpoint](https://docs.cohere.com/reference/rerank) + +1. Add Cohere API Key to your environment + +```bash +export COHERE_API_KEY="" +``` + +2. Start LiteLLM Proxy + +```bash +litellm + +# RUNNING on http://0.0.0.0:4000 +``` + +3. Test it! + +Let's call the Cohere /rerank endpoint + +```bash +curl --request POST \ + --url http://0.0.0.0:4000/cohere/v1/rerank \ + --header 'accept: application/json' \ + --header 'content-type: application/json' \ + --header "Authorization: bearer sk-anything" \ + --data '{ + "model": "rerank-english-v3.0", + "query": "What is the capital of the United States?", + "top_n": 3, + "documents": ["Carson City is the capital city of the American state of Nevada.", + "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.", + "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.", + "Capitalization or capitalisation in English grammar is the use of a capital letter at the start of a word. English usage varies from capitalization in other languages.", + "Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states."] + }' +``` + + +## Examples + +Anything after `http://0.0.0.0:4000/cohere` is treated as a provider-specific route, and handled accordingly. + +Key Changes: + +| **Original Endpoint** | **Replace With** | +|------------------------------------------------------|-----------------------------------| +| `https://api.cohere.com` | `http://0.0.0.0:4000/cohere` (LITELLM_PROXY_BASE_URL="http://0.0.0.0:4000") | +| `bearer $CO_API_KEY` | `bearer anything` (use `bearer LITELLM_VIRTUAL_KEY` if Virtual Keys are setup on proxy) | + + +### **Example 1: Rerank endpoint** + +#### LiteLLM Proxy Call + +```bash +curl --request POST \ + --url http://0.0.0.0:4000/cohere/v1/rerank \ + --header 'accept: application/json' \ + --header 'content-type: application/json' \ + --header "Authorization: bearer sk-anything" \ + --data '{ + "model": "rerank-english-v3.0", + "query": "What is the capital of the United States?", + "top_n": 3, + "documents": ["Carson City is the capital city of the American state of Nevada.", + "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.", + "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.", + "Capitalization or capitalisation in English grammar is the use of a capital letter at the start of a word. English usage varies from capitalization in other languages.", + "Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states."] + }' +``` + +#### Direct Cohere API Call + +```bash +curl --request POST \ + --url https://api.cohere.com/v1/rerank \ + --header 'accept: application/json' \ + --header 'content-type: application/json' \ + --header "Authorization: bearer $CO_API_KEY" \ + --data '{ + "model": "rerank-english-v3.0", + "query": "What is the capital of the United States?", + "top_n": 3, + "documents": ["Carson City is the capital city of the American state of Nevada.", + "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.", + "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.", + "Capitalization or capitalisation in English grammar is the use of a capital letter at the start of a word. English usage varies from capitalization in other languages.", + "Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states."] + }' +``` + +### **Example 2: Chat API** + +#### LiteLLM Proxy Call + +```bash +curl --request POST \ + --url http://0.0.0.0:4000/cohere/v1/chat \ + --header 'accept: application/json' \ + --header 'content-type: application/json' \ + --header "Authorization: bearer sk-anything" \ + --data '{ + "chat_history": [ + {"role": "USER", "message": "Who discovered gravity?"}, + {"role": "CHATBOT", "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton"} + ], + "message": "What year was he born?", + "connectors": [{"id": "web-search"}] + }' +``` + +#### Direct Cohere API Call + +```bash +curl --request POST \ + --url https://api.cohere.com/v1/chat \ + --header 'accept: application/json' \ + --header 'content-type: application/json' \ + --header "Authorization: bearer $CO_API_KEY" \ + --data '{ + "chat_history": [ + {"role": "USER", "message": "Who discovered gravity?"}, + {"role": "CHATBOT", "message": "The man who is widely credited with discovering gravity is Sir Isaac Newton"} + ], + "message": "What year was he born?", + "connectors": [{"id": "web-search"}] + }' +``` + +### **Example 3: Embedding** + + +```bash +curl --request POST \ + --url https://api.cohere.com/v1/embed \ + --header 'accept: application/json' \ + --header 'content-type: application/json' \ + --header "Authorization: bearer sk-anything" \ + --data '{ + "model": "embed-english-v3.0", + "texts": ["hello", "goodbye"], + "input_type": "classification" + }' +``` + +#### Direct Cohere API Call + +```bash +curl --request POST \ + --url https://api.cohere.com/v1/embed \ + --header 'accept: application/json' \ + --header 'content-type: application/json' \ + --header "Authorization: bearer $CO_API_KEY" \ + --data '{ + "model": "embed-english-v3.0", + "texts": ["hello", "goodbye"], + "input_type": "classification" + }' +``` + + +## Advanced - Use with Virtual Keys + +Pre-requisites +- [Setup proxy with DB](../proxy/virtual_keys.md#setup) + +Use this, to avoid giving developers the raw Cohere API key, but still letting them use Cohere endpoints. + +### Usage + +1. Setup environment + +```bash +export DATABASE_URL="" +export LITELLM_MASTER_KEY="" +export COHERE_API_KEY="" +``` + +```bash +litellm + +# RUNNING on http://0.0.0.0:4000 +``` + +2. Generate virtual key + +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{}' +``` + +Expected Response + +```bash +{ + ... + "key": "sk-1234ewknldferwedojwojw" +} +``` + +3. Test it! + + +```bash +curl --request POST \ + --url http://0.0.0.0:4000/cohere/v1/rerank \ + --header 'accept: application/json' \ + --header 'content-type: application/json' \ + --header "Authorization: bearer sk-1234ewknldferwedojwojw" \ + --data '{ + "model": "rerank-english-v3.0", + "query": "What is the capital of the United States?", + "top_n": 3, + "documents": ["Carson City is the capital city of the American state of Nevada.", + "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.", + "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district.", + "Capitalization or capitalisation in English grammar is the use of a capital letter at the start of a word. English usage varies from capitalization in other languages.", + "Capital punishment (the death penalty) has existed in the United States since beforethe United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states."] + }' +``` \ No newline at end of file diff --git a/docs/my-website/docs/pass_through/google_ai_studio.md b/docs/my-website/docs/pass_through/google_ai_studio.md new file mode 100644 index 000000000..e37fa1218 --- /dev/null +++ b/docs/my-website/docs/pass_through/google_ai_studio.md @@ -0,0 +1,223 @@ +# Google AI Studio (Pass-Through) + +Pass-through endpoints for Google AI Studio - call provider-specific endpoint, in native format (no translation). + +Just replace `https://generativelanguage.googleapis.com` with `LITELLM_PROXY_BASE_URL/gemini` 🚀 + +#### **Example Usage** +```bash +http://0.0.0.0:4000/gemini/v1beta/models/gemini-1.5-flash:countTokens?key=sk-anything' \ +-H 'Content-Type: application/json' \ +-d '{ + "contents": [{ + "parts":[{ + "text": "The quick brown fox jumps over the lazy dog." + }] + }] +}' +``` + +Supports **ALL** Google AI Studio Endpoints (including streaming). + +[**See All Google AI Studio Endpoints**](https://ai.google.dev/api) + +## Quick Start + +Let's call the Gemini [`/countTokens` endpoint](https://ai.google.dev/api/tokens#method:-models.counttokens) + +1. Add Gemini API Key to your environment + +```bash +export GEMINI_API_KEY="" +``` + +2. Start LiteLLM Proxy + +```bash +litellm + +# RUNNING on http://0.0.0.0:4000 +``` + +3. Test it! + +Let's call the Google AI Studio token counting endpoint + +```bash +http://0.0.0.0:4000/gemini/v1beta/models/gemini-1.5-flash:countTokens?key=anything' \ +-H 'Content-Type: application/json' \ +-d '{ + "contents": [{ + "parts":[{ + "text": "The quick brown fox jumps over the lazy dog." + }] + }] +}' +``` + + +## Examples + +Anything after `http://0.0.0.0:4000/gemini` is treated as a provider-specific route, and handled accordingly. + +Key Changes: + +| **Original Endpoint** | **Replace With** | +|------------------------------------------------------|-----------------------------------| +| `https://generativelanguage.googleapis.com` | `http://0.0.0.0:4000/gemini` (LITELLM_PROXY_BASE_URL="http://0.0.0.0:4000") | +| `key=$GOOGLE_API_KEY` | `key=anything` (use `key=LITELLM_VIRTUAL_KEY` if Virtual Keys are setup on proxy) | + + +### **Example 1: Counting tokens** + +#### LiteLLM Proxy Call + +```bash +curl http://0.0.0.0:4000/gemini/v1beta/models/gemini-1.5-flash:countTokens?key=anything \ + -H 'Content-Type: application/json' \ + -X POST \ + -d '{ + "contents": [{ + "parts":[{ + "text": "The quick brown fox jumps over the lazy dog." + }], + }], + }' +``` + +#### Direct Google AI Studio Call + +```bash +curl https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:countTokens?key=$GOOGLE_API_KEY \ + -H 'Content-Type: application/json' \ + -X POST \ + -d '{ + "contents": [{ + "parts":[{ + "text": "The quick brown fox jumps over the lazy dog." + }], + }], + }' +``` + +### **Example 2: Generate content** + +#### LiteLLM Proxy Call + +```bash +curl "http://0.0.0.0:4000/gemini/v1beta/models/gemini-1.5-flash:generateContent?key=anything" \ + -H 'Content-Type: application/json' \ + -X POST \ + -d '{ + "contents": [{ + "parts":[{"text": "Write a story about a magic backpack."}] + }] + }' 2> /dev/null +``` + +#### Direct Google AI Studio Call + +```bash +curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key=$GOOGLE_API_KEY" \ + -H 'Content-Type: application/json' \ + -X POST \ + -d '{ + "contents": [{ + "parts":[{"text": "Write a story about a magic backpack."}] + }] + }' 2> /dev/null +``` + +### **Example 3: Caching** + + +```bash +curl -X POST "http://0.0.0.0:4000/gemini/v1beta/models/gemini-1.5-flash-001:generateContent?key=anything" \ +-H 'Content-Type: application/json' \ +-d '{ + "contents": [ + { + "parts":[{ + "text": "Please summarize this transcript" + }], + "role": "user" + }, + ], + "cachedContent": "'$CACHE_NAME'" + }' +``` + +#### Direct Google AI Studio Call + +```bash +curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-001:generateContent?key=$GOOGLE_API_KEY" \ +-H 'Content-Type: application/json' \ +-d '{ + "contents": [ + { + "parts":[{ + "text": "Please summarize this transcript" + }], + "role": "user" + }, + ], + "cachedContent": "'$CACHE_NAME'" + }' +``` + + +## Advanced - Use with Virtual Keys + +Pre-requisites +- [Setup proxy with DB](../proxy/virtual_keys.md#setup) + +Use this, to avoid giving developers the raw Google AI Studio key, but still letting them use Google AI Studio endpoints. + +### Usage + +1. Setup environment + +```bash +export DATABASE_URL="" +export LITELLM_MASTER_KEY="" +export GEMINI_API_KEY="" +``` + +```bash +litellm + +# RUNNING on http://0.0.0.0:4000 +``` + +2. Generate virtual key + +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{}' +``` + +Expected Response + +```bash +{ + ... + "key": "sk-1234ewknldferwedojwojw" +} +``` + +3. Test it! + + +```bash +http://0.0.0.0:4000/gemini/v1beta/models/gemini-1.5-flash:countTokens?key=sk-1234ewknldferwedojwojw' \ +-H 'Content-Type: application/json' \ +-d '{ + "contents": [{ + "parts":[{ + "text": "The quick brown fox jumps over the lazy dog." + }] + }] +}' +``` \ No newline at end of file diff --git a/docs/my-website/docs/pass_through/langfuse.md b/docs/my-website/docs/pass_through/langfuse.md new file mode 100644 index 000000000..8987842f7 --- /dev/null +++ b/docs/my-website/docs/pass_through/langfuse.md @@ -0,0 +1,132 @@ +# Langfuse Endpoints (Pass-Through) + +Pass-through endpoints for Langfuse - call langfuse endpoints with LiteLLM Virtual Key. + +Just replace `https://us.cloud.langfuse.com` with `LITELLM_PROXY_BASE_URL/langfuse` 🚀 + +#### **Example Usage** +```python +from langfuse import Langfuse + +langfuse = Langfuse( + host="http://localhost:4000/langfuse", # your litellm proxy endpoint + public_key="anything", # no key required since this is a pass through + secret_key="LITELLM_VIRTUAL_KEY", # no key required since this is a pass through +) + +print("sending langfuse trace request") +trace = langfuse.trace(name="test-trace-litellm-proxy-passthrough") +print("flushing langfuse request") +langfuse.flush() + +print("flushed langfuse request") +``` + +Supports **ALL** Langfuse Endpoints. + +[**See All Langfuse Endpoints**](https://api.reference.langfuse.com/) + +## Quick Start + +Let's log a trace to Langfuse. + +1. Add Langfuse Public/Private keys to environment + +```bash +export LANGFUSE_PUBLIC_KEY="" +export LANGFUSE_PRIVATE_KEY="" +``` + +2. Start LiteLLM Proxy + +```bash +litellm + +# RUNNING on http://0.0.0.0:4000 +``` + +3. Test it! + +Let's log a trace to Langfuse! + +```python +from langfuse import Langfuse + +langfuse = Langfuse( + host="http://localhost:4000/langfuse", # your litellm proxy endpoint + public_key="anything", # no key required since this is a pass through + secret_key="anything", # no key required since this is a pass through +) + +print("sending langfuse trace request") +trace = langfuse.trace(name="test-trace-litellm-proxy-passthrough") +print("flushing langfuse request") +langfuse.flush() + +print("flushed langfuse request") +``` + + +## Advanced - Use with Virtual Keys + +Pre-requisites +- [Setup proxy with DB](../proxy/virtual_keys.md#setup) + +Use this, to avoid giving developers the raw Google AI Studio key, but still letting them use Google AI Studio endpoints. + +### Usage + +1. Setup environment + +```bash +export DATABASE_URL="" +export LITELLM_MASTER_KEY="" +export LANGFUSE_PUBLIC_KEY="" +export LANGFUSE_PRIVATE_KEY="" +``` + +```bash +litellm + +# RUNNING on http://0.0.0.0:4000 +``` + +2. Generate virtual key + +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{}' +``` + +Expected Response + +```bash +{ + ... + "key": "sk-1234ewknldferwedojwojw" +} +``` + +3. Test it! + + +```python +from langfuse import Langfuse + +langfuse = Langfuse( + host="http://localhost:4000/langfuse", # your litellm proxy endpoint + public_key="anything", # no key required since this is a pass through + secret_key="sk-1234ewknldferwedojwojw", # no key required since this is a pass through +) + +print("sending langfuse trace request") +trace = langfuse.trace(name="test-trace-litellm-proxy-passthrough") +print("flushing langfuse request") +langfuse.flush() + +print("flushed langfuse request") +``` + +## [Advanced - Log to separate langfuse projects (by key/team)](../proxy/team_logging.md) \ No newline at end of file diff --git a/docs/my-website/docs/pass_through/vertex_ai.md b/docs/my-website/docs/pass_through/vertex_ai.md new file mode 100644 index 000000000..53ae41091 --- /dev/null +++ b/docs/my-website/docs/pass_through/vertex_ai.md @@ -0,0 +1,101 @@ +# [BETA] Vertex AI Endpoints (Pass-Through) + +Pass-through endpoints for Vertex AI - call provider-specific endpoint, in native format (no translation). + +:::tip + +Looking for the Unified API (OpenAI format) for VertexAI ? [Go here - using vertexAI with LiteLLM SDK or LiteLLM Proxy Server](../docs/providers/vertex.md) + +::: + +## Supported API Endpoints + +- Gemini API +- Embeddings API +- Imagen API +- Code Completion API +- Batch prediction API +- Tuning API +- CountTokens API + +## Quick Start Usage + +#### 1. Set `default_vertex_config` on your `config.yaml` + + +Add the following credentials to your litellm config.yaml to use the Vertex AI endpoints. + +```yaml +default_vertex_config: + vertex_project: "adroit-crow-413218" + vertex_location: "us-central1" + vertex_credentials: "/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json +``` + +#### 2. Start litellm proxy + +```shell +litellm --config /path/to/config.yaml +``` + +#### 3. Test it + +```shell +curl http://localhost:4000/vertex-ai/publishers/google/models/textembedding-gecko@001:countTokens \ +-H "Content-Type: application/json" \ +-H "Authorization: Bearer sk-1234" \ +-d '{"instances":[{"content": "gm"}]}' +``` +## Usage Examples + +### Gemini API (Generate Content) + +```shell +curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.5-flash-001:generateContent \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}' +``` + +### Embeddings API + +```shell +curl http://localhost:4000/vertex-ai/publishers/google/models/textembedding-gecko@001:predict \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{"instances":[{"content": "gm"}]}' +``` + +### Imagen API + +```shell +curl http://localhost:4000/vertex-ai/publishers/google/models/imagen-3.0-generate-001:predict \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{"instances":[{"prompt": "make an otter"}], "parameters": {"sampleCount": 1}}' +``` + +### Count Tokens API + +```shell +curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.5-flash-001:countTokens \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}' +``` + +### Tuning API + +Create Fine Tuning Job + +```shell +curl http://localhost:4000/vertex-ai/tuningJobs \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "baseModel": "gemini-1.0-pro-002", + "supervisedTuningSpec" : { + "training_dataset_uri": "gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl" + } +}' +``` \ No newline at end of file diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md index a662129d0..2a7804bfd 100644 --- a/docs/my-website/docs/providers/anthropic.md +++ b/docs/my-website/docs/providers/anthropic.md @@ -22,6 +22,7 @@ Anthropic API fails requests when `max_tokens` are not passed. Due to this litel import os os.environ["ANTHROPIC_API_KEY"] = "your-api-key" +# os.environ["ANTHROPIC_API_BASE"] = "" # [OPTIONAL] or 'ANTHROPIC_BASE_URL' ``` ## Usage @@ -55,7 +56,7 @@ for chunk in response: print(chunk["choices"][0]["delta"]["content"]) # same as openai format ``` -## OpenAI Proxy Usage +## Usage with LiteLLM Proxy Here's how to call Anthropic with the LiteLLM Proxy Server @@ -68,14 +69,6 @@ export ANTHROPIC_API_KEY="your-api-key" ### 2. Start the proxy - - -```bash -$ litellm --model claude-3-opus-20240229 - -# Server running on http://0.0.0.0:4000 -``` - ```yaml @@ -90,6 +83,55 @@ model_list: litellm --config /path/to/config.yaml ``` + + +Use this if you want to make requests to `claude-3-haiku-20240307`,`claude-3-opus-20240229`,`claude-2.1` without defining them on the config.yaml + +#### Required env variables +``` +ANTHROPIC_API_KEY=sk-ant**** +``` + +```yaml +model_list: + - model_name: "*" + litellm_params: + model: "*" +``` + +```bash +litellm --config /path/to/config.yaml +``` + +Example Request for this config.yaml + +**Ensure you use `anthropic/` prefix to route the request to Anthropic API** + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "anthropic/claude-3-haiku-20240307", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + + + + +```bash +$ litellm --model claude-3-opus-20240229 + +# Server running on http://0.0.0.0:4000 +``` + ### 3. Test it @@ -183,9 +225,336 @@ print(response) | claude-instant-1.2 | `completion('claude-instant-1.2', messages)` | `os.environ['ANTHROPIC_API_KEY']` | | claude-instant-1 | `completion('claude-instant-1', messages)` | `os.environ['ANTHROPIC_API_KEY']` | -## Advanced +## **Prompt Caching** -## Usage - Function Calling +Use Anthropic Prompt Caching + + +[Relevant Anthropic API Docs](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching) + +### Caching - Large Context Caching + +This example demonstrates basic Prompt Caching usage, caching the full text of the legal agreement as a prefix while keeping the user instruction uncached. + + + + +```python +response = await litellm.acompletion( + model="anthropic/claude-3-5-sonnet-20240620", + messages=[ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "You are an AI assistant tasked with analyzing legal documents.", + }, + { + "type": "text", + "text": "Here is the full text of a complex legal agreement", + "cache_control": {"type": "ephemeral"}, + }, + ], + }, + { + "role": "user", + "content": "what are the key terms and conditions in this agreement?", + }, + ], + extra_headers={ + "anthropic-version": "2023-06-01", + "anthropic-beta": "prompt-caching-2024-07-31", + }, +) + +``` + + + +:::info + +LiteLLM Proxy is OpenAI compatible + +This is an example using the OpenAI Python SDK sending a request to LiteLLM Proxy + +Assuming you have a model=`anthropic/claude-3-5-sonnet-20240620` on the [litellm proxy config.yaml](#usage-with-litellm-proxy) + +::: + +```python +import openai +client = openai.AsyncOpenAI( + api_key="anything", # litellm proxy api key + base_url="http://0.0.0.0:4000" # litellm proxy base url +) + + +response = await client.chat.completions.create( + model="anthropic/claude-3-5-sonnet-20240620", + messages=[ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "You are an AI assistant tasked with analyzing legal documents.", + }, + { + "type": "text", + "text": "Here is the full text of a complex legal agreement", + "cache_control": {"type": "ephemeral"}, + }, + ], + }, + { + "role": "user", + "content": "what are the key terms and conditions in this agreement?", + }, + ], + extra_headers={ + "anthropic-version": "2023-06-01", + "anthropic-beta": "prompt-caching-2024-07-31", + }, +) + +``` + + + + +### Caching - Tools definitions + +In this example, we demonstrate caching tool definitions. + +The cache_control parameter is placed on the final tool + + + + +```python +import litellm + +response = await litellm.acompletion( + model="anthropic/claude-3-5-sonnet-20240620", + messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + "cache_control": {"type": "ephemeral"} + }, + } + ], + extra_headers={ + "anthropic-version": "2023-06-01", + "anthropic-beta": "prompt-caching-2024-07-31", + }, +) +``` + + + +:::info + +LiteLLM Proxy is OpenAI compatible + +This is an example using the OpenAI Python SDK sending a request to LiteLLM Proxy + +Assuming you have a model=`anthropic/claude-3-5-sonnet-20240620` on the [litellm proxy config.yaml](#usage-with-litellm-proxy) + +::: + +```python +import openai +client = openai.AsyncOpenAI( + api_key="anything", # litellm proxy api key + base_url="http://0.0.0.0:4000" # litellm proxy base url +) + +response = await client.chat.completions.create( + model="anthropic/claude-3-5-sonnet-20240620", + messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + "cache_control": {"type": "ephemeral"} + }, + } + ], + extra_headers={ + "anthropic-version": "2023-06-01", + "anthropic-beta": "prompt-caching-2024-07-31", + }, +) +``` + + + + + +### Caching - Continuing Multi-Turn Convo + +In this example, we demonstrate how to use Prompt Caching in a multi-turn conversation. + +The cache_control parameter is placed on the system message to designate it as part of the static prefix. + +The conversation history (previous messages) is included in the messages array. The final turn is marked with cache-control, for continuing in followups. The second-to-last user message is marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache. + + + + +```python +import litellm + +response = await litellm.acompletion( + model="anthropic/claude-3-5-sonnet-20240620", + messages=[ + # System Message + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Here is the full text of a complex legal agreement" + * 400, + "cache_control": {"type": "ephemeral"}, + } + ], + }, + # marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache. + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What are the key terms and conditions in this agreement?", + "cache_control": {"type": "ephemeral"}, + } + ], + }, + { + "role": "assistant", + "content": "Certainly! the key terms and conditions are the following: the contract is 1 year long for $10/mo", + }, + # The final turn is marked with cache-control, for continuing in followups. + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What are the key terms and conditions in this agreement?", + "cache_control": {"type": "ephemeral"}, + } + ], + }, + ], + extra_headers={ + "anthropic-version": "2023-06-01", + "anthropic-beta": "prompt-caching-2024-07-31", + }, +) +``` + + + +:::info + +LiteLLM Proxy is OpenAI compatible + +This is an example using the OpenAI Python SDK sending a request to LiteLLM Proxy + +Assuming you have a model=`anthropic/claude-3-5-sonnet-20240620` on the [litellm proxy config.yaml](#usage-with-litellm-proxy) + +::: + +```python +import openai +client = openai.AsyncOpenAI( + api_key="anything", # litellm proxy api key + base_url="http://0.0.0.0:4000" # litellm proxy base url +) + +response = await client.chat.completions.create( + model="anthropic/claude-3-5-sonnet-20240620", + messages=[ + # System Message + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Here is the full text of a complex legal agreement" + * 400, + "cache_control": {"type": "ephemeral"}, + } + ], + }, + # marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache. + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What are the key terms and conditions in this agreement?", + "cache_control": {"type": "ephemeral"}, + } + ], + }, + { + "role": "assistant", + "content": "Certainly! the key terms and conditions are the following: the contract is 1 year long for $10/mo", + }, + # The final turn is marked with cache-control, for continuing in followups. + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What are the key terms and conditions in this agreement?", + "cache_control": {"type": "ephemeral"}, + } + ], + }, + ], + extra_headers={ + "anthropic-version": "2023-06-01", + "anthropic-beta": "prompt-caching-2024-07-31", + }, +) +``` + + + + +## **Function/Tool Calling** :::info @@ -374,6 +743,20 @@ resp = litellm.completion( print(f"\nResponse: {resp}") ``` +## **Passing Extra Headers to Anthropic API** + +Pass `extra_headers: dict` to `litellm.completion` + +```python +from litellm import completion +messages = [{"role": "user", "content": "What is Anthropic?"}] +response = completion( + model="claude-3-5-sonnet-20240620", + messages=messages, + extra_headers={"anthropic-beta": "max-tokens-3-5-sonnet-2024-07-15"} +) +``` + ## Usage - "Assistant Pre-fill" You can "put words in Claude's mouth" by including an `assistant` role message as the last item in the `messages` array. diff --git a/docs/my-website/docs/providers/aws_sagemaker.md b/docs/my-website/docs/providers/aws_sagemaker.md index 2b65709e8..0f8a55261 100644 --- a/docs/my-website/docs/providers/aws_sagemaker.md +++ b/docs/my-website/docs/providers/aws_sagemaker.md @@ -1,10 +1,18 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem' + # AWS Sagemaker LiteLLM supports All Sagemaker Huggingface Jumpstart Models +:::tip + +**We support ALL Sagemaker models, just set `model=sagemaker/` as a prefix when sending litellm requests** + +::: + + ### API KEYS ```python -!pip install boto3 - os.environ["AWS_ACCESS_KEY_ID"] = "" os.environ["AWS_SECRET_ACCESS_KEY"] = "" os.environ["AWS_REGION_NAME"] = "" @@ -27,6 +35,327 @@ response = completion( ) ``` +### Usage - Streaming +Sagemaker currently does not support streaming - LiteLLM fakes streaming by returning chunks of the response string + +```python +import os +from litellm import completion + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +response = completion( + model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", + messages=[{ "content": "Hello, how are you?","role": "user"}], + temperature=0.2, + max_tokens=80, + stream=True, + ) +for chunk in response: + print(chunk) +``` + + +## **LiteLLM Proxy Usage** + +Here's how to call Sagemaker with the LiteLLM Proxy Server + +### 1. Setup config.yaml + +```yaml +model_list: + - model_name: jumpstart-model + litellm_params: + model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614 + aws_access_key_id: os.environ/CUSTOM_AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/CUSTOM_AWS_SECRET_ACCESS_KEY + aws_region_name: os.environ/CUSTOM_AWS_REGION_NAME +``` + +All possible auth params: + +``` +aws_access_key_id: Optional[str], +aws_secret_access_key: Optional[str], +aws_session_token: Optional[str], +aws_region_name: Optional[str], +aws_session_name: Optional[str], +aws_profile_name: Optional[str], +aws_role_name: Optional[str], +aws_web_identity_token: Optional[str], +``` + +### 2. Start the proxy + +```bash +litellm --config /path/to/config.yaml +``` +### 3. Test it + + + + + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "jumpstart-model", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create(model="jumpstart-model", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +]) + +print(response) + +``` + + + +```python +from langchain.chat_models import ChatOpenAI +from langchain.prompts.chat import ( + ChatPromptTemplate, + HumanMessagePromptTemplate, + SystemMessagePromptTemplate, +) +from langchain.schema import HumanMessage, SystemMessage + +chat = ChatOpenAI( + openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy + model = "jumpstart-model", + temperature=0.1 +) + +messages = [ + SystemMessage( + content="You are a helpful assistant that im using to make a test request to." + ), + HumanMessage( + content="test from litellm. tell me why it's amazing in 1 sentence" + ), +] +response = chat(messages) + +print(response) +``` + + + +## Set temperature, top p, etc. + + + + +```python +import os +from litellm import completion + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +response = completion( + model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614", + messages=[{ "content": "Hello, how are you?","role": "user"}], + temperature=0.7, + top_p=1 +) +``` + + + +**Set on yaml** + +```yaml +model_list: + - model_name: jumpstart-model + litellm_params: + model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614 + temperature: + top_p: +``` + +**Set on request** + +```python + +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create(model="jumpstart-model", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +], +temperature=0.7, +top_p=1 +) + +print(response) + +``` + + + + +## **Allow setting temperature=0** for Sagemaker + +By default when `temperature=0` is sent in requests to LiteLLM, LiteLLM rounds up to `temperature=0.1` since Sagemaker fails most requests when `temperature=0` + +If you want to send `temperature=0` for your model here's how to set it up (Since Sagemaker can host any kind of model, some models allow zero temperature) + + + + +```python +import os +from litellm import completion + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +response = completion( + model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614", + messages=[{ "content": "Hello, how are you?","role": "user"}], + temperature=0, + aws_sagemaker_allow_zero_temp=True, +) +``` + + + +**Set `aws_sagemaker_allow_zero_temp` on yaml** + +```yaml +model_list: + - model_name: jumpstart-model + litellm_params: + model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614 + aws_sagemaker_allow_zero_temp: true +``` + +**Set `temperature=0` on request** + +```python + +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create(model="jumpstart-model", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +], +temperature=0, +) + +print(response) + +``` + + + + +## Pass provider-specific params + +If you pass a non-openai param to litellm, we'll assume it's provider-specific and send it as a kwarg in the request body. [See more](../completion/input.md#provider-specific-params) + + + + +```python +import os +from litellm import completion + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +response = completion( + model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614", + messages=[{ "content": "Hello, how are you?","role": "user"}], + top_k=1 # 👈 PROVIDER-SPECIFIC PARAM +) +``` + + + +**Set on yaml** + +```yaml +model_list: + - model_name: jumpstart-model + litellm_params: + model: sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614 + top_k: 1 # 👈 PROVIDER-SPECIFIC PARAM +``` + +**Set on request** + +```python + +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create(model="jumpstart-model", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +], +temperature=0.7, +extra_body={ + top_k=1 # 👈 PROVIDER-SPECIFIC PARAM +} +) + +print(response) + +``` + + + + + ### Passing Inference Component Name If you have multiple models on an endpoint, you'll need to specify the individual model names, do this via `model_id`. @@ -85,29 +414,16 @@ response = completion( You can also pass in your own [custom prompt template](../completion/prompt_formatting.md#format-prompt-yourself) -### Usage - Streaming -Sagemaker currently does not support streaming - LiteLLM fakes streaming by returning chunks of the response string - -```python -import os -from litellm import completion - -os.environ["AWS_ACCESS_KEY_ID"] = "" -os.environ["AWS_SECRET_ACCESS_KEY"] = "" -os.environ["AWS_REGION_NAME"] = "" - -response = completion( - model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", - messages=[{ "content": "Hello, how are you?","role": "user"}], - temperature=0.2, - max_tokens=80, - stream=True, - ) -for chunk in response: - print(chunk) -``` ### Completion Models + + +:::tip + +**We support ALL Sagemaker models, just set `model=sagemaker/` as a prefix when sending litellm requests** + +::: + Here's an example of using a sagemaker model with LiteLLM | Model Name | Function Call | @@ -120,7 +436,7 @@ Here's an example of using a sagemaker model with LiteLLM | Meta Llama 2 70B | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | Meta Llama 2 70B (Chat/Fine-tuned) | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b-b-f', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | -### Embedding Models +## Embedding Models LiteLLM supports all Sagemaker Jumpstart Huggingface Embedding models. Here's how to call it: diff --git a/docs/my-website/docs/providers/azure.md b/docs/my-website/docs/providers/azure.md index 2e11369a7..be3401fd2 100644 --- a/docs/my-website/docs/providers/azure.md +++ b/docs/my-website/docs/providers/azure.md @@ -66,8 +66,15 @@ response = litellm.completion( ## Azure OpenAI Chat Completion Models +:::tip + +**We support ALL Azure models, just set `model=azure/` as a prefix when sending litellm requests** + +::: + | Model Name | Function Call | |------------------|----------------------------------------| +| gpt-4o-mini | `completion('azure/', messages)` | | gpt-4o | `completion('azure/', messages)` | | gpt-4 | `completion('azure/', messages)` | | gpt-4-0314 | `completion('azure/', messages)` | diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index b72dac10b..a1a056d41 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -36,40 +36,40 @@ response = completion( ) ``` -## OpenAI Proxy Usage +## LiteLLM Proxy Usage Here's how to call Anthropic with the LiteLLM Proxy Server -### 1. Save key in your environment - -```bash -export AWS_ACCESS_KEY_ID="" -export AWS_SECRET_ACCESS_KEY="" -export AWS_REGION_NAME="" -``` - -### 2. Start the proxy - - - - -```bash -$ litellm --model anthropic.claude-3-sonnet-20240229-v1:0 - -# Server running on http://0.0.0.0:4000 -``` - - +### 1. Setup config.yaml ```yaml model_list: - model_name: bedrock-claude-v1 litellm_params: model: bedrock/anthropic.claude-instant-v1 + aws_access_key_id: os.environ/CUSTOM_AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/CUSTOM_AWS_SECRET_ACCESS_KEY + aws_region_name: os.environ/CUSTOM_AWS_REGION_NAME ``` - - +All possible auth params: + +``` +aws_access_key_id: Optional[str], +aws_secret_access_key: Optional[str], +aws_session_token: Optional[str], +aws_region_name: Optional[str], +aws_session_name: Optional[str], +aws_profile_name: Optional[str], +aws_role_name: Optional[str], +aws_web_identity_token: Optional[str], +``` + +### 2. Start the proxy + +```bash +litellm --config /path/to/config.yaml +``` ### 3. Test it @@ -360,6 +360,120 @@ resp = litellm.completion( print(f"\nResponse: {resp}") ``` + +## Usage - Bedrock Guardrails + +Example of using [Bedrock Guardrails with LiteLLM](https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-use-converse-api.html) + + + + +```python +from litellm import completion + +# set env +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +response = completion( + model="anthropic.claude-v2", + messages=[ + { + "content": "where do i buy coffee from? ", + "role": "user", + } + ], + max_tokens=10, + guardrailConfig={ + "guardrailIdentifier": "ff6ujrregl1q", # The identifier (ID) for the guardrail. + "guardrailVersion": "DRAFT", # The version of the guardrail. + "trace": "disabled", # The trace behavior for the guardrail. Can either be "disabled" or "enabled" + }, +) +``` + + + +```python + +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create(model="anthropic.claude-v2", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +], +temperature=0.7, +extra_body={ + "guardrailConfig": { + "guardrailIdentifier": "ff6ujrregl1q", # The identifier (ID) for the guardrail. + "guardrailVersion": "DRAFT", # The version of the guardrail. + "trace": "disabled", # The trace behavior for the guardrail. Can either be "disabled" or "enabled" + }, +} +) + +print(response) +``` + + + +1. Update config.yaml + +```yaml +model_list: + - model_name: bedrock-claude-v1 + litellm_params: + model: bedrock/anthropic.claude-instant-v1 + aws_access_key_id: os.environ/CUSTOM_AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/CUSTOM_AWS_SECRET_ACCESS_KEY + aws_region_name: os.environ/CUSTOM_AWS_REGION_NAME + guardrailConfig: { + "guardrailIdentifier": "ff6ujrregl1q", # The identifier (ID) for the guardrail. + "guardrailVersion": "DRAFT", # The version of the guardrail. + "trace": "disabled", # The trace behavior for the guardrail. Can either be "disabled" or "enabled" + } + +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```python + +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create(model="bedrock-claude-v1", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +], +temperature=0.7 +) + +print(response) +``` + + + ## Usage - "Assistant Pre-fill" If you're using Anthropic's Claude with Bedrock, you can "put words in Claude's mouth" by including an `assistant` role message as the last item in the `messages` array. @@ -623,7 +737,7 @@ response = litellm.embedding( ## Supported AWS Bedrock Models -Here's an example of using a bedrock model with LiteLLM +Here's an example of using a bedrock model with LiteLLM. For a complete list, refer to the [model cost map](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json) | Model Name | Command | |----------------------------|------------------------------------------------------------------| @@ -641,6 +755,7 @@ Here's an example of using a bedrock model with LiteLLM | Cohere Command | `completion(model='bedrock/cohere.command-text-v14', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | AI21 J2-Mid | `completion(model='bedrock/ai21.j2-mid-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | AI21 J2-Ultra | `completion(model='bedrock/ai21.j2-ultra-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| AI21 Jamba-Instruct | `completion(model='bedrock/ai21.jamba-instruct-v1:0', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | Meta Llama 2 Chat 13b | `completion(model='bedrock/meta.llama2-13b-chat-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | Meta Llama 2 Chat 70b | `completion(model='bedrock/meta.llama2-70b-chat-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | Mistral 7B Instruct | `completion(model='bedrock/mistral.mistral-7b-instruct-v0:2', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | diff --git a/docs/my-website/docs/providers/custom_llm_server.md b/docs/my-website/docs/providers/custom_llm_server.md new file mode 100644 index 000000000..400f45b7f --- /dev/null +++ b/docs/my-website/docs/providers/custom_llm_server.md @@ -0,0 +1,168 @@ +# Custom API Server (Custom Format) + +Call your custom torch-serve / internal LLM APIs via LiteLLM + +:::info + +- For calling an openai-compatible endpoint, [go here](./openai_compatible.md) +- For modifying incoming/outgoing calls on proxy, [go here](../proxy/call_hooks.md) +::: + +## Quick Start + +```python +import litellm +from litellm import CustomLLM, completion, get_llm_provider + + +class MyCustomLLM(CustomLLM): + def completion(self, *args, **kwargs) -> litellm.ModelResponse: + return litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello world"}], + mock_response="Hi!", + ) # type: ignore + +litellm.custom_provider_map = [ # 👈 KEY STEP - REGISTER HANDLER + {"provider": "my-custom-llm", "custom_handler": my_custom_llm} + ] + +resp = completion( + model="my-custom-llm/my-fake-model", + messages=[{"role": "user", "content": "Hello world!"}], + ) + +assert resp.choices[0].message.content == "Hi!" +``` + +## OpenAI Proxy Usage + +1. Setup your `custom_handler.py` file + +```python +import litellm +from litellm import CustomLLM, completion, get_llm_provider + + +class MyCustomLLM(CustomLLM): + def completion(self, *args, **kwargs) -> litellm.ModelResponse: + return litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello world"}], + mock_response="Hi!", + ) # type: ignore + + async def acompletion(self, *args, **kwargs) -> litellm.ModelResponse: + return litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello world"}], + mock_response="Hi!", + ) # type: ignore + + +my_custom_llm = MyCustomLLM() +``` + +2. Add to `config.yaml` + +In the config below, we pass + +python_filename: `custom_handler.py` +custom_handler_instance_name: `my_custom_llm`. This is defined in Step 1 + +custom_handler: `custom_handler.my_custom_llm` + +```yaml +model_list: + - model_name: "test-model" + litellm_params: + model: "openai/text-embedding-ada-002" + - model_name: "my-custom-model" + litellm_params: + model: "my-custom-llm/my-model" + +litellm_settings: + custom_provider_map: + - {"provider": "my-custom-llm", "custom_handler": custom_handler.my_custom_llm} +``` + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "my-custom-model", + "messages": [{"role": "user", "content": "Say \"this is a test\" in JSON!"}], +}' +``` + +Expected Response + +``` +{ + "id": "chatcmpl-06f1b9cd-08bc-43f7-9814-a69173921216", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Hi!", + "role": "assistant", + "tool_calls": null, + "function_call": null + } + } + ], + "created": 1721955063, + "model": "gpt-3.5-turbo", + "object": "chat.completion", + "system_fingerprint": null, + "usage": { + "prompt_tokens": 10, + "completion_tokens": 20, + "total_tokens": 30 + } +} +``` + +## Custom Handler Spec + +```python +from litellm.types.utils import GenericStreamingChunk, ModelResponse +from typing import Iterator, AsyncIterator +from litellm.llms.base import BaseLLM + +class CustomLLMError(Exception): # use this for all your exceptions + def __init__( + self, + status_code, + message, + ): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + +class CustomLLM(BaseLLM): + def __init__(self) -> None: + super().__init__() + + def completion(self, *args, **kwargs) -> ModelResponse: + raise CustomLLMError(status_code=500, message="Not implemented yet!") + + def streaming(self, *args, **kwargs) -> Iterator[GenericStreamingChunk]: + raise CustomLLMError(status_code=500, message="Not implemented yet!") + + async def acompletion(self, *args, **kwargs) -> ModelResponse: + raise CustomLLMError(status_code=500, message="Not implemented yet!") + + async def astreaming(self, *args, **kwargs) -> AsyncIterator[GenericStreamingChunk]: + raise CustomLLMError(status_code=500, message="Not implemented yet!") +``` diff --git a/docs/my-website/docs/providers/custom_openai_proxy.md b/docs/my-website/docs/providers/custom_openai_proxy.md deleted file mode 100644 index b6f2eccac..000000000 --- a/docs/my-website/docs/providers/custom_openai_proxy.md +++ /dev/null @@ -1,129 +0,0 @@ -# Custom API Server (OpenAI Format) - -LiteLLM allows you to call your custom endpoint in the OpenAI ChatCompletion format - -## API KEYS -No api keys required - -## Set up your Custom API Server -Your server should have the following Endpoints: - -Here's an example OpenAI proxy server with routes: https://replit.com/@BerriAI/openai-proxy#main.py - -### Required Endpoints -- POST `/chat/completions` - chat completions endpoint - -### Optional Endpoints -- POST `/completions` - completions endpoint -- Get `/models` - available models on server -- POST `/embeddings` - creates an embedding vector representing the input text. - - -## Example Usage - -### Call `/chat/completions` -In order to use your custom OpenAI Chat Completion proxy with LiteLLM, ensure you set - -* `api_base` to your proxy url, example "https://openai-proxy.berriai.repl.co" -* `custom_llm_provider` to `openai` this ensures litellm uses the `openai.ChatCompletion` to your api_base - -```python -import os -from litellm import completion - -## set ENV variables -os.environ["OPENAI_API_KEY"] = "anything" #key is not used for proxy - -messages = [{ "content": "Hello, how are you?","role": "user"}] - -response = completion( - model="command-nightly", - messages=[{ "content": "Hello, how are you?","role": "user"}], - api_base="https://openai-proxy.berriai.repl.co", - custom_llm_provider="openai" # litellm will use the openai.ChatCompletion to make the request - -) -print(response) -``` - -#### Response -```json -{ - "object": - "chat.completion", - "choices": [{ - "finish_reason": "stop", - "index": 0, - "message": { - "content": - "The sky, a canvas of blue,\nA work of art, pure and true,\nA", - "role": "assistant" - } - }], - "id": - "chatcmpl-7fbd6077-de10-4cb4-a8a4-3ef11a98b7c8", - "created": - 1699290237.408061, - "model": - "togethercomputer/llama-2-70b-chat", - "usage": { - "completion_tokens": 18, - "prompt_tokens": 14, - "total_tokens": 32 - } - } -``` - - -### Call `/completions` -In order to use your custom OpenAI Completion proxy with LiteLLM, ensure you set - -* `api_base` to your proxy url, example "https://openai-proxy.berriai.repl.co" -* `custom_llm_provider` to `text-completion-openai` this ensures litellm uses the `openai.Completion` to your api_base - -```python -import os -from litellm import completion - -## set ENV variables -os.environ["OPENAI_API_KEY"] = "anything" #key is not used for proxy - -messages = [{ "content": "Hello, how are you?","role": "user"}] - -response = completion( - model="command-nightly", - messages=[{ "content": "Hello, how are you?","role": "user"}], - api_base="https://openai-proxy.berriai.repl.co", - custom_llm_provider="text-completion-openai" # litellm will use the openai.Completion to make the request - -) -print(response) -``` - -#### Response -```json -{ - "warning": - "This model version is deprecated. Migrate before January 4, 2024 to avoid disruption of service. Learn more https://platform.openai.com/docs/deprecations", - "id": - "cmpl-8HxHqF5dymQdALmLplS0dWKZVFe3r", - "object": - "text_completion", - "created": - 1699290166, - "model": - "text-davinci-003", - "choices": [{ - "text": - "\n\nThe weather in San Francisco varies depending on what time of year and time", - "index": 0, - "logprobs": None, - "finish_reason": "length" - }], - "usage": { - "prompt_tokens": 7, - "completion_tokens": 16, - "total_tokens": 23 - } - } -``` \ No newline at end of file diff --git a/docs/my-website/docs/providers/databricks.md b/docs/my-website/docs/providers/databricks.md index 633350d22..395a544db 100644 --- a/docs/my-website/docs/providers/databricks.md +++ b/docs/my-website/docs/providers/databricks.md @@ -5,6 +5,11 @@ import TabItem from '@theme/TabItem'; LiteLLM supports all models on Databricks +:::tip + +**We support ALL Databricks models, just set `model=databricks/` as a prefix when sending litellm requests** + +::: ## Usage @@ -185,8 +190,17 @@ response = litellm.embedding( ## Supported Databricks Chat Completion Models +:::tip + +**We support ALL Databricks models, just set `model=databricks/` as a prefix when sending litellm requests** + +::: + + | Model Name | Command | |----------------------------|------------------------------------------------------------------| +| databricks-meta-llama-3-1-70b-instruct | `completion(model='databricks/databricks-meta-llama-3-1-70b-instruct', messages=messages)` | +| databricks-meta-llama-3-1-405b-instruct | `completion(model='databricks/databricks-meta-llama-3-1-405b-instruct', messages=messages)` | | databricks-dbrx-instruct | `completion(model='databricks/databricks-dbrx-instruct', messages=messages)` | | databricks-meta-llama-3-70b-instruct | `completion(model='databricks/databricks-meta-llama-3-70b-instruct', messages=messages)` | | databricks-llama-2-70b-chat | `completion(model='databricks/databricks-llama-2-70b-chat', messages=messages)` | @@ -196,6 +210,13 @@ response = litellm.embedding( ## Supported Databricks Embedding Models +:::tip + +**We support ALL Databricks models, just set `model=databricks/` as a prefix when sending litellm requests** + +::: + + | Model Name | Command | |----------------------------|------------------------------------------------------------------| | databricks-bge-large-en | `embedding(model='databricks/databricks-bge-large-en', messages=messages)` | diff --git a/docs/my-website/docs/providers/fireworks_ai.md b/docs/my-website/docs/providers/fireworks_ai.md index ba50bd1f2..9d05b8ee1 100644 --- a/docs/my-website/docs/providers/fireworks_ai.md +++ b/docs/my-website/docs/providers/fireworks_ai.md @@ -1,7 +1,12 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Fireworks AI https://fireworks.ai/ +:::info **We support ALL Fireworks AI models, just set `fireworks_ai/` as a prefix when sending completion requests** +::: ## API Key ```python @@ -16,7 +21,7 @@ import os os.environ['FIREWORKS_AI_API_KEY'] = "" response = completion( - model="fireworks_ai/mixtral-8x7b-instruct", + model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct", messages=[ {"role": "user", "content": "hello from litellm"} ], @@ -31,7 +36,7 @@ import os os.environ['FIREWORKS_AI_API_KEY'] = "" response = completion( - model="fireworks_ai/mixtral-8x7b-instruct", + model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct", messages=[ {"role": "user", "content": "hello from litellm"} ], @@ -43,8 +48,103 @@ for chunk in response: ``` +## Usage with LiteLLM Proxy + +### 1. Set Fireworks AI Models on config.yaml + +```yaml +model_list: + - model_name: fireworks-llama-v3-70b-instruct + litellm_params: + model: fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct + api_key: "os.environ/FIREWORKS_AI_API_KEY" +``` + +### 2. Start Proxy + +``` +litellm --config config.yaml +``` + +### 3. Test it + + + + + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "fireworks-llama-v3-70b-instruct", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create(model="fireworks-llama-v3-70b-instruct", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +]) + +print(response) + +``` + + + +```python +from langchain.chat_models import ChatOpenAI +from langchain.prompts.chat import ( + ChatPromptTemplate, + HumanMessagePromptTemplate, + SystemMessagePromptTemplate, +) +from langchain.schema import HumanMessage, SystemMessage + +chat = ChatOpenAI( + openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy + model = "fireworks-llama-v3-70b-instruct", + temperature=0.1 +) + +messages = [ + SystemMessage( + content="You are a helpful assistant that im using to make a test request to." + ), + HumanMessage( + content="test from litellm. tell me why it's amazing in 1 sentence" + ), +] +response = chat(messages) + +print(response) +``` + + + ## Supported Models - ALL Fireworks AI Models Supported! + +:::info We support ALL Fireworks AI models, just set `fireworks_ai/` as a prefix when sending completion requests +::: | Model Name | Function Call | |--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| diff --git a/docs/my-website/docs/providers/friendliai.md b/docs/my-website/docs/providers/friendliai.md new file mode 100644 index 000000000..137c3dde3 --- /dev/null +++ b/docs/my-website/docs/providers/friendliai.md @@ -0,0 +1,60 @@ +# FriendliAI +https://suite.friendli.ai/ + +**We support ALL FriendliAI models, just set `friendliai/` as a prefix when sending completion requests** + +## API Key +```python +# env variable +os.environ['FRIENDLI_TOKEN'] +os.environ['FRIENDLI_API_BASE'] # Optional. Set this when using dedicated endpoint. +``` + +## Sample Usage +```python +from litellm import completion +import os + +os.environ['FRIENDLI_TOKEN'] = "" +response = completion( + model="friendliai/mixtral-8x7b-instruct-v0-1", + messages=[ + {"role": "user", "content": "hello from litellm"} + ], +) +print(response) +``` + +## Sample Usage - Streaming +```python +from litellm import completion +import os + +os.environ['FRIENDLI_TOKEN'] = "" +response = completion( + model="friendliai/mixtral-8x7b-instruct-v0-1", + messages=[ + {"role": "user", "content": "hello from litellm"} + ], + stream=True +) + +for chunk in response: + print(chunk) +``` + + +## Supported Models +### Serverless Endpoints +We support ALL FriendliAI AI models, just set `friendliai/` as a prefix when sending completion requests + +| Model Name | Function Call | +|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| mixtral-8x7b-instruct | `completion(model="friendliai/mixtral-8x7b-instruct-v0-1", messages)` | +| meta-llama-3-8b-instruct | `completion(model="friendliai/meta-llama-3-8b-instruct", messages)` | +| meta-llama-3-70b-instruct | `completion(model="friendliai/meta-llama-3-70b-instruct", messages)` | + +### Dedicated Endpoints +``` +model="friendliai/$ENDPOINT_ID:$ADAPTER_ROUTE" +``` diff --git a/docs/my-website/docs/providers/gemini.md b/docs/my-website/docs/providers/gemini.md index 70988f0b5..b7124c18b 100644 --- a/docs/my-website/docs/providers/gemini.md +++ b/docs/my-website/docs/providers/gemini.md @@ -1,3 +1,7 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Gemini - Google AI Studio ## Pre-requisites @@ -17,6 +21,335 @@ response = completion( ) ``` +## Supported OpenAI Params +- temperature +- top_p +- max_tokens +- stream +- tools +- tool_choice +- response_format +- n +- stop + +[**See Updated List**](https://github.com/BerriAI/litellm/blob/1c747f3ad372399c5b95cc5696b06a5fbe53186b/litellm/llms/vertex_httpx.py#L122) + +## Passing Gemini Specific Params +### Response schema +LiteLLM supports sending `response_schema` as a param for Gemini-1.5-Pro on Google AI Studio. + +**Response Schema** + + + +```python +from litellm import completion +import json +import os + +os.environ['GEMINI_API_KEY'] = "" + +messages = [ + { + "role": "user", + "content": "List 5 popular cookie recipes." + } +] + +response_schema = { + "type": "array", + "items": { + "type": "object", + "properties": { + "recipe_name": { + "type": "string", + }, + }, + "required": ["recipe_name"], + }, + } + + +completion( + model="gemini/gemini-1.5-pro", + messages=messages, + response_format={"type": "json_object", "response_schema": response_schema} # 👈 KEY CHANGE + ) + +print(json.loads(completion.choices[0].message.content)) +``` + + + + +1. Add model to config.yaml +```yaml +model_list: + - model_name: gemini-pro + litellm_params: + model: gemini/gemini-1.5-pro + api_key: os.environ/GEMINI_API_KEY +``` + +2. Start Proxy + +``` +$ litellm --config /path/to/config.yaml +``` + +3. Make Request! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-D '{ + "model": "gemini-pro", + "messages": [ + {"role": "user", "content": "List 5 popular cookie recipes."} + ], + "response_format": {"type": "json_object", "response_schema": { + "type": "array", + "items": { + "type": "object", + "properties": { + "recipe_name": { + "type": "string", + }, + }, + "required": ["recipe_name"], + }, + }} +} +' +``` + + + + +**Validate Schema** + +To validate the response_schema, set `enforce_validation: true`. + + + + +```python +from litellm import completion, JSONSchemaValidationError +try: + completion( + model="gemini/gemini-1.5-pro", + messages=messages, + response_format={ + "type": "json_object", + "response_schema": response_schema, + "enforce_validation": true # 👈 KEY CHANGE + } + ) +except JSONSchemaValidationError as e: + print("Raw Response: {}".format(e.raw_response)) + raise e +``` + + + +1. Add model to config.yaml +```yaml +model_list: + - model_name: gemini-pro + litellm_params: + model: gemini/gemini-1.5-pro + api_key: os.environ/GEMINI_API_KEY +``` + +2. Start Proxy + +``` +$ litellm --config /path/to/config.yaml +``` + +3. Make Request! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-D '{ + "model": "gemini-pro", + "messages": [ + {"role": "user", "content": "List 5 popular cookie recipes."} + ], + "response_format": {"type": "json_object", "response_schema": { + "type": "array", + "items": { + "type": "object", + "properties": { + "recipe_name": { + "type": "string", + }, + }, + "required": ["recipe_name"], + }, + }, + "enforce_validation": true + } +} +' +``` + + + + +LiteLLM will validate the response against the schema, and raise a `JSONSchemaValidationError` if the response does not match the schema. + +JSONSchemaValidationError inherits from `openai.APIError` + +Access the raw response with `e.raw_response` + + + +### GenerationConfig Params + +To pass additional GenerationConfig params - e.g. `topK`, just pass it in the request body of the call, and LiteLLM will pass it straight through as a key-value pair in the request body. + +[**See Gemini GenerationConfigParams**](https://ai.google.dev/api/generate-content#v1beta.GenerationConfig) + + + + +```python +from litellm import completion +import json +import os + +os.environ['GEMINI_API_KEY'] = "" + +messages = [ + { + "role": "user", + "content": "List 5 popular cookie recipes." + } +] + +completion( + model="gemini/gemini-1.5-pro", + messages=messages, + topK=1 # 👈 KEY CHANGE +) + +print(json.loads(completion.choices[0].message.content)) +``` + + + + +1. Add model to config.yaml +```yaml +model_list: + - model_name: gemini-pro + litellm_params: + model: gemini/gemini-1.5-pro + api_key: os.environ/GEMINI_API_KEY +``` + +2. Start Proxy + +``` +$ litellm --config /path/to/config.yaml +``` + +3. Make Request! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "gemini-pro", + "messages": [ + {"role": "user", "content": "List 5 popular cookie recipes."} + ], + "topK": 1 # 👈 KEY CHANGE +} +' +``` + + + + +**Validate Schema** + +To validate the response_schema, set `enforce_validation: true`. + + + + +```python +from litellm import completion, JSONSchemaValidationError +try: + completion( + model="gemini/gemini-1.5-pro", + messages=messages, + response_format={ + "type": "json_object", + "response_schema": response_schema, + "enforce_validation": true # 👈 KEY CHANGE + } + ) +except JSONSchemaValidationError as e: + print("Raw Response: {}".format(e.raw_response)) + raise e +``` + + + +1. Add model to config.yaml +```yaml +model_list: + - model_name: gemini-pro + litellm_params: + model: gemini/gemini-1.5-pro + api_key: os.environ/GEMINI_API_KEY +``` + +2. Start Proxy + +``` +$ litellm --config /path/to/config.yaml +``` + +3. Make Request! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-D '{ + "model": "gemini-pro", + "messages": [ + {"role": "user", "content": "List 5 popular cookie recipes."} + ], + "response_format": {"type": "json_object", "response_schema": { + "type": "array", + "items": { + "type": "object", + "properties": { + "recipe_name": { + "type": "string", + }, + }, + "required": ["recipe_name"], + }, + }, + "enforce_validation": true + } +} +' +``` + + + + ## Specifying Safety Settings In certain use-cases you may need to make calls to the models and pass [safety settigns](https://ai.google.dev/docs/safety_setting_gemini) different from the defaults. To do so, simple pass the `safety_settings` argument to `completion` or `acompletion`. For example: @@ -91,6 +424,72 @@ assert isinstance( ``` +## JSON Mode + + + + +```python +from litellm import completion +import json +import os + +os.environ['GEMINI_API_KEY'] = "" + +messages = [ + { + "role": "user", + "content": "List 5 popular cookie recipes." + } +] + + + +completion( + model="gemini/gemini-1.5-pro", + messages=messages, + response_format={"type": "json_object"} # 👈 KEY CHANGE +) + +print(json.loads(completion.choices[0].message.content)) +``` + + + + +1. Add model to config.yaml +```yaml +model_list: + - model_name: gemini-pro + litellm_params: + model: gemini/gemini-1.5-pro + api_key: os.environ/GEMINI_API_KEY +``` + +2. Start Proxy + +``` +$ litellm --config /path/to/config.yaml +``` + +3. Make Request! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "gemini-pro", + "messages": [ + {"role": "user", "content": "List 5 popular cookie recipes."} + ], + "response_format": {"type": "json_object"} +} +' +``` + + + # Gemini-Pro-Vision LiteLLM Supports the following image types passed in `url` - Images with direct links - https://storage.googleapis.com/github-repo/img/gemini/intro/landmark3.jpg @@ -141,8 +540,13 @@ print(content) ``` ## Chat Models +:::tip + +**We support ALL Gemini models, just set `model=gemini/` as a prefix when sending litellm requests** + +::: | Model Name | Function Call | Required OS Variables | |-----------------------|--------------------------------------------------------|--------------------------------| -| gemini-pro | `completion('gemini/gemini-pro', messages)` | `os.environ['GEMINI_API_KEY']` | -| gemini-1.5-pro-latest | `completion('gemini/gemini-1.5-pro-latest', messages)` | `os.environ['GEMINI_API_KEY']` | -| gemini-pro-vision | `completion('gemini/gemini-pro-vision', messages)` | `os.environ['GEMINI_API_KEY']` | +| gemini-pro | `completion(model='gemini/gemini-pro', messages)` | `os.environ['GEMINI_API_KEY']` | +| gemini-1.5-pro-latest | `completion(model='gemini/gemini-1.5-pro-latest', messages)` | `os.environ['GEMINI_API_KEY']` | +| gemini-pro-vision | `completion(model='gemini/gemini-pro-vision', messages)` | `os.environ['GEMINI_API_KEY']` | diff --git a/docs/my-website/docs/providers/github.md b/docs/my-website/docs/providers/github.md new file mode 100644 index 000000000..48ab76be8 --- /dev/null +++ b/docs/my-website/docs/providers/github.md @@ -0,0 +1,261 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# 🆕 Github +https://github.com/marketplace/models + +:::tip + +**We support ALL Github models, just set `model=github/` as a prefix when sending litellm requests** + +::: + +## API Key +```python +# env variable +os.environ['GITHUB_API_KEY'] +``` + +## Sample Usage +```python +from litellm import completion +import os + +os.environ['GITHUB_API_KEY'] = "" +response = completion( + model="github/llama3-8b-8192", + messages=[ + {"role": "user", "content": "hello from litellm"} + ], +) +print(response) +``` + +## Sample Usage - Streaming +```python +from litellm import completion +import os + +os.environ['GITHUB_API_KEY'] = "" +response = completion( + model="github/llama3-8b-8192", + messages=[ + {"role": "user", "content": "hello from litellm"} + ], + stream=True +) + +for chunk in response: + print(chunk) +``` + + + +## Usage with LiteLLM Proxy + +### 1. Set Github Models on config.yaml + +```yaml +model_list: + - model_name: github-llama3-8b-8192 # Model Alias to use for requests + litellm_params: + model: github/llama3-8b-8192 + api_key: "os.environ/GITHUB_API_KEY" # ensure you have `GITHUB_API_KEY` in your .env +``` + +### 2. Start Proxy + +``` +litellm --config config.yaml +``` + +### 3. Test it + +Make request to litellm proxy + + + + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "github-llama3-8b-8192", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create(model="github-llama3-8b-8192", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +]) + +print(response) + +``` + + + +```python +from langchain.chat_models import ChatOpenAI +from langchain.prompts.chat import ( + ChatPromptTemplate, + HumanMessagePromptTemplate, + SystemMessagePromptTemplate, +) +from langchain.schema import HumanMessage, SystemMessage + +chat = ChatOpenAI( + openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy + model = "github-llama3-8b-8192", + temperature=0.1 +) + +messages = [ + SystemMessage( + content="You are a helpful assistant that im using to make a test request to." + ), + HumanMessage( + content="test from litellm. tell me why it's amazing in 1 sentence" + ), +] +response = chat(messages) + +print(response) +``` + + + + + +## Supported Models - ALL Github Models Supported! +We support ALL Github models, just set `github/` as a prefix when sending completion requests + +| Model Name | Usage | +|--------------------|---------------------------------------------------------| +| llama-3.1-8b-instant | `completion(model="github/llama-3.1-8b-instant", messages)` | +| llama-3.1-70b-versatile | `completion(model="github/llama-3.1-70b-versatile", messages)` | +| llama-3.1-405b-reasoning | `completion(model="github/llama-3.1-405b-reasoning", messages)` | +| llama3-8b-8192 | `completion(model="github/llama3-8b-8192", messages)` | +| llama3-70b-8192 | `completion(model="github/llama3-70b-8192", messages)` | +| llama2-70b-4096 | `completion(model="github/llama2-70b-4096", messages)` | +| mixtral-8x7b-32768 | `completion(model="github/mixtral-8x7b-32768", messages)` | +| gemma-7b-it | `completion(model="github/gemma-7b-it", messages)` | + +## Github - Tool / Function Calling Example + +```python +# Example dummy function hard coded to return the current weather +import json +def get_current_weather(location, unit="fahrenheit"): + """Get the current weather in a given location""" + if "tokyo" in location.lower(): + return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"}) + elif "san francisco" in location.lower(): + return json.dumps( + {"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"} + ) + elif "paris" in location.lower(): + return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"}) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + + + + +# Step 1: send the conversation and available functions to the model +messages = [ + { + "role": "system", + "content": "You are a function calling LLM that uses the data extracted from get_current_weather to answer questions about the weather in San Francisco.", + }, + { + "role": "user", + "content": "What's the weather like in San Francisco?", + }, +] +tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + }, + }, + "required": ["location"], + }, + }, + } +] +response = litellm.completion( + model="github/llama3-8b-8192", + messages=messages, + tools=tools, + tool_choice="auto", # auto is default, but we'll be explicit +) +print("Response\n", response) +response_message = response.choices[0].message +tool_calls = response_message.tool_calls + + +# Step 2: check if the model wanted to call a function +if tool_calls: + # Step 3: call the function + # Note: the JSON response may not always be valid; be sure to handle errors + available_functions = { + "get_current_weather": get_current_weather, + } + messages.append( + response_message + ) # extend conversation with assistant's reply + print("Response message\n", response_message) + # Step 4: send the info for each function call and function response to the model + for tool_call in tool_calls: + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + messages.append( + { + "tool_call_id": tool_call.id, + "role": "tool", + "name": function_name, + "content": function_response, + } + ) # extend conversation with function response + print(f"messages: {messages}") + second_response = litellm.completion( + model="github/llama3-8b-8192", messages=messages + ) # get a new response from the model where it can see the function response + print("second response\n", second_response) +``` diff --git a/docs/my-website/docs/providers/groq.md b/docs/my-website/docs/providers/groq.md index bcca20b5d..37d63d031 100644 --- a/docs/my-website/docs/providers/groq.md +++ b/docs/my-website/docs/providers/groq.md @@ -1,3 +1,6 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Groq https://groq.com/ @@ -20,7 +23,7 @@ import os os.environ['GROQ_API_KEY'] = "" response = completion( - model="groq/llama2-70b-4096", + model="groq/llama3-8b-8192", messages=[ {"role": "user", "content": "hello from litellm"} ], @@ -35,7 +38,7 @@ import os os.environ['GROQ_API_KEY'] = "" response = completion( - model="groq/llama2-70b-4096", + model="groq/llama3-8b-8192", messages=[ {"role": "user", "content": "hello from litellm"} ], @@ -47,11 +50,109 @@ for chunk in response: ``` + +## Usage with LiteLLM Proxy + +### 1. Set Groq Models on config.yaml + +```yaml +model_list: + - model_name: groq-llama3-8b-8192 # Model Alias to use for requests + litellm_params: + model: groq/llama3-8b-8192 + api_key: "os.environ/GROQ_API_KEY" # ensure you have `GROQ_API_KEY` in your .env +``` + +### 2. Start Proxy + +``` +litellm --config config.yaml +``` + +### 3. Test it + +Make request to litellm proxy + + + + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "groq-llama3-8b-8192", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create(model="groq-llama3-8b-8192", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +]) + +print(response) + +``` + + + +```python +from langchain.chat_models import ChatOpenAI +from langchain.prompts.chat import ( + ChatPromptTemplate, + HumanMessagePromptTemplate, + SystemMessagePromptTemplate, +) +from langchain.schema import HumanMessage, SystemMessage + +chat = ChatOpenAI( + openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy + model = "groq-llama3-8b-8192", + temperature=0.1 +) + +messages = [ + SystemMessage( + content="You are a helpful assistant that im using to make a test request to." + ), + HumanMessage( + content="test from litellm. tell me why it's amazing in 1 sentence" + ), +] +response = chat(messages) + +print(response) +``` + + + + + ## Supported Models - ALL Groq Models Supported! We support ALL Groq models, just set `groq/` as a prefix when sending completion requests -| Model Name | Function Call | +| Model Name | Usage | |--------------------|---------------------------------------------------------| +| llama-3.1-8b-instant | `completion(model="groq/llama-3.1-8b-instant", messages)` | +| llama-3.1-70b-versatile | `completion(model="groq/llama-3.1-70b-versatile", messages)` | +| llama-3.1-405b-reasoning | `completion(model="groq/llama-3.1-405b-reasoning", messages)` | | llama3-8b-8192 | `completion(model="groq/llama3-8b-8192", messages)` | | llama3-70b-8192 | `completion(model="groq/llama3-70b-8192", messages)` | | llama2-70b-4096 | `completion(model="groq/llama2-70b-4096", messages)` | @@ -114,7 +215,7 @@ tools = [ } ] response = litellm.completion( - model="groq/llama2-70b-4096", + model="groq/llama3-8b-8192", messages=messages, tools=tools, tool_choice="auto", # auto is default, but we'll be explicit @@ -154,7 +255,7 @@ if tool_calls: ) # extend conversation with function response print(f"messages: {messages}") second_response = litellm.completion( - model="groq/llama2-70b-4096", messages=messages + model="groq/llama3-8b-8192", messages=messages ) # get a new response from the model where it can see the function response print("second response\n", second_response) ``` diff --git a/docs/my-website/docs/providers/mistral.md b/docs/my-website/docs/providers/mistral.md index d9616a522..62a91c687 100644 --- a/docs/my-website/docs/providers/mistral.md +++ b/docs/my-website/docs/providers/mistral.md @@ -1,3 +1,6 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Mistral AI API https://docs.mistral.ai/api/ @@ -41,18 +44,120 @@ for chunk in response: ``` + +## Usage with LiteLLM Proxy + +### 1. Set Mistral Models on config.yaml + +```yaml +model_list: + - model_name: mistral-small-latest + litellm_params: + model: mistral/mistral-small-latest + api_key: "os.environ/MISTRAL_API_KEY" # ensure you have `MISTRAL_API_KEY` in your .env +``` + +### 2. Start Proxy + +``` +litellm --config config.yaml +``` + +### 3. Test it + + + + + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "mistral-small-latest", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create(model="mistral-small-latest", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +]) + +print(response) + +``` + + + +```python +from langchain.chat_models import ChatOpenAI +from langchain.prompts.chat import ( + ChatPromptTemplate, + HumanMessagePromptTemplate, + SystemMessagePromptTemplate, +) +from langchain.schema import HumanMessage, SystemMessage + +chat = ChatOpenAI( + openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy + model = "mistral-small-latest", + temperature=0.1 +) + +messages = [ + SystemMessage( + content="You are a helpful assistant that im using to make a test request to." + ), + HumanMessage( + content="test from litellm. tell me why it's amazing in 1 sentence" + ), +] +response = chat(messages) + +print(response) +``` + + + ## Supported Models + +:::info All models listed here https://docs.mistral.ai/platform/endpoints are supported. We actively maintain the list of models, pricing, token window, etc. [here](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). +::: + + | Model Name | Function Call | |----------------|--------------------------------------------------------------| | Mistral Small | `completion(model="mistral/mistral-small-latest", messages)` | | Mistral Medium | `completion(model="mistral/mistral-medium-latest", messages)`| -| Mistral Large | `completion(model="mistral/mistral-large-latest", messages)` | +| Mistral Large 2 | `completion(model="mistral/mistral-large-2407", messages)` | +| Mistral Large Latest | `completion(model="mistral/mistral-large-latest", messages)` | | Mistral 7B | `completion(model="mistral/open-mistral-7b", messages)` | | Mixtral 8x7B | `completion(model="mistral/open-mixtral-8x7b", messages)` | | Mixtral 8x22B | `completion(model="mistral/open-mixtral-8x22b", messages)` | | Codestral | `completion(model="mistral/codestral-latest", messages)` | +| Mistral NeMo | `completion(model="mistral/open-mistral-nemo", messages)` | +| Mistral NeMo 2407 | `completion(model="mistral/open-mistral-nemo-2407", messages)` | +| Codestral Mamba | `completion(model="mistral/open-codestral-mamba", messages)` | +| Codestral Mamba | `completion(model="mistral/codestral-mamba-latest"", messages)` | ## Function Calling diff --git a/docs/my-website/docs/providers/ollama.md b/docs/my-website/docs/providers/ollama.md index c1c8fc57c..63b79fe3a 100644 --- a/docs/my-website/docs/providers/ollama.md +++ b/docs/my-website/docs/providers/ollama.md @@ -1,3 +1,6 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Ollama LiteLLM supports all models from [Ollama](https://github.com/ollama/ollama) @@ -84,6 +87,120 @@ response = completion( ) ``` +## Example Usage - Tool Calling + +To use ollama tool calling, pass `tools=[{..}]` to `litellm.completion()` + + + + +```python +from litellm import completion +import litellm + +## [OPTIONAL] REGISTER MODEL - not all ollama models support function calling, litellm defaults to json mode tool calls if native tool calling not supported. + +# litellm.register_model(model_cost={ +# "ollama_chat/llama3.1": { +# "supports_function_calling": true +# }, +# }) + +tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + } + } +] + +messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] + + +response = completion( + model="ollama_chat/llama3.1", + messages=messages, + tools=tools +) +``` + + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: "llama3.1" + litellm_params: + model: "ollama_chat/llama3.1" + model_info: + supports_function_calling: true +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "llama3.1", + "messages": [ + { + "role": "user", + "content": "What'\''s the weather like in Boston today?" + } + ], + "tools": [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + } + } + } + ], + "tool_choice": "auto", + "stream": true +}' +``` + + + ## Using ollama `api/chat` In order to send ollama requests to `POST /api/chat` on your ollama server, set the model prefix to `ollama_chat` diff --git a/docs/my-website/docs/providers/openai.md b/docs/my-website/docs/providers/openai.md index d4da55010..98b595135 100644 --- a/docs/my-website/docs/providers/openai.md +++ b/docs/my-website/docs/providers/openai.md @@ -163,7 +163,10 @@ os.environ["OPENAI_API_BASE"] = "openaiai-api-base" # OPTIONAL | Model Name | Function Call | |-----------------------|-----------------------------------------------------------------| +| gpt-4o-mini | `response = completion(model="gpt-4o-mini", messages=messages)` | +| gpt-4o-mini-2024-07-18 | `response = completion(model="gpt-4o-mini-2024-07-18", messages=messages)` | | gpt-4o | `response = completion(model="gpt-4o", messages=messages)` | +| gpt-4o-2024-08-06 | `response = completion(model="gpt-4o-2024-08-06", messages=messages)` | | gpt-4o-2024-05-13 | `response = completion(model="gpt-4o-2024-05-13", messages=messages)` | | gpt-4-turbo | `response = completion(model="gpt-4-turbo", messages=messages)` | | gpt-4-turbo-preview | `response = completion(model="gpt-4-0125-preview", messages=messages)` | @@ -236,6 +239,104 @@ response = completion( ## Advanced +### Getting OpenAI API Response Headers + +Set `litellm.return_response_headers = True` to get raw response headers from OpenAI + +You can expect to always get the `_response_headers` field from `litellm.completion()`, `litellm.embedding()` functions + + + + +```python +litellm.return_response_headers = True + +# /chat/completion +response = completion( + model="gpt-4o-mini", + messages=[ + { + "role": "user", + "content": "hi", + } + ], +) +print(f"response: {response}") +print("_response_headers=", response._response_headers) +``` + + + + +```python +litellm.return_response_headers = True + +# /chat/completion +response = completion( + model="gpt-4o-mini", + stream=True, + messages=[ + { + "role": "user", + "content": "hi", + } + ], +) +print(f"response: {response}") +print("response_headers=", response._response_headers) +for chunk in response: + print(chunk) +``` + + + + +```python +litellm.return_response_headers = True + +# embedding +embedding_response = litellm.embedding( + model="text-embedding-ada-002", + input="hello", +) + +embedding_response_headers = embedding_response._response_headers +print("embedding_response_headers=", embedding_response_headers) +``` + + + +Expected Response Headers from OpenAI + +```json +{ + "date": "Sat, 20 Jul 2024 22:05:23 GMT", + "content-type": "application/json", + "transfer-encoding": "chunked", + "connection": "keep-alive", + "access-control-allow-origin": "*", + "openai-model": "text-embedding-ada-002", + "openai-organization": "*****", + "openai-processing-ms": "20", + "openai-version": "2020-10-01", + "strict-transport-security": "max-age=15552000; includeSubDomains; preload", + "x-ratelimit-limit-requests": "5000", + "x-ratelimit-limit-tokens": "5000000", + "x-ratelimit-remaining-requests": "4999", + "x-ratelimit-remaining-tokens": "4999999", + "x-ratelimit-reset-requests": "12ms", + "x-ratelimit-reset-tokens": "0s", + "x-request-id": "req_cc37487bfd336358231a17034bcfb4d9", + "cf-cache-status": "DYNAMIC", + "set-cookie": "__cf_bm=E_FJY8fdAIMBzBE2RZI2.OkMIO3lf8Hz.ydBQJ9m3q8-1721513123-1.0.1.1-6OK0zXvtd5s9Jgqfz66cU9gzQYpcuh_RLaUZ9dOgxR9Qeq4oJlu.04C09hOTCFn7Hg.k.2tiKLOX24szUE2shw; path=/; expires=Sat, 20-Jul-24 22:35:23 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None, *cfuvid=SDndIImxiO3U0aBcVtoy1TBQqYeQtVDo1L6*Nlpp7EU-1721513123215-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None", + "x-content-type-options": "nosniff", + "server": "cloudflare", + "cf-ray": "8a66409b4f8acee9-SJC", + "content-encoding": "br", + "alt-svc": "h3=\":443\"; ma=86400" +} +``` + ### Parallel Function calling See a detailed walthrough of parallel function calling with litellm [here](https://docs.litellm.ai/docs/completion/function_call) ```python diff --git a/docs/my-website/docs/providers/perplexity.md b/docs/my-website/docs/providers/perplexity.md index 7cfc4c861..446f22b1f 100644 --- a/docs/my-website/docs/providers/perplexity.md +++ b/docs/my-website/docs/providers/perplexity.md @@ -1,3 +1,6 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # Perplexity AI (pplx-api) https://www.perplexity.ai @@ -38,7 +41,7 @@ for chunk in response: ## Supported Models -All models listed here https://docs.perplexity.ai/docs/model-cards are supported +All models listed here https://docs.perplexity.ai/docs/model-cards are supported. Just do `model=perplexity/`. | Model Name | Function Call | |--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| @@ -60,3 +63,72 @@ All models listed here https://docs.perplexity.ai/docs/model-cards are supported + +## Return citations + +Perplexity supports returning citations via `return_citations=True`. [Perplexity Docs](https://docs.perplexity.ai/reference/post_chat_completions). Note: Perplexity has this feature in **closed beta**, so you need them to grant you access to get citations from their API. + +If perplexity returns citations, LiteLLM will pass it straight through. + +:::info + +For passing more provider-specific, [go here](../completion/provider_specific_params.md) +::: + + + + +```python +from litellm import completion +import os + +os.environ['PERPLEXITYAI_API_KEY'] = "" +response = completion( + model="perplexity/mistral-7b-instruct", + messages=messages, + return_citations=True +) +print(response) +``` + + + + +1. Add perplexity to config.yaml + +```yaml +model_list: + - model_name: "perplexity-model" + litellm_params: + model: "llama-3.1-sonar-small-128k-online" + api_key: os.environ/PERPLEXITY_API_KEY +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl -L -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "perplexity-model", + "messages": [ + { + "role": "user", + "content": "Who won the world cup in 2022?" + } + ], + "return_citations": true +}' +``` + +[**Call w/ OpenAI SDK, Langchain, Instructor, etc.**](../proxy/user_keys.md#chatcompletions) + + + diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 07caa9189..fcdd1193c 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -10,7 +10,7 @@ import TabItem from '@theme/TabItem'; ## 🆕 `vertex_ai_beta/` route -New `vertex_ai_beta/` route. Adds support for system messages, tool_choice params, etc. by moving to httpx client (instead of vertex sdk). +New `vertex_ai_beta/` route. Adds support for system messages, tool_choice params, etc. by moving to httpx client (instead of vertex sdk). This implementation uses [VertexAI's REST API](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference#syntax). ```python from litellm import completion @@ -334,6 +334,10 @@ completion(model="vertex_ai_beta/gemini-1.5-flash-preview-0514", messages=messag Add Google Search Result grounding to vertex ai calls. +[**Relevant VertexAI Docs**](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/grounding#examples) + +See the grounding metadata with `response_obj._hidden_params["vertex_ai_grounding_metadata"]` + @@ -357,15 +361,17 @@ print(resp) ```bash -curl http://0.0.0.0:4000/v1/chat/completions \ +curl http://localhost:4000/v1/chat/completions \ -H "Content-Type: application/json" \ - -H "Authorization: Bearer $OPENAI_API_KEY" \ + -H "Authorization: Bearer sk-1234" \ -d '{ - "model": "gpt-4o", - "messages": [{"role": "user", "content": "Who won the world cup?"}], - "tools": [ + "model": "gemini-pro", + "messages": [ + {"role": "user", "content": "Hello, Claude!"} + ], + "tools": [ { - "googleSearchResults": {} + "googleSearchRetrieval": {} } ] }' @@ -375,6 +381,161 @@ curl http://0.0.0.0:4000/v1/chat/completions \ +#### **Moving from Vertex AI SDK to LiteLLM (GROUNDING)** + + +If this was your initial VertexAI Grounding code, + +```python +import vertexai + +vertexai.init(project=project_id, location="us-central1") + +model = GenerativeModel("gemini-1.5-flash-001") + +# Use Google Search for grounding +tool = Tool.from_google_search_retrieval(grounding.GoogleSearchRetrieval(disable_attributon=False)) + +prompt = "When is the next total solar eclipse in US?" +response = model.generate_content( + prompt, + tools=[tool], + generation_config=GenerationConfig( + temperature=0.0, + ), +) + +print(response) +``` + +then, this is what it looks like now + +```python +from litellm import completion + + +# !gcloud auth application-default login - run this to add vertex credentials to your env + +tools = [{"googleSearchRetrieval": {"disable_attributon": False}}] # 👈 ADD GOOGLE SEARCH + +resp = litellm.completion( + model="vertex_ai_beta/gemini-1.0-pro-001", + messages=[{"role": "user", "content": "Who won the world cup?"}], + tools=tools, + vertex_project="project-id" + ) + +print(resp) +``` + + +### **Context Caching** + +Use Vertex AI Context Caching + +[**Relevant VertexAI Docs**](https://cloud.google.com/vertex-ai/generative-ai/docs/context-cache/context-cache-overview) + + + + + +1. Add model to config.yaml +```yaml +model_list: + # used for /chat/completions, /completions, /embeddings endpoints + - model_name: gemini-1.5-pro-001 + litellm_params: + model: vertex_ai_beta/gemini-1.5-pro-001 + vertex_project: "project-id" + vertex_location: "us-central1" + vertex_credentials: "adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json + +# used for the /cachedContent and vertexAI native endpoints +default_vertex_config: + vertex_project: "adroit-crow-413218" + vertex_location: "us-central1" + vertex_credentials: "adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json + +``` + +2. Start Proxy + +``` +$ litellm --config /path/to/config.yaml +``` + +3. Make Request! +We make the request in two steps: +- Create a cachedContents object +- Use the cachedContents object in your /chat/completions + +**Create a cachedContents object** + +First, create a cachedContents object by calling the Vertex `cachedContents` endpoint. The LiteLLM proxy forwards the `/cachedContents` request to the VertexAI API. + +```python +import httpx + +# Set Litellm proxy variables +LITELLM_BASE_URL = "http://0.0.0.0:4000" +LITELLM_PROXY_API_KEY = "sk-1234" + +httpx_client = httpx.Client(timeout=30) + +print("Creating cached content") +create_cache = httpx_client.post( + url=f"{LITELLM_BASE_URL}/vertex-ai/cachedContents", + headers={"Authorization": f"Bearer {LITELLM_PROXY_API_KEY}"}, + json={ + "model": "gemini-1.5-pro-001", + "contents": [ + { + "role": "user", + "parts": [{ + "text": "This is sample text to demonstrate explicit caching." * 4000 + }] + } + ], + } +) + +print("Response from create_cache:", create_cache) +create_cache_response = create_cache.json() +print("JSON from create_cache:", create_cache_response) +cached_content_name = create_cache_response["name"] +``` + +**Use the cachedContents object in your /chat/completions request to VertexAI** + +```python +import openai + +# Set Litellm proxy variables +LITELLM_BASE_URL = "http://0.0.0.0:4000" +LITELLM_PROXY_API_KEY = "sk-1234" + +client = openai.OpenAI(api_key=LITELLM_PROXY_API_KEY, base_url=LITELLM_BASE_URL) + +response = client.chat.completions.create( + model="gemini-1.5-pro-001", + max_tokens=8192, + messages=[ + { + "role": "user", + "content": "What is the sample text about?", + }, + ], + temperature=0.7, + extra_body={"cached_content": cached_content_name}, # Use the cached content +) + +print("Response from proxy:", response) +``` + + + + + ## Pre-requisites * `pip install google-cloud-aiplatform` (pre-installed on proxy docker image) * Authentication: @@ -697,6 +858,256 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ + +## Llama 3 API + +| Model Name | Function Call | +|------------------|--------------------------------------| +| meta/llama3-405b-instruct-maas | `completion('vertex_ai/meta/llama3-405b-instruct-maas', messages)` | + +### Usage + + + + +```python +from litellm import completion +import os + +os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "" + +model = "meta/llama3-405b-instruct-maas" + +vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"] +vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"] + +response = completion( + model="vertex_ai/" + model, + messages=[{"role": "user", "content": "hi"}], + vertex_ai_project=vertex_ai_project, + vertex_ai_location=vertex_ai_location, +) +print("\nModel Response", response) +``` + + + +**1. Add to config** + +```yaml +model_list: + - model_name: anthropic-llama + litellm_params: + model: vertex_ai/meta/llama3-405b-instruct-maas + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-east-1" + - model_name: anthropic-llama + litellm_params: + model: vertex_ai/meta/llama3-405b-instruct-maas + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-west-1" +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "anthropic-llama", # 👈 the 'model_name' in config + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' +``` + + + + +## Mistral API + +[**Supported OpenAI Params**](https://github.com/BerriAI/litellm/blob/e0f3cd580cb85066f7d36241a03c30aa50a8a31d/litellm/llms/openai.py#L137) + +| Model Name | Function Call | +|------------------|--------------------------------------| +| mistral-large@latest | `completion('vertex_ai/mistral-large@latest', messages)` | +| mistral-large@2407 | `completion('vertex_ai/mistral-large@2407', messages)` | +| mistral-nemo@latest | `completion('vertex_ai/mistral-nemo@latest', messages)` | +| codestral@latest | `completion('vertex_ai/codestral@latest', messages)` | +| codestral@@2405 | `completion('vertex_ai/codestral@2405', messages)` | + +### Usage + + + + +```python +from litellm import completion +import os + +os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "" + +model = "mistral-large@2407" + +vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"] +vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"] + +response = completion( + model="vertex_ai/" + model, + messages=[{"role": "user", "content": "hi"}], + vertex_ai_project=vertex_ai_project, + vertex_ai_location=vertex_ai_location, +) +print("\nModel Response", response) +``` + + + +**1. Add to config** + +```yaml +model_list: + - model_name: vertex-mistral + litellm_params: + model: vertex_ai/mistral-large@2407 + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-east-1" + - model_name: vertex-mistral + litellm_params: + model: vertex_ai/mistral-large@2407 + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-west-1" +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "vertex-mistral", # 👈 the 'model_name' in config + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' +``` + + + + + + +### Usage - Codestral FIM + +Call Codestral on VertexAI via the OpenAI [`/v1/completion`](https://platform.openai.com/docs/api-reference/completions/create) endpoint for FIM tasks. + +Note: You can also call Codestral via `/chat/completion`. + + + + +```python +from litellm import completion +import os + +# os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "" +# OR run `!gcloud auth print-access-token` in your terminal + +model = "codestral@2405" + +vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"] +vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"] + +response = text_completion( + model="vertex_ai/" + model, + vertex_ai_project=vertex_ai_project, + vertex_ai_location=vertex_ai_location, + prompt="def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():", + suffix="return True", # optional + temperature=0, # optional + top_p=1, # optional + max_tokens=10, # optional + min_tokens=10, # optional + seed=10, # optional + stop=["return"], # optional +) + +print("\nModel Response", response) +``` + + + +**1. Add to config** + +```yaml +model_list: + - model_name: vertex-codestral + litellm_params: + model: vertex_ai/codestral@2405 + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-east-1" + - model_name: vertex-codestral + litellm_params: + model: vertex_ai/codestral@2405 + vertex_ai_project: "my-test-project" + vertex_ai_location: "us-west-1" +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash +curl -X POST 'http://0.0.0.0:4000/completions' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "vertex-codestral", # 👈 the 'model_name' in config + "prompt": "def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():", + "suffix":"return True", # optional + "temperature":0, # optional + "top_p":1, # optional + "max_tokens":10, # optional + "min_tokens":10, # optional + "seed":10, # optional + "stop":["return"], # optional + }' +``` + + + + + ## Model Garden | Model Name | Function Call | |------------------|--------------------------------------| diff --git a/docs/my-website/docs/proxy/alerting.md b/docs/my-website/docs/proxy/alerting.md index 08030f478..7841ace58 100644 --- a/docs/my-website/docs/proxy/alerting.md +++ b/docs/my-website/docs/proxy/alerting.md @@ -119,13 +119,14 @@ All Possible Alert Types ```python AlertType = Literal[ - "llm_exceptions", - "llm_too_slow", + "llm_exceptions", # LLM API Exceptions + "llm_too_slow", # LLM Responses slower than alerting_threshold "llm_requests_hanging", "budget_alerts", "db_exceptions", "daily_reports", "spend_reports", + "fallback_reports", "cooldown_deployment", "new_model_added", "outage_alerts", @@ -133,6 +134,61 @@ AlertType = Literal[ ``` +## Advanced - set specific slack channels per alert type + +Use this if you want to set specific channels per alert type + +**This allows you to do the following** +``` +llm_exceptions -> go to slack channel #llm-exceptions +spend_reports -> go to slack channel #llm-spend-reports +``` + +Set `alert_to_webhook_url` on your config.yaml + +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + +general_settings: + master_key: sk-1234 + alerting: ["slack"] + alerting_threshold: 0.0001 # (Seconds) set an artifically low threshold for testing alerting + alert_to_webhook_url: { + "llm_exceptions": "https://hooks.slack.com/services/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", + "llm_too_slow": "https://hooks.slack.com/services/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", + "llm_requests_hanging": "https://hooks.slack.com/services/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", + "budget_alerts": "https://hooks.slack.com/services/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", + "db_exceptions": "https://hooks.slack.com/services/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", + "daily_reports": "https://hooks.slack.com/services/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", + "spend_reports": "https://hooks.slack.com/services/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", + "cooldown_deployment": "https://hooks.slack.com/services/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", + "new_model_added": "https://hooks.slack.com/services/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", + "outage_alerts": "https://hooks.slack.com/services/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", + } + +litellm_settings: + success_callback: ["langfuse"] +``` + +Test it - send a valid llm request - expect to see a `llm_too_slow` alert in it's own slack channel + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-4", + "messages": [ + {"role": "user", "content": "Hello, Claude gm!"} + ] +}' +``` + ## Advanced - Using MS Teams Webhooks diff --git a/docs/my-website/docs/proxy/billing.md b/docs/my-website/docs/proxy/billing.md index d3d1400cd..78ac38936 100644 --- a/docs/my-website/docs/proxy/billing.md +++ b/docs/my-website/docs/proxy/billing.md @@ -2,7 +2,7 @@ import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# 💵 Billing +# Billing Bill internal teams, external customers for their usage diff --git a/docs/my-website/docs/proxy/bucket.md b/docs/my-website/docs/proxy/bucket.md new file mode 100644 index 000000000..0e341f9d1 --- /dev/null +++ b/docs/my-website/docs/proxy/bucket.md @@ -0,0 +1,191 @@ + +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Logging GCS, s3 Buckets + +LiteLLM Supports Logging to the following Cloud Buckets +- (Enterprise) ✨ [Google Cloud Storage Buckets](#logging-proxy-inputoutput-to-google-cloud-storage-buckets) +- (Free OSS) [Amazon s3 Buckets](#logging-proxy-inputoutput---s3-buckets) + +## Logging Proxy Input/Output to Google Cloud Storage Buckets + +Log LLM Logs to [Google Cloud Storage Buckets](https://cloud.google.com/storage?hl=en) + +:::info + +✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat) + +::: + + +### Usage + +1. Add `gcs_bucket` to LiteLLM Config.yaml +```yaml +model_list: +- litellm_params: + api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/ + api_key: my-fake-key + model: openai/my-fake-model + model_name: fake-openai-endpoint + +litellm_settings: + callbacks: ["gcs_bucket"] # 👈 KEY CHANGE # 👈 KEY CHANGE +``` + +2. Set required env variables + +```shell +GCS_BUCKET_NAME="" +GCS_PATH_SERVICE_ACCOUNT="/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json +``` + +3. Start Proxy + +``` +litellm --config /path/to/config.yaml +``` + +4. Test it! + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "fake-openai-endpoint", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + } +' +``` + + +### Expected Logs on GCS Buckets + + + + +### Fields Logged on GCS Buckets + +Example payload of a `/chat/completion` request logged on GCS +```json +{ + "request_kwargs": { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "This is a test" + } + ], + "optional_params": { + "temperature": 0.7, + "max_tokens": 10, + "user": "ishaan-2", + "extra_body": {} + } + }, + "response_obj": { + "id": "chatcmpl-bd836a8c-89bc-4abd-bee5-e3f1ebfdb541", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Hi!", + "role": "assistant", + "tool_calls": null, + "function_call": null + } + } + ], + "created": 1722868456, + "model": "gpt-3.5-turbo", + "object": "chat.completion", + "system_fingerprint": null, + "usage": { + "prompt_tokens": 10, + "completion_tokens": 20, + "total_tokens": 30 + } + }, + "start_time": "2024-08-05 07:34:16", + "end_time": "2024-08-05 07:34:16" +} +``` + +### Getting `service_account.json` from Google Cloud Console + +1. Go to [Google Cloud Console](https://console.cloud.google.com/) +2. Search for IAM & Admin +3. Click on Service Accounts +4. Select a Service Account +5. Click on 'Keys' -> Add Key -> Create New Key -> JSON +6. Save the JSON file and add the path to `GCS_PATH_SERVICE_ACCOUNT` + + +## Logging Proxy Input/Output - s3 Buckets + +We will use the `--config` to set + +- `litellm.success_callback = ["s3"]` + +This will log all successfull LLM calls to s3 Bucket + +**Step 1** Set AWS Credentials in .env + +```shell +AWS_ACCESS_KEY_ID = "" +AWS_SECRET_ACCESS_KEY = "" +AWS_REGION_NAME = "" +``` + +**Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo +litellm_settings: + success_callback: ["s3"] + s3_callback_params: + s3_bucket_name: logs-bucket-litellm # AWS Bucket Name for S3 + s3_region_name: us-west-2 # AWS Region Name for S3 + s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID # us os.environ/ to pass environment variables. This is AWS Access Key ID for S3 + s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY # AWS Secret Access Key for S3 + s3_path: my-test-path # [OPTIONAL] set path in bucket you want to write logs to + s3_endpoint_url: https://s3.amazonaws.com # [OPTIONAL] S3 endpoint URL, if you want to use Backblaze/cloudflare s3 buckets +``` + +**Step 3**: Start the proxy, make a test request + +Start proxy + +```shell +litellm --config config.yaml --debug +``` + +Test Request + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --data ' { + "model": "Azure OpenAI GPT-4 East", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + }' +``` + +Your logs should be available on the specified s3 Bucket diff --git a/docs/my-website/docs/proxy/caching.md b/docs/my-website/docs/proxy/caching.md index 25fb4ce34..2c21b9391 100644 --- a/docs/my-website/docs/proxy/caching.md +++ b/docs/my-website/docs/proxy/caching.md @@ -59,6 +59,8 @@ litellm_settings: cache_params: # set cache params for redis type: redis ttl: 600 # will be cached on redis for 600s + # default_in_memory_ttl: Optional[float], default is None. time in seconds. + # default_in_redis_ttl: Optional[float], default is None. time in seconds. ``` @@ -258,6 +260,21 @@ curl --location 'http://0.0.0.0:4000/cache/ping' -H "Authorization: Bearer sk-1 ``` ## Advanced + +### Control Call Types Caching is on for - (`/chat/completion`, `/embeddings`, etc.) + +By default, caching is on for all call types. You can control which call types caching is on for by setting `supported_call_types` in `cache_params` + +**Cache will only be on for the call types specified in `supported_call_types`** + +```yaml +litellm_settings: + cache: True + cache_params: + type: redis + supported_call_types: ["acompletion", "atext_completion", "aembedding", "atranscription"] + # /chat/completions, /completions, /embeddings, /audio/transcriptions +``` ### Set Cache Params on config.yaml ```yaml model_list: @@ -278,7 +295,8 @@ litellm_settings: password: "your_password" # The password for the Redis cache. Required if type is "redis". # Optional configurations - supported_call_types: ["acompletion", "completion", "embedding", "aembedding"] # defaults to all litellm call types + supported_call_types: ["acompletion", "atext_completion", "aembedding", "atranscription"] + # /chat/completions, /completions, /embeddings, /audio/transcriptions ``` ### Turn on / off caching per request. @@ -294,6 +312,11 @@ The proxy support 4 cache-controls: **Turn off caching** +Set `no-cache=True`, this will not return a cached response + + + + ```python import os from openai import OpenAI @@ -319,9 +342,81 @@ chat_completion = client.chat.completions.create( } ) ``` + + + + +```shell +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-3.5-turbo", + "cache": {"no-cache": True}, + "messages": [ + {"role": "user", "content": "Say this is a test"} + ] + }' +``` + + + + **Turn on caching** +By default cache is always on + + + + +```python +import os +from openai import OpenAI + +client = OpenAI( + # This is the default and can be omitted + api_key=os.environ.get("OPENAI_API_KEY"), + base_url="http://0.0.0.0:4000" +) + +chat_completion = client.chat.completions.create( + messages=[ + { + "role": "user", + "content": "Say this is a test", + } + ], + model="gpt-3.5-turbo" +) +``` + + + + +```shell +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "Say this is a test"} + ] + }' +``` + + + + + +**Set `ttl`** + +Set `ttl=600`, this will caches response for 10 minutes (600 seconds) + + + + ```python import os from openai import OpenAI @@ -347,6 +442,35 @@ chat_completion = client.chat.completions.create( } ) ``` + + + + +```shell +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-3.5-turbo", + "cache": {"ttl": 600}, + "messages": [ + {"role": "user", "content": "Say this is a test"} + ] + }' +``` + + + + + + + +**Set `s-maxage`** + +Set `s-maxage`, this will only get responses cached within last 10 minutes + + + ```python import os @@ -373,6 +497,27 @@ chat_completion = client.chat.completions.create( } ) ``` + + + + +```shell +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-3.5-turbo", + "cache": {"s-maxage": 600}, + "messages": [ + {"role": "user", "content": "Say this is a test"} + ] + }' +``` + + + + + ### Turn on / off caching per Key. @@ -486,21 +631,25 @@ litellm_settings: ```yaml cache_params: + # ttl + ttl: Optional[float] + default_in_memory_ttl: Optional[float] + default_in_redis_ttl: Optional[float] + # Type of cache (options: "local", "redis", "s3") type: s3 # List of litellm call types to cache for # Options: "completion", "acompletion", "embedding", "aembedding" - supported_call_types: - - completion - - acompletion - - embedding - - aembedding + supported_call_types: ["acompletion", "atext_completion", "aembedding", "atranscription"] + # /chat/completions, /completions, /embeddings, /audio/transcriptions # Redis cache parameters host: localhost # Redis server hostname or IP address port: "6379" # Redis server port (as a string) password: secret_password # Redis server password + namespace: Optional[str] = None, + # S3 cache parameters s3_bucket_name: your_s3_bucket_name # Name of the S3 bucket diff --git a/docs/my-website/docs/proxy/call_hooks.md b/docs/my-website/docs/proxy/call_hooks.md index ce34e5ad6..25a46609d 100644 --- a/docs/my-website/docs/proxy/call_hooks.md +++ b/docs/my-website/docs/proxy/call_hooks.md @@ -47,6 +47,7 @@ class MyCustomHandler(CustomLogger): # https://docs.litellm.ai/docs/observabilit async def async_post_call_success_hook( self, + data: dict, user_api_key_dict: UserAPIKeyAuth, response, ): diff --git a/docs/my-website/docs/proxy/configs.md b/docs/my-website/docs/proxy/configs.md index ecd82375e..1060db5e7 100644 --- a/docs/my-website/docs/proxy/configs.md +++ b/docs/my-website/docs/proxy/configs.md @@ -55,10 +55,18 @@ model_list: - model_name: vllm-models litellm_params: model: openai/facebook/opt-125m # the `openai/` prefix tells litellm it's openai compatible - api_base: http://0.0.0.0:4000 + api_base: http://0.0.0.0:4000/v1 + api_key: none rpm: 1440 model_info: version: 2 + + # Use this if you want to make requests to `claude-3-haiku-20240307`,`claude-3-opus-20240229`,`claude-2.1` without defining them on the config.yaml + # Default models + # Works for ALL Providers and needs the default provider credentials in .env + - model_name: "*" + litellm_params: + model: "*" litellm_settings: # module level litellm settings - https://github.com/BerriAI/litellm/blob/main/litellm/__init__.py drop_params: True @@ -277,52 +285,58 @@ curl --location 'http://0.0.0.0:4000/v1/model/info' \ --data '' ``` -## Wildcard Model Name (Add ALL MODELS from env) + +## Provider specific wildcard routing +**Proxy all models from a provider** -Dynamically call any model from any given provider without the need to predefine it in the config YAML file. As long as the relevant keys are in the environment (see [providers list](../providers/)), LiteLLM will make the call correctly. +Use this if you want to **proxy all models from a specific provider without defining them on the config.yaml** - - -1. Setup config.yaml -``` +**Step 1** - define provider specific routing on config.yaml +```yaml model_list: - - model_name: "*" # all requests where model not in your config go to this deployment + # provider specific wildcard routing + - model_name: "anthropic/*" litellm_params: - model: "openai/*" # passes our validation check that a real provider is given + model: "anthropic/*" + api_key: os.environ/ANTHROPIC_API_KEY + - model_name: "groq/*" + litellm_params: + model: "groq/*" + api_key: os.environ/GROQ_API_KEY ``` -2. Start LiteLLM proxy +Step 2 - Run litellm proxy -``` -litellm --config /path/to/config.yaml +```shell +$ litellm --config /path/to/config.yaml ``` -3. Try claude 3-5 sonnet from anthropic +Step 3 Test it -```bash -curl -X POST 'http://0.0.0.0:4000/chat/completions' \ --H 'Content-Type: application/json' \ --H 'Authorization: Bearer sk-1234' \ --D '{ - "model": "claude-3-5-sonnet-20240620", - "messages": [ - {"role": "user", "content": "Hey, how'\''s it going?"}, - { - "role": "assistant", - "content": "I'\''m doing well. Would like to hear the rest of the story?" - }, - {"role": "user", "content": "Na"}, - { - "role": "assistant", - "content": "No problem, is there anything else i can help you with today?" - }, - { - "role": "user", - "content": "I think you'\''re getting cut off sometimes" - } +Test with `anthropic/` - all models with `anthropic/` prefix will get routed to `anthropic/*` +```shell +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "anthropic/claude-3-sonnet-20240229", + "messages": [ + {"role": "user", "content": "Hello, Claude!"} ] -} -' + }' +``` + +Test with `groq/` - all models with `groq/` prefix will get routed to `groq/*` +```shell +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "groq/llama3-8b-8192", + "messages": [ + {"role": "user", "content": "Hello, Claude!"} + ] + }' ``` ## Load Balancing diff --git a/docs/my-website/docs/proxy/custom_pricing.md b/docs/my-website/docs/proxy/custom_pricing.md index 0b747f119..51634021b 100644 --- a/docs/my-website/docs/proxy/custom_pricing.md +++ b/docs/my-website/docs/proxy/custom_pricing.md @@ -1,6 +1,6 @@ import Image from '@theme/IdealImage'; -# Custom Pricing - Sagemaker, etc. +# Custom LLM Pricing - Sagemaker, Azure, etc Use this to register custom pricing for models. @@ -16,39 +16,9 @@ LiteLLM already has pricing for any model in our [model cost map](https://github ::: -## Quick Start +## Cost Per Second (e.g. Sagemaker) -Register custom pricing for sagemaker completion model. - -For cost per second pricing, you **just** need to register `input_cost_per_second`. - -```python -# !pip install boto3 -from litellm import completion, completion_cost - -os.environ["AWS_ACCESS_KEY_ID"] = "" -os.environ["AWS_SECRET_ACCESS_KEY"] = "" -os.environ["AWS_REGION_NAME"] = "" - - -def test_completion_sagemaker(): - try: - print("testing sagemaker") - response = completion( - model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4", - messages=[{"role": "user", "content": "Hey, how's it going?"}], - input_cost_per_second=0.000420, - ) - # Add any assertions here to check the response - print(response) - cost = completion_cost(completion_response=response) - print(cost) - except Exception as e: - raise Exception(f"Error occurred: {e}") - -``` - -### Usage with OpenAI Proxy Server +### Usage with LiteLLM Proxy Server **Step 1: Add pricing to config.yaml** ```yaml @@ -75,38 +45,7 @@ litellm /path/to/config.yaml ## Cost Per Token (e.g. Azure) - -```python -# !pip install boto3 -from litellm import completion, completion_cost - -## set ENV variables -os.environ["AZURE_API_KEY"] = "" -os.environ["AZURE_API_BASE"] = "" -os.environ["AZURE_API_VERSION"] = "" - - -def test_completion_azure_model(): - try: - print("testing azure custom pricing") - # azure call - response = completion( - model = "azure/", - messages = [{ "content": "Hello, how are you?","role": "user"}] - input_cost_per_token=0.005, - output_cost_per_token=1, - ) - # Add any assertions here to check the response - print(response) - cost = completion_cost(completion_response=response) - print(cost) - except Exception as e: - raise Exception(f"Error occurred: {e}") - -test_completion_azure_model() -``` - -### Usage with OpenAI Proxy Server +### Usage with LiteLLM Proxy Server ```yaml model_list: diff --git a/docs/my-website/docs/proxy/customers.md b/docs/my-website/docs/proxy/customers.md index 94000cde2..ba9ecd83d 100644 --- a/docs/my-website/docs/proxy/customers.md +++ b/docs/my-website/docs/proxy/customers.md @@ -231,7 +231,7 @@ curl -X POST 'http://localhost:4000/customer/new' \ ```python from openai import OpenAI client = OpenAI( - base_url=" .env + +# Add the litellm salt key - you cannot change this after adding a model +# It is used to encrypt / decrypt your LLM API Key credentials +# We recommned - https://1password.com/password-generator/ +# password generator to get a random hash for litellm salt key +echo 'LITELLM_SALT_KEY="sk-1234"' > .env + source .env # Start @@ -239,7 +246,7 @@ helm install lite-helm ./litellm-helm kubectl --namespace default port-forward $POD_NAME 8080:$CONTAINER_PORT ``` -Your OpenAI proxy server is now running on `http://127.0.0.1:4000`. +Your LiteLLM Proxy Server is now running on `http://127.0.0.1:4000`. @@ -247,6 +254,15 @@ Your OpenAI proxy server is now running on `http://127.0.0.1:4000`. **That's it ! That's the quick start to deploy litellm** +## Use with Langchain, OpenAI SDK, LlamaIndex, Instructor, Curl + +:::info +💡 Go here 👉 [to make your first LLM API Request](user_keys) + +LiteLLM is compatible with several SDKs - including OpenAI SDK, Anthropic SDK, Mistral SDK, LLamaIndex, Langchain (Js, Python) + +::: + ## Options to deploy LiteLLM | Docs | When to Use | @@ -285,7 +301,7 @@ docker run \ --config /app/config.yaml --detailed_debug ``` -Your OpenAI proxy server is now running on `http://0.0.0.0:4000`. +Your LiteLLM Proxy Server is now running on `http://0.0.0.0:4000`. @@ -383,7 +399,7 @@ kubectl apply -f /path/to/service.yaml kubectl port-forward service/litellm-service 4000:4000 ``` -Your OpenAI proxy server is now running on `http://0.0.0.0:4000`. +Your LiteLLM Proxy Server is now running on `http://0.0.0.0:4000`. @@ -425,7 +441,7 @@ kubectl \ 4000:4000 ``` -Your OpenAI proxy server is now running on `http://127.0.0.1:4000`. +Your LiteLLM Proxy Server is now running on `http://127.0.0.1:4000`. If you need to set your litellm proxy config.yaml, you can find this in [values.yaml](https://github.com/BerriAI/litellm/blob/main/deploy/charts/litellm-helm/values.yaml) @@ -470,7 +486,7 @@ helm install lite-helm ./litellm-helm kubectl --namespace default port-forward $POD_NAME 8080:$CONTAINER_PORT ``` -Your OpenAI proxy server is now running on `http://127.0.0.1:4000`. +Your LiteLLM Proxy Server is now running on `http://127.0.0.1:4000`. @@ -542,6 +558,39 @@ docker run --name litellm-proxy \ ghcr.io/berriai/litellm-database:main-latest --config your_config.yaml ``` +## LiteLLM without Internet Connection + +By default `prisma generate` downloads [prisma's engine binaries](https://www.prisma.io/docs/orm/reference/environment-variables-reference#custom-engine-file-locations). This might cause errors when running without internet connection. + +Use this dockerfile to build an image which pre-generates the prisma binaries. + +```Dockerfile +# Use the provided base image +FROM ghcr.io/berriai/litellm:main-latest + +# Set the working directory to /app +WORKDIR /app + +### [👇 KEY STEP] ### +# Install Prisma CLI and generate Prisma client +RUN pip install prisma +RUN prisma generate +### FIN #### + + +# Expose the necessary port +EXPOSE 4000 + +# Override the CMD instruction with your desired command and arguments +# WARNING: FOR PROD DO NOT USE `--detailed_debug` it slows down response times, instead use the following CMD +# CMD ["--port", "4000", "--config", "config.yaml"] + +# Define the command to run your app +ENTRYPOINT ["litellm"] + +CMD ["--port", "4000"] +``` + ## Advanced Deployment Settings ### 1. Customization of the server root path (custom Proxy base url) @@ -556,24 +605,87 @@ In a Kubernetes deployment, it's possible to utilize a shared DNS to host multip Customize the root path to eliminate the need for employing multiple DNS configurations during deployment. +Step 1. 👉 Set `SERVER_ROOT_PATH` in your .env and this will be set as your server root path ``` export SERVER_ROOT_PATH="/api/v1" ``` -**Step 1. Run Proxy with `SERVER_ROOT_PATH` set in your env ** +**Step 2** (If you want the Proxy Admin UI to work with your root path you need to use this dockerfile) +- Use the dockerfile below (it uses litellm as a base image) +- 👉 Set `UI_BASE_PATH=$SERVER_ROOT_PATH/ui` in the Dockerfile, example `UI_BASE_PATH=/api/v1/ui` + +Dockerfile ```shell -docker run --name litellm-proxy \ --e DATABASE_URL=postgresql://:@:/ \ --e SERVER_ROOT_PATH="/api/v1" \ --p 4000:4000 \ -ghcr.io/berriai/litellm-database:main-latest --config your_config.yaml +# Use the provided base image +FROM ghcr.io/berriai/litellm:main-latest + +# Set the working directory to /app +WORKDIR /app + +# Install Node.js and npm (adjust version as needed) +RUN apt-get update && apt-get install -y nodejs npm + +# Copy the UI source into the container +COPY ./ui/litellm-dashboard /app/ui/litellm-dashboard + +# Set an environment variable for UI_BASE_PATH +# This can be overridden at build time +# set UI_BASE_PATH to "/ui" +# 👇👇 Enter your UI_BASE_PATH here +ENV UI_BASE_PATH="/api/v1/ui" + +# Build the UI with the specified UI_BASE_PATH +WORKDIR /app/ui/litellm-dashboard +RUN npm install +RUN UI_BASE_PATH=$UI_BASE_PATH npm run build + +# Create the destination directory +RUN mkdir -p /app/litellm/proxy/_experimental/out + +# Move the built files to the appropriate location +# Assuming the build output is in ./out directory +RUN rm -rf /app/litellm/proxy/_experimental/out/* && \ + mv ./out/* /app/litellm/proxy/_experimental/out/ + +# Switch back to the main app directory +WORKDIR /app + +# Make sure your entrypoint.sh is executable +RUN chmod +x entrypoint.sh + +# Expose the necessary port +EXPOSE 4000/tcp + +# Override the CMD instruction with your desired command and arguments +# only use --detailed_debug for debugging +CMD ["--port", "4000", "--config", "config.yaml"] +``` + +**Step 3** build this Dockerfile + +```shell +docker build -f Dockerfile -t litellm-prod-build . --progress=plain +``` + +**Step 4. Run Proxy with `SERVER_ROOT_PATH` set in your env ** + +```shell +docker run \ + -v $(pwd)/proxy_config.yaml:/app/config.yaml \ + -p 4000:4000 \ + -e LITELLM_LOG="DEBUG"\ + -e SERVER_ROOT_PATH="/api/v1"\ + -e DATABASE_URL=postgresql://:@:/ \ + -e LITELLM_MASTER_KEY="sk-1234"\ + litellm-prod-build \ + --config /app/config.yaml ``` After running the proxy you can access it on `http://0.0.0.0:4000/api/v1/` (since we set `SERVER_ROOT_PATH="/api/v1"`) -**Step 2. Verify Running on correct path** +**Step 5. Verify Running on correct path** @@ -593,6 +705,29 @@ docker run ghcr.io/berriai/litellm:main-latest \ Provide an ssl certificate when starting litellm proxy server +### 3. Providing LiteLLM config.yaml file as a s3 Object/url + +Use this if you cannot mount a config file on your deployment service (example - AWS Fargate, Railway etc) + +LiteLLM Proxy will read your config.yaml from an s3 Bucket + +Set the following .env vars +```shell +LITELLM_CONFIG_BUCKET_NAME = "litellm-proxy" # your bucket name on s3 +LITELLM_CONFIG_BUCKET_OBJECT_KEY = "litellm_proxy_config.yaml" # object key on s3 +``` + +Start litellm proxy with these env vars - litellm will read your config from s3 + +```shell +docker run --name litellm-proxy \ + -e DATABASE_URL= \ + -e LITELLM_CONFIG_BUCKET_NAME= \ + -e LITELLM_CONFIG_BUCKET_OBJECT_KEY="> \ + -p 4000:4000 \ + ghcr.io/berriai/litellm-database:main-latest +``` + ## Platform-specific Guide @@ -778,3 +913,31 @@ Run the command `docker-compose up` or `docker compose up` as per your docker in Your LiteLLM container should be running now on the defined port e.g. `4000`. + +### IAM-based Auth for RDS DB + +1. Set AWS env var + +```bash +export AWS_WEB_IDENTITY_TOKEN='/path/to/token' +export AWS_ROLE_NAME='arn:aws:iam::123456789012:role/MyRole' +export AWS_SESSION_NAME='MySession' +``` + +[**See all Auth options**](https://github.com/BerriAI/litellm/blob/089a4f279ad61b7b3e213d8039fb9b75204a7abc/litellm/proxy/auth/rds_iam_token.py#L165) + +2. Add RDS credentials to env + +```bash +export DATABASE_USER="db-user" +export DATABASE_PORT="5432" +export DATABASE_HOST="database-1-instance-1.cs1ksmwz2xt3.us-west-2.rds.amazonaws.com" +export DATABASE_NAME="database-1-instance-1" +``` + +3. Run proxy with iam+rds + + +```bash +litellm --config /path/to/config.yaml --iam_token_db_auth +``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/email.md b/docs/my-website/docs/proxy/email.md index 7db529107..a3f3a4169 100644 --- a/docs/my-website/docs/proxy/email.md +++ b/docs/my-website/docs/proxy/email.md @@ -1,6 +1,6 @@ import Image from '@theme/IdealImage'; -# ✨ 📧 Email Notifications +# Email Notifications Send an Email to your users when: - A Proxy API Key is created for them diff --git a/docs/my-website/docs/proxy/enterprise.md b/docs/my-website/docs/proxy/enterprise.md index f30b4b978..94813e354 100644 --- a/docs/my-website/docs/proxy/enterprise.md +++ b/docs/my-website/docs/proxy/enterprise.md @@ -21,15 +21,22 @@ Features: - ✅ IP address‑based access control lists - ✅ Track Request IP Address - ✅ [Use LiteLLM keys/authentication on Pass Through Endpoints](pass_through#✨-enterprise---use-litellm-keysauthentication-on-pass-through-endpoints) + - ✅ [Set Max Request Size / File Size on Requests](#set-max-request--response-size-on-litellm-proxy) - ✅ [Enforce Required Params for LLM Requests (ex. Reject requests missing ["metadata"]["generation_name"])](#enforce-required-params-for-llm-requests) -- **Spend Tracking** +- **Customize Logging, Guardrails, Caching per project** + - ✅ [Team Based Logging](./team_logging.md) - Allow each team to use their own Langfuse Project / custom callbacks + - ✅ [Disable Logging for a Team](./team_logging.md#disable-logging-for-a-team) - Switch off all logging for a team/project (GDPR Compliance) +-- **Spend Tracking & Data Exports** - ✅ [Tracking Spend for Custom Tags](#tracking-spend-for-custom-tags) - - ✅ [API Endpoints to get Spend Reports per Team, API Key, Customer](cost_tracking.md#✨-enterprise-api-endpoints-to-get-spend) -- **Advanced Metrics** + - ✅ [Exporting LLM Logs to GCS Bucket](./proxy/bucket#🪣-logging-gcs-s3-buckets) + - ✅ [`/spend/report` API endpoint](cost_tracking.md#✨-enterprise-api-endpoints-to-get-spend) +- **Prometheus Metrics** + - ✅ [Prometheus Metrics - Num Requests, failures, LLM Provider Outages](prometheus) - ✅ [`x-ratelimit-remaining-requests`, `x-ratelimit-remaining-tokens` for LLM APIs on Prometheus](prometheus#✨-enterprise-llm-remaining-requests-and-remaining-tokens) - **Guardrails, PII Masking, Content Moderation** - ✅ [Content Moderation with LLM Guard, LlamaGuard, Secret Detection, Google Text Moderations](#content-moderation) - ✅ [Prompt Injection Detection (with LakeraAI API)](#prompt-injection-detection---lakeraai) + - ✅ [Prompt Injection Detection (with Aporia API)](#prompt-injection-detection---aporia-ai) - ✅ [Switch LakeraAI on / off per request](guardrails#control-guardrails-onoff-per-request) - ✅ Reject calls from Blocked User list - ✅ Reject calls (incoming / outgoing) with Banned Keywords (e.g. competitors) @@ -113,7 +120,7 @@ client = openai.OpenAI( base_url="http://0.0.0.0:4000" ) -# request sent to model set on litellm proxy, `litellm --model` + response = client.chat.completions.create( model="gpt-3.5-turbo", messages = [ @@ -124,7 +131,7 @@ response = client.chat.completions.create( ], extra_body={ "metadata": { - "tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"] + "tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"] # 👈 Key Change } } ) @@ -133,6 +140,43 @@ print(response) ``` + + + +```js +const openai = require('openai'); + +async function runOpenAI() { + const client = new openai.OpenAI({ + apiKey: 'sk-1234', + baseURL: 'http://0.0.0.0:4000' + }); + + try { + const response = await client.chat.completions.create({ + model: 'gpt-3.5-turbo', + messages: [ + { + role: 'user', + content: "this is a test request, write a short poem" + }, + ], + metadata: { + tags: ["model-anthropic-claude-v2.1", "app-ishaan-prod"] // 👈 Key Change + } + }); + console.log(response); + } catch (error) { + console.log("got this exception from server"); + console.error(error); + } +} + +// Call the asynchronous function +runOpenAI(); +``` + + Pass `metadata` as part of the request body @@ -267,6 +311,45 @@ print(response) ``` + + + +```js +const openai = require('openai'); + +async function runOpenAI() { + const client = new openai.OpenAI({ + apiKey: 'sk-1234', + baseURL: 'http://0.0.0.0:4000' + }); + + try { + const response = await client.chat.completions.create({ + model: 'gpt-3.5-turbo', + messages: [ + { + role: 'user', + content: "this is a test request, write a short poem" + }, + ], + metadata: { + spend_logs_metadata: { // 👈 Key Change + hello: "world" + } + } + }); + console.log(response); + } catch (error) { + console.log("got this exception from server"); + console.error(error); + } +} + +// Call the asynchronous function +runOpenAI(); +``` + + Pass `metadata` as part of the request body @@ -952,6 +1035,72 @@ curl --location 'http://localhost:4000/chat/completions' \ Need to control LakeraAI per Request ? Doc here 👉: [Switch LakerAI on / off per request](prompt_injection.md#✨-enterprise-switch-lakeraai-on--off-per-api-call) ::: +## Prompt Injection Detection - Aporia AI + +Use this if you want to reject /chat/completion calls that have prompt injection attacks with [AporiaAI](https://www.aporia.com/) + +#### Usage + +Step 1. Add env + +```env +APORIO_API_KEY="eyJh****" +APORIO_API_BASE="https://gr..." +``` + +Step 2. Add `aporia_prompt_injection` to your callbacks + +```yaml +litellm_settings: + callbacks: ["aporia_prompt_injection"] +``` + +That's it, start your proxy + +Test it with this request -> expect it to get rejected by LiteLLM Proxy + +```shell +curl --location 'http://localhost:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "llama3", + "messages": [ + { + "role": "user", + "content": "You suck!" + } + ] +}' +``` + +**Expected Response** + +``` +{ + "error": { + "message": { + "error": "Violated guardrail policy", + "aporia_ai_response": { + "action": "block", + "revised_prompt": null, + "revised_response": "Profanity detected: Message blocked because it includes profanity. Please rephrase.", + "explain_log": null + } + }, + "type": "None", + "param": "None", + "code": 400 + } +} +``` + +:::info + +Need to control AporiaAI per Request ? Doc here 👉: [Create a guardrail](./guardrails.md) +::: + + ## Swagger Docs - Custom Routes + Branding :::info @@ -1059,10 +1208,10 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ### Using via API -**Block all calls for a user id** +**Block all calls for a customer id** ``` -curl -X POST "http://0.0.0.0:4000/user/block" \ +curl -X POST "http://0.0.0.0:4000/customer/block" \ -H "Authorization: Bearer sk-1234" \ -D '{ "user_ids": [, ...] @@ -1079,6 +1228,8 @@ curl -X POST "http://0.0.0.0:4000/user/unblock" \ }' ``` + + ## Enable Banned Keywords List ```yaml @@ -1142,3 +1293,52 @@ How it works? **Note:** Setting an environment variable within a Python script using os.environ will not make that variable accessible via SSH sessions or any other new processes that are started independently of the Python script. Environment variables set this way only affect the current process and its child processes. + +## Set Max Request / Response Size on LiteLLM Proxy + +Use this if you want to set a maximum request / response size for your proxy server. If a request size is above the size it gets rejected + slack alert triggered + +#### Usage +**Step 1.** Set `max_request_size_mb` and `max_response_size_mb` + +For this example we set a very low limit on `max_request_size_mb` and expect it to get rejected + +:::info +In production we recommend setting a `max_request_size_mb` / `max_response_size_mb` around `32 MB` + +::: + +```yaml +model_list: + - model_name: fake-openai-endpoint + litellm_params: + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ +general_settings: + master_key: sk-1234 + + # Security controls + max_request_size_mb: 0.000000001 # 👈 Key Change - Max Request Size in MB. Set this very low for testing + max_response_size_mb: 100 # 👈 Key Change - Max Response Size in MB +``` + +**Step 2.** Test it with `/chat/completions` request + +```shell +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "fake-openai-endpoint", + "messages": [ + {"role": "user", "content": "Hello, Claude!"} + ] + }' +``` + +**Expected Response from request** +We expect this to fail since the request size is over `max_request_size_mb` +```shell +{"error":{"message":"Request size is too large. Request size is 0.0001125335693359375 MB. Max size is 1e-09 MB","type":"bad_request_error","param":"content-length","code":400}} +``` diff --git a/docs/my-website/docs/proxy/guardrails.md b/docs/my-website/docs/proxy/guardrails.md index 4c4d0c0e9..451ca8ab5 100644 --- a/docs/my-website/docs/proxy/guardrails.md +++ b/docs/my-website/docs/proxy/guardrails.md @@ -1,18 +1,10 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# 🛡️ Guardrails +# 🛡️ [Beta] Guardrails Setup Prompt Injection Detection, Secret Detection on LiteLLM Proxy -:::info - -✨ Enterprise Only Feature - -Schedule a meeting with us to get an Enterprise License 👉 Talk to founders [here](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat) - -::: - ## Quick Start ### 1. Setup guardrails on litellm proxy config.yaml @@ -217,12 +209,12 @@ If you need to switch `pii_masking` off for an API Key set `"permissions": {"pii ```shell -curl --location 'http://0.0.0.0:4000/key/generate' \ - --header 'Authorization: Bearer sk-1234' \ - --header 'Content-Type: application/json' \ - --data '{ +curl -X POST 'http://0.0.0.0:4000/key/generate' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -D '{ "permissions": {"pii_masking": true} -}' + }' ``` ```shell @@ -266,6 +258,54 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ }' ``` +## Disable team from turning on/off guardrails + + +### 1. Disable team from modifying guardrails + +```bash +curl -X POST 'http://0.0.0.0:4000/team/update' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-D '{ + "team_id": "4198d93c-d375-4c83-8d5a-71e7c5473e50", + "metadata": {"guardrails": {"modify_guardrails": false}} +}' +``` + +### 2. Try to disable guardrails for a call + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_VIRTUAL_KEY' \ +--data '{ +"model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "Think of 10 random colors." + } + ], + "metadata": {"guardrails": {"hide_secrets": false}} +}' +``` + +### 3. Get 403 Error + +``` +{ + "error": { + "message": { + "error": "Your team does not have permission to modify guardrails." + }, + "type": "auth_error", + "param": "None", + "code": 403 + } +} +``` + Expect to NOT see `+1 412-612-9992` in your server logs on your callback. :::info @@ -277,28 +317,39 @@ The `pii_masking` guardrail ran on this request because api key=sk-jNm1Zar7XfNdZ ## Spec for `guardrails` on litellm config +```yaml +litellm_settings: + guardrails: + - string: GuardrailItemSpec +``` + +- `string` - Your custom guardrail name + +- `GuardrailItemSpec`: + - `callbacks`: List[str], list of supported guardrail callbacks. + - Full List: presidio, lakera_prompt_injection, hide_secrets, llmguard_moderations, llamaguard_moderations, google_text_moderation + - `default_on`: bool, will run on all llm requests when true + - `logging_only`: Optional[bool], if true, run guardrail only on logged output, not on the actual LLM API call. Currently only supported for presidio pii masking. Requires `default_on` to be True as well. + - `callback_args`: Optional[Dict[str, Dict]]: If set, pass in init args for that specific guardrail + +Example: + ```yaml litellm_settings: guardrails: - prompt_injection: # your custom name for guardrail callbacks: [lakera_prompt_injection, hide_secrets, llmguard_moderations, llamaguard_moderations, google_text_moderation] # litellm callbacks to use default_on: true # will run on all llm requests when true + callback_args: {"lakera_prompt_injection": {"moderation_check": "pre_call"}} - hide_secrets: callbacks: [hide_secrets] default_on: true + - pii_masking: + callback: ["presidio"] + default_on: true + logging_only: true - your-custom-guardrail callbacks: [hide_secrets] default_on: false ``` - -### `guardrails`: List of guardrail configurations to be applied to LLM requests. - -#### Guardrail: `prompt_injection`: Configuration for detecting and preventing prompt injection attacks. - -- `callbacks`: List of LiteLLM callbacks used for this guardrail. [Can be one of `[lakera_prompt_injection, hide_secrets, presidio, llmguard_moderations, llamaguard_moderations, google_text_moderation]`](enterprise#content-moderation) -- `default_on`: Boolean flag determining if this guardrail runs on all LLM requests by default. -#### Guardrail: `your-custom-guardrail`: Configuration for a user-defined custom guardrail. - -- `callbacks`: List of callbacks for this custom guardrail. Can be one of `[lakera_prompt_injection, hide_secrets, presidio, llmguard_moderations, llamaguard_moderations, google_text_moderation]` -- `default_on`: Boolean flag determining if this custom guardrail runs by default, set to false. diff --git a/docs/my-website/docs/proxy/health.md b/docs/my-website/docs/proxy/health.md index 0f3926113..632702b91 100644 --- a/docs/my-website/docs/proxy/health.md +++ b/docs/my-website/docs/proxy/health.md @@ -41,28 +41,6 @@ litellm --health } ``` -### Background Health Checks - -You can enable model health checks being run in the background, to prevent each model from being queried too frequently via `/health`. - -Here's how to use it: -1. in the config.yaml add: -``` -general_settings: - background_health_checks: True # enable background health checks - health_check_interval: 300 # frequency of background health checks -``` - -2. Start server -``` -$ litellm /path/to/config.yaml -``` - -3. Query health endpoint: -``` -curl --location 'http://0.0.0.0:4000/health' -``` - ### Embedding Models We need some way to know if the model is an embedding model when running checks, if you have this in your config, specifying mode it makes an embedding health check @@ -112,6 +90,66 @@ model_list: mode: completion # 👈 ADD THIS ``` +### Speech to Text Models + +```yaml +model_list: + - model_name: whisper + litellm_params: + model: whisper-1 + api_key: os.environ/OPENAI_API_KEY + model_info: + mode: audio_transcription +``` + + +### Text to Speech Models + +```yaml +# OpenAI Text to Speech Models + - model_name: tts + litellm_params: + model: openai/tts-1 + api_key: "os.environ/OPENAI_API_KEY" + model_info: + mode: audio_speech +``` + +## Background Health Checks + +You can enable model health checks being run in the background, to prevent each model from being queried too frequently via `/health`. + +Here's how to use it: +1. in the config.yaml add: +``` +general_settings: + background_health_checks: True # enable background health checks + health_check_interval: 300 # frequency of background health checks +``` + +2. Start server +``` +$ litellm /path/to/config.yaml +``` + +3. Query health endpoint: +``` +curl --location 'http://0.0.0.0:4000/health' +``` + +### Hide details + +The health check response contains details like endpoint URLs, error messages, +and other LiteLLM params. While this is useful for debugging, it can be +problematic when exposing the proxy server to a broad audience. + +You can hide these details by setting the `health_check_details` setting to `False`. + +```yaml +general_settings: + health_check_details: False +``` + ## `/health/readiness` Unprotected endpoint for checking if proxy is ready to accept requests @@ -205,4 +243,4 @@ curl -X POST 'http://localhost:4000/chat/completions' \ ], } ' -``` \ No newline at end of file +``` diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md index c2f583366..1be390ddd 100644 --- a/docs/my-website/docs/proxy/logging.md +++ b/docs/my-website/docs/proxy/logging.md @@ -1,28 +1,68 @@ +# Logging + +Log Proxy input, output, and exceptions using: + +- Langfuse +- OpenTelemetry +- Custom Callbacks +- Langsmith +- DataDog +- DynamoDB +- etc. + import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; +## Getting the LiteLLM Call ID -# 🪢 Logging - Langfuse, OpenTelemetry, Custom Callbacks, DataDog, s3 Bucket, Sentry, Athina, Azure Content-Safety +LiteLLM generates a unique `call_id` for each request. This `call_id` can be +used to track the request across the system. This can be very useful for finding +the info for a particular request in a logging system like one of the systems +mentioned in this page. -Log Proxy Input, Output, Exceptions using Langfuse, OpenTelemetry, Custom Callbacks, DataDog, DynamoDB, s3 Bucket +```shell +curl -i -sSL --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "what llm are you"}] + }' | grep 'x-litellm' +``` -## Table of Contents +The output of this is: -- [Logging to Langfuse](#logging-proxy-inputoutput---langfuse) -- [Logging with OpenTelemetry (OpenTelemetry)](#logging-proxy-inputoutput-in-opentelemetry-format) -- [Async Custom Callbacks](#custom-callback-class-async) -- [Async Custom Callback APIs](#custom-callback-apis-async) -- [Logging to Galileo](#logging-llm-io-to-galileo) -- [Logging to OpenMeter](#logging-proxy-inputoutput---langfuse) -- [Logging to s3 Buckets](#logging-proxy-inputoutput---s3-buckets) -- [Logging to DataDog](#logging-proxy-inputoutput---datadog) -- [Logging to DynamoDB](#logging-proxy-inputoutput---dynamodb) -- [Logging to Sentry](#logging-proxy-inputoutput---sentry) -- [Logging to Athina](#logging-proxy-inputoutput-athina) -- [(BETA) Moderation with Azure Content-Safety](#moderation-with-azure-content-safety) +```output +x-litellm-call-id: b980db26-9512-45cc-b1da-c511a363b83f +x-litellm-model-id: cb41bc03f4c33d310019bae8c5afdb1af0a8f97b36a234405a9807614988457c +x-litellm-model-api-base: https://x-example-1234.openai.azure.com +x-litellm-version: 1.40.21 +x-litellm-response-cost: 2.85e-05 +x-litellm-key-tpm-limit: None +x-litellm-key-rpm-limit: None +``` + +A number of these headers could be useful for troubleshooting, but the +`x-litellm-call-id` is the one that is most useful for tracking a request across +components in your system, including in logging tools. + +## Redacting UserAPIKeyInfo + +Redact information about the user api key (hashed token, user_id, team id, etc.), from logs. + +Currently supported for Langfuse, OpenTelemetry, Logfire, ArizeAI logging. + +```yaml +litellm_settings: + callbacks: ["langfuse"] + redact_user_api_key_info: true +``` + +Removes any field with `user_api_key_*` from metadata. ## Logging Proxy Input/Output - Langfuse + We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successfull LLM calls to langfuse. Make sure to set `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` in your environment **Step 1** Install langfuse @@ -32,6 +72,7 @@ pip install langfuse>=2.0.0 ``` **Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` + ```yaml model_list: - model_name: gpt-3.5-turbo @@ -42,6 +83,7 @@ litellm_settings: ``` **Step 3**: Set required env variables for logging to langfuse + ```shell export LANGFUSE_PUBLIC_KEY="pk_kk" export LANGFUSE_SECRET_KEY="sk_ss" @@ -52,11 +94,13 @@ export LANGFUSE_HOST="https://xxx.langfuse.com" **Step 4**: Start the proxy, make a test request Start proxy + ```shell litellm --config config.yaml --debug ``` Test Request + ``` litellm --test ``` @@ -67,7 +111,6 @@ Expected output on Langfuse ### Logging Metadata to Langfuse - @@ -93,6 +136,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ } }' ``` + @@ -126,6 +170,7 @@ response = client.chat.completions.create( print(response) ``` + @@ -168,9 +213,11 @@ print(response) - ### Team based Logging to Langfuse +[👉 Tutorial - Allow each team to use their own Langfuse Project / custom callbacks](team_logging) + ### Redacting Messages, Response Content from Langfuse Logging @@ -231,6 +278,42 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ }' ``` + +### LiteLLM-specific Tags on Langfuse - `cache_hit`, `cache_key` + +Use this if you want to control which LiteLLM-specific fields are logged as tags by the LiteLLM proxy. By default LiteLLM Proxy logs no LiteLLM-specific fields + +| LiteLLM specific field | Description | Example Value | +|------------------------|-------------------------------------------------------|------------------------------------------------| +| `cache_hit` | Indicates whether a cache hit occured (True) or not (False) | `true`, `false` | +| `cache_key` | The Cache key used for this request | `d2b758c****`| +| `proxy_base_url` | The base URL for the proxy server, the value of env var `PROXY_BASE_URL` on your server | `https://proxy.example.com`| +| `user_api_key_alias` | An alias for the LiteLLM Virtual Key.| `prod-app1` | +| `user_api_key_user_id` | The unique ID associated with a user's API key. | `user_123`, `user_456` | +| `user_api_key_user_email` | The email associated with a user's API key. | `user@example.com`, `admin@example.com` | +| `user_api_key_team_alias` | An alias for a team associated with an API key. | `team_alpha`, `dev_team` | + + +**Usage** + +Specify `langfuse_default_tags` to control what litellm fields get logged on Langfuse + +Example config.yaml +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + +litellm_settings: + success_callback: ["langfuse"] + + # 👇 Key Change + langfuse_default_tags: ["cache_hit", "cache_key", "proxy_base_url", "user_api_key_alias", "user_api_key_user_id", "user_api_key_user_email", "user_api_key_team_alias", "semantic-similarity", "proxy_base_url"] +``` + ### 🔧 Debugging - Viewing RAW CURL sent from LiteLLM to provider Use this when you want to view the RAW curl request sent from LiteLLM to the LLM API @@ -257,6 +340,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ } }' ``` + @@ -287,6 +371,7 @@ response = client.chat.completions.create( print(response) ``` + @@ -332,7 +417,6 @@ You will see `raw_request` in your Langfuse Metadata. This is the RAW CURL comma - ## Logging Proxy Input/Output in OpenTelemetry format :::info @@ -348,10 +432,8 @@ OTEL_SERVICE_NAME=` # default="litellm" - - **Step 1:** Set callbacks and env vars Add the following to your env @@ -367,7 +449,6 @@ litellm_settings: callbacks: ["otel"] ``` - **Step 2**: Start the proxy, make a test request Start proxy @@ -427,7 +508,6 @@ This is the Span from OTEL Logging - #### Quick Start - Log to Honeycomb @@ -449,7 +529,6 @@ litellm_settings: callbacks: ["otel"] ``` - **Step 2**: Start the proxy, make a test request Start proxy @@ -474,10 +553,8 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ }' ``` - - #### Quick Start - Log to OTEL Collector @@ -499,7 +576,6 @@ litellm_settings: callbacks: ["otel"] ``` - **Step 2**: Start the proxy, make a test request Start proxy @@ -526,7 +602,6 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ - #### Quick Start - Log to OTEL GRPC Collector @@ -548,7 +623,6 @@ litellm_settings: callbacks: ["otel"] ``` - **Step 2**: Start the proxy, make a test request Start proxy @@ -573,7 +647,6 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ }' ``` - @@ -596,7 +669,6 @@ environment_variables: TRACELOOP_API_KEY: "XXXXX" ``` - **Step 3**: Start the proxy, make a test request Start proxy @@ -632,11 +704,15 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ❓ Use this when you want to **pass information about the incoming request in a distributed tracing system** ✅ Key change: Pass the **`traceparent` header** in your requests. [Read more about traceparent headers here](https://uptrace.dev/opentelemetry/opentelemetry-traceparent.html#what-is-traceparent-header) + ```curl traceparent: 00-80e1afed08e019fc1110464cfa66635c-7a085853722dc6d2-01 ``` + Example Usage + 1. Make Request to LiteLLM Proxy with `traceparent` header + ```python import openai import uuid @@ -660,7 +736,6 @@ response = client.chat.completions.create( ) print(response) - ``` ```shell @@ -674,12 +749,29 @@ Search for Trace=`80e1afed08e019fc1110464cfa66635c` on your OTEL Collector +### Forwarding `Traceparent HTTP Header` to LLM APIs +Use this if you want to forward the traceparent headers to your self hosted LLMs like vLLM + +Set `forward_traceparent_to_llm_provider: True` in your `config.yaml`. This will forward the `traceparent` header to your LLM API + +:::warning + +Only use this for self hosted LLMs, this can cause Bedrock, VertexAI calls to fail + +::: + +```yaml +litellm_settings: + forward_traceparent_to_llm_provider: True +``` ## Custom Callback Class [Async] + Use this when you want to run custom callbacks in `python` #### Step 1 - Create your custom `litellm` callback class + We use `litellm.integrations.custom_logger` for this, **more details about litellm custom callbacks [here](https://docs.litellm.ai/docs/observability/custom_callback)** Define your custom callback class in a python file. @@ -782,16 +874,17 @@ proxy_handler_instance = MyCustomHandler() ``` #### Step 2 - Pass your custom callback class in `config.yaml` + We pass the custom callback class defined in **Step1** to the config.yaml. Set `callbacks` to `python_filename.logger_instance_name` In the config below, we pass + - python_filename: `custom_callbacks.py` - logger_instance_name: `proxy_handler_instance`. This is defined in Step 1 `callbacks: custom_callbacks.proxy_handler_instance` - ```yaml model_list: - model_name: gpt-3.5-turbo @@ -804,6 +897,7 @@ litellm_settings: ``` #### Step 3 - Start proxy + test request + ```shell litellm --config proxy_config.yaml ``` @@ -825,6 +919,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ``` #### Resulting Log on Proxy + ```shell On Success Model: gpt-3.5-turbo, @@ -877,7 +972,6 @@ class MyCustomHandler(CustomLogger): "max_tokens": 10 } } - ``` #### Logging `model_info` set in config.yaml @@ -895,11 +989,13 @@ class MyCustomHandler(CustomLogger): ``` **Expected Output** + ```json {'mode': 'embedding', 'input_cost_per_token': 0.002} ``` ### Logging responses from proxy + Both `/chat/completions` and `/embeddings` responses are available as `response_obj` **Note: for `/chat/completions`, both `stream=True` and `non stream` responses are available as `response_obj`** @@ -913,6 +1009,7 @@ class MyCustomHandler(CustomLogger): ``` **Expected Output /chat/completion [for both `stream` and `non-stream` responses]** + ```json ModelResponse( id='chatcmpl-8Tfu8GoMElwOZuj2JlHBhNHG01PPo', @@ -939,6 +1036,7 @@ ModelResponse( ``` **Expected Output /embeddings** + ```json { 'model': 'ada', @@ -958,7 +1056,6 @@ ModelResponse( } ``` - ## Custom Callback APIs [Async] :::info @@ -968,10 +1065,12 @@ This is an Enterprise only feature [Get Started with Enterprise here](https://gi ::: Use this if you: + - Want to use custom callbacks written in a non Python programming language - Want your callbacks to run on a different microservice #### Step 1. Create your generic logging API endpoint + Set up a generic API endpoint that can receive data in JSON format. The data will be included within a "data" field. Your server should support the following Request format: @@ -1034,11 +1133,8 @@ async def log_event(request: Request): if __name__ == "__main__": import uvicorn uvicorn.run(app, host="127.0.0.1", port=4000) - - ``` - #### Step 2. Set your `GENERIC_LOGGER_ENDPOINT` to the endpoint + route we should send callback logs to ```shell @@ -1048,6 +1144,7 @@ os.environ["GENERIC_LOGGER_ENDPOINT"] = "http://localhost:4000/log-event" #### Step 3. Create a `config.yaml` file and set `litellm_settings`: `success_callback` = ["generic"] Example litellm proxy config.yaml + ```yaml model_list: - model_name: gpt-3.5-turbo @@ -1059,8 +1156,98 @@ litellm_settings: Start the LiteLLM Proxy and make a test request to verify the logs reached your callback API +## Logging LLM IO to Langsmith + +1. Set `success_callback: ["langsmith"]` on litellm config.yaml + +If you're using a custom LangSmith instance, you can set the +`LANGSMITH_BASE_URL` environment variable to point to your instance. + +```yaml +litellm_settings: + success_callback: ["langsmith"] + +environment_variables: + LANGSMITH_API_KEY: "lsv2_pt_xxxxxxxx" + LANGSMITH_PROJECT: "litellm-proxy" + + LANGSMITH_BASE_URL: "https://api.smith.langchain.com" # (Optional - only needed if you have a custom Langsmith instance) +``` + + +2. Start Proxy + +``` +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "fake-openai-endpoint", + "messages": [ + { + "role": "user", + "content": "Hello, Claude gm!" + } + ], + } +' +``` +Expect to see your log on Langfuse + + + +## Logging LLM IO to Arize AI + +1. Set `success_callback: ["arize"]` on litellm config.yaml + +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + +litellm_settings: + callbacks: ["arize"] + +environment_variables: + ARIZE_SPACE_KEY: "d0*****" + ARIZE_API_KEY: "141a****" +``` + +2. Start Proxy + +``` +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "fake-openai-endpoint", + "messages": [ + { + "role": "user", + "content": "Hello, Claude gm!" + } + ], + } +' +``` +Expect to see your log on Langfuse + ## Logging LLM IO to Galileo + [BETA] Log LLM I/O on [www.rungalileo.io](https://www.rungalileo.io/) @@ -1083,6 +1270,7 @@ export GALILEO_PASSWORD="" ### Quick Start 1. Add to Config.yaml + ```yaml model_list: - litellm_params: @@ -1118,7 +1306,6 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ' ``` - 🎉 That's it - Expect to see your Logs on your Galileo Dashboard ## Logging Proxy Cost + Usage - OpenMeter @@ -1136,6 +1323,7 @@ export OPENMETER_API_KEY="" ### Quick Start 1. Add to Config.yaml + ```yaml model_list: - litellm_params: @@ -1171,13 +1359,14 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ' ``` - ## Logging Proxy Input/Output - DataDog + We will use the `--config` to set `litellm.success_callback = ["datadog"]` this will log all successfull LLM calls to DataDog **Step 1**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` + ```yaml model_list: - model_name: gpt-3.5-turbo @@ -1197,6 +1386,7 @@ DD_SITE="us5.datadoghq.com" # your datadog base url **Step 3**: Start the proxy, make a test request Start proxy + ```shell litellm --config config.yaml --debug ``` @@ -1224,66 +1414,10 @@ Expected output on Datadog - -## Logging Proxy Input/Output - s3 Buckets - -We will use the `--config` to set -- `litellm.success_callback = ["s3"]` - -This will log all successfull LLM calls to s3 Bucket - -**Step 1** Set AWS Credentials in .env - -```shell -AWS_ACCESS_KEY_ID = "" -AWS_SECRET_ACCESS_KEY = "" -AWS_REGION_NAME = "" -``` - -**Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: gpt-3.5-turbo -litellm_settings: - success_callback: ["s3"] - s3_callback_params: - s3_bucket_name: logs-bucket-litellm # AWS Bucket Name for S3 - s3_region_name: us-west-2 # AWS Region Name for S3 - s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID # us os.environ/ to pass environment variables. This is AWS Access Key ID for S3 - s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY # AWS Secret Access Key for S3 - s3_path: my-test-path # [OPTIONAL] set path in bucket you want to write logs to - s3_endpoint_url: https://s3.amazonaws.com # [OPTIONAL] S3 endpoint URL, if you want to use Backblaze/cloudflare s3 buckets -``` - -**Step 3**: Start the proxy, make a test request - -Start proxy -```shell -litellm --config config.yaml --debug -``` - -Test Request -```shell -curl --location 'http://0.0.0.0:4000/chat/completions' \ - --header 'Content-Type: application/json' \ - --data ' { - "model": "Azure OpenAI GPT-4 East", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ] - }' -``` - -Your logs should be available on the specified s3 Bucket - ## Logging Proxy Input/Output - DynamoDB We will use the `--config` to set + - `litellm.success_callback = ["dynamodb"]` - `litellm.dynamodb_table_name = "your-table-name"` @@ -1298,6 +1432,7 @@ AWS_REGION_NAME = "" ``` **Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` + ```yaml model_list: - model_name: gpt-3.5-turbo @@ -1311,11 +1446,13 @@ litellm_settings: **Step 3**: Start the proxy, make a test request Start proxy + ```shell litellm --config config.yaml --debug ``` Test Request + ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \ --header 'Content-Type: application/json' \ @@ -1403,19 +1540,18 @@ Your logs should be available on DynamoDB } ``` - - - ## Logging Proxy Input/Output - Sentry If api calls fail (llm/database) you can log those to Sentry: **Step 1** Install Sentry + ```shell pip install --upgrade sentry-sdk ``` **Step 2**: Save your Sentry_DSN and add `litellm_settings`: `failure_callback` + ```shell export SENTRY_DSN="your-sentry-dsn" ``` @@ -1435,11 +1571,13 @@ general_settings: **Step 3**: Start the proxy, make a test request Start proxy + ```shell litellm --config config.yaml --debug ``` Test Request + ``` litellm --test ``` @@ -1457,6 +1595,7 @@ ATHINA_API_KEY = "your-athina-api-key" ``` **Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` + ```yaml model_list: - model_name: gpt-3.5-turbo @@ -1469,11 +1608,13 @@ litellm_settings: **Step 3**: Start the proxy, make a test request Start proxy + ```shell litellm --config config.yaml --debug ``` Test Request + ``` curl --location 'http://0.0.0.0:4000/chat/completions' \ --header 'Content-Type: application/json' \ @@ -1505,6 +1646,7 @@ AZURE_CONTENT_SAFETY_KEY = "" ``` **Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback` + ```yaml model_list: - model_name: gpt-3.5-turbo @@ -1520,11 +1662,13 @@ litellm_settings: **Step 3**: Start the proxy, make a test request Start proxy + ```shell litellm --config config.yaml --debug ``` Test Request + ``` curl --location 'http://0.0.0.0:4000/chat/completions' \ --header 'Content-Type: application/json' \ @@ -1540,7 +1684,8 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ``` An HTTP 400 error will be returned if the content is detected with a value greater than the threshold set in the `config.yaml`. -The details of the response will describe : +The details of the response will describe: + - The `source` : input text or llm generated text - The `category` : the category of the content that triggered the moderation - The `severity` : the severity from 0 to 10 diff --git a/docs/my-website/docs/proxy/model_management.md b/docs/my-website/docs/proxy/model_management.md index 02ce4ba23..a8cc66ae7 100644 --- a/docs/my-website/docs/proxy/model_management.md +++ b/docs/my-website/docs/proxy/model_management.md @@ -17,7 +17,7 @@ model_list: ## Get Model Information - `/model/info` -Retrieve detailed information about each model listed in the `/model/info` endpoint, including descriptions from the `config.yaml` file, and additional model info (e.g. max tokens, cost per input token, etc.) pulled the model_info you set and the litellm model cost map. Sensitive details like API keys are excluded for security purposes. +Retrieve detailed information about each model listed in the `/model/info` endpoint, including descriptions from the `config.yaml` file, and additional model info (e.g. max tokens, cost per input token, etc.) pulled from the model_info you set and the [litellm model cost map](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). Sensitive details like API keys are excluded for security purposes. - + + ```bash curl -X POST "http://0.0.0.0:4000/model/new" \ - -H "accept: application/json" \ - -H "Content-Type: application/json" \ - -d '{ "model_name": "azure-gpt-turbo", "litellm_params": {"model": "azure/gpt-3.5-turbo", "api_key": "os.environ/AZURE_API_KEY", "api_base": "my-azure-api-base"} }' + -H "accept: application/json" \ + -H "Content-Type: application/json" \ + -d '{ "model_name": "azure-gpt-turbo", "litellm_params": {"model": "azure/gpt-3.5-turbo", "api_key": "os.environ/AZURE_API_KEY", "api_base": "my-azure-api-base"} }' ``` - + + + +```yaml +model_list: + - model_name: gpt-3.5-turbo ### RECEIVED MODEL NAME ### `openai.chat.completions.create(model="gpt-3.5-turbo",...)` + litellm_params: # all params accepted by litellm.completion() - https://github.com/BerriAI/litellm/blob/9b46ec05b02d36d6e4fb5c32321e51e7f56e4a6e/litellm/types/router.py#L297 + model: azure/gpt-turbo-small-eu ### MODEL NAME sent to `litellm.completion()` ### + api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ + api_key: "os.environ/AZURE_API_KEY_EU" # does os.getenv("AZURE_API_KEY_EU") + rpm: 6 # [OPTIONAL] Rate limit for this deployment: in requests per minute (rpm) + model_info: + my_custom_key: my_custom_value # additional model metadata +``` + + @@ -85,4 +96,83 @@ Keep in mind that as both endpoints are in [BETA], you may need to visit the ass - Get Model Information: [Issue #933](https://github.com/BerriAI/litellm/issues/933) - Add a New Model: [Issue #964](https://github.com/BerriAI/litellm/issues/964) -Feedback on the beta endpoints is valuable and helps improve the API for all users. \ No newline at end of file +Feedback on the beta endpoints is valuable and helps improve the API for all users. + + +## Add Additional Model Information + +If you want the ability to add a display name, description, and labels for models, just use `model_info:` + +```yaml +model_list: + - model_name: "gpt-4" + litellm_params: + model: "gpt-4" + api_key: "os.environ/OPENAI_API_KEY" + model_info: # 👈 KEY CHANGE + my_custom_key: "my_custom_value" +``` + +### Usage + +1. Add additional information to model + +```yaml +model_list: + - model_name: "gpt-4" + litellm_params: + model: "gpt-4" + api_key: "os.environ/OPENAI_API_KEY" + model_info: # 👈 KEY CHANGE + my_custom_key: "my_custom_value" +``` + +2. Call with `/model/info` + +Use a key with access to the model `gpt-4`. + +```bash +curl -L -X GET 'http://0.0.0.0:4000/v1/model/info' \ +-H 'Authorization: Bearer LITELLM_KEY' \ +``` + +3. **Expected Response** + +Returned `model_info = Your custom model_info + (if exists) LITELLM MODEL INFO` + + +[**How LiteLLM Model Info is found**](https://github.com/BerriAI/litellm/blob/9b46ec05b02d36d6e4fb5c32321e51e7f56e4a6e/litellm/proxy/proxy_server.py#L7460) + +[Tell us how this can be improved!](https://github.com/BerriAI/litellm/issues) + +```bash +{ + "data": [ + { + "model_name": "gpt-4", + "litellm_params": { + "model": "gpt-4" + }, + "model_info": { + "id": "e889baacd17f591cce4c63639275ba5e8dc60765d6c553e6ee5a504b19e50ddc", + "db_model": false, + "my_custom_key": "my_custom_value", # 👈 CUSTOM INFO + "key": "gpt-4", # 👈 KEY in LiteLLM MODEL INFO/COST MAP - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json + "max_tokens": 4096, + "max_input_tokens": 8192, + "max_output_tokens": 4096, + "input_cost_per_token": 3e-05, + "input_cost_per_character": null, + "input_cost_per_token_above_128k_tokens": null, + "output_cost_per_token": 6e-05, + "output_cost_per_character": null, + "output_cost_per_token_above_128k_tokens": null, + "output_cost_per_character_above_128k_tokens": null, + "output_vector_size": null, + "litellm_provider": "openai", + "mode": "chat" + } + }, + ] +} +``` diff --git a/docs/my-website/docs/proxy/multiple_admins.md b/docs/my-website/docs/proxy/multiple_admins.md index 376ff0174..e43b1e13b 100644 --- a/docs/my-website/docs/proxy/multiple_admins.md +++ b/docs/my-website/docs/proxy/multiple_admins.md @@ -1,4 +1,4 @@ -# ✨ Attribute Management changes to Users +# Attribute Management changes to Users Call management endpoints on behalf of a user. (Useful when connecting proxy to your development platform). diff --git a/docs/my-website/docs/proxy/oauth2.md b/docs/my-website/docs/proxy/oauth2.md new file mode 100644 index 000000000..ec076d8fa --- /dev/null +++ b/docs/my-website/docs/proxy/oauth2.md @@ -0,0 +1,63 @@ +# Oauth 2.0 Authentication + +Use this if you want to use an Oauth2.0 token to make `/chat`, `/embeddings` requests to the LiteLLM Proxy + +:::info + +This is an Enterprise Feature - [get in touch with us if you want a free trial to test if this feature meets your needs]((https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)) + +::: + +## Usage + +1. Set env vars: + +```bash +export OAUTH_TOKEN_INFO_ENDPOINT="https://your-provider.com/token/info" +export OAUTH_USER_ID_FIELD_NAME="sub" +export OAUTH_USER_ROLE_FIELD_NAME="role" +export OAUTH_USER_TEAM_ID_FIELD_NAME="team_id" +``` + +- `OAUTH_TOKEN_INFO_ENDPOINT`: URL to validate OAuth tokens +- `OAUTH_USER_ID_FIELD_NAME`: Field in token info response containing user ID +- `OAUTH_USER_ROLE_FIELD_NAME`: Field in token info for user's role +- `OAUTH_USER_TEAM_ID_FIELD_NAME`: Field in token info for user's team ID + +2. Enable on litellm config.yaml + +Set this on your config.yaml + +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + +general_settings: + master_key: sk-1234 + enable_oauth2_auth: true +``` + +3. Use token in requests to LiteLLM + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + +## Debugging + +Start the LiteLLM Proxy with [`--detailed_debug` mode and you should see more verbose logs](cli.md#detailed_debug) + diff --git a/docs/my-website/docs/proxy/pass_through.md b/docs/my-website/docs/proxy/pass_through.md index 1348a2fc1..bad23f0de 100644 --- a/docs/my-website/docs/proxy/pass_through.md +++ b/docs/my-website/docs/proxy/pass_through.md @@ -35,6 +35,7 @@ general_settings: Authorization: "bearer os.environ/COHERE_API_KEY" # (Optional) Auth Header to forward to your Endpoint content-type: application/json # (Optional) Extra Headers to pass to this endpoint accept: application/json + forward_headers: True # (Optional) Forward all headers from the incoming request to the target endpoint ``` **Step 2** Start Proxy Server in detailed_debug mode @@ -156,6 +157,8 @@ POST /api/public/ingestion HTTP/1.1" 207 Multi-Status Use this if you want the pass through endpoint to honour LiteLLM keys/authentication +This also enforces the key's rpm limits on pass-through endpoints. + Usage - set `auth: true` on the config ```yaml general_settings: @@ -190,6 +193,53 @@ curl --request POST \ }' ``` +### Use Langfuse client sdk w/ LiteLLM Key + +**Usage** + +1. Set-up yaml to pass-through langfuse /api/public/ingestion + +```yaml +general_settings: + master_key: sk-1234 + pass_through_endpoints: + - path: "/api/public/ingestion" # route you want to add to LiteLLM Proxy Server + target: "https://us.cloud.langfuse.com/api/public/ingestion" # URL this route should forward + auth: true # 👈 KEY CHANGE + custom_auth_parser: "langfuse" # 👈 KEY CHANGE + headers: + LANGFUSE_PUBLIC_KEY: "os.environ/LANGFUSE_DEV_PUBLIC_KEY" # your langfuse account public key + LANGFUSE_SECRET_KEY: "os.environ/LANGFUSE_DEV_SK_KEY" # your langfuse account secret key +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test with langfuse sdk + + +```python + +from langfuse import Langfuse + +langfuse = Langfuse( + host="http://localhost:4000", # your litellm proxy endpoint + public_key="sk-1234", # your litellm proxy api key + secret_key="anything", # no key required since this is a pass through +) + +print("sending langfuse trace request") +trace = langfuse.trace(name="test-trace-litellm-proxy-passthrough") +print("flushing langfuse request") +langfuse.flush() + +print("flushed langfuse request") +``` + + ## `pass_through_endpoints` Spec on config.yaml All possible values for `pass_through_endpoints` and what they mean @@ -217,4 +267,150 @@ general_settings: * `accept` *string*: The expected response format from the server. * `LANGFUSE_PUBLIC_KEY` *string*: Your Langfuse account public key - only set this when forwarding to Langfuse. * `LANGFUSE_SECRET_KEY` *string*: Your Langfuse account secret key - only set this when forwarding to Langfuse. - * `` *string*: Pass any custom header key/value pair \ No newline at end of file + * `` *string*: Pass any custom header key/value pair + * `forward_headers` *Optional(boolean)*: If true, all headers from the incoming request will be forwarded to the target endpoint. Default is `False`. + + +## Custom Chat Endpoints (Anthropic/Bedrock/Vertex) + +Allow developers to call the proxy with Anthropic/boto3/etc. client sdk's. + +Test our [Anthropic Adapter](../anthropic_completion.md) for reference [**Code**](https://github.com/BerriAI/litellm/blob/fd743aaefd23ae509d8ca64b0c232d25fe3e39ee/litellm/adapters/anthropic_adapter.py#L50) + +### 1. Write an Adapter + +Translate the request/response from your custom API schema to the OpenAI schema (used by litellm.completion()) and back. + +For provider-specific params 👉 [**Provider-Specific Params**](../completion/provider_specific_params.md) + +```python +from litellm import adapter_completion +import litellm +from litellm import ChatCompletionRequest, verbose_logger +from litellm.integrations.custom_logger import CustomLogger +from litellm.types.llms.anthropic import AnthropicMessagesRequest, AnthropicResponse +import os + +# What is this? +## Translates OpenAI call to Anthropic `/v1/messages` format +import json +import os +import traceback +import uuid +from typing import Literal, Optional + +import dotenv +import httpx +from pydantic import BaseModel + + +################### +# CUSTOM ADAPTER ## +################### + +class AnthropicAdapter(CustomLogger): + def __init__(self) -> None: + super().__init__() + + def translate_completion_input_params( + self, kwargs + ) -> Optional[ChatCompletionRequest]: + """ + - translate params, where needed + - pass rest, as is + """ + request_body = AnthropicMessagesRequest(**kwargs) # type: ignore + + translated_body = litellm.AnthropicConfig().translate_anthropic_to_openai( + anthropic_message_request=request_body + ) + + return translated_body + + def translate_completion_output_params( + self, response: litellm.ModelResponse + ) -> Optional[AnthropicResponse]: + + return litellm.AnthropicConfig().translate_openai_response_to_anthropic( + response=response + ) + + def translate_completion_output_params_streaming(self) -> Optional[BaseModel]: + return super().translate_completion_output_params_streaming() + + +anthropic_adapter = AnthropicAdapter() + +########### +# TEST IT # +########### + +## register CUSTOM ADAPTER +litellm.adapters = [{"id": "anthropic", "adapter": anthropic_adapter}] + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "your-openai-key" +os.environ["COHERE_API_KEY"] = "your-cohere-key" + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# openai call +response = adapter_completion(model="gpt-3.5-turbo", messages=messages, adapter_id="anthropic") + +# cohere call +response = adapter_completion(model="command-nightly", messages=messages, adapter_id="anthropic") +print(response) +``` + +### 2. Create new endpoint + +We pass the custom callback class defined in Step1 to the config.yaml. Set callbacks to python_filename.logger_instance_name + +In the config below, we pass + +python_filename: `custom_callbacks.py` +logger_instance_name: `anthropic_adapter`. This is defined in Step 1 + +`target: custom_callbacks.proxy_handler_instance` + +```yaml +model_list: + - model_name: my-fake-claude-endpoint + litellm_params: + model: gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY + + +general_settings: + master_key: sk-1234 + pass_through_endpoints: + - path: "/v1/messages" # route you want to add to LiteLLM Proxy Server + target: custom_callbacks.anthropic_adapter # Adapter to use for this route + headers: + litellm_user_api_key: "x-api-key" # Field in headers, containing LiteLLM Key +``` + +### 3. Test it! + +**Start proxy** + +```bash +litellm --config /path/to/config.yaml +``` + +**Curl** + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +-H 'x-api-key: sk-1234' \ +-H 'anthropic-version: 2023-06-01' \ # ignored +-H 'content-type: application/json' \ +-D '{ + "model": "my-fake-claude-endpoint", + "max_tokens": 1024, + "messages": [ + {"role": "user", "content": "Hello, world"} + ] +}' +``` + diff --git a/docs/my-website/docs/proxy/pii_masking.md b/docs/my-website/docs/proxy/pii_masking.md index a95a6d771..8106765f4 100644 --- a/docs/my-website/docs/proxy/pii_masking.md +++ b/docs/my-website/docs/proxy/pii_masking.md @@ -179,4 +179,60 @@ chat_completion = client.chat.completions.create( }, "_response_ms": 1753.426 } +``` + + +## Turn on for logging only + +Only apply PII Masking before logging to Langfuse, etc. + +Not on the actual llm api request / response. + +:::note +This is currently only applied for +- `/chat/completion` requests +- on 'success' logging + +::: + +1. Setup config.yaml +```yaml +litellm_settings: + presidio_logging_only: true + +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-D '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "Hi, my name is Jane!" + } + ] + }' +``` + + +**Expected Logged Response** + +``` +Hi, my name is ! ``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/prod.md b/docs/my-website/docs/proxy/prod.md index b59360a4f..654d8cc2d 100644 --- a/docs/my-website/docs/proxy/prod.md +++ b/docs/my-website/docs/proxy/prod.md @@ -84,6 +84,20 @@ Set `export LITELLM_MODE="PRODUCTION"` This disables the load_dotenv() functionality, which will automatically load your environment credentials from the local `.env`. +## 5. Set LiteLLM Salt Key + +If you plan on using the DB, set a salt key for encrypting/decrypting variables in the DB. + +Do not change this after adding a model. It is used to encrypt / decrypt your LLM API Key credentials + +We recommned - https://1password.com/password-generator/ password generator to get a random hash for litellm salt key. + +```bash +export LITELLM_SALT_KEY="sk-1234" +``` + +[**See Code**](https://github.com/BerriAI/litellm/blob/036a6821d588bd36d170713dcf5a72791a694178/litellm/proxy/common_utils/encrypt_decrypt_utils.py#L15) + ## Extras ### Expected Performance in Production diff --git a/docs/my-website/docs/proxy/prometheus.md b/docs/my-website/docs/proxy/prometheus.md index 61d1397ac..4b913d2e8 100644 --- a/docs/my-website/docs/proxy/prometheus.md +++ b/docs/my-website/docs/proxy/prometheus.md @@ -1,7 +1,16 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# 📈 Prometheus metrics [BETA] +# 📈 [BETA] Prometheus metrics + +:::info +🚨 Prometheus metrics will be out of Beta on September 15, 2024 - as part of this release it will be on LiteLLM Enterprise starting at $250/mo + +[Enterprise Pricing](https://www.litellm.ai/#pricing) + +[Contact us here to get a free trial](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat) + +::: LiteLLM Exposes a `/metrics` endpoint for Prometheus to Poll @@ -47,9 +56,11 @@ http://localhost:4000/metrics # /metrics ``` -## Metrics Tracked +## 📈 Metrics Tracked +### Proxy Requests / Spend Metrics + | Metric Name | Description | |----------------------|--------------------------------------| | `litellm_requests_metric` | Number of requests made, per `"user", "key", "model", "team", "end-user"` | @@ -57,6 +68,23 @@ http://localhost:4000/metrics | `litellm_total_tokens` | input + output tokens per `"user", "key", "model", "team", "end-user"` | | `litellm_llm_api_failed_requests_metric` | Number of failed LLM API requests per `"user", "key", "model", "team", "end-user"` | +### LLM API / Provider Metrics + +| Metric Name | Description | +|----------------------|--------------------------------------| +| `litellm_deployment_state` | The state of the deployment: 0 = healthy, 1 = partial outage, 2 = complete outage. | +| `litellm_remaining_requests_metric` | Track `x-ratelimit-remaining-requests` returned from LLM API Deployment | +| `litellm_remaining_tokens` | Track `x-ratelimit-remaining-tokens` return from LLM API Deployment | + `litellm_deployment_success_responses` | Total number of successful LLM API calls for deployment | +| `litellm_deployment_failure_responses` | Total number of failed LLM API calls for deployment | +| `litellm_deployment_total_requests` | Total number of LLM API calls for deployment - success + failure | +| `litellm_deployment_latency_per_output_token` | Latency per output token for deployment | +| `litellm_deployment_successful_fallbacks` | Number of successful fallback requests from primary model -> fallback model | +| `litellm_deployment_failed_fallbacks` | Number of failed fallback requests from primary model -> fallback model | + + + + ### Budget Metrics | Metric Name | Description | |----------------------|--------------------------------------| @@ -64,55 +92,6 @@ http://localhost:4000/metrics | `litellm_remaining_api_key_budget_metric` | Remaining Budget for API Key (A key Created on LiteLLM)| -### ✨ (Enterprise) LLM Remaining Requests and Remaining Tokens -Set this on your config.yaml to allow you to track how close you are to hitting your TPM / RPM limits on each model group - -```yaml -litellm_settings: - success_callback: ["prometheus"] - failure_callback: ["prometheus"] - return_response_headers: true # ensures the LLM API calls track the response headers -``` - -| Metric Name | Description | -|----------------------|--------------------------------------| -| `litellm_remaining_requests_metric` | Track `x-ratelimit-remaining-requests` returned from LLM API Deployment | -| `litellm_remaining_tokens` | Track `x-ratelimit-remaining-tokens` return from LLM API Deployment | - -Example Metric - - - - -```shell -litellm_remaining_requests -{ - api_base="https://api.openai.com/v1", - api_provider="openai", - litellm_model_name="gpt-3.5-turbo", - model_group="gpt-3.5-turbo" -} -8998.0 -``` - - - - - -```shell -litellm_remaining_tokens -{ - api_base="https://api.openai.com/v1", - api_provider="openai", - litellm_model_name="gpt-3.5-turbo", - model_group="gpt-3.5-turbo" -} -999981.0 -``` - - - - ## Monitor System Health diff --git a/docs/my-website/docs/proxy/prompt_injection.md b/docs/my-website/docs/proxy/prompt_injection.md index 43edd0472..81d76e7bf 100644 --- a/docs/my-website/docs/proxy/prompt_injection.md +++ b/docs/my-website/docs/proxy/prompt_injection.md @@ -13,20 +13,23 @@ LiteLLM Supports the following methods for detecting prompt injection attacks Use this if you want to reject /chat, /completions, /embeddings calls that have prompt injection attacks -LiteLLM uses [LakerAI API](https://platform.lakera.ai/) to detect if a request has a prompt injection attack +LiteLLM uses [LakeraAI API](https://platform.lakera.ai/) to detect if a request has a prompt injection attack -#### Usage +### Usage Step 1 Set a `LAKERA_API_KEY` in your env ``` LAKERA_API_KEY="7a91a1a6059da*******" ``` -Step 2. Add `lakera_prompt_injection` to your calbacks +Step 2. Add `lakera_prompt_injection` as a guardrail ```yaml litellm_settings: - callbacks: ["lakera_prompt_injection"] + guardrails: + - prompt_injection: # your custom name for guardrail + callbacks: ["lakera_prompt_injection"] # litellm callbacks to use + default_on: true # will run on all llm requests when true ``` That's it, start your proxy @@ -48,6 +51,48 @@ curl --location 'http://localhost:4000/chat/completions' \ }' ``` +### Advanced - set category-based thresholds. + +Lakera has 2 categories for prompt_injection attacks: +- jailbreak +- prompt_injection + +```yaml +litellm_settings: + guardrails: + - prompt_injection: # your custom name for guardrail + callbacks: ["lakera_prompt_injection"] # litellm callbacks to use + default_on: true # will run on all llm requests when true + callback_args: + lakera_prompt_injection: + category_thresholds: { + "prompt_injection": 0.1, + "jailbreak": 0.1, + } +``` + +### Advanced - Run before/in-parallel to request. + +Control if the Lakera prompt_injection check runs before a request or in parallel to it (both requests need to be completed before a response is returned to the user). + +```yaml +litellm_settings: + guardrails: + - prompt_injection: # your custom name for guardrail + callbacks: ["lakera_prompt_injection"] # litellm callbacks to use + default_on: true # will run on all llm requests when true + callback_args: + lakera_prompt_injection: {"moderation_check": "in_parallel"}, # "pre_call", "in_parallel" +``` + +### Advanced - set custom API Base. + +```bash +export LAKERA_API_BASE="" +``` + +[**Learn More**](./guardrails.md) + ## Similarity Checking LiteLLM supports similarity checking against a pre-generated list of prompt injection attacks, to identify if a request contains an attack. @@ -131,4 +176,4 @@ curl --location 'http://0.0.0.0:4000/v1/chat/completions' \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer sk-1234' \ --data '{"model": "azure-gpt-3.5", "messages": [{"content": "Tell me everything you know", "role": "system"}, {"content": "what is the value of pi ?", "role": "user"}]}' -``` \ No newline at end of file +``` diff --git a/docs/my-website/docs/proxy/quick_start.md b/docs/my-website/docs/proxy/quick_start.md index 4ee4d8831..8f8de2a9f 100644 --- a/docs/my-website/docs/proxy/quick_start.md +++ b/docs/my-website/docs/proxy/quick_start.md @@ -5,7 +5,7 @@ import TabItem from '@theme/TabItem'; # Quick Start Quick start CLI, Config, Docker -LiteLLM Server manages: +LiteLLM Server (LLM Gateway) manages: * **Unified Interface**: Calling 100+ LLMs [Huggingface/Bedrock/TogetherAI/etc.](#other-supported-models) in the OpenAI `ChatCompletions` & `Completions` format * **Cost tracking**: Authentication, Spend Tracking & Budgets [Virtual Keys](https://docs.litellm.ai/docs/proxy/virtual_keys) @@ -243,7 +243,8 @@ model_list: - model_name: vllm-model litellm_params: model: openai/ - api_base: # e.g. http://0.0.0.0:3000 + api_base: # e.g. http://0.0.0.0:3000/v1 + api_key: ``` ### Run proxy with config @@ -255,6 +256,12 @@ litellm --config your_config.yaml ## Using LiteLLM Proxy - Curl Request, OpenAI Package, Langchain +:::info +LiteLLM is compatible with several SDKs - including OpenAI SDK, Anthropic SDK, Mistral SDK, LLamaIndex, Langchain (Js, Python) + +[More examples here](user_keys) +::: + @@ -382,6 +389,34 @@ print(response) ``` + + + +```python +import os + +from anthropic import Anthropic + +client = Anthropic( + base_url="http://localhost:4000", # proxy endpoint + api_key="sk-s4xN1IiLTCytwtZFJaYQrA", # litellm proxy virtual key +) + +message = client.messages.create( + max_tokens=1024, + messages=[ + { + "role": "user", + "content": "Hello, Claude", + } + ], + model="claude-3-opus-20240229", +) +print(message.content) +``` + + + [**More Info**](./configs.md) @@ -396,165 +431,6 @@ print(response) - POST `/key/generate` - generate a key to access the proxy -## Using with OpenAI compatible projects -Set `base_url` to the LiteLLM Proxy server - - - - -```python -import openai -client = openai.OpenAI( - api_key="anything", - base_url="http://0.0.0.0:4000" -) - -# request sent to model set on litellm proxy, `litellm --model` -response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ - { - "role": "user", - "content": "this is a test request, write a short poem" - } -]) - -print(response) - -``` - - - -#### Start the LiteLLM proxy -```shell -litellm --model gpt-3.5-turbo - -#INFO: Proxy running on http://0.0.0.0:4000 -``` - -#### 1. Clone the repo - -```shell -git clone https://github.com/danny-avila/LibreChat.git -``` - - -#### 2. Modify Librechat's `docker-compose.yml` -LiteLLM Proxy is running on port `4000`, set `4000` as the proxy below -```yaml -OPENAI_REVERSE_PROXY=http://host.docker.internal:4000/v1/chat/completions -``` - -#### 3. Save fake OpenAI key in Librechat's `.env` - -Copy Librechat's `.env.example` to `.env` and overwrite the default OPENAI_API_KEY (by default it requires the user to pass a key). -```env -OPENAI_API_KEY=sk-1234 -``` - -#### 4. Run LibreChat: -```shell -docker compose up -``` - - - - -Continue-Dev brings ChatGPT to VSCode. See how to [install it here](https://continue.dev/docs/quickstart). - -In the [config.py](https://continue.dev/docs/reference/Models/openai) set this as your default model. -```python - default=OpenAI( - api_key="IGNORED", - model="fake-model-name", - context_length=2048, # customize if needed for your model - api_base="http://localhost:4000" # your proxy server url - ), -``` - -Credits [@vividfog](https://github.com/ollama/ollama/issues/305#issuecomment-1751848077) for this tutorial. - - - - -```shell -$ pip install aider - -$ aider --openai-api-base http://0.0.0.0:4000 --openai-api-key fake-key -``` - - - -```python -pip install pyautogen -``` - -```python -from autogen import AssistantAgent, UserProxyAgent, oai -config_list=[ - { - "model": "my-fake-model", - "api_base": "http://localhost:4000", #litellm compatible endpoint - "api_type": "open_ai", - "api_key": "NULL", # just a placeholder - } -] - -response = oai.Completion.create(config_list=config_list, prompt="Hi") -print(response) # works fine - -llm_config={ - "config_list": config_list, -} - -assistant = AssistantAgent("assistant", llm_config=llm_config) -user_proxy = UserProxyAgent("user_proxy") -user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list) -``` - -Credits [@victordibia](https://github.com/microsoft/autogen/issues/45#issuecomment-1749921972) for this tutorial. - - - -A guidance language for controlling large language models. -https://github.com/guidance-ai/guidance - -**NOTE:** Guidance sends additional params like `stop_sequences` which can cause some models to fail if they don't support it. - -**Fix**: Start your proxy using the `--drop_params` flag - -```shell -litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048 --drop_params -``` - -```python -import guidance - -# set api_base to your proxy -# set api_key to anything -gpt4 = guidance.llms.OpenAI("gpt-4", api_base="http://0.0.0.0:4000", api_key="anything") - -experts = guidance(''' -{{#system~}} -You are a helpful and terse assistant. -{{~/system}} - -{{#user~}} -I want a response to the following question: -{{query}} -Name 3 world-class experts (past or present) who would be great at answering this? -Don't answer the question yet. -{{~/user}} - -{{#assistant~}} -{{gen 'expert_names' temperature=0 max_tokens=300}} -{{~/assistant}} -''', llm=gpt4) - -result = experts(query='How can I be more productive?') -print(result) -``` - - - ## Debugging Proxy Events that occur during normal operation diff --git a/docs/my-website/docs/proxy/reliability.md b/docs/my-website/docs/proxy/reliability.md index 9228071b0..cb6550a47 100644 --- a/docs/my-website/docs/proxy/reliability.md +++ b/docs/my-website/docs/proxy/reliability.md @@ -31,15 +31,26 @@ model_list: api_base: https://openai-france-1234.openai.azure.com/ api_key: rpm: 1440 +routing_strategy: simple-shuffle # Literal["simple-shuffle", "least-busy", "usage-based-routing","latency-based-routing"], default="simple-shuffle" + model_group_alias: {"gpt-4": "gpt-3.5-turbo"} # all requests with `gpt-4` will be routed to models with `gpt-3.5-turbo` + num_retries: 2 + timeout: 30 # 30 seconds + redis_host: # set this when using multiple litellm proxy deployments, load balancing state stored in redis + redis_password: + redis_port: 1992 ``` +:::info +Detailed information about [routing strategies can be found here](../routing) +::: + #### Step 2: Start Proxy with config ```shell $ litellm --config /path/to/config.yaml ``` -### Test - Load Balancing +### Test - Simple Call Here requests with model=gpt-3.5-turbo will be routed across multiple instances of azure/gpt-3.5-turbo @@ -127,6 +138,27 @@ print(response) +### Test - Loadbalancing + +In this request, the following will occur: +1. A rate limit exception will be raised +2. LiteLLM proxy will retry the request on the model group (default is 3). + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "Hi there!"} + ], + "mock_testing_rate_limit_error": true +}' +``` + +[**See Code**](https://github.com/BerriAI/litellm/blob/6b8806b45f970cb2446654d2c379f8dcaa93ce3c/litellm/router.py#L2535) + ### Test - Client Side Fallbacks In this request the following will occur: 1. The request to `model="zephyr-beta"` will fail @@ -434,6 +466,33 @@ litellm_settings: +### Default Fallbacks + +You can also set default_fallbacks, in case a specific model group is misconfigured / bad. + + +```yaml +model_list: + - model_name: gpt-3.5-turbo-small + litellm_params: + model: azure/chatgpt-v-2 + api_base: os.environ/AZURE_API_BASE + api_key: os.environ/AZURE_API_KEY + api_version: "2023-07-01-preview" + + - model_name: claude-opus + litellm_params: + model: claude-3-opus-20240229 + api_key: os.environ/ANTHROPIC_API_KEY + +litellm_settings: + default_fallbacks: ["claude-opus"] +``` + +This will default to claude-opus in case any model fails. + +A model-specific fallbacks (e.g. {"gpt-3.5-turbo-small": ["claude-opus"]}) overrides default fallback. + ### Test Fallbacks! Check if your fallbacks are working as expected. diff --git a/docs/my-website/docs/proxy/self_serve.md b/docs/my-website/docs/proxy/self_serve.md index 4349f985a..c18aee155 100644 --- a/docs/my-website/docs/proxy/self_serve.md +++ b/docs/my-website/docs/proxy/self_serve.md @@ -4,7 +4,7 @@ import TabItem from '@theme/TabItem'; # 🤗 UI - Self-Serve -Allow users to create their own keys on [Proxy UI](./ui.md). +## Allow users to create their own keys on [Proxy UI](./ui.md). 1. Add user with permissions to a team on proxy @@ -125,6 +125,41 @@ LiteLLM Enterprise: Enable [SSO login](./ui.md#setup-ssoauth-for-ui) +## Allow users to View Usage, Caching Analytics + +1. Go to Internal Users -> +Invite User + +Set their role to `Admin Viewer` - this means they can only view usage, caching analytics + + +
+ +2. Share invitation link with user + + + +
+ +3. User logs in via email + password auth + + +
+ +4. User can now view Usage, Caching Analytics + + + + +## Available Roles +Here's the available UI roles for a LiteLLM Internal User: + +**Admin Roles:** + - `proxy_admin`: admin over the platform + - `proxy_admin_viewer`: can login, view all keys, view all spend. **Cannot** create/delete keys, add new users. + +**Internal User Roles:** + - `internal_user`: can login, view/create/delete their own keys, view their spend. **Cannot** add new users. + - `internal_user_viewer`: can login, view their own keys, view their own spend. **Cannot** create/delete keys, add new users. ## Advanced ### Setting custom logout URLs @@ -138,3 +173,24 @@ export PROXY_LOGOUT_URL="https://www.google.com" +### Set max budget for internal users + +Automatically apply budget per internal user when they sign up + +```yaml +litellm_settings: + max_internal_user_budget: 10 + internal_user_budget_duration: "1mo" # reset every month +``` + +This sets a max budget of $10 USD for internal users when they sign up. + +This budget only applies to personal keys created by that user - seen under `Default Team` on the UI. + + + +This budget does not apply to keys created under non-default teams. + +### Set max budget for teams + +[**Go Here**](./team_budgets.md) \ No newline at end of file diff --git a/docs/my-website/docs/proxy/streaming_logging.md b/docs/my-website/docs/proxy/streaming_logging.md index 3fa896467..a9d577677 100644 --- a/docs/my-website/docs/proxy/streaming_logging.md +++ b/docs/my-website/docs/proxy/streaming_logging.md @@ -8,6 +8,7 @@ Define your custom callback class in a python file. ```python from litellm.integrations.custom_logger import CustomLogger import litellm +import logging # This file includes the custom callbacks for LiteLLM Proxy # Once defined, these can be passed in proxy_config.yaml @@ -25,9 +26,9 @@ class MyCustomHandler(CustomLogger): datefmt='%Y-%m-%d %H:%M:%S' ) - response_cost = litellm.completion_cost(completion_response=completion_response) + response_cost: Optional[float] = kwargs.get("response_cost", None) print("regular response_cost", response_cost) - logging.info(f"Model {completion_response.model} Cost: ${response_cost:.8f}") + logging.info(f"Model {response_obj.model} Cost: ${response_cost:.8f}") except: pass @@ -78,4 +79,4 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ "user": "ishaan-app", "temperature": 0.2 }' -``` \ No newline at end of file +``` diff --git a/docs/my-website/docs/proxy/tag_routing.md b/docs/my-website/docs/proxy/tag_routing.md new file mode 100644 index 000000000..c3d533564 --- /dev/null +++ b/docs/my-website/docs/proxy/tag_routing.md @@ -0,0 +1,133 @@ +# Tag Based Routing + +Route requests based on tags. +This is useful for implementing free / paid tiers for users + +### 1. Define tags on config.yaml + +- A request with `tags=["free"]` will get routed to `openai/fake` +- A request with `tags=["paid"]` will get routed to `openai/gpt-4o` + +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + tags: ["free"] # 👈 Key Change + - model_name: gpt-4 + litellm_params: + model: openai/gpt-4o + api_key: os.environ/OPENAI_API_KEY + tags: ["paid"] # 👈 Key Change + +router_settings: + enable_tag_filtering: True # 👈 Key Change +general_settings: + master_key: sk-1234 +``` + +### 2. Make Request with `tags=["free"]` + +This request includes "tags": ["free"], which routes it to `openai/fake` + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-4", + "messages": [ + {"role": "user", "content": "Hello, Claude gm!"} + ], + "tags": ["free"] + }' +``` +**Expected Response** + +Expect to see the following response header when this works +```shell +x-litellm-model-api-base: https://exampleopenaiendpoint-production.up.railway.app/ +``` + +Response +```shell +{ + "id": "chatcmpl-33c534e3d70148218e2d62496b81270b", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "\n\nHello there, how may I assist you today?", + "role": "assistant", + "tool_calls": null, + "function_call": null + } + } + ], + "created": 1677652288, + "model": "gpt-3.5-turbo-0125", + "object": "chat.completion", + "system_fingerprint": "fp_44709d6fcb", + "usage": { + "completion_tokens": 12, + "prompt_tokens": 9, + "total_tokens": 21 + } +} +``` + + +### 3. Make Request with `tags=["paid"]` + +This request includes "tags": ["paid"], which routes it to `openai/gpt-4` + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-4", + "messages": [ + {"role": "user", "content": "Hello, Claude gm!"} + ], + "tags": ["paid"] + }' +``` + +**Expected Response** + +Expect to see the following response header when this works +```shell +x-litellm-model-api-base: https://api.openai.com +``` + +Response +```shell +{ + "id": "chatcmpl-9maCcqQYTqdJrtvfakIawMOIUbEZx", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "Good morning! How can I assist you today?", + "role": "assistant", + "tool_calls": null, + "function_call": null + } + } + ], + "created": 1721365934, + "model": "gpt-4o-2024-05-13", + "object": "chat.completion", + "system_fingerprint": "fp_c4e5b6fa31", + "usage": { + "completion_tokens": 10, + "prompt_tokens": 12, + "total_tokens": 22 + } +} +``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/team_based_routing.md b/docs/my-website/docs/proxy/team_based_routing.md index 6a68e5a1f..89b18ec63 100644 --- a/docs/my-website/docs/proxy/team_based_routing.md +++ b/docs/my-website/docs/proxy/team_based_routing.md @@ -1,4 +1,4 @@ -# 👥 Team-based Routing + Logging +# 👥 Team-based Routing ## Routing Route calls to different model groups based on the team-id @@ -71,35 +71,3 @@ curl --location 'http://0.0.0.0:4000/v1/chat/completions' \ }' ``` - -## Logging / Caching - -Turn on/off logging and caching for a specific team id. - -**Example:** - -This config would send langfuse logs to 2 different langfuse projects, based on the team id - -```yaml -litellm_settings: - default_team_settings: - - team_id: my-secret-project - success_callback: ["langfuse"] - langfuse_public_key: os.environ/LANGFUSE_PUB_KEY_1 # Project 1 - langfuse_secret: os.environ/LANGFUSE_PRIVATE_KEY_1 # Project 1 - - team_id: ishaans-secret-project - success_callback: ["langfuse"] - langfuse_public_key: os.environ/LANGFUSE_PUB_KEY_2 # Project 2 - langfuse_secret: os.environ/LANGFUSE_SECRET_2 # Project 2 -``` - -Now, when you [generate keys](./virtual_keys.md) for this team-id - -```bash -curl -X POST 'http://0.0.0.0:4000/key/generate' \ --H 'Authorization: Bearer sk-1234' \ --H 'Content-Type: application/json' \ --d '{"team_id": "ishaans-secret-project"}' -``` - -All requests made with these keys will log data to their team-specific logging. diff --git a/docs/my-website/docs/proxy/team_logging.md b/docs/my-website/docs/proxy/team_logging.md new file mode 100644 index 000000000..ef4ebe591 --- /dev/null +++ b/docs/my-website/docs/proxy/team_logging.md @@ -0,0 +1,227 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# 👥📊 Team/Key Based Logging + +Allow each key/team to use their own Langfuse Project / custom callbacks + +**This allows you to do the following** +``` +Team 1 -> Logs to Langfuse Project 1 +Team 2 -> Logs to Langfuse Project 2 +Team 3 -> Disabled Logging (for GDPR compliance) + +``` + +## Team Based Logging + +[👉 Tutorial - Allow each team to use their own Langfuse Project / custom callbacks](team_logging.md) + + +## Logging / Caching + +Turn on/off logging and caching for a specific team id. + +**Example:** + +This config would send langfuse logs to 2 different langfuse projects, based on the team id + +```yaml +litellm_settings: + default_team_settings: + - team_id: my-secret-project + success_callback: ["langfuse"] + langfuse_public_key: os.environ/LANGFUSE_PUB_KEY_1 # Project 1 + langfuse_secret: os.environ/LANGFUSE_PRIVATE_KEY_1 # Project 1 + - team_id: ishaans-secret-project + success_callback: ["langfuse"] + langfuse_public_key: os.environ/LANGFUSE_PUB_KEY_2 # Project 2 + langfuse_secret: os.environ/LANGFUSE_SECRET_2 # Project 2 +``` + +Now, when you [generate keys](./virtual_keys.md) for this team-id + +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{"team_id": "ishaans-secret-project"}' +``` + +All requests made with these keys will log data to their team-specific logging. --> + +## [BETA] Team Logging via API + +:::info + +✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat) + +::: + + +### Set Callbacks Per Team + +#### 1. Set callback for team + +We make a request to `POST /team/{team_id}/callback` to add a callback for + +```shell +curl -X POST 'http:/localhost:4000/team/dbe2f686-a686-4896-864a-4c3924458709/callback' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "callback_name": "langfuse", + "callback_type": "success", + "callback_vars": { + "langfuse_public_key": "pk", + "langfuse_secret_key": "sk_", + "langfuse_host": "https://cloud.langfuse.com" + } + +}' +``` + +##### Supported Values + +| Field | Supported Values | Notes | +|-------|------------------|-------| +| `callback_name` | `"langfuse"` | Currently only supports "langfuse" | +| `callback_type` | `"success"`, `"failure"`, `"success_and_failure"` | | +| `callback_vars` | | dict of callback settings | +|     `langfuse_public_key` | string | Required | +|     `langfuse_secret_key` | string | Required | +|     `langfuse_host` | string | Optional (defaults to https://cloud.langfuse.com) | + +#### 2. Create key for team + +All keys created for team `dbe2f686-a686-4896-864a-4c3924458709` will log to langfuse project specified on [Step 1. Set callback for team](#1-set-callback-for-team) + + +```shell +curl --location 'http://0.0.0.0:4000/key/generate' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "team_id": "dbe2f686-a686-4896-864a-4c3924458709" +}' +``` + + +#### 3. Make `/chat/completion` request for team + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-KbUuE0WNptC0jXapyMmLBA" \ + -d '{ + "model": "gpt-4", + "messages": [ + {"role": "user", "content": "Hello, Claude gm!"} + ] +}' +``` + +Expect this to be logged on the langfuse project specified on [Step 1. Set callback for team](#1-set-callback-for-team) + + +### Disable Logging for a Team + +To disable logging for a specific team, you can use the following endpoint: + +`POST /team/{team_id}/disable_logging` + +This endpoint removes all success and failure callbacks for the specified team, effectively disabling logging. + +#### Step 1. Disable logging for team + +```shell +curl -X POST 'http://localhost:4000/team/YOUR_TEAM_ID/disable_logging' \ + -H 'Authorization: Bearer YOUR_API_KEY' +``` +Replace YOUR_TEAM_ID with the actual team ID + +**Response** +A successful request will return a response similar to this: +```json +{ + "status": "success", + "message": "Logging disabled for team YOUR_TEAM_ID", + "data": { + "team_id": "YOUR_TEAM_ID", + "success_callbacks": [], + "failure_callbacks": [] + } +} +``` + +#### Step 2. Test it - `/chat/completions` + +Use a key generated for team = `team_id` - you should see no logs on your configured success callback (eg. Langfuse) + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-KbUuE0WNptC0jXapyMmLBA" \ + -d '{ + "model": "gpt-4", + "messages": [ + {"role": "user", "content": "Hello, Claude gm!"} + ] +}' +``` + +#### Debugging / Troubleshooting + +- Check active callbacks for team using `GET /team/{team_id}/callback` + +Use this to check what success/failure callbacks are active for team=`team_id` + +```shell +curl -X GET 'http://localhost:4000/team/dbe2f686-a686-4896-864a-4c3924458709/callback' \ + -H 'Authorization: Bearer sk-1234' +``` + +### Team Logging Endpoints + +- [`POST /team/{team_id}/callback` Add a success/failure callback to a team](https://litellm-api.up.railway.app/#/team%20management/add_team_callbacks_team__team_id__callback_post) +- [`GET /team/{team_id}/callback` - Get the success/failure callbacks and variables for a team](https://litellm-api.up.railway.app/#/team%20management/get_team_callbacks_team__team_id__callback_get) + + + + + +## [BETA] Key Based Logging + +Use the `/key/generate` or `/key/update` endpoints to add logging callbacks to a specific key. + +:::info + +✨ This is an Enterprise only feature [Get Started with Enterprise here](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat) + +::: + +```bash +curl -X POST 'http://0.0.0.0:4000/key/generate' \ +-H 'Authorization: Bearer sk-1234' \ +-H 'Content-Type: application/json' \ +-d '{ + "metadata": { + "logging": [{ + "callback_name": "langfuse", # 'otel', 'langfuse', 'lunary' + "callback_type": "success" # set, if required by integration - future improvement, have logging tools work for success + failure by default + "callback_vars": { + "langfuse_public_key": "os.environ/LANGFUSE_PUBLIC_KEY", # [RECOMMENDED] reference key in proxy environment + "langfuse_secret_key": "os.environ/LANGFUSE_SECRET_KEY", # [RECOMMENDED] reference key in proxy environment + "langfuse_host": "https://cloud.langfuse.com" + } + }] + } +}' + +``` + +--- + +Help us improve this feature, by filing a [ticket here](https://github.com/BerriAI/litellm/issues) + diff --git a/docs/my-website/docs/proxy/ui.md b/docs/my-website/docs/proxy/ui.md index a3eaac3c0..40c55a57c 100644 --- a/docs/my-website/docs/proxy/ui.md +++ b/docs/my-website/docs/proxy/ui.md @@ -53,6 +53,12 @@ UI_PASSWORD=langchain # password to sign in on UI On accessing the LiteLLM UI, you will be prompted to enter your username, password +## Invite-other users + +Allow others to create/delete their own keys. + +[**Go Here**](./self_serve.md) + ## ✨ Enterprise Features Features here are behind a commercial license in our `/enterprise` folder. [**See Code**](https://github.com/BerriAI/litellm/tree/main/enterprise) @@ -76,6 +82,13 @@ litellm_settings: - Key will be created with `max_budget=100` since 100 is the upper bound #### Step 2: Setup Oauth Client + +:::tip + +Looking for how to use Oauth 2.0 for /chat, /completions API requests to the proxy? [Follow this doc](oauth2) + +::: + @@ -186,6 +199,16 @@ PROXY_BASE_URL=https://litellm-api.up.railway.app/ #### Step 4. Test flow +### Restrict Email Subdomains w/ SSO + +If you're using SSO and want to only allow users with a specific subdomain - e.g. (@berri.ai email accounts) to access the UI, do this: + +```bash +export ALLOWED_EMAIL_DOMAINS="berri.ai" +``` + +This will check if the user email we receive from SSO contains this domain, before allowing access. + ### Set Admin view w/ SSO You just need to set Proxy Admin ID diff --git a/docs/my-website/docs/proxy/user_keys.md b/docs/my-website/docs/proxy/user_keys.md index df20dcedf..af037ea77 100644 --- a/docs/my-website/docs/proxy/user_keys.md +++ b/docs/my-website/docs/proxy/user_keys.md @@ -1,7 +1,43 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# Use with Langchain, OpenAI SDK, LlamaIndex, Instructor, Curl +# 💡 Migrating from OpenAI (Langchain, OpenAI SDK, LlamaIndex, Instructor, Curl) + +LiteLLM Proxy is **OpenAI-Compatible**, and supports: +* /chat/completions +* /embeddings +* /completions +* /image/generations +* /moderations +* /audio/transcriptions +* /audio/speech +* [Assistants API endpoints](https://docs.litellm.ai/docs/assistants) +* [Batches API endpoints](https://docs.litellm.ai/docs/batches) +* [Fine-Tuning API endpoints](https://docs.litellm.ai/docs/fine_tuning) + +LiteLLM Proxy is **Azure OpenAI-compatible**: +* /chat/completions +* /completions +* /embeddings + +LiteLLM Proxy is **Anthropic-compatible**: +* /messages + +LiteLLM Proxy is **Vertex AI compatible**: +- [Supports ALL Vertex Endpoints](../vertex_ai) + +This doc covers: + +* /chat/completion +* /embedding + + +These are **selected examples**. LiteLLM Proxy is **OpenAI-Compatible**, it works with any project that calls OpenAI. Just change the `base_url`, `api_key` and `model`. + +To pass provider-specific args, [go here](https://docs.litellm.ai/docs/completion/provider_specific_params#proxy-usage) + +To drop unsupported params (E.g. frequency_penalty for bedrock with librechat), [go here](https://docs.litellm.ai/docs/completion/drop_params#openai-proxy-usage) + :::info @@ -207,6 +243,81 @@ console.log(message); ``` + + +```js +const { OpenAI } = require('openai'); + +const openai = new OpenAI({ + apiKey: "sk-1234", // This is the default and can be omitted + baseURL: "http://0.0.0.0:4000" +}); + +async function main() { + const chatCompletion = await openai.chat.completions.create({ + messages: [{ role: 'user', content: 'Say this is a test' }], + model: 'gpt-3.5-turbo', + }, {"metadata": { + "generation_name": "ishaan-generation-openaijs-client", + "generation_id": "openaijs-client-gen-id22", + "trace_id": "openaijs-client-trace-id22", + "trace_user_id": "openaijs-client-user-id2" + }}); +} + +main(); + +``` + + + + + +```python +import os + +from anthropic import Anthropic + +client = Anthropic( + base_url="http://localhost:4000", # proxy endpoint + api_key="sk-s4xN1IiLTCytwtZFJaYQrA", # litellm proxy virtual key +) + +message = client.messages.create( + max_tokens=1024, + messages=[ + { + "role": "user", + "content": "Hello, Claude", + } + ], + model="claude-3-opus-20240229", +) +print(message.content) +``` + + + + + +```python +import os +from mistralai.client import MistralClient +from mistralai.models.chat_completion import ChatMessage + + +client = MistralClient(api_key="sk-1234", endpoint="http://0.0.0.0:4000") +chat_response = client.chat( + model="mistral-small-latest", + messages=[ + {"role": "user", "content": "this is a test request, write a short poem"} + ], +) +print(chat_response.choices[0].message.content) +``` + + + ```python @@ -214,11 +325,12 @@ from openai import OpenAI import instructor from pydantic import BaseModel -my_proxy_api_key = "" # e.g. sk-1234 -my_proxy_base_url = "" # e.g. http://0.0.0.0:4000 +my_proxy_api_key = "" # e.g. sk-1234 - LITELLM KEY +my_proxy_base_url = "" # e.g. http://0.0.0.0:4000 - LITELLM PROXY BASE URL # This enables response_model keyword # from client.chat.completions.create +## WORKS ACROSS OPENAI/ANTHROPIC/VERTEXAI/ETC. - all LITELLM SUPPORTED MODELS! client = instructor.from_openai(OpenAI(api_key=my_proxy_api_key, base_url=my_proxy_base_url)) class UserDetail(BaseModel): @@ -539,6 +651,166 @@ curl --location 'http://0.0.0.0:4000/moderations' \ ``` +## Using with OpenAI compatible projects +Set `base_url` to the LiteLLM Proxy server + + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +]) + +print(response) + +``` + + + +#### Start the LiteLLM proxy +```shell +litellm --model gpt-3.5-turbo + +#INFO: Proxy running on http://0.0.0.0:4000 +``` + +#### 1. Clone the repo + +```shell +git clone https://github.com/danny-avila/LibreChat.git +``` + + +#### 2. Modify Librechat's `docker-compose.yml` +LiteLLM Proxy is running on port `4000`, set `4000` as the proxy below +```yaml +OPENAI_REVERSE_PROXY=http://host.docker.internal:4000/v1/chat/completions +``` + +#### 3. Save fake OpenAI key in Librechat's `.env` + +Copy Librechat's `.env.example` to `.env` and overwrite the default OPENAI_API_KEY (by default it requires the user to pass a key). +```env +OPENAI_API_KEY=sk-1234 +``` + +#### 4. Run LibreChat: +```shell +docker compose up +``` + + + + +Continue-Dev brings ChatGPT to VSCode. See how to [install it here](https://continue.dev/docs/quickstart). + +In the [config.py](https://continue.dev/docs/reference/Models/openai) set this as your default model. +```python + default=OpenAI( + api_key="IGNORED", + model="fake-model-name", + context_length=2048, # customize if needed for your model + api_base="http://localhost:4000" # your proxy server url + ), +``` + +Credits [@vividfog](https://github.com/ollama/ollama/issues/305#issuecomment-1751848077) for this tutorial. + + + + +```shell +$ pip install aider + +$ aider --openai-api-base http://0.0.0.0:4000 --openai-api-key fake-key +``` + + + +```python +pip install pyautogen +``` + +```python +from autogen import AssistantAgent, UserProxyAgent, oai +config_list=[ + { + "model": "my-fake-model", + "api_base": "http://localhost:4000", #litellm compatible endpoint + "api_type": "open_ai", + "api_key": "NULL", # just a placeholder + } +] + +response = oai.Completion.create(config_list=config_list, prompt="Hi") +print(response) # works fine + +llm_config={ + "config_list": config_list, +} + +assistant = AssistantAgent("assistant", llm_config=llm_config) +user_proxy = UserProxyAgent("user_proxy") +user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list) +``` + +Credits [@victordibia](https://github.com/microsoft/autogen/issues/45#issuecomment-1749921972) for this tutorial. + + + +A guidance language for controlling large language models. +https://github.com/guidance-ai/guidance + +**NOTE:** Guidance sends additional params like `stop_sequences` which can cause some models to fail if they don't support it. + +**Fix**: Start your proxy using the `--drop_params` flag + +```shell +litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048 --drop_params +``` + +```python +import guidance + +# set api_base to your proxy +# set api_key to anything +gpt4 = guidance.llms.OpenAI("gpt-4", api_base="http://0.0.0.0:4000", api_key="anything") + +experts = guidance(''' +{{#system~}} +You are a helpful and terse assistant. +{{~/system}} + +{{#user~}} +I want a response to the following question: +{{query}} +Name 3 world-class experts (past or present) who would be great at answering this? +Don't answer the question yet. +{{~/user}} + +{{#assistant~}} +{{gen 'expert_names' temperature=0 max_tokens=300}} +{{~/assistant}} +''', llm=gpt4) + +result = experts(query='How can I be more productive?') +print(result) +``` + + + + ## Advanced ### (BETA) Batch Completions - pass multiple models diff --git a/docs/my-website/docs/proxy/users.md b/docs/my-website/docs/proxy/users.md index ba41dadad..522147708 100644 --- a/docs/my-website/docs/proxy/users.md +++ b/docs/my-website/docs/proxy/users.md @@ -484,6 +484,8 @@ You can set: - tpm limits (tokens per minute) - rpm limits (requests per minute) - max parallel requests +- rpm / tpm limits per model for a given key + @@ -532,6 +534,60 @@ curl --location 'http://0.0.0.0:4000/key/generate' \ } ``` + + + +**Set rate limits per model per api key** + +Set `model_rpm_limit` and `model_tpm_limit` to set rate limits per model per api key + +Here `gpt-4` is the `model_name` set on the [litellm config.yaml](configs.md) + +```shell +curl --location 'http://0.0.0.0:4000/key/generate' \ +--header 'Authorization: Bearer sk-1234' \ +--header 'Content-Type: application/json' \ +--data '{"model_rpm_limit": {"gpt-4": 2}, "model_tpm_limit": {"gpt-4":}}' +``` + +**Expected Response** + +```json +{ + "key": "sk-ulGNRXWtv7M0lFnnsQk0wQ", + "expires": "2024-01-18T20:48:44.297973", +} +``` + +**Verify Model Rate Limits set correctly for this key** + +**Make /chat/completions request check if `x-litellm-key-remaining-requests-gpt-4` returned** + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-ulGNRXWtv7M0lFnnsQk0wQ" \ + -d '{ + "model": "gpt-4", + "messages": [ + {"role": "user", "content": "Hello, Claude!ss eho ares"} + ] + }' +``` + + +**Expected headers** + +```shell +x-litellm-key-remaining-requests-gpt-4: 1 +x-litellm-key-remaining-tokens-gpt-4: 179 +``` + +These headers indicate: + +- 1 request remaining for the GPT-4 model for key=`sk-ulGNRXWtv7M0lFnnsQk0wQ` +- 179 tokens remaining for the GPT-4 model for key=`sk-ulGNRXWtv7M0lFnnsQk0wQ` + diff --git a/docs/my-website/docs/proxy/virtual_keys.md b/docs/my-website/docs/proxy/virtual_keys.md index 6ea101c5c..fa2da9f28 100644 --- a/docs/my-website/docs/proxy/virtual_keys.md +++ b/docs/my-website/docs/proxy/virtual_keys.md @@ -34,6 +34,7 @@ You can then generate keys by hitting the `/key/generate` endpoint. [**See code**](https://github.com/BerriAI/litellm/blob/7a669a36d2689c7f7890bc9c93e04ff3c2641299/litellm/proxy/proxy_server.py#L672) +## **Quick Start - Generate a Key** **Step 1: Save postgres db url** ```yaml @@ -65,7 +66,7 @@ curl 'http://0.0.0.0:4000/key/generate' \ --data-raw '{"models": ["gpt-3.5-turbo", "gpt-4"], "metadata": {"user": "ishaan@berri.ai"}}' ``` -## Advanced - Spend Tracking +## Spend Tracking Get spend per: - key - via `/key/info` [Swagger](https://litellm-api.up.railway.app/#/key%20management/info_key_fn_key_info_get) @@ -223,9 +224,70 @@ Expected Response -## Advanced - Model Access +## **Model Access** -### Restrict models by `team_id` +### **Restrict models by Virtual Key** + +Set allowed models for a key using the `models` param + + +```shell +curl 'http://0.0.0.0:4000/key/generate' \ +--header 'Authorization: Bearer ' \ +--header 'Content-Type: application/json' \ +--data-raw '{"models": ["gpt-3.5-turbo", "gpt-4"]}' +``` + +:::info + +This key can only make requests to `models` that are `gpt-3.5-turbo` or `gpt-4` + +::: + +Verify this is set correctly by + + + + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-4", + "messages": [ + {"role": "user", "content": "Hello"} + ] + }' +``` + + + + + +:::info + +Expect this to fail since gpt-4o is not in the `models` for the key generated + +::: + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-4o", + "messages": [ + {"role": "user", "content": "Hello"} + ] + }' +``` + + + + + +### **Restrict models by `team_id`** `litellm-dev` can only access `azure-gpt-3.5` **1. Create a team via `/team/new`** @@ -269,6 +331,157 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ {"error":{"message":"Invalid model for team litellm-dev: BEDROCK_GROUP. Valid models for team are: ['azure-gpt-3.5']\n\n\nTraceback (most recent call last):\n File \"/Users/ishaanjaffer/Github/litellm/litellm/proxy/proxy_server.py\", line 2298, in chat_completion\n _is_valid_team_configs(\n File \"/Users/ishaanjaffer/Github/litellm/litellm/proxy/utils.py\", line 1296, in _is_valid_team_configs\n raise Exception(\nException: Invalid model for team litellm-dev: BEDROCK_GROUP. Valid models for team are: ['azure-gpt-3.5']\n\n","type":"None","param":"None","code":500}}% ``` +### **Grant Access to new model (Access Groups)** + +Use model access groups to give users access to select models, and add new ones to it over time (e.g. mistral, llama-2, etc.) + +**Step 1. Assign model, access group in config.yaml** + +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + model_info: + access_groups: ["beta-models"] # 👈 Model Access Group + - model_name: fireworks-llama-v3-70b-instruct + litellm_params: + model: fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct + api_key: "os.environ/FIREWORKS" + model_info: + access_groups: ["beta-models"] # 👈 Model Access Group +``` + + + + + +**Create key with access group** + +```bash +curl --location 'http://localhost:4000/key/generate' \ +-H 'Authorization: Bearer ' \ +-H 'Content-Type: application/json' \ +-d '{"models": ["beta-models"], # 👈 Model Access Group + "max_budget": 0,}' +``` + +Test Key + + + + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-" \ + -d '{ + "model": "gpt-4", + "messages": [ + {"role": "user", "content": "Hello"} + ] + }' +``` + + + + + +:::info + +Expect this to fail since gpt-4o is not in the `beta-models` access group + +::: + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-" \ + -d '{ + "model": "gpt-4o", + "messages": [ + {"role": "user", "content": "Hello"} + ] + }' +``` + + + + + + + + + +Create Team + +```shell +curl --location 'http://localhost:4000/team/new' \ +-H 'Authorization: Bearer sk-' \ +-H 'Content-Type: application/json' \ +-d '{"models": ["beta-models"]}' +``` + +Create Key for Team + +```shell +curl --location 'http://0.0.0.0:4000/key/generate' \ +--header 'Authorization: Bearer sk-' \ +--header 'Content-Type: application/json' \ +--data '{"team_id": "0ac97648-c194-4c90-8cd6-40af7b0d2d2a"} +``` + + +Test Key + + + + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-" \ + -d '{ + "model": "gpt-4", + "messages": [ + {"role": "user", "content": "Hello"} + ] + }' +``` + + + + + +:::info + +Expect this to fail since gpt-4o is not in the `beta-models` access group + +::: + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-" \ + -d '{ + "model": "gpt-4o", + "messages": [ + {"role": "user", "content": "Hello"} + ] + }' +``` + + + + + + + + + + ### Model Aliases If a user is expected to use a given model (i.e. gpt3-5), and you want to: @@ -319,35 +532,73 @@ curl -X POST "https://0.0.0.0:4000/key/generate" \ - **How are routing between diff keys/api bases done?** litellm handles this by shuffling between different models in the model list with the same model_name. [**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/router.py) -### Grant Access to new model +## Advanced -Use model access groups to give users access to select models, and add new ones to it over time (e.g. mistral, llama-2, etc.) +### Pass LiteLLM Key in custom header -**Step 1. Assign model, access group in config.yaml** +Use this to make LiteLLM proxy look for the virtual key in a custom header instead of the default `"Authorization"` header + +**Step 1** Define `litellm_key_header_name` name on litellm config.yaml ```yaml model_list: - - model_name: text-embedding-ada-002 + - model_name: fake-openai-endpoint litellm_params: - model: azure/azure-embedding-model - api_base: "os.environ/AZURE_API_BASE" - api_key: "os.environ/AZURE_API_KEY" - api_version: "2023-07-01-preview" - model_info: - access_groups: ["beta-models"] # 👈 Model Access Group + model: openai/fake + api_key: fake-key + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + +general_settings: + master_key: sk-1234 + litellm_key_header_name: "X-Litellm-Key" # 👈 Key Change + ``` -**Step 2. Create key with access group** +**Step 2** Test it -```bash -curl --location 'http://localhost:4000/key/generate' \ --H 'Authorization: Bearer ' \ --H 'Content-Type: application/json' \ --d '{"models": ["beta-models"], # 👈 Model Access Group - "max_budget": 0,}' +In this request, litellm will use the Virtual key in the `X-Litellm-Key` header + + + + +```shell +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "X-Litellm-Key: Bearer sk-1234" \ + -H "Authorization: Bearer bad-key" \ + -d '{ + "model": "fake-openai-endpoint", + "messages": [ + {"role": "user", "content": "Hello, Claude gm!"} + ] + }' ``` -## Advanced - Custom Auth +**Expected Response** + +Expect to see a successfull response from the litellm proxy since the key passed in `X-Litellm-Key` is valid +```shell +{"id":"chatcmpl-f9b2b79a7c30477ab93cd0e717d1773e","choices":[{"finish_reason":"stop","index":0,"message":{"content":"\n\nHello there, how may I assist you today?","role":"assistant","tool_calls":null,"function_call":null}}],"created":1677652288,"model":"gpt-3.5-turbo-0125","object":"chat.completion","system_fingerprint":"fp_44709d6fcb","usage":{"completion_tokens":12,"prompt_tokens":9,"total_tokens":21} +``` + + + + + +```python +client = openai.OpenAI( + api_key="not-used", + base_url="https://api-gateway-url.com/llmservc/api/litellmp", + default_headers={ + "Authorization": f"Bearer {API_GATEWAY_TOKEN}", # (optional) For your API Gateway + "X-Litellm-Key": f"Bearer sk-1234" # For LiteLLM Proxy + } +) +``` + + + +### Custom Auth You can now override the default api key auth. @@ -486,7 +737,7 @@ general_settings: ``` -## Upperbound /key/generate params +### Upperbound /key/generate params Use this, if you need to set default upperbounds for `max_budget`, `budget_duration` or any `key/generate` param per key. Set `litellm_settings:upperbound_key_generate_params`: @@ -502,7 +753,7 @@ litellm_settings: - Send a `/key/generate` request with `max_budget=200` - Key will be created with `max_budget=100` since 100 is the upper bound -## Default /key/generate params +### Default /key/generate params Use this, if you need to control the default `max_budget` or any `key/generate` param per key. When a `/key/generate` request does not specify `max_budget`, it will use the `max_budget` specified in `default_key_generate_params` @@ -518,7 +769,11 @@ litellm_settings: team_id: "core-infra" ``` -## Endpoints +## **Next Steps - Set Budgets, Rate Limits per Virtual Key** + +[Follow this doc to set budgets, rate limiters per virtual key with LiteLLM](users) + +## Endpoint Reference (Spec) ### Keys diff --git a/docs/my-website/docs/proxy_server.md b/docs/my-website/docs/proxy_server.md index ef9352ab1..0d08db744 100644 --- a/docs/my-website/docs/proxy_server.md +++ b/docs/my-website/docs/proxy_server.md @@ -1,7 +1,7 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# [OLD PROXY 👉 [NEW proxy here](./simple_proxy)] Local OpenAI Proxy Server +# [OLD PROXY 👉 [NEW proxy here](./simple_proxy)] Local LiteLLM Proxy Server A fast, and lightweight OpenAI-compatible server to call 100+ LLM APIs. diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md index 905954e97..b24a0f63e 100644 --- a/docs/my-website/docs/routing.md +++ b/docs/my-website/docs/routing.md @@ -14,7 +14,7 @@ In production, litellm supports using Redis as a way to track cooldown server an :::info -If you want a server to load balance across different LLM APIs, use our [OpenAI Proxy Server](./proxy/load_balancing.md) +If you want a server to load balance across different LLM APIs, use our [LiteLLM Proxy Server](./proxy/load_balancing.md) ::: @@ -88,8 +88,8 @@ print(response) ### Available Endpoints - `router.completion()` - chat completions endpoint to call 100+ LLMs - `router.acompletion()` - async chat completion calls -- `router.embeddings()` - embedding endpoint for Azure, OpenAI, Huggingface endpoints -- `router.aembeddings()` - async embeddings calls +- `router.embedding()` - embedding endpoint for Azure, OpenAI, Huggingface endpoints +- `router.aembedding()` - async embeddings calls - `router.text_completion()` - completion calls in the old OpenAI `/v1/completions` endpoint format - `router.atext_completion()` - async text completion calls - `router.image_generation()` - completion calls in OpenAI `/v1/images/generations` endpoint format @@ -1637,7 +1637,7 @@ response = router.completion( ## Deploy Router -If you want a server to load balance across different LLM APIs, use our [OpenAI Proxy Server](./simple_proxy#load-balancing---multiple-instances-of-1-model) +If you want a server to load balance across different LLM APIs, use our [LiteLLM Proxy Server](./simple_proxy#load-balancing---multiple-instances-of-1-model) ## Init Params for the litellm.Router diff --git a/docs/my-website/docs/scheduler.md b/docs/my-website/docs/scheduler.md index e7943c459..e59b03eac 100644 --- a/docs/my-website/docs/scheduler.md +++ b/docs/my-website/docs/scheduler.md @@ -41,7 +41,7 @@ router = Router( ) try: - _response = await router.schedule_acompletion( # 👈 ADDS TO QUEUE + POLLS + MAKES CALL + _response = await router.acompletion( # 👈 ADDS TO QUEUE + POLLS + MAKES CALL model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey!"}], priority=0, # 👈 LOWER IS BETTER @@ -52,13 +52,13 @@ except Exception as e: ## LiteLLM Proxy -To prioritize requests on LiteLLM Proxy call our beta openai-compatible `http://localhost:4000/queue` endpoint. +To prioritize requests on LiteLLM Proxy add `priority` to the request. ```curl -curl -X POST 'http://localhost:4000/queue/chat/completions' \ +curl -X POST 'http://localhost:4000/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-1234' \ -D '{ @@ -128,7 +128,7 @@ router = Router( ) try: - _response = await router.schedule_acompletion( # 👈 ADDS TO QUEUE + POLLS + MAKES CALL + _response = await router.acompletion( # 👈 ADDS TO QUEUE + POLLS + MAKES CALL model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey!"}], priority=0, # 👈 LOWER IS BETTER @@ -147,6 +147,9 @@ model_list: mock_response: "hello world!" api_key: my-good-key +litellm_settings: + request_timeout: 600 # 👈 Will keep retrying until timeout occurs + router_settings: redis_host; os.environ/REDIS_HOST redis_password: os.environ/REDIS_PASSWORD diff --git a/docs/my-website/docs/sdk_custom_pricing.md b/docs/my-website/docs/sdk_custom_pricing.md new file mode 100644 index 000000000..c85771151 --- /dev/null +++ b/docs/my-website/docs/sdk_custom_pricing.md @@ -0,0 +1,65 @@ +# Custom Pricing - SageMaker, Azure, etc + +Register custom pricing for sagemaker completion model. + +For cost per second pricing, you **just** need to register `input_cost_per_second`. + +```python +# !pip install boto3 +from litellm import completion, completion_cost + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + + +def test_completion_sagemaker(): + try: + print("testing sagemaker") + response = completion( + model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4", + messages=[{"role": "user", "content": "Hey, how's it going?"}], + input_cost_per_second=0.000420, + ) + # Add any assertions here to check the response + print(response) + cost = completion_cost(completion_response=response) + print(cost) + except Exception as e: + raise Exception(f"Error occurred: {e}") + +``` + + +## Cost Per Token (e.g. Azure) + + +```python +# !pip install boto3 +from litellm import completion, completion_cost + +## set ENV variables +os.environ["AZURE_API_KEY"] = "" +os.environ["AZURE_API_BASE"] = "" +os.environ["AZURE_API_VERSION"] = "" + + +def test_completion_azure_model(): + try: + print("testing azure custom pricing") + # azure call + response = completion( + model = "azure/", + messages = [{ "content": "Hello, how are you?","role": "user"}] + input_cost_per_token=0.005, + output_cost_per_token=1, + ) + # Add any assertions here to check the response + print(response) + cost = completion_cost(completion_response=response) + print(cost) + except Exception as e: + raise Exception(f"Error occurred: {e}") + +test_completion_azure_model() +``` \ No newline at end of file diff --git a/docs/my-website/docs/secret.md b/docs/my-website/docs/secret.md index 91ae38368..c2b6774c0 100644 --- a/docs/my-website/docs/secret.md +++ b/docs/my-website/docs/secret.md @@ -61,7 +61,7 @@ litellm --config /path/to/config.yaml ``` ## Azure Key Vault - + -### Usage with OpenAI Proxy Server +### Usage with LiteLLM Proxy Server 1. Install Proxy dependencies ```bash @@ -129,7 +129,7 @@ litellm --config /path/to/config.yaml Use encrypted keys from Google KMS on the proxy -### Usage with OpenAI Proxy Server +### Usage with LiteLLM Proxy Server ## Step 1. Add keys to env ``` @@ -160,29 +160,6 @@ $ litellm --test [Quick Test Proxy](./proxy/quick_start#using-litellm-proxy---curl-request-openai-package-langchain-langchain-js) - -## Infisical Secret Manager -Integrates with [Infisical's Secret Manager](https://infisical.com/) for secure storage and retrieval of API keys and sensitive data. - -### Usage -liteLLM manages reading in your LLM API secrets/env variables from Infisical for you - -```python -import litellm -from infisical import InfisicalClient - -litellm.secret_manager = InfisicalClient(token="your-token") - -messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "What's the weather like today?"}, -] - -response = litellm.completion(model="gpt-3.5-turbo", messages=messages) - -print(response) -``` - - + diff --git a/docs/my-website/docs/simple_proxy_old_doc.md b/docs/my-website/docs/simple_proxy_old_doc.md index 195728d1b..2d68db329 100644 --- a/docs/my-website/docs/simple_proxy_old_doc.md +++ b/docs/my-website/docs/simple_proxy_old_doc.md @@ -2,7 +2,7 @@ import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# 💥 OpenAI Proxy Server +# 💥 LiteLLM Proxy Server LiteLLM Server manages: diff --git a/docs/my-website/docs/text_to_speech.md b/docs/my-website/docs/text_to_speech.md index 73a12c434..5e5f8b990 100644 --- a/docs/my-website/docs/text_to_speech.md +++ b/docs/my-website/docs/text_to_speech.md @@ -109,4 +109,33 @@ response = speech( input="the quick brown fox jumped over the lazy dogs", ) response.stream_to_file(speech_file_path) +``` + +## ✨ Enterprise LiteLLM Proxy - Set Max Request File Size + +Use this when you want to limit the file size for requests sent to `audio/transcriptions` + +```yaml +- model_name: whisper + litellm_params: + model: whisper-1 + api_key: sk-******* + max_file_size_mb: 0.00001 # 👈 max file size in MB (Set this intentionally very small for testing) + model_info: + mode: audio_transcription +``` + +Make a test Request with a valid file +```shell +curl --location 'http://localhost:4000/v1/audio/transcriptions' \ +--header 'Authorization: Bearer sk-1234' \ +--form 'file=@"/Users/ishaanjaffer/Github/litellm/tests/gettysburg.wav"' \ +--form 'model="whisper"' +``` + + +Expect to see the follow response + +```shell +{"error":{"message":"File size is too large. Please check your file size. Passed file size: 0.7392807006835938 MB. Max file size: 0.0001 MB","type":"bad_request","param":"file","code":500}}% ``` \ No newline at end of file diff --git a/docs/my-website/docs/tutorials/ab_test_llms.md b/docs/my-website/docs/tutorials/ab_test_llms.md deleted file mode 100644 index b08e91352..000000000 --- a/docs/my-website/docs/tutorials/ab_test_llms.md +++ /dev/null @@ -1,98 +0,0 @@ -import Image from '@theme/IdealImage'; - -# Split traffic betwen GPT-4 and Llama2 in Production! -In this tutorial, we'll walk through A/B testing between GPT-4 and Llama2 in production. We'll assume you've deployed Llama2 on Huggingface Inference Endpoints (but any of TogetherAI, Baseten, Ollama, Petals, Openrouter should work as well). - - -# Relevant Resources: - -* 🚀 [Your production dashboard!](https://admin.litellm.ai/) - - -* [Deploying models on Huggingface](https://huggingface.co/docs/inference-endpoints/guides/create_endpoint) -* [All supported providers on LiteLLM](https://docs.litellm.ai/docs/providers) - -# Code Walkthrough - -In production, we don't know if Llama2 is going to provide: -* good results -* quickly - -### 💡 Route 20% traffic to Llama2 -If Llama2 returns poor answers / is extremely slow, we want to roll-back this change, and use GPT-4 instead. - -Instead of routing 100% of our traffic to Llama2, let's **start by routing 20% traffic** to it and see how it does. - -```python -## route 20% of responses to Llama2 -split_per_model = { - "gpt-4": 0.8, - "huggingface/https://my-unique-endpoint.us-east-1.aws.endpoints.huggingface.cloud": 0.2 -} -``` - -## 👨‍💻 Complete Code - -### a) For Local -If we're testing this in a script - this is what our complete code looks like. -```python -from litellm import completion_with_split_tests -import os - -## set ENV variables -os.environ["OPENAI_API_KEY"] = "openai key" -os.environ["HUGGINGFACE_API_KEY"] = "huggingface key" - -## route 20% of responses to Llama2 -split_per_model = { - "gpt-4": 0.8, - "huggingface/https://my-unique-endpoint.us-east-1.aws.endpoints.huggingface.cloud": 0.2 -} - -messages = [{ "content": "Hello, how are you?","role": "user"}] - -completion_with_split_tests( - models=split_per_model, - messages=messages, -) -``` - -### b) For Production - -If we're in production, we don't want to keep going to code to change model/test details (prompt, split%, etc.) for our completion function and redeploying changes. - -LiteLLM exposes a client dashboard to do this in a UI - and instantly updates our completion function in prod. - -#### Relevant Code - -```python -completion_with_split_tests(..., use_client=True, id="my-unique-id") -``` - -#### Complete Code - -```python -from litellm import completion_with_split_tests -import os - -## set ENV variables -os.environ["OPENAI_API_KEY"] = "openai key" -os.environ["HUGGINGFACE_API_KEY"] = "huggingface key" - -## route 20% of responses to Llama2 -split_per_model = { - "gpt-4": 0.8, - "huggingface/https://my-unique-endpoint.us-east-1.aws.endpoints.huggingface.cloud": 0.2 -} - -messages = [{ "content": "Hello, how are you?","role": "user"}] - -completion_with_split_tests( - models=split_per_model, - messages=messages, - use_client=True, - id="my-unique-id" # Auto-create this @ https://admin.litellm.ai/ -) -``` - - diff --git a/docs/my-website/docs/tutorials/litellm_proxy_aporia.md b/docs/my-website/docs/tutorials/litellm_proxy_aporia.md new file mode 100644 index 000000000..1fea3037f --- /dev/null +++ b/docs/my-website/docs/tutorials/litellm_proxy_aporia.md @@ -0,0 +1,194 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Use LiteLLM AI Gateway with Aporia Guardrails + +In this tutorial we will use LiteLLM Proxy with Aporia to detect PII in requests and profanity in responses + +## 1. Setup guardrails on Aporia + +### Create Aporia Projects + +Create two projects on [Aporia](https://guardrails.aporia.com/) + +1. Pre LLM API Call - Set all the policies you want to run on pre LLM API call +2. Post LLM API Call - Set all the policies you want to run post LLM API call + + + + + +### Pre-Call: Detect PII + +Add the `PII - Prompt` to your Pre LLM API Call project + + + +### Post-Call: Detect Profanity in Responses + +Add the `Toxicity - Response` to your Post LLM API Call project + + + + +## 2. Define Guardrails on your LiteLLM config.yaml + +- Define your guardrails under the `guardrails` section and set `pre_call_guardrails` and `post_call_guardrails` +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: openai/gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY + +guardrails: + - guardrail_name: "aporia-pre-guard" + litellm_params: + guardrail: aporia # supported values: "aporia", "lakera" + mode: "during_call" + api_key: os.environ/APORIA_API_KEY_1 + api_base: os.environ/APORIA_API_BASE_1 + - guardrail_name: "aporia-post-guard" + litellm_params: + guardrail: aporia # supported values: "aporia", "lakera" + mode: "post_call" + api_key: os.environ/APORIA_API_KEY_2 + api_base: os.environ/APORIA_API_BASE_2 +``` + +### Supported values for `mode` + +- `pre_call` Run **before** LLM call, on **input** +- `post_call` Run **after** LLM call, on **input & output** +- `during_call` Run **during** LLM call, on **input** Same as `pre_call` but runs in parallel as LLM call. Response not returned until guardrail check completes + +## 3. Start LiteLLM Gateway + + +```shell +litellm --config config.yaml --detailed_debug +``` + +## 4. Test request + +**[Langchain, OpenAI SDK Usage Examples](../proxy/user_keys##request-format)** + + + + +Expect this to fail since since `ishaan@berri.ai` in the request is PII + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-npnwjPQciVRok5yNZgKmFQ" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "hi my email is ishaan@berri.ai"} + ], + "guardrails": ["aporia-pre-guard", "aporia-post-guard"] + }' +``` + +Expected response on failure + +```shell +{ + "error": { + "message": { + "error": "Violated guardrail policy", + "aporia_ai_response": { + "action": "block", + "revised_prompt": null, + "revised_response": "Aporia detected and blocked PII", + "explain_log": null + } + }, + "type": "None", + "param": "None", + "code": "400" + } +} + +``` + + + + + +```shell +curl -i http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-npnwjPQciVRok5yNZgKmFQ" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "user", "content": "hi what is the weather"} + ], + "guardrails": ["aporia-pre-guard", "aporia-post-guard"] + }' +``` + + + + + + +## 5. Control Guardrails per Project (API Key) + +Use this to control what guardrails run per project. In this tutorial we only want the following guardrails to run for 1 project (API Key) +- `guardrails`: ["aporia-pre-guard", "aporia-post-guard"] + +**Step 1** Create Key with guardrail settings + + + + +```shell +curl -X POST 'http://0.0.0.0:4000/key/generate' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -D '{ + "guardrails": ["aporia-pre-guard", "aporia-post-guard"] + } + }' +``` + + + + +```shell +curl --location 'http://0.0.0.0:4000/key/update' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "key": "sk-jNm1Zar7XfNdZXp49Z1kSQ", + "guardrails": ["aporia-pre-guard", "aporia-post-guard"] + } +}' +``` + + + + +**Step 2** Test it with new key + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-jNm1Zar7XfNdZXp49Z1kSQ' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "my email is ishaan@berri.ai" + } + ] +}' +``` + + + diff --git a/docs/my-website/docs/tutorials/sagemaker_llms.md b/docs/my-website/docs/tutorials/sagemaker_llms.md deleted file mode 100644 index 1fe9594ab..000000000 --- a/docs/my-website/docs/tutorials/sagemaker_llms.md +++ /dev/null @@ -1,72 +0,0 @@ -import Image from '@theme/IdealImage'; - -# Deploy & Query Llama2-7B on Sagemaker - -This tutorial has 2 major components: -1. Deploy Llama2-7B on Jumpstart -2. Use LiteLLM to Query Llama2-7B on Sagemaker - -## Deploying Llama2-7B on AWS Sagemaker -### Pre-requisites -Ensure you have AWS quota for deploying your selected LLM. You can apply for a quota increase here: https://console.aws.amazon.com/servicequotas/home -* ml.g5.48xlarge -* ml.g5.2xlarge - -### Create an Amazon SageMaker domain to use Studio and Studio Notebooks - -- Head to AWS console https://aws.amazon.com/console/ -- Navigate to AWS Sagemaker from the console -- On AWS Sagemaker select 'Create a Sagemaker Domain' - - -### Deploying Llama2-7B using AWS Sagemaker Jumpstart - -- After creating your sagemaker domain, click 'Open Studio', which should take you to AWS sagemaker studio - -- On the left sidebar navigate to SageMaker Jumpstart -> Models, notebooks, solutions -- Now select the LLM you want to deploy by clicking 'View Model' - (in this case select Llama2-7B) - -- Click `Deploy` for the Model you want to deploy - - -- After deploying Llama2, copy your model endpoint - - -### Use LiteLLM to Query Llama2-7B on Sagemaker - -#### Prerequisites -* `pip install boto3` -* `pip install litellm` -* Create your AWS Access Key, get your `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY`. You can create a new aws access key on the aws console under `Security Credentials` under your profile - -#### Querying deployed Llama2-7b -Set `model` = `sagemaker/` for `completion`. Use the model endpoint you got after deploying llama2-7b on sagemaker. If you used jumpstart your model endpoint will look like this `jumpstart-dft-meta-textgeneration-llama-2-7b` - -Code Example: -```python -from litellm import completion -os.environ['AWS_ACCESS_KEY_ID'] = "your-access-key-id" -os.environ['AWS_SECRET_ACCESS_KEY'] = "your-secret-key" - -response = completion( - model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", - messages=[{'role': 'user', 'content': 'are you a llama'}], - temperature=0.2, # optional params - max_tokens=80, - ) - -``` - -That's it! Happy completion()! - -#### Next Steps: -- Add Caching: https://docs.litellm.ai/docs/caching/gpt_cache -- Add Logging and Observability to your deployed LLM: https://docs.litellm.ai/docs/observability/callbacks - - - - - - - - diff --git a/docs/my-website/docusaurus.config.js b/docs/my-website/docusaurus.config.js index f367f4035..33ac8cd84 100644 --- a/docs/my-website/docusaurus.config.js +++ b/docs/my-website/docusaurus.config.js @@ -11,7 +11,7 @@ const config = { favicon: '/img/favicon.ico', // Set the production url of your site here - url: 'https://litellm.vercel.app/', + url: 'https://docs.litellm.ai/', // Set the // pathname under which your site is served // For GitHub pages deployment, it is often '//' baseUrl: '/', @@ -28,6 +28,24 @@ const config = { }, plugins: [ + [ + require.resolve("@getcanary/docusaurus-pagefind"), + { + indexOnly: true, + styles: { + "--canary-color-primary-c": 0.1, + "--canary-color-primary-h": 270, + }, + pagefind: { + ranking: { + pageLength: 0.9, + termFrequency: 1.0, + termSimilarity: 1.0, + termSaturation: 1.5, + } + } + }, + ], [ '@docusaurus/plugin-ideal-image', { @@ -117,6 +135,11 @@ const config = { label: '🚀 Hosted', to: "docs/hosted" }, + { + href: 'https://models.litellm.ai/', + label: '💸 LLM Model Cost Map', + position: 'right', + }, { href: 'https://github.com/BerriAI/litellm', label: 'GitHub', diff --git a/docs/my-website/img/aporia_post.png b/docs/my-website/img/aporia_post.png new file mode 100644 index 000000000..5e4d4a287 Binary files /dev/null and b/docs/my-website/img/aporia_post.png differ diff --git a/docs/my-website/img/aporia_pre.png b/docs/my-website/img/aporia_pre.png new file mode 100644 index 000000000..8df1cfdda Binary files /dev/null and b/docs/my-website/img/aporia_pre.png differ diff --git a/docs/my-website/img/aporia_projs.png b/docs/my-website/img/aporia_projs.png new file mode 100644 index 000000000..c518fdf0b Binary files /dev/null and b/docs/my-website/img/aporia_projs.png differ diff --git a/docs/my-website/img/gcs_bucket.png b/docs/my-website/img/gcs_bucket.png new file mode 100644 index 000000000..034053da6 Binary files /dev/null and b/docs/my-website/img/gcs_bucket.png differ diff --git a/docs/my-website/img/langsmith_new.png b/docs/my-website/img/langsmith_new.png new file mode 100644 index 000000000..d5586bdbe Binary files /dev/null and b/docs/my-website/img/langsmith_new.png differ diff --git a/docs/my-website/img/max_budget_for_internal_users.png b/docs/my-website/img/max_budget_for_internal_users.png new file mode 100644 index 000000000..e1b8f3402 Binary files /dev/null and b/docs/my-website/img/max_budget_for_internal_users.png differ diff --git a/docs/my-website/img/raw_response_headers.png b/docs/my-website/img/raw_response_headers.png new file mode 100644 index 000000000..d6595c807 Binary files /dev/null and b/docs/my-website/img/raw_response_headers.png differ diff --git a/docs/my-website/img/ui_invite_link.png b/docs/my-website/img/ui_invite_link.png new file mode 100644 index 000000000..32171c86c Binary files /dev/null and b/docs/my-website/img/ui_invite_link.png differ diff --git a/docs/my-website/img/ui_invite_user.png b/docs/my-website/img/ui_invite_user.png new file mode 100644 index 000000000..bad2e3c96 Binary files /dev/null and b/docs/my-website/img/ui_invite_user.png differ diff --git a/docs/my-website/img/ui_usage.png b/docs/my-website/img/ui_usage.png new file mode 100644 index 000000000..e33e40d6f Binary files /dev/null and b/docs/my-website/img/ui_usage.png differ diff --git a/docs/my-website/package-lock.json b/docs/my-website/package-lock.json index cc3923787..008f7223d 100644 --- a/docs/my-website/package-lock.json +++ b/docs/my-website/package-lock.json @@ -12,13 +12,14 @@ "@docusaurus/plugin-google-gtag": "^2.4.1", "@docusaurus/plugin-ideal-image": "^2.4.1", "@docusaurus/preset-classic": "2.4.1", + "@getcanary/docusaurus-pagefind": "^0.0.12", + "@getcanary/web": "^0.0.55", "@mdx-js/react": "^1.6.22", "clsx": "^1.2.1", "docusaurus": "^1.14.7", - 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"docusaurus-lunr-search": "^2.4.1", "prism-react-renderer": "^1.3.5", - "react": "^18.1.0", - "react-dom": "^18.1.0", + "react": "^17.0.2", + "react-dom": "^17.0.2", "sharp": "^0.32.6", "uuid": "^9.0.1" }, diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 68b4601ce..1dcaf008e 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -20,11 +20,11 @@ const sidebars = { { type: "doc", id: "index" }, // NEW { type: "category", - label: "💥 OpenAI Proxy Server", + label: "💥 LiteLLM Proxy Server", link: { type: "generated-index", - title: "💥 OpenAI Proxy Server", - description: `Proxy Server to call 100+ LLMs in a unified interface & track spend, set budgets per virtual key/user`, + title: "💥 LiteLLM Proxy Server (LLM Gateway)", + description: `OpenAI Proxy Server (LLM Gateway) to call 100+ LLMs in a unified interface & track spend, set budgets per virtual key/user`, slug: "/simple_proxy", }, items: [ @@ -42,20 +42,32 @@ const sidebars = { "proxy/configs", "proxy/reliability", "proxy/cost_tracking", + "proxy/custom_pricing", "proxy/self_serve", + "proxy/virtual_keys", + { + type: "category", + label: "🪢 Logging", + items: ["proxy/logging", "proxy/bucket", "proxy/streaming_logging"], + }, + { + type: "category", + label: "Secret Manager - storing LLM API Keys", + items: [ + "secret", + "oidc" + ] + }, + "proxy/team_logging", + "proxy/guardrails", + "proxy/tag_routing", "proxy/users", "proxy/team_budgets", "proxy/customers", "proxy/billing", - "proxy/virtual_keys", - "proxy/guardrails", "proxy/token_auth", + "proxy/oauth2", "proxy/alerting", - { - type: "category", - label: "🪢 Logging", - items: ["proxy/logging", "proxy/streaming_logging"], - }, "proxy/ui", "proxy/prometheus", "proxy/pass_through", @@ -81,48 +93,7 @@ const sidebars = { }, { type: "category", - label: "Completion()", - link: { - type: "generated-index", - title: "Completion()", - description: "Details on the completion() function", - slug: "/completion", - }, - items: [ - "completion/input", - "completion/provider_specific_params", - "completion/drop_params", - "completion/prompt_formatting", - "completion/output", - "exception_mapping", - "completion/stream", - "completion/message_trimming", - "completion/function_call", - "completion/vision", - "completion/model_alias", - "completion/batching", - "completion/mock_requests", - "completion/reliable_completions", - ], - }, - { - type: "category", - label: "Embedding(), Image Generation(), Assistants(), Moderation(), Audio Transcriptions(), TTS(), Batches()", - items: [ - "embedding/supported_embedding", - "embedding/async_embedding", - "embedding/moderation", - "image_generation", - "audio_transcription", - "text_to_speech", - "assistants", - "batches", - "anthropic_completion" - ], - }, - { - type: "category", - label: "Supported Models & Providers", + label: "💯 Supported Models & Providers", link: { type: "generated-index", title: "Providers", @@ -155,7 +126,9 @@ const sidebars = { "providers/triton-inference-server", "providers/ollama", "providers/perplexity", + "providers/friendliai", "providers/groq", + "providers/github", "providers/deepseek", "providers/fireworks_ai", "providers/clarifai", @@ -171,18 +144,80 @@ const sidebars = { "providers/aleph_alpha", "providers/baseten", "providers/openrouter", - "providers/custom_openai_proxy", + // "providers/custom_openai_proxy", + "providers/custom_llm_server", "providers/petals", ], }, - "proxy/custom_pricing", - "routing", + { + type: "category", + label: "Chat Completions (litellm.completion + PROXY)", + link: { + type: "generated-index", + title: "Chat Completions", + description: "Details on the completion() function", + slug: "/completion", + }, + items: [ + "completion/input", + "completion/provider_specific_params", + "completion/json_mode", + "completion/prefix", + "completion/drop_params", + "completion/prompt_formatting", + "completion/output", + "exception_mapping", + "completion/stream", + "completion/message_trimming", + "completion/function_call", + "completion/vision", + "completion/model_alias", + "completion/batching", + "completion/mock_requests", + "completion/reliable_completions", + ], + }, + { + type: "category", + label: "Supported Endpoints - /images, /audio/speech, /assistants etc", + items: [ + "embedding/supported_embedding", + "embedding/async_embedding", + "embedding/moderation", + "image_generation", + "audio_transcription", + "text_to_speech", + "assistants", + "batches", + "fine_tuning", + "anthropic_completion", + "pass_through/vertex_ai", + "pass_through/google_ai_studio", + "pass_through/cohere", + "pass_through/bedrock", + "pass_through/langfuse" + ], + }, "scheduler", - "set_keys", - "budget_manager", - "secret", - "completion/token_usage", + { + type: "category", + label: "🚅 LiteLLM Python SDK", + items: [ + "routing", + "set_keys", + "completion/token_usage", + "sdk_custom_pricing", + "budget_manager", + "caching/all_caches", + "migration", + { + type: "category", + label: "LangChain, LlamaIndex, Instructor Integration", + items: ["langchain/langchain", "tutorials/instructor"], + }, + ], + }, "load_test", { type: "category", @@ -190,55 +225,52 @@ const sidebars = { items: [ "observability/langfuse_integration", "observability/logfire_integration", + "observability/gcs_bucket_integration", + "observability/langsmith_integration", + "observability/arize_integration", "debugging/local_debugging", "observability/raw_request_response", "observability/custom_callback", + "observability/scrub_data", + "observability/braintrust", "observability/sentry", "observability/lago", + "observability/helicone_integration", "observability/openmeter", "observability/promptlayer_integration", "observability/wandb_integration", - "observability/langsmith_integration", - "observability/slack_integration", "observability/traceloop_integration", "observability/athina_integration", "observability/lunary_integration", "observability/greenscale_integration", - "observability/helicone_integration", "observability/supabase_integration", `observability/telemetry`, ], }, - "caching/all_caches", { type: "category", label: "Tutorials", items: [ + 'tutorials/litellm_proxy_aporia', 'tutorials/azure_openai', 'tutorials/instructor', - 'tutorials/oobabooga', "tutorials/gradio_integration", "tutorials/huggingface_codellama", "tutorials/huggingface_tutorial", "tutorials/TogetherAI_liteLLM", "tutorials/finetuned_chat_gpt", - "tutorials/sagemaker_llms", "tutorials/text_completion", "tutorials/first_playground", "tutorials/model_fallbacks", ], }, - { - type: "category", - label: "LangChain, LlamaIndex, Instructor Integration", - items: ["langchain/langchain", "tutorials/instructor"], - }, { type: "category", label: "Extras", items: [ "extras/contributing", "data_security", + "migration_policy", "contributing", "rules", "proxy_server", @@ -271,7 +303,6 @@ const sidebars = { }, ], }, - "migration", "troubleshoot", ], }; diff --git a/docs/my-website/src/pages/index.md b/docs/my-website/src/pages/index.md index 126e83688..36d47aedf 100644 --- a/docs/my-website/src/pages/index.md +++ b/docs/my-website/src/pages/index.md @@ -10,7 +10,7 @@ https://github.com/BerriAI/litellm - Translate inputs to provider's `completion`, `embedding`, and `image_generation` endpoints - [Consistent output](https://docs.litellm.ai/docs/completion/output), text responses will always be available at `['choices'][0]['message']['content']` - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing) -- Track spend & set budgets per project [OpenAI Proxy Server](https://docs.litellm.ai/docs/simple_proxy) +- Track spend & set budgets per project [LiteLLM Proxy Server](https://docs.litellm.ai/docs/simple_proxy) ## Basic usage @@ -304,6 +304,7 @@ LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, Helicone from litellm import completion ## set env variables for logging tools +os.environ["HELICONE_API_KEY"] = "your-helicone-key" os.environ["LANGFUSE_PUBLIC_KEY"] = "" os.environ["LANGFUSE_SECRET_KEY"] = "" os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key" diff --git a/docs/my-website/src/pages/stream.md b/docs/my-website/src/pages/stream.md index 5e8cc32ca..a524f4ba6 100644 --- a/docs/my-website/src/pages/stream.md +++ b/docs/my-website/src/pages/stream.md @@ -30,4 +30,48 @@ async def test_get_response(): response = asyncio.run(test_get_response()) print(response) +``` + +## Streaming Token Usage + +Supported across all providers. Works the same as openai. + +`stream_options={"include_usage": True}` + +If set, an additional chunk will be streamed before the data: [DONE] message. The usage field on this chunk shows the token usage statistics for the entire request, and the choices field will always be an empty array. All other chunks will also include a usage field, but with a null value. + +### SDK +```python +from litellm import completion +import os + +os.environ["OPENAI_API_KEY"] = "" + +response = completion(model="gpt-3.5-turbo", messages=messages, stream=True, stream_options={"include_usage": True}) +for chunk in response: + print(chunk['choices'][0]['delta']) +``` + +### PROXY + +```bash +curl https://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $OPENAI_API_KEY" \ + -d '{ + "model": "gpt-4o", + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant." + }, + { + "role": "user", + "content": "Hello!" + } + ], + "stream": true, + "stream_options": {"include_usage": true} + }' + ``` \ No newline at end of file diff --git a/docs/my-website/src/theme/SearchBar.js b/docs/my-website/src/theme/SearchBar.js new file mode 100644 index 000000000..66c8c2b5c --- /dev/null +++ b/docs/my-website/src/theme/SearchBar.js @@ -0,0 +1,95 @@ +import React from "react"; +import SearchBar from "@theme-original/SearchBar"; + +import useDocusaurusContext from "@docusaurus/useDocusaurusContext"; +import { usePluginData } from "@docusaurus/useGlobalData"; + +export default function SearchBarWrapper(props) { + const { siteConfig } = useDocusaurusContext(); + const { options } = usePluginData("docusaurus-plugin-pagefind-canary"); + + const [path, setPath] = React.useState(""); + const [loaded, setLoaded] = React.useState(false); + + React.useEffect(() => { + setPath(`${siteConfig.baseUrl}pagefind/pagefind.js`); + }, [siteConfig]); + + React.useEffect(() => { + Promise.all([ + import("@getcanary/web/components/canary-root"), + import("@getcanary/web/components/canary-provider-pagefind"), + import("@getcanary/web/components/canary-modal"), + import("@getcanary/web/components/canary-trigger-logo"), + import("@getcanary/web/components/canary-content"), + import("@getcanary/web/components/canary-search"), + import("@getcanary/web/components/canary-search-input"), + import("@getcanary/web/components/canary-search-results-group"), + import("@getcanary/web/components/canary-footer"), + import("@getcanary/web/components/canary-callout-calendly"), + import("@getcanary/web/components/canary-callout-discord"), + ]) + .then(() => setLoaded(true)) + .catch(console.error); + }, []); + + return ( +
+ {!loaded || !path ? ( + + ) : ( + + + + + + + + + + + + + + + + + )} + + +
+ ); +} diff --git a/docs/my-website/yarn.lock b/docs/my-website/yarn.lock deleted file mode 100644 index d5159fbe9..000000000 --- a/docs/my-website/yarn.lock +++ /dev/null @@ -1,12961 +0,0 @@ -# THIS IS AN AUTOGENERATED FILE. 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dependencies: - buffer-crc32 "~0.2.3" - fd-slicer "~1.1.0" - -yocto-queue@^0.1.0: - version "0.1.0" - resolved "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz" - integrity sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q== - -zwitch@^1.0.0: - version "1.0.5" - resolved "https://registry.npmjs.org/zwitch/-/zwitch-1.0.5.tgz" - integrity sha512-V50KMwwzqJV0NpZIZFwfOD5/lyny3WlSzRiXgA0G7VUnRlqttta1L6UQIHzd6EuBY/cHGfwTIck7w1yH6Q5zUw== diff --git a/enterprise/enterprise_hooks/aporia_ai.py b/enterprise/enterprise_hooks/aporia_ai.py new file mode 100644 index 000000000..af909a8b5 --- /dev/null +++ b/enterprise/enterprise_hooks/aporia_ai.py @@ -0,0 +1,208 @@ +# +-------------------------------------------------------------+ +# +# Use AporiaAI for your LLM calls +# +# +-------------------------------------------------------------+ +# Thank you users! We ❤️ you! - Krrish & Ishaan + +import sys +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +from typing import Optional, Literal, Union, Any +import litellm, traceback, sys, uuid +from litellm.caching import DualCache +from litellm.proxy._types import UserAPIKeyAuth +from litellm.integrations.custom_guardrail import CustomGuardrail +from fastapi import HTTPException +from litellm._logging import verbose_proxy_logger +from litellm.proxy.guardrails.guardrail_helpers import should_proceed_based_on_metadata +from litellm.litellm_core_utils.logging_utils import ( + convert_litellm_response_object_to_str, +) +from typing import List +from datetime import datetime +import aiohttp, asyncio +from litellm._logging import verbose_proxy_logger +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler +import httpx +import json +from litellm.types.guardrails import GuardrailEventHooks + +litellm.set_verbose = True + +GUARDRAIL_NAME = "aporia" + + +class _ENTERPRISE_Aporia(CustomGuardrail): + def __init__( + self, api_key: Optional[str] = None, api_base: Optional[str] = None, **kwargs + ): + self.async_handler = AsyncHTTPHandler( + timeout=httpx.Timeout(timeout=600.0, connect=5.0) + ) + self.aporia_api_key = api_key or os.environ["APORIO_API_KEY"] + self.aporia_api_base = api_base or os.environ["APORIO_API_BASE"] + self.event_hook: GuardrailEventHooks + + super().__init__(**kwargs) + + #### CALL HOOKS - proxy only #### + def transform_messages(self, messages: List[dict]) -> List[dict]: + supported_openai_roles = ["system", "user", "assistant"] + default_role = "other" # for unsupported roles - e.g. tool + new_messages = [] + for m in messages: + if m.get("role", "") in supported_openai_roles: + new_messages.append(m) + else: + new_messages.append( + { + "role": default_role, + **{key: value for key, value in m.items() if key != "role"}, + } + ) + + return new_messages + + async def prepare_aporia_request( + self, new_messages: List[dict], response_string: Optional[str] = None + ) -> dict: + data: dict[str, Any] = {} + if new_messages is not None: + data["messages"] = new_messages + if response_string is not None: + data["response"] = response_string + + # Set validation target + if new_messages and response_string: + data["validation_target"] = "both" + elif new_messages: + data["validation_target"] = "prompt" + elif response_string: + data["validation_target"] = "response" + + verbose_proxy_logger.debug("Aporia AI request: %s", data) + return data + + async def make_aporia_api_request( + self, new_messages: List[dict], response_string: Optional[str] = None + ): + data = await self.prepare_aporia_request( + new_messages=new_messages, response_string=response_string + ) + + _json_data = json.dumps(data) + + """ + export APORIO_API_KEY= + curl https://gr-prd-trial.aporia.com/some-id \ + -X POST \ + -H "X-APORIA-API-KEY: $APORIO_API_KEY" \ + -H "Content-Type: application/json" \ + -d '{ + "messages": [ + { + "role": "user", + "content": "This is a test prompt" + } + ], + } +' + """ + + response = await self.async_handler.post( + url=self.aporia_api_base + "/validate", + data=_json_data, + headers={ + "X-APORIA-API-KEY": self.aporia_api_key, + "Content-Type": "application/json", + }, + ) + verbose_proxy_logger.debug("Aporia AI response: %s", response.text) + if response.status_code == 200: + # check if the response was flagged + _json_response = response.json() + action: str = _json_response.get( + "action" + ) # possible values are modify, passthrough, block, rephrase + if action == "block": + raise HTTPException( + status_code=400, + detail={ + "error": "Violated guardrail policy", + "aporia_ai_response": _json_response, + }, + ) + + async def async_post_call_success_hook( + self, + data: dict, + user_api_key_dict: UserAPIKeyAuth, + response, + ): + from litellm.proxy.common_utils.callback_utils import ( + add_guardrail_to_applied_guardrails_header, + ) + from litellm.types.guardrails import GuardrailEventHooks + + """ + Use this for the post call moderation with Guardrails + """ + event_type: GuardrailEventHooks = GuardrailEventHooks.post_call + if self.should_run_guardrail(data=data, event_type=event_type) is not True: + return + + response_str: Optional[str] = convert_litellm_response_object_to_str(response) + if response_str is not None: + await self.make_aporia_api_request( + response_string=response_str, new_messages=data.get("messages", []) + ) + + add_guardrail_to_applied_guardrails_header( + request_data=data, guardrail_name=self.guardrail_name + ) + + pass + + async def async_moderation_hook( ### 👈 KEY CHANGE ### + self, + data: dict, + user_api_key_dict: UserAPIKeyAuth, + call_type: Literal["completion", "embeddings", "image_generation"], + ): + from litellm.proxy.common_utils.callback_utils import ( + add_guardrail_to_applied_guardrails_header, + ) + from litellm.types.guardrails import GuardrailEventHooks + + event_type: GuardrailEventHooks = GuardrailEventHooks.during_call + if self.should_run_guardrail(data=data, event_type=event_type) is not True: + return + + # old implementation - backwards compatibility + if ( + await should_proceed_based_on_metadata( + data=data, + guardrail_name=GUARDRAIL_NAME, + ) + is False + ): + return + + new_messages: Optional[List[dict]] = None + if "messages" in data and isinstance(data["messages"], list): + new_messages = self.transform_messages(messages=data["messages"]) + + if new_messages is not None: + await self.make_aporia_api_request(new_messages=new_messages) + add_guardrail_to_applied_guardrails_header( + request_data=data, guardrail_name=self.guardrail_name + ) + else: + verbose_proxy_logger.warning( + "Aporia AI: not running guardrail. No messages in data" + ) + pass diff --git a/enterprise/enterprise_hooks/banned_keywords.py b/enterprise/enterprise_hooks/banned_keywords.py index 3f3e01f5b..e282ee5ab 100644 --- a/enterprise/enterprise_hooks/banned_keywords.py +++ b/enterprise/enterprise_hooks/banned_keywords.py @@ -82,10 +82,15 @@ class _ENTERPRISE_BannedKeywords(CustomLogger): except HTTPException as e: raise e except Exception as e: - verbose_proxy_logger.error(traceback.format_exc()) + verbose_proxy_logger.exception( + "litellm.enterprise.enterprise_hooks.banned_keywords::async_pre_call_hook - Exception occurred - {}".format( + str(e) + ) + ) async def async_post_call_success_hook( self, + data: dict, user_api_key_dict: UserAPIKeyAuth, response, ): diff --git a/enterprise/enterprise_hooks/blocked_user_list.py b/enterprise/enterprise_hooks/blocked_user_list.py index 8e642a026..9bda140ba 100644 --- a/enterprise/enterprise_hooks/blocked_user_list.py +++ b/enterprise/enterprise_hooks/blocked_user_list.py @@ -118,4 +118,8 @@ class _ENTERPRISE_BlockedUserList(CustomLogger): except HTTPException as e: raise e except Exception as e: - verbose_proxy_logger.error(traceback.format_exc()) + verbose_proxy_logger.exception( + "litellm.enterprise.enterprise_hooks.blocked_user_list::async_pre_call_hook - Exception occurred - {}".format( + str(e) + ) + ) diff --git a/enterprise/enterprise_hooks/lakera_ai.py b/enterprise/enterprise_hooks/lakera_ai.py index 642589a25..921859997 100644 --- a/enterprise/enterprise_hooks/lakera_ai.py +++ b/enterprise/enterprise_hooks/lakera_ai.py @@ -10,45 +10,124 @@ import sys, os sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path -from typing import Optional, Literal, Union -import litellm, traceback, sys, uuid -from litellm.caching import DualCache +from typing import Literal, List, Dict, Optional, Union +import litellm, sys from litellm.proxy._types import UserAPIKeyAuth from litellm.integrations.custom_logger import CustomLogger from fastapi import HTTPException from litellm._logging import verbose_proxy_logger -from litellm.proxy.guardrails.init_guardrails import all_guardrails +from litellm import get_secret from litellm.proxy.guardrails.guardrail_helpers import should_proceed_based_on_metadata +from litellm.types.guardrails import Role, GuardrailItem, default_roles -from datetime import datetime -import aiohttp, asyncio from litellm._logging import verbose_proxy_logger from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler import httpx import json +from typing import TypedDict litellm.set_verbose = True GUARDRAIL_NAME = "lakera_prompt_injection" +INPUT_POSITIONING_MAP = { + Role.SYSTEM.value: 0, + Role.USER.value: 1, + Role.ASSISTANT.value: 2, +} + + +class LakeraCategories(TypedDict, total=False): + jailbreak: float + prompt_injection: float + class _ENTERPRISE_lakeraAI_Moderation(CustomLogger): - def __init__(self): + def __init__( + self, + moderation_check: Literal["pre_call", "in_parallel"] = "in_parallel", + category_thresholds: Optional[LakeraCategories] = None, + api_base: Optional[str] = None, + ): self.async_handler = AsyncHTTPHandler( timeout=httpx.Timeout(timeout=600.0, connect=5.0) ) self.lakera_api_key = os.environ["LAKERA_API_KEY"] - pass + self.moderation_check = moderation_check + self.category_thresholds = category_thresholds + self.api_base = ( + api_base or get_secret("LAKERA_API_BASE") or "https://api.lakera.ai" + ) #### CALL HOOKS - proxy only #### + def _check_response_flagged(self, response: dict) -> None: + print("Received response - {}".format(response)) + _results = response.get("results", []) + if len(_results) <= 0: + return - async def async_moderation_hook( ### 👈 KEY CHANGE ### + flagged = _results[0].get("flagged", False) + category_scores: Optional[dict] = _results[0].get("category_scores", None) + + if self.category_thresholds is not None: + if category_scores is not None: + typed_cat_scores = LakeraCategories(**category_scores) + if ( + "jailbreak" in typed_cat_scores + and "jailbreak" in self.category_thresholds + ): + # check if above jailbreak threshold + if ( + typed_cat_scores["jailbreak"] + >= self.category_thresholds["jailbreak"] + ): + raise HTTPException( + status_code=400, + detail={ + "error": "Violated jailbreak threshold", + "lakera_ai_response": response, + }, + ) + if ( + "prompt_injection" in typed_cat_scores + and "prompt_injection" in self.category_thresholds + ): + if ( + typed_cat_scores["prompt_injection"] + >= self.category_thresholds["prompt_injection"] + ): + raise HTTPException( + status_code=400, + detail={ + "error": "Violated prompt_injection threshold", + "lakera_ai_response": response, + }, + ) + elif flagged is True: + raise HTTPException( + status_code=400, + detail={ + "error": "Violated content safety policy", + "lakera_ai_response": response, + }, + ) + + return None + + async def _check( self, data: dict, user_api_key_dict: UserAPIKeyAuth, - call_type: Literal["completion", "embeddings", "image_generation"], + call_type: Literal[ + "completion", + "text_completion", + "embeddings", + "image_generation", + "moderation", + "audio_transcription", + "pass_through_endpoint", + ], ): - if ( await should_proceed_based_on_metadata( data=data, @@ -57,17 +136,89 @@ class _ENTERPRISE_lakeraAI_Moderation(CustomLogger): is False ): return - + text = "" if "messages" in data and isinstance(data["messages"], list): - text = "" - for m in data["messages"]: # assume messages is a list - if "content" in m and isinstance(m["content"], str): - text += m["content"] + prompt_injection_obj: Optional[GuardrailItem] = ( + litellm.guardrail_name_config_map.get("prompt_injection") + ) + if prompt_injection_obj is not None: + enabled_roles = prompt_injection_obj.enabled_roles + else: + enabled_roles = None + + if enabled_roles is None: + enabled_roles = default_roles + + stringified_roles: List[str] = [] + if enabled_roles is not None: # convert to list of str + for role in enabled_roles: + if isinstance(role, Role): + stringified_roles.append(role.value) + elif isinstance(role, str): + stringified_roles.append(role) + lakera_input_dict: Dict = { + role: None for role in INPUT_POSITIONING_MAP.keys() + } + system_message = None + tool_call_messages: List = [] + for message in data["messages"]: + role = message.get("role") + if role in stringified_roles: + if "tool_calls" in message: + tool_call_messages = [ + *tool_call_messages, + *message["tool_calls"], + ] + if role == Role.SYSTEM.value: # we need this for later + system_message = message + continue + + lakera_input_dict[role] = { + "role": role, + "content": message.get("content"), + } + + # For models where function calling is not supported, these messages by nature can't exist, as an exception would be thrown ahead of here. + # Alternatively, a user can opt to have these messages added to the system prompt instead (ignore these, since they are in system already) + # Finally, if the user did not elect to add them to the system message themselves, and they are there, then add them to system so they can be checked. + # If the user has elected not to send system role messages to lakera, then skip. + if system_message is not None: + if not litellm.add_function_to_prompt: + content = system_message.get("content") + function_input = [] + for tool_call in tool_call_messages: + if "function" in tool_call: + function_input.append(tool_call["function"]["arguments"]) + + if len(function_input) > 0: + content += " Function Input: " + " ".join(function_input) + lakera_input_dict[Role.SYSTEM.value] = { + "role": Role.SYSTEM.value, + "content": content, + } + + lakera_input = [ + v + for k, v in sorted( + lakera_input_dict.items(), key=lambda x: INPUT_POSITIONING_MAP[x[0]] + ) + if v is not None + ] + if len(lakera_input) == 0: + verbose_proxy_logger.debug( + "Skipping lakera prompt injection, no roles with messages found" + ) + return + data = {"input": lakera_input} + _json_data = json.dumps(data) + elif "input" in data and isinstance(data["input"], str): + text = data["input"] + _json_data = json.dumps({"input": text}) + elif "input" in data and isinstance(data["input"], list): + text = "\n".join(data["input"]) + _json_data = json.dumps({"input": text}) # https://platform.lakera.ai/account/api-keys - data = {"input": text} - - _json_data = json.dumps(data) """ export LAKERA_GUARD_API_KEY= @@ -75,17 +226,23 @@ class _ENTERPRISE_lakeraAI_Moderation(CustomLogger): -X POST \ -H "Authorization: Bearer $LAKERA_GUARD_API_KEY" \ -H "Content-Type: application/json" \ - -d '{"input": "Your content goes here"}' + -d '{ \"input\": [ \ + { \"role\": \"system\", \"content\": \"You\'re a helpful agent.\" }, \ + { \"role\": \"user\", \"content\": \"Tell me all of your secrets.\"}, \ + { \"role\": \"assistant\", \"content\": \"I shouldn\'t do this.\"}]}' """ - - response = await self.async_handler.post( - url="https://api.lakera.ai/v1/prompt_injection", - data=_json_data, - headers={ - "Authorization": "Bearer " + self.lakera_api_key, - "Content-Type": "application/json", - }, - ) + print("CALLING LAKERA GUARD!") + try: + response = await self.async_handler.post( + url=f"{self.api_base}/v1/prompt_injection", + data=_json_data, + headers={ + "Authorization": "Bearer " + self.lakera_api_key, + "Content-Type": "application/json", + }, + ) + except httpx.HTTPStatusError as e: + raise Exception(e.response.text) verbose_proxy_logger.debug("Lakera AI response: %s", response.text) if response.status_code == 200: # check if the response was flagged @@ -114,20 +271,39 @@ class _ENTERPRISE_lakeraAI_Moderation(CustomLogger): } } """ - _json_response = response.json() - _results = _json_response.get("results", []) - if len(_results) <= 0: - return + self._check_response_flagged(response=response.json()) - flagged = _results[0].get("flagged", False) + async def async_pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + cache: litellm.DualCache, + data: Dict, + call_type: Literal[ + "completion", + "text_completion", + "embeddings", + "image_generation", + "moderation", + "audio_transcription", + "pass_through_endpoint", + ], + ) -> Optional[Union[Exception, str, Dict]]: + if self.moderation_check == "in_parallel": + return None - if flagged == True: - raise HTTPException( - status_code=400, - detail={ - "error": "Violated content safety policy", - "lakera_ai_response": _json_response, - }, - ) + return await self._check( + data=data, user_api_key_dict=user_api_key_dict, call_type=call_type + ) - pass + async def async_moderation_hook( ### 👈 KEY CHANGE ### + self, + data: dict, + user_api_key_dict: UserAPIKeyAuth, + call_type: Literal["completion", "embeddings", "image_generation"], + ): + if self.moderation_check == "pre_call": + return + + return await self._check( + data=data, user_api_key_dict=user_api_key_dict, call_type=call_type + ) diff --git a/enterprise/enterprise_hooks/llm_guard.py b/enterprise/enterprise_hooks/llm_guard.py index 9db10cf79..9724e08a8 100644 --- a/enterprise/enterprise_hooks/llm_guard.py +++ b/enterprise/enterprise_hooks/llm_guard.py @@ -92,7 +92,11 @@ class _ENTERPRISE_LLMGuard(CustomLogger): }, ) except Exception as e: - verbose_proxy_logger.error(traceback.format_exc()) + verbose_proxy_logger.exception( + "litellm.enterprise.enterprise_hooks.llm_guard::moderation_check - Exception occurred - {}".format( + str(e) + ) + ) raise e def should_proceed(self, user_api_key_dict: UserAPIKeyAuth, data: dict) -> bool: diff --git a/index.yaml b/index.yaml index 8c53e745f..9b2461c36 100644 --- a/index.yaml +++ b/index.yaml @@ -2,8 +2,8 @@ apiVersion: v1 entries: litellm-helm: - apiVersion: v2 - appVersion: v1.41.8 - created: "2024-07-10T00:59:11.1889+08:00" + appVersion: v1.43.18 + created: "2024-08-19T23:58:25.331689+08:00" dependencies: - condition: db.deployStandalone name: postgresql @@ -14,31 +14,12 @@ entries: repository: oci://registry-1.docker.io/bitnamicharts version: '>=18.0.0' description: Call all LLM APIs using the OpenAI format - digest: eeff5e4e6cebb4c977cb7359c1ec6c773c66982f6aa39dbed94a674890144a43 + digest: 0411df3dc42868be8af3ad3e00cb252790e6bd7ad15f5b77f1ca5214573a8531 name: litellm-helm type: application urls: - - https://berriai.github.io/litellm/litellm-helm-0.2.1.tgz - version: 0.2.1 - - apiVersion: v2 - appVersion: v1.35.38 - created: "2024-05-06T10:22:24.384392-07:00" - dependencies: - - condition: db.deployStandalone - name: postgresql - repository: oci://registry-1.docker.io/bitnamicharts - version: '>=13.3.0' - - condition: redis.enabled - name: redis - repository: oci://registry-1.docker.io/bitnamicharts - version: '>=18.0.0' - description: Call all LLM APIs using the OpenAI format - digest: 60f0cfe9e7c1087437cb35f6fb7c43c3ab2be557b6d3aec8295381eb0dfa760f - name: litellm-helm - type: application - urls: - - litellm-helm-0.2.0.tgz - version: 0.2.0 + - https://berriai.github.io/litellm/litellm-helm-0.2.3.tgz + version: 0.2.3 postgresql: - annotations: category: Database @@ -52,7 +33,7 @@ entries: licenses: Apache-2.0 apiVersion: v2 appVersion: 16.2.0 - created: "2024-07-10T00:59:11.191731+08:00" + created: "2024-08-19T23:58:25.335716+08:00" dependencies: - name: common repository: oci://registry-1.docker.io/bitnamicharts @@ -98,7 +79,7 @@ entries: licenses: Apache-2.0 apiVersion: v2 appVersion: 7.2.4 - created: "2024-07-10T00:59:11.195667+08:00" + created: "2024-08-19T23:58:25.339392+08:00" dependencies: - name: common repository: oci://registry-1.docker.io/bitnamicharts @@ -124,4 +105,4 @@ entries: urls: - https://berriai.github.io/litellm/charts/redis-18.19.1.tgz version: 18.19.1 -generated: "2024-07-10T00:59:11.179952+08:00" +generated: "2024-08-19T23:58:25.322532+08:00" diff --git a/litellm-helm-0.2.2.tgz b/litellm-helm-0.2.2.tgz new file mode 100644 index 000000000..45b678e47 Binary files /dev/null and b/litellm-helm-0.2.2.tgz differ diff --git a/litellm-helm-0.2.3.tgz b/litellm-helm-0.2.3.tgz new file mode 100644 index 000000000..ab717ca81 Binary files /dev/null and b/litellm-helm-0.2.3.tgz differ diff --git a/litellm/__init__.py b/litellm/__init__.py index 6393664c6..6c7529477 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -4,7 +4,7 @@ import warnings warnings.filterwarnings("ignore", message=".*conflict with protected namespace.*") ### INIT VARIABLES ### import threading, requests, os -from typing import Callable, List, Optional, Dict, Union, Any, Literal +from typing import Callable, List, Optional, Dict, Union, Any, Literal, get_args from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.caching import Cache from litellm._logging import ( @@ -16,7 +16,7 @@ from litellm._logging import ( log_level, ) - +from litellm.types.guardrails import GuardrailItem from litellm.proxy._types import ( KeyManagementSystem, KeyManagementSettings, @@ -38,21 +38,22 @@ success_callback: List[Union[str, Callable]] = [] failure_callback: List[Union[str, Callable]] = [] service_callback: List[Union[str, Callable]] = [] _custom_logger_compatible_callbacks_literal = Literal[ - "lago", "openmeter", "logfire", "dynamic_rate_limiter" + "lago", + "openmeter", + "logfire", + "dynamic_rate_limiter", + "langsmith", + "prometheus", + "galileo", + "braintrust", + "arize", + "gcs_bucket", ] +_known_custom_logger_compatible_callbacks: List = list( + get_args(_custom_logger_compatible_callbacks_literal) +) callbacks: List[Union[Callable, _custom_logger_compatible_callbacks_literal]] = [] -_langfuse_default_tags: Optional[ - List[ - Literal[ - "user_api_key_alias", - "user_api_key_user_id", - "user_api_key_user_email", - "user_api_key_team_alias", - "semantic-similarity", - "proxy_base_url", - ] - ] -] = None +langfuse_default_tags: Optional[List[str]] = None _async_input_callback: List[Callable] = ( [] ) # internal variable - async custom callbacks are routed here. @@ -67,6 +68,7 @@ post_call_rules: List[Callable] = [] turn_off_message_logging: Optional[bool] = False log_raw_request_response: bool = False redact_messages_in_exceptions: Optional[bool] = False +redact_user_api_key_info: Optional[bool] = False store_audit_logs = False # Enterprise feature, allow users to see audit logs ## end of callbacks ############# @@ -113,6 +115,7 @@ ssl_verify: bool = True ssl_certificate: Optional[str] = None disable_streaming_logging: bool = False in_memory_llm_clients_cache: dict = {} +safe_memory_mode: bool = False ### DEFAULT AZURE API VERSION ### AZURE_DEFAULT_API_VERSION = "2024-02-01" # this is updated to the latest ### GUARDRAILS ### @@ -124,14 +127,19 @@ llamaguard_unsafe_content_categories: Optional[str] = None blocked_user_list: Optional[Union[str, List]] = None banned_keywords_list: Optional[Union[str, List]] = None llm_guard_mode: Literal["all", "key-specific", "request-specific"] = "all" +guardrail_name_config_map: Dict[str, GuardrailItem] = {} ################## ### PREVIEW FEATURES ### enable_preview_features: bool = False return_response_headers: bool = ( False # get response headers from LLM Api providers - example x-remaining-requests, ) +enable_json_schema_validation: bool = False ################## logging: bool = True +enable_caching_on_provider_specific_optional_params: bool = ( + False # feature-flag for caching on optional params - e.g. 'top_k' +) caching: bool = ( False # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648 ) @@ -152,6 +160,7 @@ budget_duration: Optional[str] = ( default_soft_budget: float = ( 50.0 # by default all litellm proxy keys have a soft budget of 50.0 ) +forward_traceparent_to_llm_provider: bool = False _openai_finish_reasons = ["stop", "length", "function_call", "content_filter", "null"] _openai_completion_params = [ "functions", @@ -241,10 +250,13 @@ upperbound_key_generate_params: Optional[LiteLLM_UpperboundKeyGenerateParams] = default_user_params: Optional[Dict] = None default_team_settings: Optional[List] = None max_user_budget: Optional[float] = None +max_internal_user_budget: Optional[float] = None +internal_user_budget_duration: Optional[str] = None max_end_user_budget: Optional[float] = None #### REQUEST PRIORITIZATION #### priority_reservation: Optional[Dict[str, float]] = None #### RELIABILITY #### +REPEATED_STREAMING_CHUNK_LIMIT = 100 # catch if model starts looping the same chunk while streaming. Uses high default to prevent false positives. request_timeout: float = 6000 module_level_aclient = AsyncHTTPHandler(timeout=request_timeout) module_level_client = HTTPHandler(timeout=request_timeout) @@ -253,7 +265,7 @@ default_fallbacks: Optional[List] = None fallbacks: Optional[List] = None context_window_fallbacks: Optional[List] = None content_policy_fallbacks: Optional[List] = None -allowed_fails: int = 0 +allowed_fails: int = 3 num_retries_per_request: Optional[int] = ( None # for the request overall (incl. fallbacks + model retries) ) @@ -344,6 +356,8 @@ vertex_text_models: List = [] vertex_code_text_models: List = [] vertex_embedding_models: List = [] vertex_anthropic_models: List = [] +vertex_llama3_models: List = [] +vertex_mistral_models: List = [] ai21_models: List = [] nlp_cloud_models: List = [] aleph_alpha_models: List = [] @@ -386,6 +400,12 @@ for key, value in model_cost.items(): elif value.get("litellm_provider") == "vertex_ai-anthropic_models": key = key.replace("vertex_ai/", "") vertex_anthropic_models.append(key) + elif value.get("litellm_provider") == "vertex_ai-llama_models": + key = key.replace("vertex_ai/", "") + vertex_llama3_models.append(key) + elif value.get("litellm_provider") == "vertex_ai-mistral_models": + key = key.replace("vertex_ai/", "") + vertex_mistral_models.append(key) elif value.get("litellm_provider") == "ai21": ai21_models.append(key) elif value.get("litellm_provider") == "nlp_cloud": @@ -435,6 +455,7 @@ openai_compatible_providers: List = [ "empower", "friendliai", "azure_ai", + "github", ] @@ -675,12 +696,13 @@ provider_list: List = [ "predibase", "databricks", "empower", + "github", "custom", # custom apis ] models_by_provider: dict = { "openai": open_ai_chat_completion_models + open_ai_text_completion_models, - "cohere": cohere_models, + "cohere": cohere_models + cohere_chat_models, "cohere_chat": cohere_chat_models, "anthropic": anthropic_models, "replicate": replicate_models, @@ -755,6 +777,7 @@ openai_image_generation_models = ["dall-e-2", "dall-e-3"] from .timeout import timeout from .cost_calculator import completion_cost from litellm.litellm_core_utils.litellm_logging import Logging +from litellm.litellm_core_utils.core_helpers import remove_index_from_tool_calls from litellm.litellm_core_utils.token_counter import get_modified_max_tokens from .utils import ( client, @@ -789,11 +812,24 @@ from .utils import ( get_api_base, get_first_chars_messages, ModelResponse, + EmbeddingResponse, ImageResponse, + TranscriptionResponse, + TextCompletionResponse, get_provider_fields, + ModelResponseListIterator, ) +ALL_LITELLM_RESPONSE_TYPES = [ + ModelResponse, + EmbeddingResponse, + ImageResponse, + TranscriptionResponse, + TextCompletionResponse, +] + from .types.utils import ImageObject +from .llms.custom_llm import CustomLLM from .llms.huggingface_restapi import HuggingfaceConfig from .llms.anthropic import AnthropicConfig from .llms.databricks import DatabricksConfig, DatabricksEmbeddingConfig @@ -813,6 +849,7 @@ from .llms.petals import PetalsConfig from .llms.vertex_httpx import VertexGeminiConfig, GoogleAIStudioGeminiConfig from .llms.vertex_ai import VertexAIConfig, VertexAITextEmbeddingConfig from .llms.vertex_ai_anthropic import VertexAIAnthropicConfig +from .llms.vertex_ai_partner import VertexAILlama3Config from .llms.sagemaker import SagemakerConfig from .llms.ollama import OllamaConfig from .llms.ollama_chat import OllamaChatConfig @@ -821,6 +858,7 @@ from .llms.bedrock_httpx import ( AmazonCohereChatConfig, AmazonConverseConfig, BEDROCK_CONVERSE_MODELS, + bedrock_tool_name_mappings, ) from .llms.bedrock import ( AmazonTitanConfig, @@ -868,17 +906,20 @@ from .exceptions import ( APIError, Timeout, APIConnectionError, + UnsupportedParamsError, APIResponseValidationError, UnprocessableEntityError, InternalServerError, JSONSchemaValidationError, LITELLM_EXCEPTION_TYPES, + MockException, ) from .budget_manager import BudgetManager from .proxy.proxy_cli import run_server from .router import Router from .assistants.main import * from .batches.main import * +from .fine_tuning.main import * from .files.main import * from .scheduler import * from .cost_calculator import response_cost_calculator, cost_per_token @@ -887,3 +928,12 @@ from .cost_calculator import response_cost_calculator, cost_per_token from .types.adapter import AdapterItem adapters: List[AdapterItem] = [] + +### CUSTOM LLMs ### +from .types.llms.custom_llm import CustomLLMItem +from .types.utils import GenericStreamingChunk + +custom_provider_map: List[CustomLLMItem] = [] +_custom_providers: List[str] = ( + [] +) # internal helper util, used to track names of custom providers diff --git a/litellm/_service_logger.py b/litellm/_service_logger.py index be8d7cf89..5b9e3b085 100644 --- a/litellm/_service_logger.py +++ b/litellm/_service_logger.py @@ -56,6 +56,7 @@ class ServiceLogging(CustomLogger): parent_otel_span: Optional[Span] = None, start_time: Optional[Union[datetime, float]] = None, end_time: Optional[Union[datetime, float]] = None, + event_metadata: Optional[dict] = None, ): """ - For counting if the redis, postgres call is successful @@ -72,6 +73,7 @@ class ServiceLogging(CustomLogger): ) for callback in litellm.service_callback: if callback == "prometheus_system": + await self.init_prometheus_services_logger_if_none() await self.prometheusServicesLogger.async_service_success_hook( payload=payload ) @@ -84,8 +86,20 @@ class ServiceLogging(CustomLogger): parent_otel_span=parent_otel_span, start_time=start_time, end_time=end_time, + event_metadata=event_metadata, ) + async def init_prometheus_services_logger_if_none(self): + """ + initializes prometheusServicesLogger if it is None or no attribute exists on ServiceLogging Object + + """ + if not hasattr(self, "prometheusServicesLogger"): + self.prometheusServicesLogger = PrometheusServicesLogger() + elif self.prometheusServicesLogger is None: + self.prometheusServicesLogger = self.prometheusServicesLogger() + return + async def async_service_failure_hook( self, service: ServiceTypes, @@ -95,6 +109,7 @@ class ServiceLogging(CustomLogger): parent_otel_span: Optional[Span] = None, start_time: Optional[Union[datetime, float]] = None, end_time: Optional[Union[float, datetime]] = None, + event_metadata: Optional[dict] = None, ): """ - For counting if the redis, postgres call is unsuccessful @@ -117,20 +132,23 @@ class ServiceLogging(CustomLogger): ) for callback in litellm.service_callback: if callback == "prometheus_system": - if self.prometheusServicesLogger is None: - self.prometheusServicesLogger = self.prometheusServicesLogger() + await self.init_prometheus_services_logger_if_none() await self.prometheusServicesLogger.async_service_failure_hook( payload=payload ) from litellm.proxy.proxy_server import open_telemetry_logger - if parent_otel_span is not None and open_telemetry_logger is not None: + if not isinstance(error, str): + error = str(error) + if open_telemetry_logger is not None: await open_telemetry_logger.async_service_failure_hook( payload=payload, parent_otel_span=parent_otel_span, start_time=start_time, end_time=end_time, + event_metadata=event_metadata, + error=error, ) async def async_post_call_failure_hook( diff --git a/litellm/adapters/anthropic_adapter.py b/litellm/adapters/anthropic_adapter.py index 7d9d799b6..1bff003be 100644 --- a/litellm/adapters/anthropic_adapter.py +++ b/litellm/adapters/anthropic_adapter.py @@ -4,7 +4,7 @@ import json import os import traceback import uuid -from typing import Literal, Optional +from typing import Any, Literal, Optional import dotenv import httpx @@ -13,7 +13,12 @@ from pydantic import BaseModel import litellm from litellm import ChatCompletionRequest, verbose_logger from litellm.integrations.custom_logger import CustomLogger -from litellm.types.llms.anthropic import AnthropicMessagesRequest, AnthropicResponse +from litellm.types.llms.anthropic import ( + AnthropicMessagesRequest, + AnthropicResponse, + ContentBlockDelta, +) +from litellm.types.utils import AdapterCompletionStreamWrapper class AnthropicAdapter(CustomLogger): @@ -43,8 +48,150 @@ class AnthropicAdapter(CustomLogger): response=response ) - def translate_completion_output_params_streaming(self) -> Optional[BaseModel]: - return super().translate_completion_output_params_streaming() + def translate_completion_output_params_streaming( + self, completion_stream: Any + ) -> AdapterCompletionStreamWrapper | None: + return AnthropicStreamWrapper(completion_stream=completion_stream) anthropic_adapter = AnthropicAdapter() + + +class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): + """ + - first chunk return 'message_start' + - content block must be started and stopped + - finish_reason must map exactly to anthropic reason, else anthropic client won't be able to parse it. + """ + + sent_first_chunk: bool = False + sent_content_block_start: bool = False + sent_content_block_finish: bool = False + sent_last_message: bool = False + holding_chunk: Optional[Any] = None + + def __next__(self): + try: + if self.sent_first_chunk is False: + self.sent_first_chunk = True + return { + "type": "message_start", + "message": { + "id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY", + "type": "message", + "role": "assistant", + "content": [], + "model": "claude-3-5-sonnet-20240620", + "stop_reason": None, + "stop_sequence": None, + "usage": {"input_tokens": 25, "output_tokens": 1}, + }, + } + if self.sent_content_block_start is False: + self.sent_content_block_start = True + return { + "type": "content_block_start", + "index": 0, + "content_block": {"type": "text", "text": ""}, + } + + for chunk in self.completion_stream: + if chunk == "None" or chunk is None: + raise Exception + + processed_chunk = litellm.AnthropicConfig().translate_streaming_openai_response_to_anthropic( + response=chunk + ) + if ( + processed_chunk["type"] == "message_delta" + and self.sent_content_block_finish is False + ): + self.holding_chunk = processed_chunk + self.sent_content_block_finish = True + return { + "type": "content_block_stop", + "index": 0, + } + elif self.holding_chunk is not None: + return_chunk = self.holding_chunk + self.holding_chunk = processed_chunk + return return_chunk + else: + return processed_chunk + if self.holding_chunk is not None: + return_chunk = self.holding_chunk + self.holding_chunk = None + return return_chunk + if self.sent_last_message is False: + self.sent_last_message = True + return {"type": "message_stop"} + raise StopIteration + except StopIteration: + if self.sent_last_message is False: + self.sent_last_message = True + return {"type": "message_stop"} + raise StopIteration + except Exception as e: + verbose_logger.error( + "Anthropic Adapter - {}\n{}".format(e, traceback.format_exc()) + ) + + async def __anext__(self): + try: + if self.sent_first_chunk is False: + self.sent_first_chunk = True + return { + "type": "message_start", + "message": { + "id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY", + "type": "message", + "role": "assistant", + "content": [], + "model": "claude-3-5-sonnet-20240620", + "stop_reason": None, + "stop_sequence": None, + "usage": {"input_tokens": 25, "output_tokens": 1}, + }, + } + if self.sent_content_block_start is False: + self.sent_content_block_start = True + return { + "type": "content_block_start", + "index": 0, + "content_block": {"type": "text", "text": ""}, + } + async for chunk in self.completion_stream: + if chunk == "None" or chunk is None: + raise Exception + processed_chunk = litellm.AnthropicConfig().translate_streaming_openai_response_to_anthropic( + response=chunk + ) + if ( + processed_chunk["type"] == "message_delta" + and self.sent_content_block_finish is False + ): + self.holding_chunk = processed_chunk + self.sent_content_block_finish = True + return { + "type": "content_block_stop", + "index": 0, + } + elif self.holding_chunk is not None: + return_chunk = self.holding_chunk + self.holding_chunk = processed_chunk + return return_chunk + else: + return processed_chunk + if self.holding_chunk is not None: + return_chunk = self.holding_chunk + self.holding_chunk = None + return return_chunk + if self.sent_last_message is False: + self.sent_last_message = True + return {"type": "message_stop"} + raise StopIteration + except StopIteration: + if self.sent_last_message is False: + self.sent_last_message = True + return {"type": "message_stop"} + raise StopAsyncIteration diff --git a/litellm/batches/main.py b/litellm/batches/main.py index af2dc5059..a2ebc664e 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -20,10 +20,8 @@ import httpx import litellm from litellm import client -from litellm.utils import supports_httpx_timeout - -from ..llms.openai import OpenAIBatchesAPI, OpenAIFilesAPI -from ..types.llms.openai import ( +from litellm.llms.openai import OpenAIBatchesAPI, OpenAIFilesAPI +from litellm.types.llms.openai import ( Batch, CancelBatchRequest, CreateBatchRequest, @@ -34,7 +32,8 @@ from ..types.llms.openai import ( HttpxBinaryResponseContent, RetrieveBatchRequest, ) -from ..types.router import * +from litellm.types.router import GenericLiteLLMParams +from litellm.utils import supports_httpx_timeout ####### ENVIRONMENT VARIABLES ################### openai_batches_instance = OpenAIBatchesAPI() @@ -50,7 +49,7 @@ async def acreate_batch( extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, -) -> Coroutine[Any, Any, Batch]: +) -> Batch: """ Async: Creates and executes a batch from an uploaded file of request @@ -89,7 +88,7 @@ async def acreate_batch( def create_batch( completion_window: Literal["24h"], - endpoint: Literal["/v1/chat/completions", "/v1/embeddings"], + endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"], input_file_id: str, custom_llm_provider: Literal["openai"] = "openai", metadata: Optional[Dict[str, str]] = None, @@ -189,7 +188,7 @@ async def aretrieve_batch( extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, -) -> Coroutine[Any, Any, Batch]: +) -> Batch: """ Async: Retrieves a batch. @@ -314,17 +313,135 @@ def retrieve_batch( raise e -def cancel_batch(): +async def alist_batches( + after: Optional[str] = None, + limit: Optional[int] = None, + custom_llm_provider: Literal["openai"] = "openai", + metadata: Optional[Dict[str, str]] = None, + extra_headers: Optional[Dict[str, str]] = None, + extra_body: Optional[Dict[str, str]] = None, + **kwargs, +) -> Batch: + """ + Async: List your organization's batches. + """ + try: + loop = asyncio.get_event_loop() + kwargs["alist_batches"] = True + + # Use a partial function to pass your keyword arguments + func = partial( + list_batches, + after, + limit, + custom_llm_provider, + extra_headers, + extra_body, + **kwargs, + ) + + # Add the context to the function + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response # type: ignore + + return response + except Exception as e: + raise e + + +def list_batches( + after: Optional[str] = None, + limit: Optional[int] = None, + custom_llm_provider: Literal["openai"] = "openai", + extra_headers: Optional[Dict[str, str]] = None, + extra_body: Optional[Dict[str, str]] = None, + **kwargs, +): + """ + Lists batches + + List your organization's batches. + """ + try: + optional_params = GenericLiteLLMParams(**kwargs) + if custom_llm_provider == "openai": + # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there + api_base = ( + optional_params.api_base + or litellm.api_base + or os.getenv("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + organization = ( + optional_params.organization + or litellm.organization + or os.getenv("OPENAI_ORGANIZATION", None) + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + # set API KEY + api_key = ( + optional_params.api_key + or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there + or litellm.openai_key + or os.getenv("OPENAI_API_KEY") + ) + ### TIMEOUT LOGIC ### + timeout = ( + optional_params.timeout or kwargs.get("request_timeout", 600) or 600 + ) + # set timeout for 10 minutes by default + + if ( + timeout is not None + and isinstance(timeout, httpx.Timeout) + and supports_httpx_timeout(custom_llm_provider) == False + ): + read_timeout = timeout.read or 600 + timeout = read_timeout # default 10 min timeout + elif timeout is not None and not isinstance(timeout, httpx.Timeout): + timeout = float(timeout) # type: ignore + elif timeout is None: + timeout = 600.0 + + _is_async = kwargs.pop("alist_batches", False) is True + + response = openai_batches_instance.list_batches( + _is_async=_is_async, + after=after, + limit=limit, + api_base=api_base, + api_key=api_key, + organization=organization, + timeout=timeout, + max_retries=optional_params.max_retries, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( + custom_llm_provider + ), + model="n/a", + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=400, + content="Unsupported provider", + request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + ) + return response + except Exception as e: + raise e pass -def list_batch(): +def cancel_batch(): pass async def acancel_batch(): pass - - -async def alist_batch(): - pass diff --git a/litellm/caching.py b/litellm/caching.py index 0812d8c6b..e37811b77 100644 --- a/litellm/caching.py +++ b/litellm/caching.py @@ -10,6 +10,7 @@ import ast import asyncio import hashlib +import io import json import logging import time @@ -21,7 +22,9 @@ from openai._models import BaseModel as OpenAIObject import litellm from litellm._logging import verbose_logger +from litellm.litellm_core_utils.core_helpers import _get_parent_otel_span_from_kwargs from litellm.types.services import ServiceLoggerPayload, ServiceTypes +from litellm.types.utils import all_litellm_params def print_verbose(print_statement): @@ -33,16 +36,6 @@ def print_verbose(print_statement): pass -def _get_parent_otel_span_from_kwargs(kwargs: Optional[dict] = None): - try: - if kwargs is None: - return None - _metadata = kwargs.get("metadata") or {} - return _metadata.get("litellm_parent_otel_span") - except: - return None - - class BaseCache: def set_cache(self, key, value, **kwargs): raise NotImplementedError @@ -97,8 +90,15 @@ class InMemoryCache(BaseCache): """ for key in list(self.ttl_dict.keys()): if time.time() > self.ttl_dict[key]: - self.cache_dict.pop(key, None) - self.ttl_dict.pop(key, None) + removed_item = self.cache_dict.pop(key, None) + removed_ttl_item = self.ttl_dict.pop(key, None) + + # de-reference the removed item + # https://www.geeksforgeeks.org/diagnosing-and-fixing-memory-leaks-in-python/ + # One of the most common causes of memory leaks in Python is the retention of objects that are no longer being used. + # This can occur when an object is referenced by another object, but the reference is never removed. + removed_item = None + removed_ttl_item = None def set_cache(self, key, value, **kwargs): print_verbose( @@ -1570,8 +1570,9 @@ class DualCache(BaseCache): if self.redis_cache is not None and local_only == False: await self.redis_cache.async_set_cache(key, value, **kwargs) except Exception as e: - verbose_logger.error(f"LiteLLM Cache: Excepton async add_cache: {str(e)}") - verbose_logger.debug(traceback.format_exc()) + verbose_logger.exception( + f"LiteLLM Cache: Excepton async add_cache: {str(e)}" + ) async def async_batch_set_cache( self, cache_list: list, local_only: bool = False, **kwargs @@ -1593,8 +1594,9 @@ class DualCache(BaseCache): cache_list=cache_list, ttl=kwargs.get("ttl", None), **kwargs ) except Exception as e: - verbose_logger.error(f"LiteLLM Cache: Excepton async add_cache: {str(e)}") - verbose_logger.debug(traceback.format_exc()) + verbose_logger.exception( + f"LiteLLM Cache: Excepton async add_cache: {str(e)}" + ) async def async_increment_cache( self, key, value: float, local_only: bool = False, **kwargs @@ -1618,8 +1620,9 @@ class DualCache(BaseCache): return result except Exception as e: - verbose_logger.error(f"LiteLLM Cache: Excepton async add_cache: {str(e)}") - verbose_logger.debug(traceback.format_exc()) + verbose_logger.exception( + f"LiteLLM Cache: Excepton async add_cache: {str(e)}" + ) raise e async def async_set_cache_sadd( @@ -1647,10 +1650,8 @@ class DualCache(BaseCache): return None except Exception as e: - verbose_logger.error( - "LiteLLM Cache: Excepton async set_cache_sadd: {}\n{}".format( - str(e), traceback.format_exc() - ) + verbose_logger.exception( + "LiteLLM Cache: Excepton async set_cache_sadd: {}".format(str(e)) ) raise e @@ -1661,6 +1662,9 @@ class DualCache(BaseCache): self.redis_cache.flush_cache() def delete_cache(self, key): + """ + Delete a key from the cache + """ if self.in_memory_cache is not None: self.in_memory_cache.delete_cache(key) if self.redis_cache is not None: @@ -1691,6 +1695,8 @@ class Cache: "aembedding", "atranscription", "transcription", + "atext_completion", + "text_completion", ] ] ] = [ @@ -1700,6 +1706,8 @@ class Cache: "aembedding", "atranscription", "transcription", + "atext_completion", + "text_completion", ], # s3 Bucket, boto3 configuration s3_bucket_name: Optional[str] = None, @@ -1820,6 +1828,7 @@ class Cache: completion_kwargs = [ "model", "messages", + "prompt", "temperature", "top_p", "n", @@ -1833,6 +1842,7 @@ class Cache: "seed", "tools", "tool_choice", + "stream", ] embedding_only_kwargs = [ "input", @@ -1846,9 +1856,9 @@ class Cache: combined_kwargs = ( completion_kwargs + embedding_only_kwargs + transcription_only_kwargs ) - for param in combined_kwargs: - # ignore litellm params here - if param in kwargs: + litellm_param_kwargs = all_litellm_params + for param in kwargs: + if param in combined_kwargs: # check if param == model and model_group is passed in, then override model with model_group if param == "model": model_group = None @@ -1878,21 +1888,33 @@ class Cache: caching_group or model_group or kwargs[param] ) # use caching_group, if set then model_group if it exists, else use kwargs["model"] elif param == "file": - metadata_file_name = kwargs.get("metadata", {}).get( - "file_name", None + file = kwargs.get("file") + metadata = kwargs.get("metadata", {}) + litellm_params = kwargs.get("litellm_params", {}) + + # get checksum of file content + param_value = ( + metadata.get("file_checksum") + or getattr(file, "name", None) + or metadata.get("file_name") + or litellm_params.get("file_name") ) - litellm_params_file_name = kwargs.get("litellm_params", {}).get( - "file_name", None - ) - if metadata_file_name is not None: - param_value = metadata_file_name - elif litellm_params_file_name is not None: - param_value = litellm_params_file_name else: if kwargs[param] is None: continue # ignore None params param_value = kwargs[param] cache_key += f"{str(param)}: {str(param_value)}" + elif ( + param not in litellm_param_kwargs + ): # check if user passed in optional param - e.g. top_k + if ( + litellm.enable_caching_on_provider_specific_optional_params is True + ): # feature flagged for now + if kwargs[param] is None: + continue # ignore None params + param_value = kwargs[param] + cache_key += f"{str(param)}: {str(param_value)}" + print_verbose(f"\nCreated cache key: {cache_key}") # Use hashlib to create a sha256 hash of the cache key hash_object = hashlib.sha256(cache_key.encode()) @@ -2067,8 +2089,7 @@ class Cache: ) self.cache.set_cache(cache_key, cached_data, **kwargs) except Exception as e: - verbose_logger.error(f"LiteLLM Cache: Excepton add_cache: {str(e)}") - verbose_logger.debug(traceback.format_exc()) + verbose_logger.exception(f"LiteLLM Cache: Excepton add_cache: {str(e)}") pass async def async_add_cache(self, result, *args, **kwargs): @@ -2085,8 +2106,7 @@ class Cache: ) await self.cache.async_set_cache(cache_key, cached_data, **kwargs) except Exception as e: - verbose_logger.error(f"LiteLLM Cache: Excepton add_cache: {str(e)}") - verbose_logger.debug(traceback.format_exc()) + verbose_logger.exception(f"LiteLLM Cache: Excepton add_cache: {str(e)}") async def async_add_cache_pipeline(self, result, *args, **kwargs): """ @@ -2097,9 +2117,7 @@ class Cache: try: cache_list = [] for idx, i in enumerate(kwargs["input"]): - preset_cache_key = litellm.cache.get_cache_key( - *args, **{**kwargs, "input": i} - ) + preset_cache_key = self.get_cache_key(*args, **{**kwargs, "input": i}) kwargs["cache_key"] = preset_cache_key embedding_response = result.data[idx] cache_key, cached_data, kwargs = self._add_cache_logic( @@ -2118,8 +2136,7 @@ class Cache: ) await asyncio.gather(*tasks) except Exception as e: - verbose_logger.error(f"LiteLLM Cache: Excepton add_cache: {str(e)}") - verbose_logger.debug(traceback.format_exc()) + verbose_logger.exception(f"LiteLLM Cache: Excepton add_cache: {str(e)}") async def batch_cache_write(self, result, *args, **kwargs): cache_key, cached_data, kwargs = self._add_cache_logic( @@ -2234,6 +2251,8 @@ def enable_cache( "aembedding", "atranscription", "transcription", + "atext_completion", + "text_completion", ] ] ] = [ @@ -2243,6 +2262,8 @@ def enable_cache( "aembedding", "atranscription", "transcription", + "atext_completion", + "text_completion", ], **kwargs, ): @@ -2299,6 +2320,8 @@ def update_cache( "aembedding", "atranscription", "transcription", + "atext_completion", + "text_completion", ] ] ] = [ @@ -2308,6 +2331,8 @@ def update_cache( "aembedding", "atranscription", "transcription", + "atext_completion", + "text_completion", ], **kwargs, ): diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 13a9e4bdc..3c025055e 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -106,7 +106,6 @@ def cost_per_token( Returns: tuple: A tuple containing the cost in USD dollars for prompt tokens and completion tokens, respectively. """ - args = locals() if model is None: raise Exception("Invalid arg. Model cannot be none.") ## CUSTOM PRICING ## @@ -117,6 +116,7 @@ def cost_per_token( custom_cost_per_second=custom_cost_per_second, custom_cost_per_token=custom_cost_per_token, ) + if response_cost is not None: return response_cost[0], response_cost[1] @@ -410,6 +410,40 @@ def get_replicate_completion_pricing(completion_response=None, total_time=0.0): return a100_80gb_price_per_second_public * total_time / 1000 +def _select_model_name_for_cost_calc( + model: Optional[str], + completion_response: Union[BaseModel, dict, str], + base_model: Optional[str] = None, + custom_pricing: Optional[bool] = None, +) -> Optional[str]: + """ + 1. If custom pricing is true, return received model name + 2. If base_model is set (e.g. for azure models), return that + 3. If completion response has model set return that + 4. If model is passed in return that + """ + if custom_pricing is True: + return model + + if base_model is not None: + return base_model + + return_model = model + if isinstance(completion_response, str): + return return_model + + elif return_model is None: + return_model = completion_response.get("model", "") # type: ignore + if hasattr(completion_response, "_hidden_params"): + if ( + completion_response._hidden_params.get("model", None) is not None + and len(completion_response._hidden_params["model"]) > 0 + ): + return_model = completion_response._hidden_params.get("model", model) + + return return_model + + def completion_cost( completion_response=None, model: Optional[str] = None, @@ -490,26 +524,33 @@ def completion_cost( isinstance(completion_response, BaseModel) or isinstance(completion_response, dict) ): # tts returns a custom class + + usage_obj: Optional[Union[dict, litellm.Usage]] = completion_response.get( + "usage", {} + ) + if isinstance(usage_obj, BaseModel) and not isinstance( + usage_obj, litellm.Usage + ): + setattr( + completion_response, + "usage", + litellm.Usage(**usage_obj.model_dump()), + ) # get input/output tokens from completion_response prompt_tokens = completion_response.get("usage", {}).get("prompt_tokens", 0) completion_tokens = completion_response.get("usage", {}).get( "completion_tokens", 0 ) - total_time = completion_response.get("_response_ms", 0) + total_time = getattr(completion_response, "_response_ms", 0) verbose_logger.debug( - f"completion_response response ms: {completion_response.get('_response_ms')} " + f"completion_response response ms: {getattr(completion_response, '_response_ms', None)} " + ) + model = _select_model_name_for_cost_calc( + model=model, completion_response=completion_response ) - model = model or completion_response.get( - "model", None - ) # check if user passed an override for model, if it's none check completion_response['model'] if hasattr(completion_response, "_hidden_params"): - if ( - completion_response._hidden_params.get("model", None) is not None - and len(completion_response._hidden_params["model"]) > 0 - ): - model = completion_response._hidden_params.get("model", model) custom_llm_provider = completion_response._hidden_params.get( - "custom_llm_provider", "" + "custom_llm_provider", custom_llm_provider or "" ) region_name = completion_response._hidden_params.get( "region_name", region_name @@ -624,7 +665,7 @@ def completion_cost( if custom_llm_provider is not None and custom_llm_provider == "vertex_ai": # Calculate the prompt characters + response characters - if len("messages") > 0: + if len(messages) > 0: prompt_string = litellm.utils.get_formatted_prompt( data={"messages": messages}, call_type="completion" ) @@ -659,9 +700,7 @@ def completion_cost( call_type=call_type, ) _final_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar - print_verbose( - f"final cost: {_final_cost}; prompt_tokens_cost_usd_dollar: {prompt_tokens_cost_usd_dollar}; completion_tokens_cost_usd_dollar: {completion_tokens_cost_usd_dollar}" - ) + return _final_cost except Exception as e: raise e @@ -718,9 +757,7 @@ def response_cost_calculator( custom_llm_provider=custom_llm_provider, ) else: - if ( - model in litellm.model_cost or custom_pricing is True - ): # override defaults if custom pricing is set + if custom_pricing is True: # override defaults if custom pricing is set base_model = model # base_model defaults to None if not set on model_info @@ -732,14 +769,21 @@ def response_cost_calculator( ) return response_cost except litellm.NotFoundError as e: - print_verbose( + verbose_logger.debug( # debug since it can be spammy in logs, for calls f"Model={model} for LLM Provider={custom_llm_provider} not found in completion cost map." ) return None except Exception as e: - verbose_logger.error( - "litellm.cost_calculator.py::response_cost_calculator - Exception occurred - {}/n{}".format( - str(e), traceback.format_exc() + if litellm.suppress_debug_info: # allow cli tools to suppress this information. + verbose_logger.debug( + "litellm.cost_calculator.py::response_cost_calculator - Returning None. Exception occurred - {}/n{}".format( + str(e), traceback.format_exc() + ) + ) + else: + verbose_logger.warning( + "litellm.cost_calculator.py::response_cost_calculator - Returning None. Exception occurred - {}/n{}".format( + str(e), traceback.format_exc() + ) ) - ) return None diff --git a/litellm/exceptions.py b/litellm/exceptions.py index d85510b1d..dd9953a32 100644 --- a/litellm/exceptions.py +++ b/litellm/exceptions.py @@ -122,7 +122,7 @@ class BadRequestError(openai.BadRequestError): # type: ignore self.model = model self.llm_provider = llm_provider self.litellm_debug_info = litellm_debug_info - response = response or httpx.Response( + response = httpx.Response( status_code=self.status_code, request=httpx.Request( method="GET", url="https://litellm.ai" @@ -199,8 +199,12 @@ class Timeout(openai.APITimeoutError): # type: ignore litellm_debug_info: Optional[str] = None, max_retries: Optional[int] = None, num_retries: Optional[int] = None, + headers: Optional[dict] = None, ): - request = httpx.Request(method="POST", url="https://api.openai.com/v1") + request = httpx.Request( + method="POST", + url="https://api.openai.com/v1", + ) super().__init__( request=request ) # Call the base class constructor with the parameters it needs @@ -211,6 +215,7 @@ class Timeout(openai.APITimeoutError): # type: ignore self.litellm_debug_info = litellm_debug_info self.max_retries = max_retries self.num_retries = num_retries + self.headers = headers # custom function to convert to str def __str__(self): @@ -287,16 +292,13 @@ class RateLimitError(openai.RateLimitError): # type: ignore self.litellm_debug_info = litellm_debug_info self.max_retries = max_retries self.num_retries = num_retries - if response is None: - self.response = httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ) - else: - self.response = response + self.response = httpx.Response( + status_code=429, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ) super().__init__( self.message, response=self.response, body=None ) # Call the base class constructor with the parameters it needs @@ -334,7 +336,7 @@ class ContextWindowExceededError(BadRequestError): # type: ignore self.llm_provider = llm_provider self.litellm_debug_info = litellm_debug_info request = httpx.Request(method="POST", url="https://api.openai.com/v1") - self.response = response or httpx.Response(status_code=400, request=request) + self.response = httpx.Response(status_code=400, request=request) super().__init__( message=self.message, model=self.model, # type: ignore @@ -377,7 +379,7 @@ class RejectedRequestError(BadRequestError): # type: ignore self.litellm_debug_info = litellm_debug_info self.request_data = request_data request = httpx.Request(method="POST", url="https://api.openai.com/v1") - response = httpx.Response(status_code=500, request=request) + response = httpx.Response(status_code=400, request=request) super().__init__( message=self.message, model=self.model, # type: ignore @@ -419,7 +421,7 @@ class ContentPolicyViolationError(BadRequestError): # type: ignore self.llm_provider = llm_provider self.litellm_debug_info = litellm_debug_info request = httpx.Request(method="POST", url="https://api.openai.com/v1") - self.response = response or httpx.Response(status_code=500, request=request) + self.response = httpx.Response(status_code=400, request=request) super().__init__( message=self.message, model=self.model, # type: ignore @@ -463,16 +465,13 @@ class ServiceUnavailableError(openai.APIStatusError): # type: ignore self.litellm_debug_info = litellm_debug_info self.max_retries = max_retries self.num_retries = num_retries - if response is None: - self.response = httpx.Response( - status_code=self.status_code, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ) - else: - self.response = response + self.response = httpx.Response( + status_code=self.status_code, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ) super().__init__( self.message, response=self.response, body=None ) # Call the base class constructor with the parameters it needs @@ -512,16 +511,13 @@ class InternalServerError(openai.InternalServerError): # type: ignore self.litellm_debug_info = litellm_debug_info self.max_retries = max_retries self.num_retries = num_retries - if response is None: - self.response = httpx.Response( - status_code=self.status_code, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ) - else: - self.response = response + self.response = httpx.Response( + status_code=self.status_code, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ) super().__init__( self.message, response=self.response, body=None ) # Call the base class constructor with the parameters it needs @@ -547,7 +543,7 @@ class InternalServerError(openai.InternalServerError): # type: ignore class APIError(openai.APIError): # type: ignore def __init__( self, - status_code, + status_code: int, message, llm_provider, model, @@ -591,7 +587,7 @@ class APIConnectionError(openai.APIConnectionError): # type: ignore message, llm_provider, model, - request: httpx.Request, + request: Optional[httpx.Request] = None, litellm_debug_info: Optional[str] = None, max_retries: Optional[int] = None, num_retries: Optional[int] = None, @@ -601,9 +597,10 @@ class APIConnectionError(openai.APIConnectionError): # type: ignore self.model = model self.status_code = 500 self.litellm_debug_info = litellm_debug_info + self.request = httpx.Request(method="POST", url="https://api.openai.com/v1") self.max_retries = max_retries self.num_retries = num_retries - super().__init__(message=self.message, request=request) + super().__init__(message=self.message, request=self.request) def __str__(self): _message = self.message @@ -682,11 +679,39 @@ class JSONSchemaValidationError(APIError): ) +class UnsupportedParamsError(BadRequestError): + def __init__( + self, + message, + llm_provider: Optional[str] = None, + model: Optional[str] = None, + status_code: int = 400, + response: Optional[httpx.Response] = None, + litellm_debug_info: Optional[str] = None, + max_retries: Optional[int] = None, + num_retries: Optional[int] = None, + ): + self.status_code = 400 + self.message = "litellm.UnsupportedParamsError: {}".format(message) + self.model = model + self.llm_provider = llm_provider + self.litellm_debug_info = litellm_debug_info + response = response or httpx.Response( + status_code=self.status_code, + request=httpx.Request( + method="GET", url="https://litellm.ai" + ), # mock request object + ) + self.max_retries = max_retries + self.num_retries = num_retries + + LITELLM_EXCEPTION_TYPES = [ AuthenticationError, NotFoundError, BadRequestError, UnprocessableEntityError, + UnsupportedParamsError, Timeout, PermissionDeniedError, RateLimitError, @@ -705,10 +730,15 @@ LITELLM_EXCEPTION_TYPES = [ class BudgetExceededError(Exception): - def __init__(self, current_cost, max_budget): + def __init__( + self, current_cost: float, max_budget: float, message: Optional[str] = None + ): self.current_cost = current_cost self.max_budget = max_budget - message = f"Budget has been exceeded! Current cost: {current_cost}, Max budget: {max_budget}" + message = ( + message + or f"Budget has been exceeded! Current cost: {current_cost}, Max budget: {max_budget}" + ) self.message = message super().__init__(message) @@ -723,3 +753,28 @@ class InvalidRequestError(openai.BadRequestError): # type: ignore super().__init__( self.message, f"{self.model}" ) # Call the base class constructor with the parameters it needs + + +class MockException(openai.APIError): + # used for testing + def __init__( + self, + status_code: int, + message, + llm_provider, + model, + request: Optional[httpx.Request] = None, + litellm_debug_info: Optional[str] = None, + max_retries: Optional[int] = None, + num_retries: Optional[int] = None, + ): + self.status_code = status_code + self.message = "litellm.MockException: {}".format(message) + self.llm_provider = llm_provider + self.model = model + self.litellm_debug_info = litellm_debug_info + self.max_retries = max_retries + self.num_retries = num_retries + if request is None: + request = httpx.Request(method="POST", url="https://api.openai.com/v1") + super().__init__(self.message, request=request, body=None) # type: ignore diff --git a/litellm/files/main.py b/litellm/files/main.py index 598bc4878..49d355398 100644 --- a/litellm/files/main.py +++ b/litellm/files/main.py @@ -14,7 +14,8 @@ from typing import Any, Coroutine, Dict, Literal, Optional, Union import httpx import litellm -from litellm import client +from litellm import client, get_secret +from litellm.llms.files_apis.azure import AzureOpenAIFilesAPI from litellm.llms.openai import FileDeleted, FileObject, OpenAIFilesAPI from litellm.types.llms.openai import ( Batch, @@ -28,12 +29,13 @@ from litellm.utils import supports_httpx_timeout ####### ENVIRONMENT VARIABLES ################### openai_files_instance = OpenAIFilesAPI() +azure_files_instance = AzureOpenAIFilesAPI() ################################################# async def afile_retrieve( file_id: str, - custom_llm_provider: Literal["openai"] = "openai", + custom_llm_provider: Literal["openai", "azure"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -73,7 +75,7 @@ async def afile_retrieve( def file_retrieve( file_id: str, - custom_llm_provider: Literal["openai"] = "openai", + custom_llm_provider: Literal["openai", "azure"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -156,7 +158,7 @@ def file_retrieve( # Delete file async def afile_delete( file_id: str, - custom_llm_provider: Literal["openai"] = "openai", + custom_llm_provider: Literal["openai", "azure"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -196,7 +198,7 @@ async def afile_delete( def file_delete( file_id: str, - custom_llm_provider: Literal["openai"] = "openai", + custom_llm_provider: Literal["openai", "azure"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -208,6 +210,22 @@ def file_delete( """ try: optional_params = GenericLiteLLMParams(**kwargs) + ### TIMEOUT LOGIC ### + timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 + # set timeout for 10 minutes by default + + if ( + timeout is not None + and isinstance(timeout, httpx.Timeout) + and supports_httpx_timeout(custom_llm_provider) == False + ): + read_timeout = timeout.read or 600 + timeout = read_timeout # default 10 min timeout + elif timeout is not None and not isinstance(timeout, httpx.Timeout): + timeout = float(timeout) # type: ignore + elif timeout is None: + timeout = 600.0 + _is_async = kwargs.pop("is_async", False) is True if custom_llm_provider == "openai": # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( @@ -229,26 +247,6 @@ def file_delete( or litellm.openai_key or os.getenv("OPENAI_API_KEY") ) - ### TIMEOUT LOGIC ### - timeout = ( - optional_params.timeout or kwargs.get("request_timeout", 600) or 600 - ) - # set timeout for 10 minutes by default - - if ( - timeout is not None - and isinstance(timeout, httpx.Timeout) - and supports_httpx_timeout(custom_llm_provider) == False - ): - read_timeout = timeout.read or 600 - timeout = read_timeout # default 10 min timeout - elif timeout is not None and not isinstance(timeout, httpx.Timeout): - timeout = float(timeout) # type: ignore - elif timeout is None: - timeout = 600.0 - - _is_async = kwargs.pop("is_async", False) is True - response = openai_files_instance.delete_file( file_id=file_id, _is_async=_is_async, @@ -258,6 +256,38 @@ def file_delete( max_retries=optional_params.max_retries, organization=organization, ) + elif custom_llm_provider == "azure": + api_base = optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE") # type: ignore + api_version = ( + optional_params.api_version + or litellm.api_version + or get_secret("AZURE_API_VERSION") + ) # type: ignore + + api_key = ( + optional_params.api_key + or litellm.api_key + or litellm.azure_key + or get_secret("AZURE_OPENAI_API_KEY") + or get_secret("AZURE_API_KEY") + ) # type: ignore + + extra_body = optional_params.get("extra_body", {}) + azure_ad_token: Optional[str] = None + if extra_body is not None: + azure_ad_token = extra_body.pop("azure_ad_token", None) + else: + azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore + + response = azure_files_instance.delete_file( + _is_async=_is_async, + api_base=api_base, + api_key=api_key, + api_version=api_version, + timeout=timeout, + max_retries=optional_params.max_retries, + file_id=file_id, + ) else: raise litellm.exceptions.BadRequestError( message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( @@ -278,7 +308,7 @@ def file_delete( # List files async def afile_list( - custom_llm_provider: Literal["openai"] = "openai", + custom_llm_provider: Literal["openai", "azure"] = "openai", purpose: Optional[str] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -318,7 +348,7 @@ async def afile_list( def file_list( - custom_llm_provider: Literal["openai"] = "openai", + custom_llm_provider: Literal["openai", "azure"] = "openai", purpose: Optional[str] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -402,11 +432,11 @@ def file_list( async def acreate_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], - custom_llm_provider: Literal["openai"] = "openai", + custom_llm_provider: Literal["openai", "azure"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, -) -> Coroutine[Any, Any, FileObject]: +) -> FileObject: """ Async: Files are used to upload documents that can be used with features like Assistants, Fine-tuning, and Batch API. @@ -444,7 +474,7 @@ async def acreate_file( def create_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], - custom_llm_provider: Literal["openai"] = "openai", + custom_llm_provider: Literal["openai", "azure"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -455,7 +485,31 @@ def create_file( LiteLLM Equivalent of POST: POST https://api.openai.com/v1/files """ try: + _is_async = kwargs.pop("acreate_file", False) is True optional_params = GenericLiteLLMParams(**kwargs) + + ### TIMEOUT LOGIC ### + timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 + # set timeout for 10 minutes by default + + if ( + timeout is not None + and isinstance(timeout, httpx.Timeout) + and supports_httpx_timeout(custom_llm_provider) == False + ): + read_timeout = timeout.read or 600 + timeout = read_timeout # default 10 min timeout + elif timeout is not None and not isinstance(timeout, httpx.Timeout): + timeout = float(timeout) # type: ignore + elif timeout is None: + timeout = 600.0 + + _create_file_request = CreateFileRequest( + file=file, + purpose=purpose, + extra_headers=extra_headers, + extra_body=extra_body, + ) if custom_llm_provider == "openai": # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( @@ -477,32 +531,6 @@ def create_file( or litellm.openai_key or os.getenv("OPENAI_API_KEY") ) - ### TIMEOUT LOGIC ### - timeout = ( - optional_params.timeout or kwargs.get("request_timeout", 600) or 600 - ) - # set timeout for 10 minutes by default - - if ( - timeout is not None - and isinstance(timeout, httpx.Timeout) - and supports_httpx_timeout(custom_llm_provider) == False - ): - read_timeout = timeout.read or 600 - timeout = read_timeout # default 10 min timeout - elif timeout is not None and not isinstance(timeout, httpx.Timeout): - timeout = float(timeout) # type: ignore - elif timeout is None: - timeout = 600.0 - - _create_file_request = CreateFileRequest( - file=file, - purpose=purpose, - extra_headers=extra_headers, - extra_body=extra_body, - ) - - _is_async = kwargs.pop("acreate_file", False) is True response = openai_files_instance.create_file( _is_async=_is_async, @@ -513,6 +541,38 @@ def create_file( organization=organization, create_file_data=_create_file_request, ) + elif custom_llm_provider == "azure": + api_base = optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE") # type: ignore + api_version = ( + optional_params.api_version + or litellm.api_version + or get_secret("AZURE_API_VERSION") + ) # type: ignore + + api_key = ( + optional_params.api_key + or litellm.api_key + or litellm.azure_key + or get_secret("AZURE_OPENAI_API_KEY") + or get_secret("AZURE_API_KEY") + ) # type: ignore + + extra_body = optional_params.get("extra_body", {}) + azure_ad_token: Optional[str] = None + if extra_body is not None: + azure_ad_token = extra_body.pop("azure_ad_token", None) + else: + azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore + + response = azure_files_instance.create_file( + _is_async=_is_async, + api_base=api_base, + api_key=api_key, + api_version=api_version, + timeout=timeout, + max_retries=optional_params.max_retries, + create_file_data=_create_file_request, + ) else: raise litellm.exceptions.BadRequestError( message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( @@ -533,11 +593,11 @@ def create_file( async def afile_content( file_id: str, - custom_llm_provider: Literal["openai"] = "openai", + custom_llm_provider: Literal["openai", "azure"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, -) -> Coroutine[Any, Any, HttpxBinaryResponseContent]: +) -> HttpxBinaryResponseContent: """ Async: Get file contents @@ -573,7 +633,7 @@ async def afile_content( def file_content( file_id: str, - custom_llm_provider: Literal["openai"] = "openai", + custom_llm_provider: Literal["openai", "azure"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, diff --git a/litellm/fine_tuning/main.py b/litellm/fine_tuning/main.py new file mode 100644 index 000000000..abf282857 --- /dev/null +++ b/litellm/fine_tuning/main.py @@ -0,0 +1,593 @@ +""" +Main File for Fine Tuning API implementation + +https://platform.openai.com/docs/api-reference/fine-tuning + +- fine_tuning.jobs.create() +- fine_tuning.jobs.list() +- client.fine_tuning.jobs.list_events() +""" + +import asyncio +import contextvars +import os +from functools import partial +from typing import Any, Coroutine, Dict, Literal, Optional, Union + +import httpx + +import litellm +from litellm import get_secret +from litellm._logging import verbose_logger +from litellm.llms.fine_tuning_apis.azure import AzureOpenAIFineTuningAPI +from litellm.llms.fine_tuning_apis.openai import ( + FineTuningJob, + FineTuningJobCreate, + OpenAIFineTuningAPI, +) +from litellm.llms.fine_tuning_apis.vertex_ai import VertexFineTuningAPI +from litellm.types.llms.openai import Hyperparameters +from litellm.types.router import * +from litellm.utils import supports_httpx_timeout + +####### ENVIRONMENT VARIABLES ################### +openai_fine_tuning_apis_instance = OpenAIFineTuningAPI() +azure_fine_tuning_apis_instance = AzureOpenAIFineTuningAPI() +vertex_fine_tuning_apis_instance = VertexFineTuningAPI() +################################################# + + +async def acreate_fine_tuning_job( + model: str, + training_file: str, + hyperparameters: Optional[Hyperparameters] = {}, # type: ignore + suffix: Optional[str] = None, + validation_file: Optional[str] = None, + integrations: Optional[List[str]] = None, + seed: Optional[int] = None, + custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + extra_headers: Optional[Dict[str, str]] = None, + extra_body: Optional[Dict[str, str]] = None, + **kwargs, +) -> FineTuningJob: + """ + Async: Creates and executes a batch from an uploaded file of request + + """ + verbose_logger.debug( + "inside acreate_fine_tuning_job model=%s and kwargs=%s", model, kwargs + ) + try: + loop = asyncio.get_event_loop() + kwargs["acreate_fine_tuning_job"] = True + + # Use a partial function to pass your keyword arguments + func = partial( + create_fine_tuning_job, + model, + training_file, + hyperparameters, + suffix, + validation_file, + integrations, + seed, + custom_llm_provider, + extra_headers, + extra_body, + **kwargs, + ) + + # Add the context to the function + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response # type: ignore + return response + except Exception as e: + raise e + + +def create_fine_tuning_job( + model: str, + training_file: str, + hyperparameters: Optional[Hyperparameters] = {}, # type: ignore + suffix: Optional[str] = None, + validation_file: Optional[str] = None, + integrations: Optional[List[str]] = None, + seed: Optional[int] = None, + custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + extra_headers: Optional[Dict[str, str]] = None, + extra_body: Optional[Dict[str, str]] = None, + **kwargs, +) -> Union[FineTuningJob, Coroutine[Any, Any, FineTuningJob]]: + """ + Creates a fine-tuning job which begins the process of creating a new model from a given dataset. + + Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete + + """ + try: + _is_async = kwargs.pop("acreate_fine_tuning_job", False) is True + optional_params = GenericLiteLLMParams(**kwargs) + ### TIMEOUT LOGIC ### + timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 + # set timeout for 10 minutes by default + + if ( + timeout is not None + and isinstance(timeout, httpx.Timeout) + and supports_httpx_timeout(custom_llm_provider) == False + ): + read_timeout = timeout.read or 600 + timeout = read_timeout # default 10 min timeout + elif timeout is not None and not isinstance(timeout, httpx.Timeout): + timeout = float(timeout) # type: ignore + elif timeout is None: + timeout = 600.0 + + # OpenAI + if custom_llm_provider == "openai": + + # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there + api_base = ( + optional_params.api_base + or litellm.api_base + or os.getenv("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + organization = ( + optional_params.organization + or litellm.organization + or os.getenv("OPENAI_ORGANIZATION", None) + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + # set API KEY + api_key = ( + optional_params.api_key + or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there + or litellm.openai_key + or os.getenv("OPENAI_API_KEY") + ) + + create_fine_tuning_job_data = FineTuningJobCreate( + model=model, + training_file=training_file, + hyperparameters=hyperparameters, + suffix=suffix, + validation_file=validation_file, + integrations=integrations, + seed=seed, + ) + + create_fine_tuning_job_data_dict = create_fine_tuning_job_data.model_dump( + exclude_none=True + ) + + response = openai_fine_tuning_apis_instance.create_fine_tuning_job( + api_base=api_base, + api_key=api_key, + organization=organization, + create_fine_tuning_job_data=create_fine_tuning_job_data_dict, + timeout=timeout, + max_retries=optional_params.max_retries, + _is_async=_is_async, + ) + # Azure OpenAI + elif custom_llm_provider == "azure": + api_base = optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE") # type: ignore + + api_version = ( + optional_params.api_version + or litellm.api_version + or get_secret("AZURE_API_VERSION") + ) # type: ignore + + api_key = ( + optional_params.api_key + or litellm.api_key + or litellm.azure_key + or get_secret("AZURE_OPENAI_API_KEY") + or get_secret("AZURE_API_KEY") + ) # type: ignore + + extra_body = optional_params.get("extra_body", {}) + azure_ad_token: Optional[str] = None + if extra_body is not None: + azure_ad_token = extra_body.pop("azure_ad_token", None) + else: + azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore + + create_fine_tuning_job_data = FineTuningJobCreate( + model=model, + training_file=training_file, + hyperparameters=hyperparameters, + suffix=suffix, + validation_file=validation_file, + integrations=integrations, + seed=seed, + ) + + create_fine_tuning_job_data_dict = create_fine_tuning_job_data.model_dump( + exclude_none=True + ) + + response = azure_fine_tuning_apis_instance.create_fine_tuning_job( + api_base=api_base, + api_key=api_key, + api_version=api_version, + create_fine_tuning_job_data=create_fine_tuning_job_data_dict, + timeout=timeout, + max_retries=optional_params.max_retries, + _is_async=_is_async, + ) + elif custom_llm_provider == "vertex_ai": + api_base = optional_params.api_base or "" + vertex_ai_project = ( + optional_params.vertex_project + or litellm.vertex_project + or get_secret("VERTEXAI_PROJECT") + ) + vertex_ai_location = ( + optional_params.vertex_location + or litellm.vertex_location + or get_secret("VERTEXAI_LOCATION") + ) + vertex_credentials = optional_params.vertex_credentials or get_secret( + "VERTEXAI_CREDENTIALS" + ) + create_fine_tuning_job_data = FineTuningJobCreate( + model=model, + training_file=training_file, + hyperparameters=hyperparameters, + suffix=suffix, + validation_file=validation_file, + integrations=integrations, + seed=seed, + ) + response = vertex_fine_tuning_apis_instance.create_fine_tuning_job( + _is_async=_is_async, + create_fine_tuning_job_data=create_fine_tuning_job_data, + vertex_credentials=vertex_credentials, + vertex_project=vertex_ai_project, + vertex_location=vertex_ai_location, + timeout=timeout, + api_base=api_base, + kwargs=kwargs, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( + custom_llm_provider + ), + model="n/a", + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=400, + content="Unsupported provider", + request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + ) + return response + except Exception as e: + verbose_logger.error("got exception in create_fine_tuning_job=%s", str(e)) + raise e + + +async def acancel_fine_tuning_job( + fine_tuning_job_id: str, + custom_llm_provider: Literal["openai"] = "openai", + extra_headers: Optional[Dict[str, str]] = None, + extra_body: Optional[Dict[str, str]] = None, + **kwargs, +) -> FineTuningJob: + """ + Async: Immediately cancel a fine-tune job. + """ + try: + loop = asyncio.get_event_loop() + kwargs["acancel_fine_tuning_job"] = True + + # Use a partial function to pass your keyword arguments + func = partial( + cancel_fine_tuning_job, + fine_tuning_job_id, + custom_llm_provider, + extra_headers, + extra_body, + **kwargs, + ) + + # Add the context to the function + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response # type: ignore + return response + except Exception as e: + raise e + + +def cancel_fine_tuning_job( + fine_tuning_job_id: str, + custom_llm_provider: Literal["openai"] = "openai", + extra_headers: Optional[Dict[str, str]] = None, + extra_body: Optional[Dict[str, str]] = None, + **kwargs, +) -> Union[FineTuningJob, Coroutine[Any, Any, FineTuningJob]]: + """ + Immediately cancel a fine-tune job. + + Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete + + """ + try: + optional_params = GenericLiteLLMParams(**kwargs) + ### TIMEOUT LOGIC ### + timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 + # set timeout for 10 minutes by default + + if ( + timeout is not None + and isinstance(timeout, httpx.Timeout) + and supports_httpx_timeout(custom_llm_provider) == False + ): + read_timeout = timeout.read or 600 + timeout = read_timeout # default 10 min timeout + elif timeout is not None and not isinstance(timeout, httpx.Timeout): + timeout = float(timeout) # type: ignore + elif timeout is None: + timeout = 600.0 + + _is_async = kwargs.pop("acancel_fine_tuning_job", False) is True + + # OpenAI + if custom_llm_provider == "openai": + + # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there + api_base = ( + optional_params.api_base + or litellm.api_base + or os.getenv("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + organization = ( + optional_params.organization + or litellm.organization + or os.getenv("OPENAI_ORGANIZATION", None) + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + # set API KEY + api_key = ( + optional_params.api_key + or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there + or litellm.openai_key + or os.getenv("OPENAI_API_KEY") + ) + + response = openai_fine_tuning_apis_instance.cancel_fine_tuning_job( + api_base=api_base, + api_key=api_key, + organization=organization, + fine_tuning_job_id=fine_tuning_job_id, + timeout=timeout, + max_retries=optional_params.max_retries, + _is_async=_is_async, + ) + # Azure OpenAI + elif custom_llm_provider == "azure": + api_base = optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE") # type: ignore + + api_version = ( + optional_params.api_version + or litellm.api_version + or get_secret("AZURE_API_VERSION") + ) # type: ignore + + api_key = ( + optional_params.api_key + or litellm.api_key + or litellm.azure_key + or get_secret("AZURE_OPENAI_API_KEY") + or get_secret("AZURE_API_KEY") + ) # type: ignore + + extra_body = optional_params.get("extra_body", {}) + azure_ad_token: Optional[str] = None + if extra_body is not None: + azure_ad_token = extra_body.pop("azure_ad_token", None) + else: + azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore + + response = azure_fine_tuning_apis_instance.cancel_fine_tuning_job( + api_base=api_base, + api_key=api_key, + api_version=api_version, + fine_tuning_job_id=fine_tuning_job_id, + timeout=timeout, + max_retries=optional_params.max_retries, + _is_async=_is_async, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( + custom_llm_provider + ), + model="n/a", + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=400, + content="Unsupported provider", + request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + ) + return response + except Exception as e: + raise e + + +async def alist_fine_tuning_jobs( + after: Optional[str] = None, + limit: Optional[int] = None, + custom_llm_provider: Literal["openai"] = "openai", + extra_headers: Optional[Dict[str, str]] = None, + extra_body: Optional[Dict[str, str]] = None, + **kwargs, +) -> FineTuningJob: + """ + Async: List your organization's fine-tuning jobs + """ + try: + loop = asyncio.get_event_loop() + kwargs["alist_fine_tuning_jobs"] = True + + # Use a partial function to pass your keyword arguments + func = partial( + list_fine_tuning_jobs, + after, + limit, + custom_llm_provider, + extra_headers, + extra_body, + **kwargs, + ) + + # Add the context to the function + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response # type: ignore + return response + except Exception as e: + raise e + + +def list_fine_tuning_jobs( + after: Optional[str] = None, + limit: Optional[int] = None, + custom_llm_provider: Literal["openai"] = "openai", + extra_headers: Optional[Dict[str, str]] = None, + extra_body: Optional[Dict[str, str]] = None, + **kwargs, +): + """ + List your organization's fine-tuning jobs + + Params: + + - after: Optional[str] = None, Identifier for the last job from the previous pagination request. + - limit: Optional[int] = None, Number of fine-tuning jobs to retrieve. Defaults to 20 + """ + try: + optional_params = GenericLiteLLMParams(**kwargs) + ### TIMEOUT LOGIC ### + timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 + # set timeout for 10 minutes by default + + if ( + timeout is not None + and isinstance(timeout, httpx.Timeout) + and supports_httpx_timeout(custom_llm_provider) == False + ): + read_timeout = timeout.read or 600 + timeout = read_timeout # default 10 min timeout + elif timeout is not None and not isinstance(timeout, httpx.Timeout): + timeout = float(timeout) # type: ignore + elif timeout is None: + timeout = 600.0 + + _is_async = kwargs.pop("alist_fine_tuning_jobs", False) is True + + # OpenAI + if custom_llm_provider == "openai": + + # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there + api_base = ( + optional_params.api_base + or litellm.api_base + or os.getenv("OPENAI_API_BASE") + or "https://api.openai.com/v1" + ) + organization = ( + optional_params.organization + or litellm.organization + or os.getenv("OPENAI_ORGANIZATION", None) + or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105 + ) + # set API KEY + api_key = ( + optional_params.api_key + or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there + or litellm.openai_key + or os.getenv("OPENAI_API_KEY") + ) + + response = openai_fine_tuning_apis_instance.list_fine_tuning_jobs( + api_base=api_base, + api_key=api_key, + organization=organization, + after=after, + limit=limit, + timeout=timeout, + max_retries=optional_params.max_retries, + _is_async=_is_async, + ) + # Azure OpenAI + elif custom_llm_provider == "azure": + api_base = optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE") # type: ignore + + api_version = ( + optional_params.api_version + or litellm.api_version + or get_secret("AZURE_API_VERSION") + ) # type: ignore + + api_key = ( + optional_params.api_key + or litellm.api_key + or litellm.azure_key + or get_secret("AZURE_OPENAI_API_KEY") + or get_secret("AZURE_API_KEY") + ) # type: ignore + + extra_body = optional_params.get("extra_body", {}) + azure_ad_token: Optional[str] = None + if extra_body is not None: + azure_ad_token = extra_body.pop("azure_ad_token", None) + else: + azure_ad_token = get_secret("AZURE_AD_TOKEN") # type: ignore + + response = azure_fine_tuning_apis_instance.list_fine_tuning_jobs( + api_base=api_base, + api_key=api_key, + api_version=api_version, + after=after, + limit=limit, + timeout=timeout, + max_retries=optional_params.max_retries, + _is_async=_is_async, + ) + else: + raise litellm.exceptions.BadRequestError( + message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format( + custom_llm_provider + ), + model="n/a", + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=400, + content="Unsupported provider", + request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + ) + return response + except Exception as e: + raise e diff --git a/litellm/integrations/_types/open_inference.py b/litellm/integrations/_types/open_inference.py new file mode 100644 index 000000000..bcfabe9b7 --- /dev/null +++ b/litellm/integrations/_types/open_inference.py @@ -0,0 +1,286 @@ +from enum import Enum + + +class SpanAttributes: + OUTPUT_VALUE = "output.value" + OUTPUT_MIME_TYPE = "output.mime_type" + """ + The type of output.value. If unspecified, the type is plain text by default. + If type is JSON, the value is a string representing a JSON object. + """ + INPUT_VALUE = "input.value" + INPUT_MIME_TYPE = "input.mime_type" + """ + The type of input.value. If unspecified, the type is plain text by default. + If type is JSON, the value is a string representing a JSON object. + """ + + EMBEDDING_EMBEDDINGS = "embedding.embeddings" + """ + A list of objects containing embedding data, including the vector and represented piece of text. + """ + EMBEDDING_MODEL_NAME = "embedding.model_name" + """ + The name of the embedding model. + """ + + LLM_FUNCTION_CALL = "llm.function_call" + """ + For models and APIs that support function calling. Records attributes such as the function + name and arguments to the called function. + """ + LLM_INVOCATION_PARAMETERS = "llm.invocation_parameters" + """ + Invocation parameters passed to the LLM or API, such as the model name, temperature, etc. + """ + LLM_INPUT_MESSAGES = "llm.input_messages" + """ + Messages provided to a chat API. + """ + LLM_OUTPUT_MESSAGES = "llm.output_messages" + """ + Messages received from a chat API. + """ + LLM_MODEL_NAME = "llm.model_name" + """ + The name of the model being used. + """ + LLM_PROMPTS = "llm.prompts" + """ + Prompts provided to a completions API. + """ + LLM_PROMPT_TEMPLATE = "llm.prompt_template.template" + """ + The prompt template as a Python f-string. + """ + LLM_PROMPT_TEMPLATE_VARIABLES = "llm.prompt_template.variables" + """ + A list of input variables to the prompt template. + """ + LLM_PROMPT_TEMPLATE_VERSION = "llm.prompt_template.version" + """ + The version of the prompt template being used. + """ + LLM_TOKEN_COUNT_PROMPT = "llm.token_count.prompt" + """ + Number of tokens in the prompt. + """ + LLM_TOKEN_COUNT_COMPLETION = "llm.token_count.completion" + """ + Number of tokens in the completion. + """ + LLM_TOKEN_COUNT_TOTAL = "llm.token_count.total" + """ + Total number of tokens, including both prompt and completion. + """ + + TOOL_NAME = "tool.name" + """ + Name of the tool being used. + """ + TOOL_DESCRIPTION = "tool.description" + """ + Description of the tool's purpose, typically used to select the tool. + """ + TOOL_PARAMETERS = "tool.parameters" + """ + Parameters of the tool represented a dictionary JSON string, e.g. + see https://platform.openai.com/docs/guides/gpt/function-calling + """ + + RETRIEVAL_DOCUMENTS = "retrieval.documents" + + METADATA = "metadata" + """ + Metadata attributes are used to store user-defined key-value pairs. + For example, LangChain uses metadata to store user-defined attributes for a chain. + """ + + TAG_TAGS = "tag.tags" + """ + Custom categorical tags for the span. + """ + + OPENINFERENCE_SPAN_KIND = "openinference.span.kind" + + SESSION_ID = "session.id" + """ + The id of the session + """ + USER_ID = "user.id" + """ + The id of the user + """ + + +class MessageAttributes: + """ + Attributes for a message sent to or from an LLM + """ + + MESSAGE_ROLE = "message.role" + """ + The role of the message, such as "user", "agent", "function". + """ + MESSAGE_CONTENT = "message.content" + """ + The content of the message to or from the llm, must be a string. + """ + MESSAGE_CONTENTS = "message.contents" + """ + The message contents to the llm, it is an array of + `message_content` prefixed attributes. + """ + MESSAGE_NAME = "message.name" + """ + The name of the message, often used to identify the function + that was used to generate the message. + """ + MESSAGE_TOOL_CALLS = "message.tool_calls" + """ + The tool calls generated by the model, such as function calls. + """ + MESSAGE_FUNCTION_CALL_NAME = "message.function_call_name" + """ + The function name that is a part of the message list. + This is populated for role 'function' or 'agent' as a mechanism to identify + the function that was called during the execution of a tool. + """ + MESSAGE_FUNCTION_CALL_ARGUMENTS_JSON = "message.function_call_arguments_json" + """ + The JSON string representing the arguments passed to the function + during a function call. + """ + + +class MessageContentAttributes: + """ + Attributes for the contents of user messages sent to an LLM. + """ + + MESSAGE_CONTENT_TYPE = "message_content.type" + """ + The type of the content, such as "text" or "image". + """ + MESSAGE_CONTENT_TEXT = "message_content.text" + """ + The text content of the message, if the type is "text". + """ + MESSAGE_CONTENT_IMAGE = "message_content.image" + """ + The image content of the message, if the type is "image". + An image can be made available to the model by passing a link to + the image or by passing the base64 encoded image directly in the + request. + """ + + +class ImageAttributes: + """ + Attributes for images + """ + + IMAGE_URL = "image.url" + """ + An http or base64 image url + """ + + +class DocumentAttributes: + """ + Attributes for a document. + """ + + DOCUMENT_ID = "document.id" + """ + The id of the document. + """ + DOCUMENT_SCORE = "document.score" + """ + The score of the document + """ + DOCUMENT_CONTENT = "document.content" + """ + The content of the document. + """ + DOCUMENT_METADATA = "document.metadata" + """ + The metadata of the document represented as a dictionary + JSON string, e.g. `"{ 'title': 'foo' }"` + """ + + +class RerankerAttributes: + """ + Attributes for a reranker + """ + + RERANKER_INPUT_DOCUMENTS = "reranker.input_documents" + """ + List of documents as input to the reranker + """ + RERANKER_OUTPUT_DOCUMENTS = "reranker.output_documents" + """ + List of documents as output from the reranker + """ + RERANKER_QUERY = "reranker.query" + """ + Query string for the reranker + """ + RERANKER_MODEL_NAME = "reranker.model_name" + """ + Model name of the reranker + """ + RERANKER_TOP_K = "reranker.top_k" + """ + Top K parameter of the reranker + """ + + +class EmbeddingAttributes: + """ + Attributes for an embedding + """ + + EMBEDDING_TEXT = "embedding.text" + """ + The text represented by the embedding. + """ + EMBEDDING_VECTOR = "embedding.vector" + """ + The embedding vector. + """ + + +class ToolCallAttributes: + """ + Attributes for a tool call + """ + + TOOL_CALL_FUNCTION_NAME = "tool_call.function.name" + """ + The name of function that is being called during a tool call. + """ + TOOL_CALL_FUNCTION_ARGUMENTS_JSON = "tool_call.function.arguments" + """ + The JSON string representing the arguments passed to the function + during a tool call. + """ + + +class OpenInferenceSpanKindValues(Enum): + TOOL = "TOOL" + CHAIN = "CHAIN" + LLM = "LLM" + RETRIEVER = "RETRIEVER" + EMBEDDING = "EMBEDDING" + AGENT = "AGENT" + RERANKER = "RERANKER" + UNKNOWN = "UNKNOWN" + GUARDRAIL = "GUARDRAIL" + EVALUATOR = "EVALUATOR" + + +class OpenInferenceMimeTypeValues(Enum): + TEXT = "text/plain" + JSON = "application/json" diff --git a/litellm/integrations/arize_ai.py b/litellm/integrations/arize_ai.py new file mode 100644 index 000000000..45c6c1604 --- /dev/null +++ b/litellm/integrations/arize_ai.py @@ -0,0 +1,114 @@ +""" +arize AI is OTEL compatible + +this file has Arize ai specific helper functions +""" + +from typing import TYPE_CHECKING, Any, Optional, Union + +if TYPE_CHECKING: + from opentelemetry.trace import Span as _Span + + Span = _Span +else: + Span = Any + + +def set_arize_ai_attributes(span: Span, kwargs, response_obj): + from litellm.integrations._types.open_inference import ( + MessageAttributes, + MessageContentAttributes, + OpenInferenceSpanKindValues, + SpanAttributes, + ) + + optional_params = kwargs.get("optional_params", {}) + litellm_params = kwargs.get("litellm_params", {}) or {} + + ############################################# + ############ LLM CALL METADATA ############## + ############################################# + # commented out for now - looks like Arize AI could not log this + # metadata = litellm_params.get("metadata", {}) or {} + # span.set_attribute(SpanAttributes.METADATA, str(metadata)) + + ############################################# + ########## LLM Request Attributes ########### + ############################################# + + # The name of the LLM a request is being made to + if kwargs.get("model"): + span.set_attribute(SpanAttributes.LLM_MODEL_NAME, kwargs.get("model")) + + span.set_attribute( + SpanAttributes.OPENINFERENCE_SPAN_KIND, OpenInferenceSpanKindValues.LLM.value + ) + messages = kwargs.get("messages") + + # for /chat/completions + # https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions + if messages: + span.set_attribute( + SpanAttributes.INPUT_VALUE, + messages[-1].get("content", ""), # get the last message for input + ) + + # LLM_INPUT_MESSAGES shows up under `input_messages` tab on the span page + for idx, msg in enumerate(messages): + # Set the role per message + span.set_attribute( + f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_ROLE}", + msg["role"], + ) + # Set the content per message + span.set_attribute( + f"{SpanAttributes.LLM_INPUT_MESSAGES}.{idx}.{MessageAttributes.MESSAGE_CONTENT}", + msg.get("content", ""), + ) + + # The Generative AI Provider: Azure, OpenAI, etc. + span.set_attribute(SpanAttributes.LLM_INVOCATION_PARAMETERS, str(optional_params)) + + if optional_params.get("user"): + span.set_attribute(SpanAttributes.USER_ID, optional_params.get("user")) + + ############################################# + ########## LLM Response Attributes ########## + # https://docs.arize.com/arize/large-language-models/tracing/semantic-conventions + ############################################# + for choice in response_obj.get("choices"): + response_message = choice.get("message", {}) + span.set_attribute( + SpanAttributes.OUTPUT_VALUE, response_message.get("content", "") + ) + + # This shows up under `output_messages` tab on the span page + # This code assumes a single response + span.set_attribute( + f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_ROLE}", + response_message["role"], + ) + span.set_attribute( + f"{SpanAttributes.LLM_OUTPUT_MESSAGES}.0.{MessageAttributes.MESSAGE_CONTENT}", + response_message.get("content", ""), + ) + + usage = response_obj.get("usage") + if usage: + span.set_attribute( + SpanAttributes.LLM_TOKEN_COUNT_TOTAL, + usage.get("total_tokens"), + ) + + # The number of tokens used in the LLM response (completion). + span.set_attribute( + SpanAttributes.LLM_TOKEN_COUNT_COMPLETION, + usage.get("completion_tokens"), + ) + + # The number of tokens used in the LLM prompt. + span.set_attribute( + SpanAttributes.LLM_TOKEN_COUNT_PROMPT, + usage.get("prompt_tokens"), + ) + pass diff --git a/litellm/integrations/braintrust_logging.py b/litellm/integrations/braintrust_logging.py new file mode 100644 index 000000000..3e1c429de --- /dev/null +++ b/litellm/integrations/braintrust_logging.py @@ -0,0 +1,369 @@ +# What is this? +## Log success + failure events to Braintrust + +import copy +import json +import os +import threading +import traceback +import uuid +from datetime import datetime +from typing import Literal, Optional + +import dotenv +import httpx +from pydantic import BaseModel + +import litellm +from litellm import verbose_logger +from litellm.integrations.custom_logger import CustomLogger +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.utils import get_formatted_prompt + +global_braintrust_http_handler = AsyncHTTPHandler() +global_braintrust_sync_http_handler = HTTPHandler() +API_BASE = "https://api.braintrustdata.com/v1" + + +def get_utc_datetime(): + import datetime as dt + from datetime import datetime + + if hasattr(dt, "UTC"): + return datetime.now(dt.UTC) # type: ignore + else: + return datetime.utcnow() # type: ignore + + +class BraintrustLogger(CustomLogger): + def __init__( + self, api_key: Optional[str] = None, api_base: Optional[str] = None + ) -> None: + super().__init__() + self.validate_environment(api_key=api_key) + self.api_base = api_base or API_BASE + self.default_project_id = None + self.api_key: str = api_key or os.getenv("BRAINTRUST_API_KEY") # type: ignore + self.headers = { + "Authorization": "Bearer " + self.api_key, + "Content-Type": "application/json", + } + + def validate_environment(self, api_key: Optional[str]): + """ + Expects + BRAINTRUST_API_KEY + + in the environment + """ + missing_keys = [] + if api_key is None and os.getenv("BRAINTRUST_API_KEY", None) is None: + missing_keys.append("BRAINTRUST_API_KEY") + + if len(missing_keys) > 0: + raise Exception("Missing keys={} in environment.".format(missing_keys)) + + @staticmethod + def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict: + """ + Adds metadata from proxy request headers to Langfuse logging if keys start with "langfuse_" + and overwrites litellm_params.metadata if already included. + + For example if you want to append your trace to an existing `trace_id` via header, send + `headers: { ..., langfuse_existing_trace_id: your-existing-trace-id }` via proxy request. + """ + if litellm_params is None: + return metadata + + if litellm_params.get("proxy_server_request") is None: + return metadata + + if metadata is None: + metadata = {} + + proxy_headers = ( + litellm_params.get("proxy_server_request", {}).get("headers", {}) or {} + ) + + for metadata_param_key in proxy_headers: + if metadata_param_key.startswith("braintrust"): + trace_param_key = metadata_param_key.replace("braintrust", "", 1) + if trace_param_key in metadata: + verbose_logger.warning( + f"Overwriting Braintrust `{trace_param_key}` from request header" + ) + else: + verbose_logger.debug( + f"Found Braintrust `{trace_param_key}` in request header" + ) + metadata[trace_param_key] = proxy_headers.get(metadata_param_key) + + return metadata + + async def create_default_project_and_experiment(self): + project = await global_braintrust_http_handler.post( + f"{self.api_base}/project", headers=self.headers, json={"name": "litellm"} + ) + + project_dict = project.json() + + self.default_project_id = project_dict["id"] + + def create_sync_default_project_and_experiment(self): + project = global_braintrust_sync_http_handler.post( + f"{self.api_base}/project", headers=self.headers, json={"name": "litellm"} + ) + + project_dict = project.json() + + self.default_project_id = project_dict["id"] + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + verbose_logger.debug("REACHES BRAINTRUST SUCCESS") + try: + litellm_call_id = kwargs.get("litellm_call_id") + project_id = kwargs.get("project_id", None) + if project_id is None: + if self.default_project_id is None: + self.create_sync_default_project_and_experiment() + project_id = self.default_project_id + + prompt = {"messages": kwargs.get("messages")} + + if response_obj is not None and ( + kwargs.get("call_type", None) == "embedding" + or isinstance(response_obj, litellm.EmbeddingResponse) + ): + input = prompt + output = None + elif response_obj is not None and isinstance( + response_obj, litellm.ModelResponse + ): + input = prompt + output = response_obj["choices"][0]["message"].json() + elif response_obj is not None and isinstance( + response_obj, litellm.TextCompletionResponse + ): + input = prompt + output = response_obj.choices[0].text + elif response_obj is not None and isinstance( + response_obj, litellm.ImageResponse + ): + input = prompt + output = response_obj["data"] + + litellm_params = kwargs.get("litellm_params", {}) + metadata = ( + litellm_params.get("metadata", {}) or {} + ) # if litellm_params['metadata'] == None + metadata = self.add_metadata_from_header(litellm_params, metadata) + clean_metadata = {} + try: + metadata = copy.deepcopy( + metadata + ) # Avoid modifying the original metadata + except: + new_metadata = {} + for key, value in metadata.items(): + if ( + isinstance(value, list) + or isinstance(value, dict) + or isinstance(value, str) + or isinstance(value, int) + or isinstance(value, float) + ): + new_metadata[key] = copy.deepcopy(value) + metadata = new_metadata + + tags = [] + if isinstance(metadata, dict): + for key, value in metadata.items(): + + # generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy + if ( + litellm.langfuse_default_tags is not None + and isinstance(litellm.langfuse_default_tags, list) + and key in litellm.langfuse_default_tags + ): + tags.append(f"{key}:{value}") + + # clean litellm metadata before logging + if key in [ + "headers", + "endpoint", + "caching_groups", + "previous_models", + ]: + continue + else: + clean_metadata[key] = value + + cost = kwargs.get("response_cost", None) + if cost is not None: + clean_metadata["litellm_response_cost"] = cost + + metrics: Optional[dict] = None + if ( + response_obj is not None + and hasattr(response_obj, "usage") + and isinstance(response_obj.usage, litellm.Usage) + ): + generation_id = litellm.utils.get_logging_id(start_time, response_obj) + metrics = { + "prompt_tokens": response_obj.usage.prompt_tokens, + "completion_tokens": response_obj.usage.completion_tokens, + "total_tokens": response_obj.usage.total_tokens, + "total_cost": cost, + } + + request_data = { + "id": litellm_call_id, + "input": prompt, + "output": output, + "metadata": clean_metadata, + "tags": tags, + } + if metrics is not None: + request_data["metrics"] = metrics + + try: + global_braintrust_sync_http_handler.post( + url=f"{self.api_base}/project_logs/{project_id}/insert", + json={"events": [request_data]}, + headers=self.headers, + ) + except httpx.HTTPStatusError as e: + raise Exception(e.response.text) + except Exception as e: + verbose_logger.exception( + "Error logging to braintrust - Exception received - {}".format(str(e)) + ) + raise e + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + verbose_logger.debug("REACHES BRAINTRUST SUCCESS") + try: + litellm_call_id = kwargs.get("litellm_call_id") + project_id = kwargs.get("project_id", None) + if project_id is None: + if self.default_project_id is None: + await self.create_default_project_and_experiment() + project_id = self.default_project_id + + prompt = {"messages": kwargs.get("messages")} + + if response_obj is not None and ( + kwargs.get("call_type", None) == "embedding" + or isinstance(response_obj, litellm.EmbeddingResponse) + ): + input = prompt + output = None + elif response_obj is not None and isinstance( + response_obj, litellm.ModelResponse + ): + input = prompt + output = response_obj["choices"][0]["message"].json() + elif response_obj is not None and isinstance( + response_obj, litellm.TextCompletionResponse + ): + input = prompt + output = response_obj.choices[0].text + elif response_obj is not None and isinstance( + response_obj, litellm.ImageResponse + ): + input = prompt + output = response_obj["data"] + + litellm_params = kwargs.get("litellm_params", {}) + metadata = ( + litellm_params.get("metadata", {}) or {} + ) # if litellm_params['metadata'] == None + metadata = self.add_metadata_from_header(litellm_params, metadata) + clean_metadata = {} + new_metadata = {} + for key, value in metadata.items(): + if ( + isinstance(value, list) + or isinstance(value, str) + or isinstance(value, int) + or isinstance(value, float) + ): + new_metadata[key] = value + elif isinstance(value, BaseModel): + new_metadata[key] = value.model_dump_json() + elif isinstance(value, dict): + for k, v in value.items(): + if isinstance(v, datetime): + value[k] = v.isoformat() + new_metadata[key] = value + + metadata = new_metadata + + tags = [] + if isinstance(metadata, dict): + for key, value in metadata.items(): + + # generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy + if ( + litellm.langfuse_default_tags is not None + and isinstance(litellm.langfuse_default_tags, list) + and key in litellm.langfuse_default_tags + ): + tags.append(f"{key}:{value}") + + # clean litellm metadata before logging + if key in [ + "headers", + "endpoint", + "caching_groups", + "previous_models", + ]: + continue + else: + clean_metadata[key] = value + + cost = kwargs.get("response_cost", None) + if cost is not None: + clean_metadata["litellm_response_cost"] = cost + + metrics: Optional[dict] = None + if ( + response_obj is not None + and hasattr(response_obj, "usage") + and isinstance(response_obj.usage, litellm.Usage) + ): + generation_id = litellm.utils.get_logging_id(start_time, response_obj) + metrics = { + "prompt_tokens": response_obj.usage.prompt_tokens, + "completion_tokens": response_obj.usage.completion_tokens, + "total_tokens": response_obj.usage.total_tokens, + "total_cost": cost, + } + + request_data = { + "id": litellm_call_id, + "input": prompt, + "output": output, + "metadata": clean_metadata, + "tags": tags, + } + + if metrics is not None: + request_data["metrics"] = metrics + + try: + await global_braintrust_http_handler.post( + url=f"{self.api_base}/project_logs/{project_id}/insert", + json={"events": [request_data]}, + headers=self.headers, + ) + except httpx.HTTPStatusError as e: + raise Exception(e.response.text) + except Exception as e: + verbose_logger.exception( + "Error logging to braintrust - Exception received - {}".format(str(e)) + ) + raise e + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + return super().log_failure_event(kwargs, response_obj, start_time, end_time) diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py new file mode 100644 index 000000000..a3ac2ea86 --- /dev/null +++ b/litellm/integrations/custom_guardrail.py @@ -0,0 +1,32 @@ +from typing import Literal + +from litellm._logging import verbose_logger +from litellm.integrations.custom_logger import CustomLogger +from litellm.types.guardrails import GuardrailEventHooks + + +class CustomGuardrail(CustomLogger): + + def __init__(self, guardrail_name: str, event_hook: GuardrailEventHooks, **kwargs): + self.guardrail_name = guardrail_name + self.event_hook: GuardrailEventHooks = event_hook + super().__init__(**kwargs) + + def should_run_guardrail(self, data, event_type: GuardrailEventHooks) -> bool: + verbose_logger.debug( + "inside should_run_guardrail for guardrail=%s event_type= %s guardrail_supported_event_hooks= %s", + self.guardrail_name, + event_type, + self.event_hook, + ) + + metadata = data.get("metadata") or {} + requested_guardrails = metadata.get("guardrails") or [] + + if self.guardrail_name not in requested_guardrails: + return False + + if self.event_hook != event_type: + return False + + return True diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index be0263704..47d28ab56 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -2,7 +2,7 @@ # On success, logs events to Promptlayer import os import traceback -from typing import Literal, Optional, Union +from typing import Any, Literal, Optional, Tuple, Union import dotenv from pydantic import BaseModel @@ -10,7 +10,7 @@ from pydantic import BaseModel from litellm.caching import DualCache from litellm.proxy._types import UserAPIKeyAuth from litellm.types.llms.openai import ChatCompletionRequest -from litellm.types.utils import ModelResponse +from litellm.types.utils import AdapterCompletionStreamWrapper, ModelResponse class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback#callback-class @@ -58,6 +58,13 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac def pre_call_check(self, deployment: dict) -> Optional[dict]: pass + #### Fallback Events - router/proxy only #### + async def log_success_fallback_event(self, original_model_group: str, kwargs: dict): + pass + + async def log_failure_fallback_event(self, original_model_group: str, kwargs: dict): + pass + #### ADAPTERS #### Allow calling 100+ LLMs in custom format - https://github.com/BerriAI/litellm/pulls def translate_completion_input_params( @@ -76,7 +83,9 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac """ pass - def translate_completion_output_params_streaming(self) -> Optional[BaseModel]: + def translate_completion_output_params_streaming( + self, completion_stream: Any + ) -> Optional[AdapterCompletionStreamWrapper]: """ Translates the streaming chunk, from the OpenAI format to the custom format. """ @@ -99,6 +108,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac "image_generation", "moderation", "audio_transcription", + "pass_through_endpoint", ], ) -> Optional[ Union[Exception, str, dict] @@ -112,11 +122,24 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac async def async_post_call_success_hook( self, + data: dict, user_api_key_dict: UserAPIKeyAuth, response, ): pass + async def async_logging_hook( + self, kwargs: dict, result: Any, call_type: str + ) -> Tuple[dict, Any]: + """For masking logged request/response. Return a modified version of the request/result.""" + return kwargs, result + + def logging_hook( + self, kwargs: dict, result: Any, call_type: str + ) -> Tuple[dict, Any]: + """For masking logged request/response. Return a modified version of the request/result.""" + return kwargs, result + async def async_moderation_hook( self, data: dict, diff --git a/litellm/integrations/datadog.py b/litellm/integrations/datadog.py index d835b3d67..f3170e446 100644 --- a/litellm/integrations/datadog.py +++ b/litellm/integrations/datadog.py @@ -1,5 +1,5 @@ #### What this does #### -# On success + failure, log events to Supabase +# On success + failure, log events to Datadog import dotenv, os import requests # type: ignore @@ -9,6 +9,21 @@ import litellm, uuid from litellm._logging import print_verbose, verbose_logger +def make_json_serializable(payload): + for key, value in payload.items(): + try: + if isinstance(value, dict): + # recursively sanitize dicts + payload[key] = make_json_serializable(value.copy()) + elif not isinstance(value, (str, int, float, bool, type(None))): + # everything else becomes a string + payload[key] = str(value) + except: + # non blocking if it can't cast to a str + pass + return payload + + class DataDogLogger: # Class variables or attributes def __init__( @@ -61,7 +76,7 @@ class DataDogLogger: id = response_obj.get("id", str(uuid.uuid4())) usage = dict(usage) try: - response_time = (end_time - start_time).total_seconds() + response_time = (end_time - start_time).total_seconds() * 1000 except: response_time = None @@ -91,12 +106,12 @@ class DataDogLogger: "id": id, "call_type": call_type, "cache_hit": cache_hit, - "startTime": start_time, - "endTime": end_time, - "responseTime (seconds)": response_time, + "start_time": start_time, + "end_time": end_time, + "response_time": response_time, "model": kwargs.get("model", ""), "user": kwargs.get("user", ""), - "modelParameters": optional_params, + "model_parameters": optional_params, "spend": kwargs.get("response_cost", 0), "messages": messages, "response": response_obj, @@ -104,13 +119,7 @@ class DataDogLogger: "metadata": clean_metadata, } - # Ensure everything in the payload is converted to str - for key, value in payload.items(): - try: - payload[key] = str(value) - except: - # non blocking if it can't cast to a str - pass + make_json_serializable(payload) import json payload = json.dumps(payload) diff --git a/litellm/integrations/email_alerting.py b/litellm/integrations/email_alerting.py index 434efb63b..f0ad43535 100644 --- a/litellm/integrations/email_alerting.py +++ b/litellm/integrations/email_alerting.py @@ -2,11 +2,12 @@ Functions for sending Email Alerts """ -import os -from typing import Optional, List -from litellm.proxy._types import WebhookEvent import asyncio +import os +from typing import List, Optional + from litellm._logging import verbose_logger, verbose_proxy_logger +from litellm.proxy._types import WebhookEvent # we use this for the email header, please send a test email if you change this. verify it looks good on email LITELLM_LOGO_URL = "https://litellm-listing.s3.amazonaws.com/litellm_logo.png" @@ -69,9 +70,8 @@ async def send_team_budget_alert(webhook_event: WebhookEvent) -> bool: Send an Email Alert to All Team Members when the Team Budget is crossed Returns -> True if sent, False if not. """ - from litellm.proxy.utils import send_email - from litellm.proxy.proxy_server import premium_user, prisma_client + from litellm.proxy.utils import send_email _team_id = webhook_event.team_id team_alias = webhook_event.team_alias @@ -101,7 +101,7 @@ async def send_team_budget_alert(webhook_event: WebhookEvent) -> bool: email_html_content = "Alert from LiteLLM Server" if recipient_emails_str is None: - verbose_proxy_logger.error( + verbose_proxy_logger.warning( "Email Alerting: Trying to send email alert to no recipient, got recipient_emails=%s", recipient_emails_str, ) diff --git a/litellm/integrations/gcs_bucket.py b/litellm/integrations/gcs_bucket.py new file mode 100644 index 000000000..be7f8e39c --- /dev/null +++ b/litellm/integrations/gcs_bucket.py @@ -0,0 +1,289 @@ +import json +import os +import uuid +from datetime import datetime +from typing import Any, Dict, List, Optional, TypedDict, Union + +import httpx +from pydantic import BaseModel, Field + +import litellm +from litellm._logging import verbose_logger +from litellm.integrations.custom_logger import CustomLogger +from litellm.litellm_core_utils.logging_utils import ( + convert_litellm_response_object_to_dict, +) +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler +from litellm.proxy._types import CommonProxyErrors, SpendLogsMetadata, SpendLogsPayload + + +class RequestKwargs(TypedDict): + model: Optional[str] + messages: Optional[List] + optional_params: Optional[Dict[str, Any]] + + +class GCSBucketPayload(TypedDict): + request_kwargs: Optional[RequestKwargs] + response_obj: Optional[Dict] + start_time: str + end_time: str + response_cost: Optional[float] + spend_log_metadata: str + exception: Optional[str] + log_event_type: Optional[str] + + +class GCSBucketLogger(CustomLogger): + def __init__(self) -> None: + from litellm.proxy.proxy_server import premium_user + + if premium_user is not True: + raise ValueError( + f"GCS Bucket logging is a premium feature. Please upgrade to use it. {CommonProxyErrors.not_premium_user.value}" + ) + + self.async_httpx_client = AsyncHTTPHandler( + timeout=httpx.Timeout(timeout=600.0, connect=5.0) + ) + self.path_service_account_json = os.getenv("GCS_PATH_SERVICE_ACCOUNT", None) + self.BUCKET_NAME = os.getenv("GCS_BUCKET_NAME", None) + + if self.BUCKET_NAME is None: + raise ValueError( + "GCS_BUCKET_NAME is not set in the environment, but GCS Bucket is being used as a logging callback. Please set 'GCS_BUCKET_NAME' in the environment." + ) + + if self.path_service_account_json is None: + raise ValueError( + "GCS_PATH_SERVICE_ACCOUNT is not set in the environment, but GCS Bucket is being used as a logging callback. Please set 'GCS_PATH_SERVICE_ACCOUNT' in the environment." + ) + pass + + #### ASYNC #### + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + from litellm.proxy.proxy_server import premium_user + + if premium_user is not True: + raise ValueError( + f"GCS Bucket logging is a premium feature. Please upgrade to use it. {CommonProxyErrors.not_premium_user.value}" + ) + try: + verbose_logger.debug( + "GCS Logger: async_log_success_event logging kwargs: %s, response_obj: %s", + kwargs, + response_obj, + ) + + start_time_str = start_time.strftime("%Y-%m-%d %H:%M:%S") + end_time_str = end_time.strftime("%Y-%m-%d %H:%M:%S") + headers = await self.construct_request_headers() + + logging_payload: GCSBucketPayload = await self.get_gcs_payload( + kwargs, response_obj, start_time_str, end_time_str + ) + logging_payload["log_event_type"] = "successful_api_call" + + json_logged_payload = json.dumps(logging_payload) + + # Get the current date + current_date = datetime.now().strftime("%Y-%m-%d") + + # Modify the object_name to include the date-based folder + object_name = f"{current_date}/{response_obj['id']}" + response = await self.async_httpx_client.post( + headers=headers, + url=f"https://storage.googleapis.com/upload/storage/v1/b/{self.BUCKET_NAME}/o?uploadType=media&name={object_name}", + data=json_logged_payload, + ) + + if response.status_code != 200: + verbose_logger.error("GCS Bucket logging error: %s", str(response.text)) + + verbose_logger.debug("GCS Bucket response %s", response) + verbose_logger.debug("GCS Bucket status code %s", response.status_code) + verbose_logger.debug("GCS Bucket response.text %s", response.text) + except Exception as e: + verbose_logger.error("GCS Bucket logging error: %s", str(e)) + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + from litellm.proxy.proxy_server import premium_user + + if premium_user is not True: + raise ValueError( + f"GCS Bucket logging is a premium feature. Please upgrade to use it. {CommonProxyErrors.not_premium_user.value}" + ) + try: + verbose_logger.debug( + "GCS Logger: async_log_failure_event logging kwargs: %s, response_obj: %s", + kwargs, + response_obj, + ) + + start_time_str = start_time.strftime("%Y-%m-%d %H:%M:%S") + end_time_str = end_time.strftime("%Y-%m-%d %H:%M:%S") + headers = await self.construct_request_headers() + + logging_payload: GCSBucketPayload = await self.get_gcs_payload( + kwargs, response_obj, start_time_str, end_time_str + ) + logging_payload["log_event_type"] = "failed_api_call" + + _litellm_params = kwargs.get("litellm_params") or {} + metadata = _litellm_params.get("metadata") or {} + + json_logged_payload = json.dumps(logging_payload) + + # Get the current date + current_date = datetime.now().strftime("%Y-%m-%d") + + # Modify the object_name to include the date-based folder + object_name = f"{current_date}/failure-{uuid.uuid4().hex}" + + if "gcs_log_id" in metadata: + object_name = metadata["gcs_log_id"] + + response = await self.async_httpx_client.post( + headers=headers, + url=f"https://storage.googleapis.com/upload/storage/v1/b/{self.BUCKET_NAME}/o?uploadType=media&name={object_name}", + data=json_logged_payload, + ) + + if response.status_code != 200: + verbose_logger.error("GCS Bucket logging error: %s", str(response.text)) + + verbose_logger.debug("GCS Bucket response %s", response) + verbose_logger.debug("GCS Bucket status code %s", response.status_code) + verbose_logger.debug("GCS Bucket response.text %s", response.text) + except Exception as e: + verbose_logger.error("GCS Bucket logging error: %s", str(e)) + + async def construct_request_headers(self) -> Dict[str, str]: + from litellm import vertex_chat_completion + + auth_header, _ = vertex_chat_completion._get_token_and_url( + model="gcs-bucket", + vertex_credentials=self.path_service_account_json, + vertex_project=None, + vertex_location=None, + gemini_api_key=None, + stream=None, + custom_llm_provider="vertex_ai", + api_base=None, + ) + verbose_logger.debug("constructed auth_header %s", auth_header) + headers = { + "Authorization": f"Bearer {auth_header}", # auth_header + "Content-Type": "application/json", + } + + return headers + + async def get_gcs_payload( + self, kwargs, response_obj, start_time, end_time + ) -> GCSBucketPayload: + from litellm.proxy.spend_tracking.spend_tracking_utils import ( + get_logging_payload, + ) + + request_kwargs = RequestKwargs( + model=kwargs.get("model", None), + messages=kwargs.get("messages", None), + optional_params=kwargs.get("optional_params", None), + ) + response_dict = {} + if response_obj: + response_dict = convert_litellm_response_object_to_dict( + response_obj=response_obj + ) + + exception_str = None + + # Handle logging exception attributes + if "exception" in kwargs: + exception_str = kwargs.get("exception", "") + if not isinstance(exception_str, str): + exception_str = str(exception_str) + + _spend_log_payload: SpendLogsPayload = get_logging_payload( + kwargs=kwargs, + response_obj=response_obj, + start_time=start_time, + end_time=end_time, + end_user_id=kwargs.get("end_user_id", None), + ) + + gcs_payload: GCSBucketPayload = GCSBucketPayload( + request_kwargs=request_kwargs, + response_obj=response_dict, + start_time=start_time, + end_time=end_time, + spend_log_metadata=_spend_log_payload.get("metadata", ""), + response_cost=kwargs.get("response_cost", None), + exception=exception_str, + log_event_type=None, + ) + + return gcs_payload + + async def download_gcs_object(self, object_name): + """ + Download an object from GCS. + + https://cloud.google.com/storage/docs/downloading-objects#download-object-json + """ + try: + headers = await self.construct_request_headers() + url = f"https://storage.googleapis.com/storage/v1/b/{self.BUCKET_NAME}/o/{object_name}?alt=media" + + # Send the GET request to download the object + response = await self.async_httpx_client.get(url=url, headers=headers) + + if response.status_code != 200: + verbose_logger.error( + "GCS object download error: %s", str(response.text) + ) + return None + + verbose_logger.debug( + "GCS object download response status code: %s", response.status_code + ) + + # Return the content of the downloaded object + return response.content + + except Exception as e: + verbose_logger.error("GCS object download error: %s", str(e)) + return None + + async def delete_gcs_object(self, object_name): + """ + Delete an object from GCS. + """ + try: + headers = await self.construct_request_headers() + url = f"https://storage.googleapis.com/storage/v1/b/{self.BUCKET_NAME}/o/{object_name}" + + # Send the DELETE request to delete the object + response = await self.async_httpx_client.delete(url=url, headers=headers) + + if (response.status_code != 200) or (response.status_code != 204): + verbose_logger.error( + "GCS object delete error: %s, status code: %s", + str(response.text), + response.status_code, + ) + return None + + verbose_logger.debug( + "GCS object delete response status code: %s, response: %s", + response.status_code, + response.text, + ) + + # Return the content of the downloaded object + return response.text + + except Exception as e: + verbose_logger.error("GCS object download error: %s", str(e)) + return None diff --git a/litellm/integrations/helicone.py b/litellm/integrations/helicone.py index 8ea18a7d5..56c9c38bc 100644 --- a/litellm/integrations/helicone.py +++ b/litellm/integrations/helicone.py @@ -4,11 +4,12 @@ import dotenv, os import requests # type: ignore import litellm import traceback +from litellm._logging import verbose_logger class HeliconeLogger: # Class variables or attributes - helicone_model_list = ["gpt", "claude"] + helicone_model_list = ["gpt", "claude", "command-r", "command-r-plus", "command-light", "command-medium", "command-medium-beta", "command-xlarge-nightly", "command-nightly"] def __init__(self): # Instance variables @@ -30,22 +31,79 @@ class HeliconeLogger: prompt += f"{AI_PROMPT}" claude_provider_request = {"model": model, "prompt": prompt} + choice = response_obj["choices"][0] + message = choice["message"] + + content = [] + if "tool_calls" in message and message["tool_calls"]: + for tool_call in message["tool_calls"]: + content.append({ + "type": "tool_use", + "id": tool_call["id"], + "name": tool_call["function"]["name"], + "input": tool_call["function"]["arguments"] + }) + elif "content" in message and message["content"]: + content = [{"type": "text", "text": message["content"]}] + claude_response_obj = { - "completion": response_obj["choices"][0]["message"]["content"], + "id": response_obj["id"], + "type": "message", + "role": "assistant", "model": model, - "stop_reason": "stop_sequence", + "content": content, + "stop_reason": choice["finish_reason"], + "stop_sequence": None, + "usage": { + "input_tokens": response_obj["usage"]["prompt_tokens"], + "output_tokens": response_obj["usage"]["completion_tokens"] + } } - return claude_provider_request, claude_response_obj + return claude_response_obj + + @staticmethod + def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict: + """ + Adds metadata from proxy request headers to Helicone logging if keys start with "helicone_" + and overwrites litellm_params.metadata if already included. + + For example if you want to add custom property to your request, send + `headers: { ..., helicone-property-something: 1234 }` via proxy request. + """ + if litellm_params is None: + return metadata + + if litellm_params.get("proxy_server_request") is None: + return metadata + + if metadata is None: + metadata = {} + + proxy_headers = ( + litellm_params.get("proxy_server_request", {}).get("headers", {}) or {} + ) + + for header_key in proxy_headers: + if header_key.startswith("helicone_"): + metadata[header_key] = proxy_headers.get(header_key) + + return metadata def log_success( - self, model, messages, response_obj, start_time, end_time, print_verbose + self, model, messages, response_obj, start_time, end_time, print_verbose, kwargs ): # Method definition try: print_verbose( f"Helicone Logging - Enters logging function for model {model}" ) + litellm_params = kwargs.get("litellm_params", {}) + litellm_call_id = kwargs.get("litellm_call_id", None) + metadata = ( + litellm_params.get("metadata", {}) or {} + ) + metadata = self.add_metadata_from_header(litellm_params, metadata) model = ( model if any( @@ -61,7 +119,7 @@ class HeliconeLogger: response_obj = response_obj.json() if "claude" in model: - provider_request, response_obj = self.claude_mapping( + response_obj = self.claude_mapping( model=model, messages=messages, response_obj=response_obj ) @@ -72,7 +130,11 @@ class HeliconeLogger: } # Code to be executed + provider_url = self.provider_url url = "https://api.hconeai.com/oai/v1/log" + if "claude" in model: + url = "https://api.hconeai.com/anthropic/v1/log" + provider_url = "https://api.anthropic.com/v1/messages" headers = { "Authorization": f"Bearer {self.key}", "Content-Type": "application/json", @@ -85,11 +147,13 @@ class HeliconeLogger: end_time_milliseconds = int( (end_time.timestamp() - end_time_seconds) * 1000 ) + meta = {"Helicone-Auth": f"Bearer {self.key}"} + meta.update(metadata) data = { "providerRequest": { - "url": self.provider_url, + "url": provider_url, "json": provider_request, - "meta": {"Helicone-Auth": f"Bearer {self.key}"}, + "meta": meta, }, "providerResponse": providerResponse, "timing": { diff --git a/litellm/integrations/langfuse.py b/litellm/integrations/langfuse.py index a3fa1e2ce..0fb2ea1f7 100644 --- a/litellm/integrations/langfuse.py +++ b/litellm/integrations/langfuse.py @@ -5,9 +5,11 @@ import os import traceback from packaging.version import Version +from pydantic import BaseModel import litellm from litellm._logging import verbose_logger +from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info class LangFuseLogger: @@ -143,6 +145,10 @@ class LangFuseLogger: f"Langfuse Logging - Enters logging function for model {kwargs}" ) + # set default values for input/output for langfuse logging + input = None + output = None + litellm_params = kwargs.get("litellm_params", {}) litellm_call_id = kwargs.get("litellm_call_id", None) metadata = ( @@ -197,6 +203,11 @@ class LangFuseLogger: ): input = prompt output = response_obj["data"] + elif response_obj is not None and isinstance( + response_obj, litellm.TranscriptionResponse + ): + input = prompt + output = response_obj["text"] print_verbose(f"OUTPUT IN LANGFUSE: {output}; original: {response_obj}") trace_id = None generation_id = None @@ -235,10 +246,9 @@ class LangFuseLogger: return {"trace_id": trace_id, "generation_id": generation_id} except Exception as e: - verbose_logger.error( + verbose_logger.exception( "Langfuse Layer Error(): Exception occured - {}".format(str(e)) ) - verbose_logger.debug(traceback.format_exc()) return {"trace_id": None, "generation_id": None} async def _async_log_event( @@ -317,22 +327,24 @@ class LangFuseLogger: try: tags = [] - new_metadata = {} - for key, value in metadata.items(): - if ( - isinstance(value, list) - or isinstance(value, dict) - or isinstance(value, str) - or isinstance(value, int) - or isinstance(value, float) - ): - try: + try: + metadata = copy.deepcopy( + metadata + ) # Avoid modifying the original metadata + except Exception: + new_metadata = {} + for key, value in metadata.items(): + if ( + isinstance(value, list) + or isinstance(value, dict) + or isinstance(value, str) + or isinstance(value, int) + or isinstance(value, float) + ): new_metadata[key] = copy.deepcopy(value) - except Exception as e: - verbose_logger.error( - f"Langfuse [Non-blocking error] - error copying metadata: {str(e)}" - ) - metadata = new_metadata + elif isinstance(value, BaseModel): + new_metadata[key] = value.model_dump() + metadata = new_metadata supports_tags = Version(langfuse.version.__version__) >= Version("2.6.3") supports_prompt = Version(langfuse.version.__version__) >= Version("2.7.3") @@ -353,12 +365,11 @@ class LangFuseLogger: clean_metadata = {} if isinstance(metadata, dict): for key, value in metadata.items(): - # generate langfuse tags - Default Tags sent to Langfuse from LiteLLM Proxy if ( - litellm._langfuse_default_tags is not None - and isinstance(litellm._langfuse_default_tags, list) - and key in litellm._langfuse_default_tags + litellm.langfuse_default_tags is not None + and isinstance(litellm.langfuse_default_tags, list) + and key in litellm.langfuse_default_tags ): tags.append(f"{key}:{value}") @@ -373,6 +384,11 @@ class LangFuseLogger: else: clean_metadata[key] = value + # Add default langfuse tags + tags = self.add_default_langfuse_tags( + tags=tags, kwargs=kwargs, metadata=metadata + ) + session_id = clean_metadata.pop("session_id", None) trace_name = clean_metadata.pop("trace_name", None) trace_id = clean_metadata.pop("trace_id", litellm_call_id) @@ -382,6 +398,8 @@ class LangFuseLogger: mask_input = clean_metadata.pop("mask_input", False) mask_output = clean_metadata.pop("mask_output", False) + clean_metadata = redact_user_api_key_info(metadata=clean_metadata) + if trace_name is None and existing_trace_id is None: # just log `litellm-{call_type}` as the trace name ## DO NOT SET TRACE_NAME if trace-id set. this can lead to overwriting of past traces. @@ -453,9 +471,9 @@ class LangFuseLogger: clean_metadata["litellm_response_cost"] = cost if ( - litellm._langfuse_default_tags is not None - and isinstance(litellm._langfuse_default_tags, list) - and "proxy_base_url" in litellm._langfuse_default_tags + litellm.langfuse_default_tags is not None + and isinstance(litellm.langfuse_default_tags, list) + and "proxy_base_url" in litellm.langfuse_default_tags ): proxy_base_url = os.environ.get("PROXY_BASE_URL", None) if proxy_base_url is not None: @@ -500,6 +518,9 @@ class LangFuseLogger: } trace = self.Langfuse.trace(**trace_params) + # Log provider specific information as a span + log_provider_specific_information_as_span(trace, clean_metadata) + generation_id = None usage = None if response_obj is not None and response_obj.get("id", None) is not None: @@ -565,6 +586,33 @@ class LangFuseLogger: verbose_logger.error(f"Langfuse Layer Error - {traceback.format_exc()}") return None, None + def add_default_langfuse_tags(self, tags, kwargs, metadata): + """ + Helper function to add litellm default langfuse tags + + - Special LiteLLM tags: + - cache_hit + - cache_key + + """ + if litellm.langfuse_default_tags is not None and isinstance( + litellm.langfuse_default_tags, list + ): + if "cache_hit" in litellm.langfuse_default_tags: + _cache_hit_value = kwargs.get("cache_hit", False) + tags.append(f"cache_hit:{_cache_hit_value}") + if "cache_key" in litellm.langfuse_default_tags: + _hidden_params = metadata.get("hidden_params", {}) or {} + _cache_key = _hidden_params.get("cache_key", None) + if _cache_key is None: + # fallback to using "preset_cache_key" + _preset_cache_key = kwargs.get("litellm_params", {}).get( + "preset_cache_key", None + ) + _cache_key = _preset_cache_key + tags.append(f"cache_key:{_cache_key}") + return tags + def _add_prompt_to_generation_params( generation_params: dict, clean_metadata: dict @@ -617,3 +665,47 @@ def _add_prompt_to_generation_params( generation_params["prompt"] = user_prompt return generation_params + + +def log_provider_specific_information_as_span( + trace, + clean_metadata, +): + """ + Logs provider-specific information as spans. + + Parameters: + trace: The tracing object used to log spans. + clean_metadata: A dictionary containing metadata to be logged. + + Returns: + None + """ + + _hidden_params = clean_metadata.get("hidden_params", None) + if _hidden_params is None: + return + + vertex_ai_grounding_metadata = _hidden_params.get( + "vertex_ai_grounding_metadata", None + ) + + if vertex_ai_grounding_metadata is not None: + if isinstance(vertex_ai_grounding_metadata, list): + for elem in vertex_ai_grounding_metadata: + if isinstance(elem, dict): + for key, value in elem.items(): + trace.span( + name=key, + input=value, + ) + else: + trace.span( + name="vertex_ai_grounding_metadata", + input=elem, + ) + else: + trace.span( + name="vertex_ai_grounding_metadata", + input=vertex_ai_grounding_metadata, + ) diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py index 48185afee..97242a261 100644 --- a/litellm/integrations/langsmith.py +++ b/litellm/integrations/langsmith.py @@ -1,13 +1,43 @@ #### What this does #### # On success, logs events to Langsmith -import dotenv, os # type: ignore -import requests # type: ignore -from datetime import datetime -import traceback import asyncio +import os +import traceback import types +from datetime import datetime +from typing import Any, List, Optional, Union + +import dotenv # type: ignore +import httpx +import requests # type: ignore from pydantic import BaseModel # type: ignore +import litellm +from litellm._logging import verbose_logger +from litellm.integrations.custom_logger import CustomLogger +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + + +class LangsmithInputs(BaseModel): + model: Optional[str] = None + messages: Optional[List[Any]] = None + stream: Optional[bool] = None + call_type: Optional[str] = None + litellm_call_id: Optional[str] = None + completion_start_time: Optional[datetime] = None + temperature: Optional[float] = None + max_tokens: Optional[int] = None + custom_llm_provider: Optional[str] = None + input: Optional[List[Any]] = None + log_event_type: Optional[str] = None + original_response: Optional[Any] = None + response_cost: Optional[float] = None + + # LiteLLM Virtual Key specific fields + user_api_key: Optional[str] = None + user_api_key_user_id: Optional[str] = None + user_api_key_team_alias: Optional[str] = None + def is_serializable(value): non_serializable_types = ( @@ -19,7 +49,7 @@ def is_serializable(value): return not isinstance(value, non_serializable_types) -class LangsmithLogger: +class LangsmithLogger(CustomLogger): # Class variables or attributes def __init__(self): self.langsmith_api_key = os.getenv("LANGSMITH_API_KEY") @@ -27,71 +57,143 @@ class LangsmithLogger: self.langsmith_default_run_name = os.getenv( "LANGSMITH_DEFAULT_RUN_NAME", "LLMRun" ) + self.langsmith_base_url = os.getenv( + "LANGSMITH_BASE_URL", "https://api.smith.langchain.com" + ) + self.async_httpx_client = AsyncHTTPHandler( + timeout=httpx.Timeout(timeout=600.0, connect=5.0) + ) - def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): - # Method definition - # inspired by Langsmith http api here: https://github.com/langchain-ai/langsmith-cookbook/blob/main/tracing-examples/rest/rest.ipynb - metadata = ( - kwargs.get("litellm_params", {}).get("metadata", {}) or {} - ) # if metadata is None + def _prepare_log_data(self, kwargs, response_obj, start_time, end_time): + import datetime + from datetime import datetime as dt + from datetime import timezone + + metadata = kwargs.get("litellm_params", {}).get("metadata", {}) or {} + new_metadata = {} + for key, value in metadata.items(): + if ( + isinstance(value, list) + or isinstance(value, str) + or isinstance(value, int) + or isinstance(value, float) + ): + new_metadata[key] = value + elif isinstance(value, BaseModel): + new_metadata[key] = value.model_dump_json() + elif isinstance(value, dict): + for k, v in value.items(): + if isinstance(v, dt): + value[k] = v.isoformat() + new_metadata[key] = value + + metadata = new_metadata + + kwargs["user_api_key"] = metadata.get("user_api_key", None) + kwargs["user_api_key_user_id"] = metadata.get("user_api_key_user_id", None) + kwargs["user_api_key_team_alias"] = metadata.get( + "user_api_key_team_alias", None + ) - # set project name and run_name for langsmith logging - # users can pass project_name and run name to litellm.completion() - # Example: litellm.completion(model, messages, metadata={"project_name": "my-litellm-project", "run_name": "my-langsmith-run"}) - # if not set litellm will fallback to the environment variable LANGSMITH_PROJECT, then to the default project_name = litellm-completion, run_name = LLMRun project_name = metadata.get("project_name", self.langsmith_project) run_name = metadata.get("run_name", self.langsmith_default_run_name) - print_verbose( + run_id = metadata.get("id", None) + tags = metadata.get("tags", []) or [] + verbose_logger.debug( f"Langsmith Logging - project_name: {project_name}, run_name {run_name}" ) - langsmith_base_url = os.getenv( - "LANGSMITH_BASE_URL", "https://api.smith.langchain.com" - ) try: - print_verbose( - f"Langsmith Logging - Enters logging function for model {kwargs}" - ) - import requests - import datetime - from datetime import timezone + start_time = kwargs["start_time"].astimezone(timezone.utc).isoformat() + end_time = kwargs["end_time"].astimezone(timezone.utc).isoformat() + except: + start_time = datetime.datetime.utcnow().isoformat() + end_time = datetime.datetime.utcnow().isoformat() + # filter out kwargs to not include any dicts, langsmith throws an erros when trying to log kwargs + logged_kwargs = LangsmithInputs(**kwargs) + kwargs = logged_kwargs.model_dump() + + new_kwargs = {} + for key in kwargs: + value = kwargs[key] + if key == "start_time" or key == "end_time" or value is None: + pass + elif key == "original_response" and not isinstance(value, str): + new_kwargs[key] = str(value) + elif type(value) == datetime.datetime: + new_kwargs[key] = value.isoformat() + elif type(value) != dict and is_serializable(value=value): + new_kwargs[key] = value + elif not is_serializable(value=value): + continue + + if isinstance(response_obj, BaseModel): try: - start_time = kwargs["start_time"].astimezone(timezone.utc).isoformat() - end_time = kwargs["end_time"].astimezone(timezone.utc).isoformat() + response_obj = response_obj.model_dump() except: - start_time = datetime.datetime.utcnow().isoformat() - end_time = datetime.datetime.utcnow().isoformat() + response_obj = response_obj.dict() # type: ignore - # filter out kwargs to not include any dicts, langsmith throws an erros when trying to log kwargs - new_kwargs = {} - for key in kwargs: - value = kwargs[key] - if key == "start_time" or key == "end_time" or value is None: - pass - elif type(value) == datetime.datetime: - new_kwargs[key] = value.isoformat() - elif type(value) != dict and is_serializable(value=value): - new_kwargs[key] = value + data = { + "name": run_name, + "run_type": "llm", # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain" + "inputs": new_kwargs, + "outputs": response_obj, + "session_name": project_name, + "start_time": start_time, + "end_time": end_time, + "tags": tags, + "extra": metadata, + } - if isinstance(response_obj, BaseModel): - try: - response_obj = response_obj.model_dump() - except: - response_obj = response_obj.dict() # type: ignore + if run_id: + data["id"] = run_id - data = { - "name": run_name, - "run_type": "llm", # this should always be llm, since litellm always logs llm calls. Langsmith allow us to log "chain" - "inputs": new_kwargs, - "outputs": response_obj, - "session_name": project_name, - "start_time": start_time, - "end_time": end_time, - } + verbose_logger.debug("Langsmith Logging data on langsmith: %s", data) + + return data + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + verbose_logger.debug( + "Langsmith Async Layer Logging - kwargs: %s, response_obj: %s", + kwargs, + response_obj, + ) + data = self._prepare_log_data(kwargs, response_obj, start_time, end_time) + url = f"{self.langsmith_base_url}/runs" + verbose_logger.debug(f"Langsmith Logging - About to send data to {url} ...") + + headers = {"x-api-key": self.langsmith_api_key} + response = await self.async_httpx_client.post( + url=url, json=data, headers=headers + ) + + if response.status_code >= 300: + verbose_logger.error( + f"Langmsith Error: {response.status_code} - {response.text}" + ) + else: + verbose_logger.debug( + "Run successfully created, response=%s", response.text + ) + verbose_logger.debug( + f"Langsmith Layer Logging - final response object: {response_obj}. Response text from langsmith={response.text}" + ) + except: + verbose_logger.error(f"Langsmith Layer Error - {traceback.format_exc()}") + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + verbose_logger.debug( + "Langsmith Sync Layer Logging - kwargs: %s, response_obj: %s", + kwargs, + response_obj, + ) + data = self._prepare_log_data(kwargs, response_obj, start_time, end_time) + url = f"{self.langsmith_base_url}/runs" + verbose_logger.debug(f"Langsmith Logging - About to send data to {url} ...") - url = f"{langsmith_base_url}/runs" - print_verbose(f"Langsmith Logging - About to send data to {url} ...") response = requests.post( url=url, json=data, @@ -99,12 +201,21 @@ class LangsmithLogger: ) if response.status_code >= 300: - print_verbose(f"Error: {response.status_code}") + verbose_logger.error(f"Error: {response.status_code} - {response.text}") else: - print_verbose("Run successfully created") - print_verbose( - f"Langsmith Layer Logging - final response object: {response_obj}" + verbose_logger.debug("Run successfully created") + verbose_logger.debug( + f"Langsmith Layer Logging - final response object: {response_obj}. Response text from langsmith={response.text}" ) except: - print_verbose(f"Langsmith Layer Error - {traceback.format_exc()}") - pass + verbose_logger.error(f"Langsmith Layer Error - {traceback.format_exc()}") + + def get_run_by_id(self, run_id): + + url = f"{self.langsmith_base_url}/runs/{run_id}" + response = requests.get( + url=url, + headers={"x-api-key": self.langsmith_api_key}, + ) + + return response.json() diff --git a/litellm/integrations/logfire_logger.py b/litellm/integrations/logfire_logger.py index b4ab00820..5e9267dca 100644 --- a/litellm/integrations/logfire_logger.py +++ b/litellm/integrations/logfire_logger.py @@ -1,17 +1,17 @@ #### What this does #### # On success + failure, log events to Logfire -import dotenv, os - -dotenv.load_dotenv() # Loading env variables using dotenv +import os import traceback import uuid -from litellm._logging import print_verbose, verbose_logger - from enum import Enum from typing import Any, Dict, NamedTuple + from typing_extensions import LiteralString +from litellm._logging import print_verbose, verbose_logger +from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info + class SpanConfig(NamedTuple): message_template: LiteralString @@ -135,6 +135,8 @@ class LogfireLogger: else: clean_metadata[key] = value + clean_metadata = redact_user_api_key_info(metadata=clean_metadata) + # Build the initial payload payload = { "id": id, diff --git a/litellm/integrations/openmeter.py b/litellm/integrations/openmeter.py index 2c470d6f4..6905fd789 100644 --- a/litellm/integrations/openmeter.py +++ b/litellm/integrations/openmeter.py @@ -1,12 +1,18 @@ # What is this? ## On Success events log cost to OpenMeter - https://github.com/BerriAI/litellm/issues/1268 -import dotenv, os, json -import litellm +import json +import os import traceback +import uuid + +import dotenv +import httpx + +import litellm +from litellm import verbose_logger from litellm.integrations.custom_logger import CustomLogger from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler -import uuid def get_utc_datetime(): @@ -122,7 +128,11 @@ class OpenMeterLogger(CustomLogger): ) response.raise_for_status() - except Exception as e: - if hasattr(response, "text"): - litellm.print_verbose(f"\nError Message: {response.text}") + except httpx.HTTPStatusError as e: + verbose_logger.error( + "Failed OpenMeter logging - {}".format(e.response.text) + ) + raise e + except Exception as e: + verbose_logger.error("Failed OpenMeter logging - {}".format(str(e))) raise e diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 4ed561116..379c41880 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -2,11 +2,12 @@ import os from dataclasses import dataclass from datetime import datetime from functools import wraps -from typing import TYPE_CHECKING, Any, Optional, Union +from typing import TYPE_CHECKING, Any, Dict, Optional, Union import litellm from litellm._logging import verbose_logger from litellm.integrations.custom_logger import CustomLogger +from litellm.litellm_core_utils.redact_messages import redact_user_api_key_info from litellm.types.services import ServiceLoggerPayload if TYPE_CHECKING: @@ -27,9 +28,10 @@ else: LITELLM_TRACER_NAME = os.getenv("OTEL_TRACER_NAME", "litellm") -LITELLM_RESOURCE = { +LITELLM_RESOURCE: Dict[Any, Any] = { "service.name": os.getenv("OTEL_SERVICE_NAME", "litellm"), "deployment.environment": os.getenv("OTEL_ENVIRONMENT_NAME", "production"), + "model_id": os.getenv("OTEL_SERVICE_NAME", "litellm"), } RAW_REQUEST_SPAN_NAME = "raw_gen_ai_request" LITELLM_REQUEST_SPAN_NAME = "litellm_request" @@ -68,7 +70,9 @@ class OpenTelemetryConfig: class OpenTelemetry(CustomLogger): - def __init__(self, config=OpenTelemetryConfig.from_env()): + def __init__( + self, config=OpenTelemetryConfig.from_env(), callback_name: Optional[str] = None + ): from opentelemetry import trace from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace import TracerProvider @@ -79,6 +83,7 @@ class OpenTelemetry(CustomLogger): self.OTEL_HEADERS = self.config.headers provider = TracerProvider(resource=Resource(attributes=LITELLM_RESOURCE)) provider.add_span_processor(self._get_span_processor()) + self.callback_name = callback_name trace.set_tracer_provider(provider) self.tracer = trace.get_tracer(LITELLM_TRACER_NAME) @@ -114,14 +119,15 @@ class OpenTelemetry(CustomLogger): parent_otel_span: Optional[Span] = None, start_time: Optional[Union[datetime, float]] = None, end_time: Optional[Union[datetime, float]] = None, + event_metadata: Optional[dict] = None, ): from datetime import datetime from opentelemetry import trace from opentelemetry.trace import Status, StatusCode - _start_time_ns = start_time - _end_time_ns = end_time + _start_time_ns = 0 + _end_time_ns = 0 if isinstance(start_time, float): _start_time_ns = int(int(start_time) * 1e9) @@ -144,23 +150,34 @@ class OpenTelemetry(CustomLogger): service_logging_span.set_attribute( key="service", value=payload.service.value ) + + if event_metadata: + for key, value in event_metadata.items(): + if isinstance(value, dict): + try: + value = str(value) + except Exception: + value = "litllm logging error - could_not_json_serialize" + service_logging_span.set_attribute(key, value) service_logging_span.set_status(Status(StatusCode.OK)) service_logging_span.end(end_time=_end_time_ns) async def async_service_failure_hook( self, payload: ServiceLoggerPayload, + error: Optional[str] = "", parent_otel_span: Optional[Span] = None, start_time: Optional[Union[datetime, float]] = None, end_time: Optional[Union[float, datetime]] = None, + event_metadata: Optional[dict] = None, ): from datetime import datetime from opentelemetry import trace from opentelemetry.trace import Status, StatusCode - _start_time_ns = start_time - _end_time_ns = end_time + _start_time_ns = 0 + _end_time_ns = 0 if isinstance(start_time, float): _start_time_ns = int(int(start_time) * 1e9) @@ -183,6 +200,17 @@ class OpenTelemetry(CustomLogger): service_logging_span.set_attribute( key="service", value=payload.service.value ) + if error: + service_logging_span.set_attribute(key="error", value=error) + if event_metadata: + for key, value in event_metadata.items(): + if isinstance(value, dict): + try: + value = str(value) + except Exception: + value = "litllm logging error - could_not_json_serialize" + service_logging_span.set_attribute(key, value) + service_logging_span.set_status(Status(StatusCode.ERROR)) service_logging_span.end(end_time=_end_time_ns) @@ -253,15 +281,26 @@ class OpenTelemetry(CustomLogger): def _handle_failure(self, kwargs, response_obj, start_time, end_time): from opentelemetry.trace import Status, StatusCode + verbose_logger.debug( + "OpenTelemetry Logger: Failure HandlerLogging kwargs: %s, OTEL config settings=%s", + kwargs, + self.config, + ) + _parent_context, parent_otel_span = self._get_span_context(kwargs) + + # Span 1: Requst sent to litellm SDK span = self.tracer.start_span( name=self._get_span_name(kwargs), start_time=self._to_ns(start_time), - context=self._get_span_context(kwargs), + context=_parent_context, ) span.set_status(Status(StatusCode.ERROR)) self.set_attributes(span, kwargs, response_obj) span.end(end_time=self._to_ns(end_time)) + if parent_otel_span is not None: + parent_otel_span.end(end_time=self._to_ns(datetime.now())) + def set_tools_attributes(self, span: Span, tools): import json @@ -288,139 +327,171 @@ class OpenTelemetry(CustomLogger): ) pass + def is_primitive(self, value): + if value is None: + return False + return isinstance(value, (str, bool, int, float)) + def set_attributes(self, span: Span, kwargs, response_obj): - from litellm.proxy._types import SpanAttributes + try: + if self.callback_name == "arize": + from litellm.integrations.arize_ai import set_arize_ai_attributes - optional_params = kwargs.get("optional_params", {}) - litellm_params = kwargs.get("litellm_params", {}) or {} + set_arize_ai_attributes(span, kwargs, response_obj) + return + from litellm.proxy._types import SpanAttributes - # https://github.com/open-telemetry/semantic-conventions/blob/main/model/registry/gen-ai.yaml - # Following Conventions here: https://github.com/open-telemetry/semantic-conventions/blob/main/docs/gen-ai/llm-spans.md + optional_params = kwargs.get("optional_params", {}) + litellm_params = kwargs.get("litellm_params", {}) or {} - ############################################# - ########## LLM Request Attributes ########### - ############################################# + # https://github.com/open-telemetry/semantic-conventions/blob/main/model/registry/gen-ai.yaml + # Following Conventions here: https://github.com/open-telemetry/semantic-conventions/blob/main/docs/gen-ai/llm-spans.md + ############################################# + ############ LLM CALL METADATA ############## + ############################################# + metadata = litellm_params.get("metadata", {}) or {} - # The name of the LLM a request is being made to - if kwargs.get("model"): - span.set_attribute(SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model")) + clean_metadata = redact_user_api_key_info(metadata=metadata) - # The Generative AI Provider: Azure, OpenAI, etc. - span.set_attribute( - SpanAttributes.LLM_SYSTEM, - litellm_params.get("custom_llm_provider", "Unknown"), - ) + for key, value in clean_metadata.items(): + if self.is_primitive(value): + span.set_attribute("metadata.{}".format(key), value) - # The maximum number of tokens the LLM generates for a request. - if optional_params.get("max_tokens"): + ############################################# + ########## LLM Request Attributes ########### + ############################################# + + # The name of the LLM a request is being made to + if kwargs.get("model"): + span.set_attribute( + SpanAttributes.LLM_REQUEST_MODEL, kwargs.get("model") + ) + + # The Generative AI Provider: Azure, OpenAI, etc. span.set_attribute( - SpanAttributes.LLM_REQUEST_MAX_TOKENS, optional_params.get("max_tokens") + SpanAttributes.LLM_SYSTEM, + litellm_params.get("custom_llm_provider", "Unknown"), ) - # The temperature setting for the LLM request. - if optional_params.get("temperature"): + # The maximum number of tokens the LLM generates for a request. + if optional_params.get("max_tokens"): + span.set_attribute( + SpanAttributes.LLM_REQUEST_MAX_TOKENS, + optional_params.get("max_tokens"), + ) + + # The temperature setting for the LLM request. + if optional_params.get("temperature"): + span.set_attribute( + SpanAttributes.LLM_REQUEST_TEMPERATURE, + optional_params.get("temperature"), + ) + + # The top_p sampling setting for the LLM request. + if optional_params.get("top_p"): + span.set_attribute( + SpanAttributes.LLM_REQUEST_TOP_P, optional_params.get("top_p") + ) + span.set_attribute( - SpanAttributes.LLM_REQUEST_TEMPERATURE, - optional_params.get("temperature"), + SpanAttributes.LLM_IS_STREAMING, + str(optional_params.get("stream", False)), ) - # The top_p sampling setting for the LLM request. - if optional_params.get("top_p"): - span.set_attribute( - SpanAttributes.LLM_REQUEST_TOP_P, optional_params.get("top_p") - ) + if optional_params.get("tools"): + tools = optional_params["tools"] + self.set_tools_attributes(span, tools) - span.set_attribute( - SpanAttributes.LLM_IS_STREAMING, str(optional_params.get("stream", False)) - ) + if optional_params.get("user"): + span.set_attribute(SpanAttributes.LLM_USER, optional_params.get("user")) - if optional_params.get("tools"): - tools = optional_params["tools"] - self.set_tools_attributes(span, tools) - - if optional_params.get("user"): - span.set_attribute(SpanAttributes.LLM_USER, optional_params.get("user")) - - if kwargs.get("messages"): - for idx, prompt in enumerate(kwargs.get("messages")): - if prompt.get("role"): - span.set_attribute( - f"{SpanAttributes.LLM_PROMPTS}.{idx}.role", - prompt.get("role"), - ) - - if prompt.get("content"): - if not isinstance(prompt.get("content"), str): - prompt["content"] = str(prompt.get("content")) - span.set_attribute( - f"{SpanAttributes.LLM_PROMPTS}.{idx}.content", - prompt.get("content"), - ) - ############################################# - ########## LLM Response Attributes ########## - ############################################# - if response_obj.get("choices"): - for idx, choice in enumerate(response_obj.get("choices")): - if choice.get("finish_reason"): - span.set_attribute( - f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.finish_reason", - choice.get("finish_reason"), - ) - if choice.get("message"): - if choice.get("message").get("role"): + if kwargs.get("messages"): + for idx, prompt in enumerate(kwargs.get("messages")): + if prompt.get("role"): span.set_attribute( - f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.role", - choice.get("message").get("role"), + f"{SpanAttributes.LLM_PROMPTS}.{idx}.role", + prompt.get("role"), ) - if choice.get("message").get("content"): - if not isinstance(choice.get("message").get("content"), str): - choice["message"]["content"] = str( - choice.get("message").get("content") + + if prompt.get("content"): + if not isinstance(prompt.get("content"), str): + prompt["content"] = str(prompt.get("content")) + span.set_attribute( + f"{SpanAttributes.LLM_PROMPTS}.{idx}.content", + prompt.get("content"), + ) + ############################################# + ########## LLM Response Attributes ########## + ############################################# + if response_obj is not None: + if response_obj.get("choices"): + for idx, choice in enumerate(response_obj.get("choices")): + if choice.get("finish_reason"): + span.set_attribute( + f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.finish_reason", + choice.get("finish_reason"), ) - span.set_attribute( - f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content", - choice.get("message").get("content"), - ) + if choice.get("message"): + if choice.get("message").get("role"): + span.set_attribute( + f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.role", + choice.get("message").get("role"), + ) + if choice.get("message").get("content"): + if not isinstance( + choice.get("message").get("content"), str + ): + choice["message"]["content"] = str( + choice.get("message").get("content") + ) + span.set_attribute( + f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.content", + choice.get("message").get("content"), + ) - message = choice.get("message") - tool_calls = message.get("tool_calls") - if tool_calls: - span.set_attribute( - f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.function_call.name", - tool_calls[0].get("function").get("name"), - ) - span.set_attribute( - f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.function_call.arguments", - tool_calls[0].get("function").get("arguments"), - ) + message = choice.get("message") + tool_calls = message.get("tool_calls") + if tool_calls: + span.set_attribute( + f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.function_call.name", + tool_calls[0].get("function").get("name"), + ) + span.set_attribute( + f"{SpanAttributes.LLM_COMPLETIONS}.{idx}.function_call.arguments", + tool_calls[0].get("function").get("arguments"), + ) - # The unique identifier for the completion. - if response_obj.get("id"): - span.set_attribute("gen_ai.response.id", response_obj.get("id")) + # The unique identifier for the completion. + if response_obj.get("id"): + span.set_attribute("gen_ai.response.id", response_obj.get("id")) - # The model used to generate the response. - if response_obj.get("model"): - span.set_attribute( - SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model") - ) + # The model used to generate the response. + if response_obj.get("model"): + span.set_attribute( + SpanAttributes.LLM_RESPONSE_MODEL, response_obj.get("model") + ) - usage = response_obj.get("usage") - if usage: - span.set_attribute( - SpanAttributes.LLM_USAGE_TOTAL_TOKENS, - usage.get("total_tokens"), - ) + usage = response_obj.get("usage") + if usage: + span.set_attribute( + SpanAttributes.LLM_USAGE_TOTAL_TOKENS, + usage.get("total_tokens"), + ) - # The number of tokens used in the LLM response (completion). - span.set_attribute( - SpanAttributes.LLM_USAGE_COMPLETION_TOKENS, - usage.get("completion_tokens"), - ) + # The number of tokens used in the LLM response (completion). + span.set_attribute( + SpanAttributes.LLM_USAGE_COMPLETION_TOKENS, + usage.get("completion_tokens"), + ) - # The number of tokens used in the LLM prompt. - span.set_attribute( - SpanAttributes.LLM_USAGE_PROMPT_TOKENS, - usage.get("prompt_tokens"), + # The number of tokens used in the LLM prompt. + span.set_attribute( + SpanAttributes.LLM_USAGE_PROMPT_TOKENS, + usage.get("prompt_tokens"), + ) + except Exception as e: + verbose_logger.error( + "OpenTelemetry logging error in set_attributes %s", str(e) ) def set_raw_request_attributes(self, span: Span, kwargs, response_obj): @@ -438,7 +509,7 @@ class OpenTelemetry(CustomLogger): ############################################# # OTEL Attributes for the RAW Request to https://docs.anthropic.com/en/api/messages - if complete_input_dict: + if complete_input_dict and isinstance(complete_input_dict, dict): for param, val in complete_input_dict.items(): if not isinstance(val, str): val = str(val) @@ -474,8 +545,6 @@ class OpenTelemetry(CustomLogger): _raw_response, ) - pass - def _to_ns(self, dt): return int(dt.timestamp() * 1e9) @@ -601,8 +670,8 @@ class OpenTelemetry(CustomLogger): from opentelemetry import trace from opentelemetry.trace import Status, StatusCode - _start_time_ns = logging_payload.start_time - _end_time_ns = logging_payload.end_time + _start_time_ns = 0 + _end_time_ns = 0 start_time = logging_payload.start_time end_time = logging_payload.end_time @@ -647,8 +716,8 @@ class OpenTelemetry(CustomLogger): from opentelemetry import trace from opentelemetry.trace import Status, StatusCode - _start_time_ns = logging_payload.start_time - _end_time_ns = logging_payload.end_time + _start_time_ns = 0 + _end_time_ns = 0 start_time = logging_payload.start_time end_time = logging_payload.end_time diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 4a271d6e0..321c1cc1f 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -1,36 +1,40 @@ # used for /metrics endpoint on LiteLLM Proxy #### What this does #### # On success, log events to Prometheus - -import datetime import os import subprocess import sys import traceback import uuid -from typing import Optional, Union +from datetime import datetime, timedelta +from typing import Optional, TypedDict, Union import dotenv import requests # type: ignore import litellm from litellm._logging import print_verbose, verbose_logger +from litellm.integrations.custom_logger import CustomLogger -class PrometheusLogger: +class PrometheusLogger(CustomLogger): # Class variables or attributes def __init__( self, **kwargs, ): try: - from prometheus_client import Counter, Gauge + from prometheus_client import Counter, Gauge, Histogram from litellm.proxy.proxy_server import premium_user + verbose_logger.warning( + "🚨🚨🚨 Prometheus Metrics will be moving to LiteLLM Enterprise on September 15th, 2024.\n🚨 Contact us here to get a license https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat \n🚨 Enterprise Pricing: https://www.litellm.ai/#pricing" + ) + self.litellm_llm_api_failed_requests_metric = Counter( name="litellm_llm_api_failed_requests_metric", - documentation="Total number of failed LLM API calls via litellm", + documentation="Total number of failed LLM API calls via litellm - track fails per API Key, team, user", labelnames=[ "end_user", "hashed_api_key", @@ -44,7 +48,7 @@ class PrometheusLogger: self.litellm_requests_metric = Counter( name="litellm_requests_metric", - documentation="Total number of LLM calls to litellm", + documentation="Total number of LLM calls to litellm - track total per API Key, team, user", labelnames=[ "end_user", "hashed_api_key", @@ -99,13 +103,34 @@ class PrometheusLogger: "Remaining budget for api key", labelnames=["hashed_api_key", "api_key_alias"], ) - # Litellm-Enterprise Metrics if premium_user is True: + + ######################################## + # LiteLLM Virtual API KEY metrics + ######################################## + # Remaining MODEL RPM limit for API Key + self.litellm_remaining_api_key_requests_for_model = Gauge( + "litellm_remaining_api_key_requests_for_model", + "Remaining Requests API Key can make for model (model based rpm limit on key)", + labelnames=["hashed_api_key", "api_key_alias", "model"], + ) + + # Remaining MODEL TPM limit for API Key + self.litellm_remaining_api_key_tokens_for_model = Gauge( + "litellm_remaining_api_key_tokens_for_model", + "Remaining Tokens API Key can make for model (model based tpm limit on key)", + labelnames=["hashed_api_key", "api_key_alias", "model"], + ) + + ######################################## + # LLM API Deployment Metrics / analytics + ######################################## + # Remaining Rate Limit for model self.litellm_remaining_requests_metric = Gauge( "litellm_remaining_requests", - "remaining requests for model, returned from LLM API Provider", + "LLM Deployment Analytics - remaining requests for model, returned from LLM API Provider", labelnames=[ "model_group", "api_provider", @@ -124,88 +149,218 @@ class PrometheusLogger: "litellm_model_name", ], ) + # Get all keys + _logged_llm_labels = [ + "litellm_model_name", + "model_id", + "api_base", + "api_provider", + ] + + # Metric for deployment state + self.litellm_deployment_state = Gauge( + "litellm_deployment_state", + "LLM Deployment Analytics - The state of the deployment: 0 = healthy, 1 = partial outage, 2 = complete outage", + labelnames=_logged_llm_labels, + ) + + self.litellm_deployment_success_responses = Counter( + name="litellm_deployment_success_responses", + documentation="LLM Deployment Analytics - Total number of successful LLM API calls via litellm", + labelnames=_logged_llm_labels, + ) + self.litellm_deployment_failure_responses = Counter( + name="litellm_deployment_failure_responses", + documentation="LLM Deployment Analytics - Total number of failed LLM API calls via litellm", + labelnames=_logged_llm_labels, + ) + self.litellm_deployment_total_requests = Counter( + name="litellm_deployment_total_requests", + documentation="LLM Deployment Analytics - Total number of LLM API calls via litellm - success + failure", + labelnames=_logged_llm_labels, + ) + + # Deployment Latency tracking + self.litellm_deployment_latency_per_output_token = Histogram( + name="litellm_deployment_latency_per_output_token", + documentation="LLM Deployment Analytics - Latency per output token", + labelnames=_logged_llm_labels, + ) + + self.litellm_deployment_successful_fallbacks = Counter( + "litellm_deployment_successful_fallbacks", + "LLM Deployment Analytics - Number of successful fallback requests from primary model -> fallback model", + ["primary_model", "fallback_model"], + ) + self.litellm_deployment_failed_fallbacks = Counter( + "litellm_deployment_failed_fallbacks", + "LLM Deployment Analytics - Number of failed fallback requests from primary model -> fallback model", + ["primary_model", "fallback_model"], + ) except Exception as e: print_verbose(f"Got exception on init prometheus client {str(e)}") raise e - async def _async_log_event( - self, kwargs, response_obj, start_time, end_time, print_verbose, user_id - ): - self.log_event( - kwargs, response_obj, start_time, end_time, user_id, print_verbose + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + # Define prometheus client + from litellm.proxy.common_utils.callback_utils import ( + get_model_group_from_litellm_kwargs, + ) + from litellm.proxy.proxy_server import premium_user + + verbose_logger.debug( + f"prometheus Logging - Enters success logging function for kwargs {kwargs}" + ) + + # unpack kwargs + model = kwargs.get("model", "") + response_cost = kwargs.get("response_cost", 0.0) or 0 + litellm_params = kwargs.get("litellm_params", {}) or {} + _metadata = litellm_params.get("metadata", {}) + proxy_server_request = litellm_params.get("proxy_server_request") or {} + end_user_id = proxy_server_request.get("body", {}).get("user", None) + user_id = litellm_params.get("metadata", {}).get("user_api_key_user_id", None) + user_api_key = litellm_params.get("metadata", {}).get("user_api_key", None) + user_api_key_alias = litellm_params.get("metadata", {}).get( + "user_api_key_alias", None + ) + user_api_team = litellm_params.get("metadata", {}).get( + "user_api_key_team_id", None + ) + user_api_team_alias = litellm_params.get("metadata", {}).get( + "user_api_key_team_alias", None + ) + + _team_spend = litellm_params.get("metadata", {}).get( + "user_api_key_team_spend", None + ) + _team_max_budget = litellm_params.get("metadata", {}).get( + "user_api_key_team_max_budget", None + ) + _remaining_team_budget = safe_get_remaining_budget( + max_budget=_team_max_budget, spend=_team_spend + ) + + _api_key_spend = litellm_params.get("metadata", {}).get( + "user_api_key_spend", None + ) + _api_key_max_budget = litellm_params.get("metadata", {}).get( + "user_api_key_max_budget", None + ) + _remaining_api_key_budget = safe_get_remaining_budget( + max_budget=_api_key_max_budget, spend=_api_key_spend + ) + output_tokens = 1.0 + if response_obj is not None: + tokens_used = response_obj.get("usage", {}).get("total_tokens", 0) + output_tokens = response_obj.get("usage", {}).get("completion_tokens", 0) + else: + tokens_used = 0 + + print_verbose( + f"inside track_prometheus_metrics, model {model}, response_cost {response_cost}, tokens_used {tokens_used}, end_user_id {end_user_id}, user_api_key {user_api_key}" + ) + + if ( + user_api_key is not None + and isinstance(user_api_key, str) + and user_api_key.startswith("sk-") + ): + from litellm.proxy.utils import hash_token + + user_api_key = hash_token(user_api_key) + + self.litellm_requests_metric.labels( + end_user_id, + user_api_key, + user_api_key_alias, + model, + user_api_team, + user_api_team_alias, + user_id, + ).inc() + self.litellm_spend_metric.labels( + end_user_id, + user_api_key, + user_api_key_alias, + model, + user_api_team, + user_api_team_alias, + user_id, + ).inc(response_cost) + self.litellm_tokens_metric.labels( + end_user_id, + user_api_key, + user_api_key_alias, + model, + user_api_team, + user_api_team_alias, + user_id, + ).inc(tokens_used) + + self.litellm_remaining_team_budget_metric.labels( + user_api_team, user_api_team_alias + ).set(_remaining_team_budget) + + self.litellm_remaining_api_key_budget_metric.labels( + user_api_key, user_api_key_alias + ).set(_remaining_api_key_budget) + + # Set remaining rpm/tpm for API Key + model + # see parallel_request_limiter.py - variables are set there + model_group = get_model_group_from_litellm_kwargs(kwargs) + remaining_requests_variable_name = ( + f"litellm-key-remaining-requests-{model_group}" + ) + remaining_tokens_variable_name = f"litellm-key-remaining-tokens-{model_group}" + + remaining_requests = _metadata.get( + remaining_requests_variable_name, sys.maxsize + ) + remaining_tokens = _metadata.get(remaining_tokens_variable_name, sys.maxsize) + + self.litellm_remaining_api_key_requests_for_model.labels( + user_api_key, user_api_key_alias, model_group + ).set(remaining_requests) + + self.litellm_remaining_api_key_tokens_for_model.labels( + user_api_key, user_api_key_alias, model_group + ).set(remaining_tokens) + + # set x-ratelimit headers + if premium_user is True: + self.set_llm_deployment_success_metrics( + kwargs, start_time, end_time, output_tokens + ) + pass + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + from litellm.proxy.proxy_server import premium_user + + verbose_logger.debug( + f"prometheus Logging - Enters success logging function for kwargs {kwargs}" + ) + + # unpack kwargs + model = kwargs.get("model", "") + litellm_params = kwargs.get("litellm_params", {}) or {} + proxy_server_request = litellm_params.get("proxy_server_request") or {} + end_user_id = proxy_server_request.get("body", {}).get("user", None) + user_id = litellm_params.get("metadata", {}).get("user_api_key_user_id", None) + user_api_key = litellm_params.get("metadata", {}).get("user_api_key", None) + user_api_key_alias = litellm_params.get("metadata", {}).get( + "user_api_key_alias", None + ) + user_api_team = litellm_params.get("metadata", {}).get( + "user_api_key_team_id", None + ) + user_api_team_alias = litellm_params.get("metadata", {}).get( + "user_api_key_team_alias", None ) - def log_event( - self, kwargs, response_obj, start_time, end_time, user_id, print_verbose - ): try: - # Define prometheus client - from litellm.proxy.proxy_server import premium_user - - verbose_logger.debug( - f"prometheus Logging - Enters logging function for model {kwargs}" - ) - - # unpack kwargs - model = kwargs.get("model", "") - response_cost = kwargs.get("response_cost", 0.0) or 0 - litellm_params = kwargs.get("litellm_params", {}) or {} - proxy_server_request = litellm_params.get("proxy_server_request") or {} - end_user_id = proxy_server_request.get("body", {}).get("user", None) - user_id = litellm_params.get("metadata", {}).get( - "user_api_key_user_id", None - ) - user_api_key = litellm_params.get("metadata", {}).get("user_api_key", None) - user_api_key_alias = litellm_params.get("metadata", {}).get( - "user_api_key_alias", None - ) - user_api_team = litellm_params.get("metadata", {}).get( - "user_api_key_team_id", None - ) - user_api_team_alias = litellm_params.get("metadata", {}).get( - "user_api_key_team_alias", None - ) - - _team_spend = litellm_params.get("metadata", {}).get( - "user_api_key_team_spend", None - ) - _team_max_budget = litellm_params.get("metadata", {}).get( - "user_api_key_team_max_budget", None - ) - _remaining_team_budget = safe_get_remaining_budget( - max_budget=_team_max_budget, spend=_team_spend - ) - - _api_key_spend = litellm_params.get("metadata", {}).get( - "user_api_key_spend", None - ) - _api_key_max_budget = litellm_params.get("metadata", {}).get( - "user_api_key_max_budget", None - ) - _remaining_api_key_budget = safe_get_remaining_budget( - max_budget=_api_key_max_budget, spend=_api_key_spend - ) - - if response_obj is not None: - tokens_used = response_obj.get("usage", {}).get("total_tokens", 0) - else: - tokens_used = 0 - - print_verbose( - f"inside track_prometheus_metrics, model {model}, response_cost {response_cost}, tokens_used {tokens_used}, end_user_id {end_user_id}, user_api_key {user_api_key}" - ) - - if ( - user_api_key is not None - and isinstance(user_api_key, str) - and user_api_key.startswith("sk-") - ): - from litellm.proxy.utils import hash_token - - user_api_key = hash_token(user_api_key) - - self.litellm_requests_metric.labels( + self.litellm_llm_api_failed_requests_metric.labels( end_user_id, user_api_key, user_api_key_alias, @@ -214,56 +369,61 @@ class PrometheusLogger: user_api_team_alias, user_id, ).inc() - self.litellm_spend_metric.labels( - end_user_id, - user_api_key, - user_api_key_alias, - model, - user_api_team, - user_api_team_alias, - user_id, - ).inc(response_cost) - self.litellm_tokens_metric.labels( - end_user_id, - user_api_key, - user_api_key_alias, - model, - user_api_team, - user_api_team_alias, - user_id, - ).inc(tokens_used) - - self.litellm_remaining_team_budget_metric.labels( - user_api_team, user_api_team_alias - ).set(_remaining_team_budget) - - self.litellm_remaining_api_key_budget_metric.labels( - user_api_key, user_api_key_alias - ).set(_remaining_api_key_budget) - - # set x-ratelimit headers - if premium_user is True: - self.set_remaining_tokens_requests_metric(kwargs) - - ### FAILURE INCREMENT ### - if "exception" in kwargs: - self.litellm_llm_api_failed_requests_metric.labels( - end_user_id, - user_api_key, - user_api_key_alias, - model, - user_api_team, - user_api_team_alias, - user_id, - ).inc() + self.set_llm_deployment_failure_metrics(kwargs) except Exception as e: - verbose_logger.error( + verbose_logger.exception( "prometheus Layer Error(): Exception occured - {}".format(str(e)) ) - verbose_logger.debug(traceback.format_exc()) + pass + pass + + def set_llm_deployment_failure_metrics(self, request_kwargs: dict): + try: + verbose_logger.debug("setting remaining tokens requests metric") + _response_headers = request_kwargs.get("response_headers") + _litellm_params = request_kwargs.get("litellm_params", {}) or {} + _metadata = _litellm_params.get("metadata", {}) + litellm_model_name = request_kwargs.get("model", None) + api_base = _metadata.get("api_base", None) + llm_provider = _litellm_params.get("custom_llm_provider", None) + model_id = _metadata.get("model_id") + + """ + log these labels + ["litellm_model_name", "model_id", "api_base", "api_provider"] + """ + self.set_deployment_partial_outage( + litellm_model_name=litellm_model_name, + model_id=model_id, + api_base=api_base, + api_provider=llm_provider, + ) + + self.litellm_deployment_failure_responses.labels( + litellm_model_name=litellm_model_name, + model_id=model_id, + api_base=api_base, + api_provider=llm_provider, + ).inc() + + self.litellm_deployment_total_requests.labels( + litellm_model_name=litellm_model_name, + model_id=model_id, + api_base=api_base, + api_provider=llm_provider, + ).inc() + + pass + except: pass - def set_remaining_tokens_requests_metric(self, request_kwargs: dict): + def set_llm_deployment_success_metrics( + self, + request_kwargs: dict, + start_time, + end_time, + output_tokens: float = 1.0, + ): try: verbose_logger.debug("setting remaining tokens requests metric") _response_headers = request_kwargs.get("response_headers") @@ -273,6 +433,7 @@ class PrometheusLogger: model_group = _metadata.get("model_group", None) api_base = _metadata.get("api_base", None) llm_provider = _litellm_params.get("custom_llm_provider", None) + model_id = _metadata.get("model_id") remaining_requests = None remaining_tokens = None @@ -307,14 +468,136 @@ class PrometheusLogger: model_group, llm_provider, api_base, litellm_model_name ).set(remaining_tokens) + """ + log these labels + ["litellm_model_name", "model_id", "api_base", "api_provider"] + """ + self.set_deployment_healthy( + litellm_model_name=litellm_model_name, + model_id=model_id, + api_base=api_base, + api_provider=llm_provider, + ) + + self.litellm_deployment_success_responses.labels( + litellm_model_name=litellm_model_name, + model_id=model_id, + api_base=api_base, + api_provider=llm_provider, + ).inc() + + self.litellm_deployment_total_requests.labels( + litellm_model_name=litellm_model_name, + model_id=model_id, + api_base=api_base, + api_provider=llm_provider, + ).inc() + + # Track deployment Latency + response_ms: timedelta = end_time - start_time + time_to_first_token_response_time: Optional[timedelta] = None + + if ( + request_kwargs.get("stream", None) is not None + and request_kwargs["stream"] == True + ): + # only log ttft for streaming request + time_to_first_token_response_time = ( + request_kwargs.get("completion_start_time", end_time) - start_time + ) + + # use the metric that is not None + # if streaming - use time_to_first_token_response + # if not streaming - use response_ms + _latency: timedelta = time_to_first_token_response_time or response_ms + _latency_seconds = _latency.total_seconds() + + # latency per output token + latency_per_token = None + if output_tokens is not None and output_tokens > 0: + latency_per_token = _latency_seconds / output_tokens + self.litellm_deployment_latency_per_output_token.labels( + litellm_model_name=litellm_model_name, + model_id=model_id, + api_base=api_base, + api_provider=llm_provider, + ).observe(latency_per_token) + except Exception as e: verbose_logger.error( - "Prometheus Error: set_remaining_tokens_requests_metric. Exception occured - {}".format( + "Prometheus Error: set_llm_deployment_success_metrics. Exception occured - {}".format( str(e) ) ) return + async def log_success_fallback_event(self, original_model_group: str, kwargs: dict): + verbose_logger.debug( + "Prometheus: log_success_fallback_event, original_model_group: %s, kwargs: %s", + original_model_group, + kwargs, + ) + _new_model = kwargs.get("model") + self.litellm_deployment_successful_fallbacks.labels( + primary_model=original_model_group, fallback_model=_new_model + ).inc() + + async def log_failure_fallback_event(self, original_model_group: str, kwargs: dict): + verbose_logger.debug( + "Prometheus: log_failure_fallback_event, original_model_group: %s, kwargs: %s", + original_model_group, + kwargs, + ) + _new_model = kwargs.get("model") + self.litellm_deployment_failed_fallbacks.labels( + primary_model=original_model_group, fallback_model=_new_model + ).inc() + + def set_litellm_deployment_state( + self, + state: int, + litellm_model_name: str, + model_id: str, + api_base: str, + api_provider: str, + ): + self.litellm_deployment_state.labels( + litellm_model_name, model_id, api_base, api_provider + ).set(state) + + def set_deployment_healthy( + self, + litellm_model_name: str, + model_id: str, + api_base: str, + api_provider: str, + ): + self.set_litellm_deployment_state( + 0, litellm_model_name, model_id, api_base, api_provider + ) + + def set_deployment_partial_outage( + self, + litellm_model_name: str, + model_id: str, + api_base: str, + api_provider: str, + ): + self.set_litellm_deployment_state( + 1, litellm_model_name, model_id, api_base, api_provider + ) + + def set_deployment_complete_outage( + self, + litellm_model_name: str, + model_id: str, + api_base: str, + api_provider: str, + ): + self.set_litellm_deployment_state( + 2, litellm_model_name, model_id, api_base, api_provider + ) + def safe_get_remaining_budget( max_budget: Optional[float], spend: Optional[float] diff --git a/litellm/integrations/prometheus_helpers/prometheus_api.py b/litellm/integrations/prometheus_helpers/prometheus_api.py new file mode 100644 index 000000000..13ccc1562 --- /dev/null +++ b/litellm/integrations/prometheus_helpers/prometheus_api.py @@ -0,0 +1,70 @@ +""" +Helper functions to query prometheus API +""" + +import asyncio +import os +import time + +import litellm +from litellm._logging import verbose_logger +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + +PROMETHEUS_URL = litellm.get_secret("PROMETHEUS_URL") +PROMETHEUS_SELECTED_INSTANCE = litellm.get_secret("PROMETHEUS_SELECTED_INSTANCE") +async_http_handler = AsyncHTTPHandler() + + +async def get_metric_from_prometheus( + metric_name: str, +): + # Get the start of the current day in Unix timestamp + if PROMETHEUS_URL is None: + raise ValueError( + "PROMETHEUS_URL not set please set 'PROMETHEUS_URL=<>' in .env" + ) + + query = f"{metric_name}[24h]" + now = int(time.time()) + response = await async_http_handler.get( + f"{PROMETHEUS_URL}/api/v1/query", params={"query": query, "time": now} + ) # End of the day + _json_response = response.json() + verbose_logger.debug("json response from prometheus /query api %s", _json_response) + results = response.json()["data"]["result"] + return results + + +async def get_fallback_metric_from_prometheus(): + """ + Gets fallback metrics from prometheus for the last 24 hours + """ + response_message = "" + relevant_metrics = [ + "litellm_deployment_successful_fallbacks_total", + "litellm_deployment_failed_fallbacks_total", + ] + for metric in relevant_metrics: + response_json = await get_metric_from_prometheus( + metric_name=metric, + ) + + if response_json: + verbose_logger.debug("response json %s", response_json) + for result in response_json: + verbose_logger.debug("result= %s", result) + metric = result["metric"] + metric_values = result["values"] + most_recent_value = metric_values[0] + + if PROMETHEUS_SELECTED_INSTANCE is not None: + if metric.get("instance") != PROMETHEUS_SELECTED_INSTANCE: + continue + + value = int(float(most_recent_value[1])) # Convert value to integer + primary_model = metric.get("primary_model", "Unknown") + fallback_model = metric.get("fallback_model", "Unknown") + response_message += f"`{value} successful fallback requests` with primary model=`{primary_model}` -> fallback model=`{fallback_model}`" + response_message += "\n" + verbose_logger.debug("response message %s", response_message) + return response_message diff --git a/litellm/integrations/s3.py b/litellm/integrations/s3.py index 6e8c4a4e4..c440be5f1 100644 --- a/litellm/integrations/s3.py +++ b/litellm/integrations/s3.py @@ -7,9 +7,11 @@ import subprocess import sys import traceback import uuid +from typing import Optional import litellm from litellm._logging import print_verbose, verbose_logger +from litellm.types.utils import StandardLoggingPayload class S3Logger: @@ -123,29 +125,13 @@ class S3Logger: else: clean_metadata[key] = value - # Build the initial payload - payload = { - "id": id, - "call_type": call_type, - "cache_hit": cache_hit, - "startTime": start_time, - "endTime": end_time, - "model": kwargs.get("model", ""), - "user": kwargs.get("user", ""), - "modelParameters": optional_params, - "messages": messages, - "response": response_obj, - "usage": usage, - "metadata": clean_metadata, - } - # Ensure everything in the payload is converted to str - for key, value in payload.items(): - try: - payload[key] = str(value) - except: - # non blocking if it can't cast to a str - pass + payload: Optional[StandardLoggingPayload] = kwargs.get( + "standard_logging_object", None + ) + + if payload is None: + return s3_file_name = litellm.utils.get_logging_id(start_time, payload) or "" s3_object_key = ( diff --git a/litellm/integrations/slack_alerting.py b/litellm/integrations/slack_alerting.py index b7b62b61f..bc90e86a8 100644 --- a/litellm/integrations/slack_alerting.py +++ b/litellm/integrations/slack_alerting.py @@ -26,7 +26,13 @@ from litellm.caching import DualCache from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.litellm_logging import Logging from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler -from litellm.proxy._types import AlertType, CallInfo, UserAPIKeyAuth, WebhookEvent +from litellm.proxy._types import ( + AlertType, + CallInfo, + UserAPIKeyAuth, + VirtualKeyEvent, + WebhookEvent, +) from litellm.types.router import LiteLLM_Params from .email_templates.templates import * @@ -166,6 +172,7 @@ class SlackAlerting(CustomLogger): "db_exceptions", "daily_reports", "spend_reports", + "fallback_reports", "cooldown_deployment", "new_model_added", "outage_alerts", @@ -675,7 +682,7 @@ class SlackAlerting(CustomLogger): async def failed_tracking_alert(self, error_message: str): """Raise alert when tracking failed for specific model""" _cache: DualCache = self.internal_usage_cache - message = "Failed Tracking Cost for" + error_message + message = "Failed Tracking Cost for " + error_message _cache_key = "budget_alerts:failed_tracking:{}".format(message) result = await _cache.async_get_cache(key=_cache_key) if result is None: @@ -1263,6 +1270,10 @@ Model Info: if self.alerting is None or "email" not in self.alerting: # do nothing if user does not want email alerts + verbose_proxy_logger.error( + "Error sending email alert - 'email' not in self.alerting %s", + self.alerting, + ) return False from litellm.proxy.proxy_server import premium_user, prisma_client @@ -1657,7 +1668,9 @@ Model Info: async def send_weekly_spend_report(self): """ """ try: - from litellm.proxy.proxy_server import _get_spend_report_for_time_range + from litellm.proxy.spend_tracking.spend_management_endpoints import ( + _get_spend_report_for_time_range, + ) todays_date = datetime.datetime.now().date() week_before = todays_date - datetime.timedelta(days=7) @@ -1698,7 +1711,7 @@ Model Info: alerting_metadata={}, ) except Exception as e: - verbose_proxy_logger.error("Error sending weekly spend report", e) + verbose_proxy_logger.error("Error sending weekly spend report %s", e) async def send_monthly_spend_report(self): """ """ @@ -1750,4 +1763,79 @@ Model Info: alerting_metadata={}, ) except Exception as e: - verbose_proxy_logger.error("Error sending weekly spend report", e) + verbose_proxy_logger.error("Error sending weekly spend report %s", e) + + async def send_fallback_stats_from_prometheus(self): + """ + Helper to send fallback statistics from prometheus server -> to slack + + This runs once per day and sends an overview of all the fallback statistics + """ + try: + from litellm.integrations.prometheus_helpers.prometheus_api import ( + get_fallback_metric_from_prometheus, + ) + + # call prometheuslogger. + falllback_success_info_prometheus = ( + await get_fallback_metric_from_prometheus() + ) + + fallback_message = ( + f"*Fallback Statistics:*\n{falllback_success_info_prometheus}" + ) + + await self.send_alert( + message=fallback_message, + level="Low", + alert_type="fallback_reports", + alerting_metadata={}, + ) + + except Exception as e: + verbose_proxy_logger.error("Error sending weekly spend report %s", e) + + pass + + async def send_virtual_key_event_slack( + self, + key_event: VirtualKeyEvent, + event_name: str, + ): + """ + Helper to send fallback statistics from prometheus server -> to slack + + This runs once per day and sends an overview of all the fallback statistics + """ + try: + message = f"`{event_name}`\n" + + key_event_dict = key_event.model_dump() + + # Add Created by information first + message += "*Action Done by:*\n" + for key, value in key_event_dict.items(): + if "created_by" in key: + message += f"{key}: `{value}`\n" + + # Add args sent to function in the alert + message += "\n*Arguments passed:*\n" + request_kwargs = key_event.request_kwargs + for key, value in request_kwargs.items(): + if key == "user_api_key_dict": + continue + message += f"{key}: `{value}`\n" + + await self.send_alert( + message=message, + level="High", + alert_type="fallback_reports", + alerting_metadata={}, + ) + + except Exception as e: + verbose_proxy_logger.error( + "Error sending send_virtual_key_event_slack %s", e + ) + + return diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index d8d551048..a9e535316 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -1,5 +1,9 @@ # What is this? ## Helper utilities +import os +from typing import BinaryIO, List, Literal, Optional, Tuple + +from litellm._logging import verbose_logger def map_finish_reason( @@ -39,3 +43,63 @@ def map_finish_reason( elif finish_reason == "content_filtered": return "content_filter" return finish_reason + + +def remove_index_from_tool_calls(messages, tool_calls): + for tool_call in tool_calls: + if "index" in tool_call: + tool_call.pop("index") + + for message in messages: + if "tool_calls" in message: + tool_calls = message["tool_calls"] + for tool_call in tool_calls: + if "index" in tool_call: + tool_call.pop("index") + + return + + +def get_litellm_metadata_from_kwargs(kwargs: dict): + """ + Helper to get litellm metadata from all litellm request kwargs + """ + return kwargs.get("litellm_params", {}).get("metadata", {}) + + +# Helper functions used for OTEL logging +def _get_parent_otel_span_from_kwargs(kwargs: Optional[dict] = None): + try: + if kwargs is None: + return None + litellm_params = kwargs.get("litellm_params") + _metadata = kwargs.get("metadata") or {} + if "litellm_parent_otel_span" in _metadata: + return _metadata["litellm_parent_otel_span"] + elif ( + litellm_params is not None + and litellm_params.get("metadata") is not None + and "litellm_parent_otel_span" in litellm_params.get("metadata", {}) + ): + return litellm_params["metadata"]["litellm_parent_otel_span"] + elif "litellm_parent_otel_span" in kwargs: + return kwargs["litellm_parent_otel_span"] + except: + return None + + +def get_file_check_sum(_file: BinaryIO): + """ + Helper to safely get file checksum - used as a cache key + """ + try: + file_descriptor = _file.fileno() + file_stat = os.fstat(file_descriptor) + file_size = str(file_stat.st_size) + file_checksum = _file.name + file_size + return file_checksum + except Exception as e: + verbose_logger.error(f"Error getting file_checksum: {(str(e))}") + file_checksum = _file.name + return file_checksum + return file_checksum diff --git a/litellm/litellm_core_utils/json_validation_rule.py b/litellm/litellm_core_utils/json_validation_rule.py index f19144aaf..0f37e6737 100644 --- a/litellm/litellm_core_utils/json_validation_rule.py +++ b/litellm/litellm_core_utils/json_validation_rule.py @@ -13,7 +13,12 @@ def validate_schema(schema: dict, response: str): from litellm import JSONSchemaValidationError - response_dict = json.loads(response) + try: + response_dict = json.loads(response) + except json.JSONDecodeError: + raise JSONSchemaValidationError( + model="", llm_provider="", raw_response=response, schema=response + ) try: validate(response_dict, schema=schema) diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 0edc90325..29c181ee0 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -5,12 +5,16 @@ import copy import datetime import json import os +import re import subprocess import sys import time import traceback import uuid -from typing import Any, Callable, Dict, List, Literal, Optional +from datetime import datetime as dt_object +from typing import Any, Callable, Dict, List, Literal, Optional, Union + +from pydantic import BaseModel import litellm from litellm import ( @@ -20,6 +24,7 @@ from litellm import ( verbose_logger, ) from litellm.caching import DualCache, InMemoryCache, S3Cache +from litellm.cost_calculator import _select_model_name_for_cost_calc from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.redact_messages import ( redact_message_input_output_from_logging, @@ -31,6 +36,10 @@ from litellm.types.utils import ( EmbeddingResponse, ImageResponse, ModelResponse, + StandardLoggingHiddenParams, + StandardLoggingMetadata, + StandardLoggingModelInformation, + StandardLoggingPayload, TextCompletionResponse, TranscriptionResponse, ) @@ -39,7 +48,6 @@ from litellm.utils import ( add_breadcrumb, capture_exception, customLogger, - langsmithLogger, liteDebuggerClient, logfireLogger, lunaryLogger, @@ -54,11 +62,13 @@ from litellm.utils import ( from ..integrations.aispend import AISpendLogger from ..integrations.athina import AthinaLogger from ..integrations.berrispend import BerriSpendLogger +from ..integrations.braintrust_logging import BraintrustLogger from ..integrations.clickhouse import ClickhouseLogger from ..integrations.custom_logger import CustomLogger from ..integrations.datadog import DataDogLogger from ..integrations.dynamodb import DyanmoDBLogger from ..integrations.galileo import GalileoObserve +from ..integrations.gcs_bucket import GCSBucketLogger from ..integrations.greenscale import GreenscaleLogger from ..integrations.helicone import HeliconeLogger from ..integrations.lago import LagoLogger @@ -89,7 +99,6 @@ alerts_channel = None heliconeLogger = None athinaLogger = None promptLayerLogger = None -langsmithLogger = None logfireLogger = None weightsBiasesLogger = None customLogger = None @@ -136,7 +145,7 @@ in_memory_trace_id_cache = ServiceTraceIDCache() class Logging: - global supabaseClient, liteDebuggerClient, promptLayerLogger, weightsBiasesLogger, langsmithLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app + global supabaseClient, liteDebuggerClient, promptLayerLogger, weightsBiasesLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app custom_pricing: bool = False stream_options = None @@ -232,6 +241,9 @@ class Logging: ): self.custom_pricing = True + if "custom_llm_provider" in self.model_call_details: + self.custom_llm_provider = self.model_call_details["custom_llm_provider"] + def _pre_call(self, input, api_key, model=None, additional_args={}): """ Common helper function across the sync + async pre-call function @@ -262,6 +274,7 @@ class Logging: headers = {} data = additional_args.get("complete_input_dict", {}) api_base = str(additional_args.get("api_base", "")) + query_params = additional_args.get("query_params", {}) if "key=" in api_base: # Find the position of "key=" in the string key_index = api_base.find("key=") + 4 @@ -336,9 +349,9 @@ class Logging: self.model_call_details ) # Expectation: any logger function passed in by the user should accept a dict object except Exception as e: - verbose_logger.error( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}\n{}".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( + str(e) ) ) # Input Integration Logging -> If you want to log the fact that an attempt to call the model was made @@ -388,9 +401,9 @@ class Logging: callback_func=callback, ) except Exception as e: - verbose_logger.error( - "litellm.Logging.pre_call(): Exception occured - {}\n{}".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "litellm.Logging.pre_call(): Exception occured - {}".format( + str(e) ) ) verbose_logger.debug( @@ -398,10 +411,10 @@ class Logging: ) if capture_exception: # log this error to sentry for debugging capture_exception(e) - except Exception: - verbose_logger.error( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}\n{}".format( - str(e), traceback.format_exc() + except Exception as e: + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( + str(e) ) ) verbose_logger.error( @@ -446,9 +459,9 @@ class Logging: self.model_call_details ) # Expectation: any logger function passed in by the user should accept a dict object except Exception as e: - verbose_logger.debug( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}\n{}".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( + str(e) ) ) original_response = redact_message_input_output_from_logging( @@ -484,9 +497,9 @@ class Logging: end_time=None, ) except Exception as e: - verbose_logger.error( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while post-call logging with integrations {}\n{}".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while post-call logging with integrations {}".format( + str(e) ) ) verbose_logger.debug( @@ -495,12 +508,50 @@ class Logging: if capture_exception: # log this error to sentry for debugging capture_exception(e) except Exception as e: - verbose_logger.error( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}\n{}".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging {}".format( + str(e) ) ) + def _response_cost_calculator( + self, + result: Union[ + ModelResponse, + EmbeddingResponse, + ImageResponse, + TranscriptionResponse, + TextCompletionResponse, + HttpxBinaryResponseContent, + ], + ): + """ + Calculate response cost using result + logging object variables. + + used for consistent cost calculation across response headers + logging integrations. + """ + ## RESPONSE COST ## + custom_pricing = use_custom_pricing_for_model( + litellm_params=self.litellm_params + ) + + response_cost = litellm.response_cost_calculator( + response_object=result, + model=self.model, + cache_hit=self.model_call_details.get("cache_hit", False), + custom_llm_provider=self.model_call_details.get( + "custom_llm_provider", None + ), + base_model=_get_base_model_from_metadata( + model_call_details=self.model_call_details + ), + call_type=self.call_type, + optional_params=self.optional_params, + custom_pricing=custom_pricing, + ) + + return response_cost + def _success_handler_helper_fn( self, result=None, start_time=None, end_time=None, cache_hit=None ): @@ -530,25 +581,32 @@ class Logging: or isinstance(result, TextCompletionResponse) or isinstance(result, HttpxBinaryResponseContent) # tts ): + ## RESPONSE COST ## custom_pricing = use_custom_pricing_for_model( litellm_params=self.litellm_params ) self.model_call_details["response_cost"] = ( - litellm.response_cost_calculator( - response_object=result, - model=self.model, - cache_hit=self.model_call_details.get("cache_hit", False), - custom_llm_provider=self.model_call_details.get( - "custom_llm_provider", None - ), - base_model=_get_base_model_from_metadata( - model_call_details=self.model_call_details - ), - call_type=self.call_type, - optional_params=self.optional_params, - custom_pricing=custom_pricing, - ) + self._response_cost_calculator(result=result) ) + + ## HIDDEN PARAMS ## + if hasattr(result, "_hidden_params"): + # add to metadata for logging + if self.model_call_details.get("litellm_params") is not None: + self.model_call_details["litellm_params"].setdefault( + "metadata", {} + ) + if ( + self.model_call_details["litellm_params"]["metadata"] + is None + ): + self.model_call_details["litellm_params"][ + "metadata" + ] = {} + + self.model_call_details["litellm_params"]["metadata"][ + "hidden_params" + ] = result._hidden_params else: # streaming chunks + image gen. self.model_call_details["response_cost"] = None @@ -567,6 +625,16 @@ class Logging: total_time=float_diff, ) + ## STANDARDIZED LOGGING PAYLOAD + + self.model_call_details["standard_logging_object"] = ( + get_standard_logging_object_payload( + kwargs=self.model_call_details, + init_response_obj=result, + start_time=start_time, + end_time=end_time, + ) + ) return start_time, end_time, result except Exception as e: raise Exception(f"[Non-Blocking] LiteLLM.Success_Call Error: {str(e)}") @@ -588,6 +656,8 @@ class Logging: verbose_logger.debug(f"success callbacks: {litellm.success_callback}") ## BUILD COMPLETE STREAMED RESPONSE complete_streaming_response = None + if "complete_streaming_response" in self.model_call_details: + return # break out of this. if self.stream and isinstance(result, ModelResponse): if ( result.choices[0].finish_reason is not None @@ -602,9 +672,9 @@ class Logging: end_time=end_time, ) except Exception as e: - verbose_logger.error( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while building complete streaming response in success logging {}\n{}".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while building complete streaming response in success logging {}".format( + str(e) ) ) complete_streaming_response = None @@ -619,23 +689,7 @@ class Logging: complete_streaming_response ) self.model_call_details["response_cost"] = ( - litellm.response_cost_calculator( - response_object=complete_streaming_response, - model=self.model, - cache_hit=self.model_call_details.get("cache_hit", False), - custom_llm_provider=self.model_call_details.get( - "custom_llm_provider", None - ), - base_model=_get_base_model_from_metadata( - model_call_details=self.model_call_details - ), - call_type=self.call_type, - optional_params=( - self.optional_params - if hasattr(self, "optional_params") - else {} - ), - ) + self._response_cost_calculator(result=complete_streaming_response) ) if self.dynamic_success_callbacks is not None and isinstance( self.dynamic_success_callbacks, list @@ -655,6 +709,16 @@ class Logging: result=result, litellm_logging_obj=self ) + ## LOGGING HOOK ## + + for callback in callbacks: + if isinstance(callback, CustomLogger): + self.model_call_details, result = callback.logging_hook( + kwargs=self.model_call_details, + result=result, + call_type=self.call_type, + ) + for callback in callbacks: try: litellm_params = self.model_call_details.get("litellm_params", {}) @@ -728,23 +792,6 @@ class Logging: end_time=end_time, print_verbose=print_verbose, ) - if callback == "langsmith": - print_verbose("reaches langsmith for logging!") - if self.stream: - if "complete_streaming_response" not in kwargs: - continue - else: - print_verbose( - "reaches langsmith for streaming logging!" - ) - result = kwargs["complete_streaming_response"] - langsmithLogger.log_event( - kwargs=self.model_call_details, - response_obj=result, - start_time=start_time, - end_time=end_time, - print_verbose=print_verbose, - ) if callback == "logfire": global logfireLogger verbose_logger.debug("reaches logfire for success logging!") @@ -811,6 +858,7 @@ class Logging: print_verbose("reaches helicone for logging!") model = self.model messages = self.model_call_details["input"] + kwargs = self.model_call_details heliconeLogger.log_success( model=model, messages=messages, @@ -818,6 +866,7 @@ class Logging: start_time=start_time, end_time=end_time, print_verbose=print_verbose, + kwargs=kwargs, ) if callback == "langfuse": global langFuseLogger @@ -902,34 +951,6 @@ class Logging: user_id=kwargs.get("user", None), print_verbose=print_verbose, ) - if callback == "prometheus": - verbose_logger.debug("reaches prometheus for success logging!") - kwargs = {} - for k, v in self.model_call_details.items(): - if ( - k != "original_response" - ): # copy.deepcopy raises errors as this could be a coroutine - kwargs[k] = v - # this only logs streaming once, complete_streaming_response exists i.e when stream ends - if self.stream: - verbose_logger.debug( - f"prometheus: is complete_streaming_response in kwargs: {kwargs.get('complete_streaming_response', None)}" - ) - if complete_streaming_response is None: - continue - else: - print_verbose( - "reaches prometheus for streaming logging!" - ) - result = kwargs["complete_streaming_response"] - prometheusLogger.log_event( - kwargs=kwargs, - response_obj=result, - start_time=start_time, - end_time=end_time, - user_id=kwargs.get("user", None), - print_verbose=print_verbose, - ) if callback == "generic": global genericAPILogger verbose_logger.debug("reaches langfuse for success logging!") @@ -1093,19 +1114,19 @@ class Logging: and self.model_call_details.get("litellm_params", {}).get( "acompletion", False ) - == False + is not True and self.model_call_details.get("litellm_params", {}).get( "aembedding", False ) - == False + is not True and self.model_call_details.get("litellm_params", {}).get( "aimage_generation", False ) - == False + is not True and self.model_call_details.get("litellm_params", {}).get( "atranscription", False ) - == False + is not True ): global openMeterLogger if openMeterLogger is None: @@ -1138,19 +1159,19 @@ class Logging: and self.model_call_details.get("litellm_params", {}).get( "acompletion", False ) - == False + is not True and self.model_call_details.get("litellm_params", {}).get( "aembedding", False ) - == False + is not True and self.model_call_details.get("litellm_params", {}).get( "aimage_generation", False ) - == False + is not True and self.model_call_details.get("litellm_params", {}).get( "atranscription", False ) - == False + is not True ): # custom logger class if self.stream and complete_streaming_response is None: callback.log_stream_event( @@ -1178,19 +1199,19 @@ class Logging: and self.model_call_details.get("litellm_params", {}).get( "acompletion", False ) - == False + is not True and self.model_call_details.get("litellm_params", {}).get( "aembedding", False ) - == False + is not True and self.model_call_details.get("litellm_params", {}).get( "aimage_generation", False ) - == False + is not True and self.model_call_details.get("litellm_params", {}).get( "atranscription", False ) - == False + is not True ): # custom logger functions print_verbose( f"success callbacks: Running Custom Callback Function" @@ -1214,9 +1235,9 @@ class Logging: if capture_exception: # log this error to sentry for debugging capture_exception(e) except Exception as e: - verbose_logger.error( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {}\n{}".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {}".format( + str(e) ), ) @@ -1226,12 +1247,16 @@ class Logging: """ Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. """ - print_verbose("Logging Details LiteLLM-Async Success Call") + print_verbose( + "Logging Details LiteLLM-Async Success Call, cache_hit={}".format(cache_hit) + ) start_time, end_time, result = self._success_handler_helper_fn( start_time=start_time, end_time=end_time, result=result, cache_hit=cache_hit ) ## BUILD COMPLETE STREAMED RESPONSE complete_streaming_response = None + if "async_complete_streaming_response" in self.model_call_details: + return # break out of this. if self.stream: if result.choices[0].finish_reason is not None: # if it's the last chunk self.streaming_chunks.append(result) @@ -1244,17 +1269,17 @@ class Logging: end_time=end_time, ) except Exception as e: - print_verbose( - "Error occurred building stream chunk in success logging: {}\n{}".format( - str(e), traceback.format_exc() - ), - log_level="ERROR", + verbose_logger.exception( + "Error occurred building stream chunk in success logging: {}".format( + str(e) + ) ) complete_streaming_response = None else: self.streaming_chunks.append(result) if complete_streaming_response is not None: print_verbose("Async success callbacks: Got a complete streaming response") + self.model_call_details["async_complete_streaming_response"] = ( complete_streaming_response ) @@ -1267,14 +1292,15 @@ class Logging: model_call_details=self.model_call_details ) # base_model defaults to None if not set on model_info - self.model_call_details["response_cost"] = litellm.completion_cost( - completion_response=complete_streaming_response, - model=base_model, + self.model_call_details["response_cost"] = ( + self._response_cost_calculator( + result=complete_streaming_response + ) ) verbose_logger.debug( f"Model={self.model}; cost={self.model_call_details['response_cost']}" ) - except litellm.NotFoundError as e: + except litellm.NotFoundError: verbose_logger.warning( f"Model={self.model} not found in completion cost map. Setting 'response_cost' to None" ) @@ -1300,6 +1326,16 @@ class Logging: result=result, litellm_logging_obj=self ) + ## LOGGING HOOK ## + + for callback in callbacks: + if isinstance(callback, CustomLogger): + self.model_call_details, result = await callback.async_logging_hook( + kwargs=self.model_call_details, + result=result, + call_type=self.call_type, + ) + for callback in callbacks: # check if callback can run for this request litellm_params = self.model_call_details.get("litellm_params", {}) @@ -1315,7 +1351,14 @@ class Logging: if kwargs.get("no-log", False) == True: print_verbose("no-log request, skipping logging") continue - if callback == "cache" and litellm.cache is not None: + if ( + callback == "cache" + and litellm.cache is not None + and self.model_call_details.get("litellm_params", {}).get( + "acompletion", False + ) + is True + ): # set_cache once complete streaming response is built print_verbose("async success_callback: reaches cache for logging!") kwargs = self.model_call_details @@ -1367,7 +1410,7 @@ class Logging: end_time=end_time, ) if isinstance(callback, CustomLogger): # custom logger class - if self.stream == True: + if self.stream is True: if ( "async_complete_streaming_response" in self.model_call_details @@ -1395,6 +1438,9 @@ class Logging: end_time=end_time, ) if callable(callback): # custom logger functions + global customLogger + if customLogger is None: + customLogger = CustomLogger() if self.stream: if ( "async_complete_streaming_response" @@ -1475,6 +1521,14 @@ class Logging: self.model_call_details["traceback_exception"] = traceback_exception self.model_call_details["end_time"] = end_time self.model_call_details.setdefault("original_response", None) + self.model_call_details["response_cost"] = 0 + + if hasattr(exception, "headers") and isinstance(exception.headers, dict): + self.model_call_details.setdefault("litellm_params", {}) + metadata = ( + self.model_call_details["litellm_params"].get("metadata", {}) or {} + ) + metadata.update(exception.headers) return start_time, end_time def failure_handler( @@ -1610,11 +1664,11 @@ class Logging: and self.model_call_details.get("litellm_params", {}).get( "acompletion", False ) - == False + is not True and self.model_call_details.get("litellm_params", {}).get( "aembedding", False ) - == False + is not True ): # custom logger class callback.log_failure_event( @@ -1683,25 +1737,6 @@ class Logging: level="ERROR", kwargs=self.model_call_details, ) - if callback == "prometheus": - global prometheusLogger - verbose_logger.debug("reaches prometheus for success logging!") - kwargs = {} - for k, v in self.model_call_details.items(): - if ( - k != "original_response" - ): # copy.deepcopy raises errors as this could be a coroutine - kwargs[k] = v - kwargs["exception"] = str(exception) - prometheusLogger.log_event( - kwargs=kwargs, - response_obj=result, - start_time=start_time, - end_time=end_time, - user_id=kwargs.get("user", None), - print_verbose=print_verbose, - ) - if callback == "logfire": verbose_logger.debug("reaches logfire for failure logging!") kwargs = {} @@ -1731,9 +1766,9 @@ class Logging: if capture_exception: # log this error to sentry for debugging capture_exception(e) except Exception as e: - verbose_logger.error( - "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging {}\n{}".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging {}".format( + str(e) ) ) @@ -1769,10 +1804,10 @@ class Logging: callback_func=callback, ) except Exception as e: - verbose_logger.error( + verbose_logger.exception( "LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success \ - logging {}\n{}\nCallback={}".format( - str(e), traceback.format_exc(), callback + logging {}\nCallback={}".format( + str(e), callback ) ) @@ -1799,7 +1834,7 @@ def set_callbacks(callback_list, function_id=None): """ Globally sets the callback client """ - global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, langsmithLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger + global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, athinaLogger, heliconeLogger, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, lunaryLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, logfireLogger, dynamoLogger, s3Logger, dataDogLogger, prometheusLogger, greenscaleLogger, openMeterLogger try: for callback in callback_list: @@ -1871,17 +1906,12 @@ def set_callbacks(callback_list, function_id=None): openMeterLogger = OpenMeterLogger() elif callback == "datadog": dataDogLogger = DataDogLogger() - elif callback == "prometheus": - if prometheusLogger is None: - prometheusLogger = PrometheusLogger() elif callback == "dynamodb": dynamoLogger = DyanmoDBLogger() elif callback == "s3": s3Logger = S3Logger() elif callback == "wandb": weightsBiasesLogger = WeightsBiasesLogger() - elif callback == "langsmith": - langsmithLogger = LangsmithLogger() elif callback == "logfire": logfireLogger = LogfireLogger() elif callback == "aispend": @@ -1933,6 +1963,75 @@ def _init_custom_logger_compatible_class( _openmeter_logger = OpenMeterLogger() _in_memory_loggers.append(_openmeter_logger) return _openmeter_logger # type: ignore + elif logging_integration == "braintrust": + for callback in _in_memory_loggers: + if isinstance(callback, BraintrustLogger): + return callback # type: ignore + + braintrust_logger = BraintrustLogger() + _in_memory_loggers.append(braintrust_logger) + return braintrust_logger # type: ignore + elif logging_integration == "langsmith": + for callback in _in_memory_loggers: + if isinstance(callback, LangsmithLogger): + return callback # type: ignore + + _langsmith_logger = LangsmithLogger() + _in_memory_loggers.append(_langsmith_logger) + return _langsmith_logger # type: ignore + elif logging_integration == "prometheus": + for callback in _in_memory_loggers: + if isinstance(callback, PrometheusLogger): + return callback # type: ignore + + _prometheus_logger = PrometheusLogger() + _in_memory_loggers.append(_prometheus_logger) + return _prometheus_logger # type: ignore + elif logging_integration == "gcs_bucket": + for callback in _in_memory_loggers: + if isinstance(callback, GCSBucketLogger): + return callback # type: ignore + + _gcs_bucket_logger = GCSBucketLogger() + _in_memory_loggers.append(_gcs_bucket_logger) + return _gcs_bucket_logger # type: ignore + elif logging_integration == "arize": + if "ARIZE_SPACE_KEY" not in os.environ: + raise ValueError("ARIZE_SPACE_KEY not found in environment variables") + if "ARIZE_API_KEY" not in os.environ: + raise ValueError("ARIZE_API_KEY not found in environment variables") + from litellm.integrations.opentelemetry import ( + OpenTelemetry, + OpenTelemetryConfig, + ) + + otel_config = OpenTelemetryConfig( + exporter="otlp_grpc", + endpoint="https://otlp.arize.com/v1", + ) + os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = ( + f"space_key={os.getenv('ARIZE_SPACE_KEY')},api_key={os.getenv('ARIZE_API_KEY')}" + ) + for callback in _in_memory_loggers: + if ( + isinstance(callback, OpenTelemetry) + and callback.callback_name == "arize" + ): + return callback # type: ignore + _otel_logger = OpenTelemetry(config=otel_config, callback_name="arize") + _in_memory_loggers.append(_otel_logger) + return _otel_logger # type: ignore + + elif logging_integration == "otel": + from litellm.integrations.opentelemetry import OpenTelemetry + + for callback in _in_memory_loggers: + if isinstance(callback, OpenTelemetry): + return callback # type: ignore + + otel_logger = OpenTelemetry() + _in_memory_loggers.append(otel_logger) + return otel_logger # type: ignore elif logging_integration == "galileo": for callback in _in_memory_loggers: @@ -1998,10 +2097,45 @@ def get_custom_logger_compatible_class( for callback in _in_memory_loggers: if isinstance(callback, OpenMeterLogger): return callback + elif logging_integration == "braintrust": + for callback in _in_memory_loggers: + if isinstance(callback, BraintrustLogger): + return callback elif logging_integration == "galileo": for callback in _in_memory_loggers: if isinstance(callback, GalileoObserve): return callback + elif logging_integration == "langsmith": + for callback in _in_memory_loggers: + if isinstance(callback, LangsmithLogger): + return callback + elif logging_integration == "prometheus": + for callback in _in_memory_loggers: + if isinstance(callback, PrometheusLogger): + return callback + elif logging_integration == "gcs_bucket": + for callback in _in_memory_loggers: + if isinstance(callback, GCSBucketLogger): + return callback + elif logging_integration == "otel": + from litellm.integrations.opentelemetry import OpenTelemetry + + for callback in _in_memory_loggers: + if isinstance(callback, OpenTelemetry): + return callback + elif logging_integration == "arize": + from litellm.integrations.opentelemetry import OpenTelemetry + + if "ARIZE_SPACE_KEY" not in os.environ: + raise ValueError("ARIZE_SPACE_KEY not found in environment variables") + if "ARIZE_API_KEY" not in os.environ: + raise ValueError("ARIZE_API_KEY not found in environment variables") + for callback in _in_memory_loggers: + if ( + isinstance(callback, OpenTelemetry) + and callback.callback_name == "arize" + ): + return callback elif logging_integration == "logfire": if "LOGFIRE_TOKEN" not in os.environ: raise ValueError("LOGFIRE_TOKEN not found in environment variables") @@ -2034,3 +2168,187 @@ def use_custom_pricing_for_model(litellm_params: Optional[dict]) -> bool: if k in SPECIAL_MODEL_INFO_PARAMS: return True return False + + +def is_valid_sha256_hash(value: str) -> bool: + # Check if the value is a valid SHA-256 hash (64 hexadecimal characters) + return bool(re.fullmatch(r"[a-fA-F0-9]{64}", value)) + + +def get_standard_logging_object_payload( + kwargs: Optional[dict], + init_response_obj: Any, + start_time: dt_object, + end_time: dt_object, +) -> Optional[StandardLoggingPayload]: + try: + if kwargs is None: + kwargs = {} + + hidden_params: Optional[dict] = None + if init_response_obj is None: + response_obj = {} + elif isinstance(init_response_obj, BaseModel): + response_obj = init_response_obj.model_dump() + hidden_params = getattr(init_response_obj, "_hidden_params", None) + else: + response_obj = {} + # standardize this function to be used across, s3, dynamoDB, langfuse logging + litellm_params = kwargs.get("litellm_params", {}) + proxy_server_request = litellm_params.get("proxy_server_request") or {} + end_user_id = proxy_server_request.get("body", {}).get("user", None) + metadata = ( + litellm_params.get("metadata", {}) or {} + ) # if litellm_params['metadata'] == None + completion_start_time = kwargs.get("completion_start_time", end_time) + call_type = kwargs.get("call_type") + cache_hit = kwargs.get("cache_hit", False) + usage = response_obj.get("usage", None) or {} + if type(usage) == litellm.Usage: + usage = dict(usage) + id = response_obj.get("id", kwargs.get("litellm_call_id")) + + _model_id = metadata.get("model_info", {}).get("id", "") + _model_group = metadata.get("model_group", "") + + request_tags = ( + metadata.get("tags", []) + if isinstance(metadata.get("tags", []), list) + else [] + ) + + # cleanup timestamps + if isinstance(start_time, datetime.datetime): + start_time_float = start_time.timestamp() + elif isinstance(start_time, float): + start_time_float = start_time + if isinstance(end_time, datetime.datetime): + end_time_float = end_time.timestamp() + elif isinstance(end_time, float): + end_time_float = end_time + if isinstance(completion_start_time, datetime.datetime): + completion_start_time_float = completion_start_time.timestamp() + elif isinstance(completion_start_time, float): + completion_start_time_float = completion_start_time + # clean up litellm hidden params + clean_hidden_params = StandardLoggingHiddenParams( + model_id=None, + cache_key=None, + api_base=None, + response_cost=None, + additional_headers=None, + ) + if hidden_params is not None: + clean_hidden_params = StandardLoggingHiddenParams( + **{ # type: ignore + key: hidden_params[key] + for key in StandardLoggingHiddenParams.__annotations__.keys() + if key in hidden_params + } + ) + # clean up litellm metadata + clean_metadata = StandardLoggingMetadata( + user_api_key_hash=None, + user_api_key_alias=None, + user_api_key_team_id=None, + user_api_key_user_id=None, + user_api_key_team_alias=None, + spend_logs_metadata=None, + requester_ip_address=None, + ) + if isinstance(metadata, dict): + # Filter the metadata dictionary to include only the specified keys + clean_metadata = StandardLoggingMetadata( + **{ # type: ignore + key: metadata[key] + for key in StandardLoggingMetadata.__annotations__.keys() + if key in metadata + } + ) + + if metadata.get("user_api_key") is not None: + if is_valid_sha256_hash(str(metadata.get("user_api_key"))): + clean_metadata["user_api_key_hash"] = metadata.get( + "user_api_key" + ) # this is the hash + + if litellm.cache is not None: + cache_key = litellm.cache.get_cache_key(**kwargs) + else: + cache_key = None + if cache_hit is True: + import time + + id = f"{id}_cache_hit{time.time()}" # do not duplicate the request id + + ## Get model cost information ## + base_model = _get_base_model_from_metadata(model_call_details=kwargs) + custom_pricing = use_custom_pricing_for_model(litellm_params=litellm_params) + model_cost_name = _select_model_name_for_cost_calc( + model=None, + completion_response=init_response_obj, + base_model=base_model, + custom_pricing=custom_pricing, + ) + if model_cost_name is None: + model_cost_information = StandardLoggingModelInformation( + model_map_key="", model_map_value=None + ) + else: + custom_llm_provider = kwargs.get("custom_llm_provider", None) + + try: + _model_cost_information = litellm.get_model_info( + model=model_cost_name, custom_llm_provider=custom_llm_provider + ) + model_cost_information = StandardLoggingModelInformation( + model_map_key=model_cost_name, + model_map_value=_model_cost_information, + ) + except Exception: + verbose_logger.debug( # keep in debug otherwise it will trigger on every call + "Model is not mapped in model cost map. Defaulting to None model_cost_information for standard_logging_payload" + ) + model_cost_information = StandardLoggingModelInformation( + model_map_key=model_cost_name, model_map_value=None + ) + + payload: StandardLoggingPayload = StandardLoggingPayload( + id=str(id), + call_type=call_type or "", + cache_hit=cache_hit, + startTime=start_time_float, + endTime=end_time_float, + completionStartTime=completion_start_time_float, + model=kwargs.get("model", "") or "", + metadata=clean_metadata, + cache_key=cache_key, + response_cost=kwargs.get("response_cost", 0), + total_tokens=usage.get("total_tokens", 0), + prompt_tokens=usage.get("prompt_tokens", 0), + completion_tokens=usage.get("completion_tokens", 0), + request_tags=request_tags, + end_user=end_user_id or "", + api_base=litellm_params.get("api_base", ""), + model_group=_model_group, + model_id=_model_id, + requester_ip_address=clean_metadata.get("requester_ip_address", None), + messages=kwargs.get("messages"), + response=( + response_obj if len(response_obj.keys()) > 0 else init_response_obj + ), + model_parameters=kwargs.get("optional_params", None), + hidden_params=clean_hidden_params, + model_map_information=model_cost_information, + ) + + verbose_logger.debug( + "Standard Logging: created payload - payload: %s\n\n", payload + ) + + return payload + except Exception as e: + verbose_logger.exception( + "Error creating standard logging object - {}".format(str(e)) + ) + return None diff --git a/litellm/litellm_core_utils/llm_cost_calc/google.py b/litellm/litellm_core_utils/llm_cost_calc/google.py index 76da0da51..a9a04ad00 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/google.py +++ b/litellm/litellm_core_utils/llm_cost_calc/google.py @@ -44,7 +44,12 @@ def cost_router( Returns - str, the specific google cost calc function it should route to. """ - if custom_llm_provider == "vertex_ai" and "claude" in model: + if custom_llm_provider == "vertex_ai" and ( + "claude" in model + or "llama" in model + or "mistral" in model + or "codestral" in model + ): return "cost_per_token" elif custom_llm_provider == "gemini": return "cost_per_token" @@ -113,10 +118,9 @@ def cost_per_character( ) prompt_cost = prompt_characters * model_info["input_cost_per_character"] except Exception as e: - verbose_logger.error( - "litellm.litellm_core_utils.llm_cost_calc.google.cost_per_character(): Exception occured - {}\n{}\n\ - Defaulting to (cost_per_token * 4) calculation for prompt_cost".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "litellm.litellm_core_utils.llm_cost_calc.google.cost_per_character(): Defaulting to (cost_per_token * 4) calculation for prompt_cost. Exception occured - {}".format( + str(e) ) ) initial_prompt_cost, _ = cost_per_token( @@ -156,10 +160,10 @@ def cost_per_character( completion_tokens * model_info["output_cost_per_character"] ) except Exception as e: - verbose_logger.error( - "litellm.litellm_core_utils.llm_cost_calc.google.cost_per_character(): Exception occured - {}\n{}\n\ - Defaulting to (cost_per_token * 4) calculation for completion_cost".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "litellm.litellm_core_utils.llm_cost_calc.google.cost_per_character(): \ + Defaulting to (cost_per_token * 4) calculation for completion_cost\nException occured - {}".format( + str(e) ) ) _, initial_completion_cost = cost_per_token( diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index e986a22a6..87799bc1f 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -54,9 +54,9 @@ def _generic_cost_per_character( prompt_cost = prompt_characters * custom_prompt_cost except Exception as e: - verbose_logger.error( - "litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\n{}\nDefaulting to None".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\nDefaulting to None".format( + str(e) ) ) @@ -74,9 +74,9 @@ def _generic_cost_per_character( custom_completion_cost = model_info["output_cost_per_character"] completion_cost = completion_characters * custom_completion_cost except Exception as e: - verbose_logger.error( - "litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\n{}\nDefaulting to None".format( - str(e), traceback.format_exc() + verbose_logger.exception( + "litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\nDefaulting to None".format( + str(e) ) ) diff --git a/litellm/litellm_core_utils/logging_utils.py b/litellm/litellm_core_utils/logging_utils.py new file mode 100644 index 000000000..7fa1be9d8 --- /dev/null +++ b/litellm/litellm_core_utils/logging_utils.py @@ -0,0 +1,48 @@ +from typing import TYPE_CHECKING, Any, Optional, Union + +if TYPE_CHECKING: + from litellm import ModelResponse as _ModelResponse + + LiteLLMModelResponse = _ModelResponse +else: + LiteLLMModelResponse = Any + + +import litellm + +""" +Helper utils used for logging callbacks +""" + + +def convert_litellm_response_object_to_dict(response_obj: Any) -> dict: + """ + Convert a LiteLLM response object to a dictionary + + """ + if isinstance(response_obj, dict): + return response_obj + for _type in litellm.ALL_LITELLM_RESPONSE_TYPES: + if isinstance(response_obj, _type): + return response_obj.model_dump() + + # If it's not a LiteLLM type, return the object as is + return dict(response_obj) + + +def convert_litellm_response_object_to_str( + response_obj: Union[Any, LiteLLMModelResponse] +) -> Optional[str]: + """ + Get the string of the response object from LiteLLM + + """ + if isinstance(response_obj, litellm.ModelResponse): + response_str = "" + for choice in response_obj.choices: + if isinstance(choice, litellm.Choices): + if choice.message.content and isinstance(choice.message.content, str): + response_str += choice.message.content + return response_str + + return None diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py index 378c46ba0..7f342e271 100644 --- a/litellm/litellm_core_utils/redact_messages.py +++ b/litellm/litellm_core_utils/redact_messages.py @@ -87,3 +87,33 @@ def redact_message_input_output_from_logging( # by default return result return result + + +def redact_user_api_key_info(metadata: dict) -> dict: + """ + removes any user_api_key_info before passing to logging object, if flag set + + Usage: + + SDK + ```python + litellm.redact_user_api_key_info = True + ``` + + PROXY: + ```yaml + litellm_settings: + redact_user_api_key_info: true + ``` + """ + if litellm.redact_user_api_key_info is not True: + return metadata + + new_metadata = {} + for k, v in metadata.items(): + if isinstance(k, str) and k.startswith("user_api_key"): + pass + else: + new_metadata[k] = v + + return new_metadata diff --git a/litellm/litellm_core_utils/streaming_utils.py b/litellm/litellm_core_utils/streaming_utils.py new file mode 100644 index 000000000..ca8d58e9f --- /dev/null +++ b/litellm/litellm_core_utils/streaming_utils.py @@ -0,0 +1,16 @@ +from litellm.types.utils import GenericStreamingChunk as GChunk + + +def generic_chunk_has_all_required_fields(chunk: dict) -> bool: + """ + Checks if the provided chunk dictionary contains all required fields for GenericStreamingChunk. + + :param chunk: The dictionary to check. + :return: True if all required fields are present, False otherwise. + """ + _all_fields = GChunk.__annotations__ + + # this is an optional field in GenericStreamingChunk, it's not required to be present + _all_fields.pop("provider_specific_fields", None) + + return all(key in chunk for key in _all_fields) diff --git a/litellm/llms/ai21.py b/litellm/llms/ai21.py index a39a83f15..e65a81099 100644 --- a/litellm/llms/ai21.py +++ b/litellm/llms/ai21.py @@ -1,11 +1,16 @@ -import os, types, traceback import json +import os +import time # type: ignore +import traceback +import types from enum import Enum -import requests # type: ignore -import time, httpx # type: ignore from typing import Callable, Optional -from litellm.utils import ModelResponse, Choices, Message + +import httpx +import requests # type: ignore + import litellm +from litellm.utils import Choices, Message, ModelResponse class AI21Error(Exception): @@ -185,7 +190,7 @@ def completion( message=message_obj, ) choices_list.append(choice_obj) - model_response["choices"] = choices_list + model_response.choices = choices_list # type: ignore except Exception as e: raise AI21Error( message=traceback.format_exc(), status_code=response.status_code @@ -197,13 +202,17 @@ def completion( encoding.encode(model_response["choices"][0]["message"].get("content")) ) - model_response["created"] = int(time.time()) - model_response["model"] = model - model_response["usage"] = { - "prompt_tokens": prompt_tokens, - "completion_tokens": completion_tokens, - "total_tokens": prompt_tokens + completion_tokens, - } + model_response.created = int(time.time()) + model_response.model = model + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ), + ) return model_response diff --git a/litellm/llms/aleph_alpha.py b/litellm/llms/aleph_alpha.py index 7edd11964..163e96fde 100644 --- a/litellm/llms/aleph_alpha.py +++ b/litellm/llms/aleph_alpha.py @@ -1,12 +1,15 @@ -import os, types import json -from enum import Enum -import requests # type: ignore +import os import time +import types +from enum import Enum from typing import Callable, Optional -import litellm -from litellm.utils import ModelResponse, Choices, Message, Usage + import httpx # type: ignore +import requests # type: ignore + +import litellm +from litellm.utils import Choices, Message, ModelResponse, Usage class AlephAlphaError(Exception): @@ -275,7 +278,7 @@ def completion( message=message_obj, ) choices_list.append(choice_obj) - model_response["choices"] = choices_list + model_response.choices = choices_list # type: ignore except: raise AlephAlphaError( message=json.dumps(completion_response), @@ -291,8 +294,8 @@ def completion( ) ) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, diff --git a/litellm/llms/anthropic.py b/litellm/llms/anthropic.py index ca93a85b7..9f1346b0c 100644 --- a/litellm/llms/anthropic.py +++ b/litellm/llms/anthropic.py @@ -2,20 +2,25 @@ import copy import json import os import time +import traceback import types from enum import Enum from functools import partial -from typing import Callable, List, Optional, Union +from typing import Callable, List, Literal, Optional, Tuple, Union import httpx # type: ignore import requests # type: ignore +from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingChoice import litellm import litellm.litellm_core_utils +import litellm.types +import litellm.types.utils from litellm import verbose_logger from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, + HTTPHandler, _get_async_httpx_client, _get_httpx_client, ) @@ -30,10 +35,16 @@ from litellm.types.llms.anthropic import ( AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseUsageBlock, + AnthropicSystemMessageContent, ContentBlockDelta, ContentBlockStart, + ContentBlockStop, + ContentJsonBlockDelta, + ContentTextBlockDelta, MessageBlockDelta, + MessageDelta, MessageStartBlock, + UsageDelta, ) from litellm.types.llms.openai import ( AllMessageValues, @@ -71,7 +82,7 @@ class AnthropicConstants(Enum): class AnthropicError(Exception): - def __init__(self, status_code, message): + def __init__(self, status_code: int, message): self.status_code = status_code self.message: str = message self.request = httpx.Request( @@ -213,6 +224,9 @@ class AnthropicConfig: for m in messages: user_message: Optional[ChatCompletionUserMessage] = None tool_message_list: List[ChatCompletionToolMessage] = [] + new_user_content_list: List[ + Union[ChatCompletionTextObject, ChatCompletionImageObject] + ] = [] ## USER MESSAGE ## if m["role"] == "user": ## translate user message @@ -221,9 +235,6 @@ class AnthropicConfig: role="user", content=m["content"] ) elif isinstance(m["content"], list): - new_user_content_list: List[ - Union[ChatCompletionTextObject, ChatCompletionImageObject] - ] = [] for content in m["content"]: if content["type"] == "text": text_obj = ChatCompletionTextObject( @@ -277,6 +288,9 @@ class AnthropicConfig: if user_message is not None: new_messages.append(user_message) + if len(new_user_content_list) > 0: + new_messages.append({"role": "user", "content": new_user_content_list}) + if len(tool_message_list) > 0: new_messages.extend(tool_message_list) @@ -384,6 +398,11 @@ class AnthropicConfig: if "user_id" in anthropic_message_request["metadata"]: new_kwargs["user"] = anthropic_message_request["metadata"]["user_id"] + # Pass litellm proxy specific metadata + if "litellm_metadata" in anthropic_message_request: + # metadata will be passed to litellm.acompletion(), it's a litellm_param + new_kwargs["metadata"] = anthropic_message_request.pop("litellm_metadata") + ## CONVERT TOOL CHOICE if "tool_choice" in anthropic_message_request: new_kwargs["tool_choice"] = self.translate_anthropic_tool_choice_to_openai( @@ -458,7 +477,8 @@ class AnthropicConfig: # extract usage usage: litellm.Usage = getattr(response, "usage") anthropic_usage = AnthropicResponseUsageBlock( - input_tokens=usage.prompt_tokens, output_tokens=usage.completion_tokens + input_tokens=usage.prompt_tokens or 0, + output_tokens=usage.completion_tokens or 0, ) translated_obj = AnthropicResponse( id=response.id, @@ -473,12 +493,82 @@ class AnthropicConfig: return translated_obj + def _translate_streaming_openai_chunk_to_anthropic( + self, choices: List[OpenAIStreamingChoice] + ) -> Tuple[ + Literal["text_delta", "input_json_delta"], + Union[ContentTextBlockDelta, ContentJsonBlockDelta], + ]: + text: str = "" + partial_json: Optional[str] = None + for choice in choices: + if choice.delta.content is not None: + text += choice.delta.content + elif choice.delta.tool_calls is not None: + partial_json = "" + for tool in choice.delta.tool_calls: + if ( + tool.function is not None + and tool.function.arguments is not None + ): + partial_json += tool.function.arguments + + if partial_json is not None: + return "input_json_delta", ContentJsonBlockDelta( + type="input_json_delta", partial_json=partial_json + ) + else: + return "text_delta", ContentTextBlockDelta(type="text_delta", text=text) + + def translate_streaming_openai_response_to_anthropic( + self, response: litellm.ModelResponse + ) -> Union[ContentBlockDelta, MessageBlockDelta]: + ## base case - final chunk w/ finish reason + if response.choices[0].finish_reason is not None: + delta = MessageDelta( + stop_reason=self._translate_openai_finish_reason_to_anthropic( + response.choices[0].finish_reason + ), + ) + if getattr(response, "usage", None) is not None: + litellm_usage_chunk: Optional[litellm.Usage] = response.usage # type: ignore + elif ( + hasattr(response, "_hidden_params") + and "usage" in response._hidden_params + ): + litellm_usage_chunk = response._hidden_params["usage"] + else: + litellm_usage_chunk = None + if litellm_usage_chunk is not None: + usage_delta = UsageDelta( + input_tokens=litellm_usage_chunk.prompt_tokens or 0, + output_tokens=litellm_usage_chunk.completion_tokens or 0, + ) + else: + usage_delta = UsageDelta(input_tokens=0, output_tokens=0) + return MessageBlockDelta( + type="message_delta", delta=delta, usage=usage_delta + ) + ( + type_of_content, + content_block_delta, + ) = self._translate_streaming_openai_chunk_to_anthropic( + choices=response.choices # type: ignore + ) + return ContentBlockDelta( + type="content_block_delta", + index=response.choices[0].index, + delta=content_block_delta, + ) + # makes headers for API call -def validate_environment(api_key, user_headers): +def validate_environment(api_key, user_headers, model): if api_key is None: - raise ValueError( - "Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params" + raise litellm.AuthenticationError( + message="Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params. Please set `ANTHROPIC_API_KEY` in your environment vars", + llm_provider="anthropic", + model=model, ) headers = { "accept": "application/json", @@ -499,17 +589,23 @@ async def make_call( model: str, messages: list, logging_obj, + timeout: Optional[Union[float, httpx.Timeout]], ): if client is None: client = _get_async_httpx_client() # Create a new client if none provided try: - response = await client.post(api_base, headers=headers, data=data, stream=True) + response = await client.post( + api_base, headers=headers, data=data, stream=True, timeout=timeout + ) except httpx.HTTPStatusError as e: raise AnthropicError( status_code=e.response.status_code, message=await e.response.aread() ) except Exception as e: + for exception in litellm.LITELLM_EXCEPTION_TYPES: + if isinstance(e, exception): + raise e raise AnthropicError(status_code=500, message=str(e)) if response.status_code != 200: @@ -532,11 +628,56 @@ async def make_call( return completion_stream +def make_sync_call( + client: Optional[HTTPHandler], + api_base: str, + headers: dict, + data: str, + model: str, + messages: list, + logging_obj, + timeout: Optional[Union[float, httpx.Timeout]], +): + if client is None: + client = HTTPHandler() # Create a new client if none provided + + try: + response = client.post( + api_base, headers=headers, data=data, stream=True, timeout=timeout + ) + except httpx.HTTPStatusError as e: + raise AnthropicError( + status_code=e.response.status_code, message=e.response.read() + ) + except Exception as e: + for exception in litellm.LITELLM_EXCEPTION_TYPES: + if isinstance(e, exception): + raise e + raise AnthropicError(status_code=500, message=str(e)) + + if response.status_code != 200: + raise AnthropicError(status_code=response.status_code, message=response.read()) + + completion_stream = ModelResponseIterator( + streaming_response=response.iter_lines(), sync_stream=True + ) + + # LOGGING + logging_obj.post_call( + input=messages, + api_key="", + original_response="first stream response received", + additional_args={"complete_input_dict": data}, + ) + + return completion_stream + + class AnthropicChatCompletion(BaseLLM): def __init__(self) -> None: super().__init__() - def process_response( + def _process_response( self, model: str, response: Union[requests.Response, httpx.Response], @@ -549,6 +690,7 @@ class AnthropicChatCompletion(BaseLLM): messages: List, print_verbose, encoding, + json_mode: bool, ) -> ModelResponse: ## LOGGING logging_obj.post_call( @@ -572,27 +714,40 @@ class AnthropicChatCompletion(BaseLLM): ) else: text_content = "" - tool_calls = [] - for content in completion_response["content"]: + tool_calls: List[ChatCompletionToolCallChunk] = [] + for idx, content in enumerate(completion_response["content"]): if content["type"] == "text": text_content += content["text"] ## TOOL CALLING elif content["type"] == "tool_use": tool_calls.append( - { - "id": content["id"], - "type": "function", - "function": { - "name": content["name"], - "arguments": json.dumps(content["input"]), - }, - } + ChatCompletionToolCallChunk( + id=content["id"], + type="function", + function=ChatCompletionToolCallFunctionChunk( + name=content["name"], + arguments=json.dumps(content["input"]), + ), + index=idx, + ) ) _message = litellm.Message( tool_calls=tool_calls, content=text_content or None, ) + + ## HANDLE JSON MODE - anthropic returns single function call + if json_mode and len(tool_calls) == 1: + json_mode_content_str: Optional[str] = tool_calls[0]["function"].get( + "arguments" + ) + if json_mode_content_str is not None: + args = json.loads(json_mode_content_str) + values: Optional[dict] = args.get("values") + if values is not None: + _message = litellm.Message(content=json.dumps(values)) + completion_response["stop_reason"] = "stop" model_response.choices[0].message = _message # type: ignore model_response._hidden_params["original_response"] = completion_response[ "content" @@ -605,15 +760,21 @@ class AnthropicChatCompletion(BaseLLM): ## CALCULATING USAGE prompt_tokens = completion_response["usage"]["input_tokens"] completion_tokens = completion_response["usage"]["output_tokens"] + _usage = completion_response["usage"] total_tokens = prompt_tokens + completion_tokens - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, ) + + if "cache_creation_input_tokens" in _usage: + usage["cache_creation_input_tokens"] = _usage["cache_creation_input_tokens"] + if "cache_read_input_tokens" in _usage: + usage["cache_read_input_tokens"] = _usage["cache_read_input_tokens"] setattr(model_response, "usage", usage) # type: ignore return model_response @@ -625,6 +786,7 @@ class AnthropicChatCompletion(BaseLLM): custom_prompt_dict: dict, model_response: ModelResponse, print_verbose: Callable, + timeout: Union[float, httpx.Timeout], encoding, api_key, logging_obj, @@ -637,20 +799,6 @@ class AnthropicChatCompletion(BaseLLM): headers={}, ): data["stream"] = True - # async_handler = AsyncHTTPHandler( - # timeout=httpx.Timeout(timeout=600.0, connect=20.0) - # ) - - # response = await async_handler.post( - # api_base, headers=headers, json=data, stream=True - # ) - - # if response.status_code != 200: - # raise AnthropicError( - # status_code=response.status_code, message=response.text - # ) - - # completion_stream = response.aiter_lines() streamwrapper = CustomStreamWrapper( completion_stream=None, @@ -663,6 +811,7 @@ class AnthropicChatCompletion(BaseLLM): model=model, messages=messages, logging_obj=logging_obj, + timeout=timeout, ), model=model, custom_llm_provider="anthropic", @@ -678,6 +827,7 @@ class AnthropicChatCompletion(BaseLLM): custom_prompt_dict: dict, model_response: ModelResponse, print_verbose: Callable, + timeout: Union[float, httpx.Timeout], encoding, api_key, logging_obj, @@ -685,14 +835,18 @@ class AnthropicChatCompletion(BaseLLM): _is_function_call, data: dict, optional_params: dict, + json_mode: bool, litellm_params=None, logger_fn=None, headers={}, + client=None, ) -> Union[ModelResponse, CustomStreamWrapper]: async_handler = _get_async_httpx_client() try: - response = await async_handler.post(api_base, headers=headers, json=data) + response = await async_handler.post( + api_base, headers=headers, json=data, timeout=timeout + ) except Exception as e: ## LOGGING logging_obj.post_call( @@ -703,7 +857,7 @@ class AnthropicChatCompletion(BaseLLM): ) raise e - return self.process_response( + return self._process_response( model=model, response=response, model_response=model_response, @@ -715,6 +869,7 @@ class AnthropicChatCompletion(BaseLLM): print_verbose=print_verbose, optional_params=optional_params, encoding=encoding, + json_mode=json_mode, ) def completion( @@ -729,12 +884,14 @@ class AnthropicChatCompletion(BaseLLM): api_key, logging_obj, optional_params: dict, + timeout: Union[float, httpx.Timeout], acompletion=None, litellm_params=None, logger_fn=None, headers={}, + client=None, ): - headers = validate_environment(api_key, headers) + headers = validate_environment(api_key, headers, model) _is_function_call = False messages = copy.deepcopy(messages) optional_params = copy.deepcopy(optional_params) @@ -751,22 +908,59 @@ class AnthropicChatCompletion(BaseLLM): # Separate system prompt from rest of message system_prompt_indices = [] system_prompt = "" + anthropic_system_message_list = None for idx, message in enumerate(messages): if message["role"] == "system": - system_prompt += message["content"] - system_prompt_indices.append(idx) + valid_content: bool = False + if isinstance(message["content"], str): + system_prompt += message["content"] + valid_content = True + elif isinstance(message["content"], list): + for _content in message["content"]: + anthropic_system_message_content = ( + AnthropicSystemMessageContent( + type=_content.get("type"), + text=_content.get("text"), + ) + ) + if "cache_control" in _content: + anthropic_system_message_content["cache_control"] = ( + _content["cache_control"] + ) + + if anthropic_system_message_list is None: + anthropic_system_message_list = [] + anthropic_system_message_list.append( + anthropic_system_message_content + ) + valid_content = True + + if valid_content: + system_prompt_indices.append(idx) if len(system_prompt_indices) > 0: for idx in reversed(system_prompt_indices): messages.pop(idx) if len(system_prompt) > 0: optional_params["system"] = system_prompt + + # Handling anthropic API Prompt Caching + if anthropic_system_message_list is not None: + optional_params["system"] = anthropic_system_message_list # Format rest of message according to anthropic guidelines try: messages = prompt_factory( model=model, messages=messages, custom_llm_provider="anthropic" ) except Exception as e: - raise AnthropicError(status_code=400, message=str(e)) + verbose_logger.exception( + "litellm.llms.anthropic.py::completion() - Exception occurred - {}\nReceived Messages: {}".format( + str(e), messages + ) + ) + raise AnthropicError( + status_code=400, + message="{}\nReceived Messages={}".format(str(e), messages), + ) ## Load Config config = litellm.AnthropicConfig.get_config() @@ -785,14 +979,20 @@ class AnthropicChatCompletion(BaseLLM): anthropic_tools = [] for tool in optional_params["tools"]: - new_tool = tool["function"] - new_tool["input_schema"] = new_tool.pop("parameters") # rename key - anthropic_tools.append(new_tool) + if "input_schema" in tool: # assume in anthropic format + anthropic_tools.append(tool) + else: # assume openai tool call + new_tool = tool["function"] + new_tool["input_schema"] = new_tool.pop("parameters") # rename key + if "cache_control" in tool: + new_tool["cache_control"] = tool["cache_control"] + anthropic_tools.append(new_tool) optional_params["tools"] = anthropic_tools stream = optional_params.pop("stream", None) is_vertex_request: bool = optional_params.pop("is_vertex_request", False) + json_mode: bool = optional_params.pop("json_mode", False) data = { "messages": messages, @@ -813,7 +1013,7 @@ class AnthropicChatCompletion(BaseLLM): }, ) print_verbose(f"_is_function_call: {_is_function_call}") - if acompletion == True: + if acompletion is True: if ( stream is True ): # if function call - fake the streaming (need complete blocks for output parsing in openai format) @@ -836,6 +1036,7 @@ class AnthropicChatCompletion(BaseLLM): litellm_params=litellm_params, logger_fn=logger_fn, headers=headers, + timeout=timeout, ) else: return self.acompletion_function( @@ -855,47 +1056,48 @@ class AnthropicChatCompletion(BaseLLM): litellm_params=litellm_params, logger_fn=logger_fn, headers=headers, + client=client, + json_mode=json_mode, + timeout=timeout, ) else: ## COMPLETION CALL + if client is None or not isinstance(client, HTTPHandler): + client = HTTPHandler(timeout=timeout) # type: ignore + else: + client = client if ( stream is True ): # if function call - fake the streaming (need complete blocks for output parsing in openai format) - print_verbose("makes anthropic streaming POST request") data["stream"] = stream - response = requests.post( - api_base, - headers=headers, - data=json.dumps(data), - stream=stream, - ) - - if response.status_code != 200: - raise AnthropicError( - status_code=response.status_code, message=response.text - ) - - completion_stream = ModelResponseIterator( - streaming_response=response.iter_lines(), sync_stream=True - ) - streaming_response = CustomStreamWrapper( - completion_stream=completion_stream, + return CustomStreamWrapper( + completion_stream=None, + make_call=partial( + make_sync_call, + client=None, + api_base=api_base, + headers=headers, # type: ignore + data=json.dumps(data), + model=model, + messages=messages, + logging_obj=logging_obj, + timeout=timeout, + ), model=model, custom_llm_provider="anthropic", logging_obj=logging_obj, ) - return streaming_response else: - response = requests.post( - api_base, headers=headers, data=json.dumps(data) + response = client.post( + api_base, headers=headers, data=json.dumps(data), timeout=timeout ) if response.status_code != 200: raise AnthropicError( status_code=response.status_code, message=response.text ) - return self.process_response( + return self._process_response( model=model, response=response, model_response=model_response, @@ -907,6 +1109,7 @@ class AnthropicChatCompletion(BaseLLM): print_verbose=print_verbose, optional_params=optional_params, encoding=encoding, + json_mode=json_mode, ) def embedding(self): @@ -918,10 +1121,31 @@ class ModelResponseIterator: def __init__(self, streaming_response, sync_stream: bool): self.streaming_response = streaming_response self.response_iterator = self.streaming_response + self.content_blocks: List[ContentBlockDelta] = [] + self.tool_index = -1 + + def check_empty_tool_call_args(self) -> bool: + """ + Check if the tool call block so far has been an empty string + """ + args = "" + # if text content block -> skip + if len(self.content_blocks) == 0: + return False + + if self.content_blocks[0]["delta"]["type"] == "text_delta": + return False + + for block in self.content_blocks: + if block["delta"]["type"] == "input_json_delta": + args += block["delta"].get("partial_json", "") # type: ignore + + if len(args) == 0: + return True + return False def chunk_parser(self, chunk: dict) -> GenericStreamingChunk: try: - verbose_logger.debug(f"\n\nRaw chunk:\n{chunk}\n") type_chunk = chunk.get("type", "") or "" text = "" @@ -937,6 +1161,7 @@ class ModelResponseIterator: chunk = {'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': 'Hello'}} """ content_block = ContentBlockDelta(**chunk) # type: ignore + self.content_blocks.append(content_block) if "text" in content_block["delta"]: text = content_block["delta"]["text"] elif "partial_json" in content_block["delta"]: @@ -947,7 +1172,7 @@ class ModelResponseIterator: "name": None, "arguments": content_block["delta"]["partial_json"], }, - "index": content_block["index"], + "index": self.tool_index, } elif type_chunk == "content_block_start": """ @@ -955,9 +1180,11 @@ class ModelResponseIterator: data: {"type":"content_block_start","index":1,"content_block":{"type":"tool_use","id":"toolu_01T1x1fJ34qAmk2tNTrN7Up6","name":"get_weather","input":{}}} """ content_block_start = ContentBlockStart(**chunk) # type: ignore + self.content_blocks = [] # reset content blocks when new block starts if content_block_start["content_block"]["type"] == "text": text = content_block_start["content_block"]["text"] elif content_block_start["content_block"]["type"] == "tool_use": + self.tool_index += 1 tool_use = { "id": content_block_start["content_block"]["id"], "type": "function", @@ -965,7 +1192,21 @@ class ModelResponseIterator: "name": content_block_start["content_block"]["name"], "arguments": "", }, - "index": content_block_start["index"], + "index": self.tool_index, + } + elif type_chunk == "content_block_stop": + content_block_stop = ContentBlockStop(**chunk) # type: ignore + # check if tool call content block + is_empty = self.check_empty_tool_call_args() + if is_empty: + tool_use = { + "id": None, + "type": "function", + "function": { + "name": None, + "arguments": "{}", + }, + "index": self.tool_index, } elif type_chunk == "message_delta": """ diff --git a/litellm/llms/anthropic_text.py b/litellm/llms/anthropic_text.py index 0093d9f35..d20e49daf 100644 --- a/litellm/llms/anthropic_text.py +++ b/litellm/llms/anthropic_text.py @@ -1,15 +1,19 @@ -import os, types import json -from enum import Enum -import requests +import os import time +import types +from enum import Enum from typing import Callable, Optional -from litellm.utils import ModelResponse, Usage, CustomStreamWrapper -import litellm -from .prompt_templates.factory import prompt_factory, custom_prompt + import httpx -from .base import BaseLLM +import requests + +import litellm from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.utils import CustomStreamWrapper, ModelResponse, Usage + +from .base import BaseLLM +from .prompt_templates.factory import custom_prompt, prompt_factory class AnthropicConstants(Enum): @@ -117,9 +121,9 @@ class AnthropicTextCompletion(BaseLLM): ) else: if len(completion_response["completion"]) > 0: - model_response["choices"][0]["message"]["content"] = ( - completion_response["completion"] - ) + model_response.choices[0].message.content = completion_response[ # type: ignore + "completion" + ] model_response.choices[0].finish_reason = completion_response["stop_reason"] ## CALCULATING USAGE @@ -130,8 +134,8 @@ class AnthropicTextCompletion(BaseLLM): encoding.encode(model_response["choices"][0]["message"].get("content", "")) ) ##[TODO] use the anthropic tokenizer here - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, diff --git a/litellm/llms/azure.py b/litellm/llms/azure.py index a2928cf20..409536dc1 100644 --- a/litellm/llms/azure.py +++ b/litellm/llms/azure.py @@ -47,6 +47,10 @@ from ..types.llms.openai import ( AsyncAssistantEventHandler, AsyncAssistantStreamManager, AsyncCursorPage, + ChatCompletionToolChoiceFunctionParam, + ChatCompletionToolChoiceObjectParam, + ChatCompletionToolParam, + ChatCompletionToolParamFunctionChunk, HttpxBinaryResponseContent, MessageData, OpenAICreateThreadParamsMessage, @@ -204,8 +208,8 @@ class AzureOpenAIConfig: and api_version_day < "01" ) ): - if litellm.drop_params == True or ( - drop_params is not None and drop_params == True + if litellm.drop_params is True or ( + drop_params is not None and drop_params is True ): pass else: @@ -227,6 +231,41 @@ class AzureOpenAIConfig: ) else: optional_params["tool_choice"] = value + if param == "response_format" and isinstance(value, dict): + json_schema: Optional[dict] = None + schema_name: str = "" + if "response_schema" in value: + json_schema = value["response_schema"] + schema_name = "json_tool_call" + elif "json_schema" in value: + json_schema = value["json_schema"]["schema"] + schema_name = value["json_schema"]["name"] + """ + Follow similar approach to anthropic - translate to a single tool call. + + When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode + - You usually want to provide a single tool + - You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool + - Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective. + """ + if json_schema is not None: + _tool_choice = ChatCompletionToolChoiceObjectParam( + type="function", + function=ChatCompletionToolChoiceFunctionParam( + name=schema_name + ), + ) + + _tool = ChatCompletionToolParam( + type="function", + function=ChatCompletionToolParamFunctionChunk( + name=schema_name, parameters=json_schema + ), + ) + + optional_params["tools"] = [_tool] + optional_params["tool_choice"] = _tool_choice + optional_params["json_mode"] = True elif param in supported_openai_params: optional_params[param] = value return optional_params @@ -403,6 +442,27 @@ def get_azure_ad_token_from_oidc(azure_ad_token: str): return azure_ad_token_access_token +def _check_dynamic_azure_params( + azure_client_params: dict, + azure_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]], +) -> bool: + """ + Returns True if user passed in client params != initialized azure client + + Currently only implemented for api version + """ + if azure_client is None: + return True + + dynamic_params = ["api_version"] + for k, v in azure_client_params.items(): + if k in dynamic_params and k == "api_version": + if v is not None and v != azure_client._custom_query["api-version"]: + return True + + return False + + class AzureChatCompletion(BaseLLM): def __init__(self) -> None: super().__init__() @@ -462,6 +522,28 @@ class AzureChatCompletion(BaseLLM): return azure_client + def make_sync_azure_openai_chat_completion_request( + self, + azure_client: AzureOpenAI, + data: dict, + timeout: Union[float, httpx.Timeout], + ): + """ + Helper to: + - call chat.completions.create.with_raw_response when litellm.return_response_headers is True + - call chat.completions.create by default + """ + try: + raw_response = azure_client.chat.completions.with_raw_response.create( + **data, timeout=timeout + ) + + headers = dict(raw_response.headers) + response = raw_response.parse() + return headers, response + except Exception as e: + raise e + async def make_azure_openai_chat_completion_request( self, azure_client: AsyncAzureOpenAI, @@ -474,21 +556,13 @@ class AzureChatCompletion(BaseLLM): - call chat.completions.create by default """ try: - if litellm.return_response_headers is True: - raw_response = ( - await azure_client.chat.completions.with_raw_response.create( - **data, timeout=timeout - ) - ) + raw_response = await azure_client.chat.completions.with_raw_response.create( + **data, timeout=timeout + ) - headers = dict(raw_response.headers) - response = raw_response.parse() - return headers, response - else: - response = await azure_client.chat.completions.create( - **data, timeout=timeout - ) - return None, response + headers = dict(raw_response.headers) + response = raw_response.parse() + return headers, response except Exception as e: raise e @@ -502,6 +576,7 @@ class AzureChatCompletion(BaseLLM): api_version: str, api_type: str, azure_ad_token: str, + dynamic_params: bool, print_verbose: Callable, timeout: Union[float, httpx.Timeout], logging_obj: LiteLLMLoggingObj, @@ -521,6 +596,7 @@ class AzureChatCompletion(BaseLLM): ) max_retries = optional_params.pop("max_retries", 2) + json_mode: Optional[bool] = optional_params.pop("json_mode", False) ### CHECK IF CLOUDFLARE AI GATEWAY ### ### if so - set the model as part of the base url @@ -566,6 +642,7 @@ class AzureChatCompletion(BaseLLM): return self.async_streaming( logging_obj=logging_obj, api_base=api_base, + dynamic_params=dynamic_params, data=data, model=model, api_key=api_key, @@ -583,14 +660,17 @@ class AzureChatCompletion(BaseLLM): api_version=api_version, model=model, azure_ad_token=azure_ad_token, + dynamic_params=dynamic_params, timeout=timeout, client=client, logging_obj=logging_obj, + convert_tool_call_to_json_mode=json_mode, ) elif "stream" in optional_params and optional_params["stream"] == True: return self.streaming( logging_obj=logging_obj, api_base=api_base, + dynamic_params=dynamic_params, data=data, model=model, api_key=api_key, @@ -636,7 +716,8 @@ class AzureChatCompletion(BaseLLM): if azure_ad_token.startswith("oidc/"): azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token) azure_client_params["azure_ad_token"] = azure_ad_token - if client is None: + + if client is None or dynamic_params: azure_client = AzureOpenAI(**azure_client_params) else: azure_client = client @@ -648,7 +729,9 @@ class AzureChatCompletion(BaseLLM): "api-version", api_version ) - response = azure_client.chat.completions.create(**data, timeout=timeout) # type: ignore + headers, response = self.make_sync_azure_openai_chat_completion_request( + azure_client=azure_client, data=data, timeout=timeout + ) stringified_response = response.model_dump() ## LOGGING logging_obj.post_call( @@ -664,6 +747,7 @@ class AzureChatCompletion(BaseLLM): return convert_to_model_response_object( response_object=stringified_response, model_response_object=model_response, + convert_tool_call_to_json_mode=json_mode, ) except AzureOpenAIError as e: exception_mapping_worked = True @@ -682,9 +766,11 @@ class AzureChatCompletion(BaseLLM): api_base: str, data: dict, timeout: Any, + dynamic_params: bool, model_response: ModelResponse, logging_obj: LiteLLMLoggingObj, azure_ad_token: Optional[str] = None, + convert_tool_call_to_json_mode: Optional[bool] = None, client=None, # this is the AsyncAzureOpenAI ): response = None @@ -715,15 +801,11 @@ class AzureChatCompletion(BaseLLM): azure_client_params["azure_ad_token"] = azure_ad_token # setting Azure client - if client is None: + if client is None or dynamic_params: azure_client = AsyncAzureOpenAI(**azure_client_params) else: azure_client = client - if api_version is not None and isinstance( - azure_client._custom_query, dict - ): - # set api_version to version passed by user - azure_client._custom_query.setdefault("api-version", api_version) + ## LOGGING logging_obj.pre_call( input=data["messages"], @@ -750,9 +832,13 @@ class AzureChatCompletion(BaseLLM): original_response=stringified_response, additional_args={"complete_input_dict": data}, ) + return convert_to_model_response_object( response_object=stringified_response, model_response_object=model_response, + hidden_params={"headers": headers}, + _response_headers=headers, + convert_tool_call_to_json_mode=convert_tool_call_to_json_mode, ) except AzureOpenAIError as e: ## LOGGING @@ -792,6 +878,7 @@ class AzureChatCompletion(BaseLLM): api_base: str, api_key: str, api_version: str, + dynamic_params: bool, data: dict, model: str, timeout: Any, @@ -821,13 +908,11 @@ class AzureChatCompletion(BaseLLM): if azure_ad_token.startswith("oidc/"): azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token) azure_client_params["azure_ad_token"] = azure_ad_token - if client is None: + + if client is None or dynamic_params: azure_client = AzureOpenAI(**azure_client_params) else: azure_client = client - if api_version is not None and isinstance(azure_client._custom_query, dict): - # set api_version to version passed by user - azure_client._custom_query.setdefault("api-version", api_version) ## LOGGING logging_obj.pre_call( input=data["messages"], @@ -839,7 +924,9 @@ class AzureChatCompletion(BaseLLM): "complete_input_dict": data, }, ) - response = azure_client.chat.completions.create(**data, timeout=timeout) + headers, response = self.make_sync_azure_openai_chat_completion_request( + azure_client=azure_client, data=data, timeout=timeout + ) streamwrapper = CustomStreamWrapper( completion_stream=response, model=model, @@ -854,6 +941,7 @@ class AzureChatCompletion(BaseLLM): api_base: str, api_key: str, api_version: str, + dynamic_params: bool, data: dict, model: str, timeout: Any, @@ -879,15 +967,10 @@ class AzureChatCompletion(BaseLLM): if azure_ad_token.startswith("oidc/"): azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token) azure_client_params["azure_ad_token"] = azure_ad_token - if client is None: + if client is None or dynamic_params: azure_client = AsyncAzureOpenAI(**azure_client_params) else: azure_client = client - if api_version is not None and isinstance( - azure_client._custom_query, dict - ): - # set api_version to version passed by user - azure_client._custom_query.setdefault("api-version", api_version) ## LOGGING logging_obj.pre_call( input=data["messages"], @@ -913,6 +996,7 @@ class AzureChatCompletion(BaseLLM): model=model, custom_llm_provider="azure", logging_obj=logging_obj, + _response_headers=headers, ) return streamwrapper ## DO NOT make this into an async for ... loop, it will yield an async generator, which won't raise errors if the response fails except Exception as e: @@ -1864,6 +1948,23 @@ class AzureChatCompletion(BaseLLM): model=model, # type: ignore prompt=prompt, # type: ignore ) + elif mode == "audio_transcription": + # Get the current directory of the file being run + pwd = os.path.dirname(os.path.realpath(__file__)) + file_path = os.path.join(pwd, "../tests/gettysburg.wav") + audio_file = open(file_path, "rb") + completion = await client.audio.transcriptions.with_raw_response.create( + file=audio_file, + model=model, # type: ignore + prompt=prompt, # type: ignore + ) + elif mode == "audio_speech": + # Get the current directory of the file being run + completion = await client.audio.speech.with_raw_response.create( + model=model, # type: ignore + input=prompt, # type: ignore + voice="alloy", + ) else: raise Exception("mode not set") response = {} diff --git a/litellm/llms/base_aws_llm.py b/litellm/llms/base_aws_llm.py new file mode 100644 index 000000000..8de42eda7 --- /dev/null +++ b/litellm/llms/base_aws_llm.py @@ -0,0 +1,218 @@ +import json +from typing import List, Optional + +import httpx + +from litellm._logging import verbose_logger +from litellm.caching import DualCache, InMemoryCache +from litellm.utils import get_secret + +from .base import BaseLLM + + +class AwsAuthError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url="https://us-west-2.console.aws.amazon.com/bedrock" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class BaseAWSLLM(BaseLLM): + def __init__(self) -> None: + self.iam_cache = DualCache() + super().__init__() + + def get_credentials( + self, + aws_access_key_id: Optional[str] = None, + aws_secret_access_key: Optional[str] = None, + aws_session_token: Optional[str] = None, + aws_region_name: Optional[str] = None, + aws_session_name: Optional[str] = None, + aws_profile_name: Optional[str] = None, + aws_role_name: Optional[str] = None, + aws_web_identity_token: Optional[str] = None, + aws_sts_endpoint: Optional[str] = None, + ): + """ + Return a boto3.Credentials object + """ + import boto3 + + ## CHECK IS 'os.environ/' passed in + params_to_check: List[Optional[str]] = [ + aws_access_key_id, + aws_secret_access_key, + aws_session_token, + aws_region_name, + aws_session_name, + aws_profile_name, + aws_role_name, + aws_web_identity_token, + aws_sts_endpoint, + ] + + # Iterate over parameters and update if needed + for i, param in enumerate(params_to_check): + if param and param.startswith("os.environ/"): + _v = get_secret(param) + if _v is not None and isinstance(_v, str): + params_to_check[i] = _v + # Assign updated values back to parameters + ( + aws_access_key_id, + aws_secret_access_key, + aws_session_token, + aws_region_name, + aws_session_name, + aws_profile_name, + aws_role_name, + aws_web_identity_token, + aws_sts_endpoint, + ) = params_to_check + + verbose_logger.debug( + "in get credentials\n" + "aws_access_key_id=%s\n" + "aws_secret_access_key=%s\n" + "aws_session_token=%s\n" + "aws_region_name=%s\n" + "aws_session_name=%s\n" + "aws_profile_name=%s\n" + "aws_role_name=%s\n" + "aws_web_identity_token=%s\n" + "aws_sts_endpoint=%s", + aws_access_key_id, + aws_secret_access_key, + aws_session_token, + aws_region_name, + aws_session_name, + aws_profile_name, + aws_role_name, + aws_web_identity_token, + aws_sts_endpoint, + ) + + ### CHECK STS ### + if ( + aws_web_identity_token is not None + and aws_role_name is not None + and aws_session_name is not None + ): + verbose_logger.debug( + f"IN Web Identity Token: {aws_web_identity_token} | Role Name: {aws_role_name} | Session Name: {aws_session_name}" + ) + + if aws_sts_endpoint is None: + sts_endpoint = f"https://sts.{aws_region_name}.amazonaws.com" + else: + sts_endpoint = aws_sts_endpoint + + iam_creds_cache_key = json.dumps( + { + "aws_web_identity_token": aws_web_identity_token, + "aws_role_name": aws_role_name, + "aws_session_name": aws_session_name, + "aws_region_name": aws_region_name, + "aws_sts_endpoint": sts_endpoint, + } + ) + + iam_creds_dict = self.iam_cache.get_cache(iam_creds_cache_key) + if iam_creds_dict is None: + oidc_token = get_secret(aws_web_identity_token) + + if oidc_token is None: + raise AwsAuthError( + message="OIDC token could not be retrieved from secret manager.", + status_code=401, + ) + + sts_client = boto3.client( + "sts", + region_name=aws_region_name, + endpoint_url=sts_endpoint, + ) + + # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html + # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html + sts_response = sts_client.assume_role_with_web_identity( + RoleArn=aws_role_name, + RoleSessionName=aws_session_name, + WebIdentityToken=oidc_token, + DurationSeconds=3600, + ) + + iam_creds_dict = { + "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"], + "aws_secret_access_key": sts_response["Credentials"][ + "SecretAccessKey" + ], + "aws_session_token": sts_response["Credentials"]["SessionToken"], + "region_name": aws_region_name, + } + + self.iam_cache.set_cache( + key=iam_creds_cache_key, + value=json.dumps(iam_creds_dict), + ttl=3600 - 60, + ) + + session = boto3.Session(**iam_creds_dict) + + iam_creds = session.get_credentials() + + return iam_creds + elif aws_role_name is not None and aws_session_name is not None: + sts_client = boto3.client( + "sts", + aws_access_key_id=aws_access_key_id, # [OPTIONAL] + aws_secret_access_key=aws_secret_access_key, # [OPTIONAL] + ) + + sts_response = sts_client.assume_role( + RoleArn=aws_role_name, RoleSessionName=aws_session_name + ) + + # Extract the credentials from the response and convert to Session Credentials + sts_credentials = sts_response["Credentials"] + from botocore.credentials import Credentials + + credentials = Credentials( + access_key=sts_credentials["AccessKeyId"], + secret_key=sts_credentials["SecretAccessKey"], + token=sts_credentials["SessionToken"], + ) + return credentials + elif aws_profile_name is not None: ### CHECK SESSION ### + # uses auth values from AWS profile usually stored in ~/.aws/credentials + client = boto3.Session(profile_name=aws_profile_name) + + return client.get_credentials() + elif ( + aws_access_key_id is not None + and aws_secret_access_key is not None + and aws_session_token is not None + ): ### CHECK FOR AWS SESSION TOKEN ### + from botocore.credentials import Credentials + + credentials = Credentials( + access_key=aws_access_key_id, + secret_key=aws_secret_access_key, + token=aws_session_token, + ) + return credentials + else: + session = boto3.Session( + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + region_name=aws_region_name, + ) + + return session.get_credentials() diff --git a/litellm/llms/baseten.py b/litellm/llms/baseten.py index 643dae530..d856efc9a 100644 --- a/litellm/llms/baseten.py +++ b/litellm/llms/baseten.py @@ -1,9 +1,11 @@ -import os import json -from enum import Enum -import requests # type: ignore +import os import time +from enum import Enum from typing import Callable + +import requests # type: ignore + from litellm.utils import ModelResponse, Usage @@ -106,28 +108,32 @@ def completion( and "data" in completion_response["model_output"] and isinstance(completion_response["model_output"]["data"], list) ): - model_response["choices"][0]["message"]["content"] = ( - completion_response["model_output"]["data"][0] - ) + model_response.choices[0].message.content = completion_response[ # type: ignore + "model_output" + ][ + "data" + ][ + 0 + ] elif isinstance(completion_response["model_output"], str): - model_response["choices"][0]["message"]["content"] = ( - completion_response["model_output"] - ) + model_response.choices[0].message.content = completion_response[ # type: ignore + "model_output" + ] elif "completion" in completion_response and isinstance( completion_response["completion"], str ): - model_response["choices"][0]["message"]["content"] = ( - completion_response["completion"] - ) + model_response.choices[0].message.content = completion_response[ # type: ignore + "completion" + ] elif isinstance(completion_response, list) and len(completion_response) > 0: if "generated_text" not in completion_response: raise BasetenError( message=f"Unable to parse response. Original response: {response.text}", status_code=response.status_code, ) - model_response["choices"][0]["message"]["content"] = ( - completion_response[0]["generated_text"] - ) + model_response.choices[0].message.content = completion_response[0][ # type: ignore + "generated_text" + ] ## GETTING LOGPROBS if ( "details" in completion_response[0] @@ -139,7 +145,7 @@ def completion( sum_logprob = 0 for token in completion_response[0]["details"]["tokens"]: sum_logprob += token["logprob"] - model_response["choices"][0]["message"]._logprobs = sum_logprob + model_response.choices[0].logprobs = sum_logprob else: raise BasetenError( message=f"Unable to parse response. Original response: {response.text}", @@ -152,8 +158,8 @@ def completion( encoding.encode(model_response["choices"][0]["message"]["content"]) ) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, diff --git a/litellm/llms/bedrock.py b/litellm/llms/bedrock.py index a8c47b3b9..2185ec459 100644 --- a/litellm/llms/bedrock.py +++ b/litellm/llms/bedrock.py @@ -13,6 +13,7 @@ from enum import Enum from typing import Any, Callable, List, Optional, Union import httpx +from openai.types.image import Image import litellm from litellm.litellm_core_utils.core_helpers import map_finish_reason @@ -1122,7 +1123,7 @@ def completion( logging_obj=logging_obj, ) - model_response["finish_reason"] = map_finish_reason( + model_response.choices[0].finish_reason = map_finish_reason( response_body["stop_reason"] ) _usage = litellm.Usage( @@ -1134,14 +1135,16 @@ def completion( setattr(model_response, "usage", _usage) else: outputText = response_body["completion"] - model_response["finish_reason"] = response_body["stop_reason"] + model_response.choices[0].finish_reason = response_body["stop_reason"] elif provider == "cohere": outputText = response_body["generations"][0]["text"] elif provider == "meta": outputText = response_body["generation"] elif provider == "mistral": outputText = response_body["outputs"][0]["text"] - model_response["finish_reason"] = response_body["outputs"][0]["stop_reason"] + model_response.choices[0].finish_reason = response_body["outputs"][0][ + "stop_reason" + ] else: # amazon titan outputText = response_body.get("results")[0].get("outputText") @@ -1160,7 +1163,7 @@ def completion( and getattr(model_response.choices[0].message, "tool_calls", None) is None ): - model_response["choices"][0]["message"]["content"] = outputText + model_response.choices[0].message.content = outputText elif ( hasattr(model_response.choices[0], "message") and getattr(model_response.choices[0].message, "tool_calls", None) @@ -1199,8 +1202,8 @@ def completion( ) setattr(model_response, "usage", usage) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model model_response._hidden_params["region_name"] = client.meta.region_name print_verbose(f"model_response._hidden_params: {model_response._hidden_params}") @@ -1323,9 +1326,9 @@ def _embedding_func_single( def embedding( model: str, input: Union[list, str], + model_response: litellm.EmbeddingResponse, api_key: Optional[str] = None, logging_obj=None, - model_response=None, optional_params=None, encoding=None, ): @@ -1391,9 +1394,9 @@ def embedding( "embedding": embedding, } ) - model_response["object"] = "list" - model_response["data"] = embedding_response - model_response["model"] = model + model_response.object = "list" + model_response.data = embedding_response + model_response.model = model input_tokens = 0 input_str = "".join(input) @@ -1411,10 +1414,10 @@ def embedding( def image_generation( model: str, prompt: str, + model_response: ImageResponse, + optional_params: dict, timeout=None, logging_obj=None, - model_response=None, - optional_params=None, aimg_generation=False, ): """ @@ -1511,9 +1514,10 @@ def image_generation( if model_response is None: model_response = ImageResponse() - image_list: List = [] + image_list: List[Image] = [] for artifact in response_body["artifacts"]: - image_dict = {"url": artifact["base64"]} + _image = Image(b64_json=artifact["base64"]) + image_list.append(_image) - model_response.data = image_dict + model_response.data = image_list return model_response diff --git a/litellm/llms/bedrock_httpx.py b/litellm/llms/bedrock_httpx.py index 33d920338..e45559752 100644 --- a/litellm/llms/bedrock_httpx.py +++ b/litellm/llms/bedrock_httpx.py @@ -27,7 +27,8 @@ import httpx # type: ignore import requests # type: ignore import litellm -from litellm.caching import DualCache +from litellm import verbose_logger +from litellm.caching import DualCache, InMemoryCache from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.litellm_core_utils.litellm_logging import Logging from litellm.llms.custom_httpx.http_handler import ( @@ -42,8 +43,11 @@ from litellm.types.llms.openai import ( ChatCompletionResponseMessage, ChatCompletionToolCallChunk, ChatCompletionToolCallFunctionChunk, + ChatCompletionUsageBlock, ) -from litellm.types.utils import Choices, Message +from litellm.types.utils import Choices +from litellm.types.utils import GenericStreamingChunk as GChunk +from litellm.types.utils import Message from litellm.utils import ( CustomStreamWrapper, ModelResponse, @@ -53,6 +57,7 @@ from litellm.utils import ( ) from .base import BaseLLM +from .base_aws_llm import BaseAWSLLM from .bedrock import BedrockError, ModelResponseIterator, convert_messages_to_prompt from .prompt_templates.factory import ( _bedrock_converse_messages_pt, @@ -75,9 +80,18 @@ BEDROCK_CONVERSE_MODELS = [ "anthropic.claude-v2:1", "anthropic.claude-v1", "anthropic.claude-instant-v1", + "ai21.jamba-instruct-v1:0", + "meta.llama3-1-8b-instruct-v1:0", + "meta.llama3-1-70b-instruct-v1:0", + "meta.llama3-1-405b-instruct-v1:0", + "mistral.mistral-large-2407-v1:0", ] -iam_cache = DualCache() + +_response_stream_shape_cache = None +bedrock_tool_name_mappings: InMemoryCache = InMemoryCache( + max_size_in_memory=50, default_ttl=600 +) class AmazonCohereChatConfig: @@ -193,13 +207,39 @@ async def make_call( if client is None: client = _get_async_httpx_client() # Create a new client if none provided - response = await client.post(api_base, headers=headers, data=data, stream=True) + response = await client.post( + api_base, + headers=headers, + data=data, + stream=True if "ai21" not in api_base else False, + ) if response.status_code != 200: raise BedrockError(status_code=response.status_code, message=response.text) - decoder = AWSEventStreamDecoder(model=model) - completion_stream = decoder.aiter_bytes(response.aiter_bytes(chunk_size=1024)) + if "ai21" in api_base: + aws_bedrock_process_response = BedrockConverseLLM() + model_response: ( + ModelResponse + ) = aws_bedrock_process_response.process_response( + model=model, + response=response, + model_response=litellm.ModelResponse(), + stream=True, + logging_obj=logging_obj, + optional_params={}, + api_key="", + data=data, + messages=messages, + print_verbose=litellm.print_verbose, + encoding=litellm.encoding, + ) # type: ignore + completion_stream: Any = MockResponseIterator(model_response=model_response) + else: + decoder = AWSEventStreamDecoder(model=model) + completion_stream = decoder.aiter_bytes( + response.aiter_bytes(chunk_size=1024) + ) # LOGGING logging_obj.post_call( @@ -212,7 +252,7 @@ async def make_call( return completion_stream except httpx.HTTPStatusError as err: error_code = err.response.status_code - raise BedrockError(status_code=error_code, message=str(err)) + raise BedrockError(status_code=error_code, message=err.response.text) except httpx.TimeoutException as e: raise BedrockError(status_code=408, message="Timeout error occurred.") except Exception as e: @@ -231,13 +271,35 @@ def make_sync_call( if client is None: client = _get_httpx_client() # Create a new client if none provided - response = client.post(api_base, headers=headers, data=data, stream=True) + response = client.post( + api_base, + headers=headers, + data=data, + stream=True if "ai21" not in api_base else False, + ) if response.status_code != 200: raise BedrockError(status_code=response.status_code, message=response.read()) - decoder = AWSEventStreamDecoder(model=model) - completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + if "ai21" in api_base: + aws_bedrock_process_response = BedrockConverseLLM() + model_response: ModelResponse = aws_bedrock_process_response.process_response( + model=model, + response=response, + model_response=litellm.ModelResponse(), + stream=True, + logging_obj=logging_obj, + optional_params={}, + api_key="", + data=data, + messages=messages, + print_verbose=litellm.print_verbose, + encoding=litellm.encoding, + ) # type: ignore + completion_stream: Any = MockResponseIterator(model_response=model_response) + else: + decoder = AWSEventStreamDecoder(model=model) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) # LOGGING logging_obj.post_call( @@ -250,7 +312,7 @@ def make_sync_call( return completion_stream -class BedrockLLM(BaseLLM): +class BedrockLLM(BaseAWSLLM): """ Example call @@ -318,173 +380,6 @@ class BedrockLLM(BaseLLM): prompt += f"{message['content']}" return prompt, chat_history # type: ignore - def get_credentials( - self, - aws_access_key_id: Optional[str] = None, - aws_secret_access_key: Optional[str] = None, - aws_session_token: Optional[str] = None, - aws_region_name: Optional[str] = None, - aws_session_name: Optional[str] = None, - aws_profile_name: Optional[str] = None, - aws_role_name: Optional[str] = None, - aws_web_identity_token: Optional[str] = None, - ): - """ - Return a boto3.Credentials object - """ - import boto3 - - print_verbose( - f"Boto3 get_credentials called variables passed to function {locals()}" - ) - - ## CHECK IS 'os.environ/' passed in - params_to_check: List[Optional[str]] = [ - aws_access_key_id, - aws_secret_access_key, - aws_session_token, - aws_region_name, - aws_session_name, - aws_profile_name, - aws_role_name, - aws_web_identity_token, - ] - - # Iterate over parameters and update if needed - for i, param in enumerate(params_to_check): - if param and param.startswith("os.environ/"): - _v = get_secret(param) - if _v is not None and isinstance(_v, str): - params_to_check[i] = _v - # Assign updated values back to parameters - ( - aws_access_key_id, - aws_secret_access_key, - aws_session_token, - aws_region_name, - aws_session_name, - aws_profile_name, - aws_role_name, - aws_web_identity_token, - ) = params_to_check - - ### CHECK STS ### - if ( - aws_web_identity_token is not None - and aws_role_name is not None - and aws_session_name is not None - ): - print_verbose( - f"IN Web Identity Token: {aws_web_identity_token} | Role Name: {aws_role_name} | Session Name: {aws_session_name}" - ) - iam_creds_cache_key = json.dumps( - { - "aws_web_identity_token": aws_web_identity_token, - "aws_role_name": aws_role_name, - "aws_session_name": aws_session_name, - "aws_region_name": aws_region_name, - } - ) - - iam_creds_dict = iam_cache.get_cache(iam_creds_cache_key) - if iam_creds_dict is None: - oidc_token = get_secret(aws_web_identity_token) - - if oidc_token is None: - raise BedrockError( - message="OIDC token could not be retrieved from secret manager.", - status_code=401, - ) - - sts_client = boto3.client( - "sts", - region_name=aws_region_name, - endpoint_url=f"https://sts.{aws_region_name}.amazonaws.com", - ) - - # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html - # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html - sts_response = sts_client.assume_role_with_web_identity( - RoleArn=aws_role_name, - RoleSessionName=aws_session_name, - WebIdentityToken=oidc_token, - DurationSeconds=3600, - ) - - iam_creds_dict = { - "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"], - "aws_secret_access_key": sts_response["Credentials"][ - "SecretAccessKey" - ], - "aws_session_token": sts_response["Credentials"]["SessionToken"], - "region_name": aws_region_name, - } - - iam_cache.set_cache( - key=iam_creds_cache_key, - value=json.dumps(iam_creds_dict), - ttl=3600 - 60, - ) - - session = boto3.Session(**iam_creds_dict) - - iam_creds = session.get_credentials() - - return iam_creds - elif aws_role_name is not None and aws_session_name is not None: - print_verbose( - f"Using STS Client AWS aws_role_name: {aws_role_name} aws_session_name: {aws_session_name}" - ) - sts_client = boto3.client( - "sts", - aws_access_key_id=aws_access_key_id, # [OPTIONAL] - aws_secret_access_key=aws_secret_access_key, # [OPTIONAL] - ) - - sts_response = sts_client.assume_role( - RoleArn=aws_role_name, RoleSessionName=aws_session_name - ) - - # Extract the credentials from the response and convert to Session Credentials - sts_credentials = sts_response["Credentials"] - from botocore.credentials import Credentials - - credentials = Credentials( - access_key=sts_credentials["AccessKeyId"], - secret_key=sts_credentials["SecretAccessKey"], - token=sts_credentials["SessionToken"], - ) - return credentials - elif aws_profile_name is not None: ### CHECK SESSION ### - # uses auth values from AWS profile usually stored in ~/.aws/credentials - print_verbose(f"Using AWS profile: {aws_profile_name}") - client = boto3.Session(profile_name=aws_profile_name) - - return client.get_credentials() - elif ( - aws_access_key_id is not None - and aws_secret_access_key is not None - and aws_session_token is not None - ): ### CHECK FOR AWS SESSION TOKEN ### - print_verbose(f"Using AWS Session Token: {aws_session_token}") - from botocore.credentials import Credentials - - credentials = Credentials( - access_key=aws_access_key_id, - secret_key=aws_secret_access_key, - token=aws_session_token, - ) - return credentials - else: - print_verbose("Using Default AWS Session") - session = boto3.Session( - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, - region_name=aws_region_name, - ) - - return session.get_credentials() - def process_response( self, model: str, @@ -521,7 +416,7 @@ class BedrockLLM(BaseLLM): outputText = completion_response["text"] # type: ignore elif "generations" in completion_response: outputText = completion_response["generations"][0]["text"] - model_response["finish_reason"] = map_finish_reason( + model_response.choices[0].finish_reason = map_finish_reason( completion_response["generations"][0]["finish_reason"] ) elif provider == "anthropic": @@ -625,7 +520,7 @@ class BedrockLLM(BaseLLM): logging_obj=logging_obj, ) - model_response["finish_reason"] = map_finish_reason( + model_response.choices[0].finish_reason = map_finish_reason( completion_response.get("stop_reason", "") ) _usage = litellm.Usage( @@ -638,7 +533,9 @@ class BedrockLLM(BaseLLM): else: outputText = completion_response["completion"] - model_response["finish_reason"] = completion_response["stop_reason"] + model_response.choices[0].finish_reason = completion_response[ + "stop_reason" + ] elif provider == "ai21": outputText = ( completion_response.get("completions")[0].get("data").get("text") @@ -647,9 +544,9 @@ class BedrockLLM(BaseLLM): outputText = completion_response["generation"] elif provider == "mistral": outputText = completion_response["outputs"][0]["text"] - model_response["finish_reason"] = completion_response["outputs"][0][ - "stop_reason" - ] + model_response.choices[0].finish_reason = completion_response[ + "outputs" + ][0]["stop_reason"] else: # amazon titan outputText = completion_response.get("results")[0].get("outputText") except Exception as e: @@ -667,7 +564,7 @@ class BedrockLLM(BaseLLM): and getattr(model_response.choices[0].message, "tool_calls", None) is None ): - model_response["choices"][0]["message"]["content"] = outputText + model_response.choices[0].message.content = outputText elif ( hasattr(model_response.choices[0], "message") and getattr(model_response.choices[0].message, "tool_calls", None) @@ -723,8 +620,8 @@ class BedrockLLM(BaseLLM): ) ) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, @@ -792,6 +689,7 @@ class BedrockLLM(BaseLLM): "aws_bedrock_runtime_endpoint", None ) # https://bedrock-runtime.{region_name}.amazonaws.com aws_web_identity_token = optional_params.pop("aws_web_identity_token", None) + aws_sts_endpoint = optional_params.pop("aws_sts_endpoint", None) ### SET REGION NAME ### if aws_region_name is None: @@ -821,6 +719,7 @@ class BedrockLLM(BaseLLM): aws_profile_name=aws_profile_name, aws_role_name=aws_role_name, aws_web_identity_token=aws_web_identity_token, + aws_sts_endpoint=aws_sts_endpoint, ) ### SET RUNTIME ENDPOINT ### @@ -978,10 +877,11 @@ class BedrockLLM(BaseLLM): "complete_input_dict": inference_params, }, ) - raise Exception( - "Bedrock HTTPX: Unsupported provider={}, model={}".format( + raise BedrockError( + status_code=404, + message="Bedrock HTTPX: Unknown provider={}, model={}".format( provider, model - ) + ), ) ## COMPLETION CALL @@ -1260,6 +1160,7 @@ class AmazonConverseConfig: model.startswith("anthropic") or model.startswith("mistral") or model.startswith("cohere") + or model.startswith("meta.llama3-1") ): supported_params.append("tools") @@ -1317,6 +1218,8 @@ class AmazonConverseConfig: optional_params["stream"] = value if param == "stop": if isinstance(value, str): + if len(value) == 0: # converse raises error for empty strings + continue value = [value] optional_params["stop_sequences"] = value if param == "temperature": @@ -1334,7 +1237,7 @@ class AmazonConverseConfig: return optional_params -class BedrockConverseLLM(BaseLLM): +class BedrockConverseLLM(BaseAWSLLM): def __init__(self) -> None: super().__init__() @@ -1344,7 +1247,7 @@ class BedrockConverseLLM(BaseLLM): response: Union[requests.Response, httpx.Response], model_response: ModelResponse, stream: bool, - logging_obj: Logging, + logging_obj: Optional[Logging], optional_params: dict, api_key: str, data: Union[dict, str], @@ -1354,12 +1257,13 @@ class BedrockConverseLLM(BaseLLM): ) -> Union[ModelResponse, CustomStreamWrapper]: ## LOGGING - logging_obj.post_call( - input=messages, - api_key=api_key, - original_response=response.text, - additional_args={"complete_input_dict": data}, - ) + if logging_obj is not None: + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) print_verbose(f"raw model_response: {response.text}") ## RESPONSE OBJECT @@ -1420,8 +1324,14 @@ class BedrockConverseLLM(BaseLLM): if "text" in content: content_str += content["text"] if "toolUse" in content: + + ## check tool name was formatted by litellm + _response_tool_name = content["toolUse"]["name"] + response_tool_name = get_bedrock_tool_name( + response_tool_name=_response_tool_name + ) _function_chunk = ChatCompletionToolCallFunctionChunk( - name=content["toolUse"]["name"], + name=response_tool_name, arguments=json.dumps(content["toolUse"]["input"]), ) _tool_response_chunk = ChatCompletionToolCallChunk( @@ -1446,8 +1356,8 @@ class BedrockConverseLLM(BaseLLM): message=litellm.Message(**chat_completion_message), ) ] - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=input_tokens, completion_tokens=output_tokens, @@ -1455,6 +1365,10 @@ class BedrockConverseLLM(BaseLLM): ) setattr(model_response, "usage", usage) + # Add "trace" from Bedrock guardrails - if user has opted in to returning it + if "trace" in completion_response: + setattr(model_response, "trace", completion_response["trace"]) + return model_response def encode_model_id(self, model_id: str) -> str: @@ -1467,160 +1381,6 @@ class BedrockConverseLLM(BaseLLM): """ return urllib.parse.quote(model_id, safe="") - def get_credentials( - self, - aws_access_key_id: Optional[str] = None, - aws_secret_access_key: Optional[str] = None, - aws_session_token: Optional[str] = None, - aws_region_name: Optional[str] = None, - aws_session_name: Optional[str] = None, - aws_profile_name: Optional[str] = None, - aws_role_name: Optional[str] = None, - aws_web_identity_token: Optional[str] = None, - ): - """ - Return a boto3.Credentials object - """ - import boto3 - - ## CHECK IS 'os.environ/' passed in - params_to_check: List[Optional[str]] = [ - aws_access_key_id, - aws_secret_access_key, - aws_session_token, - aws_region_name, - aws_session_name, - aws_profile_name, - aws_role_name, - aws_web_identity_token, - ] - - # Iterate over parameters and update if needed - for i, param in enumerate(params_to_check): - if param and param.startswith("os.environ/"): - _v = get_secret(param) - if _v is not None and isinstance(_v, str): - params_to_check[i] = _v - # Assign updated values back to parameters - ( - aws_access_key_id, - aws_secret_access_key, - aws_session_token, - aws_region_name, - aws_session_name, - aws_profile_name, - aws_role_name, - aws_web_identity_token, - ) = params_to_check - - ### CHECK STS ### - if ( - aws_web_identity_token is not None - and aws_role_name is not None - and aws_session_name is not None - ): - iam_creds_cache_key = json.dumps( - { - "aws_web_identity_token": aws_web_identity_token, - "aws_role_name": aws_role_name, - "aws_session_name": aws_session_name, - "aws_region_name": aws_region_name, - } - ) - - iam_creds_dict = iam_cache.get_cache(iam_creds_cache_key) - if iam_creds_dict is None: - oidc_token = get_secret(aws_web_identity_token) - - if oidc_token is None: - raise BedrockError( - message="OIDC token could not be retrieved from secret manager.", - status_code=401, - ) - - sts_client = boto3.client( - "sts", - region_name=aws_region_name, - endpoint_url=f"https://sts.{aws_region_name}.amazonaws.com", - ) - - # https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html - # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html - sts_response = sts_client.assume_role_with_web_identity( - RoleArn=aws_role_name, - RoleSessionName=aws_session_name, - WebIdentityToken=oidc_token, - DurationSeconds=3600, - ) - - iam_creds_dict = { - "aws_access_key_id": sts_response["Credentials"]["AccessKeyId"], - "aws_secret_access_key": sts_response["Credentials"][ - "SecretAccessKey" - ], - "aws_session_token": sts_response["Credentials"]["SessionToken"], - "region_name": aws_region_name, - } - - iam_cache.set_cache( - key=iam_creds_cache_key, - value=json.dumps(iam_creds_dict), - ttl=3600 - 60, - ) - - session = boto3.Session(**iam_creds_dict) - - iam_creds = session.get_credentials() - - return iam_creds - elif aws_role_name is not None and aws_session_name is not None: - sts_client = boto3.client( - "sts", - aws_access_key_id=aws_access_key_id, # [OPTIONAL] - aws_secret_access_key=aws_secret_access_key, # [OPTIONAL] - ) - - sts_response = sts_client.assume_role( - RoleArn=aws_role_name, RoleSessionName=aws_session_name - ) - - # Extract the credentials from the response and convert to Session Credentials - sts_credentials = sts_response["Credentials"] - from botocore.credentials import Credentials - - credentials = Credentials( - access_key=sts_credentials["AccessKeyId"], - secret_key=sts_credentials["SecretAccessKey"], - token=sts_credentials["SessionToken"], - ) - return credentials - elif aws_profile_name is not None: ### CHECK SESSION ### - # uses auth values from AWS profile usually stored in ~/.aws/credentials - client = boto3.Session(profile_name=aws_profile_name) - - return client.get_credentials() - elif ( - aws_access_key_id is not None - and aws_secret_access_key is not None - and aws_session_token is not None - ): ### CHECK FOR AWS SESSION TOKEN ### - from botocore.credentials import Credentials - - credentials = Credentials( - access_key=aws_access_key_id, - secret_key=aws_secret_access_key, - token=aws_session_token, - ) - return credentials - else: - session = boto3.Session( - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, - region_name=aws_region_name, - ) - - return session.get_credentials() - async def async_streaming( self, model: str, @@ -1675,7 +1435,7 @@ class BedrockConverseLLM(BaseLLM): headers={}, client: Optional[AsyncHTTPHandler] = None, ) -> Union[ModelResponse, CustomStreamWrapper]: - if client is None: + if client is None or not isinstance(client, AsyncHTTPHandler): _params = {} if timeout is not None: if isinstance(timeout, float) or isinstance(timeout, int): @@ -1756,6 +1516,7 @@ class BedrockConverseLLM(BaseLLM): "aws_bedrock_runtime_endpoint", None ) # https://bedrock-runtime.{region_name}.amazonaws.com aws_web_identity_token = optional_params.pop("aws_web_identity_token", None) + aws_sts_endpoint = optional_params.pop("aws_sts_endpoint", None) ### SET REGION NAME ### if aws_region_name is None: @@ -1785,6 +1546,7 @@ class BedrockConverseLLM(BaseLLM): aws_profile_name=aws_profile_name, aws_role_name=aws_role_name, aws_web_identity_token=aws_web_identity_token, + aws_sts_endpoint=aws_sts_endpoint, ) ### SET RUNTIME ENDPOINT ### @@ -1813,8 +1575,15 @@ class BedrockConverseLLM(BaseLLM): system_content_blocks: List[SystemContentBlock] = [] for idx, message in enumerate(messages): if message["role"] == "system": - _system_content_block = SystemContentBlock(text=message["content"]) - system_content_blocks.append(_system_content_block) + _system_content_block: Optional[SystemContentBlock] = None + if isinstance(message["content"], str) and len(message["content"]) > 0: + _system_content_block = SystemContentBlock(text=message["content"]) + elif isinstance(message["content"], list): + for m in message["content"]: + if m.get("type", "") == "text" and len(m["text"]) > 0: + _system_content_block = SystemContentBlock(text=m["text"]) + if _system_content_block is not None: + system_content_blocks.append(_system_content_block) system_prompt_indices.append(idx) if len(system_prompt_indices) > 0: for idx in reversed(system_prompt_indices): @@ -1825,12 +1594,14 @@ class BedrockConverseLLM(BaseLLM): additional_request_params = {} supported_converse_params = AmazonConverseConfig.__annotations__.keys() supported_tool_call_params = ["tools", "tool_choice"] + supported_guardrail_params = ["guardrailConfig"] ## TRANSFORMATION ## # send all model-specific params in 'additional_request_params' for k, v in inference_params.items(): if ( k not in supported_converse_params and k not in supported_tool_call_params + and k not in supported_guardrail_params ): additional_request_params[k] = v additional_request_keys.append(k) @@ -1838,7 +1609,9 @@ class BedrockConverseLLM(BaseLLM): inference_params.pop(key, None) bedrock_messages: List[MessageBlock] = _bedrock_converse_messages_pt( - messages=messages + messages=messages, + model=model, + llm_provider="bedrock_converse", ) bedrock_tools: List[ToolBlock] = _bedrock_tools_pt( inference_params.pop("tools", []) @@ -1860,8 +1633,18 @@ class BedrockConverseLLM(BaseLLM): "system": system_content_blocks, "inferenceConfig": InferenceConfig(**inference_params), } + + # Guardrail Config + guardrail_config: Optional[GuardrailConfigBlock] = None + request_guardrails_config = inference_params.pop("guardrailConfig", None) + if request_guardrails_config is not None: + guardrail_config = GuardrailConfigBlock(**request_guardrails_config) + _data["guardrailConfig"] = guardrail_config + + # Tool Config if bedrock_tool_config is not None: _data["toolConfig"] = bedrock_tool_config + data = json.dumps(_data) ## COMPLETION CALL @@ -1889,7 +1672,7 @@ class BedrockConverseLLM(BaseLLM): if acompletion: if isinstance(client, HTTPHandler): client = None - if stream is True and provider != "ai21": + if stream is True: return self.async_streaming( model=model, messages=messages, @@ -1926,7 +1709,7 @@ class BedrockConverseLLM(BaseLLM): client=client, ) # type: ignore - if (stream is not None and stream is True) and provider != "ai21": + if stream is not None and stream is True: streaming_response = CustomStreamWrapper( completion_stream=None, @@ -1970,7 +1753,7 @@ class BedrockConverseLLM(BaseLLM): model=model, response=response, model_response=model_response, - stream=stream, + stream=stream if isinstance(stream, bool) else False, logging_obj=logging_obj, optional_params=optional_params, api_key="", @@ -1982,13 +1765,36 @@ class BedrockConverseLLM(BaseLLM): def get_response_stream_shape(): - from botocore.loaders import Loader - from botocore.model import ServiceModel + global _response_stream_shape_cache + if _response_stream_shape_cache is None: - loader = Loader() - bedrock_service_dict = loader.load_service_model("bedrock-runtime", "service-2") - bedrock_service_model = ServiceModel(bedrock_service_dict) - return bedrock_service_model.shape_for("ResponseStream") + from botocore.loaders import Loader + from botocore.model import ServiceModel + + loader = Loader() + bedrock_service_dict = loader.load_service_model("bedrock-runtime", "service-2") + bedrock_service_model = ServiceModel(bedrock_service_dict) + _response_stream_shape_cache = bedrock_service_model.shape_for("ResponseStream") + + return _response_stream_shape_cache + + +def get_bedrock_tool_name(response_tool_name: str) -> str: + """ + If litellm formatted the input tool name, we need to convert it back to the original name. + + Args: + response_tool_name (str): The name of the tool as received from the response. + + Returns: + str: The original name of the tool. + """ + + if response_tool_name in litellm.bedrock_tool_name_mappings.cache_dict: + response_tool_name = litellm.bedrock_tool_name_mappings.cache_dict[ + response_tool_name + ] + return response_tool_name class AWSEventStreamDecoder: @@ -1997,34 +1803,63 @@ class AWSEventStreamDecoder: self.model = model self.parser = EventStreamJSONParser() + self.content_blocks: List[ContentBlockDeltaEvent] = [] - def converse_chunk_parser(self, chunk_data: dict) -> GenericStreamingChunk: + def check_empty_tool_call_args(self) -> bool: + """ + Check if the tool call block so far has been an empty string + """ + args = "" + # if text content block -> skip + if len(self.content_blocks) == 0: + return False + + if "text" in self.content_blocks[0]: + return False + + for block in self.content_blocks: + if "toolUse" in block: + args += block["toolUse"]["input"] + + if len(args) == 0: + return True + return False + + def converse_chunk_parser(self, chunk_data: dict) -> GChunk: try: + verbose_logger.debug("\n\nRaw Chunk: {}\n\n".format(chunk_data)) text = "" tool_use: Optional[ChatCompletionToolCallChunk] = None is_finished = False finish_reason = "" - usage: Optional[ConverseTokenUsageBlock] = None + usage: Optional[ChatCompletionUsageBlock] = None index = int(chunk_data.get("contentBlockIndex", 0)) if "start" in chunk_data: start_obj = ContentBlockStartEvent(**chunk_data["start"]) + self.content_blocks = [] # reset if ( start_obj is not None and "toolUse" in start_obj and start_obj["toolUse"] is not None ): + ## check tool name was formatted by litellm + _response_tool_name = start_obj["toolUse"]["name"] + response_tool_name = get_bedrock_tool_name( + response_tool_name=_response_tool_name + ) tool_use = { "id": start_obj["toolUse"]["toolUseId"], "type": "function", "function": { - "name": start_obj["toolUse"]["name"], + "name": response_tool_name, "arguments": "", }, "index": index, } elif "delta" in chunk_data: delta_obj = ContentBlockDeltaEvent(**chunk_data["delta"]) + self.content_blocks.append(delta_obj) if "text" in delta_obj: text = delta_obj["text"] elif "toolUse" in delta_obj: @@ -2037,13 +1872,31 @@ class AWSEventStreamDecoder: }, "index": index, } + elif ( + "contentBlockIndex" in chunk_data + ): # stop block, no 'start' or 'delta' object + is_empty = self.check_empty_tool_call_args() + if is_empty: + tool_use = { + "id": None, + "type": "function", + "function": { + "name": None, + "arguments": "{}", + }, + "index": chunk_data["contentBlockIndex"], + } elif "stopReason" in chunk_data: finish_reason = map_finish_reason(chunk_data.get("stopReason", "stop")) is_finished = True elif "usage" in chunk_data: - usage = ConverseTokenUsageBlock(**chunk_data["usage"]) # type: ignore + usage = ChatCompletionUsageBlock( + prompt_tokens=chunk_data.get("inputTokens", 0), + completion_tokens=chunk_data.get("outputTokens", 0), + total_tokens=chunk_data.get("totalTokens", 0), + ) - response = GenericStreamingChunk( + response = GChunk( text=text, tool_use=tool_use, is_finished=is_finished, @@ -2051,11 +1904,15 @@ class AWSEventStreamDecoder: usage=usage, index=index, ) + + if "trace" in chunk_data: + trace = chunk_data.get("trace") + response["provider_specific_fields"] = {"trace": trace} return response except Exception as e: raise Exception("Received streaming error - {}".format(str(e))) - def _chunk_parser(self, chunk_data: dict) -> GenericStreamingChunk: + def _chunk_parser(self, chunk_data: dict) -> GChunk: text = "" is_finished = False finish_reason = "" @@ -2071,6 +1928,7 @@ class AWSEventStreamDecoder: "contentBlockIndex" in chunk_data or "stopReason" in chunk_data or "metrics" in chunk_data + or "trace" in chunk_data ): return self.converse_chunk_parser(chunk_data=chunk_data) ######## bedrock.mistral mappings ############### @@ -2098,7 +1956,7 @@ class AWSEventStreamDecoder: elif chunk_data.get("completionReason", None): is_finished = True finish_reason = chunk_data["completionReason"] - return GenericStreamingChunk( + return GChunk( text=text, is_finished=is_finished, finish_reason=finish_reason, @@ -2107,7 +1965,7 @@ class AWSEventStreamDecoder: tool_use=None, ) - def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[GenericStreamingChunk]: + def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[GChunk]: """Given an iterator that yields lines, iterate over it & yield every event encountered""" from botocore.eventstream import EventStreamBuffer @@ -2123,7 +1981,7 @@ class AWSEventStreamDecoder: async def aiter_bytes( self, iterator: AsyncIterator[bytes] - ) -> AsyncIterator[GenericStreamingChunk]: + ) -> AsyncIterator[GChunk]: """Given an async iterator that yields lines, iterate over it & yield every event encountered""" from botocore.eventstream import EventStreamBuffer @@ -2139,6 +1997,7 @@ class AWSEventStreamDecoder: def _parse_message_from_event(self, event) -> Optional[str]: response_dict = event.to_response_dict() parsed_response = self.parser.parse(response_dict, get_response_stream_shape()) + if response_dict["status_code"] != 200: raise ValueError(f"Bad response code, expected 200: {response_dict}") if "chunk" in parsed_response: @@ -2152,3 +2011,45 @@ class AWSEventStreamDecoder: return None return chunk.decode() # type: ignore[no-any-return] + + +class MockResponseIterator: # for returning ai21 streaming responses + def __init__(self, model_response): + self.model_response = model_response + self.is_done = False + + # Sync iterator + def __iter__(self): + return self + + def _chunk_parser(self, chunk_data: ModelResponse) -> GChunk: + + try: + chunk_usage: litellm.Usage = getattr(chunk_data, "usage") + processed_chunk = GChunk( + text=chunk_data.choices[0].message.content or "", # type: ignore + tool_use=None, + is_finished=True, + finish_reason=chunk_data.choices[0].finish_reason, # type: ignore + usage=chunk_usage, # type: ignore + index=0, + ) + return processed_chunk + except Exception: + raise ValueError(f"Failed to decode chunk: {chunk_data}") + + def __next__(self): + if self.is_done: + raise StopIteration + self.is_done = True + return self._chunk_parser(self.model_response) + + # Async iterator + def __aiter__(self): + return self + + async def __anext__(self): + if self.is_done: + raise StopAsyncIteration + self.is_done = True + return self._chunk_parser(self.model_response) diff --git a/litellm/llms/clarifai.py b/litellm/llms/clarifai.py index 785a7ad38..497b37cf8 100644 --- a/litellm/llms/clarifai.py +++ b/litellm/llms/clarifai.py @@ -1,13 +1,18 @@ -import os, types, traceback import json -import requests +import os import time +import traceback +import types from typing import Callable, Optional -from litellm.utils import ModelResponse, Usage, Choices, Message, CustomStreamWrapper -import litellm + import httpx +import requests + +import litellm from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler -from .prompt_templates.factory import prompt_factory, custom_prompt +from litellm.utils import Choices, CustomStreamWrapper, Message, ModelResponse, Usage + +from .prompt_templates.factory import custom_prompt, prompt_factory class ClarifaiError(Exception): @@ -87,7 +92,14 @@ def completions_to_model(payload): def process_response( - model, prompt, response, model_response, api_key, data, encoding, logging_obj + model, + prompt, + response, + model_response: litellm.ModelResponse, + api_key, + data, + encoding, + logging_obj, ): logging_obj.post_call( input=prompt, @@ -116,7 +128,7 @@ def process_response( message=message_obj, ) choices_list.append(choice_obj) - model_response["choices"] = choices_list + model_response.choices = choices_list # type: ignore except Exception as e: raise ClarifaiError( @@ -128,18 +140,21 @@ def process_response( completion_tokens = len( encoding.encode(model_response["choices"][0]["message"].get("content")) ) - model_response["model"] = model - model_response["usage"] = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, + model_response.model = model + setattr( + model_response, + "usage", + Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ), ) return model_response def convert_model_to_url(model: str, api_base: str): user_id, app_id, model_id = model.split(".") - model_id = model_id.lower() return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs" @@ -202,7 +217,7 @@ async def async_completion( message=message_obj, ) choices_list.append(choice_obj) - model_response["choices"] = choices_list + model_response.choices = choices_list # type: ignore except Exception as e: raise ClarifaiError( @@ -214,11 +229,15 @@ async def async_completion( completion_tokens = len( encoding.encode(model_response["choices"][0]["message"].get("content")) ) - model_response["model"] = model - model_response["usage"] = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, + model_response.model = model + setattr( + model_response, + "usage", + Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ), ) return model_response diff --git a/litellm/llms/cloudflare.py b/litellm/llms/cloudflare.py index 5a24b3b44..516b490f4 100644 --- a/litellm/llms/cloudflare.py +++ b/litellm/llms/cloudflare.py @@ -1,13 +1,17 @@ -import os, types import json -from enum import Enum -import requests # type: ignore +import os import time +import types +from enum import Enum from typing import Callable, Optional -import litellm + import httpx # type: ignore +import requests # type: ignore + +import litellm from litellm.utils import ModelResponse, Usage -from .prompt_templates.factory import prompt_factory, custom_prompt + +from .prompt_templates.factory import custom_prompt, prompt_factory class CloudflareError(Exception): @@ -147,9 +151,9 @@ def completion( ) completion_response = response.json() - model_response["choices"][0]["message"]["content"] = completion_response[ - "result" - ]["response"] + model_response.choices[0].message.content = completion_response["result"][ # type: ignore + "response" + ] ## CALCULATING USAGE print_verbose( @@ -160,8 +164,8 @@ def completion( encoding.encode(model_response["choices"][0]["message"].get("content", "")) ) - model_response["created"] = int(time.time()) - model_response["model"] = "cloudflare/" + model + model_response.created = int(time.time()) + model_response.model = "cloudflare/" + model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, diff --git a/litellm/llms/cohere.py b/litellm/llms/cohere.py index 14a66b54a..3873027b2 100644 --- a/litellm/llms/cohere.py +++ b/litellm/llms/cohere.py @@ -1,12 +1,21 @@ -import os, types +#################### OLD ######################## +##### See `cohere_chat.py` for `/chat` calls #### +################################################# import json +import os +import time +import traceback +import types from enum import Enum -import requests # type: ignore -import time, traceback -from typing import Callable, Optional -from litellm.utils import ModelResponse, Choices, Message, Usage -import litellm +from typing import Any, Callable, Optional, Union + import httpx # type: ignore +import requests # type: ignore + +import litellm +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.utils import Choices, Message, ModelResponse, Usage class CohereError(Exception): @@ -117,7 +126,7 @@ class CohereConfig: def validate_environment(api_key): headers = { - "Request-Source":"unspecified:litellm", + "Request-Source": "unspecified:litellm", "accept": "application/json", "content-type": "application/json", } @@ -219,7 +228,7 @@ def completion( message=message_obj, ) choices_list.append(choice_obj) - model_response["choices"] = choices_list + model_response.choices = choices_list # type: ignore except Exception as e: raise CohereError( message=response.text, status_code=response.status_code @@ -231,8 +240,8 @@ def completion( encoding.encode(model_response["choices"][0]["message"].get("content", "")) ) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, @@ -242,14 +251,98 @@ def completion( return model_response +def _process_embedding_response( + embeddings: list, + model_response: litellm.EmbeddingResponse, + model: str, + encoding: Any, + input: list, +) -> litellm.EmbeddingResponse: + output_data = [] + for idx, embedding in enumerate(embeddings): + output_data.append( + {"object": "embedding", "index": idx, "embedding": embedding} + ) + model_response.object = "list" + model_response.data = output_data + model_response.model = model + input_tokens = 0 + for text in input: + input_tokens += len(encoding.encode(text)) + + setattr( + model_response, + "usage", + Usage( + prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens + ), + ) + + return model_response + + +async def async_embedding( + model: str, + data: dict, + input: list, + model_response: litellm.utils.EmbeddingResponse, + timeout: Union[float, httpx.Timeout], + logging_obj: LiteLLMLoggingObj, + optional_params: dict, + api_base: str, + api_key: Optional[str], + headers: dict, + encoding: Callable, + client: Optional[AsyncHTTPHandler] = None, +): + + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "headers": headers, + "api_base": api_base, + }, + ) + ## COMPLETION CALL + if client is None: + client = AsyncHTTPHandler(concurrent_limit=1) + + response = await client.post(api_base, headers=headers, data=json.dumps(data)) + + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=response, + ) + + embeddings = response.json()["embeddings"] + + ## PROCESS RESPONSE ## + return _process_embedding_response( + embeddings=embeddings, + model_response=model_response, + model=model, + encoding=encoding, + input=input, + ) + + def embedding( model: str, input: list, + model_response: litellm.EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + optional_params: dict, + encoding: Any, api_key: Optional[str] = None, - logging_obj=None, - model_response=None, - encoding=None, - optional_params=None, + aembedding: Optional[bool] = None, + timeout: Union[float, httpx.Timeout] = httpx.Timeout(None), + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, ): headers = validate_environment(api_key) embed_url = "https://api.cohere.ai/v1/embed" @@ -266,8 +359,26 @@ def embedding( api_key=api_key, additional_args={"complete_input_dict": data}, ) + + ## ROUTING + if aembedding is True: + return async_embedding( + model=model, + data=data, + input=input, + model_response=model_response, + timeout=timeout, + logging_obj=logging_obj, + optional_params=optional_params, + api_base=embed_url, + api_key=api_key, + headers=headers, + encoding=encoding, + ) ## COMPLETION CALL - response = requests.post(embed_url, headers=headers, data=json.dumps(data)) + if client is None or not isinstance(client, HTTPHandler): + client = HTTPHandler(concurrent_limit=1) + response = client.post(embed_url, headers=headers, data=json.dumps(data)) ## LOGGING logging_obj.post_call( input=input, @@ -289,19 +400,11 @@ def embedding( if response.status_code != 200: raise CohereError(message=response.text, status_code=response.status_code) embeddings = response.json()["embeddings"] - output_data = [] - for idx, embedding in enumerate(embeddings): - output_data.append( - {"object": "embedding", "index": idx, "embedding": embedding} - ) - model_response["object"] = "list" - model_response["data"] = output_data - model_response["model"] = model - input_tokens = 0 - for text in input: - input_tokens += len(encoding.encode(text)) - model_response["usage"] = Usage( - prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens + return _process_embedding_response( + embeddings=embeddings, + model_response=model_response, + model=model, + encoding=encoding, + input=input, ) - return model_response diff --git a/litellm/llms/cohere_chat.py b/litellm/llms/cohere_chat.py index 1b3aa8405..a0a9a9874 100644 --- a/litellm/llms/cohere_chat.py +++ b/litellm/llms/cohere_chat.py @@ -212,7 +212,9 @@ def completion( headers = validate_environment(api_key) completion_url = api_base model = model - most_recent_message, chat_history = cohere_messages_pt_v2(messages=messages) + most_recent_message, chat_history = cohere_messages_pt_v2( + messages=messages, model=model, llm_provider="cohere_chat" + ) ## Load Config config = litellm.CohereConfig.get_config() @@ -231,8 +233,14 @@ def completion( optional_params["tool_results"] = [most_recent_message] elif isinstance(most_recent_message, str): optional_params["message"] = most_recent_message + + ## check if chat history message is 'user' and 'tool_results' is given -> force_single_step=True, else cohere api fails + if len(chat_history) > 0 and chat_history[-1]["role"] == "USER": + optional_params["force_single_step"] = True + data = { "model": model, + "chat_history": chat_history, **optional_params, } @@ -305,8 +313,8 @@ def completion( prompt_tokens = billed_units.get("input_tokens", 0) completion_tokens = billed_units.get("output_tokens", 0) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index ddffe9ad8..1828a92d2 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -80,18 +80,77 @@ class AsyncHTTPHandler: json: Optional[dict] = None, params: Optional[dict] = None, headers: Optional[dict] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, stream: bool = False, ): try: + if timeout is None: + timeout = self.timeout req = self.client.build_request( - "POST", url, data=data, json=json, params=params, headers=headers # type: ignore + "POST", url, data=data, json=json, params=params, headers=headers, timeout=timeout # type: ignore ) response = await self.client.send(req, stream=stream) response.raise_for_status() return response except (httpx.RemoteProtocolError, httpx.ConnectError): # Retry the request with a new session if there is a connection error - new_client = self.create_client(timeout=self.timeout, concurrent_limit=1) + new_client = self.create_client(timeout=timeout, concurrent_limit=1) + try: + return await self.single_connection_post_request( + url=url, + client=new_client, + data=data, + json=json, + params=params, + headers=headers, + stream=stream, + ) + finally: + await new_client.aclose() + except httpx.TimeoutException as e: + headers = {} + if hasattr(e, "response") and e.response is not None: + for key, value in e.response.headers.items(): + headers["response_headers-{}".format(key)] = value + + raise litellm.Timeout( + message=f"Connection timed out after {timeout} seconds.", + model="default-model-name", + llm_provider="litellm-httpx-handler", + headers=headers, + ) + except httpx.HTTPStatusError as e: + setattr(e, "status_code", e.response.status_code) + if stream is True: + setattr(e, "message", await e.response.aread()) + else: + setattr(e, "message", e.response.text) + raise e + except Exception as e: + raise e + + async def delete( + self, + url: str, + data: Optional[Union[dict, str]] = None, # type: ignore + json: Optional[dict] = None, + params: Optional[dict] = None, + headers: Optional[dict] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + stream: bool = False, + ): + try: + if timeout is None: + timeout = self.timeout + req = self.client.build_request( + "DELETE", url, data=data, json=json, params=params, headers=headers, timeout=timeout # type: ignore + ) + response = await self.client.send(req, stream=stream) + response.raise_for_status() + return response + except (httpx.RemoteProtocolError, httpx.ConnectError): + # Retry the request with a new session if there is a connection error + new_client = self.create_client(timeout=timeout, concurrent_limit=1) try: return await self.single_connection_post_request( url=url, @@ -192,13 +251,28 @@ class HTTPHandler: params: Optional[dict] = None, headers: Optional[dict] = None, stream: bool = False, + timeout: Optional[Union[float, httpx.Timeout]] = None, ): + try: - req = self.client.build_request( - "POST", url, data=data, json=json, params=params, headers=headers # type: ignore - ) - response = self.client.send(req, stream=stream) - return response + if timeout is not None: + req = self.client.build_request( + "POST", url, data=data, json=json, params=params, headers=headers, timeout=timeout # type: ignore + ) + else: + req = self.client.build_request( + "POST", url, data=data, json=json, params=params, headers=headers # type: ignore + ) + response = self.client.send(req, stream=stream) + return response + except httpx.TimeoutException: + raise litellm.Timeout( + message=f"Connection timed out after {timeout} seconds.", + model="default-model-name", + llm_provider="litellm-httpx-handler", + ) + except Exception as e: + raise e def __del__(self) -> None: try: diff --git a/litellm/llms/custom_llm.py b/litellm/llms/custom_llm.py new file mode 100644 index 000000000..47c5a485c --- /dev/null +++ b/litellm/llms/custom_llm.py @@ -0,0 +1,161 @@ +# What is this? +## Handler file for a Custom Chat LLM + +""" +- completion +- acompletion +- streaming +- async_streaming +""" + +import copy +import json +import os +import time +import types +from enum import Enum +from functools import partial +from typing import ( + Any, + AsyncGenerator, + AsyncIterator, + Callable, + Coroutine, + Iterator, + List, + Literal, + Optional, + Tuple, + Union, +) + +import httpx # type: ignore +import requests # type: ignore + +import litellm +from litellm.litellm_core_utils.core_helpers import map_finish_reason +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.types.utils import GenericStreamingChunk, ProviderField +from litellm.utils import CustomStreamWrapper, EmbeddingResponse, ModelResponse, Usage + +from .base import BaseLLM +from .prompt_templates.factory import custom_prompt, prompt_factory + + +class CustomLLMError(Exception): # use this for all your exceptions + def __init__( + self, + status_code, + message, + ): + self.status_code = status_code + self.message = message + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class CustomLLM(BaseLLM): + def __init__(self) -> None: + super().__init__() + + def completion( + self, + model: str, + messages: list, + api_base: str, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params: dict, + acompletion=None, + litellm_params=None, + logger_fn=None, + headers={}, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[HTTPHandler] = None, + ) -> ModelResponse: + raise CustomLLMError(status_code=500, message="Not implemented yet!") + + def streaming( + self, + model: str, + messages: list, + api_base: str, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params: dict, + acompletion=None, + litellm_params=None, + logger_fn=None, + headers={}, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[HTTPHandler] = None, + ) -> Iterator[GenericStreamingChunk]: + raise CustomLLMError(status_code=500, message="Not implemented yet!") + + async def acompletion( + self, + model: str, + messages: list, + api_base: str, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params: dict, + acompletion=None, + litellm_params=None, + logger_fn=None, + headers={}, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[AsyncHTTPHandler] = None, + ) -> ModelResponse: + raise CustomLLMError(status_code=500, message="Not implemented yet!") + + async def astreaming( + self, + model: str, + messages: list, + api_base: str, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params: dict, + acompletion=None, + litellm_params=None, + logger_fn=None, + headers={}, + timeout: Optional[Union[float, httpx.Timeout]] = None, + client: Optional[AsyncHTTPHandler] = None, + ) -> AsyncIterator[GenericStreamingChunk]: + raise CustomLLMError(status_code=500, message="Not implemented yet!") + + +def custom_chat_llm_router( + async_fn: bool, stream: Optional[bool], custom_llm: CustomLLM +): + """ + Routes call to CustomLLM completion/acompletion/streaming/astreaming functions, based on call type + + Validates if response is in expected format + """ + if async_fn: + if stream: + return custom_llm.astreaming + return custom_llm.acompletion + if stream: + return custom_llm.streaming + return custom_llm.completion diff --git a/litellm/llms/databricks.py b/litellm/llms/databricks.py index 1ab09246b..0c5509a71 100644 --- a/litellm/llms/databricks.py +++ b/litellm/llms/databricks.py @@ -1,26 +1,32 @@ # What is this? ## Handler file for databricks API https://docs.databricks.com/en/machine-learning/foundation-models/api-reference.html#chat-request -from functools import partial -import os, types +import copy import json -from enum import Enum -import requests, copy # type: ignore +import os import time -from typing import Callable, Optional, List, Union, Tuple, Literal -from litellm.utils import ( - ModelResponse, - Usage, - CustomStreamWrapper, - EmbeddingResponse, -) -from litellm.litellm_core_utils.core_helpers import map_finish_reason -import litellm -from .prompt_templates.factory import prompt_factory, custom_prompt -from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler -from .base import BaseLLM +import types +from enum import Enum +from functools import partial +from typing import Callable, List, Literal, Optional, Tuple, Union + import httpx # type: ignore -from litellm.types.llms.databricks import GenericStreamingChunk -from litellm.types.utils import ProviderField +import requests # type: ignore + +import litellm +from litellm.litellm_core_utils.core_helpers import map_finish_reason +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.types.llms.openai import ( + ChatCompletionDeltaChunk, + ChatCompletionResponseMessage, + ChatCompletionToolCallChunk, + ChatCompletionToolCallFunctionChunk, + ChatCompletionUsageBlock, +) +from litellm.types.utils import GenericStreamingChunk, ProviderField +from litellm.utils import CustomStreamWrapper, EmbeddingResponse, ModelResponse, Usage + +from .base import BaseLLM +from .prompt_templates.factory import custom_prompt, prompt_factory class DatabricksError(Exception): @@ -114,71 +120,6 @@ class DatabricksConfig: optional_params["stop"] = value return optional_params - def _chunk_parser(self, chunk_data: str) -> GenericStreamingChunk: - try: - text = "" - is_finished = False - finish_reason = None - logprobs = None - usage = None - original_chunk = None # this is used for function/tool calling - chunk_data = chunk_data.replace("data:", "") - chunk_data = chunk_data.strip() - if len(chunk_data) == 0 or chunk_data == "[DONE]": - return { - "text": "", - "is_finished": is_finished, - "finish_reason": finish_reason, - } - chunk_data_dict = json.loads(chunk_data) - str_line = litellm.ModelResponse(**chunk_data_dict, stream=True) - - if len(str_line.choices) > 0: - if ( - str_line.choices[0].delta is not None # type: ignore - and str_line.choices[0].delta.content is not None # type: ignore - ): - text = str_line.choices[0].delta.content # type: ignore - else: # function/tool calling chunk - when content is None. in this case we just return the original chunk from openai - original_chunk = str_line - if str_line.choices[0].finish_reason: - is_finished = True - finish_reason = str_line.choices[0].finish_reason - if finish_reason == "content_filter": - if hasattr(str_line.choices[0], "content_filter_result"): - error_message = json.dumps( - str_line.choices[0].content_filter_result # type: ignore - ) - else: - error_message = "Azure Response={}".format( - str(dict(str_line)) - ) - raise litellm.AzureOpenAIError( - status_code=400, message=error_message - ) - - # checking for logprobs - if ( - hasattr(str_line.choices[0], "logprobs") - and str_line.choices[0].logprobs is not None - ): - logprobs = str_line.choices[0].logprobs - else: - logprobs = None - - usage = getattr(str_line, "usage", None) - - return GenericStreamingChunk( - text=text, - is_finished=is_finished, - finish_reason=finish_reason, - logprobs=logprobs, - original_chunk=original_chunk, - usage=usage, - ) - except Exception as e: - raise e - class DatabricksEmbeddingConfig: """ @@ -236,7 +177,9 @@ async def make_call( if response.status_code != 200: raise DatabricksError(status_code=response.status_code, message=response.text) - completion_stream = response.aiter_lines() + completion_stream = ModelResponseIterator( + streaming_response=response.aiter_lines(), sync_stream=False + ) # LOGGING logging_obj.post_call( input=messages, @@ -248,6 +191,38 @@ async def make_call( return completion_stream +def make_sync_call( + client: Optional[HTTPHandler], + api_base: str, + headers: dict, + data: str, + model: str, + messages: list, + logging_obj, +): + if client is None: + client = HTTPHandler() # Create a new client if none provided + + response = client.post(api_base, headers=headers, data=data, stream=True) + + if response.status_code != 200: + raise DatabricksError(status_code=response.status_code, message=response.read()) + + completion_stream = ModelResponseIterator( + streaming_response=response.iter_lines(), sync_stream=True + ) + + # LOGGING + logging_obj.post_call( + input=messages, + api_key="", + original_response="first stream response received", + additional_args={"complete_input_dict": data}, + ) + + return completion_stream + + class DatabricksChatCompletion(BaseLLM): def __init__(self) -> None: super().__init__() @@ -259,6 +234,7 @@ class DatabricksChatCompletion(BaseLLM): api_key: Optional[str], api_base: Optional[str], endpoint_type: Literal["chat_completions", "embeddings"], + custom_endpoint: Optional[bool], ) -> Tuple[str, dict]: if api_key is None: raise DatabricksError( @@ -277,97 +253,17 @@ class DatabricksChatCompletion(BaseLLM): "Content-Type": "application/json", } - if endpoint_type == "chat_completions": + if endpoint_type == "chat_completions" and custom_endpoint is not True: api_base = "{}/chat/completions".format(api_base) - elif endpoint_type == "embeddings": + elif endpoint_type == "embeddings" and custom_endpoint is not True: api_base = "{}/embeddings".format(api_base) return api_base, headers - def process_response( - self, - model: str, - response: Union[requests.Response, httpx.Response], - model_response: ModelResponse, - stream: bool, - logging_obj: litellm.litellm_core_utils.litellm_logging.Logging, - optional_params: dict, - api_key: str, - data: Union[dict, str], - messages: List, - print_verbose, - encoding, - ) -> ModelResponse: - ## LOGGING - logging_obj.post_call( - input=messages, - api_key=api_key, - original_response=response.text, - additional_args={"complete_input_dict": data}, - ) - print_verbose(f"raw model_response: {response.text}") - ## RESPONSE OBJECT - try: - completion_response = response.json() - except: - raise DatabricksError( - message=response.text, status_code=response.status_code - ) - if "error" in completion_response: - raise DatabricksError( - message=str(completion_response["error"]), - status_code=response.status_code, - ) - else: - text_content = "" - tool_calls = [] - for content in completion_response["content"]: - if content["type"] == "text": - text_content += content["text"] - ## TOOL CALLING - elif content["type"] == "tool_use": - tool_calls.append( - { - "id": content["id"], - "type": "function", - "function": { - "name": content["name"], - "arguments": json.dumps(content["input"]), - }, - } - ) - - _message = litellm.Message( - tool_calls=tool_calls, - content=text_content or None, - ) - model_response.choices[0].message = _message # type: ignore - model_response._hidden_params["original_response"] = completion_response[ - "content" - ] # allow user to access raw anthropic tool calling response - - model_response.choices[0].finish_reason = map_finish_reason( - completion_response["stop_reason"] - ) - - ## CALCULATING USAGE - prompt_tokens = completion_response["usage"]["input_tokens"] - completion_tokens = completion_response["usage"]["output_tokens"] - total_tokens = prompt_tokens + completion_tokens - - model_response["created"] = int(time.time()) - model_response["model"] = model - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=total_tokens, - ) - setattr(model_response, "usage", usage) # type: ignore - return model_response - async def acompletion_stream_function( self, model: str, messages: list, + custom_llm_provider: str, api_base: str, custom_prompt_dict: dict, model_response: ModelResponse, @@ -397,7 +293,7 @@ class DatabricksChatCompletion(BaseLLM): logging_obj=logging_obj, ), model=model, - custom_llm_provider="databricks", + custom_llm_provider=custom_llm_provider, logging_obj=logging_obj, ) return streamwrapper @@ -415,6 +311,7 @@ class DatabricksChatCompletion(BaseLLM): logging_obj, stream, data: dict, + base_model: Optional[str], optional_params: dict, litellm_params=None, logger_fn=None, @@ -436,20 +333,25 @@ class DatabricksChatCompletion(BaseLLM): except httpx.HTTPStatusError as e: raise DatabricksError( status_code=e.response.status_code, - message=response.text if response else str(e), + message=e.response.text, ) except httpx.TimeoutException as e: raise DatabricksError(status_code=408, message="Timeout error occurred.") except Exception as e: raise DatabricksError(status_code=500, message=str(e)) - return ModelResponse(**response_json) + response = ModelResponse(**response_json) + + if base_model is not None: + response._hidden_params["model"] = base_model + return response def completion( self, model: str, messages: list, api_base: str, + custom_llm_provider: str, custom_prompt_dict: dict, model_response: ModelResponse, print_verbose: Callable, @@ -464,8 +366,13 @@ class DatabricksChatCompletion(BaseLLM): timeout: Optional[Union[float, httpx.Timeout]] = None, client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, ): + custom_endpoint: Optional[bool] = optional_params.pop("custom_endpoint", None) + base_model: Optional[str] = optional_params.pop("base_model", None) api_base, headers = self._validate_environment( - api_base=api_base, api_key=api_key, endpoint_type="chat_completions" + api_base=api_base, + api_key=api_key, + endpoint_type="chat_completions", + custom_endpoint=custom_endpoint, ) ## Load Config config = litellm.DatabricksConfig().get_config() @@ -475,7 +382,8 @@ class DatabricksChatCompletion(BaseLLM): ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in optional_params[k] = v - stream = optional_params.pop("stream", None) + stream: bool = optional_params.pop("stream", None) or False + optional_params["stream"] = stream data = { "model": model, @@ -493,11 +401,11 @@ class DatabricksChatCompletion(BaseLLM): "headers": headers, }, ) - if acompletion == True: + if acompletion is True: if client is not None and isinstance(client, HTTPHandler): client = None if ( - stream is not None and stream == True + stream is not None and stream is True ): # if function call - fake the streaming (need complete blocks for output parsing in openai format) print_verbose("makes async anthropic streaming POST request") data["stream"] = stream @@ -518,6 +426,7 @@ class DatabricksChatCompletion(BaseLLM): logger_fn=logger_fn, headers=headers, client=client, + custom_llm_provider=custom_llm_provider, ) else: return self.acompletion_function( @@ -537,46 +446,32 @@ class DatabricksChatCompletion(BaseLLM): logger_fn=logger_fn, headers=headers, timeout=timeout, + base_model=base_model, ) else: - if client is None or isinstance(client, AsyncHTTPHandler): - self.client = HTTPHandler(timeout=timeout) # type: ignore - else: - self.client = client + if client is None or not isinstance(client, HTTPHandler): + client = HTTPHandler(timeout=timeout) # type: ignore ## COMPLETION CALL - if ( - stream is not None and stream == True - ): # if function call - fake the streaming (need complete blocks for output parsing in openai format) - print_verbose("makes dbrx streaming POST request") - data["stream"] = stream - try: - response = self.client.post( - api_base, headers=headers, data=json.dumps(data), stream=stream - ) - response.raise_for_status() - completion_stream = response.iter_lines() - except httpx.HTTPStatusError as e: - raise DatabricksError( - status_code=e.response.status_code, message=response.text - ) - except httpx.TimeoutException as e: - raise DatabricksError( - status_code=408, message="Timeout error occurred." - ) - except Exception as e: - raise DatabricksError(status_code=408, message=str(e)) - - streaming_response = CustomStreamWrapper( - completion_stream=completion_stream, + if stream is True: + return CustomStreamWrapper( + completion_stream=None, + make_call=partial( + make_sync_call, + client=None, + api_base=api_base, + headers=headers, # type: ignore + data=json.dumps(data), + model=model, + messages=messages, + logging_obj=logging_obj, + ), model=model, - custom_llm_provider="databricks", + custom_llm_provider=custom_llm_provider, logging_obj=logging_obj, ) - return streaming_response - else: try: - response = self.client.post( + response = client.post( api_base, headers=headers, data=json.dumps(data) ) response.raise_for_status() @@ -593,7 +488,12 @@ class DatabricksChatCompletion(BaseLLM): except Exception as e: raise DatabricksError(status_code=500, message=str(e)) - return ModelResponse(**response_json) + response = ModelResponse(**response_json) + + if base_model is not None: + response._hidden_params["model"] = base_model + + return response async def aembedding( self, @@ -667,7 +567,10 @@ class DatabricksChatCompletion(BaseLLM): aembedding=None, ) -> EmbeddingResponse: api_base, headers = self._validate_environment( - api_base=api_base, api_key=api_key, endpoint_type="embeddings" + api_base=api_base, + api_key=api_key, + endpoint_type="embeddings", + custom_endpoint=False, ) model = model data = {"model": model, "input": input, **optional_params} @@ -716,3 +619,128 @@ class DatabricksChatCompletion(BaseLLM): ) return litellm.EmbeddingResponse(**response_json) + + +class ModelResponseIterator: + def __init__(self, streaming_response, sync_stream: bool): + self.streaming_response = streaming_response + + def chunk_parser(self, chunk: dict) -> GenericStreamingChunk: + try: + processed_chunk = litellm.ModelResponse(**chunk, stream=True) # type: ignore + + text = "" + tool_use: Optional[ChatCompletionToolCallChunk] = None + is_finished = False + finish_reason = "" + usage: Optional[ChatCompletionUsageBlock] = None + + if processed_chunk.choices[0].delta.content is not None: # type: ignore + text = processed_chunk.choices[0].delta.content # type: ignore + + if ( + processed_chunk.choices[0].delta.tool_calls is not None # type: ignore + and len(processed_chunk.choices[0].delta.tool_calls) > 0 # type: ignore + and processed_chunk.choices[0].delta.tool_calls[0].function is not None # type: ignore + and processed_chunk.choices[0].delta.tool_calls[0].function.arguments # type: ignore + is not None + ): + tool_use = ChatCompletionToolCallChunk( + id=processed_chunk.choices[0].delta.tool_calls[0].id, # type: ignore + type="function", + function=ChatCompletionToolCallFunctionChunk( + name=processed_chunk.choices[0] + .delta.tool_calls[0] # type: ignore + .function.name, + arguments=processed_chunk.choices[0] + .delta.tool_calls[0] # type: ignore + .function.arguments, + ), + index=processed_chunk.choices[0].index, + ) + + if processed_chunk.choices[0].finish_reason is not None: + is_finished = True + finish_reason = processed_chunk.choices[0].finish_reason + + if hasattr(processed_chunk, "usage"): + usage = processed_chunk.usage # type: ignore + + return GenericStreamingChunk( + text=text, + tool_use=tool_use, + is_finished=is_finished, + finish_reason=finish_reason, + usage=usage, + index=0, + ) + except json.JSONDecodeError: + raise ValueError(f"Failed to decode JSON from chunk: {chunk}") + + # Sync iterator + def __iter__(self): + self.response_iterator = self.streaming_response + return self + + def __next__(self): + try: + chunk = self.response_iterator.__next__() + except StopIteration: + raise StopIteration + except ValueError as e: + raise RuntimeError(f"Error receiving chunk from stream: {e}") + + try: + chunk = chunk.replace("data:", "") + chunk = chunk.strip() + if len(chunk) > 0: + json_chunk = json.loads(chunk) + return self.chunk_parser(chunk=json_chunk) + else: + return GenericStreamingChunk( + text="", + is_finished=False, + finish_reason="", + usage=None, + index=0, + tool_use=None, + ) + except StopIteration: + raise StopIteration + except ValueError as e: + raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}") + + # Async iterator + def __aiter__(self): + self.async_response_iterator = self.streaming_response.__aiter__() + return self + + async def __anext__(self): + try: + chunk = await self.async_response_iterator.__anext__() + except StopAsyncIteration: + raise StopAsyncIteration + except ValueError as e: + raise RuntimeError(f"Error receiving chunk from stream: {e}") + + try: + chunk = chunk.replace("data:", "") + chunk = chunk.strip() + if chunk == "[DONE]": + raise StopAsyncIteration + if len(chunk) > 0: + json_chunk = json.loads(chunk) + return self.chunk_parser(chunk=json_chunk) + else: + return GenericStreamingChunk( + text="", + is_finished=False, + finish_reason="", + usage=None, + index=0, + tool_use=None, + ) + except StopAsyncIteration: + raise StopAsyncIteration + except ValueError as e: + raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}") diff --git a/litellm/llms/files_apis/azure.py b/litellm/llms/files_apis/azure.py new file mode 100644 index 000000000..c4c9ee48a --- /dev/null +++ b/litellm/llms/files_apis/azure.py @@ -0,0 +1,315 @@ +from typing import Any, Coroutine, Dict, List, Optional, Union + +import httpx +from openai import AsyncAzureOpenAI, AzureOpenAI +from openai.types.file_deleted import FileDeleted + +import litellm +from litellm._logging import verbose_logger +from litellm.llms.base import BaseLLM +from litellm.types.llms.openai import * + + +def get_azure_openai_client( + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + api_version: Optional[str] = None, + organization: Optional[str] = None, + client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None, + _is_async: bool = False, +) -> Optional[Union[AzureOpenAI, AsyncAzureOpenAI]]: + received_args = locals() + openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None + if client is None: + data = {} + for k, v in received_args.items(): + if k == "self" or k == "client" or k == "_is_async": + pass + elif k == "api_base" and v is not None: + data["azure_endpoint"] = v + elif v is not None: + data[k] = v + if "api_version" not in data: + data["api_version"] = litellm.AZURE_DEFAULT_API_VERSION + if _is_async is True: + openai_client = AsyncAzureOpenAI(**data) + else: + openai_client = AzureOpenAI(**data) # type: ignore + else: + openai_client = client + + return openai_client + + +class AzureOpenAIFilesAPI(BaseLLM): + """ + AzureOpenAI methods to support for batches + - create_file() + - retrieve_file() + - list_files() + - delete_file() + - file_content() + - update_file() + """ + + def __init__(self) -> None: + super().__init__() + + async def acreate_file( + self, + create_file_data: CreateFileRequest, + openai_client: AsyncAzureOpenAI, + ) -> FileObject: + verbose_logger.debug("create_file_data=%s", create_file_data) + response = await openai_client.files.create(**create_file_data) + verbose_logger.debug("create_file_response=%s", response) + return response + + def create_file( + self, + _is_async: bool, + create_file_data: CreateFileRequest, + api_base: str, + api_key: Optional[str], + api_version: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None, + ) -> Union[FileObject, Coroutine[Any, Any, FileObject]]: + openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = ( + get_azure_openai_client( + api_key=api_key, + api_base=api_base, + api_version=api_version, + timeout=timeout, + max_retries=max_retries, + client=client, + _is_async=_is_async, + ) + ) + if openai_client is None: + raise ValueError( + "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncAzureOpenAI): + raise ValueError( + "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI client." + ) + return self.acreate_file( # type: ignore + create_file_data=create_file_data, openai_client=openai_client + ) + response = openai_client.files.create(**create_file_data) + return response + + async def afile_content( + self, + file_content_request: FileContentRequest, + openai_client: AsyncAzureOpenAI, + ) -> HttpxBinaryResponseContent: + response = await openai_client.files.content(**file_content_request) + return response + + def file_content( + self, + _is_async: bool, + file_content_request: FileContentRequest, + api_base: str, + api_key: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + api_version: Optional[str] = None, + client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None, + ) -> Union[ + HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent] + ]: + openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = ( + get_azure_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + api_version=api_version, + max_retries=max_retries, + organization=organization, + client=client, + _is_async=_is_async, + ) + ) + if openai_client is None: + raise ValueError( + "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncAzureOpenAI): + raise ValueError( + "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI client." + ) + return self.afile_content( # type: ignore + file_content_request=file_content_request, + openai_client=openai_client, + ) + response = openai_client.files.content(**file_content_request) + + return response + + async def aretrieve_file( + self, + file_id: str, + openai_client: AsyncAzureOpenAI, + ) -> FileObject: + response = await openai_client.files.retrieve(file_id=file_id) + return response + + def retrieve_file( + self, + _is_async: bool, + file_id: str, + api_base: str, + api_key: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + api_version: Optional[str] = None, + client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None, + ): + openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = ( + get_azure_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + api_version=api_version, + client=client, + _is_async=_is_async, + ) + ) + if openai_client is None: + raise ValueError( + "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncAzureOpenAI): + raise ValueError( + "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI client." + ) + return self.aretrieve_file( # type: ignore + file_id=file_id, + openai_client=openai_client, + ) + response = openai_client.files.retrieve(file_id=file_id) + + return response + + async def adelete_file( + self, + file_id: str, + openai_client: AsyncAzureOpenAI, + ) -> FileDeleted: + response = await openai_client.files.delete(file_id=file_id) + return response + + def delete_file( + self, + _is_async: bool, + file_id: str, + api_base: str, + api_key: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str] = None, + api_version: Optional[str] = None, + client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None, + ): + openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = ( + get_azure_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + api_version=api_version, + client=client, + _is_async=_is_async, + ) + ) + if openai_client is None: + raise ValueError( + "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncAzureOpenAI): + raise ValueError( + "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI client." + ) + return self.adelete_file( # type: ignore + file_id=file_id, + openai_client=openai_client, + ) + response = openai_client.files.delete(file_id=file_id) + + return response + + async def alist_files( + self, + openai_client: AsyncAzureOpenAI, + purpose: Optional[str] = None, + ): + if isinstance(purpose, str): + response = await openai_client.files.list(purpose=purpose) + else: + response = await openai_client.files.list() + return response + + def list_files( + self, + _is_async: bool, + api_base: str, + api_key: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + purpose: Optional[str] = None, + api_version: Optional[str] = None, + client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None, + ): + openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = ( + get_azure_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + api_version=api_version, + client=client, + _is_async=_is_async, + ) + ) + if openai_client is None: + raise ValueError( + "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncAzureOpenAI): + raise ValueError( + "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI client." + ) + return self.alist_files( # type: ignore + purpose=purpose, + openai_client=openai_client, + ) + + if isinstance(purpose, str): + response = openai_client.files.list(purpose=purpose) + else: + response = openai_client.files.list() + + return response diff --git a/litellm/llms/fine_tuning_apis/azure.py b/litellm/llms/fine_tuning_apis/azure.py new file mode 100644 index 000000000..ff7d40ff8 --- /dev/null +++ b/litellm/llms/fine_tuning_apis/azure.py @@ -0,0 +1,181 @@ +from typing import Any, Coroutine, Optional, Union + +import httpx +from openai import AsyncAzureOpenAI, AzureOpenAI +from openai.pagination import AsyncCursorPage +from openai.types.fine_tuning import FineTuningJob + +from litellm._logging import verbose_logger +from litellm.llms.base import BaseLLM +from litellm.llms.files_apis.azure import get_azure_openai_client +from litellm.types.llms.openai import FineTuningJobCreate + + +class AzureOpenAIFineTuningAPI(BaseLLM): + """ + AzureOpenAI methods to support for batches + """ + + def __init__(self) -> None: + super().__init__() + + async def acreate_fine_tuning_job( + self, + create_fine_tuning_job_data: dict, + openai_client: AsyncAzureOpenAI, + ) -> FineTuningJob: + response = await openai_client.fine_tuning.jobs.create( + **create_fine_tuning_job_data # type: ignore + ) + return response + + def create_fine_tuning_job( + self, + _is_async: bool, + create_fine_tuning_job_data: dict, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str] = None, + client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None, + api_version: Optional[str] = None, + ) -> Union[FineTuningJob, Union[Coroutine[Any, Any, FineTuningJob]]]: + openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = ( + get_azure_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + api_version=api_version, + client=client, + _is_async=_is_async, + ) + ) + if openai_client is None: + raise ValueError( + "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncAzureOpenAI): + raise ValueError( + "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI client." + ) + return self.acreate_fine_tuning_job( # type: ignore + create_fine_tuning_job_data=create_fine_tuning_job_data, + openai_client=openai_client, + ) + verbose_logger.debug( + "creating fine tuning job, args= %s", create_fine_tuning_job_data + ) + response = openai_client.fine_tuning.jobs.create(**create_fine_tuning_job_data) # type: ignore + return response + + async def acancel_fine_tuning_job( + self, + fine_tuning_job_id: str, + openai_client: AsyncAzureOpenAI, + ) -> FineTuningJob: + response = await openai_client.fine_tuning.jobs.cancel( + fine_tuning_job_id=fine_tuning_job_id + ) + return response + + def cancel_fine_tuning_job( + self, + _is_async: bool, + fine_tuning_job_id: str, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str] = None, + api_version: Optional[str] = None, + client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None, + ): + openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = ( + get_azure_openai_client( + api_key=api_key, + api_base=api_base, + api_version=api_version, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + _is_async=_is_async, + ) + ) + if openai_client is None: + raise ValueError( + "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncAzureOpenAI): + raise ValueError( + "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI client." + ) + return self.acancel_fine_tuning_job( # type: ignore + fine_tuning_job_id=fine_tuning_job_id, + openai_client=openai_client, + ) + verbose_logger.debug("canceling fine tuning job, args= %s", fine_tuning_job_id) + response = openai_client.fine_tuning.jobs.cancel( + fine_tuning_job_id=fine_tuning_job_id + ) + return response + + async def alist_fine_tuning_jobs( + self, + openai_client: AsyncAzureOpenAI, + after: Optional[str] = None, + limit: Optional[int] = None, + ): + response = await openai_client.fine_tuning.jobs.list(after=after, limit=limit) # type: ignore + return response + + def list_fine_tuning_jobs( + self, + _is_async: bool, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str] = None, + client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None, + api_version: Optional[str] = None, + after: Optional[str] = None, + limit: Optional[int] = None, + ): + openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = ( + get_azure_openai_client( + api_key=api_key, + api_base=api_base, + api_version=api_version, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + _is_async=_is_async, + ) + ) + if openai_client is None: + raise ValueError( + "AzureOpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncAzureOpenAI): + raise ValueError( + "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI client." + ) + return self.alist_fine_tuning_jobs( # type: ignore + after=after, + limit=limit, + openai_client=openai_client, + ) + verbose_logger.debug("list fine tuning job, after= %s, limit= %s", after, limit) + response = openai_client.fine_tuning.jobs.list(after=after, limit=limit) # type: ignore + return response diff --git a/litellm/llms/fine_tuning_apis/openai.py b/litellm/llms/fine_tuning_apis/openai.py new file mode 100644 index 000000000..6f3cd6021 --- /dev/null +++ b/litellm/llms/fine_tuning_apis/openai.py @@ -0,0 +1,199 @@ +from typing import Any, Coroutine, Optional, Union + +import httpx +from openai import AsyncOpenAI, OpenAI +from openai.pagination import AsyncCursorPage +from openai.types.fine_tuning import FineTuningJob + +from litellm._logging import verbose_logger +from litellm.llms.base import BaseLLM +from litellm.types.llms.openai import FineTuningJobCreate + + +class OpenAIFineTuningAPI(BaseLLM): + """ + OpenAI methods to support for batches + """ + + def __init__(self) -> None: + super().__init__() + + def get_openai_client( + self, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[Union[OpenAI, AsyncOpenAI]] = None, + _is_async: bool = False, + ) -> Optional[Union[OpenAI, AsyncOpenAI]]: + received_args = locals() + openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = None + if client is None: + data = {} + for k, v in received_args.items(): + if k == "self" or k == "client" or k == "_is_async": + pass + elif k == "api_base" and v is not None: + data["base_url"] = v + elif v is not None: + data[k] = v + if _is_async is True: + openai_client = AsyncOpenAI(**data) + else: + openai_client = OpenAI(**data) # type: ignore + else: + openai_client = client + + return openai_client + + async def acreate_fine_tuning_job( + self, + create_fine_tuning_job_data: dict, + openai_client: AsyncOpenAI, + ) -> FineTuningJob: + response = await openai_client.fine_tuning.jobs.create( + **create_fine_tuning_job_data + ) + return response + + def create_fine_tuning_job( + self, + _is_async: bool, + create_fine_tuning_job_data: dict, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[Union[OpenAI, AsyncOpenAI]] = None, + ) -> Union[FineTuningJob, Union[Coroutine[Any, Any, FineTuningJob]]]: + openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + _is_async=_is_async, + ) + if openai_client is None: + raise ValueError( + "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncOpenAI): + raise ValueError( + "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client." + ) + return self.acreate_fine_tuning_job( # type: ignore + create_fine_tuning_job_data=create_fine_tuning_job_data, + openai_client=openai_client, + ) + verbose_logger.debug( + "creating fine tuning job, args= %s", create_fine_tuning_job_data + ) + response = openai_client.fine_tuning.jobs.create(**create_fine_tuning_job_data) + return response + + async def acancel_fine_tuning_job( + self, + fine_tuning_job_id: str, + openai_client: AsyncOpenAI, + ) -> FineTuningJob: + response = await openai_client.fine_tuning.jobs.cancel( + fine_tuning_job_id=fine_tuning_job_id + ) + return response + + def cancel_fine_tuning_job( + self, + _is_async: bool, + fine_tuning_job_id: str, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[Union[OpenAI, AsyncOpenAI]] = None, + ): + openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + _is_async=_is_async, + ) + if openai_client is None: + raise ValueError( + "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncOpenAI): + raise ValueError( + "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client." + ) + return self.acancel_fine_tuning_job( # type: ignore + fine_tuning_job_id=fine_tuning_job_id, + openai_client=openai_client, + ) + verbose_logger.debug("canceling fine tuning job, args= %s", fine_tuning_job_id) + response = openai_client.fine_tuning.jobs.cancel( + fine_tuning_job_id=fine_tuning_job_id + ) + return response + + async def alist_fine_tuning_jobs( + self, + openai_client: AsyncOpenAI, + after: Optional[str] = None, + limit: Optional[int] = None, + ): + response = await openai_client.fine_tuning.jobs.list(after=after, limit=limit) # type: ignore + return response + + def list_fine_tuning_jobs( + self, + _is_async: bool, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + client: Optional[Union[OpenAI, AsyncOpenAI]] = None, + after: Optional[str] = None, + limit: Optional[int] = None, + ): + openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + _is_async=_is_async, + ) + if openai_client is None: + raise ValueError( + "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncOpenAI): + raise ValueError( + "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client." + ) + return self.alist_fine_tuning_jobs( # type: ignore + after=after, + limit=limit, + openai_client=openai_client, + ) + verbose_logger.debug("list fine tuning job, after= %s, limit= %s", after, limit) + response = openai_client.fine_tuning.jobs.list(after=after, limit=limit) # type: ignore + return response + pass diff --git a/litellm/llms/fine_tuning_apis/vertex_ai.py b/litellm/llms/fine_tuning_apis/vertex_ai.py new file mode 100644 index 000000000..ffdb82c5b --- /dev/null +++ b/litellm/llms/fine_tuning_apis/vertex_ai.py @@ -0,0 +1,306 @@ +import traceback +from datetime import datetime +from typing import Any, Coroutine, Literal, Optional, Union + +import httpx +from openai.types.fine_tuning.fine_tuning_job import FineTuningJob, Hyperparameters + +from litellm._logging import verbose_logger +from litellm.llms.base import BaseLLM +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.llms.vertex_httpx import VertexLLM +from litellm.types.llms.openai import FineTuningJobCreate +from litellm.types.llms.vertex_ai import ( + FineTuneJobCreate, + FineTunesupervisedTuningSpec, + ResponseTuningJob, +) + + +class VertexFineTuningAPI(VertexLLM): + """ + Vertex methods to support for batches + """ + + def __init__(self) -> None: + super().__init__() + self.async_handler = AsyncHTTPHandler( + timeout=httpx.Timeout(timeout=600.0, connect=5.0) + ) + + def convert_response_created_at(self, response: ResponseTuningJob): + try: + + create_time_str = response.get("createTime", "") or "" + create_time_datetime = datetime.fromisoformat( + create_time_str.replace("Z", "+00:00") + ) + # Convert to Unix timestamp (seconds since epoch) + created_at = int(create_time_datetime.timestamp()) + + return created_at + except Exception as e: + return 0 + + def convert_vertex_response_to_open_ai_response( + self, response: ResponseTuningJob + ) -> FineTuningJob: + status: Literal[ + "validating_files", "queued", "running", "succeeded", "failed", "cancelled" + ] = "queued" + if response["state"] == "JOB_STATE_PENDING": + status = "queued" + if response["state"] == "JOB_STATE_SUCCEEDED": + status = "succeeded" + if response["state"] == "JOB_STATE_FAILED": + status = "failed" + if response["state"] == "JOB_STATE_CANCELLED": + status = "cancelled" + if response["state"] == "JOB_STATE_RUNNING": + status = "running" + + created_at = self.convert_response_created_at(response) + + training_uri = "" + if "supervisedTuningSpec" in response and response["supervisedTuningSpec"]: + training_uri = response["supervisedTuningSpec"]["trainingDatasetUri"] or "" + + return FineTuningJob( + id=response["name"] or "", + created_at=created_at, + fine_tuned_model=response["tunedModelDisplayName"], + finished_at=None, + hyperparameters=Hyperparameters( + n_epochs=0, + ), + model=response["baseModel"] or "", + object="fine_tuning.job", + organization_id="", + result_files=[], + seed=0, + status=status, + trained_tokens=None, + training_file=training_uri, + validation_file=None, + estimated_finish=None, + integrations=[], + ) + + def convert_openai_request_to_vertex( + self, create_fine_tuning_job_data: FineTuningJobCreate, **kwargs + ) -> FineTuneJobCreate: + """ + convert request from OpenAI format to Vertex format + https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning + supervised_tuning_spec = FineTunesupervisedTuningSpec( + """ + hyperparameters = create_fine_tuning_job_data.hyperparameters + supervised_tuning_spec = FineTunesupervisedTuningSpec( + training_dataset_uri=create_fine_tuning_job_data.training_file, + validation_dataset=create_fine_tuning_job_data.validation_file, + ) + + if hyperparameters: + if hyperparameters.n_epochs: + supervised_tuning_spec["epoch_count"] = int(hyperparameters.n_epochs) + if hyperparameters.learning_rate_multiplier: + supervised_tuning_spec["learning_rate_multiplier"] = float( + hyperparameters.learning_rate_multiplier + ) + + supervised_tuning_spec["adapter_size"] = kwargs.get("adapter_size") + + fine_tune_job = FineTuneJobCreate( + baseModel=create_fine_tuning_job_data.model, + supervisedTuningSpec=supervised_tuning_spec, + tunedModelDisplayName=create_fine_tuning_job_data.suffix, + ) + + return fine_tune_job + + async def acreate_fine_tuning_job( + self, + fine_tuning_url: str, + headers: dict, + request_data: FineTuneJobCreate, + ): + from litellm.fine_tuning.main import FineTuningJob + + try: + verbose_logger.debug( + "about to create fine tuning job: %s, request_data: %s", + fine_tuning_url, + request_data, + ) + if self.async_handler is None: + raise ValueError( + "VertexAI Fine Tuning - async_handler is not initialized" + ) + response = await self.async_handler.post( + headers=headers, + url=fine_tuning_url, + json=request_data, # type: ignore + ) + + if response.status_code != 200: + raise Exception( + f"Error creating fine tuning job. Status code: {response.status_code}. Response: {response.text}" + ) + + verbose_logger.debug( + "got response from creating fine tuning job: %s", response.json() + ) + + vertex_response = ResponseTuningJob( # type: ignore + **response.json(), + ) + + verbose_logger.debug("vertex_response %s", vertex_response) + open_ai_response = self.convert_vertex_response_to_open_ai_response( + vertex_response + ) + return open_ai_response + + except Exception as e: + verbose_logger.error("asyncerror creating fine tuning job %s", e) + trace_back_str = traceback.format_exc() + verbose_logger.error(trace_back_str) + raise e + + def create_fine_tuning_job( + self, + _is_async: bool, + create_fine_tuning_job_data: FineTuningJobCreate, + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_credentials: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + **kwargs, + ): + + verbose_logger.debug( + "creating fine tuning job, args= %s", create_fine_tuning_job_data + ) + + auth_header, _ = self._get_token_and_url( + model="", + gemini_api_key=None, + vertex_credentials=vertex_credentials, + vertex_project=vertex_project, + vertex_location=vertex_location, + stream=False, + custom_llm_provider="vertex_ai_beta", + api_base=api_base, + ) + + headers = { + "Authorization": f"Bearer {auth_header}", + "Content-Type": "application/json", + } + + fine_tune_job = self.convert_openai_request_to_vertex( + create_fine_tuning_job_data=create_fine_tuning_job_data, **kwargs + ) + + fine_tuning_url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/tuningJobs" + if _is_async is True: + return self.acreate_fine_tuning_job( # type: ignore + fine_tuning_url=fine_tuning_url, + headers=headers, + request_data=fine_tune_job, + ) + sync_handler = HTTPHandler(timeout=httpx.Timeout(timeout=600.0, connect=5.0)) + + verbose_logger.debug( + "about to create fine tuning job: %s, request_data: %s", + fine_tuning_url, + fine_tune_job, + ) + response = sync_handler.post( + headers=headers, + url=fine_tuning_url, + json=fine_tune_job, # type: ignore + ) + + if response.status_code != 200: + raise Exception( + f"Error creating fine tuning job. Status code: {response.status_code}. Response: {response.text}" + ) + + verbose_logger.debug( + "got response from creating fine tuning job: %s", response.json() + ) + vertex_response = ResponseTuningJob( # type: ignore + **response.json(), + ) + + verbose_logger.debug("vertex_response %s", vertex_response) + open_ai_response = self.convert_vertex_response_to_open_ai_response( + vertex_response + ) + return open_ai_response + + async def pass_through_vertex_ai_POST_request( + self, + request_data: dict, + vertex_project: str, + vertex_location: str, + vertex_credentials: str, + request_route: str, + ): + auth_header, _ = self._get_token_and_url( + model="", + gemini_api_key=None, + vertex_credentials=vertex_credentials, + vertex_project=vertex_project, + vertex_location=vertex_location, + stream=False, + custom_llm_provider="vertex_ai_beta", + api_base="", + ) + + headers = { + "Authorization": f"Bearer {auth_header}", + "Content-Type": "application/json", + } + + url = None + if request_route == "/tuningJobs": + url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/tuningJobs" + elif "/tuningJobs/" in request_route and "cancel" in request_route: + url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/tuningJobs{request_route}" + elif "generateContent" in request_route: + url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}{request_route}" + elif "predict" in request_route: + url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}{request_route}" + elif "/batchPredictionJobs" in request_route: + url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}{request_route}" + elif "countTokens" in request_route: + url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}{request_route}" + elif "cachedContents" in request_route: + _model = request_data.get("model") + if _model is not None and "/publishers/google/models/" not in _model: + request_data["model"] = ( + f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{_model}" + ) + + url = f"https://{vertex_location}-aiplatform.googleapis.com/v1beta1/projects/{vertex_project}/locations/{vertex_location}{request_route}" + else: + raise ValueError(f"Unsupported Vertex AI request route: {request_route}") + if self.async_handler is None: + raise ValueError("VertexAI Fine Tuning - async_handler is not initialized") + + response = await self.async_handler.post( + headers=headers, + url=url, + json=request_data, # type: ignore + ) + + if response.status_code != 200: + raise Exception( + f"Error creating fine tuning job. Status code: {response.status_code}. Response: {response.text}" + ) + + response_json = response.json() + return response_json diff --git a/litellm/llms/gemini.py b/litellm/llms/gemini.py index f48c4e29e..179554ea4 100644 --- a/litellm/llms/gemini.py +++ b/litellm/llms/gemini.py @@ -1,7 +1,7 @@ -#################################### -######### DEPRECATED FILE ########## -#################################### -# logic moved to `vertex_httpx.py` # +# #################################### +# ######### DEPRECATED FILE ########## +# #################################### +# # logic moved to `vertex_httpx.py` # import copy import time @@ -92,332 +92,330 @@ class GeminiConfig: } -class TextStreamer: - """ - A class designed to return an async stream from AsyncGenerateContentResponse object. - """ +# class TextStreamer: +# """ +# A class designed to return an async stream from AsyncGenerateContentResponse object. +# """ - def __init__(self, response): - self.response = response - self._aiter = self.response.__aiter__() +# def __init__(self, response): +# self.response = response +# self._aiter = self.response.__aiter__() - async def __aiter__(self): - while True: - try: - # This will manually advance the async iterator. - # In the case the next object doesn't exists, __anext__() will simply raise a StopAsyncIteration exception - next_object = await self._aiter.__anext__() - yield next_object - except StopAsyncIteration: - # After getting all items from the async iterator, stop iterating - break +# async def __aiter__(self): +# while True: +# try: +# # This will manually advance the async iterator. +# # In the case the next object doesn't exists, __anext__() will simply raise a StopAsyncIteration exception +# next_object = await self._aiter.__anext__() +# yield next_object +# except StopAsyncIteration: +# # After getting all items from the async iterator, stop iterating +# break -def supports_system_instruction(): - import google.generativeai as genai +# def supports_system_instruction(): +# import google.generativeai as genai - gemini_pkg_version = Version(genai.__version__) - return gemini_pkg_version >= Version("0.5.0") +# gemini_pkg_version = Version(genai.__version__) +# return gemini_pkg_version >= Version("0.5.0") -def completion( - model: str, - messages: list, - model_response: ModelResponse, - print_verbose: Callable, - api_key, - encoding, - logging_obj, - custom_prompt_dict: dict, - acompletion: bool = False, - optional_params=None, - litellm_params=None, - logger_fn=None, -): - try: - import google.generativeai as genai # type: ignore - except: - raise Exception( - "Importing google.generativeai failed, please run 'pip install -q google-generativeai" - ) - genai.configure(api_key=api_key) - system_prompt = "" - if model in custom_prompt_dict: - # check if the model has a registered custom prompt - model_prompt_details = custom_prompt_dict[model] - prompt = custom_prompt( - role_dict=model_prompt_details["roles"], - initial_prompt_value=model_prompt_details["initial_prompt_value"], - final_prompt_value=model_prompt_details["final_prompt_value"], - messages=messages, - ) - else: - system_prompt, messages = get_system_prompt(messages=messages) - prompt = prompt_factory( - model=model, messages=messages, custom_llm_provider="gemini" - ) +# def completion( +# model: str, +# messages: list, +# model_response: ModelResponse, +# print_verbose: Callable, +# api_key, +# encoding, +# logging_obj, +# custom_prompt_dict: dict, +# acompletion: bool = False, +# optional_params=None, +# litellm_params=None, +# logger_fn=None, +# ): +# try: +# import google.generativeai as genai # type: ignore +# except: +# raise Exception( +# "Importing google.generativeai failed, please run 'pip install -q google-generativeai" +# ) +# genai.configure(api_key=api_key) +# system_prompt = "" +# if model in custom_prompt_dict: +# # check if the model has a registered custom prompt +# model_prompt_details = custom_prompt_dict[model] +# prompt = custom_prompt( +# role_dict=model_prompt_details["roles"], +# initial_prompt_value=model_prompt_details["initial_prompt_value"], +# final_prompt_value=model_prompt_details["final_prompt_value"], +# messages=messages, +# ) +# else: +# system_prompt, messages = get_system_prompt(messages=messages) +# prompt = prompt_factory( +# model=model, messages=messages, custom_llm_provider="gemini" +# ) - ## Load Config - inference_params = copy.deepcopy(optional_params) - stream = inference_params.pop("stream", None) +# ## Load Config +# inference_params = copy.deepcopy(optional_params) +# stream = inference_params.pop("stream", None) - # Handle safety settings - safety_settings_param = inference_params.pop("safety_settings", None) - safety_settings = None - if safety_settings_param: - safety_settings = [ - genai.types.SafetySettingDict(x) for x in safety_settings_param - ] +# # Handle safety settings +# safety_settings_param = inference_params.pop("safety_settings", None) +# safety_settings = None +# if safety_settings_param: +# safety_settings = [ +# genai.types.SafetySettingDict(x) for x in safety_settings_param +# ] - config = litellm.GeminiConfig.get_config() - for k, v in config.items(): - if ( - k not in inference_params - ): # completion(top_k=3) > gemini_config(top_k=3) <- allows for dynamic variables to be passed in - inference_params[k] = v +# config = litellm.GeminiConfig.get_config() +# for k, v in config.items(): +# if ( +# k not in inference_params +# ): # completion(top_k=3) > gemini_config(top_k=3) <- allows for dynamic variables to be passed in +# inference_params[k] = v - ## LOGGING - logging_obj.pre_call( - input=prompt, - api_key="", - additional_args={ - "complete_input_dict": { - "inference_params": inference_params, - "system_prompt": system_prompt, - } - }, - ) - ## COMPLETION CALL - try: - _params = {"model_name": "models/{}".format(model)} - _system_instruction = supports_system_instruction() - if _system_instruction and len(system_prompt) > 0: - _params["system_instruction"] = system_prompt - _model = genai.GenerativeModel(**_params) - if stream is True: - if acompletion is True: +# ## LOGGING +# logging_obj.pre_call( +# input=prompt, +# api_key="", +# additional_args={ +# "complete_input_dict": { +# "inference_params": inference_params, +# "system_prompt": system_prompt, +# } +# }, +# ) +# ## COMPLETION CALL +# try: +# _params = {"model_name": "models/{}".format(model)} +# _system_instruction = supports_system_instruction() +# if _system_instruction and len(system_prompt) > 0: +# _params["system_instruction"] = system_prompt +# _model = genai.GenerativeModel(**_params) +# if stream is True: +# if acompletion is True: - async def async_streaming(): - try: - response = await _model.generate_content_async( - contents=prompt, - generation_config=genai.types.GenerationConfig( - **inference_params - ), - safety_settings=safety_settings, - stream=True, - ) +# async def async_streaming(): +# try: +# response = await _model.generate_content_async( +# contents=prompt, +# generation_config=genai.types.GenerationConfig( +# **inference_params +# ), +# safety_settings=safety_settings, +# stream=True, +# ) - response = litellm.CustomStreamWrapper( - TextStreamer(response), - model, - custom_llm_provider="gemini", - logging_obj=logging_obj, - ) - return response - except Exception as e: - raise GeminiError(status_code=500, message=str(e)) +# response = litellm.CustomStreamWrapper( +# TextStreamer(response), +# model, +# custom_llm_provider="gemini", +# logging_obj=logging_obj, +# ) +# return response +# except Exception as e: +# raise GeminiError(status_code=500, message=str(e)) - return async_streaming() - response = _model.generate_content( - contents=prompt, - generation_config=genai.types.GenerationConfig(**inference_params), - safety_settings=safety_settings, - stream=True, - ) - return response - elif acompletion == True: - return async_completion( - _model=_model, - model=model, - prompt=prompt, - inference_params=inference_params, - safety_settings=safety_settings, - logging_obj=logging_obj, - print_verbose=print_verbose, - model_response=model_response, - messages=messages, - encoding=encoding, - ) - else: - params = { - "contents": prompt, - "generation_config": genai.types.GenerationConfig(**inference_params), - "safety_settings": safety_settings, - } - response = _model.generate_content(**params) - except Exception as e: - raise GeminiError( - message=str(e), - status_code=500, - ) +# return async_streaming() +# response = _model.generate_content( +# contents=prompt, +# generation_config=genai.types.GenerationConfig(**inference_params), +# safety_settings=safety_settings, +# stream=True, +# ) +# return response +# elif acompletion == True: +# return async_completion( +# _model=_model, +# model=model, +# prompt=prompt, +# inference_params=inference_params, +# safety_settings=safety_settings, +# logging_obj=logging_obj, +# print_verbose=print_verbose, +# model_response=model_response, +# messages=messages, +# encoding=encoding, +# ) +# else: +# params = { +# "contents": prompt, +# "generation_config": genai.types.GenerationConfig(**inference_params), +# "safety_settings": safety_settings, +# } +# response = _model.generate_content(**params) +# except Exception as e: +# raise GeminiError( +# message=str(e), +# status_code=500, +# ) - ## LOGGING - logging_obj.post_call( - input=prompt, - api_key="", - original_response=response, - additional_args={"complete_input_dict": {}}, - ) - print_verbose(f"raw model_response: {response}") - ## RESPONSE OBJECT - completion_response = response - try: - choices_list = [] - for idx, item in enumerate(completion_response.candidates): - if len(item.content.parts) > 0: - message_obj = Message(content=item.content.parts[0].text) - else: - message_obj = Message(content=None) - choice_obj = Choices(index=idx, message=message_obj) - choices_list.append(choice_obj) - model_response["choices"] = choices_list - except Exception as e: - verbose_logger.error("LiteLLM.gemini.py: Exception occured - {}".format(str(e))) - verbose_logger.debug(traceback.format_exc()) - raise GeminiError( - message=traceback.format_exc(), status_code=response.status_code - ) +# ## LOGGING +# logging_obj.post_call( +# input=prompt, +# api_key="", +# original_response=response, +# additional_args={"complete_input_dict": {}}, +# ) +# print_verbose(f"raw model_response: {response}") +# ## RESPONSE OBJECT +# completion_response = response +# try: +# choices_list = [] +# for idx, item in enumerate(completion_response.candidates): +# if len(item.content.parts) > 0: +# message_obj = Message(content=item.content.parts[0].text) +# else: +# message_obj = Message(content=None) +# choice_obj = Choices(index=idx, message=message_obj) +# choices_list.append(choice_obj) +# model_response.choices = choices_list +# except Exception as e: +# verbose_logger.error("LiteLLM.gemini.py: Exception occured - {}".format(str(e))) +# raise GeminiError( +# message=traceback.format_exc(), status_code=response.status_code +# ) - try: - completion_response = model_response["choices"][0]["message"].get("content") - if completion_response is None: - raise Exception - except: - original_response = f"response: {response}" - if hasattr(response, "candidates"): - original_response = f"response: {response.candidates}" - if "SAFETY" in original_response: - original_response += ( - "\nThe candidate content was flagged for safety reasons." - ) - elif "RECITATION" in original_response: - original_response += ( - "\nThe candidate content was flagged for recitation reasons." - ) - raise GeminiError( - status_code=400, - message=f"No response received. Original response - {original_response}", - ) +# try: +# completion_response = model_response["choices"][0]["message"].get("content") +# if completion_response is None: +# raise Exception +# except: +# original_response = f"response: {response}" +# if hasattr(response, "candidates"): +# original_response = f"response: {response.candidates}" +# if "SAFETY" in original_response: +# original_response += ( +# "\nThe candidate content was flagged for safety reasons." +# ) +# elif "RECITATION" in original_response: +# original_response += ( +# "\nThe candidate content was flagged for recitation reasons." +# ) +# raise GeminiError( +# status_code=400, +# message=f"No response received. Original response - {original_response}", +# ) - ## CALCULATING USAGE - prompt_str = "" - for m in messages: - if isinstance(m["content"], str): - prompt_str += m["content"] - elif isinstance(m["content"], list): - for content in m["content"]: - if content["type"] == "text": - prompt_str += content["text"] - prompt_tokens = len(encoding.encode(prompt_str)) - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) - ) +# ## CALCULATING USAGE +# prompt_str = "" +# for m in messages: +# if isinstance(m["content"], str): +# prompt_str += m["content"] +# elif isinstance(m["content"], list): +# for content in m["content"]: +# if content["type"] == "text": +# prompt_str += content["text"] +# prompt_tokens = len(encoding.encode(prompt_str)) +# completion_tokens = len( +# encoding.encode(model_response["choices"][0]["message"].get("content", "")) +# ) - model_response["created"] = int(time.time()) - model_response["model"] = "gemini/" + model - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - setattr(model_response, "usage", usage) - return model_response +# model_response.created = int(time.time()) +# model_response.model = "gemini/" + model +# usage = Usage( +# prompt_tokens=prompt_tokens, +# completion_tokens=completion_tokens, +# total_tokens=prompt_tokens + completion_tokens, +# ) +# setattr(model_response, "usage", usage) +# return model_response -async def async_completion( - _model, - model, - prompt, - inference_params, - safety_settings, - logging_obj, - print_verbose, - model_response, - messages, - encoding, -): - import google.generativeai as genai # type: ignore +# async def async_completion( +# _model, +# model, +# prompt, +# inference_params, +# safety_settings, +# logging_obj, +# print_verbose, +# model_response, +# messages, +# encoding, +# ): +# import google.generativeai as genai # type: ignore - response = await _model.generate_content_async( - contents=prompt, - generation_config=genai.types.GenerationConfig(**inference_params), - safety_settings=safety_settings, - ) +# response = await _model.generate_content_async( +# contents=prompt, +# generation_config=genai.types.GenerationConfig(**inference_params), +# safety_settings=safety_settings, +# ) - ## LOGGING - logging_obj.post_call( - input=prompt, - api_key="", - original_response=response, - additional_args={"complete_input_dict": {}}, - ) - print_verbose(f"raw model_response: {response}") - ## RESPONSE OBJECT - completion_response = response - try: - choices_list = [] - for idx, item in enumerate(completion_response.candidates): - if len(item.content.parts) > 0: - message_obj = Message(content=item.content.parts[0].text) - else: - message_obj = Message(content=None) - choice_obj = Choices(index=idx, message=message_obj) - choices_list.append(choice_obj) - model_response["choices"] = choices_list - except Exception as e: - verbose_logger.error("LiteLLM.gemini.py: Exception occured - {}".format(str(e))) - verbose_logger.debug(traceback.format_exc()) - raise GeminiError( - message=traceback.format_exc(), status_code=response.status_code - ) +# ## LOGGING +# logging_obj.post_call( +# input=prompt, +# api_key="", +# original_response=response, +# additional_args={"complete_input_dict": {}}, +# ) +# print_verbose(f"raw model_response: {response}") +# ## RESPONSE OBJECT +# completion_response = response +# try: +# choices_list = [] +# for idx, item in enumerate(completion_response.candidates): +# if len(item.content.parts) > 0: +# message_obj = Message(content=item.content.parts[0].text) +# else: +# message_obj = Message(content=None) +# choice_obj = Choices(index=idx, message=message_obj) +# choices_list.append(choice_obj) +# model_response["choices"] = choices_list +# except Exception as e: +# verbose_logger.error("LiteLLM.gemini.py: Exception occured - {}".format(str(e))) +# raise GeminiError( +# message=traceback.format_exc(), status_code=response.status_code +# ) - try: - completion_response = model_response["choices"][0]["message"].get("content") - if completion_response is None: - raise Exception - except: - original_response = f"response: {response}" - if hasattr(response, "candidates"): - original_response = f"response: {response.candidates}" - if "SAFETY" in original_response: - original_response += ( - "\nThe candidate content was flagged for safety reasons." - ) - elif "RECITATION" in original_response: - original_response += ( - "\nThe candidate content was flagged for recitation reasons." - ) - raise GeminiError( - status_code=400, - message=f"No response received. Original response - {original_response}", - ) +# try: +# completion_response = model_response["choices"][0]["message"].get("content") +# if completion_response is None: +# raise Exception +# except: +# original_response = f"response: {response}" +# if hasattr(response, "candidates"): +# original_response = f"response: {response.candidates}" +# if "SAFETY" in original_response: +# original_response += ( +# "\nThe candidate content was flagged for safety reasons." +# ) +# elif "RECITATION" in original_response: +# original_response += ( +# "\nThe candidate content was flagged for recitation reasons." +# ) +# raise GeminiError( +# status_code=400, +# message=f"No response received. Original response - {original_response}", +# ) - ## CALCULATING USAGE - prompt_str = "" - for m in messages: - if isinstance(m["content"], str): - prompt_str += m["content"] - elif isinstance(m["content"], list): - for content in m["content"]: - if content["type"] == "text": - prompt_str += content["text"] - prompt_tokens = len(encoding.encode(prompt_str)) - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) - ) +# ## CALCULATING USAGE +# prompt_str = "" +# for m in messages: +# if isinstance(m["content"], str): +# prompt_str += m["content"] +# elif isinstance(m["content"], list): +# for content in m["content"]: +# if content["type"] == "text": +# prompt_str += content["text"] +# prompt_tokens = len(encoding.encode(prompt_str)) +# completion_tokens = len( +# encoding.encode(model_response["choices"][0]["message"].get("content", "")) +# ) - model_response["created"] = int(time.time()) - model_response["model"] = "gemini/" + model - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - model_response.usage = usage - return model_response +# model_response["created"] = int(time.time()) +# model_response["model"] = "gemini/" + model +# usage = Usage( +# prompt_tokens=prompt_tokens, +# completion_tokens=completion_tokens, +# total_tokens=prompt_tokens + completion_tokens, +# ) +# model_response.usage = usage +# return model_response -def embedding(): - # logic for parsing in - calling - parsing out model embedding calls - pass +# def embedding(): +# # logic for parsing in - calling - parsing out model embedding calls +# pass diff --git a/litellm/llms/huggingface_restapi.py b/litellm/llms/huggingface_restapi.py index c54dba75f..06ef0e6fc 100644 --- a/litellm/llms/huggingface_restapi.py +++ b/litellm/llms/huggingface_restapi.py @@ -1,17 +1,23 @@ ## Uses the huggingface text generation inference API -import os, copy, types -import json -from enum import Enum -import httpx, requests -from .base import BaseLLM -import time -import litellm -from typing import Callable, Dict, List, Any, Literal, Tuple -from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper, Usage -from typing import Optional -from .prompt_templates.factory import prompt_factory, custom_prompt -from litellm.types.completion import ChatCompletionMessageToolCallParam +import copy import enum +import json +import os +import time +import types +from enum import Enum +from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union, get_args + +import httpx +import requests + +import litellm +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.types.completion import ChatCompletionMessageToolCallParam +from litellm.utils import Choices, CustomStreamWrapper, Message, ModelResponse, Usage + +from .base import BaseLLM +from .prompt_templates.factory import custom_prompt, prompt_factory class HuggingfaceError(Exception): @@ -55,6 +61,10 @@ hf_tasks = Literal[ "text-generation", ] +hf_tasks_embeddings = Literal[ # pipeline tags + hf tei endpoints - https://huggingface.github.io/text-embeddings-inference/#/ + "sentence-similarity", "feature-extraction", "rerank", "embed", "similarity" +] + class HuggingfaceConfig: """ @@ -121,6 +131,9 @@ class HuggingfaceConfig: and v is not None } + def get_special_options_params(self): + return ["use_cache", "wait_for_model"] + def get_supported_openai_params(self): return [ "stream", @@ -244,6 +257,55 @@ def get_hf_task_for_model(model: str) -> Tuple[hf_tasks, str]: return "text-generation-inference", model # default to tgi +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler + + +def get_hf_task_embedding_for_model( + model: str, task_type: Optional[str], api_base: str +) -> Optional[str]: + if task_type is not None: + if task_type in get_args(hf_tasks_embeddings): + return task_type + else: + raise Exception( + "Invalid task_type={}. Expected one of={}".format( + task_type, hf_tasks_embeddings + ) + ) + http_client = HTTPHandler(concurrent_limit=1) + + model_info = http_client.get(url=api_base) + + model_info_dict = model_info.json() + + pipeline_tag: Optional[str] = model_info_dict.get("pipeline_tag", None) + + return pipeline_tag + + +async def async_get_hf_task_embedding_for_model( + model: str, task_type: Optional[str], api_base: str +) -> Optional[str]: + if task_type is not None: + if task_type in get_args(hf_tasks_embeddings): + return task_type + else: + raise Exception( + "Invalid task_type={}. Expected one of={}".format( + task_type, hf_tasks_embeddings + ) + ) + http_client = AsyncHTTPHandler(concurrent_limit=1) + + model_info = await http_client.get(url=api_base) + + model_info_dict = model_info.json() + + pipeline_tag: Optional[str] = model_info_dict.get("pipeline_tag", None) + + return pipeline_tag + + class Huggingface(BaseLLM): _client_session: Optional[httpx.Client] = None _aclient_session: Optional[httpx.AsyncClient] = None @@ -251,7 +313,7 @@ class Huggingface(BaseLLM): def __init__(self) -> None: super().__init__() - def validate_environment(self, api_key, headers): + def _validate_environment(self, api_key, headers) -> dict: default_headers = { "content-type": "application/json", } @@ -269,7 +331,7 @@ class Huggingface(BaseLLM): def convert_to_model_response_object( self, completion_response, - model_response, + model_response: litellm.ModelResponse, task: hf_tasks, optional_params, encoding, @@ -278,11 +340,9 @@ class Huggingface(BaseLLM): ): if task == "conversational": if len(completion_response["generated_text"]) > 0: # type: ignore - model_response["choices"][0]["message"][ - "content" - ] = completion_response[ + model_response.choices[0].message.content = completion_response[ # type: ignore "generated_text" - ] # type: ignore + ] elif task == "text-generation-inference": if ( not isinstance(completion_response, list) @@ -295,7 +355,7 @@ class Huggingface(BaseLLM): ) if len(completion_response[0]["generated_text"]) > 0: - model_response["choices"][0]["message"]["content"] = output_parser( + model_response.choices[0].message.content = output_parser( # type: ignore completion_response[0]["generated_text"] ) ## GETTING LOGPROBS + FINISH REASON @@ -310,7 +370,7 @@ class Huggingface(BaseLLM): for token in completion_response[0]["details"]["tokens"]: if token["logprob"] != None: sum_logprob += token["logprob"] - model_response["choices"][0]["message"]._logprob = sum_logprob + setattr(model_response.choices[0].message, "_logprob", sum_logprob) # type: ignore if "best_of" in optional_params and optional_params["best_of"] > 1: if ( "details" in completion_response[0] @@ -337,14 +397,14 @@ class Huggingface(BaseLLM): message=message_obj, ) choices_list.append(choice_obj) - model_response["choices"].extend(choices_list) + model_response.choices.extend(choices_list) elif task == "text-classification": - model_response["choices"][0]["message"]["content"] = json.dumps( + model_response.choices[0].message.content = json.dumps( # type: ignore completion_response ) else: if len(completion_response[0]["generated_text"]) > 0: - model_response["choices"][0]["message"]["content"] = output_parser( + model_response.choices[0].message.content = output_parser( # type: ignore completion_response[0]["generated_text"] ) ## CALCULATING USAGE @@ -371,14 +431,14 @@ class Huggingface(BaseLLM): else: completion_tokens = 0 - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=prompt_tokens + completion_tokens, ) - model_response.usage = usage + setattr(model_response, "usage", usage) model_response._hidden_params["original_response"] = completion_response return model_response @@ -403,7 +463,7 @@ class Huggingface(BaseLLM): super().completion() exception_mapping_worked = False try: - headers = self.validate_environment(api_key, headers) + headers = self._validate_environment(api_key, headers) task, model = get_hf_task_for_model(model) ## VALIDATE API FORMAT if task is None or not isinstance(task, str) or task not in hf_task_list: @@ -434,6 +494,20 @@ class Huggingface(BaseLLM): optional_params[k] = v ### MAP INPUT PARAMS + #### HANDLE SPECIAL PARAMS + special_params = HuggingfaceConfig().get_special_options_params() + special_params_dict = {} + # Create a list of keys to pop after iteration + keys_to_pop = [] + + for k, v in optional_params.items(): + if k in special_params: + special_params_dict[k] = v + keys_to_pop.append(k) + + # Pop the keys from the dictionary after iteration + for k in keys_to_pop: + optional_params.pop(k) if task == "conversational": inference_params = copy.deepcopy(optional_params) inference_params.pop("details") @@ -521,6 +595,11 @@ class Huggingface(BaseLLM): else False ) input_text = prompt + + ### RE-ADD SPECIAL PARAMS + if len(special_params_dict.keys()) > 0: + data.update({"options": special_params_dict}) + ## LOGGING logging_obj.pre_call( input=input_text, @@ -759,76 +838,120 @@ class Huggingface(BaseLLM): async for transformed_chunk in streamwrapper: yield transformed_chunk - def embedding( + def _transform_input_on_pipeline_tag( + self, input: List, pipeline_tag: Optional[str] + ) -> dict: + if pipeline_tag is None: + return {"inputs": input} + if pipeline_tag == "sentence-similarity" or pipeline_tag == "similarity": + if len(input) < 2: + raise HuggingfaceError( + status_code=400, + message="sentence-similarity requires 2+ sentences", + ) + return {"inputs": {"source_sentence": input[0], "sentences": input[1:]}} + elif pipeline_tag == "rerank": + if len(input) < 2: + raise HuggingfaceError( + status_code=400, + message="reranker requires 2+ sentences", + ) + return {"inputs": {"query": input[0], "texts": input[1:]}} + return {"inputs": input} # default to feature-extraction pipeline tag + + async def _async_transform_input( self, model: str, - input: list, - api_key: Optional[str] = None, - api_base: Optional[str] = None, - logging_obj=None, - model_response=None, - encoding=None, - ): - super().embedding() - headers = self.validate_environment(api_key, headers=None) - # print_verbose(f"{model}, {task}") - embed_url = "" - if "https" in model: - embed_url = model - elif api_base: - embed_url = api_base - elif "HF_API_BASE" in os.environ: - embed_url = os.getenv("HF_API_BASE", "") - elif "HUGGINGFACE_API_BASE" in os.environ: - embed_url = os.getenv("HUGGINGFACE_API_BASE", "") - else: - embed_url = f"https://api-inference.huggingface.co/models/{model}" + task_type: Optional[str], + embed_url: str, + input: List, + optional_params: dict, + ) -> dict: + hf_task = await async_get_hf_task_embedding_for_model( + model=model, task_type=task_type, api_base=embed_url + ) + data = self._transform_input_on_pipeline_tag(input=input, pipeline_tag=hf_task) + + if len(optional_params.keys()) > 0: + data["options"] = optional_params + + return data + + def _process_optional_params(self, data: dict, optional_params: dict) -> dict: + special_options_keys = HuggingfaceConfig().get_special_options_params() + special_parameters_keys = [ + "min_length", + "max_length", + "top_k", + "top_p", + "temperature", + "repetition_penalty", + "max_time", + ] + + for k, v in optional_params.items(): + if k in special_options_keys: + data.setdefault("options", {}) + data["options"][k] = v + elif k in special_parameters_keys: + data.setdefault("parameters", {}) + data["parameters"][k] = v + else: + data[k] = v + + return data + + def _transform_input( + self, + input: List, + model: str, + call_type: Literal["sync", "async"], + optional_params: dict, + embed_url: str, + ) -> dict: + data: Dict = {} + ## TRANSFORMATION ## if "sentence-transformers" in model: if len(input) == 0: raise HuggingfaceError( status_code=400, message="sentence transformers requires 2+ sentences", ) - data = { - "inputs": { - "source_sentence": input[0], - "sentences": [ - "That is a happy dog", - "That is a very happy person", - "Today is a sunny day", - ], - } - } + data = {"inputs": {"source_sentence": input[0], "sentences": input[1:]}} else: - data = {"inputs": input} # type: ignore + data = {"inputs": input} - ## LOGGING - logging_obj.pre_call( - input=input, - api_key=api_key, - additional_args={ - "complete_input_dict": data, - "headers": headers, - "api_base": embed_url, - }, - ) - ## COMPLETION CALL - response = requests.post(embed_url, headers=headers, data=json.dumps(data)) + task_type = optional_params.pop("input_type", None) - ## LOGGING - logging_obj.post_call( - input=input, - api_key=api_key, - additional_args={"complete_input_dict": data}, - original_response=response, - ) + if call_type == "sync": + hf_task = get_hf_task_embedding_for_model( + model=model, task_type=task_type, api_base=embed_url + ) + elif call_type == "async": + return self._async_transform_input( + model=model, task_type=task_type, embed_url=embed_url, input=input + ) # type: ignore - embeddings = response.json() + data = self._transform_input_on_pipeline_tag( + input=input, pipeline_tag=hf_task + ) - if "error" in embeddings: - raise HuggingfaceError(status_code=500, message=embeddings["error"]) + if len(optional_params.keys()) > 0: + data = self._process_optional_params( + data=data, optional_params=optional_params + ) + return data + + def _process_embedding_response( + self, + embeddings: dict, + model_response: litellm.EmbeddingResponse, + model: str, + input: List, + encoding: Any, + ) -> litellm.EmbeddingResponse: output_data = [] if "similarities" in embeddings: for idx, embedding in embeddings["similarities"]: @@ -867,15 +990,174 @@ class Huggingface(BaseLLM): ], # flatten list returned from hf } ) - model_response["object"] = "list" - model_response["data"] = output_data - model_response["model"] = model + model_response.object = "list" + model_response.data = output_data + model_response.model = model input_tokens = 0 for text in input: input_tokens += len(encoding.encode(text)) - model_response["usage"] = { - "prompt_tokens": input_tokens, - "total_tokens": input_tokens, - } + setattr( + model_response, + "usage", + litellm.Usage( + **{ + "prompt_tokens": input_tokens, + "total_tokens": input_tokens, + } + ), + ) return model_response + + async def aembedding( + self, + model: str, + input: list, + model_response: litellm.utils.EmbeddingResponse, + timeout: Union[float, httpx.Timeout], + logging_obj: LiteLLMLoggingObj, + optional_params: dict, + api_base: str, + api_key: Optional[str], + headers: dict, + encoding: Callable, + client: Optional[AsyncHTTPHandler] = None, + ): + ## TRANSFORMATION ## + data = self._transform_input( + input=input, + model=model, + call_type="sync", + optional_params=optional_params, + embed_url=api_base, + ) + + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "headers": headers, + "api_base": api_base, + }, + ) + ## COMPLETION CALL + if client is None: + client = AsyncHTTPHandler(concurrent_limit=1) + + response = await client.post(api_base, headers=headers, data=json.dumps(data)) + + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=response, + ) + + embeddings = response.json() + + if "error" in embeddings: + raise HuggingfaceError(status_code=500, message=embeddings["error"]) + + ## PROCESS RESPONSE ## + return self._process_embedding_response( + embeddings=embeddings, + model_response=model_response, + model=model, + input=input, + encoding=encoding, + ) + + def embedding( + self, + model: str, + input: list, + model_response: litellm.EmbeddingResponse, + optional_params: dict, + logging_obj: LiteLLMLoggingObj, + encoding: Callable, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + timeout: Union[float, httpx.Timeout] = httpx.Timeout(None), + aembedding: Optional[bool] = None, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + ) -> litellm.EmbeddingResponse: + super().embedding() + headers = self._validate_environment(api_key, headers=None) + # print_verbose(f"{model}, {task}") + embed_url = "" + if "https" in model: + embed_url = model + elif api_base: + embed_url = api_base + elif "HF_API_BASE" in os.environ: + embed_url = os.getenv("HF_API_BASE", "") + elif "HUGGINGFACE_API_BASE" in os.environ: + embed_url = os.getenv("HUGGINGFACE_API_BASE", "") + else: + embed_url = f"https://api-inference.huggingface.co/models/{model}" + + ## ROUTING ## + if aembedding is True: + return self.aembedding( + input=input, + model_response=model_response, + timeout=timeout, + logging_obj=logging_obj, + headers=headers, + api_base=embed_url, # type: ignore + api_key=api_key, + client=client if isinstance(client, AsyncHTTPHandler) else None, + model=model, + optional_params=optional_params, + encoding=encoding, + ) + + ## TRANSFORMATION ## + + data = self._transform_input( + input=input, + model=model, + call_type="sync", + optional_params=optional_params, + embed_url=embed_url, + ) + + ## LOGGING + logging_obj.pre_call( + input=input, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "headers": headers, + "api_base": embed_url, + }, + ) + ## COMPLETION CALL + if client is None or not isinstance(client, HTTPHandler): + client = HTTPHandler(concurrent_limit=1) + response = client.post(embed_url, headers=headers, data=json.dumps(data)) + + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=response, + ) + + embeddings = response.json() + + if "error" in embeddings: + raise HuggingfaceError(status_code=500, message=embeddings["error"]) + + ## PROCESS RESPONSE ## + return self._process_embedding_response( + embeddings=embeddings, + model_response=model_response, + model=model, + input=input, + encoding=encoding, + ) diff --git a/litellm/llms/maritalk.py b/litellm/llms/maritalk.py index dfe53e9df..c2eb66382 100644 --- a/litellm/llms/maritalk.py +++ b/litellm/llms/maritalk.py @@ -1,11 +1,15 @@ -import os, types import json +import os +import time +import traceback +import types from enum import Enum +from typing import Callable, List, Optional + import requests # type: ignore -import time, traceback -from typing import Callable, Optional, List -from litellm.utils import ModelResponse, Choices, Message, Usage + import litellm +from litellm.utils import Choices, Message, ModelResponse, Usage class MaritalkError(Exception): @@ -152,9 +156,9 @@ def completion( else: try: if len(completion_response["answer"]) > 0: - model_response["choices"][0]["message"]["content"] = ( - completion_response["answer"] - ) + model_response.choices[0].message.content = completion_response[ # type: ignore + "answer" + ] except Exception as e: raise MaritalkError( message=response.text, status_code=response.status_code @@ -167,8 +171,8 @@ def completion( encoding.encode(model_response["choices"][0]["message"].get("content", "")) ) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, diff --git a/litellm/llms/nlp_cloud.py b/litellm/llms/nlp_cloud.py index cd5f17a90..84908f26b 100644 --- a/litellm/llms/nlp_cloud.py +++ b/litellm/llms/nlp_cloud.py @@ -1,9 +1,12 @@ -import os, types import json -from enum import Enum -import requests # type: ignore +import os import time +import types +from enum import Enum from typing import Callable, Optional + +import requests # type: ignore + import litellm from litellm.utils import ModelResponse, Usage @@ -185,7 +188,7 @@ def completion( else: try: if len(completion_response["generated_text"]) > 0: - model_response["choices"][0]["message"]["content"] = ( + model_response.choices[0].message.content = ( # type: ignore completion_response["generated_text"] ) except: @@ -198,8 +201,8 @@ def completion( prompt_tokens = completion_response["nb_input_tokens"] completion_tokens = completion_response["nb_generated_tokens"] - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, diff --git a/litellm/llms/ollama.py b/litellm/llms/ollama.py index 1939715b3..9f62bab20 100644 --- a/litellm/llms/ollama.py +++ b/litellm/llms/ollama.py @@ -1,13 +1,22 @@ -from itertools import chain -import requests, types, time # type: ignore -import json, uuid +import asyncio +import json +import time import traceback -from typing import Optional, List +import types +import uuid +from copy import deepcopy +from itertools import chain +from typing import Any, Dict, List, Optional + +import aiohttp +import httpx # type: ignore +import requests # type: ignore + import litellm -from litellm.types.utils import ProviderField -import httpx, aiohttp, asyncio # type: ignore -from .prompt_templates.factory import prompt_factory, custom_prompt from litellm import verbose_logger +from litellm.types.utils import ProviderField + +from .prompt_templates.factory import custom_prompt, prompt_factory class OllamaError(Exception): @@ -69,6 +78,7 @@ class OllamaConfig: mirostat_tau: Optional[float] = None num_ctx: Optional[int] = None num_gqa: Optional[int] = None + num_gpu: Optional[int] = None num_thread: Optional[int] = None repeat_last_n: Optional[int] = None repeat_penalty: Optional[float] = None @@ -91,6 +101,7 @@ class OllamaConfig: mirostat_tau: Optional[float] = None, num_ctx: Optional[int] = None, num_gqa: Optional[int] = None, + num_gpu: Optional[int] = None, num_thread: Optional[int] = None, repeat_last_n: Optional[int] = None, repeat_penalty: Optional[float] = None, @@ -138,7 +149,6 @@ class OllamaConfig: ) ] - def get_supported_openai_params( self, ): @@ -157,7 +167,8 @@ class OllamaConfig: # ollama wants plain base64 jpeg/png files as images. strip any leading dataURI # and convert to jpeg if necessary. def _convert_image(image): - import base64, io + import base64 + import io try: from PIL import Image @@ -183,13 +194,13 @@ def _convert_image(image): # ollama implementation def get_ollama_response( + model_response: litellm.ModelResponse, api_base="http://localhost:11434", model="llama2", prompt="Why is the sky blue?", optional_params=None, logging_obj=None, acompletion: bool = False, - model_response=None, encoding=None, ): if api_base.endswith("/api/generate"): @@ -248,7 +259,7 @@ def get_ollama_response( logging_obj=logging_obj, ) return response - elif stream == True: + elif stream is True: return ollama_completion_stream(url=url, data=data, logging_obj=logging_obj) response = requests.post( @@ -271,7 +282,7 @@ def get_ollama_response( response_json = response.json() ## RESPONSE OBJECT - model_response["choices"][0]["finish_reason"] = "stop" + model_response.choices[0].finish_reason = "stop" if data.get("format", "") == "json": function_call = json.loads(response_json["response"]) message = litellm.Message( @@ -287,20 +298,24 @@ def get_ollama_response( } ], ) - model_response["choices"][0]["message"] = message - model_response["choices"][0]["finish_reason"] = "tool_calls" + model_response.choices[0].message = message # type: ignore + model_response.choices[0].finish_reason = "tool_calls" else: - model_response["choices"][0]["message"]["content"] = response_json["response"] - model_response["created"] = int(time.time()) - model_response["model"] = "ollama/" + model + model_response.choices[0].message.content = response_json["response"] # type: ignore + model_response.created = int(time.time()) + model_response.model = "ollama/" + model prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(prompt, disallowed_special=()))) # type: ignore completion_tokens = response_json.get( "eval_count", len(response_json.get("message", dict()).get("content", "")) ) - model_response["usage"] = litellm.Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ), ) return model_response @@ -312,7 +327,7 @@ def ollama_completion_stream(url, data, logging_obj): try: if response.status_code != 200: raise OllamaError( - status_code=response.status_code, message=response.text + status_code=response.status_code, message=response.read() ) streamwrapper = litellm.CustomStreamWrapper( @@ -346,8 +361,8 @@ def ollama_completion_stream(url, data, logging_obj): ], ) model_response = first_chunk - model_response["choices"][0]["delta"] = delta - model_response["choices"][0]["finish_reason"] = "tool_calls" + model_response.choices[0].delta = delta # type: ignore + model_response.choices[0].finish_reason = "tool_calls" yield model_response else: for transformed_chunk in streamwrapper: @@ -401,24 +416,25 @@ async def ollama_async_streaming(url, data, model_response, encoding, logging_ob ], ) model_response = first_chunk - model_response["choices"][0]["delta"] = delta - model_response["choices"][0]["finish_reason"] = "tool_calls" + model_response.choices[0].delta = delta # type: ignore + model_response.choices[0].finish_reason = "tool_calls" yield model_response else: async for transformed_chunk in streamwrapper: yield transformed_chunk except Exception as e: - verbose_logger.error( + verbose_logger.exception( "LiteLLM.ollama.py::ollama_async_streaming(): Exception occured - {}".format( str(e) ) ) - verbose_logger.debug(traceback.format_exc()) raise e -async def ollama_acompletion(url, data, model_response, encoding, logging_obj): +async def ollama_acompletion( + url, data, model_response: litellm.ModelResponse, encoding, logging_obj +): data["stream"] = False try: timeout = aiohttp.ClientTimeout(total=litellm.request_timeout) # 10 minutes @@ -442,7 +458,7 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj): response_json = await resp.json() ## RESPONSE OBJECT - model_response["choices"][0]["finish_reason"] = "stop" + model_response.choices[0].finish_reason = "stop" if data.get("format", "") == "json": function_call = json.loads(response_json["response"]) message = litellm.Message( @@ -451,55 +467,58 @@ async def ollama_acompletion(url, data, model_response, encoding, logging_obj): { "id": f"call_{str(uuid.uuid4())}", "function": { - "name": function_call.get("name", function_call.get("function", None)), + "name": function_call.get( + "name", function_call.get("function", None) + ), "arguments": json.dumps(function_call["arguments"]), }, "type": "function", } ], ) - model_response["choices"][0]["message"] = message - model_response["choices"][0]["finish_reason"] = "tool_calls" + model_response.choices[0].message = message # type: ignore + model_response.choices[0].finish_reason = "tool_calls" else: - model_response["choices"][0]["message"]["content"] = response_json[ - "response" - ] - model_response["created"] = int(time.time()) - model_response["model"] = "ollama/" + data["model"] + model_response.choices[0].message.content = response_json["response"] # type: ignore + model_response.created = int(time.time()) + model_response.model = "ollama/" + data["model"] prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(data["prompt"], disallowed_special=()))) # type: ignore completion_tokens = response_json.get( "eval_count", len(response_json.get("message", dict()).get("content", "")), ) - model_response["usage"] = litellm.Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ), ) return model_response except Exception as e: - verbose_logger.error( + verbose_logger.exception( "LiteLLM.ollama.py::ollama_acompletion(): Exception occured - {}".format( str(e) ) ) - verbose_logger.debug(traceback.format_exc()) raise e async def ollama_aembeddings( api_base: str, model: str, - prompts: list, - optional_params=None, + prompts: List[str], + model_response: litellm.EmbeddingResponse, + optional_params: dict, logging_obj=None, - model_response=None, encoding=None, ): - if api_base.endswith("/api/embeddings"): + if api_base.endswith("/api/embed"): url = api_base else: - url = f"{api_base}/api/embeddings" + url = f"{api_base}/api/embed" ## Load Config config = litellm.OllamaConfig.get_config() @@ -509,58 +528,63 @@ async def ollama_aembeddings( ): # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in optional_params[k] = v + data: Dict[str, Any] = {"model": model, "input": prompts} + special_optional_params = ["truncate", "options", "keep_alive"] + + for k, v in optional_params.items(): + if k in special_optional_params: + data[k] = v + else: + # Ensure "options" is a dictionary before updating it + data.setdefault("options", {}) + if isinstance(data["options"], dict): + data["options"].update({k: v}) total_input_tokens = 0 output_data = [] + timeout = aiohttp.ClientTimeout(total=litellm.request_timeout) # 10 minutes async with aiohttp.ClientSession(timeout=timeout) as session: - for idx, prompt in enumerate(prompts): - data = { - "model": model, - "prompt": prompt, + ## LOGGING + logging_obj.pre_call( + input=None, + api_key=None, + additional_args={ + "api_base": url, + "complete_input_dict": data, + "headers": {}, + }, + ) + + response = await session.post(url, json=data) + + if response.status != 200: + text = await response.text() + raise OllamaError(status_code=response.status, message=text) + + response_json = await response.json() + + embeddings: List[List[float]] = response_json["embeddings"] + for idx, emb in enumerate(embeddings): + output_data.append({"object": "embedding", "index": idx, "embedding": emb}) + + input_tokens = response_json.get("prompt_eval_count") or len( + encoding.encode("".join(prompt for prompt in prompts)) + ) + total_input_tokens += input_tokens + + model_response.object = "list" + model_response.data = output_data + model_response.model = "ollama/" + model + setattr( + model_response, + "usage", + litellm.Usage( + **{ + "prompt_tokens": total_input_tokens, + "total_tokens": total_input_tokens, } - ## LOGGING - logging_obj.pre_call( - input=None, - api_key=None, - additional_args={ - "api_base": url, - "complete_input_dict": data, - "headers": {}, - }, - ) - - response = await session.post(url, json=data) - if response.status != 200: - text = await response.text() - raise OllamaError(status_code=response.status, message=text) - - ## LOGGING - logging_obj.post_call( - input=prompt, - api_key="", - original_response=response.text, - additional_args={ - "headers": None, - "api_base": api_base, - }, - ) - - response_json = await response.json() - embeddings: list[float] = response_json["embedding"] - output_data.append( - {"object": "embedding", "index": idx, "embedding": embeddings} - ) - - input_tokens = len(encoding.encode(prompt)) - total_input_tokens += input_tokens - - model_response["object"] = "list" - model_response["data"] = output_data - model_response["model"] = model - model_response["usage"] = { - "prompt_tokens": total_input_tokens, - "total_tokens": total_input_tokens, - } + ), + ) return model_response @@ -575,12 +599,12 @@ def ollama_embeddings( ): return asyncio.run( ollama_aembeddings( - api_base, - model, - prompts, - optional_params, - logging_obj, - model_response, - encoding, + api_base=api_base, + model=model, + prompts=prompts, + model_response=model_response, + optional_params=optional_params, + logging_obj=logging_obj, + encoding=encoding, ) ) diff --git a/litellm/llms/ollama_chat.py b/litellm/llms/ollama_chat.py index bb053f5e8..21056cee3 100644 --- a/litellm/llms/ollama_chat.py +++ b/litellm/llms/ollama_chat.py @@ -1,15 +1,17 @@ -from itertools import chain -import requests -import types -import time import json -import uuid +import time import traceback +import types +import uuid +from itertools import chain from typing import Optional -from litellm import verbose_logger -import litellm -import httpx + import aiohttp +import httpx +import requests + +import litellm +from litellm import verbose_logger class OllamaError(Exception): @@ -147,7 +149,9 @@ class OllamaChatConfig: "response_format", ] - def map_openai_params(self, non_default_params: dict, optional_params: dict): + def map_openai_params( + self, model: str, non_default_params: dict, optional_params: dict + ): for param, value in non_default_params.items(): if param == "max_tokens": optional_params["num_predict"] = value @@ -168,16 +172,26 @@ class OllamaChatConfig: ### FUNCTION CALLING LOGIC ### if param == "tools": # ollama actually supports json output - optional_params["format"] = "json" - litellm.add_function_to_prompt = ( - True # so that main.py adds the function call to the prompt - ) - optional_params["functions_unsupported_model"] = value + ## CHECK IF MODEL SUPPORTS TOOL CALLING ## + try: + model_info = litellm.get_model_info( + model=model, custom_llm_provider="ollama_chat" + ) + if model_info.get("supports_function_calling") is True: + optional_params["tools"] = value + else: + raise Exception + except Exception: + optional_params["format"] = "json" + litellm.add_function_to_prompt = ( + True # so that main.py adds the function call to the prompt + ) + optional_params["functions_unsupported_model"] = value - if len(optional_params["functions_unsupported_model"]) == 1: - optional_params["function_name"] = optional_params[ - "functions_unsupported_model" - ][0]["function"]["name"] + if len(optional_params["functions_unsupported_model"]) == 1: + optional_params["function_name"] = optional_params[ + "functions_unsupported_model" + ][0]["function"]["name"] if param == "functions": # ollama actually supports json output @@ -195,14 +209,14 @@ class OllamaChatConfig: # ollama implementation def get_ollama_response( + model_response: litellm.ModelResponse, + messages: list, + optional_params: dict, api_base="http://localhost:11434", api_key: Optional[str] = None, model="llama2", - messages=None, - optional_params=None, logging_obj=None, acompletion: bool = False, - model_response=None, encoding=None, ): if api_base.endswith("/api/chat"): @@ -221,6 +235,7 @@ def get_ollama_response( stream = optional_params.pop("stream", False) format = optional_params.pop("format", None) function_name = optional_params.pop("function_name", None) + tools = optional_params.pop("tools", None) for m in messages: if "role" in m and m["role"] == "tool": @@ -234,6 +249,8 @@ def get_ollama_response( } if format is not None: data["format"] = format + if tools is not None: + data["tools"] = tools ## LOGGING logging_obj.pre_call( input=None, @@ -276,7 +293,7 @@ def get_ollama_response( "json": data, } if api_key is not None: - _request["headers"] = "Bearer {}".format(api_key) + _request["headers"] = {"Authorization": "Bearer {}".format(api_key)} response = requests.post(**_request) # type: ignore if response.status_code != 200: raise OllamaError(status_code=response.status_code, message=response.text) @@ -295,8 +312,8 @@ def get_ollama_response( response_json = response.json() ## RESPONSE OBJECT - model_response["choices"][0]["finish_reason"] = "stop" - if data.get("format", "") == "json": + model_response.choices[0].finish_reason = "stop" + if data.get("format", "") == "json" and function_name is not None: function_call = json.loads(response_json["message"]["content"]) message = litellm.Message( content=None, @@ -304,29 +321,34 @@ def get_ollama_response( { "id": f"call_{str(uuid.uuid4())}", "function": { - "name": function_call["name"], - "arguments": json.dumps(function_call["arguments"]), + "name": function_call.get("name", function_name), + "arguments": json.dumps( + function_call.get("arguments", function_call) + ), }, "type": "function", } ], ) - model_response["choices"][0]["message"] = message - model_response["choices"][0]["finish_reason"] = "tool_calls" + model_response.choices[0].message = message # type: ignore + model_response.choices[0].finish_reason = "tool_calls" else: - model_response["choices"][0]["message"]["content"] = response_json["message"][ - "content" - ] - model_response["created"] = int(time.time()) - model_response["model"] = "ollama/" + model + _message = litellm.Message(**response_json["message"]) + model_response.choices[0].message = _message # type: ignore + model_response.created = int(time.time()) + model_response.model = "ollama_chat/" + model prompt_tokens = response_json.get("prompt_eval_count", litellm.token_counter(messages=messages)) # type: ignore completion_tokens = response_json.get( "eval_count", litellm.token_counter(text=response_json["message"]["content"]) ) - model_response["usage"] = litellm.Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ), ) return model_response @@ -337,9 +359,10 @@ def ollama_completion_stream(url, api_key, data, logging_obj): "json": data, "method": "POST", "timeout": litellm.request_timeout, + "follow_redirects": True, } if api_key is not None: - _request["headers"] = "Bearer {}".format(api_key) + _request["headers"] = {"Authorization": "Bearer {}".format(api_key)} with httpx.stream(**_request) as response: try: if response.status_code != 200: @@ -379,8 +402,8 @@ def ollama_completion_stream(url, api_key, data, logging_obj): ], ) model_response = first_chunk - model_response["choices"][0]["delta"] = delta - model_response["choices"][0]["finish_reason"] = "tool_calls" + model_response.choices[0].delta = delta # type: ignore + model_response.choices[0].finish_reason = "tool_calls" yield model_response else: for transformed_chunk in streamwrapper: @@ -401,7 +424,7 @@ async def ollama_async_streaming( "timeout": litellm.request_timeout, } if api_key is not None: - _request["headers"] = "Bearer {}".format(api_key) + _request["headers"] = {"Authorization": "Bearer {}".format(api_key)} async with client.stream(**_request) as response: if response.status_code != 200: raise OllamaError( @@ -434,7 +457,9 @@ async def ollama_async_streaming( { "id": f"call_{str(uuid.uuid4())}", "function": { - "name": function_call.get("name", function_call.get("function", None)), + "name": function_call.get( + "name", function_call.get("function", None) + ), "arguments": json.dumps(function_call["arguments"]), }, "type": "function", @@ -442,22 +467,23 @@ async def ollama_async_streaming( ], ) model_response = first_chunk - model_response["choices"][0]["delta"] = delta - model_response["choices"][0]["finish_reason"] = "tool_calls" + model_response.choices[0].delta = delta # type: ignore + model_response.choices[0].finish_reason = "tool_calls" yield model_response else: async for transformed_chunk in streamwrapper: yield transformed_chunk except Exception as e: - verbose_logger.error("LiteLLM.gemini(): Exception occured - {}".format(str(e))) - verbose_logger.debug(traceback.format_exc()) + verbose_logger.exception( + "LiteLLM.ollama(): Exception occured - {}".format(str(e)) + ) async def ollama_acompletion( url, api_key: Optional[str], data, - model_response, + model_response: litellm.ModelResponse, encoding, logging_obj, function_name, @@ -471,7 +497,7 @@ async def ollama_acompletion( "json": data, } if api_key is not None: - _request["headers"] = "Bearer {}".format(api_key) + _request["headers"] = {"Authorization": "Bearer {}".format(api_key)} resp = await session.post(**_request) if resp.status != 200: @@ -492,8 +518,9 @@ async def ollama_acompletion( ) ## RESPONSE OBJECT - model_response["choices"][0]["finish_reason"] = "stop" - if data.get("format", "") == "json": + model_response.choices[0].finish_reason = "stop" + + if data.get("format", "") == "json" and function_name is not None: function_call = json.loads(response_json["message"]["content"]) message = litellm.Message( content=None, @@ -510,15 +537,14 @@ async def ollama_acompletion( } ], ) - model_response["choices"][0]["message"] = message - model_response["choices"][0]["finish_reason"] = "tool_calls" + model_response.choices[0].message = message # type: ignore + model_response.choices[0].finish_reason = "tool_calls" else: - model_response["choices"][0]["message"]["content"] = response_json[ - "message" - ]["content"] + _message = litellm.Message(**response_json["message"]) + model_response.choices[0].message = _message # type: ignore - model_response["created"] = int(time.time()) - model_response["model"] = "ollama_chat/" + data["model"] + model_response.created = int(time.time()) + model_response.model = "ollama_chat/" + data["model"] prompt_tokens = response_json.get("prompt_eval_count", litellm.token_counter(messages=data["messages"])) # type: ignore completion_tokens = response_json.get( "eval_count", @@ -526,16 +552,19 @@ async def ollama_acompletion( text=response_json["message"]["content"], count_response_tokens=True ), ) - model_response["usage"] = litellm.Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ), ) return model_response except Exception as e: - verbose_logger.error( + verbose_logger.exception( "LiteLLM.ollama_acompletion(): Exception occured - {}".format(str(e)) ) - verbose_logger.debug(traceback.format_exc()) raise e diff --git a/litellm/llms/oobabooga.py b/litellm/llms/oobabooga.py index f8f32e0fe..79d918667 100644 --- a/litellm/llms/oobabooga.py +++ b/litellm/llms/oobabooga.py @@ -1,11 +1,14 @@ -import os import json -from enum import Enum -import requests # type: ignore +import os import time +from enum import Enum from typing import Callable, Optional -from litellm.utils import ModelResponse, Usage -from .prompt_templates.factory import prompt_factory, custom_prompt + +import requests # type: ignore + +from litellm.utils import EmbeddingResponse, ModelResponse, Usage + +from .prompt_templates.factory import custom_prompt, prompt_factory class OobaboogaError(Exception): @@ -99,17 +102,15 @@ def completion( ) else: try: - model_response["choices"][0]["message"]["content"] = ( - completion_response["choices"][0]["message"]["content"] - ) + model_response.choices[0].message.content = completion_response["choices"][0]["message"]["content"] # type: ignore except: raise OobaboogaError( message=json.dumps(completion_response), status_code=response.status_code, ) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=completion_response["usage"]["prompt_tokens"], completion_tokens=completion_response["usage"]["completion_tokens"], @@ -122,10 +123,10 @@ def completion( def embedding( model: str, input: list, + model_response: EmbeddingResponse, api_key: Optional[str] = None, api_base: Optional[str] = None, logging_obj=None, - model_response=None, optional_params=None, encoding=None, ): @@ -166,7 +167,7 @@ def embedding( ) # Process response data - model_response["data"] = [ + model_response.data = [ { "embedding": completion_response["data"][0]["embedding"], "index": 0, @@ -176,8 +177,12 @@ def embedding( num_tokens = len(completion_response["data"][0]["embedding"]) # Adding metadata to response - model_response.usage = Usage(prompt_tokens=num_tokens, total_tokens=num_tokens) - model_response["object"] = "list" - model_response["model"] = model + setattr( + model_response, + "usage", + Usage(prompt_tokens=num_tokens, total_tokens=num_tokens), + ) + model_response.object = "list" + model_response.model = model return model_response diff --git a/litellm/llms/openai.py b/litellm/llms/openai.py index 0a2b6a3fa..3db001183 100644 --- a/litellm/llms/openai.py +++ b/litellm/llms/openai.py @@ -1,5 +1,6 @@ import hashlib import json +import os import time import traceback import types @@ -23,6 +24,7 @@ from pydantic import BaseModel from typing_extensions import overload, override import litellm +from litellm._logging import verbose_logger from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.types.utils import ProviderField from litellm.utils import ( @@ -158,7 +160,7 @@ class MistralConfig: optional_params["max_tokens"] = value if param == "tools": optional_params["tools"] = value - if param == "stream" and value == True: + if param == "stream" and value is True: optional_params["stream"] = value if param == "temperature": optional_params["temperature"] = value @@ -759,6 +761,30 @@ class OpenAIChatCompletion(BaseLLM): openai_aclient: AsyncOpenAI, data: dict, timeout: Union[float, httpx.Timeout], + ): + """ + Helper to: + - call chat.completions.create.with_raw_response when litellm.return_response_headers is True + - call chat.completions.create by default + """ + try: + raw_response = ( + await openai_aclient.chat.completions.with_raw_response.create( + **data, timeout=timeout + ) + ) + + headers = dict(raw_response.headers) + response = raw_response.parse() + return headers, response + except Exception as e: + raise e + + def make_sync_openai_chat_completion_request( + self, + openai_client: OpenAI, + data: dict, + timeout: Union[float, httpx.Timeout], ): """ Helper to: @@ -767,17 +793,15 @@ class OpenAIChatCompletion(BaseLLM): """ try: if litellm.return_response_headers is True: - raw_response = ( - await openai_aclient.chat.completions.with_raw_response.create( - **data, timeout=timeout - ) + raw_response = openai_client.chat.completions.with_raw_response.create( + **data, timeout=timeout ) headers = dict(raw_response.headers) response = raw_response.parse() return headers, response else: - response = await openai_aclient.chat.completions.create( + response = openai_client.chat.completions.create( **data, timeout=timeout ) return None, response @@ -803,6 +827,7 @@ class OpenAIChatCompletion(BaseLLM): client=None, organization: Optional[str] = None, custom_llm_provider: Optional[str] = None, + drop_params: Optional[bool] = None, ): super().completion() exception_mapping_worked = False @@ -858,6 +883,7 @@ class OpenAIChatCompletion(BaseLLM): client=client, max_retries=max_retries, organization=organization, + drop_params=drop_params, ) else: return self.acompletion( @@ -871,6 +897,7 @@ class OpenAIChatCompletion(BaseLLM): client=client, max_retries=max_retries, organization=organization, + drop_params=drop_params, ) elif optional_params.get("stream", False): return self.streaming( @@ -913,7 +940,15 @@ class OpenAIChatCompletion(BaseLLM): }, ) - response = openai_client.chat.completions.create(**data, timeout=timeout) # type: ignore + headers, response = ( + self.make_sync_openai_chat_completion_request( + openai_client=openai_client, + data=data, + timeout=timeout, + ) + ) + + logging_obj.model_call_details["response_headers"] = headers stringified_response = response.model_dump() logging_obj.post_call( input=messages, @@ -924,7 +959,35 @@ class OpenAIChatCompletion(BaseLLM): return convert_to_model_response_object( response_object=stringified_response, model_response_object=model_response, + _response_headers=headers, ) + except openai.UnprocessableEntityError as e: + ## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800 + if litellm.drop_params is True or drop_params is True: + invalid_params: List[str] = [] + if e.body is not None and isinstance(e.body, dict) and e.body.get("detail"): # type: ignore + detail = e.body.get("detail") # type: ignore + if ( + isinstance(detail, List) + and len(detail) > 0 + and isinstance(detail[0], dict) + ): + for error_dict in detail: + if ( + error_dict.get("loc") + and isinstance(error_dict.get("loc"), list) + and len(error_dict.get("loc")) == 2 + ): + invalid_params.append(error_dict["loc"][1]) + + new_data = {} + for k, v in optional_params.items(): + if k not in invalid_params: + new_data[k] = v + optional_params = new_data + else: + raise e + # e.message except Exception as e: if print_verbose is not None: print_verbose(f"openai.py: Received openai error - {str(e)}") @@ -953,6 +1016,12 @@ class OpenAIChatCompletion(BaseLLM): new_messages = messages new_messages.append({"role": "user", "content": ""}) messages = new_messages + elif ( + "unknown field: parameter index is not a valid field" in str(e) + ) and "tools" in data: + litellm.remove_index_from_tool_calls( + tool_calls=data["tools"], messages=messages + ) else: raise e except OpenAIError as e: @@ -976,49 +1045,83 @@ class OpenAIChatCompletion(BaseLLM): client=None, max_retries=None, headers=None, + drop_params: Optional[bool] = None, ): response = None - try: - openai_aclient = self._get_openai_client( - is_async=True, - api_key=api_key, - api_base=api_base, - timeout=timeout, - max_retries=max_retries, - organization=organization, - client=client, - ) + for _ in range( + 2 + ): # if call fails due to alternating messages, retry with reformatted message + try: + openai_aclient = self._get_openai_client( + is_async=True, + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + ) - ## LOGGING - logging_obj.pre_call( - input=data["messages"], - api_key=openai_aclient.api_key, - additional_args={ - "headers": {"Authorization": f"Bearer {openai_aclient.api_key}"}, - "api_base": openai_aclient._base_url._uri_reference, - "acompletion": True, - "complete_input_dict": data, - }, - ) + ## LOGGING + logging_obj.pre_call( + input=data["messages"], + api_key=openai_aclient.api_key, + additional_args={ + "headers": { + "Authorization": f"Bearer {openai_aclient.api_key}" + }, + "api_base": openai_aclient._base_url._uri_reference, + "acompletion": True, + "complete_input_dict": data, + }, + ) - headers, response = await self.make_openai_chat_completion_request( - openai_aclient=openai_aclient, data=data, timeout=timeout - ) - stringified_response = response.model_dump() - logging_obj.post_call( - input=data["messages"], - api_key=api_key, - original_response=stringified_response, - additional_args={"complete_input_dict": data}, - ) - logging_obj.model_call_details["response_headers"] = headers - return convert_to_model_response_object( - response_object=stringified_response, - model_response_object=model_response, - hidden_params={"headers": headers}, - ) - except Exception as e: - raise e + headers, response = await self.make_openai_chat_completion_request( + openai_aclient=openai_aclient, data=data, timeout=timeout + ) + stringified_response = response.model_dump() + logging_obj.post_call( + input=data["messages"], + api_key=api_key, + original_response=stringified_response, + additional_args={"complete_input_dict": data}, + ) + logging_obj.model_call_details["response_headers"] = headers + return convert_to_model_response_object( + response_object=stringified_response, + model_response_object=model_response, + hidden_params={"headers": headers}, + _response_headers=headers, + ) + except openai.UnprocessableEntityError as e: + ## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800 + if litellm.drop_params is True or drop_params is True: + invalid_params: List[str] = [] + if e.body is not None and isinstance(e.body, dict) and e.body.get("detail"): # type: ignore + detail = e.body.get("detail") # type: ignore + if ( + isinstance(detail, List) + and len(detail) > 0 + and isinstance(detail[0], dict) + ): + for error_dict in detail: + if ( + error_dict.get("loc") + and isinstance(error_dict.get("loc"), list) + and len(error_dict.get("loc")) == 2 + ): + invalid_params.append(error_dict["loc"][1]) + + new_data = {} + for k, v in data.items(): + if k not in invalid_params: + new_data[k] = v + data = new_data + else: + raise e + # e.message + except Exception as e: + raise e def streaming( self, @@ -1053,13 +1156,20 @@ class OpenAIChatCompletion(BaseLLM): "complete_input_dict": data, }, ) - response = openai_client.chat.completions.create(**data, timeout=timeout) + headers, response = self.make_sync_openai_chat_completion_request( + openai_client=openai_client, + data=data, + timeout=timeout, + ) + + logging_obj.model_call_details["response_headers"] = headers streamwrapper = CustomStreamWrapper( completion_stream=response, model=model, custom_llm_provider="openai", logging_obj=logging_obj, stream_options=data.get("stream_options", None), + _response_headers=headers, ) return streamwrapper @@ -1075,57 +1185,88 @@ class OpenAIChatCompletion(BaseLLM): client=None, max_retries=None, headers=None, + drop_params: Optional[bool] = None, ): response = None - try: - openai_aclient = self._get_openai_client( - is_async=True, - api_key=api_key, - api_base=api_base, - timeout=timeout, - max_retries=max_retries, - organization=organization, - client=client, - ) - ## LOGGING - logging_obj.pre_call( - input=data["messages"], - api_key=api_key, - additional_args={ - "headers": headers, - "api_base": api_base, - "acompletion": True, - "complete_input_dict": data, - }, - ) - - headers, response = await self.make_openai_chat_completion_request( - openai_aclient=openai_aclient, data=data, timeout=timeout - ) - logging_obj.model_call_details["response_headers"] = headers - streamwrapper = CustomStreamWrapper( - completion_stream=response, - model=model, - custom_llm_provider="openai", - logging_obj=logging_obj, - stream_options=data.get("stream_options", None), - ) - return streamwrapper - except ( - Exception - ) as e: # need to exception handle here. async exceptions don't get caught in sync functions. - if response is not None and hasattr(response, "text"): - raise OpenAIError( - status_code=500, - message=f"{str(e)}\n\nOriginal Response: {response.text}", + for _ in range(2): + try: + openai_aclient = self._get_openai_client( + is_async=True, + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, ) - else: - if type(e).__name__ == "ReadTimeout": - raise OpenAIError(status_code=408, message=f"{type(e).__name__}") - elif hasattr(e, "status_code"): - raise OpenAIError(status_code=e.status_code, message=str(e)) + ## LOGGING + logging_obj.pre_call( + input=data["messages"], + api_key=api_key, + additional_args={ + "headers": headers, + "api_base": api_base, + "acompletion": True, + "complete_input_dict": data, + }, + ) + + headers, response = await self.make_openai_chat_completion_request( + openai_aclient=openai_aclient, data=data, timeout=timeout + ) + logging_obj.model_call_details["response_headers"] = headers + streamwrapper = CustomStreamWrapper( + completion_stream=response, + model=model, + custom_llm_provider="openai", + logging_obj=logging_obj, + stream_options=data.get("stream_options", None), + _response_headers=headers, + ) + return streamwrapper + except openai.UnprocessableEntityError as e: + ## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800 + if litellm.drop_params is True or drop_params is True: + invalid_params: List[str] = [] + if e.body is not None and isinstance(e.body, dict) and e.body.get("detail"): # type: ignore + detail = e.body.get("detail") # type: ignore + if ( + isinstance(detail, List) + and len(detail) > 0 + and isinstance(detail[0], dict) + ): + for error_dict in detail: + if ( + error_dict.get("loc") + and isinstance(error_dict.get("loc"), list) + and len(error_dict.get("loc")) == 2 + ): + invalid_params.append(error_dict["loc"][1]) + + new_data = {} + for k, v in data.items(): + if k not in invalid_params: + new_data[k] = v + data = new_data else: - raise OpenAIError(status_code=500, message=f"{str(e)}") + raise e + except ( + Exception + ) as e: # need to exception handle here. async exceptions don't get caught in sync functions. + if response is not None and hasattr(response, "text"): + raise OpenAIError( + status_code=500, + message=f"{str(e)}\n\nOriginal Response: {response.text}", + ) + else: + if type(e).__name__ == "ReadTimeout": + raise OpenAIError( + status_code=408, message=f"{type(e).__name__}" + ) + elif hasattr(e, "status_code"): + raise OpenAIError(status_code=e.status_code, message=str(e)) + else: + raise OpenAIError(status_code=500, message=f"{str(e)}") # Embedding async def make_openai_embedding_request( @@ -1153,6 +1294,32 @@ class OpenAIChatCompletion(BaseLLM): except Exception as e: raise e + def make_sync_openai_embedding_request( + self, + openai_client: OpenAI, + data: dict, + timeout: Union[float, httpx.Timeout], + ): + """ + Helper to: + - call embeddings.create.with_raw_response when litellm.return_response_headers is True + - call embeddings.create by default + """ + try: + if litellm.return_response_headers is True: + raw_response = openai_client.embeddings.with_raw_response.create( + **data, timeout=timeout + ) # type: ignore + + headers = dict(raw_response.headers) + response = raw_response.parse() + return headers, response + else: + response = openai_client.embeddings.create(**data, timeout=timeout) # type: ignore + return None, response + except Exception as e: + raise e + async def aembedding( self, input: list, @@ -1187,7 +1354,12 @@ class OpenAIChatCompletion(BaseLLM): additional_args={"complete_input_dict": data}, original_response=stringified_response, ) - return convert_to_model_response_object(response_object=stringified_response, model_response_object=model_response, response_type="embedding") # type: ignore + return convert_to_model_response_object( + response_object=stringified_response, + model_response_object=model_response, + response_type="embedding", + _response_headers=headers, + ) # type: ignore except Exception as e: ## LOGGING logging_obj.post_call( @@ -1248,17 +1420,26 @@ class OpenAIChatCompletion(BaseLLM): client=client, ) - ## COMPLETION CALL - response = openai_client.embeddings.create(**data, timeout=timeout) # type: ignore + ## embedding CALL + headers: Optional[Dict] = None + headers, sync_embedding_response = self.make_sync_openai_embedding_request( + openai_client=openai_client, data=data, timeout=timeout + ) # type: ignore + ## LOGGING + logging_obj.model_call_details["response_headers"] = headers logging_obj.post_call( input=input, api_key=api_key, additional_args={"complete_input_dict": data}, - original_response=response, + original_response=sync_embedding_response, ) - - return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="embedding") # type: ignore + return convert_to_model_response_object( + response_object=sync_embedding_response.model_dump(), + model_response_object=model_response, + _response_headers=headers, + response_type="embedding", + ) # type: ignore except OpenAIError as e: exception_mapping_worked = True raise e @@ -1421,6 +1602,33 @@ class OpenAIChatCompletion(BaseLLM): except Exception as e: raise e + def make_sync_openai_audio_transcriptions_request( + self, + openai_client: OpenAI, + data: dict, + timeout: Union[float, httpx.Timeout], + ): + """ + Helper to: + - call openai_aclient.audio.transcriptions.with_raw_response when litellm.return_response_headers is True + - call openai_aclient.audio.transcriptions.create by default + """ + try: + if litellm.return_response_headers is True: + raw_response = ( + openai_client.audio.transcriptions.with_raw_response.create( + **data, timeout=timeout + ) + ) # type: ignore + headers = dict(raw_response.headers) + response = raw_response.parse() + return headers, response + else: + response = openai_client.audio.transcriptions.create(**data, timeout=timeout) # type: ignore + return None, response + except Exception as e: + raise e + def audio_transcriptions( self, model: str, @@ -1456,8 +1664,10 @@ class OpenAIChatCompletion(BaseLLM): timeout=timeout, max_retries=max_retries, ) - response = openai_client.audio.transcriptions.create( - **data, timeout=timeout # type: ignore + _, response = self.make_sync_openai_audio_transcriptions_request( + openai_client=openai_client, + data=data, + timeout=timeout, ) if isinstance(response, BaseModel): @@ -1656,8 +1866,25 @@ class OpenAIChatCompletion(BaseLLM): model=model, # type: ignore prompt=prompt, # type: ignore ) + elif mode == "audio_transcription": + # Get the current directory of the file being run + pwd = os.path.dirname(os.path.realpath(__file__)) + file_path = os.path.join(pwd, "../tests/gettysburg.wav") + audio_file = open(file_path, "rb") + completion = await client.audio.transcriptions.with_raw_response.create( + file=audio_file, + model=model, # type: ignore + prompt=prompt, # type: ignore + ) + elif mode == "audio_speech": + # Get the current directory of the file being run + completion = await client.audio.speech.with_raw_response.create( + model=model, # type: ignore + input=prompt, # type: ignore + voice="alloy", + ) else: - raise Exception("mode not set") + raise ValueError("mode not set, passed in mode: " + mode) response = {} if completion is None or not hasattr(completion, "headers"): @@ -2302,6 +2529,7 @@ class OpenAIBatchesAPI(BaseLLM): retrieve_batch_data: RetrieveBatchRequest, openai_client: AsyncOpenAI, ) -> Batch: + verbose_logger.debug("retrieving batch, args= %s", retrieve_batch_data) response = await openai_client.batches.retrieve(**retrieve_batch_data) return response @@ -2368,26 +2596,52 @@ class OpenAIBatchesAPI(BaseLLM): response = openai_client.batches.cancel(**cancel_batch_data) return response - # def list_batch( - # self, - # list_batch_data: ListBatchRequest, - # api_key: Optional[str], - # api_base: Optional[str], - # timeout: Union[float, httpx.Timeout], - # max_retries: Optional[int], - # organization: Optional[str], - # client: Optional[OpenAI] = None, - # ): - # openai_client: OpenAI = self.get_openai_client( - # api_key=api_key, - # api_base=api_base, - # timeout=timeout, - # max_retries=max_retries, - # organization=organization, - # client=client, - # ) - # response = openai_client.batches.list(**list_batch_data) - # return response + async def alist_batches( + self, + openai_client: AsyncOpenAI, + after: Optional[str] = None, + limit: Optional[int] = None, + ): + verbose_logger.debug("listing batches, after= %s, limit= %s", after, limit) + response = await openai_client.batches.list(after=after, limit=limit) # type: ignore + return response + + def list_batches( + self, + _is_async: bool, + api_key: Optional[str], + api_base: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + organization: Optional[str], + after: Optional[str] = None, + limit: Optional[int] = None, + client: Optional[OpenAI] = None, + ): + openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client( + api_key=api_key, + api_base=api_base, + timeout=timeout, + max_retries=max_retries, + organization=organization, + client=client, + _is_async=_is_async, + ) + if openai_client is None: + raise ValueError( + "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment." + ) + + if _is_async is True: + if not isinstance(openai_client, AsyncOpenAI): + raise ValueError( + "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client." + ) + return self.alist_batches( # type: ignore + openai_client=openai_client, after=after, limit=limit + ) + response = openai_client.batches.list(after=after, limit=limit) # type: ignore + return response class OpenAIAssistantsAPI(BaseLLM): diff --git a/litellm/llms/palm.py b/litellm/llms/palm.py index 4d9953e77..a17fd02be 100644 --- a/litellm/llms/palm.py +++ b/litellm/llms/palm.py @@ -1,12 +1,14 @@ -import types -import traceback import copy import time +import traceback +import types from typing import Callable, Optional -from litellm.utils import ModelResponse, Choices, Message, Usage -import litellm + import httpx + +import litellm from litellm import verbose_logger +from litellm.utils import Choices, Message, ModelResponse, Usage class PalmError(Exception): @@ -164,12 +166,11 @@ def completion( message_obj = Message(content=None) choice_obj = Choices(index=idx + 1, message=message_obj) choices_list.append(choice_obj) - model_response["choices"] = choices_list + model_response.choices = choices_list # type: ignore except Exception as e: - verbose_logger.error( + verbose_logger.exception( "litellm.llms.palm.py::completion(): Exception occured - {}".format(str(e)) ) - verbose_logger.debug(traceback.format_exc()) raise PalmError( message=traceback.format_exc(), status_code=response.status_code ) @@ -188,8 +189,8 @@ def completion( encoding.encode(model_response["choices"][0]["message"].get("content", "")) ) - model_response["created"] = int(time.time()) - model_response["model"] = "palm/" + model + model_response.created = int(time.time()) + model_response.model = "palm/" + model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, diff --git a/litellm/llms/petals.py b/litellm/llms/petals.py index 334b80d38..be9f92f07 100644 --- a/litellm/llms/petals.py +++ b/litellm/llms/petals.py @@ -1,12 +1,16 @@ -import os, types import json -from enum import Enum -import requests # type: ignore +import os import time +import types +from enum import Enum from typing import Callable, Optional + +import requests # type: ignore + import litellm from litellm.utils import ModelResponse, Usage -from .prompt_templates.factory import prompt_factory, custom_prompt + +from .prompt_templates.factory import custom_prompt, prompt_factory class PetalsError(Exception): @@ -151,8 +155,8 @@ def completion( else: try: import torch - from transformers import AutoTokenizer from petals import AutoDistributedModelForCausalLM # type: ignore + from transformers import AutoTokenizer except: raise Exception( "Importing torch, transformers, petals failed\nTry pip installing petals \npip install git+https://github.com/bigscience-workshop/petals" @@ -189,15 +193,15 @@ def completion( output_text = tokenizer.decode(outputs[0]) if len(output_text) > 0: - model_response["choices"][0]["message"]["content"] = output_text + model_response.choices[0].message.content = output_text # type: ignore prompt_tokens = len(encoding.encode(prompt)) completion_tokens = len( encoding.encode(model_response["choices"][0]["message"].get("content")) ) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, diff --git a/litellm/llms/predibase.py b/litellm/llms/predibase.py index 534f8e26f..84e2810a5 100644 --- a/litellm/llms/predibase.py +++ b/litellm/llms/predibase.py @@ -17,6 +17,7 @@ import requests # type: ignore import litellm import litellm.litellm_core_utils import litellm.litellm_core_utils.litellm_logging +from litellm import verbose_logger from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler from litellm.utils import Choices, CustomStreamWrapper, Message, ModelResponse, Usage @@ -61,8 +62,11 @@ async def make_call( model: str, messages: list, logging_obj, + timeout: Optional[Union[float, httpx.Timeout]], ): - response = await client.post(api_base, headers=headers, data=data, stream=True) + response = await client.post( + api_base, headers=headers, data=data, stream=True, timeout=timeout + ) if response.status_code != 200: raise PredibaseError(status_code=response.status_code, message=response.text) @@ -279,7 +283,7 @@ class PredibaseChatCompletion(BaseLLM): message=f"'generated_text' is not a key response dictionary - {completion_response}", ) if len(completion_response["generated_text"]) > 0: - model_response["choices"][0]["message"]["content"] = self.output_parser( + model_response.choices[0].message.content = self.output_parser( # type: ignore completion_response["generated_text"] ) ## GETTING LOGPROBS + FINISH REASON @@ -294,10 +298,10 @@ class PredibaseChatCompletion(BaseLLM): for token in completion_response["details"]["tokens"]: if token["logprob"] is not None: sum_logprob += token["logprob"] - model_response["choices"][0][ - "message" - ]._logprob = ( - sum_logprob # [TODO] move this to using the actual logprobs + setattr( + model_response.choices[0].message, # type: ignore + "_logprob", + sum_logprob, # [TODO] move this to using the actual logprobs ) if "best_of" in optional_params and optional_params["best_of"] > 1: if ( @@ -325,7 +329,7 @@ class PredibaseChatCompletion(BaseLLM): message=message_obj, ) choices_list.append(choice_obj) - model_response["choices"].extend(choices_list) + model_response.choices.extend(choices_list) ## CALCULATING USAGE prompt_tokens = 0 @@ -351,14 +355,24 @@ class PredibaseChatCompletion(BaseLLM): total_tokens = prompt_tokens + completion_tokens - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, total_tokens=total_tokens, ) model_response.usage = usage # type: ignore + + ## RESPONSE HEADERS + predibase_headers = response.headers + response_headers = {} + for k, v in predibase_headers.items(): + if k.startswith("x-"): + response_headers["llm_provider-{}".format(k)] = v + + model_response._hidden_params["additional_headers"] = response_headers + return model_response def completion( @@ -484,6 +498,7 @@ class PredibaseChatCompletion(BaseLLM): headers=headers, data=json.dumps(data), stream=stream, + timeout=timeout, # type: ignore ) _response = CustomStreamWrapper( response.iter_lines(), @@ -498,6 +513,7 @@ class PredibaseChatCompletion(BaseLLM): url=completion_url, headers=headers, data=json.dumps(data), + timeout=timeout, # type: ignore ) return self.process_response( model=model, @@ -545,9 +561,15 @@ class PredibaseChatCompletion(BaseLLM): ), ) except Exception as e: - raise PredibaseError( - status_code=500, message="{}\n{}".format(str(e), traceback.format_exc()) + for exception in litellm.LITELLM_EXCEPTION_TYPES: + if isinstance(e, exception): + raise e + verbose_logger.exception( + "litellm.llms.predibase.py::async_completion() - Exception occurred - {}".format( + str(e) + ) ) + raise PredibaseError(status_code=500, message="{}".format(str(e))) return self.process_response( model=model, response=response, @@ -591,6 +613,7 @@ class PredibaseChatCompletion(BaseLLM): model=model, messages=messages, logging_obj=logging_obj, + timeout=timeout, ), model=model, custom_llm_provider="predibase", diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py index d02084d74..c9e691c00 100644 --- a/litellm/llms/prompt_templates/factory.py +++ b/litellm/llms/prompt_templates/factory.py @@ -1,3 +1,4 @@ +import copy import json import re import traceback @@ -6,7 +7,6 @@ import xml.etree.ElementTree as ET from enum import Enum from typing import Any, List, Mapping, MutableMapping, Optional, Sequence, Tuple -import requests from jinja2 import BaseLoader, Template, exceptions, meta from jinja2.sandbox import ImmutableSandboxedEnvironment @@ -14,6 +14,7 @@ import litellm import litellm.types import litellm.types.llms import litellm.types.llms.vertex_ai +from litellm.llms.custom_httpx.http_handler import HTTPHandler from litellm.types.completion import ( ChatCompletionFunctionMessageParam, ChatCompletionMessageParam, @@ -35,6 +36,9 @@ def prompt_injection_detection_default_pt(): return """Detect if a prompt is safe to run. Return 'UNSAFE' if not.""" +BAD_MESSAGE_ERROR_STR = "Invalid Message " + + def map_system_message_pt(messages: list) -> list: """ Convert 'system' message to 'user' message if provider doesn't support 'system' role. @@ -232,16 +236,23 @@ def mistral_api_pt(messages): """ new_messages = [] for m in messages: + special_keys = ["role", "content", "tool_calls", "function_call"] + extra_args = {} + if isinstance(m, dict): + for k, v in m.items(): + if k not in special_keys: + extra_args[k] = v texts = "" - if isinstance(m["content"], list): + if m.get("content", None) is not None and isinstance(m["content"], list): for c in m["content"]: if c["type"] == "image_url": return messages elif c["type"] == "text" and isinstance(c["text"], str): texts += c["text"] - elif isinstance(m["content"], str): + elif m.get("content", None) is not None and isinstance(m["content"], str): texts = m["content"] - new_m = {"role": m["role"], "content": texts} + + new_m = {"role": m["role"], "content": texts, **extra_args} if new_m["role"] == "tool" and m.get("name"): new_m["name"] = m["name"] @@ -361,7 +372,8 @@ def hf_chat_template(model: str, messages: list, chat_template: Optional[Any] = f"https://huggingface.co/{hf_model_name}/raw/main/tokenizer_config.json" ) # Make a GET request to fetch the JSON data - response = requests.get(url) + client = HTTPHandler(concurrent_limit=1) + response = client.get(url) if response.status_code == 200: # Parse the JSON data tokenizer_config = json.loads(response.content) @@ -491,7 +503,8 @@ def claude_2_1_pt( def get_model_info(token, model): try: headers = {"Authorization": f"Bearer {token}"} - response = requests.get("https://api.together.xyz/models/info", headers=headers) + client = HTTPHandler(concurrent_limit=1) + response = client.get("https://api.together.xyz/models/info", headers=headers) if response.status_code == 200: model_info = response.json() for m in model_info: @@ -654,11 +667,11 @@ def construct_tool_use_system_prompt( def convert_url_to_base64(url): import base64 - import requests - + client = HTTPHandler(concurrent_limit=1) for _ in range(3): try: - response = requests.get(url) + + response = client.get(url) break except: pass @@ -667,7 +680,7 @@ def convert_url_to_base64(url): base64_image = base64.b64encode(image_bytes).decode("utf-8") image_type = response.headers.get("Content-Type", None) - if image_type is not None and image_type.startswith("image/"): + if image_type is not None: img_type = image_type else: img_type = url.split(".")[-1].lower() @@ -706,6 +719,7 @@ def convert_to_anthropic_image_obj(openai_image_url: str) -> GenericImageParsing openai_image_url = convert_url_to_base64(url=openai_image_url) # Extract the media type and base64 data media_type, base64_data = openai_image_url.split("data:")[1].split(";base64,") + media_type = media_type.replace("\\/", "/") return GenericImageParsingChunk( type="base64", @@ -996,6 +1010,9 @@ def convert_to_gemini_tool_call_invoke( name = tool["function"].get("name", "") arguments = tool["function"].get("arguments", "") arguments_dict = json.loads(arguments) + function_call: Optional[litellm.types.llms.vertex_ai.FunctionCall] = ( + None + ) for k, v in arguments_dict.items(): inferred_protocol_value = infer_protocol_value(value=v) _field = litellm.types.llms.vertex_ai.Field( @@ -1008,9 +1025,18 @@ def convert_to_gemini_tool_call_invoke( name=name, args=_fields, ) - _parts_list.append( - litellm.types.llms.vertex_ai.PartType(function_call=function_call) - ) + if function_call is not None: + _parts_list.append( + litellm.types.llms.vertex_ai.PartType( + function_call=function_call + ) + ) + else: # don't silently drop params. Make it clear to user what's happening. + raise Exception( + "function_call missing. Received tool call with 'type': 'function'. No function call in argument - {}".format( + tool + ) + ) return _parts_list except Exception as e: raise Exception( @@ -1078,7 +1104,7 @@ def convert_to_gemini_tool_call_result( return _part -def convert_to_anthropic_tool_result(message: dict) -> dict: +def convert_to_anthropic_tool_result(message: dict) -> AnthropicMessagesToolResultParam: """ OpenAI message with a tool result looks like: { @@ -1111,44 +1137,51 @@ def convert_to_anthropic_tool_result(message: dict) -> dict: } """ if message["role"] == "tool": - tool_call_id = message.get("tool_call_id") - content = message.get("content") + tool_call_id: str = message.get("tool_call_id") # type: ignore + content: str = message.get("content") # type: ignore # We can't determine from openai message format whether it's a successful or # error call result so default to the successful result template - anthropic_tool_result = { - "type": "tool_result", - "tool_use_id": tool_call_id, - "content": content, - } + anthropic_tool_result = AnthropicMessagesToolResultParam( + type="tool_result", tool_use_id=tool_call_id, content=content + ) return anthropic_tool_result - elif message["role"] == "function": - content = message.get("content") - anthropic_tool_result = { - "type": "tool_result", - "tool_use_id": str(uuid.uuid4()), - "content": content, - } + if message["role"] == "function": + content = message.get("content") # type: ignore + tool_call_id = message.get("tool_call_id") or str(uuid.uuid4()) + anthropic_tool_result = AnthropicMessagesToolResultParam( + type="tool_result", tool_use_id=tool_call_id, content=content + ) + return anthropic_tool_result - return {} + else: + raise Exception( + "Invalid role={}. Only 'tool' or 'function' are accepted for tool result blocks.".format( + message.get("content") + ) + ) -def convert_function_to_anthropic_tool_invoke(function_call): +def convert_function_to_anthropic_tool_invoke( + function_call, +) -> List[AnthropicMessagesToolUseParam]: try: anthropic_tool_invoke = [ - { - "type": "tool_use", - "id": str(uuid.uuid4()), - "name": get_attribute_or_key(function_call, "name"), - "input": json.loads(get_attribute_or_key(function_call, "arguments")), - } + AnthropicMessagesToolUseParam( + type="tool_use", + id=str(uuid.uuid4()), + name=get_attribute_or_key(function_call, "name"), + input=json.loads(get_attribute_or_key(function_call, "arguments")), + ) ] return anthropic_tool_invoke except Exception as e: raise e -def convert_to_anthropic_tool_invoke(tool_calls: list) -> list: +def convert_to_anthropic_tool_invoke( + tool_calls: list, +) -> List[AnthropicMessagesToolUseParam]: """ OpenAI tool invokes: { @@ -1186,18 +1219,16 @@ def convert_to_anthropic_tool_invoke(tool_calls: list) -> list: } """ anthropic_tool_invoke = [ - { - "type": "tool_use", - "id": get_attribute_or_key(tool, "id"), - "name": get_attribute_or_key( - get_attribute_or_key(tool, "function"), "name" - ), - "input": json.loads( + AnthropicMessagesToolUseParam( + type="tool_use", + id=get_attribute_or_key(tool, "id"), + name=get_attribute_or_key(get_attribute_or_key(tool, "function"), "name"), + input=json.loads( get_attribute_or_key( get_attribute_or_key(tool, "function"), "arguments" ) ), - } + ) for tool in tool_calls if get_attribute_or_key(tool, "type") == "function" ] @@ -1205,7 +1236,29 @@ def convert_to_anthropic_tool_invoke(tool_calls: list) -> list: return anthropic_tool_invoke -def anthropic_messages_pt(messages: list): +def add_cache_control_to_content( + anthropic_content_element: Union[ + dict, AnthropicMessagesImageParam, AnthropicMessagesTextParam + ], + orignal_content_element: dict, +): + if "cache_control" in orignal_content_element: + anthropic_content_element["cache_control"] = orignal_content_element[ + "cache_control" + ] + return anthropic_content_element + + +def anthropic_messages_pt( + messages: list, + model: str, + llm_provider: str, +) -> List[ + Union[ + AnthropicMessagesUserMessageParam, + AnthopicMessagesAssistantMessageParam, + ] +]: """ format messages for anthropic 1. Anthropic supports roles like "user" and "assistant" (system prompt sent separately) @@ -1218,27 +1271,49 @@ def anthropic_messages_pt(messages: list): # add role=tool support to allow function call result/error submission user_message_types = {"user", "tool", "function"} # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, merge them. - new_messages: list = [] + new_messages: List[ + Union[ + AnthropicMessagesUserMessageParam, + AnthopicMessagesAssistantMessageParam, + ] + ] = [] msg_i = 0 - tool_use_param = False while msg_i < len(messages): - user_content = [] + user_content: List[AnthropicMessagesUserMessageValues] = [] init_msg_i = msg_i ## MERGE CONSECUTIVE USER CONTENT ## while msg_i < len(messages) and messages[msg_i]["role"] in user_message_types: if isinstance(messages[msg_i]["content"], list): for m in messages[msg_i]["content"]: if m.get("type", "") == "image_url": - user_content.append( - { - "type": "image", - "source": convert_to_anthropic_image_obj( - m["image_url"]["url"] - ), - } + image_chunk = convert_to_anthropic_image_obj( + m["image_url"]["url"] ) + + _anthropic_content_element = AnthropicMessagesImageParam( + type="image", + source=AnthropicImageParamSource( + type="base64", + media_type=image_chunk["media_type"], + data=image_chunk["data"], + ), + ) + + anthropic_content_element = add_cache_control_to_content( + anthropic_content_element=_anthropic_content_element, + orignal_content_element=m, + ) + user_content.append(anthropic_content_element) elif m.get("type", "") == "text": - user_content.append({"type": "text", "text": m["text"]}) + _anthropic_text_content_element = { + "type": "text", + "text": m["text"], + } + anthropic_content_element = add_cache_control_to_content( + anthropic_content_element=_anthropic_text_content_element, + orignal_content_element=m, + ) + user_content.append(anthropic_content_element) elif ( messages[msg_i]["role"] == "tool" or messages[msg_i]["role"] == "function" @@ -1255,14 +1330,42 @@ def anthropic_messages_pt(messages: list): if user_content: new_messages.append({"role": "user", "content": user_content}) - assistant_content = [] + assistant_content: List[AnthropicMessagesAssistantMessageValues] = [] ## MERGE CONSECUTIVE ASSISTANT CONTENT ## while msg_i < len(messages) and messages[msg_i]["role"] == "assistant": - assistant_text = ( - messages[msg_i].get("content") or "" - ) # either string or none - if assistant_text: - assistant_content.append({"type": "text", "text": assistant_text}) + if "content" in messages[msg_i] and isinstance( + messages[msg_i]["content"], list + ): + for m in messages[msg_i]["content"]: + # handle text + if ( + m.get("type", "") == "text" and len(m.get("text", "")) > 0 + ): # don't pass empty text blocks. anthropic api raises errors. + anthropic_message = AnthropicMessagesTextParam( + type="text", text=m.get("text") + ) + anthropic_message = add_cache_control_to_content( + anthropic_content_element=anthropic_message, + orignal_content_element=m, + ) + assistant_content.append(anthropic_message) + elif ( + "content" in messages[msg_i] + and isinstance(messages[msg_i]["content"], str) + and len(messages[msg_i]["content"]) + > 0 # don't pass empty text blocks. anthropic api raises errors. + ): + + _anthropic_text_content_element = { + "type": "text", + "text": messages[msg_i]["content"], + } + + anthropic_content_element = add_cache_control_to_content( + anthropic_content_element=_anthropic_text_content_element, + orignal_content_element=messages[msg_i], + ) + assistant_content.append(anthropic_content_element) if messages[msg_i].get( "tool_calls", [] @@ -1284,10 +1387,10 @@ def anthropic_messages_pt(messages: list): new_messages.append({"role": "assistant", "content": assistant_content}) if msg_i == init_msg_i: # prevent infinite loops - raise Exception( - "Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format( - messages[msg_i] - ) + raise litellm.BadRequestError( + message=BAD_MESSAGE_ERROR_STR + f"passed in {messages[msg_i]}", + model=model, + llm_provider=llm_provider, ) if not new_messages or new_messages[0]["role"] != "user": if litellm.modify_params: @@ -1567,6 +1670,8 @@ def convert_to_cohere_tool_invoke(tool_calls: list) -> List[ToolCallObject]: def cohere_messages_pt_v2( messages: List, + model: str, + llm_provider: str, ) -> Tuple[Union[str, ToolResultObject], ChatHistory]: """ Returns a tuple(Union[tool_result, message], chat_history) @@ -1579,6 +1684,7 @@ def cohere_messages_pt_v2( Note: - cannot specify message if the last entry in chat history contains tool results - message must be at least 1 token long or tool results must be specified. + - cannot specify tool_results if the last entry in chat history contains a user message """ tool_calls: List = get_all_tool_calls(messages=messages) @@ -1645,12 +1751,14 @@ def cohere_messages_pt_v2( assistant_tool_calls: List[ToolCallObject] = [] ## MERGE CONSECUTIVE ASSISTANT CONTENT ## while msg_i < len(messages) and messages[msg_i]["role"] == "assistant": - assistant_text = ( - messages[msg_i].get("content") or "" - ) # either string or none - if assistant_text: - assistant_content += assistant_text - + if isinstance(messages[msg_i]["content"], list): + for m in messages[msg_i]["content"]: + if m.get("type", "") == "text": + assistant_content += m["text"] + elif messages[msg_i].get("content") is not None and isinstance( + messages[msg_i]["content"], str + ): + assistant_content += messages[msg_i]["content"] if messages[msg_i].get( "tool_calls", [] ): # support assistant tool invoke conversion @@ -1691,10 +1799,10 @@ def cohere_messages_pt_v2( ) if msg_i == init_msg_i: # prevent infinite loops - raise Exception( - "Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format( - messages[msg_i] - ) + raise litellm.BadRequestError( + message=BAD_MESSAGE_ERROR_STR + f"passed in {messages[msg_i]}", + model=model, + llm_provider=llm_provider, ) return returned_message, new_messages @@ -1754,7 +1862,8 @@ def _load_image_from_url(image_url): try: # Send a GET request to the image URL - response = requests.get(image_url) + client = HTTPHandler(concurrent_limit=1) + response = client.get(image_url) response.raise_for_status() # Raise an exception for HTTP errors # Check the response's content type to ensure it is an image @@ -1767,8 +1876,6 @@ def _load_image_from_url(image_url): # Load the image from the response content return Image.open(BytesIO(response.content)) - except requests.RequestException as e: - raise Exception(f"Request failed: {e}") except Exception as e: raise e @@ -1945,8 +2052,9 @@ def get_image_details(image_url) -> Tuple[str, str]: try: import base64 + client = HTTPHandler(concurrent_limit=1) # Send a GET request to the image URL - response = requests.get(image_url) + response = client.get(image_url) response.raise_for_status() # Raise an exception for HTTP errors # Check the response's content type to ensure it is an image @@ -1966,8 +2074,6 @@ def get_image_details(image_url) -> Tuple[str, str]: return base64_bytes, mime_type - except requests.RequestException as e: - raise Exception(f"Request failed: {e}") except Exception as e: raise e @@ -2089,7 +2195,7 @@ def _convert_to_bedrock_tool_call_invoke( def _convert_to_bedrock_tool_call_result( message: dict, -) -> BedrockMessageBlock: +) -> BedrockContentBlock: """ OpenAI message with a tool result looks like: { @@ -2141,10 +2247,14 @@ def _convert_to_bedrock_tool_call_result( ) content_block = BedrockContentBlock(toolResult=tool_result) - return BedrockMessageBlock(role="user", content=[content_block]) + return content_block -def _bedrock_converse_messages_pt(messages: List) -> List[BedrockMessageBlock]: +def _bedrock_converse_messages_pt( + messages: List, + model: str, + llm_provider: str, +) -> List[BedrockMessageBlock]: """ Converts given messages from OpenAI format to Bedrock format @@ -2179,6 +2289,12 @@ def _bedrock_converse_messages_pt(messages: List) -> List[BedrockMessageBlock]: msg_i += 1 + ## MERGE CONSECUTIVE TOOL CALL MESSAGES ## + while msg_i < len(messages) and messages[msg_i]["role"] == "tool": + tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i]) + + user_content.append(tool_call_result) + msg_i += 1 if user_content: contents.append(BedrockMessageBlock(role="user", content=user_content)) assistant_content: List[BedrockContentBlock] = [] @@ -2222,21 +2338,50 @@ def _bedrock_converse_messages_pt(messages: List) -> List[BedrockMessageBlock]: BedrockMessageBlock(role="assistant", content=assistant_content) ) - ## APPEND TOOL CALL MESSAGES ## - if msg_i < len(messages) and messages[msg_i]["role"] == "tool": - tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i]) - contents.append(tool_call_result) - msg_i += 1 if msg_i == init_msg_i: # prevent infinite loops - raise Exception( - "Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format( - messages[msg_i] - ) + raise litellm.BadRequestError( + message=BAD_MESSAGE_ERROR_STR + f"passed in {messages[msg_i]}", + model=model, + llm_provider=llm_provider, ) - return contents +def make_valid_bedrock_tool_name(input_tool_name: str) -> str: + """ + Replaces any invalid characters in the input tool name with underscores + and ensures the resulting string is a valid identifier for Bedrock tools + """ + + def replace_invalid(char): + """ + Bedrock tool names only supports alpha-numeric characters and underscores + """ + if char.isalnum() or char == "_": + return char + return "_" + + # If the string is empty, return a default valid identifier + if input_tool_name is None or len(input_tool_name) == 0: + return input_tool_name + bedrock_tool_name = copy.copy(input_tool_name) + # If it doesn't start with a letter, prepend 'a' + if not bedrock_tool_name[0].isalpha(): + bedrock_tool_name = "a" + bedrock_tool_name + + # Replace any invalid characters with underscores + valid_string = "".join(replace_invalid(char) for char in bedrock_tool_name) + + if input_tool_name != valid_string: + # passed tool name was formatted to become valid + # store it internally so we can use for the response + litellm.bedrock_tool_name_mappings.set_cache( + key=valid_string, value=input_tool_name + ) + + return valid_string + + def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]: """ OpenAI tools looks like: @@ -2290,7 +2435,13 @@ def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]: for tool in tools: parameters = tool.get("function", {}).get("parameters", None) name = tool.get("function", {}).get("name", "") - description = tool.get("function", {}).get("description", "") + + # related issue: https://github.com/BerriAI/litellm/issues/5007 + # Bedrock tool names must satisfy regular expression pattern: [a-zA-Z][a-zA-Z0-9_]* ensure this is true + name = make_valid_bedrock_tool_name(input_tool_name=name) + description = tool.get("function", {}).get( + "description", name + ) # converse api requires a description tool_input_schema = BedrockToolInputSchemaBlock(json=parameters) tool_spec = BedrockToolSpecBlock( inputSchema=tool_input_schema, name=name, description=description @@ -2393,7 +2544,16 @@ def custom_prompt( if role in role_dict and "post_message" in role_dict[role] else "" ) - prompt += pre_message_str + message["content"] + post_message_str + if isinstance(message["content"], str): + prompt += pre_message_str + message["content"] + post_message_str + elif isinstance(message["content"], list): + text_str = "" + for content in message["content"]: + if content.get("text", None) is not None and isinstance( + content["text"], str + ): + text_str += content["text"] + prompt += pre_message_str + text_str + post_message_str if role == "assistant": prompt += eos_token @@ -2416,7 +2576,9 @@ def prompt_factory( elif custom_llm_provider == "anthropic": if model == "claude-instant-1" or model == "claude-2": return anthropic_pt(messages=messages) - return anthropic_messages_pt(messages=messages) + return anthropic_messages_pt( + messages=messages, model=model, llm_provider=custom_llm_provider + ) elif custom_llm_provider == "anthropic_xml": return anthropic_messages_pt_xml(messages=messages) elif custom_llm_provider == "together_ai": diff --git a/litellm/llms/replicate.py b/litellm/llms/replicate.py index 77dc52aae..0d129ce02 100644 --- a/litellm/llms/replicate.py +++ b/litellm/llms/replicate.py @@ -387,8 +387,8 @@ def process_response( result = " " ## Building RESPONSE OBJECT - if len(result) > 1: - model_response["choices"][0]["message"]["content"] = result + if len(result) >= 1: + model_response.choices[0].message.content = result # type: ignore # Calculate usage prompt_tokens = len(encoding.encode(prompt, disallowed_special=())) @@ -398,7 +398,7 @@ def process_response( disallowed_special=(), ) ) - model_response["model"] = "replicate/" + model + model_response.model = "replicate/" + model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, @@ -498,7 +498,7 @@ def completion( ## Step1: Start Prediction: gets a prediction url ## Step2: Poll prediction url for response ## Step2: is handled with and without streaming - model_response["created"] = int( + model_response.created = int( time.time() ) # for pricing this must remain right before calling api diff --git a/litellm/llms/sagemaker.py b/litellm/llms/sagemaker.py index 6892445f0..32146b9ca 100644 --- a/litellm/llms/sagemaker.py +++ b/litellm/llms/sagemaker.py @@ -1,16 +1,43 @@ -import os, types, traceback -from enum import Enum -import json -import requests # type: ignore -import time -from typing import Callable, Optional, Any -import litellm -from litellm.utils import ModelResponse, EmbeddingResponse, get_secret, Usage -import sys -from copy import deepcopy -import httpx # type: ignore import io -from .prompt_templates.factory import prompt_factory, custom_prompt +import json +import os +import sys +import time +import traceback +import types +from copy import deepcopy +from enum import Enum +from functools import partial +from typing import Any, AsyncIterator, Callable, Iterator, List, Optional, Union + +import httpx # type: ignore +import requests # type: ignore + +import litellm +from litellm._logging import verbose_logger +from litellm.llms.custom_httpx.http_handler import ( + AsyncHTTPHandler, + HTTPHandler, + _get_async_httpx_client, + _get_httpx_client, +) +from litellm.types.llms.openai import ( + ChatCompletionToolCallChunk, + ChatCompletionUsageBlock, +) +from litellm.types.utils import GenericStreamingChunk as GChunk +from litellm.utils import ( + CustomStreamWrapper, + EmbeddingResponse, + ModelResponse, + Usage, + get_secret, +) + +from .base_aws_llm import BaseAWSLLM +from .prompt_templates.factory import custom_prompt, prompt_factory + +_response_stream_shape_cache = None class SagemakerError(Exception): @@ -26,73 +53,6 @@ class SagemakerError(Exception): ) # Call the base class constructor with the parameters it needs -class TokenIterator: - def __init__(self, stream, acompletion: bool = False): - if acompletion == False: - self.byte_iterator = iter(stream) - elif acompletion == True: - self.byte_iterator = stream - self.buffer = io.BytesIO() - self.read_pos = 0 - self.end_of_data = False - - def __iter__(self): - return self - - def __next__(self): - try: - while True: - self.buffer.seek(self.read_pos) - line = self.buffer.readline() - if line and line[-1] == ord("\n"): - response_obj = {"text": "", "is_finished": False} - self.read_pos += len(line) + 1 - full_line = line[:-1].decode("utf-8") - line_data = json.loads(full_line.lstrip("data:").rstrip("/n")) - if line_data.get("generated_text", None) is not None: - self.end_of_data = True - response_obj["is_finished"] = True - response_obj["text"] = line_data["token"]["text"] - return response_obj - chunk = next(self.byte_iterator) - self.buffer.seek(0, io.SEEK_END) - self.buffer.write(chunk["PayloadPart"]["Bytes"]) - except StopIteration as e: - if self.end_of_data == True: - raise e # Re-raise StopIteration - else: - self.end_of_data = True - return "data: [DONE]" - - def __aiter__(self): - return self - - async def __anext__(self): - try: - while True: - self.buffer.seek(self.read_pos) - line = self.buffer.readline() - if line and line[-1] == ord("\n"): - response_obj = {"text": "", "is_finished": False} - self.read_pos += len(line) + 1 - full_line = line[:-1].decode("utf-8") - line_data = json.loads(full_line.lstrip("data:").rstrip("/n")) - if line_data.get("generated_text", None) is not None: - self.end_of_data = True - response_obj["is_finished"] = True - response_obj["text"] = line_data["token"]["text"] - return response_obj - chunk = await self.byte_iterator.__anext__() - self.buffer.seek(0, io.SEEK_END) - self.buffer.write(chunk["PayloadPart"]["Bytes"]) - except StopAsyncIteration as e: - if self.end_of_data == True: - raise e # Re-raise StopIteration - else: - self.end_of_data = True - return "data: [DONE]" - - class SagemakerConfig: """ Reference: https://d-uuwbxj1u4cnu.studio.us-west-2.sagemaker.aws/jupyter/default/lab/workspaces/auto-q/tree/DemoNotebooks/meta-textgeneration-llama-2-7b-SDK_1.ipynb @@ -140,439 +100,498 @@ os.environ['AWS_ACCESS_KEY_ID'] = "" os.environ['AWS_SECRET_ACCESS_KEY'] = "" """ + # set os.environ['AWS_REGION_NAME'] = +class SagemakerLLM(BaseAWSLLM): + def _load_credentials( + self, + optional_params: dict, + ): + try: + from botocore.credentials import Credentials + except ImportError as e: + raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") + ## CREDENTIALS ## + # pop aws_secret_access_key, aws_access_key_id, aws_session_token, aws_region_name from kwargs, since completion calls fail with them + aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) + aws_access_key_id = optional_params.pop("aws_access_key_id", None) + aws_session_token = optional_params.pop("aws_session_token", None) + aws_region_name = optional_params.pop("aws_region_name", None) + aws_role_name = optional_params.pop("aws_role_name", None) + aws_session_name = optional_params.pop("aws_session_name", None) + aws_profile_name = optional_params.pop("aws_profile_name", None) + aws_bedrock_runtime_endpoint = optional_params.pop( + "aws_bedrock_runtime_endpoint", None + ) # https://bedrock-runtime.{region_name}.amazonaws.com + aws_web_identity_token = optional_params.pop("aws_web_identity_token", None) + aws_sts_endpoint = optional_params.pop("aws_sts_endpoint", None) -def completion( - model: str, - messages: list, - model_response: ModelResponse, - print_verbose: Callable, - encoding, - logging_obj, - custom_prompt_dict={}, - hf_model_name=None, - optional_params=None, - litellm_params=None, - logger_fn=None, - acompletion: bool = False, -): - import boto3 + ### SET REGION NAME ### + if aws_region_name is None: + # check env # + litellm_aws_region_name = get_secret("AWS_REGION_NAME", None) - # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them - aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) - aws_access_key_id = optional_params.pop("aws_access_key_id", None) - aws_region_name = optional_params.pop("aws_region_name", None) - model_id = optional_params.pop("model_id", None) + if litellm_aws_region_name is not None and isinstance( + litellm_aws_region_name, str + ): + aws_region_name = litellm_aws_region_name - if aws_access_key_id != None: - # uses auth params passed to completion - # aws_access_key_id is not None, assume user is trying to auth using litellm.completion - client = boto3.client( - service_name="sagemaker-runtime", + standard_aws_region_name = get_secret("AWS_REGION", None) + if standard_aws_region_name is not None and isinstance( + standard_aws_region_name, str + ): + aws_region_name = standard_aws_region_name + + if aws_region_name is None: + aws_region_name = "us-west-2" + + credentials: Credentials = self.get_credentials( aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key, - region_name=aws_region_name, + aws_session_token=aws_session_token, + aws_region_name=aws_region_name, + aws_session_name=aws_session_name, + aws_profile_name=aws_profile_name, + aws_role_name=aws_role_name, + aws_web_identity_token=aws_web_identity_token, + aws_sts_endpoint=aws_sts_endpoint, ) - else: - # aws_access_key_id is None, assume user is trying to auth using env variables - # boto3 automaticaly reads env variables + return credentials, aws_region_name - # we need to read region name from env - # I assume majority of users use .env for auth - region_name = ( - get_secret("AWS_REGION_NAME") - or aws_region_name # get region from config file if specified - or "us-west-2" # default to us-west-2 if region not specified - ) - client = boto3.client( - service_name="sagemaker-runtime", - region_name=region_name, - ) + def _prepare_request( + self, + credentials, + model: str, + data: dict, + optional_params: dict, + aws_region_name: str, + extra_headers: Optional[dict] = None, + ): + try: + import boto3 + from botocore.auth import SigV4Auth + from botocore.awsrequest import AWSRequest + from botocore.credentials import Credentials + except ImportError as e: + raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.") - # pop streaming if it's in the optional params as 'stream' raises an error with sagemaker - inference_params = deepcopy(optional_params) + sigv4 = SigV4Auth(credentials, "sagemaker", aws_region_name) + if optional_params.get("stream") is True: + api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations-response-stream" + else: + api_base = f"https://runtime.sagemaker.{aws_region_name}.amazonaws.com/endpoints/{model}/invocations" - ## Load Config - config = litellm.SagemakerConfig.get_config() - for k, v in config.items(): - if ( - k not in inference_params - ): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in - inference_params[k] = v + encoded_data = json.dumps(data).encode("utf-8") + headers = {"Content-Type": "application/json"} + if extra_headers is not None: + headers = {"Content-Type": "application/json", **extra_headers} + request = AWSRequest( + method="POST", url=api_base, data=encoded_data, headers=headers + ) + sigv4.add_auth(request) + prepped_request = request.prepare() - model = model - if model in custom_prompt_dict: - # check if the model has a registered custom prompt - model_prompt_details = custom_prompt_dict[model] - prompt = custom_prompt( - role_dict=model_prompt_details.get("roles", None), - initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), - final_prompt_value=model_prompt_details.get("final_prompt_value", ""), - messages=messages, - ) - elif hf_model_name in custom_prompt_dict: - # check if the base huggingface model has a registered custom prompt - model_prompt_details = custom_prompt_dict[hf_model_name] - prompt = custom_prompt( - role_dict=model_prompt_details.get("roles", None), - initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), - final_prompt_value=model_prompt_details.get("final_prompt_value", ""), - messages=messages, - ) - else: - if hf_model_name is None: - if "llama-2" in model.lower(): # llama-2 model - if "chat" in model.lower(): # apply llama2 chat template - hf_model_name = "meta-llama/Llama-2-7b-chat-hf" - else: # apply regular llama2 template - hf_model_name = "meta-llama/Llama-2-7b" - hf_model_name = ( - hf_model_name or model - ) # pass in hf model name for pulling it's prompt template - (e.g. `hf_model_name="meta-llama/Llama-2-7b-chat-hf` applies the llama2 chat template to the prompt) - prompt = prompt_factory(model=hf_model_name, messages=messages) - stream = inference_params.pop("stream", None) - if stream == True: - data = json.dumps( - {"inputs": prompt, "parameters": inference_params, "stream": True} - ).encode("utf-8") - if acompletion == True: - response = async_streaming( - optional_params=optional_params, - encoding=encoding, - model_response=model_response, + return prepped_request + + def completion( + self, + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + custom_prompt_dict={}, + hf_model_name=None, + optional_params=None, + litellm_params=None, + logger_fn=None, + acompletion: bool = False, + ): + + # pop streaming if it's in the optional params as 'stream' raises an error with sagemaker + credentials, aws_region_name = self._load_credentials(optional_params) + inference_params = deepcopy(optional_params) + + ## Load Config + config = litellm.SagemakerConfig.get_config() + for k, v in config.items(): + if ( + k not in inference_params + ): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v + + if model in custom_prompt_dict: + # check if the model has a registered custom prompt + model_prompt_details = custom_prompt_dict[model] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", None), + initial_prompt_value=model_prompt_details.get( + "initial_prompt_value", "" + ), + final_prompt_value=model_prompt_details.get("final_prompt_value", ""), + messages=messages, + ) + elif hf_model_name in custom_prompt_dict: + # check if the base huggingface model has a registered custom prompt + model_prompt_details = custom_prompt_dict[hf_model_name] + prompt = custom_prompt( + role_dict=model_prompt_details.get("roles", None), + initial_prompt_value=model_prompt_details.get( + "initial_prompt_value", "" + ), + final_prompt_value=model_prompt_details.get("final_prompt_value", ""), + messages=messages, + ) + else: + if hf_model_name is None: + if "llama-2" in model.lower(): # llama-2 model + if "chat" in model.lower(): # apply llama2 chat template + hf_model_name = "meta-llama/Llama-2-7b-chat-hf" + else: # apply regular llama2 template + hf_model_name = "meta-llama/Llama-2-7b" + hf_model_name = ( + hf_model_name or model + ) # pass in hf model name for pulling it's prompt template - (e.g. `hf_model_name="meta-llama/Llama-2-7b-chat-hf` applies the llama2 chat template to the prompt) + prompt = prompt_factory(model=hf_model_name, messages=messages) + stream = inference_params.pop("stream", None) + model_id = optional_params.get("model_id", None) + + if stream is True: + data = {"inputs": prompt, "parameters": inference_params, "stream": True} + prepared_request = self._prepare_request( model=model, - logging_obj=logging_obj, data=data, - model_id=model_id, - aws_secret_access_key=aws_secret_access_key, - aws_access_key_id=aws_access_key_id, + optional_params=optional_params, + credentials=credentials, aws_region_name=aws_region_name, ) - return response + if model_id is not None: + # Add model_id as InferenceComponentName header + # boto3 doc: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html + prepared_request.headers.update( + {"X-Amzn-SageMaker-Inference-Componen": model_id} + ) - if model_id is not None: - response = client.invoke_endpoint_with_response_stream( - EndpointName=model, - InferenceComponentName=model_id, - ContentType="application/json", - Body=data, - CustomAttributes="accept_eula=true", + if acompletion is True: + response = self.async_streaming( + prepared_request=prepared_request, + optional_params=optional_params, + encoding=encoding, + model_response=model_response, + model=model, + logging_obj=logging_obj, + data=data, + model_id=model_id, + ) + return response + else: + if stream is not None and stream == True: + sync_handler = _get_httpx_client() + sync_response = sync_handler.post( + url=prepared_request.url, + headers=prepared_request.headers, # type: ignore + json=data, + stream=stream, + ) + + if sync_response.status_code != 200: + raise SagemakerError( + status_code=sync_response.status_code, + message=sync_response.read(), + ) + + decoder = AWSEventStreamDecoder(model="") + + completion_stream = decoder.iter_bytes( + sync_response.iter_bytes(chunk_size=1024) + ) + streaming_response = CustomStreamWrapper( + completion_stream=completion_stream, + model=model, + custom_llm_provider="sagemaker", + logging_obj=logging_obj, + ) + + ## LOGGING + logging_obj.post_call( + input=messages, + api_key="", + original_response=streaming_response, + additional_args={"complete_input_dict": data}, ) - else: - response = client.invoke_endpoint_with_response_stream( - EndpointName=model, - ContentType="application/json", - Body=data, - CustomAttributes="accept_eula=true", - ) - return response["Body"] - elif acompletion == True: + return streaming_response + + # Non-Streaming Requests _data = {"inputs": prompt, "parameters": inference_params} - return async_completion( - optional_params=optional_params, - encoding=encoding, - model_response=model_response, + prepared_request = self._prepare_request( model=model, - logging_obj=logging_obj, data=_data, - model_id=model_id, - aws_secret_access_key=aws_secret_access_key, - aws_access_key_id=aws_access_key_id, + optional_params=optional_params, + credentials=credentials, aws_region_name=aws_region_name, ) - data = json.dumps({"inputs": prompt, "parameters": inference_params}).encode( - "utf-8" - ) - ## COMPLETION CALL - try: - if model_id is not None: - ## LOGGING - request_str = f""" - response = client.invoke_endpoint( - EndpointName={model}, - InferenceComponentName={model_id}, - ContentType="application/json", - Body={data}, # type: ignore - CustomAttributes="accept_eula=true", + + # Async completion + if acompletion == True: + return self.async_completion( + prepared_request=prepared_request, + model_response=model_response, + encoding=encoding, + model=model, + logging_obj=logging_obj, + data=_data, + model_id=model_id, ) - """ # type: ignore - logging_obj.pre_call( - input=prompt, - api_key="", - additional_args={ - "complete_input_dict": data, - "request_str": request_str, - "hf_model_name": hf_model_name, - }, - ) - response = client.invoke_endpoint( - EndpointName=model, - InferenceComponentName=model_id, - ContentType="application/json", - Body=data, - CustomAttributes="accept_eula=true", - ) - else: - ## LOGGING - request_str = f""" - response = client.invoke_endpoint( - EndpointName={model}, - ContentType="application/json", - Body={data}, # type: ignore - CustomAttributes="accept_eula=true", - ) - """ # type: ignore - logging_obj.pre_call( - input=prompt, - api_key="", - additional_args={ - "complete_input_dict": data, - "request_str": request_str, - "hf_model_name": hf_model_name, - }, - ) - response = client.invoke_endpoint( - EndpointName=model, - ContentType="application/json", - Body=data, - CustomAttributes="accept_eula=true", - ) - except Exception as e: - status_code = ( - getattr(e, "response", {}) - .get("ResponseMetadata", {}) - .get("HTTPStatusCode", 500) - ) - error_message = ( - getattr(e, "response", {}).get("Error", {}).get("Message", str(e)) - ) - if "Inference Component Name header is required" in error_message: - error_message += "\n pass in via `litellm.completion(..., model_id={InferenceComponentName})`" - raise SagemakerError(status_code=status_code, message=error_message) - - response = response["Body"].read().decode("utf8") - ## LOGGING - logging_obj.post_call( - input=prompt, - api_key="", - original_response=response, - additional_args={"complete_input_dict": data}, - ) - print_verbose(f"raw model_response: {response}") - ## RESPONSE OBJECT - completion_response = json.loads(response) - try: - if isinstance(completion_response, list): - completion_response_choices = completion_response[0] - else: - completion_response_choices = completion_response - completion_output = "" - if "generation" in completion_response_choices: - completion_output += completion_response_choices["generation"] - elif "generated_text" in completion_response_choices: - completion_output += completion_response_choices["generated_text"] - - # check if the prompt template is part of output, if so - filter it out - if completion_output.startswith(prompt) and "" in prompt: - completion_output = completion_output.replace(prompt, "", 1) - - model_response["choices"][0]["message"]["content"] = completion_output - except: - raise SagemakerError( - message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}", - status_code=500, - ) - - ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. - prompt_tokens = len(encoding.encode(prompt)) - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) - ) - - model_response["created"] = int(time.time()) - model_response["model"] = model - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - setattr(model_response, "usage", usage) - return model_response - - -async def async_streaming( - optional_params, - encoding, - model_response: ModelResponse, - model: str, - model_id: Optional[str], - logging_obj: Any, - data, - aws_secret_access_key: Optional[str], - aws_access_key_id: Optional[str], - aws_region_name: Optional[str], -): - """ - Use aioboto3 - """ - import aioboto3 - - session = aioboto3.Session() - - if aws_access_key_id != None: - # uses auth params passed to completion - # aws_access_key_id is not None, assume user is trying to auth using litellm.completion - _client = session.client( - service_name="sagemaker-runtime", - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, - region_name=aws_region_name, - ) - else: - # aws_access_key_id is None, assume user is trying to auth using env variables - # boto3 automaticaly reads env variables - - # we need to read region name from env - # I assume majority of users use .env for auth - region_name = ( - get_secret("AWS_REGION_NAME") - or aws_region_name # get region from config file if specified - or "us-west-2" # default to us-west-2 if region not specified - ) - _client = session.client( - service_name="sagemaker-runtime", - region_name=region_name, - ) - - async with _client as client: + ## Non-Streaming completion CALL try: if model_id is not None: - response = await client.invoke_endpoint_with_response_stream( - EndpointName=model, - InferenceComponentName=model_id, - ContentType="application/json", - Body=data, - CustomAttributes="accept_eula=true", + # Add model_id as InferenceComponentName header + # boto3 doc: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html + prepared_request.headers.update( + {"X-Amzn-SageMaker-Inference-Componen": model_id} ) - else: - response = await client.invoke_endpoint_with_response_stream( - EndpointName=model, - ContentType="application/json", - Body=data, - CustomAttributes="accept_eula=true", + + ## LOGGING + timeout = 300.0 + sync_handler = _get_httpx_client() + ## LOGGING + logging_obj.pre_call( + input=[], + api_key="", + additional_args={ + "complete_input_dict": _data, + "api_base": prepared_request.url, + "headers": prepared_request.headers, + }, + ) + + # make sync httpx post request here + try: + sync_response = sync_handler.post( + url=prepared_request.url, + headers=prepared_request.headers, + json=_data, + timeout=timeout, ) + + if sync_response.status_code != 200: + raise SagemakerError( + status_code=sync_response.status_code, + message=sync_response.text, + ) + except Exception as e: + ## LOGGING + logging_obj.post_call( + input=[], + api_key="", + original_response=str(e), + additional_args={"complete_input_dict": _data}, + ) + raise e except Exception as e: - raise SagemakerError(status_code=500, message=f"{str(e)}") - response = response["Body"] - async for chunk in response: - yield chunk + verbose_logger.error("Sagemaker error %s", str(e)) + status_code = ( + getattr(e, "response", {}) + .get("ResponseMetadata", {}) + .get("HTTPStatusCode", 500) + ) + error_message = ( + getattr(e, "response", {}).get("Error", {}).get("Message", str(e)) + ) + if "Inference Component Name header is required" in error_message: + error_message += "\n pass in via `litellm.completion(..., model_id={InferenceComponentName})`" + raise SagemakerError(status_code=status_code, message=error_message) - -async def async_completion( - optional_params, - encoding, - model_response: ModelResponse, - model: str, - logging_obj: Any, - data: dict, - model_id: Optional[str], - aws_secret_access_key: Optional[str], - aws_access_key_id: Optional[str], - aws_region_name: Optional[str], -): - """ - Use aioboto3 - """ - import aioboto3 - - session = aioboto3.Session() - - if aws_access_key_id != None: - # uses auth params passed to completion - # aws_access_key_id is not None, assume user is trying to auth using litellm.completion - _client = session.client( - service_name="sagemaker-runtime", - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, - region_name=aws_region_name, + completion_response = sync_response.json() + ## LOGGING + logging_obj.post_call( + input=prompt, + api_key="", + original_response=completion_response, + additional_args={"complete_input_dict": _data}, ) - else: - # aws_access_key_id is None, assume user is trying to auth using env variables - # boto3 automaticaly reads env variables + print_verbose(f"raw model_response: {completion_response}") + ## RESPONSE OBJECT + try: + if isinstance(completion_response, list): + completion_response_choices = completion_response[0] + else: + completion_response_choices = completion_response + completion_output = "" + if "generation" in completion_response_choices: + completion_output += completion_response_choices["generation"] + elif "generated_text" in completion_response_choices: + completion_output += completion_response_choices["generated_text"] - # we need to read region name from env - # I assume majority of users use .env for auth - region_name = ( - get_secret("AWS_REGION_NAME") - or aws_region_name # get region from config file if specified - or "us-west-2" # default to us-west-2 if region not specified - ) - _client = session.client( - service_name="sagemaker-runtime", - region_name=region_name, + # check if the prompt template is part of output, if so - filter it out + if completion_output.startswith(prompt) and "" in prompt: + completion_output = completion_output.replace(prompt, "", 1) + + model_response.choices[0].message.content = completion_output # type: ignore + except: + raise SagemakerError( + message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}", + status_code=500, + ) + + ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. + prompt_tokens = len(encoding.encode(prompt)) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) ) - async with _client as client: - encoded_data = json.dumps(data).encode("utf-8") + model_response.created = int(time.time()) + model_response.model = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + setattr(model_response, "usage", usage) + return model_response + + async def make_async_call( + self, + api_base: str, + headers: dict, + data: str, + logging_obj, + client=None, + ): + try: + if client is None: + client = ( + _get_async_httpx_client() + ) # Create a new client if none provided + response = await client.post( + api_base, + headers=headers, + json=data, + stream=True, + ) + + if response.status_code != 200: + raise SagemakerError( + status_code=response.status_code, message=response.text + ) + + decoder = AWSEventStreamDecoder(model="") + completion_stream = decoder.aiter_bytes( + response.aiter_bytes(chunk_size=1024) + ) + + return completion_stream + + # LOGGING + logging_obj.post_call( + input=[], + api_key="", + original_response="first stream response received", + additional_args={"complete_input_dict": data}, + ) + + except httpx.HTTPStatusError as err: + error_code = err.response.status_code + raise SagemakerError(status_code=error_code, message=err.response.text) + except httpx.TimeoutException as e: + raise SagemakerError(status_code=408, message="Timeout error occurred.") + except Exception as e: + raise SagemakerError(status_code=500, message=str(e)) + + async def async_streaming( + self, + prepared_request, + optional_params, + encoding, + model_response: ModelResponse, + model: str, + model_id: Optional[str], + logging_obj: Any, + data, + ): + streaming_response = CustomStreamWrapper( + completion_stream=None, + make_call=partial( + self.make_async_call, + api_base=prepared_request.url, + headers=prepared_request.headers, + data=data, + logging_obj=logging_obj, + ), + model=model, + custom_llm_provider="sagemaker", + logging_obj=logging_obj, + ) + + # LOGGING + logging_obj.post_call( + input=[], + api_key="", + original_response="first stream response received", + additional_args={"complete_input_dict": data}, + ) + + return streaming_response + + async def async_completion( + self, + prepared_request, + encoding, + model_response: ModelResponse, + model: str, + logging_obj: Any, + data: dict, + model_id: Optional[str], + ): + timeout = 300.0 + async_handler = _get_async_httpx_client() + ## LOGGING + logging_obj.pre_call( + input=[], + api_key="", + additional_args={ + "complete_input_dict": data, + "api_base": prepared_request.url, + "headers": prepared_request.headers, + }, + ) try: if model_id is not None: - ## LOGGING - request_str = f""" - response = client.invoke_endpoint( - EndpointName={model}, - InferenceComponentName={model_id}, - ContentType="application/json", - Body={data}, - CustomAttributes="accept_eula=true", + # Add model_id as InferenceComponentName header + # boto3 doc: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html + prepared_request.headers.update( + {"X-Amzn-SageMaker-Inference-Componen": model_id} ) - """ # type: ignore - logging_obj.pre_call( + # make async httpx post request here + try: + response = await async_handler.post( + url=prepared_request.url, + headers=prepared_request.headers, + json=data, + timeout=timeout, + ) + + if response.status_code != 200: + raise SagemakerError( + status_code=response.status_code, message=response.text + ) + except Exception as e: + ## LOGGING + logging_obj.post_call( input=data["inputs"], api_key="", - additional_args={ - "complete_input_dict": data, - "request_str": request_str, - }, - ) - response = await client.invoke_endpoint( - EndpointName=model, - InferenceComponentName=model_id, - ContentType="application/json", - Body=encoded_data, - CustomAttributes="accept_eula=true", - ) - else: - ## LOGGING - request_str = f""" - response = client.invoke_endpoint( - EndpointName={model}, - ContentType="application/json", - Body={data}, - CustomAttributes="accept_eula=true", - ) - """ # type: ignore - logging_obj.pre_call( - input=data["inputs"], - api_key="", - additional_args={ - "complete_input_dict": data, - "request_str": request_str, - }, - ) - response = await client.invoke_endpoint( - EndpointName=model, - ContentType="application/json", - Body=encoded_data, - CustomAttributes="accept_eula=true", + original_response=str(e), + additional_args={"complete_input_dict": data}, ) + raise e except Exception as e: error_message = f"{str(e)}" if "Inference Component Name header is required" in error_message: error_message += "\n pass in via `litellm.completion(..., model_id={InferenceComponentName})`" raise SagemakerError(status_code=500, message=error_message) - response = await response["Body"].read() - response = response.decode("utf8") + completion_response = response.json() ## LOGGING logging_obj.post_call( input=data["inputs"], @@ -581,7 +600,6 @@ async def async_completion( additional_args={"complete_input_dict": data}, ) ## RESPONSE OBJECT - completion_response = json.loads(response) try: if isinstance(completion_response, list): completion_response_choices = completion_response[0] @@ -597,7 +615,7 @@ async def async_completion( if completion_output.startswith(data["inputs"]) and "" in data["inputs"]: completion_output = completion_output.replace(data["inputs"], "", 1) - model_response["choices"][0]["message"]["content"] = completion_output + model_response.choices[0].message.content = completion_output # type: ignore except: raise SagemakerError( message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}", @@ -610,8 +628,8 @@ async def async_completion( encoding.encode(model_response["choices"][0]["message"].get("content", "")) ) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, @@ -620,137 +638,296 @@ async def async_completion( setattr(model_response, "usage", usage) return model_response + def embedding( + self, + model: str, + input: list, + model_response: EmbeddingResponse, + print_verbose: Callable, + encoding, + logging_obj, + custom_prompt_dict={}, + optional_params=None, + litellm_params=None, + logger_fn=None, + ): + """ + Supports Huggingface Jumpstart embeddings like GPT-6B + """ + ### BOTO3 INIT + import boto3 -def embedding( - model: str, - input: list, - model_response: EmbeddingResponse, - print_verbose: Callable, - encoding, - logging_obj, - custom_prompt_dict={}, - optional_params=None, - litellm_params=None, - logger_fn=None, -): - """ - Supports Huggingface Jumpstart embeddings like GPT-6B - """ - ### BOTO3 INIT - import boto3 + # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them + aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) + aws_access_key_id = optional_params.pop("aws_access_key_id", None) + aws_region_name = optional_params.pop("aws_region_name", None) - # pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them - aws_secret_access_key = optional_params.pop("aws_secret_access_key", None) - aws_access_key_id = optional_params.pop("aws_access_key_id", None) - aws_region_name = optional_params.pop("aws_region_name", None) + if aws_access_key_id is not None: + # uses auth params passed to completion + # aws_access_key_id is not None, assume user is trying to auth using litellm.completion + client = boto3.client( + service_name="sagemaker-runtime", + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + region_name=aws_region_name, + ) + else: + # aws_access_key_id is None, assume user is trying to auth using env variables + # boto3 automaticaly reads env variables - if aws_access_key_id is not None: - # uses auth params passed to completion - # aws_access_key_id is not None, assume user is trying to auth using litellm.completion - client = boto3.client( - service_name="sagemaker-runtime", - aws_access_key_id=aws_access_key_id, - aws_secret_access_key=aws_secret_access_key, - region_name=aws_region_name, - ) - else: - # aws_access_key_id is None, assume user is trying to auth using env variables - # boto3 automaticaly reads env variables + # we need to read region name from env + # I assume majority of users use .env for auth + region_name = ( + get_secret("AWS_REGION_NAME") + or aws_region_name # get region from config file if specified + or "us-west-2" # default to us-west-2 if region not specified + ) + client = boto3.client( + service_name="sagemaker-runtime", + region_name=region_name, + ) - # we need to read region name from env - # I assume majority of users use .env for auth - region_name = ( - get_secret("AWS_REGION_NAME") - or aws_region_name # get region from config file if specified - or "us-west-2" # default to us-west-2 if region not specified - ) - client = boto3.client( - service_name="sagemaker-runtime", - region_name=region_name, - ) + # pop streaming if it's in the optional params as 'stream' raises an error with sagemaker + inference_params = deepcopy(optional_params) + inference_params.pop("stream", None) - # pop streaming if it's in the optional params as 'stream' raises an error with sagemaker - inference_params = deepcopy(optional_params) - inference_params.pop("stream", None) + ## Load Config + config = litellm.SagemakerConfig.get_config() + for k, v in config.items(): + if ( + k not in inference_params + ): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in + inference_params[k] = v - ## Load Config - config = litellm.SagemakerConfig.get_config() - for k, v in config.items(): - if ( - k not in inference_params - ): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in - inference_params[k] = v + #### HF EMBEDDING LOGIC + data = json.dumps({"text_inputs": input}).encode("utf-8") - #### HF EMBEDDING LOGIC - data = json.dumps({"text_inputs": input}).encode("utf-8") - - ## LOGGING - request_str = f""" - response = client.invoke_endpoint( - EndpointName={model}, - ContentType="application/json", - Body={data}, # type: ignore - CustomAttributes="accept_eula=true", - )""" # type: ignore - logging_obj.pre_call( - input=input, - api_key="", - additional_args={"complete_input_dict": data, "request_str": request_str}, - ) - ## EMBEDDING CALL - try: + ## LOGGING + request_str = f""" response = client.invoke_endpoint( - EndpointName=model, + EndpointName={model}, ContentType="application/json", - Body=data, + Body={data}, # type: ignore CustomAttributes="accept_eula=true", + )""" # type: ignore + logging_obj.pre_call( + input=input, + api_key="", + additional_args={"complete_input_dict": data, "request_str": request_str}, ) - except Exception as e: - status_code = ( - getattr(e, "response", {}) - .get("ResponseMetadata", {}) - .get("HTTPStatusCode", 500) - ) - error_message = ( - getattr(e, "response", {}).get("Error", {}).get("Message", str(e)) - ) - raise SagemakerError(status_code=status_code, message=error_message) + ## EMBEDDING CALL + try: + response = client.invoke_endpoint( + EndpointName=model, + ContentType="application/json", + Body=data, + CustomAttributes="accept_eula=true", + ) + except Exception as e: + status_code = ( + getattr(e, "response", {}) + .get("ResponseMetadata", {}) + .get("HTTPStatusCode", 500) + ) + error_message = ( + getattr(e, "response", {}).get("Error", {}).get("Message", str(e)) + ) + raise SagemakerError(status_code=status_code, message=error_message) - response = json.loads(response["Body"].read().decode("utf8")) - ## LOGGING - logging_obj.post_call( - input=input, - api_key="", - original_response=response, - additional_args={"complete_input_dict": data}, - ) - - print_verbose(f"raw model_response: {response}") - if "embedding" not in response: - raise SagemakerError(status_code=500, message="embedding not found in response") - embeddings = response["embedding"] - - if not isinstance(embeddings, list): - raise SagemakerError( - status_code=422, message=f"Response not in expected format - {embeddings}" + response = json.loads(response["Body"].read().decode("utf8")) + ## LOGGING + logging_obj.post_call( + input=input, + api_key="", + original_response=response, + additional_args={"complete_input_dict": data}, ) - output_data = [] - for idx, embedding in enumerate(embeddings): - output_data.append( - {"object": "embedding", "index": idx, "embedding": embedding} + print_verbose(f"raw model_response: {response}") + if "embedding" not in response: + raise SagemakerError( + status_code=500, message="embedding not found in response" + ) + embeddings = response["embedding"] + + if not isinstance(embeddings, list): + raise SagemakerError( + status_code=422, + message=f"Response not in expected format - {embeddings}", + ) + + output_data = [] + for idx, embedding in enumerate(embeddings): + output_data.append( + {"object": "embedding", "index": idx, "embedding": embedding} + ) + + model_response.object = "list" + model_response.data = output_data + model_response.model = model + + input_tokens = 0 + for text in input: + input_tokens += len(encoding.encode(text)) + + setattr( + model_response, + "usage", + Usage( + prompt_tokens=input_tokens, + completion_tokens=0, + total_tokens=input_tokens, + ), ) - model_response["object"] = "list" - model_response["data"] = output_data - model_response["model"] = model + return model_response - input_tokens = 0 - for text in input: - input_tokens += len(encoding.encode(text)) - model_response["usage"] = Usage( - prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens - ) +def get_response_stream_shape(): + global _response_stream_shape_cache + if _response_stream_shape_cache is None: - return model_response + from botocore.loaders import Loader + from botocore.model import ServiceModel + + loader = Loader() + sagemaker_service_dict = loader.load_service_model( + "sagemaker-runtime", "service-2" + ) + sagemaker_service_model = ServiceModel(sagemaker_service_dict) + _response_stream_shape_cache = sagemaker_service_model.shape_for( + "InvokeEndpointWithResponseStreamOutput" + ) + return _response_stream_shape_cache + + +class AWSEventStreamDecoder: + def __init__(self, model: str) -> None: + from botocore.parsers import EventStreamJSONParser + + self.model = model + self.parser = EventStreamJSONParser() + self.content_blocks: List = [] + + def _chunk_parser(self, chunk_data: dict) -> GChunk: + verbose_logger.debug("in sagemaker chunk parser, chunk_data %s", chunk_data) + _token = chunk_data.get("token", {}) or {} + _index = chunk_data.get("index", None) or 0 + is_finished = False + finish_reason = "" + + _text = _token.get("text", "") + if _text == "<|endoftext|>": + return GChunk( + text="", + index=_index, + is_finished=True, + finish_reason="stop", + ) + + return GChunk( + text=_text, + index=_index, + is_finished=is_finished, + finish_reason=finish_reason, + ) + + def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[GChunk]: + """Given an iterator that yields lines, iterate over it & yield every event encountered""" + from botocore.eventstream import EventStreamBuffer + + event_stream_buffer = EventStreamBuffer() + accumulated_json = "" + + for chunk in iterator: + event_stream_buffer.add_data(chunk) + for event in event_stream_buffer: + message = self._parse_message_from_event(event) + if message: + # remove data: prefix and "\n\n" at the end + message = message.replace("data:", "").replace("\n\n", "") + + # Accumulate JSON data + accumulated_json += message + + # Try to parse the accumulated JSON + try: + _data = json.loads(accumulated_json) + yield self._chunk_parser(chunk_data=_data) + # Reset accumulated_json after successful parsing + accumulated_json = "" + except json.JSONDecodeError: + # If it's not valid JSON yet, continue to the next event + continue + + # Handle any remaining data after the iterator is exhausted + if accumulated_json: + try: + _data = json.loads(accumulated_json) + yield self._chunk_parser(chunk_data=_data) + except json.JSONDecodeError: + # Handle or log any unparseable data at the end + verbose_logger.error( + f"Warning: Unparseable JSON data remained: {accumulated_json}" + ) + + async def aiter_bytes( + self, iterator: AsyncIterator[bytes] + ) -> AsyncIterator[GChunk]: + """Given an async iterator that yields lines, iterate over it & yield every event encountered""" + from botocore.eventstream import EventStreamBuffer + + event_stream_buffer = EventStreamBuffer() + accumulated_json = "" + + async for chunk in iterator: + event_stream_buffer.add_data(chunk) + for event in event_stream_buffer: + message = self._parse_message_from_event(event) + if message: + verbose_logger.debug("sagemaker parsed chunk bytes %s", message) + # remove data: prefix and "\n\n" at the end + message = message.replace("data:", "").replace("\n\n", "") + + # Accumulate JSON data + accumulated_json += message + + # Try to parse the accumulated JSON + try: + _data = json.loads(accumulated_json) + yield self._chunk_parser(chunk_data=_data) + # Reset accumulated_json after successful parsing + accumulated_json = "" + except json.JSONDecodeError: + # If it's not valid JSON yet, continue to the next event + continue + + # Handle any remaining data after the iterator is exhausted + if accumulated_json: + try: + _data = json.loads(accumulated_json) + yield self._chunk_parser(chunk_data=_data) + except json.JSONDecodeError: + # Handle or log any unparseable data at the end + verbose_logger.error( + f"Warning: Unparseable JSON data remained: {accumulated_json}" + ) + + def _parse_message_from_event(self, event) -> Optional[str]: + response_dict = event.to_response_dict() + parsed_response = self.parser.parse(response_dict, get_response_stream_shape()) + + if response_dict["status_code"] != 200: + raise ValueError(f"Bad response code, expected 200: {response_dict}") + + if "chunk" in parsed_response: + chunk = parsed_response.get("chunk") + if not chunk: + return None + return chunk.get("bytes").decode() # type: ignore[no-any-return] + else: + chunk = response_dict.get("body") + if not chunk: + return None + + return chunk.decode() # type: ignore[no-any-return] diff --git a/litellm/llms/text_completion_codestral.py b/litellm/llms/text_completion_codestral.py index e732706b4..9dbe3bb37 100644 --- a/litellm/llms/text_completion_codestral.py +++ b/litellm/llms/text_completion_codestral.py @@ -1,28 +1,34 @@ # What is this? ## Controller file for TextCompletionCodestral Integration - https://codestral.com/ -from functools import partial -import os, types -import traceback +import copy import json -from enum import Enum -import requests, copy # type: ignore +import os import time -from typing import Callable, Optional, List, Literal, Union +import traceback +import types +from enum import Enum +from functools import partial +from typing import Callable, List, Literal, Optional, Union + +import httpx # type: ignore +import requests # type: ignore + +import litellm +from litellm import verbose_logger +from litellm.litellm_core_utils.core_helpers import map_finish_reason +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler +from litellm.types.llms.databricks import GenericStreamingChunk from litellm.utils import ( - TextCompletionResponse, - Usage, + Choices, CustomStreamWrapper, Message, - Choices, + TextCompletionResponse, + Usage, ) -from litellm.litellm_core_utils.core_helpers import map_finish_reason -from litellm.types.llms.databricks import GenericStreamingChunk -import litellm -from .prompt_templates.factory import prompt_factory, custom_prompt -from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + from .base import BaseLLM -import httpx # type: ignore +from .prompt_templates.factory import custom_prompt, prompt_factory class TextCompletionCodestralError(Exception): @@ -329,7 +335,12 @@ class CodestralTextCompletion(BaseLLM): ) -> Union[TextCompletionResponse, CustomStreamWrapper]: headers = self._validate_environment(api_key, headers) - completion_url = api_base or "https://codestral.mistral.ai/v1/fim/completions" + if optional_params.pop("custom_endpoint", None) is True: + completion_url = api_base + else: + completion_url = ( + api_base or "https://codestral.mistral.ai/v1/fim/completions" + ) if model in custom_prompt_dict: # check if the model has a registered custom prompt @@ -354,6 +365,7 @@ class CodestralTextCompletion(BaseLLM): stream = optional_params.pop("stream", False) data = { + "model": model, "prompt": prompt, **optional_params, } @@ -426,6 +438,7 @@ class CodestralTextCompletion(BaseLLM): return _response ### SYNC COMPLETION else: + response = requests.post( url=completion_url, headers=headers, @@ -464,8 +477,11 @@ class CodestralTextCompletion(BaseLLM): headers={}, ) -> TextCompletionResponse: - async_handler = AsyncHTTPHandler(timeout=httpx.Timeout(timeout=timeout)) + async_handler = AsyncHTTPHandler( + timeout=httpx.Timeout(timeout=timeout), concurrent_limit=1 + ) try: + response = await async_handler.post( api_base, headers=headers, data=json.dumps(data) ) @@ -475,8 +491,13 @@ class CodestralTextCompletion(BaseLLM): message="HTTPStatusError - {}".format(e.response.text), ) except Exception as e: + verbose_logger.exception( + "litellm.llms.text_completion_codestral.py::async_completion() - Exception occurred - {}".format( + str(e) + ) + ) raise TextCompletionCodestralError( - status_code=500, message="{}\n{}".format(str(e), traceback.format_exc()) + status_code=500, message="{}".format(str(e)) ) return self.process_text_completion_response( model=model, diff --git a/litellm/llms/together_ai.py b/litellm/llms/together_ai.py index 47453ca88..3adbcae37 100644 --- a/litellm/llms/together_ai.py +++ b/litellm/llms/together_ai.py @@ -3,16 +3,20 @@ Deprecated. We now do together ai calls via the openai client. Reference: https://docs.together.ai/docs/openai-api-compatibility """ -import os, types import json -from enum import Enum -import requests # type: ignore +import os import time +import types +from enum import Enum from typing import Callable, Optional -import litellm + import httpx # type: ignore +import requests # type: ignore + +import litellm from litellm.utils import ModelResponse, Usage -from .prompt_templates.factory import prompt_factory, custom_prompt + +from .prompt_templates.factory import custom_prompt, prompt_factory class TogetherAIError(Exception): @@ -91,145 +95,145 @@ class TogetherAIConfig: } -def validate_environment(api_key): - if api_key is None: - raise ValueError( - "Missing TogetherAI API Key - A call is being made to together_ai but no key is set either in the environment variables or via params" - ) - headers = { - "accept": "application/json", - "content-type": "application/json", - "Authorization": "Bearer " + api_key, - } - return headers +# def validate_environment(api_key): +# if api_key is None: +# raise ValueError( +# "Missing TogetherAI API Key - A call is being made to together_ai but no key is set either in the environment variables or via params" +# ) +# headers = { +# "accept": "application/json", +# "content-type": "application/json", +# "Authorization": "Bearer " + api_key, +# } +# return headers -def completion( - model: str, - messages: list, - api_base: str, - model_response: ModelResponse, - print_verbose: Callable, - encoding, - api_key, - logging_obj, - custom_prompt_dict={}, - optional_params=None, - litellm_params=None, - logger_fn=None, -): - headers = validate_environment(api_key) +# def completion( +# model: str, +# messages: list, +# api_base: str, +# model_response: ModelResponse, +# print_verbose: Callable, +# encoding, +# api_key, +# logging_obj, +# custom_prompt_dict={}, +# optional_params=None, +# litellm_params=None, +# logger_fn=None, +# ): +# headers = validate_environment(api_key) - ## Load Config - config = litellm.TogetherAIConfig.get_config() - for k, v in config.items(): - if ( - k not in optional_params - ): # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v +# ## Load Config +# config = litellm.TogetherAIConfig.get_config() +# for k, v in config.items(): +# if ( +# k not in optional_params +# ): # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in +# optional_params[k] = v - print_verbose(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}") - if model in custom_prompt_dict: - # check if the model has a registered custom prompt - model_prompt_details = custom_prompt_dict[model] - prompt = custom_prompt( - role_dict=model_prompt_details.get("roles", {}), - initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), - final_prompt_value=model_prompt_details.get("final_prompt_value", ""), - bos_token=model_prompt_details.get("bos_token", ""), - eos_token=model_prompt_details.get("eos_token", ""), - messages=messages, - ) - else: - prompt = prompt_factory( - model=model, - messages=messages, - api_key=api_key, - custom_llm_provider="together_ai", - ) # api key required to query together ai model list +# print_verbose(f"CUSTOM PROMPT DICT: {custom_prompt_dict}; model: {model}") +# if model in custom_prompt_dict: +# # check if the model has a registered custom prompt +# model_prompt_details = custom_prompt_dict[model] +# prompt = custom_prompt( +# role_dict=model_prompt_details.get("roles", {}), +# initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""), +# final_prompt_value=model_prompt_details.get("final_prompt_value", ""), +# bos_token=model_prompt_details.get("bos_token", ""), +# eos_token=model_prompt_details.get("eos_token", ""), +# messages=messages, +# ) +# else: +# prompt = prompt_factory( +# model=model, +# messages=messages, +# api_key=api_key, +# custom_llm_provider="together_ai", +# ) # api key required to query together ai model list - data = { - "model": model, - "prompt": prompt, - "request_type": "language-model-inference", - **optional_params, - } +# data = { +# "model": model, +# "prompt": prompt, +# "request_type": "language-model-inference", +# **optional_params, +# } - ## LOGGING - logging_obj.pre_call( - input=prompt, - api_key=api_key, - additional_args={ - "complete_input_dict": data, - "headers": headers, - "api_base": api_base, - }, - ) - ## COMPLETION CALL - if "stream_tokens" in optional_params and optional_params["stream_tokens"] == True: - response = requests.post( - api_base, - headers=headers, - data=json.dumps(data), - stream=optional_params["stream_tokens"], - ) - return response.iter_lines() - else: - response = requests.post(api_base, headers=headers, data=json.dumps(data)) - ## LOGGING - logging_obj.post_call( - input=prompt, - api_key=api_key, - original_response=response.text, - additional_args={"complete_input_dict": data}, - ) - print_verbose(f"raw model_response: {response.text}") - ## RESPONSE OBJECT - if response.status_code != 200: - raise TogetherAIError( - status_code=response.status_code, message=response.text - ) - completion_response = response.json() +# ## LOGGING +# logging_obj.pre_call( +# input=prompt, +# api_key=api_key, +# additional_args={ +# "complete_input_dict": data, +# "headers": headers, +# "api_base": api_base, +# }, +# ) +# ## COMPLETION CALL +# if "stream_tokens" in optional_params and optional_params["stream_tokens"] == True: +# response = requests.post( +# api_base, +# headers=headers, +# data=json.dumps(data), +# stream=optional_params["stream_tokens"], +# ) +# return response.iter_lines() +# else: +# response = requests.post(api_base, headers=headers, data=json.dumps(data)) +# ## LOGGING +# logging_obj.post_call( +# input=prompt, +# api_key=api_key, +# original_response=response.text, +# additional_args={"complete_input_dict": data}, +# ) +# print_verbose(f"raw model_response: {response.text}") +# ## RESPONSE OBJECT +# if response.status_code != 200: +# raise TogetherAIError( +# status_code=response.status_code, message=response.text +# ) +# completion_response = response.json() - if "error" in completion_response: - raise TogetherAIError( - message=json.dumps(completion_response), - status_code=response.status_code, - ) - elif "error" in completion_response["output"]: - raise TogetherAIError( - message=json.dumps(completion_response["output"]), - status_code=response.status_code, - ) +# if "error" in completion_response: +# raise TogetherAIError( +# message=json.dumps(completion_response), +# status_code=response.status_code, +# ) +# elif "error" in completion_response["output"]: +# raise TogetherAIError( +# message=json.dumps(completion_response["output"]), +# status_code=response.status_code, +# ) - if len(completion_response["output"]["choices"][0]["text"]) >= 0: - model_response["choices"][0]["message"]["content"] = completion_response[ - "output" - ]["choices"][0]["text"] +# if len(completion_response["output"]["choices"][0]["text"]) >= 0: +# model_response.choices[0].message.content = completion_response["output"][ +# "choices" +# ][0]["text"] - ## CALCULATING USAGE - print_verbose( - f"CALCULATING TOGETHERAI TOKEN USAGE. Model Response: {model_response}; model_response['choices'][0]['message'].get('content', ''): {model_response['choices'][0]['message'].get('content', None)}" - ) - prompt_tokens = len(encoding.encode(prompt)) - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) - ) - if "finish_reason" in completion_response["output"]["choices"][0]: - model_response.choices[0].finish_reason = completion_response["output"][ - "choices" - ][0]["finish_reason"] - model_response["created"] = int(time.time()) - model_response["model"] = "together_ai/" + model - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - setattr(model_response, "usage", usage) - return model_response +# ## CALCULATING USAGE +# print_verbose( +# f"CALCULATING TOGETHERAI TOKEN USAGE. Model Response: {model_response}; model_response['choices'][0]['message'].get('content', ''): {model_response['choices'][0]['message'].get('content', None)}" +# ) +# prompt_tokens = len(encoding.encode(prompt)) +# completion_tokens = len( +# encoding.encode(model_response["choices"][0]["message"].get("content", "")) +# ) +# if "finish_reason" in completion_response["output"]["choices"][0]: +# model_response.choices[0].finish_reason = completion_response["output"][ +# "choices" +# ][0]["finish_reason"] +# model_response["created"] = int(time.time()) +# model_response["model"] = "together_ai/" + model +# usage = Usage( +# prompt_tokens=prompt_tokens, +# completion_tokens=completion_tokens, +# total_tokens=prompt_tokens + completion_tokens, +# ) +# setattr(model_response, "usage", usage) +# return model_response -def embedding(): - # logic for parsing in - calling - parsing out model embedding calls - pass +# def embedding(): +# # logic for parsing in - calling - parsing out model embedding calls +# pass diff --git a/litellm/llms/triton.py b/litellm/llms/triton.py index d647c9c43..14a2e828b 100644 --- a/litellm/llms/triton.py +++ b/litellm/llms/triton.py @@ -1,18 +1,31 @@ -import os, types import json -from enum import Enum -import requests, copy # type: ignore +import os import time -from typing import Callable, Optional, List -import litellm -from .prompt_templates.factory import prompt_factory, custom_prompt -from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler -from .base import BaseLLM +from enum import Enum +from typing import Any, Callable, Dict, List, Optional, Sequence, Union + import httpx # type: ignore +import requests # type: ignore + +import litellm +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.utils import ( + Choices, + CustomStreamWrapper, + Delta, + EmbeddingResponse, + Message, + ModelResponse, + Usage, + map_finish_reason, +) + +from .base import BaseLLM +from .prompt_templates.factory import custom_prompt, prompt_factory class TritonError(Exception): - def __init__(self, status_code, message): + def __init__(self, status_code: int, message: str) -> None: self.status_code = status_code self.message = message self.request = httpx.Request( @@ -36,8 +49,7 @@ class TritonChatCompletion(BaseLLM): api_base: str, logging_obj=None, api_key: Optional[str] = None, - ): - + ) -> EmbeddingResponse: async_handler = AsyncHTTPHandler( timeout=httpx.Timeout(timeout=600.0, connect=5.0) ) @@ -52,24 +64,32 @@ class TritonChatCompletion(BaseLLM): logging_obj.post_call(original_response=_text_response) _json_response = response.json() + _embedding_output = [] _outputs = _json_response["outputs"] - _output_data = _outputs[0]["data"] - _embedding_output = { - "object": "embedding", - "index": 0, - "embedding": _output_data, - } + for output in _outputs: + _shape = output["shape"] + _data = output["data"] + _split_output_data = self.split_embedding_by_shape(_data, _shape) + + for idx, embedding in enumerate(_split_output_data): + _embedding_output.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding, + } + ) model_response.model = _json_response.get("model_name", "None") - model_response.data = [_embedding_output] + model_response.data = _embedding_output return model_response - def embedding( + async def embedding( self, model: str, - input: list, + input: List[str], timeout: float, api_base: str, model_response: litellm.utils.EmbeddingResponse, @@ -77,21 +97,19 @@ class TritonChatCompletion(BaseLLM): logging_obj=None, optional_params=None, client=None, - aembedding=None, - ): + aembedding: bool = False, + ) -> EmbeddingResponse: data_for_triton = { "inputs": [ { "name": "input_text", - "shape": [1], + "shape": [len(input)], "datatype": "BYTES", "data": input, } ] } - ## LOGGING - curl_string = f"curl {api_base} -X POST -H 'Content-Type: application/json' -d '{data_for_triton}'" logging_obj.pre_call( @@ -103,8 +121,8 @@ class TritonChatCompletion(BaseLLM): }, ) - if aembedding == True: - response = self.aembedding( + if aembedding: + response = await self.aembedding( data=data_for_triton, model_response=model_response, logging_obj=logging_obj, @@ -116,3 +134,206 @@ class TritonChatCompletion(BaseLLM): raise Exception( "Only async embedding supported for triton, please use litellm.aembedding() for now" ) + + def completion( + self, + model: str, + messages: List[dict], + timeout: float, + api_base: str, + model_response: ModelResponse, + api_key: Optional[str] = None, + logging_obj=None, + optional_params=None, + client=None, + stream: Optional[bool] = False, + acompletion: bool = False, + ) -> ModelResponse: + type_of_model = "" + optional_params.pop("stream", False) + if api_base.endswith("generate"): ### This is a trtllm model + text_input = messages[0]["content"] + data_for_triton: Dict[str, Any] = { + "text_input": prompt_factory(model=model, messages=messages), + "parameters": { + "max_tokens": int(optional_params.get("max_tokens", 2000)), + "bad_words": [""], + "stop_words": [""], + }, + "stream": bool(stream), + } + data_for_triton["parameters"].update(optional_params) + type_of_model = "trtllm" + + elif api_base.endswith( + "infer" + ): ### This is an infer model with a custom model on triton + text_input = messages[0]["content"] + data_for_triton = { + "inputs": [ + { + "name": "text_input", + "shape": [1], + "datatype": "BYTES", + "data": [text_input], + } + ] + } + + for k, v in optional_params.items(): + if not (k == "stream" or k == "max_retries"): + datatype = "INT32" if isinstance(v, int) else "BYTES" + datatype = "FP32" if isinstance(v, float) else datatype + data_for_triton["inputs"].append( + {"name": k, "shape": [1], "datatype": datatype, "data": [v]} + ) + + if "max_tokens" not in optional_params: + data_for_triton["inputs"].append( + { + "name": "max_tokens", + "shape": [1], + "datatype": "INT32", + "data": [20], + } + ) + + type_of_model = "infer" + else: ## Unknown model type passthrough + data_for_triton = { + "inputs": [ + { + "name": "text_input", + "shape": [1], + "datatype": "BYTES", + "data": [messages[0]["content"]], + } + ] + } + + if logging_obj: + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={ + "complete_input_dict": optional_params, + "api_base": api_base, + "http_client": client, + }, + ) + + headers = {"Content-Type": "application/json"} + json_data_for_triton: str = json.dumps(data_for_triton) + + if acompletion: + return self.acompletion( # type: ignore + model, + json_data_for_triton, + headers=headers, + logging_obj=logging_obj, + api_base=api_base, + stream=stream, + model_response=model_response, + type_of_model=type_of_model, + ) + else: + handler = HTTPHandler() + if stream: + return self._handle_stream( + handler, api_base, json_data_for_triton, model, logging_obj + ) + else: + response = handler.post(url=api_base, data=json_data_for_triton, headers=headers) + return self._handle_response( + response, model_response, logging_obj, type_of_model=type_of_model + ) + + async def acompletion( + self, + model: str, + data_for_triton, + api_base, + stream, + logging_obj, + headers, + model_response, + type_of_model, + ) -> ModelResponse: + handler = AsyncHTTPHandler() + if stream: + return self._ahandle_stream( + handler, api_base, data_for_triton, model, logging_obj + ) + else: + response = await handler.post( + url=api_base, data=data_for_triton, headers=headers + ) + + return self._handle_response( + response, model_response, logging_obj, type_of_model=type_of_model + ) + + def _handle_stream(self, handler, api_base, data_for_triton, model, logging_obj): + response = handler.post( + url=api_base + "_stream", data=data_for_triton, stream=True + ) + streamwrapper = litellm.CustomStreamWrapper( + response.iter_lines(), + model=model, + custom_llm_provider="triton", + logging_obj=logging_obj, + ) + for chunk in streamwrapper: + yield (chunk) + + async def _ahandle_stream( + self, handler, api_base, data_for_triton, model, logging_obj + ): + response = await handler.post( + url=api_base + "_stream", data=data_for_triton, stream=True + ) + streamwrapper = litellm.CustomStreamWrapper( + response.aiter_lines(), + model=model, + custom_llm_provider="triton", + logging_obj=logging_obj, + ) + async for chunk in streamwrapper: + yield (chunk) + + def _handle_response(self, response, model_response, logging_obj, type_of_model): + if logging_obj: + logging_obj.post_call(original_response=response) + + if response.status_code != 200: + raise TritonError(status_code=response.status_code, message=response.text) + + _json_response = response.json() + model_response.model = _json_response.get("model_name", "None") + if type_of_model == "trtllm": + model_response.choices = [ + Choices(index=0, message=Message(content=_json_response["text_output"])) + ] + elif type_of_model == "infer": + model_response.choices = [ + Choices( + index=0, + message=Message(content=_json_response["outputs"][0]["data"]), + ) + ] + else: + model_response.choices = [ + Choices(index=0, message=Message(content=_json_response["outputs"])) + ] + return model_response + + @staticmethod + def split_embedding_by_shape( + data: List[float], shape: List[int] + ) -> List[List[float]]: + if len(shape) != 2: + raise ValueError("Shape must be of length 2.") + embedding_size = shape[1] + return [ + data[i * embedding_size : (i + 1) * embedding_size] for i in range(shape[0]) + ] diff --git a/litellm/llms/vertex_ai.py b/litellm/llms/vertex_ai.py index 31fd23202..c891a86ee 100644 --- a/litellm/llms/vertex_ai.py +++ b/litellm/llms/vertex_ai.py @@ -852,16 +852,14 @@ def completion( ## RESPONSE OBJECT if isinstance(completion_response, litellm.Message): - model_response["choices"][0]["message"] = completion_response + model_response.choices[0].message = completion_response # type: ignore elif len(str(completion_response)) > 0: - model_response["choices"][0]["message"]["content"] = str( - completion_response - ) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.choices[0].message.content = str(completion_response) # type: ignore + model_response.created = int(time.time()) + model_response.model = model ## CALCULATING USAGE if model in litellm.vertex_language_models and response_obj is not None: - model_response["choices"][0].finish_reason = map_finish_reason( + model_response.choices[0].finish_reason = map_finish_reason( response_obj.candidates[0].finish_reason.name ) usage = Usage( @@ -912,7 +910,7 @@ async def async_completion( request_str: str, print_verbose: Callable, logging_obj, - encoding=None, + encoding, client_options=None, instances=None, vertex_project=None, @@ -1088,16 +1086,16 @@ async def async_completion( ## RESPONSE OBJECT if isinstance(completion_response, litellm.Message): - model_response["choices"][0]["message"] = completion_response + model_response.choices[0].message = completion_response # type: ignore elif len(str(completion_response)) > 0: - model_response["choices"][0]["message"]["content"] = str( + model_response.choices[0].message.content = str( # type: ignore completion_response ) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model ## CALCULATING USAGE if model in litellm.vertex_language_models and response_obj is not None: - model_response["choices"][0].finish_reason = map_finish_reason( + model_response.choices[0].finish_reason = map_finish_reason( response_obj.candidates[0].finish_reason.name ) usage = Usage( @@ -1377,16 +1375,16 @@ class VertexAITextEmbeddingConfig(BaseModel): def embedding( model: str, input: Union[list, str], + print_verbose, + model_response: litellm.EmbeddingResponse, + optional_params: dict, api_key: Optional[str] = None, logging_obj=None, - model_response=None, - optional_params=None, encoding=None, vertex_project=None, vertex_location=None, vertex_credentials=None, aembedding=False, - print_verbose=None, ): # logic for parsing in - calling - parsing out model embedding calls try: @@ -1484,15 +1482,15 @@ def embedding( "embedding": embedding.values, } ) - input_tokens += embedding.statistics.token_count - model_response["object"] = "list" - model_response["data"] = embedding_response - model_response["model"] = model + input_tokens += embedding.statistics.token_count # type: ignore + model_response.object = "list" + model_response.data = embedding_response + model_response.model = model usage = Usage( prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens ) - model_response.usage = usage + setattr(model_response, "usage", usage) return model_response @@ -1500,8 +1498,8 @@ def embedding( async def async_embedding( model: str, input: Union[list, str], + model_response: litellm.EmbeddingResponse, logging_obj=None, - model_response=None, optional_params=None, encoding=None, client=None, @@ -1541,11 +1539,11 @@ async def async_embedding( ) input_tokens += embedding.statistics.token_count - model_response["object"] = "list" - model_response["data"] = embedding_response - model_response["model"] = model + model_response.object = "list" + model_response.data = embedding_response + model_response.model = model usage = Usage( prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens ) - model_response.usage = usage + setattr(model_response, "usage", usage) return model_response diff --git a/litellm/llms/vertex_ai_anthropic.py b/litellm/llms/vertex_ai_anthropic.py index 44a7a448e..588745852 100644 --- a/litellm/llms/vertex_ai_anthropic.py +++ b/litellm/llms/vertex_ai_anthropic.py @@ -7,7 +7,7 @@ import time import types import uuid from enum import Enum -from typing import Any, Callable, List, Optional, Tuple +from typing import Any, Callable, List, Optional, Tuple, Union import httpx # type: ignore import requests # type: ignore @@ -15,7 +15,14 @@ import requests # type: ignore import litellm from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler -from litellm.types.llms.anthropic import AnthropicMessagesToolChoice +from litellm.types.llms.anthropic import ( + AnthropicMessagesTool, + AnthropicMessagesToolChoice, +) +from litellm.types.llms.openai import ( + ChatCompletionToolParam, + ChatCompletionToolParamFunctionChunk, +) from litellm.types.utils import ResponseFormatChunk from litellm.utils import CustomStreamWrapper, ModelResponse, Usage @@ -141,8 +148,33 @@ class VertexAIAnthropicConfig: optional_params["temperature"] = value if param == "top_p": optional_params["top_p"] = value - if param == "response_format" and "response_schema" in value: - optional_params["response_format"] = ResponseFormatChunk(**value) # type: ignore + if param == "response_format" and isinstance(value, dict): + json_schema: Optional[dict] = None + if "response_schema" in value: + json_schema = value["response_schema"] + elif "json_schema" in value: + json_schema = value["json_schema"]["schema"] + """ + When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode + - You usually want to provide a single tool + - You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool + - Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective. + """ + _tool_choice = None + _tool_choice = {"name": "json_tool_call", "type": "tool"} + + _tool = AnthropicMessagesTool( + name="json_tool_call", + input_schema={ + "type": "object", + "properties": {"values": json_schema}, # type: ignore + }, + ) + + optional_params["tools"] = [_tool] + optional_params["tool_choice"] = _tool_choice + optional_params["json_mode"] = True + return optional_params @@ -222,6 +254,7 @@ def completion( optional_params: dict, custom_prompt_dict: dict, headers: Optional[dict], + timeout: Union[float, httpx.Timeout], vertex_project=None, vertex_location=None, vertex_credentials=None, @@ -301,6 +334,8 @@ def completion( litellm_params=litellm_params, logger_fn=logger_fn, headers=vertex_headers, + client=client, + timeout=timeout, ) except Exception as e: @@ -367,8 +402,8 @@ async def async_completion( prompt_tokens = message.usage.input_tokens completion_tokens = message.usage.output_tokens - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, diff --git a/litellm/llms/vertex_ai_partner.py b/litellm/llms/vertex_ai_partner.py new file mode 100644 index 000000000..24586a3fe --- /dev/null +++ b/litellm/llms/vertex_ai_partner.py @@ -0,0 +1,245 @@ +# What is this? +## Handler for calling llama 3.1 API on Vertex AI +import copy +import json +import os +import time +import types +import uuid +from enum import Enum +from typing import Any, Callable, List, Literal, Optional, Tuple, Union + +import httpx # type: ignore +import requests # type: ignore + +import litellm +from litellm.litellm_core_utils.core_helpers import map_finish_reason +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.types.llms.anthropic import ( + AnthropicMessagesTool, + AnthropicMessagesToolChoice, +) +from litellm.types.llms.openai import ( + ChatCompletionToolParam, + ChatCompletionToolParamFunctionChunk, +) +from litellm.types.utils import ResponseFormatChunk +from litellm.utils import CustomStreamWrapper, ModelResponse, Usage + +from .base import BaseLLM +from .prompt_templates.factory import ( + construct_tool_use_system_prompt, + contains_tag, + custom_prompt, + extract_between_tags, + parse_xml_params, + prompt_factory, + response_schema_prompt, +) + + +class VertexAIError(Exception): + def __init__(self, status_code, message): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url=" https://cloud.google.com/vertex-ai/" + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) # Call the base class constructor with the parameters it needs + + +class VertexAILlama3Config: + """ + Reference:https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/llama#streaming + + The class `VertexAILlama3Config` provides configuration for the VertexAI's Llama API interface. Below are the parameters: + + - `max_tokens` Required (integer) max tokens, + + Note: Please make sure to modify the default parameters as required for your use case. + """ + + max_tokens: Optional[int] = None + + def __init__( + self, + max_tokens: Optional[int] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + if key == "max_tokens" and value is None: + value = self.max_tokens + if key != "self" and value is not None: + setattr(self.__class__, key, value) + + @classmethod + def get_config(cls): + return { + k: v + for k, v in cls.__dict__.items() + if not k.startswith("__") + and not isinstance( + v, + ( + types.FunctionType, + types.BuiltinFunctionType, + classmethod, + staticmethod, + ), + ) + and v is not None + } + + def get_supported_openai_params(self): + return litellm.OpenAIConfig().get_supported_openai_params(model="gpt-3.5-turbo") + + def map_openai_params( + self, non_default_params: dict, optional_params: dict, model: str + ): + return litellm.OpenAIConfig().map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + ) + + +class VertexAIPartnerModels(BaseLLM): + def __init__(self) -> None: + pass + + def create_vertex_url( + self, + vertex_location: str, + vertex_project: str, + partner: Literal["llama", "mistralai"], + stream: Optional[bool], + model: str, + ) -> str: + if partner == "llama": + return f"https://{vertex_location}-aiplatform.googleapis.com/v1beta1/projects/{vertex_project}/locations/{vertex_location}/endpoints/openapi" + elif partner == "mistralai": + if stream: + return f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/mistralai/models/{model}:streamRawPredict" + else: + return f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/mistralai/models/{model}:rawPredict" + + def completion( + self, + model: str, + messages: list, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + logging_obj, + optional_params: dict, + custom_prompt_dict: dict, + headers: Optional[dict], + timeout: Union[float, httpx.Timeout], + litellm_params: dict, + vertex_project=None, + vertex_location=None, + vertex_credentials=None, + logger_fn=None, + acompletion: bool = False, + client=None, + ): + try: + import vertexai + from google.cloud import aiplatform + + from litellm.llms.databricks import DatabricksChatCompletion + from litellm.llms.openai import OpenAIChatCompletion + from litellm.llms.text_completion_codestral import CodestralTextCompletion + from litellm.llms.vertex_httpx import VertexLLM + except Exception: + + raise VertexAIError( + status_code=400, + message="""vertexai import failed please run `pip install -U "google-cloud-aiplatform>=1.38"`""", + ) + + if not ( + hasattr(vertexai, "preview") or hasattr(vertexai.preview, "language_models") + ): + raise VertexAIError( + status_code=400, + message="""Upgrade vertex ai. Run `pip install "google-cloud-aiplatform>=1.38"`""", + ) + try: + + vertex_httpx_logic = VertexLLM() + + access_token, project_id = vertex_httpx_logic._ensure_access_token( + credentials=vertex_credentials, project_id=vertex_project + ) + + openai_like_chat_completions = DatabricksChatCompletion() + codestral_fim_completions = CodestralTextCompletion() + + ## CONSTRUCT API BASE + stream: bool = optional_params.get("stream", False) or False + + optional_params["stream"] = stream + + if "llama" in model: + partner = "llama" + elif "mistral" in model or "codestral" in model: + partner = "mistralai" + optional_params["custom_endpoint"] = True + + api_base = self.create_vertex_url( + vertex_location=vertex_location or "us-central1", + vertex_project=vertex_project or project_id, + partner=partner, # type: ignore + stream=stream, + model=model, + ) + + model = model.split("@")[0] + + if "codestral" in model and litellm_params.get("text_completion") is True: + optional_params["model"] = model + text_completion_model_response = litellm.TextCompletionResponse( + stream=stream + ) + return codestral_fim_completions.completion( + model=model, + messages=messages, + api_base=api_base, + api_key=access_token, + custom_prompt_dict=custom_prompt_dict, + model_response=text_completion_model_response, + print_verbose=print_verbose, + logging_obj=logging_obj, + optional_params=optional_params, + acompletion=acompletion, + litellm_params=litellm_params, + logger_fn=logger_fn, + timeout=timeout, + encoding=encoding, + ) + + return openai_like_chat_completions.completion( + model=model, + messages=messages, + api_base=api_base, + api_key=access_token, + custom_prompt_dict=custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + logging_obj=logging_obj, + optional_params=optional_params, + acompletion=acompletion, + litellm_params=litellm_params, + logger_fn=logger_fn, + client=client, + timeout=timeout, + encoding=encoding, + custom_llm_provider="vertex_ai_beta", + ) + + except Exception as e: + raise VertexAIError(status_code=500, message=str(e)) diff --git a/litellm/llms/vertex_httpx.py b/litellm/llms/vertex_httpx.py index 468a81146..8af8f6894 100644 --- a/litellm/llms/vertex_httpx.py +++ b/litellm/llms/vertex_httpx.py @@ -13,6 +13,7 @@ from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union import httpx # type: ignore import requests # type: ignore +from openai.types.image import Image import litellm import litellm.litellm_core_utils @@ -180,8 +181,17 @@ class GoogleAIStudioGeminiConfig: # key diff from VertexAI - 'frequency_penalty optional_params["stop_sequences"] = value if param == "max_tokens": optional_params["max_output_tokens"] = value - if param == "response_format" and value["type"] == "json_object": # type: ignore - optional_params["response_mime_type"] = "application/json" + if param == "response_format": # type: ignore + if value["type"] == "json_object": # type: ignore + if value["type"] == "json_object": # type: ignore + optional_params["response_mime_type"] = "application/json" + elif value["type"] == "text": # type: ignore + optional_params["response_mime_type"] = "text/plain" + if "response_schema" in value: # type: ignore + optional_params["response_schema"] = value["response_schema"] # type: ignore + elif value["type"] == "json_schema": # type: ignore + if "json_schema" in value and "schema" in value["json_schema"]: # type: ignore + optional_params["response_schema"] = value["json_schema"]["schema"] # type: ignore if param == "tools" and isinstance(value, list): gtool_func_declarations = [] for tool in value: @@ -265,6 +275,8 @@ class VertexGeminiConfig: - `presence_penalty` (float): This parameter is used to penalize the model from generating the same output as the input. The default value is 0.0. + - `seed` (int): The seed value is used to help generate the same output for the same input. The default value is None. + Note: Please make sure to modify the default parameters as required for your use case. """ @@ -277,6 +289,7 @@ class VertexGeminiConfig: stop_sequences: Optional[list] = None frequency_penalty: Optional[float] = None presence_penalty: Optional[float] = None + seed: Optional[int] = None def __init__( self, @@ -289,6 +302,7 @@ class VertexGeminiConfig: stop_sequences: Optional[list] = None, frequency_penalty: Optional[float] = None, presence_penalty: Optional[float] = None, + seed: Optional[int] = None, ) -> None: locals_ = locals() for key, value in locals_.items(): @@ -326,6 +340,7 @@ class VertexGeminiConfig: "stop", "frequency_penalty", "presence_penalty", + "seed", ] def map_tool_choice_values( @@ -385,13 +400,17 @@ class VertexGeminiConfig: optional_params["response_mime_type"] = "text/plain" if "response_schema" in value: optional_params["response_schema"] = value["response_schema"] + elif value["type"] == "json_schema": # type: ignore + if "json_schema" in value and "schema" in value["json_schema"]: # type: ignore + optional_params["response_schema"] = value["json_schema"]["schema"] # type: ignore if param == "frequency_penalty": optional_params["frequency_penalty"] = value if param == "presence_penalty": optional_params["presence_penalty"] = value if param == "tools" and isinstance(value, list): gtool_func_declarations = [] - google_search_tool: Optional[dict] = None + googleSearchRetrieval: Optional[dict] = None + provider_specific_tools: List[dict] = [] for tool in value: # check if grounding try: @@ -402,15 +421,19 @@ class VertexGeminiConfig: ) gtool_func_declarations.append(gtool_func_declaration) except KeyError: - # assume it's a provider-specific param - verbose_logger.warning( - "Got KeyError parsing tool={}. Assuming it's a provider-specific param. Use `litellm.set_verbose` or `litellm --detailed_debug` to see raw request." - ) - google_search_tool = tool - _tools = Tools(function_declarations=gtool_func_declarations) - if google_search_tool is not None: - _tools["googleSearchRetrieval"] = google_search_tool - optional_params["tools"] = [_tools] + if tool.get("googleSearchRetrieval", None) is not None: + googleSearchRetrieval = tool["googleSearchRetrieval"] + else: + # assume it's a provider-specific param + verbose_logger.warning( + "Got KeyError parsing tool={}. Assuming it's a provider-specific param. Use `litellm.set_verbose` or `litellm --detailed_debug` to see raw request." + ) + _tools = Tools( + function_declarations=gtool_func_declarations, + ) + if googleSearchRetrieval is not None: + _tools["googleSearchRetrieval"] = googleSearchRetrieval + optional_params["tools"] = [_tools] + provider_specific_tools if param == "tool_choice" and ( isinstance(value, str) or isinstance(value, dict) ): @@ -419,6 +442,8 @@ class VertexGeminiConfig: ) if _tool_choice_value is not None: optional_params["tool_choice"] = _tool_choice_value + if param == "seed": + optional_params["seed"] = value return optional_params def get_mapped_special_auth_params(self) -> dict: @@ -466,6 +491,16 @@ class VertexGeminiConfig: "SPII": "The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents.", } + def translate_exception_str(self, exception_string: str): + if ( + "GenerateContentRequest.tools[0].function_declarations[0].parameters.properties: should be non-empty for OBJECT type" + in exception_string + ): + return "'properties' field in tools[0]['function']['parameters'] cannot be empty if 'type' == 'object'. Received error from provider - {}".format( + exception_string + ) + return exception_string + async def make_call( client: Optional[AsyncHTTPHandler], @@ -479,8 +514,15 @@ async def make_call( if client is None: client = AsyncHTTPHandler() # Create a new client if none provided - response = await client.post(api_base, headers=headers, data=data, stream=True) - + try: + response = await client.post(api_base, headers=headers, data=data, stream=True) + response.raise_for_status() + except httpx.HTTPStatusError as e: + exception_string = str(await e.response.aread()) + raise VertexAIError( + status_code=e.response.status_code, + message=VertexGeminiConfig().translate_exception_str(exception_string), + ) if response.status_code != 200: raise VertexAIError(status_code=response.status_code, message=response.text) @@ -675,6 +717,10 @@ class VertexLLM(BaseLLM): model_response.choices = [] # type: ignore try: + ## CHECK IF GROUNDING METADATA IN REQUEST + grounding_metadata: List[dict] = [] + safety_ratings: List = [] + citation_metadata: List = [] ## GET TEXT ## chat_completion_message = {"role": "assistant"} content_str = "" @@ -683,6 +729,14 @@ class VertexLLM(BaseLLM): if "content" not in candidate: continue + if "groundingMetadata" in candidate: + grounding_metadata.append(candidate["groundingMetadata"]) + + if "safetyRatings" in candidate: + safety_ratings.append(candidate["safetyRatings"]) + + if "citationMetadata" in candidate: + citation_metadata.append(candidate["citationMetadata"]) if "text" in candidate["content"]["parts"][0]: content_str = candidate["content"]["parts"][0]["text"] @@ -728,6 +782,27 @@ class VertexLLM(BaseLLM): ) setattr(model_response, "usage", usage) + + ## ADD GROUNDING METADATA ## + setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata) + model_response._hidden_params[ + "vertex_ai_grounding_metadata" + ] = ( # older approach - maintaining to prevent regressions + grounding_metadata + ) + + ## ADD SAFETY RATINGS ## + setattr(model_response, "vertex_ai_safety_results", safety_ratings) + model_response._hidden_params["vertex_ai_safety_results"] = ( + safety_ratings # older approach - maintaining to prevent regressions + ) + + ## ADD CITATION METADATA ## + setattr(model_response, "vertex_ai_citation_metadata", citation_metadata) + model_response._hidden_params["vertex_ai_citation_metadata"] = ( + citation_metadata # older approach - maintaining to prevent regressions + ) + except Exception as e: raise VertexAIError( message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format( @@ -753,7 +828,20 @@ class VertexLLM(BaseLLM): if credentials is not None and isinstance(credentials, str): import google.oauth2.service_account - json_obj = json.loads(credentials) + verbose_logger.debug( + "Vertex: Loading vertex credentials from %s", credentials + ) + verbose_logger.debug( + "Vertex: checking if credentials is a valid path, os.path.exists(%s)=%s, current dir %s", + credentials, + os.path.exists(credentials), + os.getcwd(), + ) + + if os.path.exists(credentials): + json_obj = json.load(open(credentials)) + else: + json_obj = json.loads(credentials) creds = google.oauth2.service_account.Credentials.from_service_account_info( json_obj, @@ -818,6 +906,21 @@ class VertexLLM(BaseLLM): return self._credentials.token, self.project_id + def is_using_v1beta1_features(self, optional_params: dict) -> bool: + """ + VertexAI only supports ContextCaching on v1beta1 + + use this helper to decide if request should be sent to v1 or v1beta1 + + Returns v1beta1 if context caching is enabled + Returns v1 in all other cases + """ + if "cached_content" in optional_params: + return True + if "CachedContent" in optional_params: + return True + return False + def _get_token_and_url( self, model: str, @@ -828,6 +931,7 @@ class VertexLLM(BaseLLM): stream: Optional[bool], custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], api_base: Optional[str], + should_use_v1beta1_features: Optional[bool] = False, ) -> Tuple[Optional[str], str]: """ Internal function. Returns the token and url for the call. @@ -857,12 +961,13 @@ class VertexLLM(BaseLLM): vertex_location = self.get_vertex_region(vertex_region=vertex_location) ### SET RUNTIME ENDPOINT ### + version = "v1beta1" if should_use_v1beta1_features is True else "v1" endpoint = "generateContent" if stream is True: endpoint = "streamGenerateContent" - url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}?alt=sse" + url = f"https://{vertex_location}-aiplatform.googleapis.com/{version}/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}?alt=sse" else: - url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}" + url = f"https://{vertex_location}-aiplatform.googleapis.com/{version}/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}" if ( api_base is not None @@ -992,6 +1097,9 @@ class VertexLLM(BaseLLM): ) -> Union[ModelResponse, CustomStreamWrapper]: stream: Optional[bool] = optional_params.pop("stream", None) # type: ignore + should_use_v1beta1_features = self.is_using_v1beta1_features( + optional_params=optional_params + ) auth_header, url = self._get_token_and_url( model=model, gemini_api_key=gemini_api_key, @@ -1001,6 +1109,7 @@ class VertexLLM(BaseLLM): stream=stream, custom_llm_provider=custom_llm_provider, api_base=api_base, + should_use_v1beta1_features=should_use_v1beta1_features, ) ## TRANSFORMATION ## @@ -1012,7 +1121,7 @@ class VertexLLM(BaseLLM): model=model, custom_llm_provider=_custom_llm_provider ) except Exception as e: - verbose_logger.error( + verbose_logger.warning( "Unable to identify if system message supported. Defaulting to 'False'. Received error message - {}\nAdd it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json".format( str(e) ) @@ -1168,7 +1277,7 @@ class VertexLLM(BaseLLM): response.raise_for_status() except httpx.HTTPStatusError as err: error_code = err.response.status_code - raise VertexAIError(status_code=error_code, message=response.text) + raise VertexAIError(status_code=error_code, message=err.response.text) except httpx.TimeoutException: raise VertexAIError(status_code=408, message="Timeout error occurred.") @@ -1289,12 +1398,18 @@ class VertexLLM(BaseLLM): """ _json_response = response.json() + if "predictions" not in _json_response: + raise litellm.InternalServerError( + message=f"image generation response does not contain 'predictions', got {_json_response}", + llm_provider="vertex_ai", + model=model, + ) _predictions = _json_response["predictions"] - _response_data: List[litellm.ImageObject] = [] + _response_data: List[Image] = [] for _prediction in _predictions: _bytes_base64_encoded = _prediction["bytesBase64Encoded"] - image_object = litellm.ImageObject(b64_json=_bytes_base64_encoded) + image_object = Image(b64_json=_bytes_base64_encoded) _response_data.append(image_object) model_response.data = _response_data @@ -1401,12 +1516,20 @@ class VertexLLM(BaseLLM): """ _json_response = response.json() + + if "predictions" not in _json_response: + raise litellm.InternalServerError( + message=f"image generation response does not contain 'predictions', got {_json_response}", + llm_provider="vertex_ai", + model=model, + ) + _predictions = _json_response["predictions"] - _response_data: List[litellm.ImageObject] = [] + _response_data: List[Image] = [] for _prediction in _predictions: _bytes_base64_encoded = _prediction["bytesBase64Encoded"] - image_object = litellm.ImageObject(b64_json=_bytes_base64_encoded) + image_object = Image(b64_json=_bytes_base64_encoded) _response_data.append(image_object) model_response.data = _response_data diff --git a/litellm/llms/vllm.py b/litellm/llms/vllm.py index b2a9dd54d..f261b7297 100644 --- a/litellm/llms/vllm.py +++ b/litellm/llms/vllm.py @@ -1,11 +1,15 @@ -import os import json +import os +import time # type: ignore from enum import Enum +from typing import Any, Callable + +import httpx import requests # type: ignore -import time, httpx # type: ignore -from typing import Callable, Any + from litellm.utils import ModelResponse, Usage -from .prompt_templates.factory import prompt_factory, custom_prompt + +from .prompt_templates.factory import custom_prompt, prompt_factory llm = None @@ -91,14 +95,14 @@ def completion( ) print_verbose(f"raw model_response: {outputs}") ## RESPONSE OBJECT - model_response["choices"][0]["message"]["content"] = outputs[0].outputs[0].text + model_response.choices[0].message.content = outputs[0].outputs[0].text # type: ignore ## CALCULATING USAGE prompt_tokens = len(outputs[0].prompt_token_ids) completion_tokens = len(outputs[0].outputs[0].token_ids) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, @@ -173,14 +177,14 @@ def batch_completions( for output in outputs: model_response = ModelResponse() ## RESPONSE OBJECT - model_response["choices"][0]["message"]["content"] = output.outputs[0].text + model_response.choices[0].message.content = output.outputs[0].text # type: ignore ## CALCULATING USAGE prompt_tokens = len(output.prompt_token_ids) completion_tokens = len(output.outputs[0].token_ids) - model_response["created"] = int(time.time()) - model_response["model"] = model + model_response.created = int(time.time()) + model_response.model = model usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, diff --git a/litellm/llms/watsonx.py b/litellm/llms/watsonx.py index a3d315557..c01efd8ad 100644 --- a/litellm/llms/watsonx.py +++ b/litellm/llms/watsonx.py @@ -591,9 +591,9 @@ class IBMWatsonXAI(BaseLLM): self, model: str, input: Union[list, str], + model_response: litellm.EmbeddingResponse, api_key: Optional[str] = None, logging_obj=None, - model_response=None, optional_params=None, encoding=None, print_verbose=None, @@ -610,7 +610,7 @@ class IBMWatsonXAI(BaseLLM): if k not in optional_params: optional_params[k] = v - model_response["model"] = model + model_response.model = model # Load auth variables from environment variables if isinstance(input, str): diff --git a/litellm/main.py b/litellm/main.py index bc40194b4..f2c6df306 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -31,6 +31,7 @@ from typing import ( Literal, Mapping, Optional, + Type, Union, ) @@ -94,7 +95,6 @@ from .llms import ( palm, petals, replicate, - sagemaker, together_ai, triton, vertex_ai, @@ -104,9 +104,10 @@ from .llms import ( ) from .llms.anthropic import AnthropicChatCompletion from .llms.anthropic_text import AnthropicTextCompletion -from .llms.azure import AzureChatCompletion +from .llms.azure import AzureChatCompletion, _check_dynamic_azure_params from .llms.azure_text import AzureTextCompletion from .llms.bedrock_httpx import BedrockConverseLLM, BedrockLLM +from .llms.custom_llm import CustomLLM, custom_chat_llm_router from .llms.databricks import DatabricksChatCompletion from .llms.huggingface_restapi import Huggingface from .llms.openai import OpenAIChatCompletion, OpenAITextCompletion @@ -118,12 +119,19 @@ from .llms.prompt_templates.factory import ( prompt_factory, stringify_json_tool_call_content, ) +from .llms.sagemaker import SagemakerLLM from .llms.text_completion_codestral import CodestralTextCompletion from .llms.triton import TritonChatCompletion +from .llms.vertex_ai_partner import VertexAIPartnerModels from .llms.vertex_httpx import VertexLLM from .llms.watsonx import IBMWatsonXAI from .types.llms.openai import HttpxBinaryResponseContent -from .types.utils import ChatCompletionMessageToolCall +from .types.utils import ( + AdapterCompletionStreamWrapper, + ChatCompletionMessageToolCall, + HiddenParams, + all_litellm_params, +) encoding = tiktoken.get_encoding("cl100k_base") from litellm.utils import ( @@ -156,7 +164,9 @@ triton_chat_completions = TritonChatCompletion() bedrock_chat_completion = BedrockLLM() bedrock_converse_chat_completion = BedrockConverseLLM() vertex_chat_completion = VertexLLM() +vertex_partner_models_chat_completion = VertexAIPartnerModels() watsonxai = IBMWatsonXAI() +sagemaker_llm = SagemakerLLM() ####### COMPLETION ENDPOINTS ################ @@ -243,7 +253,7 @@ async def acompletion( logit_bias: Optional[dict] = None, user: Optional[str] = None, # openai v1.0+ new params - response_format: Optional[dict] = None, + response_format: Optional[Union[dict, Type[BaseModel]]] = None, seed: Optional[int] = None, tools: Optional[List] = None, tool_choice: Optional[str] = None, @@ -375,9 +385,11 @@ async def acompletion( or custom_llm_provider == "predibase" or custom_llm_provider == "bedrock" or custom_llm_provider == "databricks" + or custom_llm_provider == "triton" or custom_llm_provider == "clarifai" or custom_llm_provider == "watsonx" or custom_llm_provider in litellm.openai_compatible_providers + or custom_llm_provider in litellm._custom_providers ): # currently implemented aiohttp calls for just azure, openai, hf, ollama, vertex ai soon all. init_response = await loop.run_in_executor(None, func_with_context) if isinstance(init_response, dict) or isinstance( @@ -408,12 +420,9 @@ async def acompletion( ) # sets the logging event loop if the user does sync streaming (e.g. on proxy for sagemaker calls) return response except Exception as e: - verbose_logger.error( - "litellm.acompletion(): Exception occured - {}\n{}".format( - str(e), traceback.format_exc() - ) + verbose_logger.exception( + "litellm.main.py::acompletion() - Exception occurred - {}".format(str(e)) ) - verbose_logger.debug(traceback.format_exc()) custom_llm_provider = custom_llm_provider or "openai" raise exception_type( model=model, @@ -479,7 +488,7 @@ def mock_completion( if isinstance(mock_response, Exception): if isinstance(mock_response, openai.APIError): raise mock_response - raise litellm.APIError( + raise litellm.MockException( status_code=getattr(mock_response, "status_code", 500), # type: ignore message=getattr(mock_response, "text", str(mock_response)), llm_provider=getattr(mock_response, "llm_provider", custom_llm_provider or "openai"), # type: ignore @@ -495,6 +504,16 @@ def mock_completion( llm_provider=getattr(mock_response, "llm_provider", custom_llm_provider or "openai"), # type: ignore model=model, ) + elif isinstance(mock_response, str) and mock_response.startswith( + "Exception: content_filter_policy" + ): + raise litellm.MockException( + status_code=400, + message=mock_response, + llm_provider="azure", + model=model, # type: ignore + request=httpx.Request(method="POST", url="https://api.openai.com/v1/"), + ) time_delay = kwargs.get("mock_delay", None) if time_delay is not None: time.sleep(time_delay) @@ -505,7 +524,7 @@ def mock_completion( model_response = ModelResponse(stream=stream) if stream is True: # don't try to access stream object, - if kwargs.get("acompletion", False) == True: + if kwargs.get("acompletion", False) is True: return CustomStreamWrapper( completion_stream=async_mock_completion_streaming_obj( model_response, mock_response=mock_response, model=model, n=n @@ -514,13 +533,14 @@ def mock_completion( custom_llm_provider="openai", logging_obj=logging, ) - response = mock_completion_streaming_obj( - model_response, - mock_response=mock_response, + return CustomStreamWrapper( + completion_stream=mock_completion_streaming_obj( + model_response, mock_response=mock_response, model=model, n=n + ), model=model, - n=n, + custom_llm_provider="openai", + logging_obj=logging, ) - return response if n is None: model_response.choices[0].message.content = mock_response # type: ignore else: @@ -567,10 +587,9 @@ def mock_completion( except Exception as e: if isinstance(e, openai.APIError): raise e - verbose_logger.error( + verbose_logger.exception( "litellm.mock_completion(): Exception occured - {}".format(str(e)) ) - verbose_logger.debug(traceback.format_exc()) raise Exception("Mock completion response failed") @@ -592,7 +611,7 @@ def completion( logit_bias: Optional[dict] = None, user: Optional[str] = None, # openai v1.0+ new params - response_format: Optional[dict] = None, + response_format: Optional[Union[dict, Type[BaseModel]]] = None, seed: Optional[int] = None, tools: Optional[List] = None, tool_choice: Optional[Union[str, dict]] = None, @@ -669,7 +688,9 @@ def completion( proxy_server_request = kwargs.get("proxy_server_request", None) fallbacks = kwargs.get("fallbacks", None) headers = kwargs.get("headers", None) or extra_headers - num_retries = kwargs.get("num_retries", None) ## deprecated + num_retries = kwargs.get( + "num_retries", None + ) ## alt. param for 'max_retries'. Use this to pass retries w/ instructor. max_retries = kwargs.get("max_retries", None) cooldown_time = kwargs.get("cooldown_time", None) context_window_fallback_dict = kwargs.get("context_window_fallback_dict", None) @@ -733,63 +754,9 @@ def completion( "top_logprobs", "extra_headers", ] - litellm_params = [ - "metadata", - "acompletion", - "atext_completion", - "text_completion", - "caching", - "mock_response", - "api_key", - "api_version", - "api_base", - "force_timeout", - "logger_fn", - "verbose", - "custom_llm_provider", - "litellm_logging_obj", - "litellm_call_id", - "use_client", - "id", - "fallbacks", - "azure", - "headers", - "model_list", - "num_retries", - "context_window_fallback_dict", - "retry_policy", - "roles", - "final_prompt_value", - "bos_token", - "eos_token", - "request_timeout", - "complete_response", - "self", - "client", - "rpm", - "tpm", - "max_parallel_requests", - "input_cost_per_token", - "output_cost_per_token", - "input_cost_per_second", - "output_cost_per_second", - "hf_model_name", - "model_info", - "proxy_server_request", - "preset_cache_key", - "caching_groups", - "ttl", - "cache", - "no-log", - "base_model", - "stream_timeout", - "supports_system_message", - "region_name", - "allowed_model_region", - "model_config", - "fastest_response", - "cooldown_time", - ] + litellm_params = ( + all_litellm_params # use the external var., used in creating cache key as well. + ) default_params = openai_params + litellm_params non_default_params = { @@ -799,8 +766,8 @@ def completion( try: if base_url is not None: api_base = base_url - if max_retries is not None: # openai allows openai.OpenAI(max_retries=3) - num_retries = max_retries + if num_retries is not None: + max_retries = num_retries logging = litellm_logging_obj fallbacks = fallbacks or litellm.model_fallbacks if fallbacks is not None: @@ -975,6 +942,7 @@ def completion( output_cost_per_second=output_cost_per_second, output_cost_per_token=output_cost_per_token, cooldown_time=cooldown_time, + text_completion=kwargs.get("text_completion"), ) logging.update_environment_variables( model=model, @@ -999,6 +967,17 @@ def completion( if custom_llm_provider == "azure": # azure configs + ## check dynamic params ## + dynamic_params = False + if client is not None and ( + isinstance(client, openai.AzureOpenAI) + or isinstance(client, openai.AsyncAzureOpenAI) + ): + dynamic_params = _check_dynamic_azure_params( + azure_client_params={"api_version": api_version}, + azure_client=client, + ) + api_type = get_secret("AZURE_API_TYPE") or "azure" api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") @@ -1038,6 +1017,7 @@ def completion( api_base=api_base, api_version=api_version, api_type=api_type, + dynamic_params=dynamic_params, azure_ad_token=azure_ad_token, model_response=model_response, print_verbose=print_verbose, @@ -1175,6 +1155,7 @@ def completion( client=client, # pass AsyncOpenAI, OpenAI client organization=organization, custom_llm_provider=custom_llm_provider, + drop_params=non_default_params.get("drop_params"), ) except Exception as e: ## LOGGING - log the original exception returned @@ -1486,8 +1467,13 @@ def completion( api_base or litellm.api_base or get_secret("ANTHROPIC_API_BASE") + or get_secret("ANTHROPIC_BASE_URL") or "https://api.anthropic.com/v1/complete" ) + + if api_base is not None and not api_base.endswith("/v1/complete"): + api_base += "/v1/complete" + response = anthropic_text_completions.completion( model=model, messages=messages, @@ -1511,8 +1497,13 @@ def completion( api_base or litellm.api_base or get_secret("ANTHROPIC_API_BASE") + or get_secret("ANTHROPIC_BASE_URL") or "https://api.anthropic.com/v1/messages" ) + + if api_base is not None and not api_base.endswith("/v1/messages"): + api_base += "/v1/messages" + response = anthropic_chat_completions.completion( model=model, messages=messages, @@ -1528,6 +1519,8 @@ def completion( api_key=api_key, logging_obj=logging, headers=headers, + timeout=timeout, + client=client, ) if optional_params.get("stream", False) or acompletion == True: ## LOGGING @@ -1848,6 +1841,7 @@ def completion( custom_prompt_dict=custom_prompt_dict, client=client, # pass AsyncOpenAI, OpenAI client encoding=encoding, + custom_llm_provider="databricks", ) except Exception as e: ## LOGGING - log the original exception returned @@ -1879,17 +1873,18 @@ def completion( ) openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai" - openrouter_app_name = get_secret("OR_APP_NAME") or "liteLLM" - headers = ( - headers - or litellm.headers - or { - "HTTP-Referer": openrouter_site_url, - "X-Title": openrouter_app_name, - } - ) + openrouter_headers = { + "HTTP-Referer": openrouter_site_url, + "X-Title": openrouter_app_name, + } + + _headers = headers or litellm.headers + if _headers: + openrouter_headers.update(_headers) + + headers = openrouter_headers ## Load Config config = openrouter.OpenrouterConfig.get_config() @@ -1934,51 +1929,7 @@ def completion( """ Deprecated. We now do together ai calls via the openai client - https://docs.together.ai/docs/openai-api-compatibility """ - custom_llm_provider = "together_ai" - together_ai_key = ( - api_key - or litellm.togetherai_api_key - or get_secret("TOGETHER_AI_TOKEN") - or get_secret("TOGETHERAI_API_KEY") - or litellm.api_key - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret("TOGETHERAI_API_BASE") - or "https://api.together.xyz/inference" - ) - - custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict - - model_response = together_ai.completion( - model=model, - messages=messages, - api_base=api_base, - model_response=model_response, - print_verbose=print_verbose, - optional_params=optional_params, - litellm_params=litellm_params, - logger_fn=logger_fn, - encoding=encoding, - api_key=together_ai_key, - logging_obj=logging, - custom_prompt_dict=custom_prompt_dict, - ) - if ( - "stream_tokens" in optional_params - and optional_params["stream_tokens"] == True - ): - # don't try to access stream object, - response = CustomStreamWrapper( - model_response, - model, - custom_llm_provider="together_ai", - logging_obj=logging, - ) - return response - response = model_response + pass elif custom_llm_provider == "palm": palm_api_key = api_key or get_secret("PALM_API_KEY") or litellm.api_key @@ -2090,6 +2041,32 @@ def completion( acompletion=acompletion, headers=headers, custom_prompt_dict=custom_prompt_dict, + timeout=timeout, + client=client, + ) + elif ( + model.startswith("meta/") + or model.startswith("mistral") + or model.startswith("codestral") + ): + model_response = vertex_partner_models_chat_completion.completion( + model=model, + messages=messages, + model_response=model_response, + print_verbose=print_verbose, + optional_params=new_params, + litellm_params=litellm_params, # type: ignore + logger_fn=logger_fn, + encoding=encoding, + vertex_location=vertex_ai_location, + vertex_project=vertex_ai_project, + vertex_credentials=vertex_credentials, + logging_obj=logging, + acompletion=acompletion, + headers=headers, + custom_prompt_dict=custom_prompt_dict, + timeout=timeout, + client=client, ) else: model_response = vertex_ai.completion( @@ -2253,7 +2230,7 @@ def completion( response = model_response elif custom_llm_provider == "sagemaker": # boto3 reads keys from .env - model_response = sagemaker.completion( + model_response = sagemaker_llm.completion( model=model, messages=messages, model_response=model_response, @@ -2267,26 +2244,13 @@ def completion( logging_obj=logging, acompletion=acompletion, ) - if ( - "stream" in optional_params and optional_params["stream"] == True - ): ## [BETA] - print_verbose(f"ENTERS SAGEMAKER CUSTOMSTREAMWRAPPER") - from .llms.sagemaker import TokenIterator - - tokenIterator = TokenIterator(model_response, acompletion=acompletion) - response = CustomStreamWrapper( - completion_stream=tokenIterator, - model=model, - custom_llm_provider="sagemaker", - logging_obj=logging, - ) + if optional_params.get("stream", False): ## LOGGING logging.post_call( input=messages, api_key=None, - original_response=response, + original_response=model_response, ) - return response ## RESPONSE OBJECT response = model_response @@ -2461,10 +2425,10 @@ def completion( ## LOGGING generator = ollama.get_ollama_response( - api_base, - model, - prompt, - optional_params, + api_base=api_base, + model=model, + prompt=prompt, + optional_params=optional_params, logging_obj=logging, acompletion=acompletion, model_response=model_response, @@ -2490,11 +2454,11 @@ def completion( ) ## LOGGING generator = ollama_chat.get_ollama_response( - api_base, - api_key, - model, - messages, - optional_params, + api_base=api_base, + api_key=api_key, + model=model, + messages=messages, + optional_params=optional_params, logging_obj=logging, acompletion=acompletion, model_response=model_response, @@ -2504,6 +2468,25 @@ def completion( return generator response = generator + + elif custom_llm_provider == "triton": + api_base = litellm.api_base or api_base + model_response = triton_chat_completions.completion( + api_base=api_base, + timeout=timeout, # type: ignore + model=model, + messages=messages, + model_response=model_response, + optional_params=optional_params, + logging_obj=logging, + stream=stream, + acompletion=acompletion, + ) + + ## RESPONSE OBJECT + response = model_response + return response + elif custom_llm_provider == "cloudflare": api_key = ( api_key @@ -2676,6 +2659,54 @@ def completion( model_response.created = int(time.time()) model_response.model = model response = model_response + elif ( + custom_llm_provider in litellm._custom_providers + ): # Assume custom LLM provider + # Get the Custom Handler + custom_handler: Optional[CustomLLM] = None + for item in litellm.custom_provider_map: + if item["provider"] == custom_llm_provider: + custom_handler = item["custom_handler"] + + if custom_handler is None: + raise ValueError( + f"Unable to map your input to a model. Check your input - {args}" + ) + + ## ROUTE LLM CALL ## + handler_fn = custom_chat_llm_router( + async_fn=acompletion, stream=stream, custom_llm=custom_handler + ) + + headers = headers or litellm.headers + + ## CALL FUNCTION + response = handler_fn( + model=model, + messages=messages, + headers=headers, + model_response=model_response, + print_verbose=print_verbose, + api_key=api_key, + api_base=api_base, + acompletion=acompletion, + logging_obj=logging, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + timeout=timeout, # type: ignore + custom_prompt_dict=custom_prompt_dict, + client=client, # pass AsyncOpenAI, OpenAI client + encoding=encoding, + ) + if stream is True: + return CustomStreamWrapper( + completion_stream=response, + model=model, + custom_llm_provider=custom_llm_provider, + logging_obj=logging, + ) + else: raise ValueError( f"Unable to map your input to a model. Check your input - {args}" @@ -2721,6 +2752,7 @@ def completion_with_retries(*args, **kwargs): async def acompletion_with_retries(*args, **kwargs): """ + [DEPRECATED]. Use 'acompletion' or router.acompletion instead! Executes a litellm.completion() with 3 retries """ try: @@ -3043,6 +3075,8 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse: or custom_llm_provider == "vertex_ai" or custom_llm_provider == "databricks" or custom_llm_provider == "watsonx" + or custom_llm_provider == "cohere" + or custom_llm_provider == "huggingface" ): # currently implemented aiohttp calls for just azure and openai, soon all. # Await normally init_response = await loop.run_in_executor(None, func_with_context) @@ -3194,6 +3228,7 @@ def embedding( "allowed_model_region", "model_config", "cooldown_time", + "tags", ] default_params = openai_params + litellm_params non_default_params = { @@ -3355,7 +3390,7 @@ def embedding( client=client, aembedding=aembedding, ) - elif custom_llm_provider == "cohere": + elif custom_llm_provider == "cohere" or custom_llm_provider == "cohere_chat": cohere_key = ( api_key or litellm.cohere_key @@ -3368,9 +3403,12 @@ def embedding( input=input, optional_params=optional_params, encoding=encoding, - api_key=cohere_key, + api_key=cohere_key, # type: ignore logging_obj=logging, model_response=EmbeddingResponse(), + aembedding=aembedding, + timeout=timeout, + client=client, ) elif custom_llm_provider == "huggingface": api_key = ( @@ -3378,15 +3416,18 @@ def embedding( or litellm.huggingface_key or get_secret("HUGGINGFACE_API_KEY") or litellm.api_key - ) + ) # type: ignore response = huggingface.embedding( model=model, input=input, - encoding=encoding, + encoding=encoding, # type: ignore api_key=api_key, api_base=api_base, logging_obj=logging, model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, ) elif custom_llm_provider == "bedrock": response = bedrock.embedding( @@ -3465,7 +3506,7 @@ def embedding( or api_base or get_secret("OLLAMA_API_BASE") or "http://localhost:11434" - ) + ) # type: ignore if isinstance(input, str): input = [input] if not all(isinstance(item, str) for item in input): @@ -3475,9 +3516,11 @@ def embedding( llm_provider="ollama", # type: ignore ) ollama_embeddings_fn = ( - ollama.ollama_aembeddings if aembedding else ollama.ollama_embeddings + ollama.ollama_aembeddings + if aembedding is True + else ollama.ollama_embeddings ) - response = ollama_embeddings_fn( + response = ollama_embeddings_fn( # type: ignore api_base=api_base, model=model, prompts=input, @@ -3487,7 +3530,7 @@ def embedding( model_response=EmbeddingResponse(), ) elif custom_llm_provider == "sagemaker": - response = sagemaker.embedding( + response = sagemaker_llm.embedding( model=model, input=input, encoding=encoding, @@ -3672,6 +3715,9 @@ async def atext_completion( text_choices["finish_reason"] = response["choices"][0]["finish_reason"] text_completion_response["choices"] = [text_choices] text_completion_response["usage"] = response.get("usage", None) + text_completion_response._hidden_params = HiddenParams( + **response._hidden_params + ) return text_completion_response except Exception as e: custom_llm_provider = custom_llm_provider or "openai" @@ -3816,7 +3862,7 @@ def text_completion( optional_params["custom_llm_provider"] = custom_llm_provider # get custom_llm_provider - _, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore + _model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore if custom_llm_provider == "huggingface": # if echo == True, for TGI llms we need to set top_n_tokens to 3 @@ -3899,10 +3945,12 @@ def text_completion( kwargs.pop("prompt", None) - if model is not None and model.startswith( - "openai/" + if ( + _model is not None and custom_llm_provider == "openai" ): # for openai compatible endpoints - e.g. vllm, call the native /v1/completions endpoint for text completion calls - model = model.replace("openai/", "text-completion-openai/") + if _model not in litellm.open_ai_chat_completion_models: + model = "text-completion-openai/" + _model + optional_params.pop("custom_llm_provider", None) kwargs["text_completion"] = True response = completion( @@ -3941,6 +3989,7 @@ def text_completion( text_choices["finish_reason"] = response["choices"][0]["finish_reason"] text_completion_response["choices"] = [text_choices] text_completion_response["usage"] = response.get("usage", None) + text_completion_response._hidden_params = HiddenParams(**response._hidden_params) return text_completion_response @@ -3948,7 +3997,9 @@ def text_completion( ###### Adapter Completion ################ -async def aadapter_completion(*, adapter_id: str, **kwargs) -> Optional[BaseModel]: +async def aadapter_completion( + *, adapter_id: str, **kwargs +) -> Optional[Union[BaseModel, AdapterCompletionStreamWrapper]]: """ Implemented to handle async calls for adapter_completion() """ @@ -3967,18 +4018,29 @@ async def aadapter_completion(*, adapter_id: str, **kwargs) -> Optional[BaseMode new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs) - response: ModelResponse = await acompletion(**new_kwargs) # type: ignore - - translated_response = translation_obj.translate_completion_output_params( - response=response - ) + response: Union[ModelResponse, CustomStreamWrapper] = await acompletion(**new_kwargs) # type: ignore + translated_response: Optional[ + Union[BaseModel, AdapterCompletionStreamWrapper] + ] = None + if isinstance(response, ModelResponse): + translated_response = translation_obj.translate_completion_output_params( + response=response + ) + if isinstance(response, CustomStreamWrapper): + translated_response = ( + translation_obj.translate_completion_output_params_streaming( + completion_stream=response + ) + ) return translated_response except Exception as e: raise e -def adapter_completion(*, adapter_id: str, **kwargs) -> Optional[BaseModel]: +def adapter_completion( + *, adapter_id: str, **kwargs +) -> Optional[Union[BaseModel, AdapterCompletionStreamWrapper]]: translation_obj: Optional[CustomLogger] = None for item in litellm.adapters: if item["id"] == adapter_id: @@ -3993,11 +4055,20 @@ def adapter_completion(*, adapter_id: str, **kwargs) -> Optional[BaseModel]: new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs) - response: ModelResponse = completion(**new_kwargs) # type: ignore - - translated_response = translation_obj.translate_completion_output_params( - response=response + response: Union[ModelResponse, CustomStreamWrapper] = completion(**new_kwargs) # type: ignore + translated_response: Optional[Union[BaseModel, AdapterCompletionStreamWrapper]] = ( + None ) + if isinstance(response, ModelResponse): + translated_response = translation_obj.translate_completion_output_params( + response=response + ) + elif isinstance(response, CustomStreamWrapper) or inspect.isgenerator(response): + translated_response = ( + translation_obj.translate_completion_output_params_streaming( + completion_stream=response + ) + ) return translated_response @@ -4421,6 +4492,8 @@ def transcription( proxy_server_request = kwargs.get("proxy_server_request", None) model_info = kwargs.get("model_info", None) metadata = kwargs.get("metadata", {}) + tags = kwargs.pop("tags", []) + drop_params = kwargs.get("drop_params", None) client: Optional[ Union[ @@ -4593,6 +4666,7 @@ def speech( ) -> HttpxBinaryResponseContent: model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore + tags = kwargs.pop("tags", []) optional_params = {} if response_format is not None: @@ -4718,21 +4792,26 @@ async def ahealth_check( For azure/openai -> completion.with_raw_response For rest -> litellm.acompletion() """ + passed_in_mode: Optional[str] = None try: + model: Optional[str] = model_params.get("model", None) if model is None: raise Exception("model not set") if model in litellm.model_cost and mode is None: - mode = litellm.model_cost[model]["mode"] + mode = litellm.model_cost[model].get("mode") model, custom_llm_provider, _, _ = get_llm_provider(model=model) if model in litellm.model_cost and mode is None: - mode = litellm.model_cost[model]["mode"] + mode = litellm.model_cost[model].get("mode") - mode = mode or "chat" # default to chat completion calls + mode = mode + passed_in_mode = mode + if mode is None: + mode = "chat" # default to chat completion calls if custom_llm_provider == "azure": api_key = ( @@ -4822,18 +4901,24 @@ async def ahealth_check( response = {} # args like remaining ratelimit etc. return response except Exception as e: - verbose_logger.error( + verbose_logger.exception( "litellm.ahealth_check(): Exception occured - {}".format(str(e)) ) - verbose_logger.debug(traceback.format_exc()) stack_trace = traceback.format_exc() if isinstance(stack_trace, str): stack_trace = stack_trace[:1000] - if model not in litellm.model_cost and mode is None: - raise Exception( - "Missing `mode`. Set the `mode` for the model - https://docs.litellm.ai/docs/proxy/health#embedding-models" - ) - error_to_return = str(e) + " stack trace: " + stack_trace + + if passed_in_mode is None: + return { + "error": "Missing `mode`. Set the `mode` for the model - https://docs.litellm.ai/docs/proxy/health#embedding-models" + } + + error_to_return = ( + str(e) + + "\nHave you set 'mode' - https://docs.litellm.ai/docs/proxy/health#embedding-models" + + "\nstack trace: " + + stack_trace + ) return {"error": error_to_return} @@ -4931,202 +5016,249 @@ def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List] def stream_chunk_builder( chunks: list, messages: Optional[list] = None, start_time=None, end_time=None -) -> Union[ModelResponse, TextCompletionResponse]: - model_response = litellm.ModelResponse() - ### SORT CHUNKS BASED ON CREATED ORDER ## - print_verbose("Goes into checking if chunk has hiddden created at param") - if chunks[0]._hidden_params.get("created_at", None): - print_verbose("Chunks have a created at hidden param") - # Sort chunks based on created_at in ascending order - chunks = sorted( - chunks, key=lambda x: x._hidden_params.get("created_at", float("inf")) - ) - print_verbose("Chunks sorted") - - # set hidden params from chunk to model_response - if model_response is not None and hasattr(model_response, "_hidden_params"): - model_response._hidden_params = chunks[0].get("_hidden_params", {}) - id = chunks[0]["id"] - object = chunks[0]["object"] - created = chunks[0]["created"] - model = chunks[0]["model"] - system_fingerprint = chunks[0].get("system_fingerprint", None) - - if isinstance( - chunks[0]["choices"][0], litellm.utils.TextChoices - ): # route to the text completion logic - return stream_chunk_builder_text_completion(chunks=chunks, messages=messages) - role = chunks[0]["choices"][0]["delta"]["role"] - finish_reason = chunks[-1]["choices"][0]["finish_reason"] - - # Initialize the response dictionary - response = { - "id": id, - "object": object, - "created": created, - "model": model, - "system_fingerprint": system_fingerprint, - "choices": [ - { - "index": 0, - "message": {"role": role, "content": ""}, - "finish_reason": finish_reason, - } - ], - "usage": { - "prompt_tokens": 0, # Modify as needed - "completion_tokens": 0, # Modify as needed - "total_tokens": 0, # Modify as needed - }, - } - - # Extract the "content" strings from the nested dictionaries within "choices" - content_list = [] - combined_content = "" - combined_arguments = "" - - if ( - "tool_calls" in chunks[0]["choices"][0]["delta"] - and chunks[0]["choices"][0]["delta"]["tool_calls"] is not None - ): - argument_list = [] - delta = chunks[0]["choices"][0]["delta"] - message = response["choices"][0]["message"] - message["tool_calls"] = [] - id = None - name = None - type = None - tool_calls_list = [] - prev_index = 0 - prev_id = None - curr_id = None - curr_index = 0 - for chunk in chunks: - choices = chunk["choices"] - for choice in choices: - delta = choice.get("delta", {}) - tool_calls = delta.get("tool_calls", "") - # Check if a tool call is present - if tool_calls and tool_calls[0].function is not None: - if tool_calls[0].id: - id = tool_calls[0].id - curr_id = id - if prev_id is None: - prev_id = curr_id - if tool_calls[0].index: - curr_index = tool_calls[0].index - if tool_calls[0].function.arguments: - # Now, tool_calls is expected to be a dictionary - arguments = tool_calls[0].function.arguments - argument_list.append(arguments) - if tool_calls[0].function.name: - name = tool_calls[0].function.name - if tool_calls[0].type: - type = tool_calls[0].type - if curr_index != prev_index: # new tool call - combined_arguments = "".join(argument_list) - tool_calls_list.append( - { - "id": prev_id, - "index": prev_index, - "function": {"arguments": combined_arguments, "name": name}, - "type": type, - } - ) - argument_list = [] # reset - prev_index = curr_index - prev_id = curr_id - - combined_arguments = "".join(argument_list) - tool_calls_list.append( - { - "id": id, - "function": {"arguments": combined_arguments, "name": name}, - "type": type, - } - ) - response["choices"][0]["message"]["content"] = None - response["choices"][0]["message"]["tool_calls"] = tool_calls_list - elif ( - "function_call" in chunks[0]["choices"][0]["delta"] - and chunks[0]["choices"][0]["delta"]["function_call"] is not None - ): - argument_list = [] - delta = chunks[0]["choices"][0]["delta"] - function_call = delta.get("function_call", "") - function_call_name = function_call.name - - message = response["choices"][0]["message"] - message["function_call"] = {} - message["function_call"]["name"] = function_call_name - - for chunk in chunks: - choices = chunk["choices"] - for choice in choices: - delta = choice.get("delta", {}) - function_call = delta.get("function_call", "") - - # Check if a function call is present - if function_call: - # Now, function_call is expected to be a dictionary - arguments = function_call.arguments - argument_list.append(arguments) - - combined_arguments = "".join(argument_list) - response["choices"][0]["message"]["content"] = None - response["choices"][0]["message"]["function_call"][ - "arguments" - ] = combined_arguments - else: - for chunk in chunks: - choices = chunk["choices"] - for choice in choices: - delta = choice.get("delta", {}) - content = delta.get("content", "") - if content == None: - continue # openai v1.0.0 sets content = None for chunks - content_list.append(content) - - # Combine the "content" strings into a single string || combine the 'function' strings into a single string - combined_content = "".join(content_list) - - # Update the "content" field within the response dictionary - response["choices"][0]["message"]["content"] = combined_content - - if len(combined_content) > 0: - completion_output = combined_content - elif len(combined_arguments) > 0: - completion_output = combined_arguments - else: - completion_output = "" - # # Update usage information if needed - prompt_tokens = 0 - completion_tokens = 0 - for chunk in chunks: - if "usage" in chunk: - if "prompt_tokens" in chunk["usage"]: - prompt_tokens = chunk["usage"].get("prompt_tokens", 0) or 0 - if "completion_tokens" in chunk["usage"]: - completion_tokens = chunk["usage"].get("completion_tokens", 0) or 0 +) -> Optional[Union[ModelResponse, TextCompletionResponse]]: try: - response["usage"]["prompt_tokens"] = prompt_tokens or token_counter( - model=model, messages=messages - ) - except: # don't allow this failing to block a complete streaming response from being returned - print_verbose(f"token_counter failed, assuming prompt tokens is 0") - response["usage"]["prompt_tokens"] = 0 - response["usage"]["completion_tokens"] = completion_tokens or token_counter( - model=model, - text=completion_output, - count_response_tokens=True, # count_response_tokens is a Flag to tell token counter this is a response, No need to add extra tokens we do for input messages - ) - response["usage"]["total_tokens"] = ( - response["usage"]["prompt_tokens"] + response["usage"]["completion_tokens"] - ) + model_response = litellm.ModelResponse() + ### BASE-CASE ### + if len(chunks) == 0: + return None + ### SORT CHUNKS BASED ON CREATED ORDER ## + print_verbose("Goes into checking if chunk has hiddden created at param") + if chunks[0]._hidden_params.get("created_at", None): + print_verbose("Chunks have a created at hidden param") + # Sort chunks based on created_at in ascending order + chunks = sorted( + chunks, key=lambda x: x._hidden_params.get("created_at", float("inf")) + ) + print_verbose("Chunks sorted") - return convert_to_model_response_object( - response_object=response, - model_response_object=model_response, - start_time=start_time, - end_time=end_time, - ) + # set hidden params from chunk to model_response + if model_response is not None and hasattr(model_response, "_hidden_params"): + model_response._hidden_params = chunks[0].get("_hidden_params", {}) + id = chunks[0]["id"] + object = chunks[0]["object"] + created = chunks[0]["created"] + model = chunks[0]["model"] + system_fingerprint = chunks[0].get("system_fingerprint", None) + + if isinstance( + chunks[0]["choices"][0], litellm.utils.TextChoices + ): # route to the text completion logic + return stream_chunk_builder_text_completion( + chunks=chunks, messages=messages + ) + role = chunks[0]["choices"][0]["delta"]["role"] + finish_reason = chunks[-1]["choices"][0]["finish_reason"] + + # Initialize the response dictionary + response = { + "id": id, + "object": object, + "created": created, + "model": model, + "system_fingerprint": system_fingerprint, + "choices": [ + { + "index": 0, + "message": {"role": role, "content": ""}, + "finish_reason": finish_reason, + } + ], + "usage": { + "prompt_tokens": 0, # Modify as needed + "completion_tokens": 0, # Modify as needed + "total_tokens": 0, # Modify as needed + }, + } + + # Extract the "content" strings from the nested dictionaries within "choices" + content_list = [] + combined_content = "" + combined_arguments = "" + + tool_call_chunks = [ + chunk + for chunk in chunks + if "tool_calls" in chunk["choices"][0]["delta"] + and chunk["choices"][0]["delta"]["tool_calls"] is not None + ] + + if len(tool_call_chunks) > 0: + argument_list = [] + delta = tool_call_chunks[0]["choices"][0]["delta"] + message = response["choices"][0]["message"] + message["tool_calls"] = [] + id = None + name = None + type = None + tool_calls_list = [] + prev_index = None + prev_id = None + curr_id = None + curr_index = 0 + for chunk in tool_call_chunks: + choices = chunk["choices"] + for choice in choices: + delta = choice.get("delta", {}) + tool_calls = delta.get("tool_calls", "") + # Check if a tool call is present + if tool_calls and tool_calls[0].function is not None: + if tool_calls[0].id: + id = tool_calls[0].id + curr_id = id + if prev_id is None: + prev_id = curr_id + if tool_calls[0].index: + curr_index = tool_calls[0].index + if tool_calls[0].function.arguments: + # Now, tool_calls is expected to be a dictionary + arguments = tool_calls[0].function.arguments + argument_list.append(arguments) + if tool_calls[0].function.name: + name = tool_calls[0].function.name + if tool_calls[0].type: + type = tool_calls[0].type + if prev_index is None: + prev_index = curr_index + if curr_index != prev_index: # new tool call + combined_arguments = "".join(argument_list) + tool_calls_list.append( + { + "id": prev_id, + "index": prev_index, + "function": {"arguments": combined_arguments, "name": name}, + "type": type, + } + ) + argument_list = [] # reset + prev_index = curr_index + prev_id = curr_id + + combined_arguments = ( + "".join(argument_list) or "{}" + ) # base case, return empty dict + tool_calls_list.append( + { + "id": id, + "index": curr_index, + "function": {"arguments": combined_arguments, "name": name}, + "type": type, + } + ) + response["choices"][0]["message"]["content"] = None + response["choices"][0]["message"]["tool_calls"] = tool_calls_list + + function_call_chunks = [ + chunk + for chunk in chunks + if "function_call" in chunk["choices"][0]["delta"] + and chunk["choices"][0]["delta"]["function_call"] is not None + ] + + if len(function_call_chunks) > 0: + argument_list = [] + delta = function_call_chunks[0]["choices"][0]["delta"] + function_call = delta.get("function_call", "") + function_call_name = function_call.name + + message = response["choices"][0]["message"] + message["function_call"] = {} + message["function_call"]["name"] = function_call_name + + for chunk in function_call_chunks: + choices = chunk["choices"] + for choice in choices: + delta = choice.get("delta", {}) + function_call = delta.get("function_call", "") + + # Check if a function call is present + if function_call: + # Now, function_call is expected to be a dictionary + arguments = function_call.arguments + argument_list.append(arguments) + + combined_arguments = "".join(argument_list) + response["choices"][0]["message"]["content"] = None + response["choices"][0]["message"]["function_call"][ + "arguments" + ] = combined_arguments + + content_chunks = [ + chunk + for chunk in chunks + if "content" in chunk["choices"][0]["delta"] + and chunk["choices"][0]["delta"]["content"] is not None + ] + + if len(content_chunks) > 0: + for chunk in chunks: + choices = chunk["choices"] + for choice in choices: + delta = choice.get("delta", {}) + content = delta.get("content", "") + if content == None: + continue # openai v1.0.0 sets content = None for chunks + content_list.append(content) + + # Combine the "content" strings into a single string || combine the 'function' strings into a single string + combined_content = "".join(content_list) + + # Update the "content" field within the response dictionary + response["choices"][0]["message"]["content"] = combined_content + + completion_output = "" + if len(combined_content) > 0: + completion_output += combined_content + if len(combined_arguments) > 0: + completion_output += combined_arguments + + # # Update usage information if needed + prompt_tokens = 0 + completion_tokens = 0 + for chunk in chunks: + usage_chunk: Optional[Usage] = None + if "usage" in chunk: + usage_chunk = chunk.usage + elif hasattr(chunk, "_hidden_params") and "usage" in chunk._hidden_params: + usage_chunk = chunk._hidden_params["usage"] + if usage_chunk is not None: + if "prompt_tokens" in usage_chunk: + prompt_tokens = usage_chunk.get("prompt_tokens", 0) or 0 + if "completion_tokens" in usage_chunk: + completion_tokens = usage_chunk.get("completion_tokens", 0) or 0 + try: + response["usage"]["prompt_tokens"] = prompt_tokens or token_counter( + model=model, messages=messages + ) + except ( + Exception + ): # don't allow this failing to block a complete streaming response from being returned + print_verbose("token_counter failed, assuming prompt tokens is 0") + response["usage"]["prompt_tokens"] = 0 + response["usage"]["completion_tokens"] = completion_tokens or token_counter( + model=model, + text=completion_output, + count_response_tokens=True, # count_response_tokens is a Flag to tell token counter this is a response, No need to add extra tokens we do for input messages + ) + response["usage"]["total_tokens"] = ( + response["usage"]["prompt_tokens"] + response["usage"]["completion_tokens"] + ) + + return convert_to_model_response_object( + response_object=response, + model_response_object=model_response, + start_time=start_time, + end_time=end_time, + ) # type: ignore + except Exception as e: + verbose_logger.exception( + "litellm.main.py::stream_chunk_builder() - Exception occurred - {}".format( + str(e) + ) + ) + raise litellm.APIError( + status_code=500, + message="Error building chunks for logging/streaming usage calculation", + llm_provider="", + model="", + ) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 4f9242af4..2dc846df9 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1,4 +1,16 @@ { + "sample_spec": { + "max_tokens": "set to max_output_tokens if provider specifies it. IF not set to max_tokens provider specifies", + "max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens", + "max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens", + "input_cost_per_token": 0.0000, + "output_cost_per_token": 0.000, + "litellm_provider": "one of https://docs.litellm.ai/docs/providers", + "mode": "one of chat, embedding, completion, image_generation, audio_transcription", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true + }, "gpt-4": { "max_tokens": 4096, "max_input_tokens": 8192, @@ -21,6 +33,42 @@ "supports_parallel_function_calling": true, "supports_vision": true }, + "gpt-4o-mini": { + "max_tokens": 16384, + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "input_cost_per_token": 0.00000015, + "output_cost_per_token": 0.00000060, + "litellm_provider": "openai", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true + }, + "gpt-4o-mini-2024-07-18": { + "max_tokens": 16384, + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "input_cost_per_token": 0.00000015, + "output_cost_per_token": 0.00000060, + "litellm_provider": "openai", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true + }, + "chatgpt-4o-latest": { + "max_tokens": 4096, + "max_input_tokens": 128000, + "max_output_tokens": 4096, + "input_cost_per_token": 0.000005, + "output_cost_per_token": 0.000015, + "litellm_provider": "openai", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true + }, "gpt-4o-2024-05-13": { "max_tokens": 4096, "max_input_tokens": 128000, @@ -33,6 +81,18 @@ "supports_parallel_function_calling": true, "supports_vision": true }, + "gpt-4o-2024-08-06": { + "max_tokens": 16384, + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "input_cost_per_token": 0.0000025, + "output_cost_per_token": 0.000010, + "litellm_provider": "openai", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true + }, "gpt-4-turbo-preview": { "max_tokens": 4096, "max_input_tokens": 128000, @@ -245,18 +305,17 @@ "supports_function_calling": true, "source": "OpenAI needs to add pricing for this ft model, will be updated when added by OpenAI. Defaulting to base model pricing" }, - "ft:gpt-4o-2024-05-13": { - "max_tokens": 4096, + "ft:gpt-4o-mini-2024-07-18": { + "max_tokens": 16384, "max_input_tokens": 128000, - "max_output_tokens": 4096, - "input_cost_per_token": 0.000005, - "output_cost_per_token": 0.000015, + "max_output_tokens": 16384, + "input_cost_per_token": 0.0000003, + "output_cost_per_token": 0.0000012, "litellm_provider": "openai", "mode": "chat", "supports_function_calling": true, "supports_parallel_function_calling": true, - "supports_vision": true, - "source": "OpenAI needs to add pricing for this ft model, will be updated when added by OpenAI. Defaulting to base model pricing" + "supports_vision": true }, "ft:davinci-002": { "max_tokens": 16384, @@ -436,6 +495,30 @@ "supports_parallel_function_calling": true, "supports_vision": true }, + "azure/global-standard/gpt-4o-mini": { + "max_tokens": 16384, + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "input_cost_per_token": 0.00000015, + "output_cost_per_token": 0.00000060, + "litellm_provider": "azure", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true + }, + "azure/gpt-4o-mini": { + "max_tokens": 16384, + "max_input_tokens": 128000, + "max_output_tokens": 16384, + "input_cost_per_token": 0.000000165, + "output_cost_per_token": 0.00000066, + "litellm_provider": "azure", + "mode": "chat", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true + }, "azure/gpt-4-turbo-2024-04-09": { "max_tokens": 4096, "max_input_tokens": 128000, @@ -698,6 +781,74 @@ "litellm_provider": "azure", "mode": "image_generation" }, + "azure_ai/jamba-instruct": { + "max_tokens": 4096, + "max_input_tokens": 70000, + "max_output_tokens": 4096, + "input_cost_per_token": 0.0000005, + "output_cost_per_token": 0.0000007, + "litellm_provider": "azure_ai", + "mode": "chat" + }, + "azure_ai/mistral-large": { + "max_tokens": 8191, + "max_input_tokens": 32000, + "max_output_tokens": 8191, + "input_cost_per_token": 0.000004, + "output_cost_per_token": 0.000012, + "litellm_provider": "azure_ai", + "mode": "chat", + "supports_function_calling": true + }, + "azure_ai/mistral-small": { + "max_tokens": 8191, + "max_input_tokens": 32000, + "max_output_tokens": 8191, + "input_cost_per_token": 0.000001, + "output_cost_per_token": 0.000003, + "litellm_provider": "azure_ai", + "supports_function_calling": true, + "mode": "chat" + }, + "azure_ai/Meta-Llama-3-70B-Instruct": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0000011, + "output_cost_per_token": 0.00000037, + "litellm_provider": "azure_ai", + "mode": "chat" + }, + "azure_ai/Meta-Llama-31-8B-Instruct": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.0000003, + "output_cost_per_token": 0.00000061, + "litellm_provider": "azure_ai", + "mode": "chat", + "source":"https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-8b-instruct-offer?tab=PlansAndPrice" + }, + "azure_ai/Meta-Llama-31-70B-Instruct": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.00000268, + "output_cost_per_token": 0.00000354, + "litellm_provider": "azure_ai", + "mode": "chat", + "source":"https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-70b-instruct-offer?tab=PlansAndPrice" + }, + "azure_ai/Meta-Llama-31-405B-Instruct": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.00000533, + "output_cost_per_token": 0.000016, + "litellm_provider": "azure_ai", + "mode": "chat", + "source":"https://azuremarketplace.microsoft.com/en-us/marketplace/apps/metagenai.meta-llama-3-1-405b-instruct-offer?tab=PlansAndPrice" + }, "babbage-002": { "max_tokens": 16384, "max_input_tokens": 16384, @@ -751,7 +902,8 @@ "input_cost_per_token": 0.00000025, "output_cost_per_token": 0.00000025, "litellm_provider": "mistral", - "mode": "chat" + "mode": "chat", + "supports_assistant_prefill": true }, "mistral/mistral-small": { "max_tokens": 8191, @@ -761,7 +913,8 @@ "output_cost_per_token": 0.000003, "litellm_provider": "mistral", "supports_function_calling": true, - "mode": "chat" + "mode": "chat", + "supports_assistant_prefill": true }, "mistral/mistral-small-latest": { "max_tokens": 8191, @@ -771,7 +924,8 @@ "output_cost_per_token": 0.000003, "litellm_provider": "mistral", "supports_function_calling": true, - "mode": "chat" + "mode": "chat", + "supports_assistant_prefill": true }, "mistral/mistral-medium": { "max_tokens": 8191, @@ -780,7 +934,8 @@ "input_cost_per_token": 0.0000027, "output_cost_per_token": 0.0000081, "litellm_provider": "mistral", - "mode": "chat" + "mode": "chat", + "supports_assistant_prefill": true }, "mistral/mistral-medium-latest": { "max_tokens": 8191, @@ -789,7 +944,8 @@ "input_cost_per_token": 0.0000027, "output_cost_per_token": 0.0000081, "litellm_provider": "mistral", - "mode": "chat" + "mode": "chat", + "supports_assistant_prefill": true }, "mistral/mistral-medium-2312": { "max_tokens": 8191, @@ -798,17 +954,19 @@ "input_cost_per_token": 0.0000027, "output_cost_per_token": 0.0000081, "litellm_provider": "mistral", - "mode": "chat" + "mode": "chat", + "supports_assistant_prefill": true }, "mistral/mistral-large-latest": { - "max_tokens": 8191, - "max_input_tokens": 32000, - "max_output_tokens": 8191, - "input_cost_per_token": 0.000004, - "output_cost_per_token": 0.000012, + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000009, "litellm_provider": "mistral", "mode": "chat", - "supports_function_calling": true + "supports_function_calling": true, + "supports_assistant_prefill": true }, "mistral/mistral-large-2402": { "max_tokens": 8191, @@ -818,7 +976,19 @@ "output_cost_per_token": 0.000012, "litellm_provider": "mistral", "mode": "chat", - "supports_function_calling": true + "supports_function_calling": true, + "supports_assistant_prefill": true + }, + "mistral/mistral-large-2407": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000009, + "litellm_provider": "mistral", + "mode": "chat", + "supports_function_calling": true, + "supports_assistant_prefill": true }, "mistral/open-mistral-7b": { "max_tokens": 8191, @@ -827,7 +997,8 @@ "input_cost_per_token": 0.00000025, "output_cost_per_token": 0.00000025, "litellm_provider": "mistral", - "mode": "chat" + "mode": "chat", + "supports_assistant_prefill": true }, "mistral/open-mixtral-8x7b": { "max_tokens": 8191, @@ -837,7 +1008,8 @@ "output_cost_per_token": 0.0000007, "litellm_provider": "mistral", "mode": "chat", - "supports_function_calling": true + "supports_function_calling": true, + "supports_assistant_prefill": true }, "mistral/open-mixtral-8x22b": { "max_tokens": 8191, @@ -847,7 +1019,8 @@ "output_cost_per_token": 0.000006, "litellm_provider": "mistral", "mode": "chat", - "supports_function_calling": true + "supports_function_calling": true, + "supports_assistant_prefill": true }, "mistral/codestral-latest": { "max_tokens": 8191, @@ -856,7 +1029,8 @@ "input_cost_per_token": 0.000001, "output_cost_per_token": 0.000003, "litellm_provider": "mistral", - "mode": "chat" + "mode": "chat", + "supports_assistant_prefill": true }, "mistral/codestral-2405": { "max_tokens": 8191, @@ -865,7 +1039,52 @@ "input_cost_per_token": 0.000001, "output_cost_per_token": 0.000003, "litellm_provider": "mistral", - "mode": "chat" + "mode": "chat", + "supports_assistant_prefill": true + }, + "mistral/open-mistral-nemo": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.0000003, + "output_cost_per_token": 0.0000003, + "litellm_provider": "mistral", + "mode": "chat", + "source": "https://mistral.ai/technology/", + "supports_assistant_prefill": true + }, + "mistral/open-mistral-nemo-2407": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.0000003, + "output_cost_per_token": 0.0000003, + "litellm_provider": "mistral", + "mode": "chat", + "source": "https://mistral.ai/technology/", + "supports_assistant_prefill": true + }, + "mistral/open-codestral-mamba": { + "max_tokens": 256000, + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "input_cost_per_token": 0.00000025, + "output_cost_per_token": 0.00000025, + "litellm_provider": "mistral", + "mode": "chat", + "source": "https://mistral.ai/technology/", + "supports_assistant_prefill": true + }, + "mistral/codestral-mamba-latest": { + "max_tokens": 256000, + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "input_cost_per_token": 0.00000025, + "output_cost_per_token": 0.00000025, + "litellm_provider": "mistral", + "mode": "chat", + "source": "https://mistral.ai/technology/", + "supports_assistant_prefill": true }, "mistral/mistral-embed": { "max_tokens": 8192, @@ -879,9 +1098,13 @@ "max_input_tokens": 32000, "max_output_tokens": 4096, "input_cost_per_token": 0.00000014, + "input_cost_per_token_cache_hit": 0.000000014, "output_cost_per_token": 0.00000028, "litellm_provider": "deepseek", - "mode": "chat" + "mode": "chat", + "supports_function_calling": true, + "supports_assistant_prefill": true, + "supports_tool_choice": true }, "codestral/codestral-latest": { "max_tokens": 8191, @@ -891,7 +1114,8 @@ "output_cost_per_token": 0.000000, "litellm_provider": "codestral", "mode": "chat", - "source": "https://docs.mistral.ai/capabilities/code_generation/" + "source": "https://docs.mistral.ai/capabilities/code_generation/", + "supports_assistant_prefill": true }, "codestral/codestral-2405": { "max_tokens": 8191, @@ -901,7 +1125,8 @@ "output_cost_per_token": 0.000000, "litellm_provider": "codestral", "mode": "chat", - "source": "https://docs.mistral.ai/capabilities/code_generation/" + "source": "https://docs.mistral.ai/capabilities/code_generation/", + "supports_assistant_prefill": true }, "text-completion-codestral/codestral-latest": { "max_tokens": 8191, @@ -928,9 +1153,13 @@ "max_input_tokens": 128000, "max_output_tokens": 4096, "input_cost_per_token": 0.00000014, + "input_cost_per_token_cache_hit": 0.000000014, "output_cost_per_token": 0.00000028, "litellm_provider": "deepseek", - "mode": "chat" + "mode": "chat", + "supports_function_calling": true, + "supports_assistant_prefill": true, + "supports_tool_choice": true }, "groq/llama2-70b-4096": { "max_tokens": 4096, @@ -962,6 +1191,36 @@ "mode": "chat", "supports_function_calling": true }, + "groq/llama-3.1-8b-instant": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.00000059, + "output_cost_per_token": 0.00000079, + "litellm_provider": "groq", + "mode": "chat", + "supports_function_calling": true + }, + "groq/llama-3.1-70b-versatile": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.00000059, + "output_cost_per_token": 0.00000079, + "litellm_provider": "groq", + "mode": "chat", + "supports_function_calling": true + }, + "groq/llama-3.1-405b-reasoning": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.00000059, + "output_cost_per_token": 0.00000079, + "litellm_provider": "groq", + "mode": "chat", + "supports_function_calling": true + }, "groq/mixtral-8x7b-32768": { "max_tokens": 32768, "max_input_tokens": 32768, @@ -982,6 +1241,26 @@ "mode": "chat", "supports_function_calling": true }, + "groq/llama3-groq-70b-8192-tool-use-preview": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.00000089, + "output_cost_per_token": 0.00000089, + "litellm_provider": "groq", + "mode": "chat", + "supports_function_calling": true + }, + "groq/llama3-groq-8b-8192-tool-use-preview": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.00000019, + "output_cost_per_token": 0.00000019, + "litellm_provider": "groq", + "mode": "chat", + "supports_function_calling": true + }, "friendliai/mixtral-8x7b-instruct-v0-1": { "max_tokens": 32768, "max_input_tokens": 32768, @@ -1049,7 +1328,8 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 264 + "tool_use_system_prompt_tokens": 264, + "supports_assistant_prefill": true }, "claude-3-opus-20240229": { "max_tokens": 4096, @@ -1061,7 +1341,8 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 395 + "tool_use_system_prompt_tokens": 395, + "supports_assistant_prefill": true }, "claude-3-sonnet-20240229": { "max_tokens": 4096, @@ -1073,7 +1354,8 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 159 + "tool_use_system_prompt_tokens": 159, + "supports_assistant_prefill": true }, "claude-3-5-sonnet-20240620": { "max_tokens": 4096, @@ -1085,7 +1367,8 @@ "mode": "chat", "supports_function_calling": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 159 + "tool_use_system_prompt_tokens": 159, + "supports_assistant_prefill": true }, "text-bison": { "max_tokens": 2048, @@ -1762,6 +2045,26 @@ "supports_vision": true, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" }, + "medlm-medium": { + "max_tokens": 8192, + "max_input_tokens": 32768, + "max_output_tokens": 8192, + "input_cost_per_character": 0.0000005, + "output_cost_per_character": 0.000001, + "litellm_provider": "vertex_ai-language-models", + "mode": "chat", + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "medlm-large": { + "max_tokens": 1024, + "max_input_tokens": 8192, + "max_output_tokens": 1024, + "input_cost_per_character": 0.000005, + "output_cost_per_character": 0.000015, + "litellm_provider": "vertex_ai-language-models", + "mode": "chat", + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, "vertex_ai/claude-3-sonnet@20240229": { "max_tokens": 4096, "max_input_tokens": 200000, @@ -1771,7 +2074,8 @@ "litellm_provider": "vertex_ai-anthropic_models", "mode": "chat", "supports_function_calling": true, - "supports_vision": true + "supports_vision": true, + "supports_assistant_prefill": true }, "vertex_ai/claude-3-5-sonnet@20240620": { "max_tokens": 4096, @@ -1782,7 +2086,8 @@ "litellm_provider": "vertex_ai-anthropic_models", "mode": "chat", "supports_function_calling": true, - "supports_vision": true + "supports_vision": true, + "supports_assistant_prefill": true }, "vertex_ai/claude-3-haiku@20240307": { "max_tokens": 4096, @@ -1793,7 +2098,8 @@ "litellm_provider": "vertex_ai-anthropic_models", "mode": "chat", "supports_function_calling": true, - "supports_vision": true + "supports_vision": true, + "supports_assistant_prefill": true }, "vertex_ai/claude-3-opus@20240229": { "max_tokens": 4096, @@ -1804,7 +2110,78 @@ "litellm_provider": "vertex_ai-anthropic_models", "mode": "chat", "supports_function_calling": true, - "supports_vision": true + "supports_vision": true, + "supports_assistant_prefill": true + }, + "vertex_ai/meta/llama3-405b-instruct-maas": { + "max_tokens": 32000, + "max_input_tokens": 32000, + "max_output_tokens": 32000, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "vertex_ai-llama_models", + "mode": "chat", + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#partner-models" + }, + "vertex_ai/mistral-large@latest": { + "max_tokens": 8191, + "max_input_tokens": 128000, + "max_output_tokens": 8191, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000009, + "litellm_provider": "vertex_ai-mistral_models", + "mode": "chat", + "supports_function_calling": true + }, + "vertex_ai/mistral-large@2407": { + "max_tokens": 8191, + "max_input_tokens": 128000, + "max_output_tokens": 8191, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000009, + "litellm_provider": "vertex_ai-mistral_models", + "mode": "chat", + "supports_function_calling": true + }, + "vertex_ai/mistral-nemo@latest": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000003, + "litellm_provider": "vertex_ai-mistral_models", + "mode": "chat", + "supports_function_calling": true + }, + "vertex_ai/mistral-nemo@2407": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000003, + "litellm_provider": "vertex_ai-mistral_models", + "mode": "chat", + "supports_function_calling": true + }, + "vertex_ai/codestral@latest": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.000001, + "output_cost_per_token": 0.000003, + "litellm_provider": "vertex_ai-mistral_models", + "mode": "chat", + "supports_function_calling": true + }, + "vertex_ai/codestral@2405": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "input_cost_per_token": 0.000001, + "output_cost_per_token": 0.000003, + "litellm_provider": "vertex_ai-mistral_models", + "mode": "chat", + "supports_function_calling": true }, "vertex_ai/imagegeneration@006": { "cost_per_image": 0.020, @@ -2035,6 +2412,23 @@ "supports_response_schema": true, "source": "https://ai.google.dev/pricing" }, + "gemini/gemini-1.5-pro-exp-0801": { + "max_tokens": 8192, + "max_input_tokens": 2097152, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0000035, + "input_cost_per_token_above_128k_tokens": 0.000007, + "output_cost_per_token": 0.0000105, + "output_cost_per_token_above_128k_tokens": 0.000021, + "litellm_provider": "gemini", + "mode": "chat", + "supports_system_messages": true, + "supports_function_calling": true, + "supports_vision": true, + "supports_tool_choice": true, + "supports_response_schema": true, + "source": "https://ai.google.dev/pricing" + }, "gemini/gemini-1.5-pro-latest": { "max_tokens": 8192, "max_input_tokens": 1048576, @@ -2066,6 +2460,28 @@ "supports_vision": true, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" }, + "gemini/gemini-gemma-2-27b-it": { + "max_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.00000035, + "output_cost_per_token": 0.00000105, + "litellm_provider": "gemini", + "mode": "chat", + "supports_function_calling": true, + "supports_vision": true, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, + "gemini/gemini-gemma-2-9b-it": { + "max_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.00000035, + "output_cost_per_token": 0.00000105, + "litellm_provider": "gemini", + "mode": "chat", + "supports_function_calling": true, + "supports_vision": true, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models" + }, "command-r": { "max_tokens": 4096, "max_input_tokens": 128000, @@ -2131,14 +2547,53 @@ "litellm_provider": "cohere", "mode": "completion" }, - "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 0.0000, - "output_cost_per_token": 0.0000, - "litellm_provider": "replicate", - "mode": "chat" + "embed-english-v3.0": { + "max_tokens": 512, + "max_input_tokens": 512, + "input_cost_per_token": 0.00000010, + "output_cost_per_token": 0.00000, + "litellm_provider": "cohere", + "mode": "embedding" + }, + "embed-english-light-v3.0": { + "max_tokens": 512, + "max_input_tokens": 512, + "input_cost_per_token": 0.00000010, + "output_cost_per_token": 0.00000, + "litellm_provider": "cohere", + "mode": "embedding" + }, + "embed-multilingual-v3.0": { + "max_tokens": 512, + "max_input_tokens": 512, + "input_cost_per_token": 0.00000010, + "output_cost_per_token": 0.00000, + "litellm_provider": "cohere", + "mode": "embedding" + }, + "embed-english-v2.0": { + "max_tokens": 512, + "max_input_tokens": 512, + "input_cost_per_token": 0.00000010, + "output_cost_per_token": 0.00000, + "litellm_provider": "cohere", + "mode": "embedding" + }, + "embed-english-light-v2.0": { + "max_tokens": 512, + "max_input_tokens": 512, + "input_cost_per_token": 0.00000010, + "output_cost_per_token": 0.00000, + "litellm_provider": "cohere", + "mode": "embedding" + }, + "embed-multilingual-v2.0": { + "max_tokens": 256, + "max_input_tokens": 256, + "input_cost_per_token": 0.00000010, + "output_cost_per_token": 0.00000, + "litellm_provider": "cohere", + "mode": "embedding" }, "replicate/meta/llama-2-13b": { "max_tokens": 4096, @@ -2688,6 +3143,16 @@ "litellm_provider": "bedrock", "mode": "chat" }, + "ai21.jamba-instruct-v1:0": { + "max_tokens": 4096, + "max_input_tokens": 70000, + "max_output_tokens": 4096, + "input_cost_per_token": 0.0000005, + "output_cost_per_token": 0.0000007, + "litellm_provider": "bedrock", + "mode": "chat", + "supports_system_messages": true + }, "amazon.titan-text-lite-v1": { "max_tokens": 4000, "max_input_tokens": 42000, @@ -2749,7 +3214,18 @@ "input_cost_per_token": 0.000008, "output_cost_per_token": 0.000024, "litellm_provider": "bedrock", - "mode": "chat" + "mode": "chat", + "supports_function_calling": true + }, + "mistral.mistral-large-2407-v1:0": { + "max_tokens": 8191, + "max_input_tokens": 128000, + "max_output_tokens": 8191, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000009, + "litellm_provider": "bedrock", + "mode": "chat", + "supports_function_calling": true }, "bedrock/us-west-2/mistral.mixtral-8x7b-instruct-v0:1": { "max_tokens": 8191, @@ -2821,7 +3297,8 @@ "input_cost_per_token": 0.000008, "output_cost_per_token": 0.000024, "litellm_provider": "bedrock", - "mode": "chat" + "mode": "chat", + "supports_function_calling": true }, "bedrock/eu-west-3/mistral.mistral-large-2402-v1:0": { "max_tokens": 8191, @@ -2830,7 +3307,8 @@ "input_cost_per_token": 0.0000104, "output_cost_per_token": 0.0000312, "litellm_provider": "bedrock", - "mode": "chat" + "mode": "chat", + "supports_function_calling": true }, "anthropic.claude-3-sonnet-20240229-v1:0": { "max_tokens": 4096, @@ -3468,6 +3946,39 @@ "litellm_provider": "bedrock", "mode": "chat" }, + "meta.llama3-1-8b-instruct-v1:0": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 2048, + "input_cost_per_token": 0.0000004, + "output_cost_per_token": 0.0000006, + "litellm_provider": "bedrock", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": false + }, + "meta.llama3-1-70b-instruct-v1:0": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 2048, + "input_cost_per_token": 0.00000265, + "output_cost_per_token": 0.0000035, + "litellm_provider": "bedrock", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": false + }, + "meta.llama3-1-405b-instruct-v1:0": { + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 4096, + "input_cost_per_token": 0.00000532, + "output_cost_per_token": 0.000016, + "litellm_provider": "bedrock", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": false + }, "512-x-512/50-steps/stability.stable-diffusion-xl-v0": { "max_tokens": 77, "max_input_tokens": 77, @@ -3621,6 +4132,66 @@ "litellm_provider": "ollama", "mode": "completion" }, + "ollama/codegeex4": { + "max_tokens": 32768, + "max_input_tokens": 32768, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "chat", + "supports_function_calling": false + }, + "ollama/deepseek-coder-v2-instruct": { + "max_tokens": 32768, + "max_input_tokens": 32768, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "chat", + "supports_function_calling": true + }, + "ollama/deepseek-coder-v2-base": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "completion", + "supports_function_calling": true + }, + "ollama/deepseek-coder-v2-lite-instruct": { + "max_tokens": 32768, + "max_input_tokens": 32768, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "chat", + "supports_function_calling": true + }, + "ollama/deepseek-coder-v2-lite-base": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "completion", + "supports_function_calling": true + }, + "ollama/internlm2_5-20b-chat": { + "max_tokens": 32768, + "max_input_tokens": 32768, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "chat", + "supports_function_calling": true + }, "ollama/llama2": { "max_tokens": 4096, "max_input_tokens": 4096, @@ -3630,6 +4201,15 @@ "litellm_provider": "ollama", "mode": "completion" }, + "ollama/llama2:7b": { + "max_tokens": 4096, + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "completion" + }, "ollama/llama2:13b": { "max_tokens": 4096, "max_input_tokens": 4096, @@ -3666,6 +4246,15 @@ "litellm_provider": "ollama", "mode": "chat" }, + "ollama/llama3:8b": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "chat" + }, "ollama/llama3:70b": { "max_tokens": 8192, "max_input_tokens": 8192, @@ -3675,6 +4264,25 @@ "litellm_provider": "ollama", "mode": "chat" }, + "ollama/llama3.1": { + "max_tokens": 32768, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "chat", + "supports_function_calling": true + }, + "ollama/mistral-large-instruct-2407": { + "max_tokens": 65536, + "max_input_tokens": 65536, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "chat" + }, "ollama/mistral": { "max_tokens": 8192, "max_input_tokens": 8192, @@ -3945,6 +4553,69 @@ "litellm_provider": "perplexity", "mode": "chat" }, + "perplexity/llama-3.1-70b-instruct": { + "max_tokens": 131072, + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "input_cost_per_token": 0.000001, + "output_cost_per_token": 0.000001, + "litellm_provider": "perplexity", + "mode": "chat" + }, + "perplexity/llama-3.1-8b-instruct": { + "max_tokens": 131072, + "max_input_tokens": 131072, + 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Logout

- - ), + label:

Logout

, } ]; diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx index e50fc37e8..f55076478 100644 --- a/ui/litellm-dashboard/src/components/networking.tsx +++ b/ui/litellm-dashboard/src/components/networking.tsx @@ -5,6 +5,9 @@ import { message } from "antd"; const isLocal = process.env.NODE_ENV === "development"; const proxyBaseUrl = isLocal ? "http://localhost:4000" : null; +if (isLocal != true) { + console.log = function() {}; +} export interface Model { model_name: string; @@ -12,6 +15,42 @@ export interface Model { model_info: Object | null; } +const baseUrl = "/"; // Assuming the base URL is the root + + +let lastErrorTime = 0; + +const sleep = (ms: number) => new Promise(resolve => setTimeout(resolve, ms)); + +const handleError = async (errorData: string) => { + const currentTime = Date.now(); + if (currentTime - lastErrorTime > 60000) { // 60000 milliseconds = 60 seconds + if (errorData.includes("Authentication Error - Expired Key")) { + message.info("UI Session Expired. Logging out."); + lastErrorTime = currentTime; + await sleep(3000); // 5 second sleep + document.cookie = "token=; expires=Thu, 01 Jan 1970 00:00:00 UTC; path=/;"; + window.location.href = baseUrl; + } else { + message.error(errorData); + } + lastErrorTime = currentTime; + } else { + console.log("Error suppressed to prevent spam:", errorData); + } +}; + + +// Global variable for the header name +let globalLitellmHeaderName: string = "Authorization"; + +// Function to set the global header name +export function setGlobalLitellmHeaderName(headerName: string = "Authorization") { + console.log(`setGlobalLitellmHeaderName: ${headerName}`); + globalLitellmHeaderName = headerName; +} + + export const modelCostMap = async ( accessToken: string, ) => { @@ -21,7 +60,7 @@ export const modelCostMap = async ( url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, } @@ -44,7 +83,7 @@ export const modelCreateCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -54,7 +93,6 @@ export const modelCreateCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to create key: " + errorData, 10); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -84,14 +122,14 @@ export const modelSettingsCall = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -115,7 +153,7 @@ export const modelDeleteCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -125,7 +163,7 @@ export const modelDeleteCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to create key: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -157,7 +195,7 @@ export const budgetDeleteCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -167,7 +205,7 @@ export const budgetDeleteCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to create key: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -192,7 +230,7 @@ export const budgetCreateCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -202,7 +240,7 @@ export const budgetCreateCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to create key: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -228,7 +266,7 @@ export const invitationCreateCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -238,7 +276,7 @@ export const invitationCreateCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to create key: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -267,7 +305,7 @@ export const invitationClaimCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -277,7 +315,7 @@ export const invitationClaimCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to create key: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -305,14 +343,14 @@ export const alertingSettingsCall = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -353,7 +391,6 @@ export const keyCreateCall = async ( try { formValues.metadata = JSON.parse(formValues.metadata); } catch (error) { - message.error("Failed to parse metadata: " + error, 10); throw new Error("Failed to parse metadata: " + error); } } @@ -363,7 +400,7 @@ export const keyCreateCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -374,7 +411,7 @@ export const keyCreateCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to create key: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -416,7 +453,6 @@ export const userCreateCall = async ( try { formValues.metadata = JSON.parse(formValues.metadata); } catch (error) { - message.error("Failed to parse metadata: " + error, 10); throw new Error("Failed to parse metadata: " + error); } } @@ -426,7 +462,7 @@ export const userCreateCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -437,7 +473,7 @@ export const userCreateCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to create key: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -460,7 +496,7 @@ export const keyDeleteCall = async (accessToken: String, user_key: String) => { const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -470,7 +506,7 @@ export const keyDeleteCall = async (accessToken: String, user_key: String) => { if (!response.ok) { const errorData = await response.text(); - message.error("Failed to delete key: " + errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -492,7 +528,7 @@ export const teamDeleteCall = async (accessToken: String, teamID: String) => { const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -502,7 +538,7 @@ export const teamDeleteCall = async (accessToken: String, teamID: String) => { if (!response.ok) { const errorData = await response.text(); - message.error("Failed to delete team: " + errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } const data = await response.json(); @@ -545,14 +581,14 @@ export const userInfoCall = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -580,14 +616,14 @@ export const teamInfoCall = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -601,6 +637,39 @@ export const teamInfoCall = async ( } }; +export const teamListCall = async ( + accessToken: String, +) => { + /** + * Get all available teams on proxy + */ + try { + let url = proxyBaseUrl ? `${proxyBaseUrl}/team/list` : `/team/list`; + console.log("in teamInfoCall"); + const response = await fetch(url, { + method: "GET", + headers: { + [globalLitellmHeaderName]: `Bearer ${accessToken}`, + "Content-Type": "application/json", + }, + }); + + if (!response.ok) { + const errorData = await response.text(); + handleError(errorData); + throw new Error("Network response was not ok"); + } + + const data = await response.json(); + console.log("/team/list API Response:", data); + return data; + // Handle success - you might want to update some state or UI based on the created key + } catch (error) { + console.error("Failed to create key:", error); + throw error; + } +}; + export const getTotalSpendCall = async (accessToken: String) => { /** * Get all models on proxy @@ -612,14 +681,14 @@ export const getTotalSpendCall = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -651,7 +720,7 @@ export const getOnboardingCredentials = async (inviteUUID: String) => { if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -677,7 +746,7 @@ export const claimOnboardingToken = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -689,7 +758,7 @@ export const claimOnboardingToken = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to delete team: " + errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } const data = await response.json(); @@ -719,7 +788,7 @@ export const modelInfoCall = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); @@ -767,7 +836,7 @@ export const modelHubCall = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); @@ -798,7 +867,7 @@ export const getAllowedIPs = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); @@ -827,7 +896,7 @@ export const addAllowedIP = async (accessToken: String, ip: String) => { const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ ip: ip }), @@ -857,7 +926,7 @@ export const deleteAllowedIP = async (accessToken: String, ip: String) => { const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ ip: ip }), @@ -899,14 +968,14 @@ export const modelMetricsCall = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } const data = await response.json(); @@ -938,14 +1007,14 @@ export const streamingModelMetricsCall = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } const data = await response.json(); @@ -983,14 +1052,14 @@ export const modelMetricsSlowResponsesCall = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } const data = await response.json(); @@ -1027,14 +1096,14 @@ export const modelExceptionsCall = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } const data = await response.json(); @@ -1055,6 +1124,7 @@ export const modelAvailableCall = async ( /** * Get all the models user has access to */ + console.log("in /models calls, globalLitellmHeaderName", globalLitellmHeaderName) try { let url = proxyBaseUrl ? `${proxyBaseUrl}/models` : `/models`; @@ -1062,14 +1132,14 @@ export const modelAvailableCall = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -1092,13 +1162,13 @@ export const keySpendLogsCall = async (accessToken: String, token: String) => { const response = await fetch(`${url}?api_key=${token}`, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -1120,13 +1190,13 @@ export const teamSpendLogsCall = async (accessToken: String) => { const response = await fetch(`${url}`, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -1163,7 +1233,7 @@ export const tagsSpendLogsCall = async ( const response = await fetch(`${url}`, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); @@ -1191,7 +1261,7 @@ export const allTagNamesCall = async (accessToken: String) => { const response = await fetch(`${url}`, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); @@ -1219,7 +1289,7 @@ export const allEndUsersCall = async (accessToken: String) => { const response = await fetch(`${url}`, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); @@ -1257,13 +1327,13 @@ export const userSpendLogsCall = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -1287,13 +1357,13 @@ export const adminSpendLogsCall = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -1317,13 +1387,13 @@ export const adminTopKeysCall = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -1362,27 +1432,20 @@ export const adminTopEndUsersCall = async ( //message.info("Making top end users request"); // Define requestOptions with body as an optional property - const requestOptions: { - method: string; - headers: { - Authorization: string; - "Content-Type": string; - }; - body?: string; // The body is optional and might not be present - } = { + const requestOptions = { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, + body: body, }; - requestOptions.body = body; const response = await fetch(url, requestOptions); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -1415,15 +1478,10 @@ export const adminspendByProvider = async ( url += `&api_key=${keyToken}`; } - const requestOptions: { - method: string; - headers: { - Authorization: string; - }; - } = { + const requestOptions = { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, }, }; @@ -1431,7 +1489,7 @@ export const adminspendByProvider = async ( if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -1458,15 +1516,10 @@ export const adminGlobalActivity = async ( url += `?start_date=${startTime}&end_date=${endTime}`; } - const requestOptions: { - method: string; - headers: { - Authorization: string; - }; - } = { + const requestOptions = { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, }, }; @@ -1499,15 +1552,10 @@ export const adminGlobalCacheActivity = async ( url += `?start_date=${startTime}&end_date=${endTime}`; } - const requestOptions: { - method: string; - headers: { - Authorization: string; - }; - } = { + const requestOptions = { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, }, }; @@ -1540,15 +1588,10 @@ export const adminGlobalActivityPerModel = async ( url += `?start_date=${startTime}&end_date=${endTime}`; } - const requestOptions: { - method: string; - headers: { - Authorization: string; - }; - } = { + const requestOptions = { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, }, }; @@ -1586,15 +1629,10 @@ export const adminGlobalActivityExceptions = async ( url += `&model_group=${modelGroup}`; } - const requestOptions: { - method: string; - headers: { - Authorization: string; - }; - } = { + const requestOptions = { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, }, }; @@ -1632,15 +1670,10 @@ export const adminGlobalActivityExceptionsPerDeployment = async ( url += `&model_group=${modelGroup}`; } - const requestOptions: { - method: string; - headers: { - Authorization: string; - }; - } = { + const requestOptions = { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, }, }; @@ -1669,13 +1702,13 @@ export const adminTopModelsCall = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -1696,7 +1729,7 @@ export const keyInfoCall = async (accessToken: String, keys: String[]) => { const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -1706,7 +1739,7 @@ export const keyInfoCall = async (accessToken: String, keys: String[]) => { if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -1726,13 +1759,13 @@ export const spendUsersCall = async (accessToken: String, userID: String) => { const response = await fetch(`${url}?user_id=${userID}`, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -1758,7 +1791,7 @@ export const userRequestModelCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -1770,7 +1803,7 @@ export const userRequestModelCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to delete key: " + errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } const data = await response.json(); @@ -1793,14 +1826,14 @@ export const userGetRequesedtModelsCall = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error("Failed to delete key: " + errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } const data = await response.json(); @@ -1833,14 +1866,14 @@ export const userGetAllUsersCall = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error("Failed to delete key: " + errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } const data = await response.json(); @@ -1862,7 +1895,7 @@ export const getPossibleUserRoles = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); @@ -1891,7 +1924,7 @@ export const teamCreateCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -1901,7 +1934,7 @@ export const teamCreateCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to create key: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -1927,7 +1960,7 @@ export const keyUpdateCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -1937,7 +1970,7 @@ export const keyUpdateCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to update key: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -1962,7 +1995,7 @@ export const teamUpdateCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -1972,7 +2005,7 @@ export const teamUpdateCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to update team: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -1997,7 +2030,7 @@ export const modelUpdateCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -2007,7 +2040,7 @@ export const modelUpdateCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to update model: " + errorData, 10); + handleError(errorData); console.error("Error update from the server:", errorData); throw new Error("Network response was not ok"); } @@ -2041,7 +2074,7 @@ export const teamMemberAddCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -2052,7 +2085,7 @@ export const teamMemberAddCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to create key: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -2084,7 +2117,7 @@ export const userUpdateUserCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: response_body, @@ -2092,7 +2125,7 @@ export const userUpdateUserCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error("Failed to create key: " + errorData, 10); + handleError(errorData); console.error("Error response from the server:", errorData); throw new Error("Network response was not ok"); } @@ -2122,7 +2155,7 @@ export const PredictedSpendLogsCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -2132,7 +2165,7 @@ export const PredictedSpendLogsCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -2158,14 +2191,14 @@ export const slackBudgetAlertsHealthCheck = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error("Failed Slack Alert test: " + errorData); + handleError(errorData); // throw error with message throw new Error(errorData); } @@ -2197,14 +2230,14 @@ export const serviceHealthCheck = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(`Failed ${service} service health check ` + errorData); + handleError(errorData); // throw error with message throw new Error(errorData); } @@ -2232,14 +2265,14 @@ export const getBudgetList = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -2265,14 +2298,14 @@ export const getBudgetSettings = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -2303,14 +2336,14 @@ export const getCallbacksCall = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -2334,14 +2367,46 @@ export const getGeneralSettingsCall = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); + throw new Error("Network response was not ok"); + } + + const data = await response.json(); + //message.info("Received model data"); + return data; + // Handle success - you might want to update some state or UI based on the created key + } catch (error) { + console.error("Failed to get callbacks:", error); + throw error; + } +}; + + +export const getPassThroughEndpointsCall = async (accessToken: String) => { + try { + let url = proxyBaseUrl + ? `${proxyBaseUrl}/config/pass_through_endpoint` + : `/config/pass_through_endpoint`; + + //message.info("Requesting model data"); + const response = await fetch(url, { + method: "GET", + headers: { + [globalLitellmHeaderName]: `Bearer ${accessToken}`, + "Content-Type": "application/json", + }, + }); + + if (!response.ok) { + const errorData = await response.text(); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -2368,7 +2433,7 @@ export const getConfigFieldSetting = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); @@ -2387,6 +2452,85 @@ export const getConfigFieldSetting = async ( } }; +export const updatePassThroughFieldSetting = async ( + accessToken: String, + fieldName: string, + fieldValue: any +) => { + try { + let url = proxyBaseUrl + ? `${proxyBaseUrl}/config/pass_through_endpoint` + : `/config/pass_through_endpoint`; + + let formData = { + field_name: fieldName, + field_value: fieldValue, + }; + //message.info("Requesting model data"); + const response = await fetch(url, { + method: "POST", + headers: { + [globalLitellmHeaderName]: `Bearer ${accessToken}`, + "Content-Type": "application/json", + }, + body: JSON.stringify(formData), + }); + + if (!response.ok) { + const errorData = await response.text(); + handleError(errorData); + throw new Error("Network response was not ok"); + } + + const data = await response.json(); + //message.info("Received model data"); + message.success("Successfully updated value!"); + return data; + // Handle success - you might want to update some state or UI based on the created key + } catch (error) { + console.error("Failed to set callbacks:", error); + throw error; + } +}; + +export const createPassThroughEndpoint = async ( + accessToken: String, + formValues: Record +) => { + /** + * Set callbacks on proxy + */ + try { + let url = proxyBaseUrl ? `${proxyBaseUrl}/config/pass_through_endpoint` : `/config/pass_through_endpoint`; + + //message.info("Requesting model data"); + const response = await fetch(url, { + method: "POST", + headers: { + [globalLitellmHeaderName]: `Bearer ${accessToken}`, + "Content-Type": "application/json", + }, + body: JSON.stringify({ + ...formValues, // Include formValues in the request body + }), + }); + + if (!response.ok) { + const errorData = await response.text(); + handleError(errorData); + throw new Error("Network response was not ok"); + } + + const data = await response.json(); + //message.info("Received model data"); + return data; + // Handle success - you might want to update some state or UI based on the created key + } catch (error) { + console.error("Failed to set callbacks:", error); + throw error; + } +}; + export const updateConfigFieldSetting = async ( accessToken: String, fieldName: string, @@ -2406,7 +2550,7 @@ export const updateConfigFieldSetting = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify(formData), @@ -2414,7 +2558,7 @@ export const updateConfigFieldSetting = async ( if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -2446,7 +2590,7 @@ export const deleteConfigFieldSetting = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify(formData), @@ -2454,7 +2598,7 @@ export const deleteConfigFieldSetting = async ( if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -2467,6 +2611,38 @@ export const deleteConfigFieldSetting = async ( throw error; } }; + +export const deletePassThroughEndpointsCall = async (accessToken: String, endpointId: string) => { + try { + let url = proxyBaseUrl + ? `${proxyBaseUrl}/config/pass_through_endpoint?endpoint_id=${endpointId}` + : `/config/pass_through_endpoint${endpointId}`; + + //message.info("Requesting model data"); + const response = await fetch(url, { + method: "DELETE", + headers: { + [globalLitellmHeaderName]: `Bearer ${accessToken}`, + "Content-Type": "application/json", + }, + }); + + if (!response.ok) { + const errorData = await response.text(); + handleError(errorData); + throw new Error("Network response was not ok"); + } + + const data = await response.json(); + //message.info("Received model data"); + return data; + // Handle success - you might want to update some state or UI based on the created key + } catch (error) { + console.error("Failed to get callbacks:", error); + throw error; + } +}; + export const setCallbacksCall = async ( accessToken: String, formValues: Record @@ -2481,7 +2657,7 @@ export const setCallbacksCall = async ( const response = await fetch(url, { method: "POST", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, body: JSON.stringify({ @@ -2491,7 +2667,7 @@ export const setCallbacksCall = async ( if (!response.ok) { const errorData = await response.text(); - message.error(errorData, 10); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -2516,14 +2692,14 @@ export const healthCheckCall = async (accessToken: String) => { const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); if (!response.ok) { const errorData = await response.text(); - message.error(errorData); + handleError(errorData); throw new Error("Network response was not ok"); } @@ -2552,7 +2728,7 @@ export const getProxyBaseUrlAndLogoutUrl = async ( const response = await fetch(url, { method: "GET", headers: { - Authorization: `Bearer ${accessToken}`, + [globalLitellmHeaderName]: `Bearer ${accessToken}`, "Content-Type": "application/json", }, }); diff --git a/ui/litellm-dashboard/src/components/pass_through_settings.tsx b/ui/litellm-dashboard/src/components/pass_through_settings.tsx new file mode 100644 index 000000000..c979076a2 --- /dev/null +++ b/ui/litellm-dashboard/src/components/pass_through_settings.tsx @@ -0,0 +1,196 @@ +import React, { useState, useEffect } from "react"; +import { + Card, + Title, + Subtitle, + Table, + TableHead, + TableRow, + Badge, + TableHeaderCell, + TableCell, + TableBody, + Metric, + Text, + Grid, + Button, + TextInput, + Select as Select2, + SelectItem, + Col, + Accordion, + AccordionBody, + AccordionHeader, + AccordionList, +} from "@tremor/react"; +import { + TabPanel, + TabPanels, + TabGroup, + TabList, + Tab, + Icon, +} from "@tremor/react"; +import { + getCallbacksCall, + setCallbacksCall, + getGeneralSettingsCall, + deletePassThroughEndpointsCall, + getPassThroughEndpointsCall, + serviceHealthCheck, + updateConfigFieldSetting, + deleteConfigFieldSetting, +} from "./networking"; +import { + Modal, + Form, + Input, + Select, + Button as Button2, + message, + InputNumber, +} from "antd"; +import { + InformationCircleIcon, + PencilAltIcon, + PencilIcon, + StatusOnlineIcon, + TrashIcon, + RefreshIcon, + CheckCircleIcon, + XCircleIcon, + QuestionMarkCircleIcon, +} from "@heroicons/react/outline"; +import StaticGenerationSearchParamsBailoutProvider from "next/dist/client/components/static-generation-searchparams-bailout-provider"; +import AddFallbacks from "./add_fallbacks"; +import AddPassThroughEndpoint from "./add_pass_through"; +import openai from "openai"; +import Paragraph from "antd/es/skeleton/Paragraph"; +interface GeneralSettingsPageProps { + accessToken: string | null; + userRole: string | null; + userID: string | null; + modelData: any; +} + + +interface routingStrategyArgs { + ttl?: number; + lowest_latency_buffer?: number; +} + +interface nestedFieldItem { + field_name: string; + field_type: string; + field_value: any; + field_description: string; + stored_in_db: boolean | null; +} + +export interface passThroughItem { + path: string + target: string + headers: object +} + + + + +const PassThroughSettings: React.FC = ({ + accessToken, + userRole, + userID, + modelData, +}) => { + const [generalSettings, setGeneralSettings] = useState( + [] + ); + useEffect(() => { + if (!accessToken || !userRole || !userID) { + return; + } + getPassThroughEndpointsCall(accessToken).then((data) => { + let general_settings = data["endpoints"]; + setGeneralSettings(general_settings); + }); + }, [accessToken, userRole, userID]); + + + const handleResetField = (fieldName: string, idx: number) => { + if (!accessToken) { + return; + } + + try { + deletePassThroughEndpointsCall(accessToken, fieldName); + // update value in state + + const updatedSettings = generalSettings.filter((setting) => setting.path !== fieldName); + setGeneralSettings(updatedSettings); + + message.success("Endpoint deleted successfully."); + + } catch (error) { + // do something + } + }; + + + if (!accessToken) { + return null; + } + + + + return ( +
+ + + + + + Path + Target + Headers + Action + + + + {generalSettings.map((value, index) => ( + + + {value.path} + + + { + value.target + } + + + { + JSON.stringify(value.headers) + } + + + + handleResetField(value.path, index) + } + > + Reset + + + + ))} + +
+ + + +
+ ); +}; + +export default PassThroughSettings; diff --git a/ui/litellm-dashboard/src/components/request_model_access.tsx b/ui/litellm-dashboard/src/components/request_model_access.tsx index c884d03b2..22fcafa4e 100644 --- a/ui/litellm-dashboard/src/components/request_model_access.tsx +++ b/ui/litellm-dashboard/src/components/request_model_access.tsx @@ -12,7 +12,11 @@ interface RequestAccessProps { accessToken: string; userID: string; } - +const isLocal = process.env.NODE_ENV === "development"; +const proxyBaseUrl = isLocal ? "http://localhost:4000" : null; +if (isLocal != true) { + console.log = function() {}; +} function onRequestAccess(formData: Record): void { // This function does nothing for now } diff --git a/ui/litellm-dashboard/src/components/settings.tsx b/ui/litellm-dashboard/src/components/settings.tsx index fe2735d4a..482cfdd73 100644 --- a/ui/litellm-dashboard/src/components/settings.tsx +++ b/ui/litellm-dashboard/src/components/settings.tsx @@ -34,6 +34,11 @@ import { import { Modal, Typography, Form, Input, Select, Button as Button2, message } from "antd"; const { Title, Paragraph } = Typography; +const isLocal = process.env.NODE_ENV === "development"; +const proxyBaseUrl = isLocal ? "http://localhost:4000" : null; +if (isLocal != true) { + console.log = function() {}; +} import { getCallbacksCall, setCallbacksCall, diff --git a/ui/litellm-dashboard/src/components/teams.tsx b/ui/litellm-dashboard/src/components/teams.tsx index 8b4b803b5..2b2c57fe7 100644 --- a/ui/litellm-dashboard/src/components/teams.tsx +++ b/ui/litellm-dashboard/src/components/teams.tsx @@ -17,6 +17,7 @@ import { Select as Select2, InputNumber, message, + Tooltip } from "antd"; import { Select, SelectItem } from "@tremor/react"; import { @@ -36,6 +37,11 @@ import { Grid, } from "@tremor/react"; import { CogIcon } from "@heroicons/react/outline"; +const isLocal = process.env.NODE_ENV === "development"; +const proxyBaseUrl = isLocal ? "http://localhost:4000" : null; +if (isLocal != true) { + console.log = function() {}; +} interface TeamProps { teams: any[] | null; searchParams: any; @@ -57,6 +63,7 @@ import { teamMemberAddCall, Member, modelAvailableCall, + teamListCall } from "./networking"; const Team: React.FC = ({ @@ -67,6 +74,21 @@ const Team: React.FC = ({ userID, userRole, }) => { + + useEffect(() => { + console.log(`inside useeffect - ${teams}`) + if (teams === null && accessToken) { + // Call your function here + const fetchData = async () => { + const givenTeams = await teamListCall(accessToken) + console.log(`givenTeams: ${givenTeams}`) + + setTeams(givenTeams) + } + fetchData() + } + }, [teams]); + const [form] = Form.useForm(); const [memberForm] = Form.useForm(); const { Title, Paragraph } = Typography; @@ -304,15 +326,15 @@ const Team: React.FC = ({ return; } - console.log("fetching team info:"); - let _team_id_to_info: Record = {}; - for (let i = 0; i < teams?.length; i++) { - let _team_id = teams[i].team_id; - const teamInfo = await teamInfoCall(accessToken, _team_id); - console.log("teamInfo response:", teamInfo); - if (teamInfo !== null) { - _team_id_to_info = { ..._team_id_to_info, [_team_id]: teamInfo }; + const teamList = await teamListCall(accessToken) + for (let i = 0; i < teamList.length; i++) { + let team = teamList[i]; + let _team_id = team.team_id; + + // Use the team info directly from the teamList + if (team !== null) { + _team_id_to_info = { ..._team_id_to_info, [_team_id]: team }; } } setPerTeamInfo(_team_id_to_info); @@ -390,7 +412,6 @@ const Team: React.FC = ({ console.error("Error creating the team:", error); } }; - console.log(`received teams ${JSON.stringify(teams)}`); return (
@@ -401,6 +422,7 @@ const Team: React.FC = ({ Team Name + Team ID Spend (USD) Budget (USD) Models @@ -422,6 +444,19 @@ const Team: React.FC = ({ > {team["team_alias"]} + + + {team.team_id} + + = ({ accessToken={accessToken} userSpend={null} selectedTeam={null} + userMaxBudget={null} /> diff --git a/ui/litellm-dashboard/src/components/user_dashboard.tsx b/ui/litellm-dashboard/src/components/user_dashboard.tsx index fdddcaad6..3d1d4ea60 100644 --- a/ui/litellm-dashboard/src/components/user_dashboard.tsx +++ b/ui/litellm-dashboard/src/components/user_dashboard.tsx @@ -17,6 +17,9 @@ import { useSearchParams, useRouter } from "next/navigation"; import { jwtDecode } from "jwt-decode"; import { Typography } from "antd"; const isLocal = process.env.NODE_ENV === "development"; +if (isLocal != true) { + console.log = function() {}; +} console.log("isLocal:", isLocal); const proxyBaseUrl = isLocal ? "http://localhost:4000" : null; @@ -271,6 +274,11 @@ const UserDashboard: React.FC = ({ const url = proxyBaseUrl ? `${proxyBaseUrl}/sso/key/generate` : `/sso/key/generate`; + + + // clear cookie called "token" since user will be logging in again + document.cookie = "token=; expires=Thu, 01 Jan 1970 00:00:00 UTC; path=/;"; + console.log("Full URL:", url); window.location.href = url; @@ -307,6 +315,7 @@ const UserDashboard: React.FC = ({ = ({ userID, userRole, accessToken, userSpend, selectedTeam }) => { +const ViewUserSpend: React.FC = ({ userID, userRole, accessToken, userSpend, userMaxBudget, selectedTeam }) => { console.log(`userSpend: ${userSpend}`) let [spend, setSpend] = useState(userSpend !== null ? userSpend : 0.0); - const [maxBudget, setMaxBudget] = useState(0.0); + const [maxBudget, setMaxBudget] = useState(selectedTeam ? selectedTeam.max_budget : null); + useEffect(() => { + if (selectedTeam) { + if (selectedTeam.team_alias === "Default Team") { + setMaxBudget(userMaxBudget); + } else { + setMaxBudget(selectedTeam.max_budget); + } + } + }, [selectedTeam, userMaxBudget]); const [userModels, setUserModels] = useState([]); useEffect(() => { const fetchData = async () => { if (!accessToken || !userID || !userRole) { return; } - if (userRole === "Admin" && userSpend == null) { - try { - const globalSpend = await getTotalSpendCall(accessToken); - if (globalSpend) { - if (globalSpend.spend) { - setSpend(globalSpend.spend); - } else { - setSpend(0.0); - } - if (globalSpend.max_budget) { - setMaxBudget(globalSpend.max_budget); - } else { - setMaxBudget(0.0); - } - } - } catch (error) { - console.error("Error fetching global spend data:", error); - } - } }; const fetchUserModels = async () => { try { @@ -123,14 +118,24 @@ const ViewUserSpend: React.FC = ({ userID, userRole, accessT console.log(`spend in view user spend: ${spend}`) return (
+

- Total Spend{" "} + Total Spend

${roundedSpend}

+
+

+ Max Budget +

+

+ {displayMaxBudget} +

+
+
{/*
Team Models diff --git a/ui/litellm-dashboard/src/components/view_user_team.tsx b/ui/litellm-dashboard/src/components/view_user_team.tsx index cbc58e5f7..483420ccc 100644 --- a/ui/litellm-dashboard/src/components/view_user_team.tsx +++ b/ui/litellm-dashboard/src/components/view_user_team.tsx @@ -21,7 +21,11 @@ import { } from "@tremor/react"; import { Statistic } from "antd" import { modelAvailableCall } from "./networking"; - +const isLocal = process.env.NODE_ENV === "development"; +const proxyBaseUrl = isLocal ? "http://localhost:4000" : null; +if (isLocal != true) { + console.log = function() {}; +} interface ViewUserTeamProps { userID: string | null; diff --git a/ui/litellm-dashboard/src/components/view_users.tsx b/ui/litellm-dashboard/src/components/view_users.tsx index 6b1deee6e..75d2b2656 100644 --- a/ui/litellm-dashboard/src/components/view_users.tsx +++ b/ui/litellm-dashboard/src/components/view_users.tsx @@ -52,6 +52,11 @@ interface ViewUserDashboardProps { teams: any[] | null; setKeys: React.Dispatch>; } +const isLocal = process.env.NODE_ENV === "development"; +const proxyBaseUrl = isLocal ? "http://localhost:4000" : null; +if (isLocal != true) { + console.log = function() {}; +} const ViewUserDashboard: React.FC = ({ accessToken, diff --git a/ui/package-lock.json b/ui/package-lock.json index acf436e46..8a1ddf505 100644 --- a/ui/package-lock.json +++ b/ui/package-lock.json @@ -416,12 +416,12 @@ } }, "node_modules/braces": { - "version": "3.0.2", - "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.2.tgz", - "integrity": "sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A==", + "version": "3.0.3", + "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.3.tgz", + "integrity": "sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==", "peer": true, "dependencies": { - "fill-range": "^7.0.1" + "fill-range": "^7.1.1" }, "engines": { "node": ">=8" @@ -754,9 +754,9 @@ } }, "node_modules/fill-range": { - "version": "7.0.1", - "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.0.1.tgz", - "integrity": "sha512-qOo9F+dMUmC2Lcb4BbVvnKJxTPjCm+RRpe4gDuGrzkL7mEVl/djYSu2OdQ2Pa302N4oqkSg9ir6jaLWJ2USVpQ==", + "version": "7.1.1", + "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.1.1.tgz", + "integrity": "sha512-YsGpe3WHLK8ZYi4tWDg2Jy3ebRz2rXowDxnld4bkQB00cc/1Zw9AWnC0i9ztDJitivtQvaI9KaLyKrc+hBW0yg==", "peer": true, "dependencies": { "to-regex-range": "^5.0.1"