diff --git a/cookbook/liteLLM_clarifai_Demo.ipynb b/cookbook/liteLLM_clarifai_Demo.ipynb new file mode 100644 index 0000000000..40ef2fcf93 --- /dev/null +++ b/cookbook/liteLLM_clarifai_Demo.ipynb @@ -0,0 +1,187 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LiteLLM Clarifai \n", + "This notebook walks you through on how to use liteLLM integration of Clarifai and call LLM model from clarifai with response in openAI output format." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-Requisites" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#install necessary packages\n", + "!pip install litellm\n", + "!pip install clarifai" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To obtain Clarifai Personal Access Token follow the steps mentioned in the [link](https://docs.clarifai.com/clarifai-basics/authentication/personal-access-tokens/)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "## Set Clarifai Credentials\n", + "import os\n", + "os.environ[\"CLARIFAI_API_KEY\"]= \"YOUR_CLARIFAI_PAT\" # Clarifai PAT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mistral-large" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import litellm\n", + "\n", + "litellm.set_verbose=False" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mistral large response : ModelResponse(id='chatcmpl-6eed494d-7ae2-4870-b9c2-6a64d50a6151', choices=[Choices(finish_reason='stop', index=1, message=Message(content=\"In the grand tapestry of time, where tales unfold,\\nLies the chronicle of ages, a sight to behold.\\nA tale of empires rising, and kings of old,\\nOf civilizations lost, and stories untold.\\n\\nOnce upon a yesterday, in a time so vast,\\nHumans took their first steps, casting shadows in the past.\\nFrom the cradle of mankind, a journey they embarked,\\nThrough stone and bronze and iron, their skills they sharpened and marked.\\n\\nEgyptians built pyramids, reaching for the skies,\\nWhile Greeks sought wisdom, truth, in philosophies that lie.\\nRoman legions marched, their empire to expand,\\nAnd in the East, the Silk Road joined the world, hand in hand.\\n\\nThe Middle Ages came, with knights in shining armor,\\nFeudal lords and serfs, a time of both clamor and calm order.\\nThen Renaissance bloomed, like a flower in the sun,\\nA rebirth of art and science, a new age had begun.\\n\\nAcross the vast oceans, explorers sailed with courage bold,\\nDiscovering new lands, stories of adventure, untold.\\nIndustrial Revolution churned, progress in its wake,\\nMachines and factories, a whole new world to make.\\n\\nTwo World Wars raged, a testament to man's strife,\\nYet from the ashes rose hope, a renewed will for life.\\nInto the modern era, technology took flight,\\nConnecting every corner, bathed in digital light.\\n\\nHistory, a symphony, a melody of time,\\nA testament to human will, resilience so sublime.\\nIn every page, a lesson, in every tale, a guide,\\nFor understanding our past, shapes our future's tide.\", role='assistant'))], created=1713896412, model='https://api.clarifai.com/v2/users/mistralai/apps/completion/models/mistral-large/outputs', object='chat.completion', system_fingerprint=None, usage=Usage(prompt_tokens=13, completion_tokens=338, total_tokens=351))\n" + ] + } + ], + "source": [ + "from litellm import completion\n", + "\n", + "messages = [{\"role\": \"user\",\"content\": \"\"\"Write a poem about history?\"\"\"}]\n", + "response=completion(\n", + " model=\"clarifai/mistralai.completion.mistral-large\",\n", + " messages=messages,\n", + " )\n", + "\n", + "print(f\"Mistral large response : {response}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Claude-2.1 " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Claude-2.1 response : ModelResponse(id='chatcmpl-d126c919-4db4-4aa3-ac8f-7edea41e0b93', choices=[Choices(finish_reason='stop', index=1, message=Message(content=\" Here's a poem I wrote about history:\\n\\nThe Tides of Time\\n\\nThe tides of time ebb and flow,\\nCarrying stories of long ago.\\nFigures and events come into light,\\nShaping the future with all their might.\\n\\nKingdoms rise, empires fall, \\nLeaving traces that echo down every hall.\\nRevolutions bring change with a fiery glow,\\nToppling structures from long ago.\\n\\nExplorers traverse each ocean and land,\\nSeeking treasures they don't understand.\\nWhile artists and writers try to make their mark,\\nHoping their works shine bright in the dark.\\n\\nThe cycle repeats again and again,\\nAs humanity struggles to learn from its pain.\\nThough the players may change on history's stage,\\nThe themes stay the same from age to age.\\n\\nWar and peace, life and death,\\nLove and strife with every breath.\\nThe tides of time continue their dance,\\nAs we join in, by luck or by chance.\\n\\nSo we study the past to light the way forward, \\nHeeding warnings from stories told and heard.\\nThe future unfolds from this unending flow -\\nWhere the tides of time ultimately go.\", role='assistant'))], created=1713896579, model='https://api.clarifai.com/v2/users/anthropic/apps/completion/models/claude-2_1/outputs', object='chat.completion', system_fingerprint=None, usage=Usage(prompt_tokens=12, completion_tokens=232, total_tokens=244))\n" + ] + } + ], + "source": [ + "from litellm import completion\n", + "\n", + "messages = [{\"role\": \"user\",\"content\": \"\"\"Write a poem about history?\"\"\"}]\n", + "response=completion(\n", + " model=\"clarifai/anthropic.completion.claude-2_1\",\n", + " messages=messages,\n", + " )\n", + "\n", + "print(f\"Claude-2.1 response : {response}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### OpenAI GPT-4 (Streaming)\n", + "Though clarifai doesn't support streaming, still you can call stream and get the response in standard StreamResponse format of liteLLM" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ModelResponse(id='chatcmpl-40ae19af-3bf0-4eb4-99f2-33aec3ba84af', choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(content=\"In the quiet corners of time's grand hall,\\nLies the tale of rise and fall.\\nFrom ancient ruins to modern sprawl,\\nHistory, the greatest story of them all.\\n\\nEmpires have risen, empires have decayed,\\nThrough the eons, memories have stayed.\\nIn the book of time, history is laid,\\nA tapestry of events, meticulously displayed.\\n\\nThe pyramids of Egypt, standing tall,\\nThe Roman Empire's mighty sprawl.\\nFrom Alexander's conquest, to the Berlin Wall,\\nHistory, a silent witness to it all.\\n\\nIn the shadow of the past we tread,\\nWhere once kings and prophets led.\\nTheir stories in our hearts are spread,\\nEchoes of their words, in our minds are read.\\n\\nBattles fought and victories won,\\nActs of courage under the sun.\\nTales of love, of deeds done,\\nIn history's grand book, they all run.\\n\\nHeroes born, legends made,\\nIn the annals of time, they'll never fade.\\nTheir triumphs and failures all displayed,\\nIn the eternal march of history's parade.\\n\\nThe ink of the past is forever dry,\\nBut its lessons, we cannot deny.\\nIn its stories, truths lie,\\nIn its wisdom, we rely.\\n\\nHistory, a mirror to our past,\\nA guide for the future vast.\\nThrough its lens, we're ever cast,\\nIn the drama of life, forever vast.\", role='assistant', function_call=None, tool_calls=None), logprobs=None)], created=1714744515, model='https://api.clarifai.com/v2/users/openai/apps/chat-completion/models/GPT-4/outputs', object='chat.completion.chunk', system_fingerprint=None)\n", + "ModelResponse(id='chatcmpl-40ae19af-3bf0-4eb4-99f2-33aec3ba84af', choices=[StreamingChoices(finish_reason='stop', index=0, delta=Delta(content=None, role=None, function_call=None, tool_calls=None), logprobs=None)], created=1714744515, model='https://api.clarifai.com/v2/users/openai/apps/chat-completion/models/GPT-4/outputs', object='chat.completion.chunk', system_fingerprint=None)\n" + ] + } + ], + "source": [ + "from litellm import completion\n", + "\n", + "messages = [{\"role\": \"user\",\"content\": \"\"\"Write a poem about history?\"\"\"}]\n", + "response = completion(\n", + " model=\"clarifai/openai.chat-completion.GPT-4\",\n", + " messages=messages,\n", + " stream=True,\n", + " api_key = \"c75cc032415e45368be331fdd2c06db0\")\n", + "\n", + "for chunk in response:\n", + " print(chunk)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/my-website/docs/completion/batching.md b/docs/my-website/docs/completion/batching.md index 05683b3ddb..09f59f743d 100644 --- a/docs/my-website/docs/completion/batching.md +++ b/docs/my-website/docs/completion/batching.md @@ -4,6 +4,12 @@ LiteLLM allows you to: * Send 1 completion call to many models: Return Fastest Response * Send 1 completion call to many models: Return All Responses +:::info + +Trying to do batch completion on LiteLLM Proxy ? Go here: https://docs.litellm.ai/docs/proxy/user_keys#beta-batch-completions---pass-model-as-list + +::: + ## Send multiple completion calls to 1 model In the batch_completion method, you provide a list of `messages` where each sub-list of messages is passed to `litellm.completion()`, allowing you to process multiple prompts efficiently in a single API call. diff --git a/docs/my-website/docs/observability/langfuse_integration.md b/docs/my-website/docs/observability/langfuse_integration.md index ebf20b6335..7ba204497a 100644 --- a/docs/my-website/docs/observability/langfuse_integration.md +++ b/docs/my-website/docs/observability/langfuse_integration.md @@ -136,6 +136,7 @@ response = completion( "existing_trace_id": "trace-id22", "trace_metadata": {"key": "updated_trace_value"}, # The new value to use for the langfuse Trace Metadata "update_trace_keys": ["input", "output", "trace_metadata"], # Updates the trace input & output to be this generations input & output also updates the Trace Metadata to match the passed in value + "debug_langfuse": True, # Will log the exact metadata sent to litellm for the trace/generation as `metadata_passed_to_litellm` }, ) diff --git a/docs/my-website/docs/providers/clarifai.md b/docs/my-website/docs/providers/clarifai.md new file mode 100644 index 0000000000..acc8c54bef --- /dev/null +++ b/docs/my-website/docs/providers/clarifai.md @@ -0,0 +1,177 @@ + +# Clarifai +Anthropic, OpenAI, Mistral, Llama and Gemini LLMs are Supported on Clarifai. + +## Pre-Requisites + +`pip install clarifai` + +`pip install litellm` + +## Required Environment Variables +To obtain your Clarifai Personal access token follow this [link](https://docs.clarifai.com/clarifai-basics/authentication/personal-access-tokens/). Optionally the PAT can also be passed in `completion` function. + +```python +os.environ["CALRIFAI_API_KEY"] = "YOUR_CLARIFAI_PAT" # CLARIFAI_PAT +``` + +## Usage + +```python +import os +from litellm import completion + +os.environ["CLARIFAI_API_KEY"] = "" + +response = completion( + model="clarifai/mistralai.completion.mistral-large", + messages=[{ "content": "Tell me a joke about physics?","role": "user"}] +) +``` + +**Output** +```json +{ + "id": "chatcmpl-572701ee-9ab2-411c-ac75-46c1ba18e781", + "choices": [ + { + "finish_reason": "stop", + "index": 1, + "message": { + "content": "Sure, here's a physics joke for you:\n\nWhy can't you trust an atom?\n\nBecause they make up everything!", + "role": "assistant" + } + } + ], + "created": 1714410197, + "model": "https://api.clarifai.com/v2/users/mistralai/apps/completion/models/mistral-large/outputs", + "object": "chat.completion", + "system_fingerprint": null, + "usage": { + "prompt_tokens": 14, + "completion_tokens": 24, + "total_tokens": 38 + } + } +``` + +## Clarifai models +liteLLM supports non-streaming requests to all models on [Clarifai community](https://clarifai.com/explore/models?filterData=%5B%7B%22field%22%3A%22use_cases%22%2C%22value%22%3A%5B%22llm%22%5D%7D%5D&page=1&perPage=24) + +Example Usage - Note: liteLLM supports all models deployed on Clarifai + +## Llama LLMs +| Model Name | Function Call | +---------------------------|---------------------------------| +| clarifai/meta.Llama-2.llama2-7b-chat | `completion('clarifai/meta.Llama-2.llama2-7b-chat', messages)` +| clarifai/meta.Llama-2.llama2-13b-chat | `completion('clarifai/meta.Llama-2.llama2-13b-chat', messages)` +| clarifai/meta.Llama-2.llama2-70b-chat | `completion('clarifai/meta.Llama-2.llama2-70b-chat', messages)` | +| clarifai/meta.Llama-2.codeLlama-70b-Python | `completion('clarifai/meta.Llama-2.codeLlama-70b-Python', messages)`| +| clarifai/meta.Llama-2.codeLlama-70b-Instruct | `completion('clarifai/meta.Llama-2.codeLlama-70b-Instruct', messages)` | + +## Mistal LLMs +| Model Name | Function Call | +|---------------------------------------------|------------------------------------------------------------------------| +| clarifai/mistralai.completion.mixtral-8x22B | `completion('clarifai/mistralai.completion.mixtral-8x22B', messages)` | +| clarifai/mistralai.completion.mistral-large | `completion('clarifai/mistralai.completion.mistral-large', messages)` | +| clarifai/mistralai.completion.mistral-medium | `completion('clarifai/mistralai.completion.mistral-medium', messages)` | +| clarifai/mistralai.completion.mistral-small | `completion('clarifai/mistralai.completion.mistral-small', messages)` | +| clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1 | `completion('clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1', messages)` +| clarifai/mistralai.completion.mistral-7B-OpenOrca | `completion('clarifai/mistralai.completion.mistral-7B-OpenOrca', messages)` | +| clarifai/mistralai.completion.openHermes-2-mistral-7B | `completion('clarifai/mistralai.completion.openHermes-2-mistral-7B', messages)` | + + +## Jurassic LLMs +| Model Name | Function Call | +|-----------------------------------------------|---------------------------------------------------------------------| +| clarifai/ai21.complete.Jurassic2-Grande | `completion('clarifai/ai21.complete.Jurassic2-Grande', messages)` | +| clarifai/ai21.complete.Jurassic2-Grande-Instruct | `completion('clarifai/ai21.complete.Jurassic2-Grande-Instruct', messages)` | +| clarifai/ai21.complete.Jurassic2-Jumbo-Instruct | `completion('clarifai/ai21.complete.Jurassic2-Jumbo-Instruct', messages)` | +| clarifai/ai21.complete.Jurassic2-Jumbo | `completion('clarifai/ai21.complete.Jurassic2-Jumbo', messages)` | +| clarifai/ai21.complete.Jurassic2-Large | `completion('clarifai/ai21.complete.Jurassic2-Large', messages)` | + +## Wizard LLMs + +| Model Name | Function Call | +|-----------------------------------------------|---------------------------------------------------------------------| +| clarifai/wizardlm.generate.wizardCoder-Python-34B | `completion('clarifai/wizardlm.generate.wizardCoder-Python-34B', messages)` | +| clarifai/wizardlm.generate.wizardLM-70B | `completion('clarifai/wizardlm.generate.wizardLM-70B', messages)` | +| clarifai/wizardlm.generate.wizardLM-13B | `completion('clarifai/wizardlm.generate.wizardLM-13B', messages)` | +| clarifai/wizardlm.generate.wizardCoder-15B | `completion('clarifai/wizardlm.generate.wizardCoder-15B', messages)` | + +## Anthropic models + +| Model Name | Function Call | +|-----------------------------------------------|---------------------------------------------------------------------| +| clarifai/anthropic.completion.claude-v1 | `completion('clarifai/anthropic.completion.claude-v1', messages)` | +| clarifai/anthropic.completion.claude-instant-1_2 | `completion('clarifai/anthropic.completion.claude-instant-1_2', messages)` | +| clarifai/anthropic.completion.claude-instant | `completion('clarifai/anthropic.completion.claude-instant', messages)` | +| clarifai/anthropic.completion.claude-v2 | `completion('clarifai/anthropic.completion.claude-v2', messages)` | +| clarifai/anthropic.completion.claude-2_1 | `completion('clarifai/anthropic.completion.claude-2_1', messages)` | +| clarifai/anthropic.completion.claude-3-opus | `completion('clarifai/anthropic.completion.claude-3-opus', messages)` | +| clarifai/anthropic.completion.claude-3-sonnet | `completion('clarifai/anthropic.completion.claude-3-sonnet', messages)` | + +## OpenAI GPT LLMs + +| Model Name | Function Call | +|-----------------------------------------------|---------------------------------------------------------------------| +| clarifai/openai.chat-completion.GPT-4 | `completion('clarifai/openai.chat-completion.GPT-4', messages)` | +| clarifai/openai.chat-completion.GPT-3_5-turbo | `completion('clarifai/openai.chat-completion.GPT-3_5-turbo', messages)` | +| clarifai/openai.chat-completion.gpt-4-turbo | `completion('clarifai/openai.chat-completion.gpt-4-turbo', messages)` | +| clarifai/openai.completion.gpt-3_5-turbo-instruct | `completion('clarifai/openai.completion.gpt-3_5-turbo-instruct', messages)` | + +## GCP LLMs + +| Model Name | Function Call | +|-----------------------------------------------|---------------------------------------------------------------------| +| clarifai/gcp.generate.gemini-1_5-pro | `completion('clarifai/gcp.generate.gemini-1_5-pro', messages)` | +| clarifai/gcp.generate.imagen-2 | `completion('clarifai/gcp.generate.imagen-2', messages)` | +| clarifai/gcp.generate.code-gecko | `completion('clarifai/gcp.generate.code-gecko', messages)` | +| clarifai/gcp.generate.code-bison | `completion('clarifai/gcp.generate.code-bison', messages)` | +| clarifai/gcp.generate.text-bison | `completion('clarifai/gcp.generate.text-bison', messages)` | +| clarifai/gcp.generate.gemma-2b-it | `completion('clarifai/gcp.generate.gemma-2b-it', messages)` | +| clarifai/gcp.generate.gemma-7b-it | `completion('clarifai/gcp.generate.gemma-7b-it', messages)` | +| clarifai/gcp.generate.gemini-pro | `completion('clarifai/gcp.generate.gemini-pro', messages)` | +| clarifai/gcp.generate.gemma-1_1-7b-it | `completion('clarifai/gcp.generate.gemma-1_1-7b-it', messages)` | + +## Cohere LLMs +| Model Name | Function Call | +|-----------------------------------------------|---------------------------------------------------------------------| +| clarifai/cohere.generate.cohere-generate-command | `completion('clarifai/cohere.generate.cohere-generate-command', messages)` | + clarifai/cohere.generate.command-r-plus' | `completion('clarifai/clarifai/cohere.generate.command-r-plus', messages)`| + +## Databricks LLMs + +| Model Name | Function Call | +|---------------------------------------------------|---------------------------------------------------------------------| +| clarifai/databricks.drbx.dbrx-instruct | `completion('clarifai/databricks.drbx.dbrx-instruct', messages)` | +| clarifai/databricks.Dolly-v2.dolly-v2-12b | `completion('clarifai/databricks.Dolly-v2.dolly-v2-12b', messages)`| + +## Microsoft LLMs + +| Model Name | Function Call | +|---------------------------------------------------|---------------------------------------------------------------------| +| clarifai/microsoft.text-generation.phi-2 | `completion('clarifai/microsoft.text-generation.phi-2', messages)` | +| clarifai/microsoft.text-generation.phi-1_5 | `completion('clarifai/microsoft.text-generation.phi-1_5', messages)`| + +## Salesforce models + +| Model Name | Function Call | +|-----------------------------------------------------------|-------------------------------------------------------------------------------| +| clarifai/salesforce.blip.general-english-image-caption-blip-2 | `completion('clarifai/salesforce.blip.general-english-image-caption-blip-2', messages)` | +| clarifai/salesforce.xgen.xgen-7b-8k-instruct | `completion('clarifai/salesforce.xgen.xgen-7b-8k-instruct', messages)` | + + +## Other Top performing LLMs + +| Model Name | Function Call | +|---------------------------------------------------|---------------------------------------------------------------------| +| clarifai/deci.decilm.deciLM-7B-instruct | `completion('clarifai/deci.decilm.deciLM-7B-instruct', messages)` | +| clarifai/upstage.solar.solar-10_7b-instruct | `completion('clarifai/upstage.solar.solar-10_7b-instruct', messages)` | +| clarifai/openchat.openchat.openchat-3_5-1210 | `completion('clarifai/openchat.openchat.openchat-3_5-1210', messages)` | +| clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B | `completion('clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B', messages)` | +| clarifai/fblgit.una-cybertron.una-cybertron-7b-v2 | `completion('clarifai/fblgit.una-cybertron.una-cybertron-7b-v2', messages)` | +| clarifai/tiiuae.falcon.falcon-40b-instruct | `completion('clarifai/tiiuae.falcon.falcon-40b-instruct', messages)` | +| clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat | `completion('clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat', messages)` | +| clarifai/bigcode.code.StarCoder | `completion('clarifai/bigcode.code.StarCoder', messages)` | +| clarifai/mosaicml.mpt.mpt-7b-instruct | `completion('clarifai/mosaicml.mpt.mpt-7b-instruct', messages)` | diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md index 932ea4f577..538a81d4b3 100644 --- a/docs/my-website/docs/proxy/logging.md +++ b/docs/my-website/docs/proxy/logging.md @@ -3,7 +3,7 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# 🔎 Logging - Custom Callbacks, DataDog, Langfuse, s3 Bucket, Sentry, OpenTelemetry, Athina +# 🔎 Logging - Custom Callbacks, DataDog, Langfuse, s3 Bucket, Sentry, OpenTelemetry, Athina, Azure Content-Safety Log Proxy Input, Output, Exceptions using Custom Callbacks, Langfuse, OpenTelemetry, LangFuse, DynamoDB, s3 Bucket @@ -17,6 +17,7 @@ Log Proxy Input, Output, Exceptions using Custom Callbacks, Langfuse, OpenTeleme - [Logging to Sentry](#logging-proxy-inputoutput---sentry) - [Logging to Traceloop (OpenTelemetry)](#logging-proxy-inputoutput-traceloop-opentelemetry) - [Logging to Athina](#logging-proxy-inputoutput-athina) +- [(BETA) Moderation with Azure Content-Safety](#moderation-with-azure-content-safety) ## Custom Callback Class [Async] Use this when you want to run custom callbacks in `python` @@ -1037,3 +1038,86 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ ] }' ``` + +## (BETA) Moderation with Azure Content Safety + +[Azure Content-Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety) is a Microsoft Azure service that provides content moderation APIs to detect potential offensive, harmful, or risky content in text. + +We will use the `--config` to set `litellm.success_callback = ["azure_content_safety"]` this will moderate all LLM calls using Azure Content Safety. + +**Step 0** Deploy Azure Content Safety + +Deploy an Azure Content-Safety instance from the Azure Portal and get the `endpoint` and `key`. + +**Step 1** Set Athina API key + +```shell +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 + litellm_params: + model: gpt-3.5-turbo +litellm_settings: + callbacks: ["azure_content_safety"] + azure_content_safety_params: + endpoint: "" + key: "os.environ/AZURE_CONTENT_SAFETY_KEY" +``` + +**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' \ + --data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "Hi, how are you?" + } + ] + }' +``` + +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 `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 + +**Step 4**: Customizing Azure Content Safety Thresholds + +You can customize the thresholds for each category by setting the `thresholds` in the `config.yaml` + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo +litellm_settings: + callbacks: ["azure_content_safety"] + azure_content_safety_params: + endpoint: "" + key: "os.environ/AZURE_CONTENT_SAFETY_KEY" + thresholds: + Hate: 6 + SelfHarm: 8 + Sexual: 6 + Violence: 4 +``` + +:::info +`thresholds` are not required by default, but you can tune the values to your needs. +Default values is `4` for all categories +::: \ No newline at end of file diff --git a/docs/my-website/docs/proxy/reliability.md b/docs/my-website/docs/proxy/reliability.md index bd04216dd1..e39a6765fc 100644 --- a/docs/my-website/docs/proxy/reliability.md +++ b/docs/my-website/docs/proxy/reliability.md @@ -151,7 +151,7 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ }' ``` -## Advanced - Context Window Fallbacks +## Advanced - Context Window Fallbacks (Pre-Call Checks + Fallbacks) **Before call is made** check if a call is within model context window with **`enable_pre_call_checks: true`**. @@ -232,16 +232,16 @@ 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_info: - base_model: azure/gpt-4-1106-preview # 2. 👈 (azure-only) SET BASE MODEL + api_base: os.environ/AZURE_API_BASE + api_key: os.environ/AZURE_API_KEY + api_version: "2023-07-01-preview" + model_info: + base_model: azure/gpt-4-1106-preview # 2. 👈 (azure-only) SET BASE MODEL - model_name: gpt-3.5-turbo-large litellm_params: - model: gpt-3.5-turbo-1106 - api_key: os.environ/OPENAI_API_KEY + model: gpt-3.5-turbo-1106 + api_key: os.environ/OPENAI_API_KEY - model_name: claude-opus litellm_params: @@ -287,6 +287,69 @@ print(response) +## Advanced - EU-Region Filtering (Pre-Call Checks) + +**Before call is made** check if a call is within model context window with **`enable_pre_call_checks: true`**. + +Set 'region_name' of deployment. + +**Note:** LiteLLM can automatically infer region_name for Vertex AI, Bedrock, and IBM WatsonxAI based on your litellm params. For Azure, set `litellm.enable_preview = True`. + +**1. Set Config** + +```yaml +router_settings: + enable_pre_call_checks: true # 1. Enable pre-call checks + +model_list: +- model_name: gpt-3.5-turbo + 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" + region_name: "eu" # 👈 SET EU-REGION + +- model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo-1106 + api_key: os.environ/OPENAI_API_KEY + +- model_name: gemini-pro + litellm_params: + model: vertex_ai/gemini-pro-1.5 + vertex_project: adroit-crow-1234 + vertex_location: us-east1 # 👈 AUTOMATICALLY INFERS 'region_name' +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + +**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.with_raw_response.create( + model="gpt-3.5-turbo", + messages = [{"role": "user", "content": "Who was Alexander?"}] +) + +print(response) + +print(f"response.headers.get('x-litellm-model-api-base')") +``` + ## Advanced - Custom Timeouts, Stream Timeouts - Per Model For each model you can set `timeout` & `stream_timeout` under `litellm_params` ```yaml diff --git a/docs/my-website/docs/proxy/user_keys.md b/docs/my-website/docs/proxy/user_keys.md index fa78b37c11..7aba832eb8 100644 --- a/docs/my-website/docs/proxy/user_keys.md +++ b/docs/my-website/docs/proxy/user_keys.md @@ -365,6 +365,90 @@ curl --location 'http://0.0.0.0:4000/moderations' \ ## Advanced +### (BETA) Batch Completions - pass `model` as List + +Use this when you want to send 1 request to N Models + +#### Expected Request Format + +This same request will be sent to the following model groups on the [litellm proxy config.yaml](https://docs.litellm.ai/docs/proxy/configs) +- `model_name="llama3"` +- `model_name="gpt-3.5-turbo"` + +```shell +curl --location 'http://localhost:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": ["llama3", "gpt-3.5-turbo"], + "max_tokens": 10, + "user": "litellm2", + "messages": [ + { + "role": "user", + "content": "is litellm getting better" + } + ] +}' +``` + + +#### Expected Response Format + +Get a list of responses when `model` is passed as a list + +```json +[ + { + "id": "chatcmpl-3dbd5dd8-7c82-4ca3-bf1f-7c26f497cf2b", + "choices": [ + { + "finish_reason": "length", + "index": 0, + "message": { + "content": "The Elder Scrolls IV: Oblivion!\n\nReleased", + "role": "assistant" + } + } + ], + "created": 1715459876, + "model": "groq/llama3-8b-8192", + "object": "chat.completion", + "system_fingerprint": "fp_179b0f92c9", + "usage": { + "completion_tokens": 10, + "prompt_tokens": 12, + "total_tokens": 22 + } + }, + { + "id": "chatcmpl-9NnldUfFLmVquFHSX4yAtjCw8PGei", + "choices": [ + { + "finish_reason": "length", + "index": 0, + "message": { + "content": "TES4 could refer to The Elder Scrolls IV:", + "role": "assistant" + } + } + ], + "created": 1715459877, + "model": "gpt-3.5-turbo-0125", + "object": "chat.completion", + "system_fingerprint": null, + "usage": { + "completion_tokens": 10, + "prompt_tokens": 9, + "total_tokens": 19 + } + } +] +``` + + + + ### Pass User LLM API Keys, Fallbacks Allow your end-users to pass their model list, api base, OpenAI API key (any LiteLLM supported provider) to make requests diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md index 0b0c7713c2..b1afad2fbe 100644 --- a/docs/my-website/docs/routing.md +++ b/docs/my-website/docs/routing.md @@ -879,13 +879,11 @@ router = Router(model_list: Optional[list] = None, cache_responses=True) ``` -## Pre-Call Checks (Context Window) +## Pre-Call Checks (Context Window, EU-Regions) Enable pre-call checks to filter out: 1. deployments with context window limit < messages for a call. -2. deployments that have exceeded rate limits when making concurrent calls. (eg. `asyncio.gather(*[ - router.acompletion(model="gpt-3.5-turbo", messages=m) for m in list_of_messages - ])`) +2. deployments outside of eu-region @@ -900,10 +898,14 @@ router = Router(model_list=model_list, enable_pre_call_checks=True) # 👈 Set t **2. Set Model List** -For azure deployments, set the base model. Pick the base model from [this list](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json), all the azure models start with `azure/`. +For context window checks on azure deployments, set the base model. Pick the base model from [this list](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json), all the azure models start with `azure/`. - - +For 'eu-region' filtering, Set 'region_name' of deployment. + +**Note:** We automatically infer region_name for Vertex AI, Bedrock, and IBM WatsonxAI based on your litellm params. For Azure, set `litellm.enable_preview = True`. + + +[**See Code**](https://github.com/BerriAI/litellm/blob/d33e49411d6503cb634f9652873160cd534dec96/litellm/router.py#L2958) ```python model_list = [ @@ -914,10 +916,9 @@ model_list = [ "api_key": os.getenv("AZURE_API_KEY"), "api_version": os.getenv("AZURE_API_VERSION"), "api_base": os.getenv("AZURE_API_BASE"), - }, - "model_info": { + "region_name": "eu" # 👈 SET 'EU' REGION NAME "base_model": "azure/gpt-35-turbo", # 👈 (Azure-only) SET BASE MODEL - } + }, }, { "model_name": "gpt-3.5-turbo", # model group name @@ -926,54 +927,26 @@ model_list = [ "api_key": os.getenv("OPENAI_API_KEY"), }, }, + { + "model_name": "gemini-pro", + "litellm_params: { + "model": "vertex_ai/gemini-pro-1.5", + "vertex_project": "adroit-crow-1234", + "vertex_location": "us-east1" # 👈 AUTOMATICALLY INFERS 'region_name' + } + } ] router = Router(model_list=model_list, enable_pre_call_checks=True) ``` - - - - -```python -model_list = [ - { - "model_name": "gpt-3.5-turbo-small", # model group name - "litellm_params": { # params for litellm completion/embedding call - "model": "azure/chatgpt-v-2", - "api_key": os.getenv("AZURE_API_KEY"), - "api_version": os.getenv("AZURE_API_VERSION"), - "api_base": os.getenv("AZURE_API_BASE"), - }, - "model_info": { - "base_model": "azure/gpt-35-turbo", # 👈 (Azure-only) SET BASE MODEL - } - }, - { - "model_name": "gpt-3.5-turbo-large", # model group name - "litellm_params": { # params for litellm completion/embedding call - "model": "gpt-3.5-turbo-1106", - "api_key": os.getenv("OPENAI_API_KEY"), - }, - }, - { - "model_name": "claude-opus", - "litellm_params": { call - "model": "claude-3-opus-20240229", - "api_key": os.getenv("ANTHROPIC_API_KEY"), - }, - }, - ] - -router = Router(model_list=model_list, enable_pre_call_checks=True, context_window_fallbacks=[{"gpt-3.5-turbo-small": ["gpt-3.5-turbo-large", "claude-opus"]}]) -``` - - - - **3. Test it!** + + + + ```python """ - Give a gpt-3.5-turbo model group with different context windows (4k vs. 16k) @@ -983,7 +956,6 @@ router = Router(model_list=model_list, enable_pre_call_checks=True, context_wind from litellm import Router import os -try: model_list = [ { "model_name": "gpt-3.5-turbo", # model group name @@ -992,6 +964,7 @@ model_list = [ "api_key": os.getenv("AZURE_API_KEY"), "api_version": os.getenv("AZURE_API_VERSION"), "api_base": os.getenv("AZURE_API_BASE"), + "base_model": "azure/gpt-35-turbo", }, "model_info": { "base_model": "azure/gpt-35-turbo", @@ -1021,6 +994,59 @@ response = router.completion( print(f"response: {response}") ``` + + +```python +""" +- Give 2 gpt-3.5-turbo deployments, in eu + non-eu regions +- Make a call +- Assert it picks the eu-region model +""" + +from litellm import Router +import os + +model_list = [ + { + "model_name": "gpt-3.5-turbo", # model group name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "region_name": "eu" + }, + "model_info": { + "id": "1" + } + }, + { + "model_name": "gpt-3.5-turbo", # model group name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-1106", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "model_info": { + "id": "2" + } + }, +] + +router = Router(model_list=model_list, enable_pre_call_checks=True) + +response = router.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Who was Alexander?"}], +) + +print(f"response: {response}") + +print(f"response id: {response._hidden_params['model_id']}") +``` + + + + :::info diff --git a/litellm/__init__.py b/litellm/__init__.py index 67170c68d0..6c7b26617e 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -71,6 +71,7 @@ azure_key: Optional[str] = None anthropic_key: Optional[str] = None replicate_key: Optional[str] = None cohere_key: Optional[str] = None +clarifai_key: Optional[str] = None maritalk_key: Optional[str] = None ai21_key: Optional[str] = None ollama_key: Optional[str] = None @@ -101,6 +102,9 @@ 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" ################## +### PREVIEW FEATURES ### +enable_preview_features: bool = False +################## logging: bool = True caching: bool = ( False # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648 @@ -401,6 +405,73 @@ replicate_models: List = [ "replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad", ] +clarifai_models: List = [ + 'clarifai/meta.Llama-3.Llama-3-8B-Instruct', + 'clarifai/gcp.generate.gemma-1_1-7b-it', + 'clarifai/mistralai.completion.mixtral-8x22B', + 'clarifai/cohere.generate.command-r-plus', + 'clarifai/databricks.drbx.dbrx-instruct', + 'clarifai/mistralai.completion.mistral-large', + 'clarifai/mistralai.completion.mistral-medium', + 'clarifai/mistralai.completion.mistral-small', + 'clarifai/mistralai.completion.mixtral-8x7B-Instruct-v0_1', + 'clarifai/gcp.generate.gemma-2b-it', + 'clarifai/gcp.generate.gemma-7b-it', + 'clarifai/deci.decilm.deciLM-7B-instruct', + 'clarifai/mistralai.completion.mistral-7B-Instruct', + 'clarifai/gcp.generate.gemini-pro', + 'clarifai/anthropic.completion.claude-v1', + 'clarifai/anthropic.completion.claude-instant-1_2', + 'clarifai/anthropic.completion.claude-instant', + 'clarifai/anthropic.completion.claude-v2', + 'clarifai/anthropic.completion.claude-2_1', + 'clarifai/meta.Llama-2.codeLlama-70b-Python', + 'clarifai/meta.Llama-2.codeLlama-70b-Instruct', + 'clarifai/openai.completion.gpt-3_5-turbo-instruct', + 'clarifai/meta.Llama-2.llama2-7b-chat', + 'clarifai/meta.Llama-2.llama2-13b-chat', + 'clarifai/meta.Llama-2.llama2-70b-chat', + 'clarifai/openai.chat-completion.gpt-4-turbo', + 'clarifai/microsoft.text-generation.phi-2', + 'clarifai/meta.Llama-2.llama2-7b-chat-vllm', + 'clarifai/upstage.solar.solar-10_7b-instruct', + 'clarifai/openchat.openchat.openchat-3_5-1210', + 'clarifai/togethercomputer.stripedHyena.stripedHyena-Nous-7B', + 'clarifai/gcp.generate.text-bison', + 'clarifai/meta.Llama-2.llamaGuard-7b', + 'clarifai/fblgit.una-cybertron.una-cybertron-7b-v2', + 'clarifai/openai.chat-completion.GPT-4', + 'clarifai/openai.chat-completion.GPT-3_5-turbo', + 'clarifai/ai21.complete.Jurassic2-Grande', + 'clarifai/ai21.complete.Jurassic2-Grande-Instruct', + 'clarifai/ai21.complete.Jurassic2-Jumbo-Instruct', + 'clarifai/ai21.complete.Jurassic2-Jumbo', + 'clarifai/ai21.complete.Jurassic2-Large', + 'clarifai/cohere.generate.cohere-generate-command', + 'clarifai/wizardlm.generate.wizardCoder-Python-34B', + 'clarifai/wizardlm.generate.wizardLM-70B', + 'clarifai/tiiuae.falcon.falcon-40b-instruct', + 'clarifai/togethercomputer.RedPajama.RedPajama-INCITE-7B-Chat', + 'clarifai/gcp.generate.code-gecko', + 'clarifai/gcp.generate.code-bison', + 'clarifai/mistralai.completion.mistral-7B-OpenOrca', + 'clarifai/mistralai.completion.openHermes-2-mistral-7B', + 'clarifai/wizardlm.generate.wizardLM-13B', + 'clarifai/huggingface-research.zephyr.zephyr-7B-alpha', + 'clarifai/wizardlm.generate.wizardCoder-15B', + 'clarifai/microsoft.text-generation.phi-1_5', + 'clarifai/databricks.Dolly-v2.dolly-v2-12b', + 'clarifai/bigcode.code.StarCoder', + 'clarifai/salesforce.xgen.xgen-7b-8k-instruct', + 'clarifai/mosaicml.mpt.mpt-7b-instruct', + 'clarifai/anthropic.completion.claude-3-opus', + 'clarifai/anthropic.completion.claude-3-sonnet', + 'clarifai/gcp.generate.gemini-1_5-pro', + 'clarifai/gcp.generate.imagen-2', + 'clarifai/salesforce.blip.general-english-image-caption-blip-2', +] + + huggingface_models: List = [ "meta-llama/Llama-2-7b-hf", "meta-llama/Llama-2-7b-chat-hf", @@ -506,6 +577,7 @@ provider_list: List = [ "text-completion-openai", "cohere", "cohere_chat", + "clarifai", "anthropic", "replicate", "huggingface", @@ -656,6 +728,7 @@ from .llms.predibase import PredibaseConfig from .llms.anthropic_text import AnthropicTextConfig from .llms.replicate import ReplicateConfig from .llms.cohere import CohereConfig +from .llms.clarifai import ClarifaiConfig from .llms.ai21 import AI21Config from .llms.together_ai import TogetherAIConfig from .llms.cloudflare import CloudflareConfig diff --git a/litellm/exceptions.py b/litellm/exceptions.py index d8b0a7c55a..d239f1e128 100644 --- a/litellm/exceptions.py +++ b/litellm/exceptions.py @@ -9,25 +9,12 @@ ## LiteLLM versions of the OpenAI Exception Types -from openai import ( - AuthenticationError, - BadRequestError, - NotFoundError, - RateLimitError, - APIStatusError, - OpenAIError, - APIError, - APITimeoutError, - APIConnectionError, - APIResponseValidationError, - UnprocessableEntityError, - PermissionDeniedError, -) +import openai import httpx from typing import Optional -class AuthenticationError(AuthenticationError): # type: ignore +class AuthenticationError(openai.AuthenticationError): # type: ignore def __init__(self, message, llm_provider, model, response: httpx.Response): self.status_code = 401 self.message = message @@ -39,7 +26,7 @@ class AuthenticationError(AuthenticationError): # type: ignore # raise when invalid models passed, example gpt-8 -class NotFoundError(NotFoundError): # type: ignore +class NotFoundError(openai.NotFoundError): # type: ignore def __init__(self, message, model, llm_provider, response: httpx.Response): self.status_code = 404 self.message = message @@ -50,7 +37,7 @@ class NotFoundError(NotFoundError): # type: ignore ) # Call the base class constructor with the parameters it needs -class BadRequestError(BadRequestError): # type: ignore +class BadRequestError(openai.BadRequestError): # type: ignore def __init__( self, message, model, llm_provider, response: Optional[httpx.Response] = None ): @@ -69,7 +56,7 @@ class BadRequestError(BadRequestError): # type: ignore ) # Call the base class constructor with the parameters it needs -class UnprocessableEntityError(UnprocessableEntityError): # type: ignore +class UnprocessableEntityError(openai.UnprocessableEntityError): # type: ignore def __init__(self, message, model, llm_provider, response: httpx.Response): self.status_code = 422 self.message = message @@ -80,7 +67,7 @@ class UnprocessableEntityError(UnprocessableEntityError): # type: ignore ) # Call the base class constructor with the parameters it needs -class Timeout(APITimeoutError): # type: ignore +class Timeout(openai.APITimeoutError): # type: ignore def __init__(self, message, model, llm_provider): request = httpx.Request(method="POST", url="https://api.openai.com/v1") super().__init__( @@ -96,7 +83,7 @@ class Timeout(APITimeoutError): # type: ignore return str(self.message) -class PermissionDeniedError(PermissionDeniedError): # type:ignore +class PermissionDeniedError(openai.PermissionDeniedError): # type:ignore def __init__(self, message, llm_provider, model, response: httpx.Response): self.status_code = 403 self.message = message @@ -107,7 +94,7 @@ class PermissionDeniedError(PermissionDeniedError): # type:ignore ) # Call the base class constructor with the parameters it needs -class RateLimitError(RateLimitError): # type: ignore +class RateLimitError(openai.RateLimitError): # type: ignore def __init__(self, message, llm_provider, model, response: httpx.Response): self.status_code = 429 self.message = message @@ -148,7 +135,7 @@ class ContentPolicyViolationError(BadRequestError): # type: ignore ) # Call the base class constructor with the parameters it needs -class ServiceUnavailableError(APIStatusError): # type: ignore +class ServiceUnavailableError(openai.APIStatusError): # type: ignore def __init__(self, message, llm_provider, model, response: httpx.Response): self.status_code = 503 self.message = message @@ -160,7 +147,7 @@ class ServiceUnavailableError(APIStatusError): # type: ignore # raise this when the API returns an invalid response object - https://github.com/openai/openai-python/blob/1be14ee34a0f8e42d3f9aa5451aa4cb161f1781f/openai/api_requestor.py#L401 -class APIError(APIError): # type: ignore +class APIError(openai.APIError): # type: ignore def __init__( self, status_code, message, llm_provider, model, request: httpx.Request ): @@ -172,7 +159,7 @@ class APIError(APIError): # type: ignore # raised if an invalid request (not get, delete, put, post) is made -class APIConnectionError(APIConnectionError): # type: ignore +class APIConnectionError(openai.APIConnectionError): # type: ignore def __init__(self, message, llm_provider, model, request: httpx.Request): self.message = message self.llm_provider = llm_provider @@ -182,7 +169,7 @@ class APIConnectionError(APIConnectionError): # type: ignore # raised if an invalid request (not get, delete, put, post) is made -class APIResponseValidationError(APIResponseValidationError): # type: ignore +class APIResponseValidationError(openai.APIResponseValidationError): # type: ignore def __init__(self, message, llm_provider, model): self.message = message self.llm_provider = llm_provider @@ -192,7 +179,7 @@ class APIResponseValidationError(APIResponseValidationError): # type: ignore super().__init__(response=response, body=None, message=message) -class OpenAIError(OpenAIError): # type: ignore +class OpenAIError(openai.OpenAIError): # type: ignore def __init__(self, original_exception): self.status_code = original_exception.http_status super().__init__( @@ -214,7 +201,7 @@ class BudgetExceededError(Exception): ## DEPRECATED ## -class InvalidRequestError(BadRequestError): # type: ignore +class InvalidRequestError(openai.BadRequestError): # type: ignore def __init__(self, message, model, llm_provider): self.status_code = 400 self.message = message diff --git a/litellm/integrations/langfuse.py b/litellm/integrations/langfuse.py index f27d199680..0d9c0640cc 100644 --- a/litellm/integrations/langfuse.py +++ b/litellm/integrations/langfuse.py @@ -321,6 +321,7 @@ class LangFuseLogger: trace_id = clean_metadata.pop("trace_id", None) existing_trace_id = clean_metadata.pop("existing_trace_id", None) update_trace_keys = clean_metadata.pop("update_trace_keys", []) + debug = clean_metadata.pop("debug_langfuse", None) if trace_name is None and existing_trace_id is None: # just log `litellm-{call_type}` as the trace name @@ -374,6 +375,13 @@ class LangFuseLogger: else: trace_params["output"] = output + if debug == True or (isinstance(debug, str) and debug.lower() == "true"): + if "metadata" in trace_params: + # log the raw_metadata in the trace + trace_params["metadata"]["metadata_passed_to_litellm"] = metadata + else: + trace_params["metadata"] = {"metadata_passed_to_litellm": metadata} + cost = kwargs.get("response_cost", None) print_verbose(f"trace: {cost}") @@ -424,7 +432,6 @@ class LangFuseLogger: "url": url, "headers": clean_headers, } - trace = self.Langfuse.trace(**trace_params) generation_id = None @@ -465,7 +472,29 @@ class LangFuseLogger: } if supports_prompt: - generation_params["prompt"] = clean_metadata.pop("prompt", None) + user_prompt = clean_metadata.pop("prompt", None) + if user_prompt is None: + pass + elif isinstance(user_prompt, dict): + from langfuse.model import ( + TextPromptClient, + ChatPromptClient, + Prompt_Text, + Prompt_Chat, + ) + + if user_prompt.get("type", "") == "chat": + _prompt_chat = Prompt_Chat(**user_prompt) + generation_params["prompt"] = ChatPromptClient( + prompt=_prompt_chat + ) + elif user_prompt.get("type", "") == "text": + _prompt_text = Prompt_Text(**user_prompt) + generation_params["prompt"] = TextPromptClient( + prompt=_prompt_text + ) + else: + generation_params["prompt"] = user_prompt if output is not None and isinstance(output, str) and level == "ERROR": generation_params["status_message"] = output diff --git a/litellm/integrations/lunary.py b/litellm/integrations/lunary.py index 52316f3154..2e16e44a14 100644 --- a/litellm/integrations/lunary.py +++ b/litellm/integrations/lunary.py @@ -3,7 +3,6 @@ from datetime import datetime, timezone import traceback import importlib -import sys import packaging @@ -15,13 +14,33 @@ def parse_usage(usage): "prompt": usage["prompt_tokens"] if "prompt_tokens" in usage else 0, } +def parse_tool_calls(tool_calls): + if tool_calls is None: + return None + + def clean_tool_call(tool_call): + + serialized = { + "type": tool_call.type, + "id": tool_call.id, + "function": { + "name": tool_call.function.name, + "arguments": tool_call.function.arguments, + } + } + + return serialized + + return [clean_tool_call(tool_call) for tool_call in tool_calls] + def parse_messages(input): + if input is None: return None def clean_message(message): - # if is strin, return as is + # if is string, return as is if isinstance(message, str): return message @@ -35,9 +54,7 @@ def parse_messages(input): # Only add tool_calls and function_call to res if they are set if message.get("tool_calls"): - serialized["tool_calls"] = message.get("tool_calls") - if message.get("function_call"): - serialized["function_call"] = message.get("function_call") + serialized["tool_calls"] = parse_tool_calls(message.get("tool_calls")) return serialized @@ -92,8 +109,13 @@ class LunaryLogger: print_verbose(f"Lunary Logging - Logging request for model {model}") litellm_params = kwargs.get("litellm_params", {}) + optional_params = kwargs.get("optional_params", {}) metadata = litellm_params.get("metadata", {}) or {} + if optional_params: + # merge into extra + extra = {**extra, **optional_params} + tags = litellm_params.pop("tags", None) or [] if extra: @@ -103,7 +125,7 @@ class LunaryLogger: # keep only serializable types for param, value in extra.items(): - if not isinstance(value, (str, int, bool, float)): + if not isinstance(value, (str, int, bool, float)) and param != "tools": try: extra[param] = str(value) except: @@ -139,7 +161,7 @@ class LunaryLogger: metadata=metadata, runtime="litellm", tags=tags, - extra=extra, + params=extra, ) self.lunary_client.track_event( diff --git a/litellm/llms/azure.py b/litellm/llms/azure.py index f416d14377..02fe4a08f2 100644 --- a/litellm/llms/azure.py +++ b/litellm/llms/azure.py @@ -8,14 +8,16 @@ from litellm.utils import ( CustomStreamWrapper, convert_to_model_response_object, TranscriptionResponse, + get_secret, ) -from typing import Callable, Optional, BinaryIO +from typing import Callable, Optional, BinaryIO, List from litellm import OpenAIConfig import litellm, json import httpx # type: ignore from .custom_httpx.azure_dall_e_2 import CustomHTTPTransport, AsyncCustomHTTPTransport from openai import AzureOpenAI, AsyncAzureOpenAI import uuid +import os class AzureOpenAIError(Exception): @@ -105,6 +107,12 @@ class AzureOpenAIConfig(OpenAIConfig): optional_params["azure_ad_token"] = value return optional_params + def get_eu_regions(self) -> List[str]: + """ + Source: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-4-and-gpt-4-turbo-model-availability + """ + return ["europe", "sweden", "switzerland", "france", "uk"] + def select_azure_base_url_or_endpoint(azure_client_params: dict): # azure_client_params = { @@ -126,6 +134,51 @@ def select_azure_base_url_or_endpoint(azure_client_params: dict): return azure_client_params +def get_azure_ad_token_from_oidc(azure_ad_token: str): + azure_client_id = os.getenv("AZURE_CLIENT_ID", None) + azure_tenant = os.getenv("AZURE_TENANT_ID", None) + + if azure_client_id is None or azure_tenant is None: + raise AzureOpenAIError( + status_code=422, + message="AZURE_CLIENT_ID and AZURE_TENANT_ID must be set", + ) + + oidc_token = get_secret(azure_ad_token) + + if oidc_token is None: + raise AzureOpenAIError( + status_code=401, + message="OIDC token could not be retrieved from secret manager.", + ) + + req_token = httpx.post( + f"https://login.microsoftonline.com/{azure_tenant}/oauth2/v2.0/token", + data={ + "client_id": azure_client_id, + "grant_type": "client_credentials", + "scope": "https://cognitiveservices.azure.com/.default", + "client_assertion_type": "urn:ietf:params:oauth:client-assertion-type:jwt-bearer", + "client_assertion": oidc_token, + }, + ) + + if req_token.status_code != 200: + raise AzureOpenAIError( + status_code=req_token.status_code, + message=req_token.text, + ) + + possible_azure_ad_token = req_token.json().get("access_token", None) + + if possible_azure_ad_token is None: + raise AzureOpenAIError( + status_code=422, message="Azure AD Token not returned" + ) + + return possible_azure_ad_token + + class AzureChatCompletion(BaseLLM): def __init__(self) -> None: super().__init__() @@ -137,6 +190,8 @@ class AzureChatCompletion(BaseLLM): if api_key is not None: headers["api-key"] = api_key elif azure_ad_token is not None: + if azure_ad_token.startswith("oidc/"): + azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token) headers["Authorization"] = f"Bearer {azure_ad_token}" return headers @@ -189,6 +244,9 @@ class AzureChatCompletion(BaseLLM): if api_key is not None: azure_client_params["api_key"] = api_key elif azure_ad_token is not None: + 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 acompletion is True: @@ -276,6 +334,8 @@ class AzureChatCompletion(BaseLLM): if api_key is not None: azure_client_params["api_key"] = api_key elif azure_ad_token is not None: + 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: azure_client = AzureOpenAI(**azure_client_params) @@ -351,6 +411,8 @@ class AzureChatCompletion(BaseLLM): if api_key is not None: azure_client_params["api_key"] = api_key elif azure_ad_token is not None: + 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 # setting Azure client @@ -422,6 +484,8 @@ class AzureChatCompletion(BaseLLM): if api_key is not None: azure_client_params["api_key"] = api_key elif azure_ad_token is not None: + 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: azure_client = AzureOpenAI(**azure_client_params) @@ -478,6 +542,8 @@ class AzureChatCompletion(BaseLLM): if api_key is not None: azure_client_params["api_key"] = api_key elif azure_ad_token is not None: + 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: azure_client = AsyncAzureOpenAI(**azure_client_params) @@ -599,6 +665,8 @@ class AzureChatCompletion(BaseLLM): if api_key is not None: azure_client_params["api_key"] = api_key elif azure_ad_token is not None: + 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 ## LOGGING @@ -755,6 +823,8 @@ class AzureChatCompletion(BaseLLM): if api_key is not None: azure_client_params["api_key"] = api_key elif azure_ad_token is not None: + 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 aimg_generation == True: @@ -833,6 +903,8 @@ class AzureChatCompletion(BaseLLM): if api_key is not None: azure_client_params["api_key"] = api_key elif azure_ad_token is not None: + 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 max_retries is not None: diff --git a/litellm/llms/bedrock.py b/litellm/llms/bedrock.py index 08433ba18b..4314032e76 100644 --- a/litellm/llms/bedrock.py +++ b/litellm/llms/bedrock.py @@ -52,6 +52,16 @@ class AmazonBedrockGlobalConfig: optional_params[mapped_params[param]] = value return optional_params + def get_eu_regions(self) -> List[str]: + """ + Source: https://www.aws-services.info/bedrock.html + """ + return [ + "eu-west-1", + "eu-west-3", + "eu-central-1", + ] + class AmazonTitanConfig: """ @@ -551,6 +561,7 @@ def init_bedrock_client( 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, extra_headers: Optional[dict] = None, timeout: Optional[Union[float, httpx.Timeout]] = None, ): @@ -567,6 +578,7 @@ def init_bedrock_client( aws_session_name, aws_profile_name, aws_role_name, + aws_web_identity_token, ] # Iterate over parameters and update if needed @@ -582,6 +594,7 @@ def init_bedrock_client( aws_session_name, aws_profile_name, aws_role_name, + aws_web_identity_token, ) = params_to_check ### SET REGION NAME @@ -620,7 +633,38 @@ def init_bedrock_client( config = boto3.session.Config() ### CHECK STS ### - if aws_role_name is not None and aws_session_name is not None: + if aws_web_identity_token is not None and aws_role_name is not None and aws_session_name is not 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" + ) + + # 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, + ) + + client = boto3.client( + service_name="bedrock-runtime", + 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=region_name, + endpoint_url=endpoint_url, + config=config, + ) + elif aws_role_name is not None and aws_session_name is not None: # use sts if role name passed in sts_client = boto3.client( "sts", @@ -755,6 +799,7 @@ def completion( aws_bedrock_runtime_endpoint = optional_params.pop( "aws_bedrock_runtime_endpoint", None ) + aws_web_identity_token = optional_params.pop("aws_web_identity_token", None) # use passed in BedrockRuntime.Client if provided, otherwise create a new one client = optional_params.pop("aws_bedrock_client", None) @@ -769,6 +814,7 @@ def completion( aws_role_name=aws_role_name, aws_session_name=aws_session_name, aws_profile_name=aws_profile_name, + aws_web_identity_token=aws_web_identity_token, extra_headers=extra_headers, timeout=timeout, ) @@ -1291,6 +1337,7 @@ def embedding( aws_bedrock_runtime_endpoint = optional_params.pop( "aws_bedrock_runtime_endpoint", None ) + aws_web_identity_token = optional_params.pop("aws_web_identity_token", None) # use passed in BedrockRuntime.Client if provided, otherwise create a new one client = init_bedrock_client( @@ -1298,6 +1345,7 @@ def embedding( aws_secret_access_key=aws_secret_access_key, aws_region_name=aws_region_name, aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint, + aws_web_identity_token=aws_web_identity_token, aws_role_name=aws_role_name, aws_session_name=aws_session_name, ) @@ -1380,6 +1428,7 @@ def image_generation( aws_bedrock_runtime_endpoint = optional_params.pop( "aws_bedrock_runtime_endpoint", None ) + aws_web_identity_token = optional_params.pop("aws_web_identity_token", None) # use passed in BedrockRuntime.Client if provided, otherwise create a new one client = init_bedrock_client( @@ -1387,6 +1436,7 @@ def image_generation( aws_secret_access_key=aws_secret_access_key, aws_region_name=aws_region_name, aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint, + aws_web_identity_token=aws_web_identity_token, aws_role_name=aws_role_name, aws_session_name=aws_session_name, timeout=timeout, diff --git a/litellm/llms/clarifai.py b/litellm/llms/clarifai.py new file mode 100644 index 0000000000..e07a8d9e8a --- /dev/null +++ b/litellm/llms/clarifai.py @@ -0,0 +1,328 @@ +import os, types, traceback +import json +import requests +import time +from typing import Callable, Optional +from litellm.utils import ModelResponse, Usage, Choices, Message, CustomStreamWrapper +import litellm +import httpx +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler +from .prompt_templates.factory import prompt_factory, custom_prompt + + +class ClarifaiError(Exception): + def __init__(self, status_code, message, url): + self.status_code = status_code + self.message = message + self.request = httpx.Request( + method="POST", url=url + ) + self.response = httpx.Response(status_code=status_code, request=self.request) + super().__init__( + self.message + ) + +class ClarifaiConfig: + """ + Reference: https://clarifai.com/meta/Llama-2/models/llama2-70b-chat + TODO fill in the details + """ + max_tokens: Optional[int] = None + temperature: Optional[int] = None + top_k: Optional[int] = None + + def __init__( + self, + max_tokens: Optional[int] = None, + temperature: Optional[int] = None, + top_k: Optional[int] = None, + ) -> None: + locals_ = locals() + for key, value in locals_.items(): + 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 validate_environment(api_key): + headers = { + "accept": "application/json", + "content-type": "application/json", + } + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + return headers + +def completions_to_model(payload): + # if payload["n"] != 1: + # raise HTTPException( + # status_code=422, + # detail="Only one generation is supported. Please set candidate_count to 1.", + # ) + + params = {} + if temperature := payload.get("temperature"): + params["temperature"] = temperature + if max_tokens := payload.get("max_tokens"): + params["max_tokens"] = max_tokens + return { + "inputs": [{"data": {"text": {"raw": payload["prompt"]}}}], + "model": {"output_info": {"params": params}}, +} + +def process_response( + model, + prompt, + response, + model_response, + api_key, + data, + encoding, + logging_obj + ): + logging_obj.post_call( + input=prompt, + api_key=api_key, + original_response=response.text, + additional_args={"complete_input_dict": data}, + ) + ## RESPONSE OBJECT + try: + completion_response = response.json() + except Exception: + raise ClarifaiError( + message=response.text, status_code=response.status_code, url=model + ) + # print(completion_response) + try: + choices_list = [] + for idx, item in enumerate(completion_response["outputs"]): + if len(item["data"]["text"]["raw"]) > 0: + message_obj = Message(content=item["data"]["text"]["raw"]) + else: + message_obj = Message(content=None) + choice_obj = Choices( + finish_reason="stop", + index=idx + 1, #check + message=message_obj, + ) + choices_list.append(choice_obj) + model_response["choices"] = choices_list + + except Exception as e: + raise ClarifaiError( + message=traceback.format_exc(), status_code=response.status_code, url=model + ) + + # Calculate Usage + prompt_tokens = len(encoding.encode(prompt)) + 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, + ) + return model_response + +def convert_model_to_url(model: str, api_base: str): + user_id, app_id, model_id = model.split(".") + return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs" + +def get_prompt_model_name(url: str): + clarifai_model_name = url.split("/")[-2] + if "claude" in clarifai_model_name: + return "anthropic", clarifai_model_name.replace("_", ".") + if ("llama" in clarifai_model_name)or ("mistral" in clarifai_model_name): + return "", "meta-llama/llama-2-chat" + else: + return "", clarifai_model_name + +async def async_completion( + model: str, + prompt: str, + api_base: str, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + data=None, + optional_params=None, + litellm_params=None, + logger_fn=None, + headers={}): + + async_handler = AsyncHTTPHandler( + timeout=httpx.Timeout(timeout=600.0, connect=5.0) + ) + response = await async_handler.post( + api_base, headers=headers, data=json.dumps(data) + ) + + return process_response( + model=model, + prompt=prompt, + response=response, + model_response=model_response, + api_key=api_key, + data=data, + encoding=encoding, + logging_obj=logging_obj, + ) + +def completion( + model: str, + messages: list, + api_base: str, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + custom_prompt_dict={}, + acompletion=False, + optional_params=None, + litellm_params=None, + logger_fn=None, +): + headers = validate_environment(api_key) + model = convert_model_to_url(model, api_base) + prompt = " ".join(message["content"] for message in messages) # TODO + + ## Load Config + config = litellm.ClarifaiConfig.get_config() + for k, v in config.items(): + if ( + k not in optional_params + ): + optional_params[k] = v + + custom_llm_provider, orig_model_name = get_prompt_model_name(model) + if custom_llm_provider == "anthropic": + prompt = prompt_factory( + model=orig_model_name, + messages=messages, + api_key=api_key, + custom_llm_provider="clarifai" + ) + else: + prompt = prompt_factory( + model=orig_model_name, + messages=messages, + api_key=api_key, + custom_llm_provider=custom_llm_provider + ) + # print(prompt); exit(0) + + data = { + "prompt": prompt, + **optional_params, + } + data = completions_to_model(data) + + + ## LOGGING + logging_obj.pre_call( + input=prompt, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "headers": headers, + "api_base": api_base, + }, + ) + if acompletion==True: + return async_completion( + model=model, + prompt=prompt, + api_base=api_base, + custom_prompt_dict=custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + encoding=encoding, + api_key=api_key, + logging_obj=logging_obj, + data=data, + optional_params=optional_params, + litellm_params=litellm_params, + logger_fn=logger_fn, + headers=headers, + ) + else: + ## COMPLETION CALL + response = requests.post( + model, + headers=headers, + data=json.dumps(data), + ) + # print(response.content); exit() + + if response.status_code != 200: + raise ClarifaiError(status_code=response.status_code, message=response.text, url=model) + + if "stream" in optional_params and optional_params["stream"] == True: + completion_stream = response.iter_lines() + stream_response = CustomStreamWrapper( + completion_stream=completion_stream, + model=model, + custom_llm_provider="clarifai", + logging_obj=logging_obj, + ) + return stream_response + + else: + return process_response( + model=model, + prompt=prompt, + response=response, + model_response=model_response, + api_key=api_key, + data=data, + encoding=encoding, + logging_obj=logging_obj) + + +class ModelResponseIterator: + def __init__(self, model_response): + self.model_response = model_response + self.is_done = False + + # Sync iterator + def __iter__(self): + return self + + def __next__(self): + if self.is_done: + raise StopIteration + self.is_done = True + return 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.model_response \ No newline at end of file diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py index 24a076dd0c..cf593369c4 100644 --- a/litellm/llms/prompt_templates/factory.py +++ b/litellm/llms/prompt_templates/factory.py @@ -1509,6 +1509,11 @@ def prompt_factory( model="meta-llama/Meta-Llama-3-8B-Instruct", messages=messages, ) + + elif custom_llm_provider == "clarifai": + if "claude" in model: + return anthropic_pt(messages=messages) + elif custom_llm_provider == "perplexity": for message in messages: message.pop("name", None) diff --git a/litellm/llms/vertex_ai.py b/litellm/llms/vertex_ai.py index a61c07df01..d3bb2c78ab 100644 --- a/litellm/llms/vertex_ai.py +++ b/litellm/llms/vertex_ai.py @@ -198,6 +198,23 @@ class VertexAIConfig: optional_params[mapped_params[param]] = value return optional_params + def get_eu_regions(self) -> List[str]: + """ + Source: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/locations#available-regions + """ + return [ + "europe-central2", + "europe-north1", + "europe-southwest1", + "europe-west1", + "europe-west2", + "europe-west3", + "europe-west4", + "europe-west6", + "europe-west8", + "europe-west9", + ] + import asyncio diff --git a/litellm/llms/watsonx.py b/litellm/llms/watsonx.py index 99f2d18baf..34176a23a4 100644 --- a/litellm/llms/watsonx.py +++ b/litellm/llms/watsonx.py @@ -1,12 +1,26 @@ from enum import Enum import json, types, time # noqa: E401 -from contextlib import contextmanager -from typing import Callable, Dict, Optional, Any, Union, List +from contextlib import asynccontextmanager, contextmanager +from typing import ( + Callable, + Dict, + Generator, + AsyncGenerator, + Iterator, + AsyncIterator, + Optional, + Any, + Union, + List, + ContextManager, + AsyncContextManager, +) import httpx # type: ignore import requests # type: ignore import litellm -from litellm.utils import ModelResponse, get_secret, Usage +from litellm.utils import ModelResponse, Usage, get_secret +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler from .base import BaseLLM from .prompt_templates import factory as ptf @@ -149,6 +163,15 @@ class IBMWatsonXAIConfig: optional_params[mapped_params[param]] = value return optional_params + def get_eu_regions(self) -> List[str]: + """ + Source: https://www.ibm.com/docs/en/watsonx/saas?topic=integrations-regional-availability + """ + return [ + "eu-de", + "eu-gb", + ] + def convert_messages_to_prompt(model, messages, provider, custom_prompt_dict): # handle anthropic prompts and amazon titan prompts @@ -188,11 +211,12 @@ class WatsonXAIEndpoint(str, Enum): ) EMBEDDINGS = "/ml/v1/text/embeddings" PROMPTS = "/ml/v1/prompts" + AVAILABLE_MODELS = "/ml/v1/foundation_model_specs" class IBMWatsonXAI(BaseLLM): """ - Class to interface with IBM Watsonx.ai API for text generation and embeddings. + Class to interface with IBM watsonx.ai API for text generation and embeddings. Reference: https://cloud.ibm.com/apidocs/watsonx-ai """ @@ -343,7 +367,7 @@ class IBMWatsonXAI(BaseLLM): ) if token is None and api_key is not None: # generate the auth token - if print_verbose: + if print_verbose is not None: print_verbose("Generating IAM token for Watsonx.ai") token = self.generate_iam_token(api_key) elif token is None and api_key is None: @@ -378,10 +402,11 @@ class IBMWatsonXAI(BaseLLM): print_verbose: Callable, encoding, logging_obj, - optional_params: dict, - litellm_params: Optional[dict] = None, + optional_params=None, + acompletion=None, + litellm_params=None, logger_fn=None, - timeout: Optional[float] = None, + timeout=None, ): """ Send a text generation request to the IBM Watsonx.ai API. @@ -402,12 +427,12 @@ class IBMWatsonXAI(BaseLLM): model, messages, provider, custom_prompt_dict ) - def process_text_request(request_params: dict) -> ModelResponse: - with self._manage_response( - request_params, logging_obj=logging_obj, input=prompt, timeout=timeout - ) as resp: - json_resp = resp.json() - + def process_text_gen_response(json_resp: dict) -> ModelResponse: + if "results" not in json_resp: + raise WatsonXAIError( + status_code=500, + message=f"Error: Invalid response from Watsonx.ai API: {json_resp}", + ) generated_text = json_resp["results"][0]["generated_text"] prompt_tokens = json_resp["results"][0]["input_token_count"] completion_tokens = json_resp["results"][0]["generated_token_count"] @@ -415,36 +440,70 @@ class IBMWatsonXAI(BaseLLM): model_response["finish_reason"] = json_resp["results"][0]["stop_reason"] model_response["created"] = int(time.time()) model_response["model"] = model - setattr( - model_response, - "usage", - Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ), + 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 process_stream_request( - request_params: dict, + def process_stream_response( + stream_resp: Union[Iterator[str], AsyncIterator], ) -> litellm.CustomStreamWrapper: + streamwrapper = litellm.CustomStreamWrapper( + stream_resp, + model=model, + custom_llm_provider="watsonx", + logging_obj=logging_obj, + ) + return streamwrapper + + # create the function to manage the request to watsonx.ai + self.request_manager = RequestManager(logging_obj) + + def handle_text_request(request_params: dict) -> ModelResponse: + with self.request_manager.request( + request_params, + input=prompt, + timeout=timeout, + ) as resp: + json_resp = resp.json() + + return process_text_gen_response(json_resp) + + async def handle_text_request_async(request_params: dict) -> ModelResponse: + async with self.request_manager.async_request( + request_params, + input=prompt, + timeout=timeout, + ) as resp: + json_resp = resp.json() + return process_text_gen_response(json_resp) + + def handle_stream_request(request_params: dict) -> litellm.CustomStreamWrapper: # stream the response - generated chunks will be handled # by litellm.utils.CustomStreamWrapper.handle_watsonx_stream - with self._manage_response( + with self.request_manager.request( request_params, - logging_obj=logging_obj, stream=True, input=prompt, timeout=timeout, ) as resp: - response = litellm.CustomStreamWrapper( - resp.iter_lines(), - model=model, - custom_llm_provider="watsonx", - logging_obj=logging_obj, - ) - return response + streamwrapper = process_stream_response(resp.iter_lines()) + return streamwrapper + + async def handle_stream_request_async(request_params: dict) -> litellm.CustomStreamWrapper: + # stream the response - generated chunks will be handled + # by litellm.utils.CustomStreamWrapper.handle_watsonx_stream + async with self.request_manager.async_request( + request_params, + stream=True, + input=prompt, + timeout=timeout, + ) as resp: + streamwrapper = process_stream_response(resp.aiter_lines()) + return streamwrapper try: ## Get the response from the model @@ -455,10 +514,18 @@ class IBMWatsonXAI(BaseLLM): optional_params=optional_params, print_verbose=print_verbose, ) - if stream: - return process_stream_request(req_params) + if stream and (acompletion is True): + # stream and async text generation + return handle_stream_request_async(req_params) + elif stream: + # streaming text generation + return handle_stream_request(req_params) + elif (acompletion is True): + # async text generation + return handle_text_request_async(req_params) else: - return process_text_request(req_params) + # regular text generation + return handle_text_request(req_params) except WatsonXAIError as e: raise e except Exception as e: @@ -473,6 +540,7 @@ class IBMWatsonXAI(BaseLLM): model_response=None, optional_params=None, encoding=None, + aembedding=None, ): """ Send a text embedding request to the IBM Watsonx.ai API. @@ -507,9 +575,6 @@ class IBMWatsonXAI(BaseLLM): } request_params = dict(version=api_params["api_version"]) url = api_params["url"].rstrip("/") + WatsonXAIEndpoint.EMBEDDINGS - # request = httpx.Request( - # "POST", url, headers=headers, json=payload, params=request_params - # ) req_params = { "method": "POST", "url": url, @@ -517,25 +582,49 @@ class IBMWatsonXAI(BaseLLM): "json": payload, "params": request_params, } - with self._manage_response( - req_params, logging_obj=logging_obj, input=input - ) as resp: - json_resp = resp.json() + request_manager = RequestManager(logging_obj) - results = json_resp.get("results", []) - embedding_response = [] - for idx, result in enumerate(results): - embedding_response.append( - {"object": "embedding", "index": idx, "embedding": result["embedding"]} + def process_embedding_response(json_resp: dict) -> ModelResponse: + results = json_resp.get("results", []) + embedding_response = [] + for idx, result in enumerate(results): + embedding_response.append( + { + "object": "embedding", + "index": idx, + "embedding": result["embedding"], + } + ) + model_response["object"] = "list" + model_response["data"] = embedding_response + model_response["model"] = model + input_tokens = json_resp.get("input_token_count", 0) + model_response.usage = Usage( + prompt_tokens=input_tokens, + completion_tokens=0, + total_tokens=input_tokens, ) - model_response["object"] = "list" - model_response["data"] = embedding_response - model_response["model"] = model - input_tokens = json_resp.get("input_token_count", 0) - model_response.usage = Usage( - prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens - ) - return model_response + return model_response + + def handle_embedding(request_params: dict) -> ModelResponse: + with request_manager.request(request_params, input=input) as resp: + json_resp = resp.json() + return process_embedding_response(json_resp) + + async def handle_aembedding(request_params: dict) -> ModelResponse: + async with request_manager.async_request(request_params, input=input) as resp: + json_resp = resp.json() + return process_embedding_response(json_resp) + + try: + if aembedding is True: + return handle_embedding(req_params) + else: + return handle_aembedding(req_params) + except WatsonXAIError as e: + raise e + except Exception as e: + raise WatsonXAIError(status_code=500, message=str(e)) def generate_iam_token(self, api_key=None, **params): headers = {} @@ -558,52 +647,144 @@ class IBMWatsonXAI(BaseLLM): self.token = iam_access_token return iam_access_token - @contextmanager - def _manage_response( - self, - request_params: dict, - logging_obj: Any, - stream: bool = False, - input: Optional[Any] = None, - timeout: Optional[float] = None, - ): - request_str = ( - f"response = {request_params['method']}(\n" - f"\turl={request_params['url']},\n" - f"\tjson={request_params['json']},\n" - f")" - ) - logging_obj.pre_call( - input=input, - api_key=request_params["headers"].get("Authorization"), - additional_args={ - "complete_input_dict": request_params["json"], - "request_str": request_str, - }, - ) - if timeout: - request_params["timeout"] = timeout - try: - if stream: - resp = requests.request( - **request_params, - stream=True, - ) - resp.raise_for_status() - yield resp - else: - resp = requests.request(**request_params) - resp.raise_for_status() - yield resp - except Exception as e: - raise WatsonXAIError(status_code=500, message=str(e)) - if not stream: - logging_obj.post_call( + def get_available_models(self, *, ids_only: bool = True, **params): + api_params = self._get_api_params(params) + headers = { + "Authorization": f"Bearer {api_params['token']}", + "Content-Type": "application/json", + "Accept": "application/json", + } + request_params = dict(version=api_params["api_version"]) + url = api_params["url"].rstrip("/") + WatsonXAIEndpoint.AVAILABLE_MODELS + req_params = dict(method="GET", url=url, headers=headers, params=request_params) + with RequestManager(logging_obj=None).request(req_params) as resp: + json_resp = resp.json() + if not ids_only: + return json_resp + return [res["model_id"] for res in json_resp["resources"]] + +class RequestManager: + """ + Returns a context manager that manages the response from the request. + if async_ is True, returns an async context manager, otherwise returns a regular context manager. + + Usage: + ```python + request_params = dict(method="POST", url="https://api.example.com", headers={"Authorization" : "Bearer token"}, json={"key": "value"}) + request_manager = RequestManager(logging_obj=logging_obj) + async with request_manager.request(request_params) as resp: + ... + # or + with request_manager.async_request(request_params) as resp: + ... + ``` + """ + + def __init__(self, logging_obj=None): + self.logging_obj = logging_obj + + def pre_call( + self, + request_params: dict, + input: Optional[Any] = None, + ): + if self.logging_obj is None: + return + request_str = ( + f"response = {request_params['method']}(\n" + f"\turl={request_params['url']},\n" + f"\tjson={request_params.get('json')},\n" + f")" + ) + self.logging_obj.pre_call( + input=input, + api_key=request_params["headers"].get("Authorization"), + additional_args={ + "complete_input_dict": request_params.get("json"), + "request_str": request_str, + }, + ) + + def post_call(self, resp, request_params): + if self.logging_obj is None: + return + self.logging_obj.post_call( input=input, api_key=request_params["headers"].get("Authorization"), original_response=json.dumps(resp.json()), additional_args={ "status_code": resp.status_code, - "complete_input_dict": request_params["json"], + "complete_input_dict": request_params.get( + "data", request_params.get("json") + ), }, ) + + @contextmanager + def request( + self, + request_params: dict, + stream: bool = False, + input: Optional[Any] = None, + timeout=None, + ) -> Generator[requests.Response, None, None]: + """ + Returns a context manager that yields the response from the request. + """ + self.pre_call(request_params, input) + if timeout: + request_params["timeout"] = timeout + if stream: + request_params["stream"] = stream + try: + resp = requests.request(**request_params) + if not resp.ok: + raise WatsonXAIError( + status_code=resp.status_code, + message=f"Error {resp.status_code} ({resp.reason}): {resp.text}", + ) + yield resp + except Exception as e: + raise WatsonXAIError(status_code=500, message=str(e)) + if not stream: + self.post_call(resp, request_params) + + @asynccontextmanager + async def async_request( + self, + request_params: dict, + stream: bool = False, + input: Optional[Any] = None, + timeout=None, + ) -> AsyncGenerator[httpx.Response, None]: + self.pre_call(request_params, input) + if timeout: + request_params["timeout"] = timeout + if stream: + request_params["stream"] = stream + try: + # async with AsyncHTTPHandler(timeout=timeout) as client: + self.async_handler = AsyncHTTPHandler( + timeout=httpx.Timeout( + timeout=request_params.pop("timeout", 600.0), connect=5.0 + ), + ) + # async_handler.client.verify = False + if "json" in request_params: + request_params["data"] = json.dumps(request_params.pop("json", {})) + method = request_params.pop("method") + if method.upper() == "POST": + resp = await self.async_handler.post(**request_params) + else: + resp = await self.async_handler.get(**request_params) + if resp.status_code not in [200, 201]: + raise WatsonXAIError( + status_code=resp.status_code, + message=f"Error {resp.status_code} ({resp.reason}): {resp.text}", + ) + yield resp + # await async_handler.close() + except Exception as e: + raise WatsonXAIError(status_code=500, message=str(e)) + if not stream: + self.post_call(resp, request_params) \ No newline at end of file diff --git a/litellm/main.py b/litellm/main.py index 8e150f3e6f..16188b253c 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -56,6 +56,7 @@ from .llms import ( ollama, ollama_chat, cloudflare, + clarifai, cohere, cohere_chat, petals, @@ -1212,6 +1213,60 @@ def completion( ) response = model_response + elif ("clarifai" in model + or custom_llm_provider == "clarifai" + or model in litellm.clarifai_models + ): + clarifai_key = None + clarifai_key = ( + api_key + or litellm.clarifai_key + or litellm.api_key + or get_secret("CLARIFAI_API_KEY") + or get_secret("CLARIFAI_API_TOKEN") + ) + + api_base = ( + api_base + or litellm.api_base + or get_secret("CLARIFAI_API_BASE") + or "https://api.clarifai.com/v2" + ) + + custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict + model_response = clarifai.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, + acompletion=acompletion, + logger_fn=logger_fn, + encoding=encoding, # for calculating input/output tokens + api_key=clarifai_key, + logging_obj=logging, + custom_prompt_dict=custom_prompt_dict, + ) + + if "stream" in optional_params and optional_params["stream"] == True: + # don't try to access stream object, + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=model_response, + ) + + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=clarifai_key, + original_response=model_response, + ) + response = model_response elif custom_llm_provider == "anthropic": api_key = ( diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 10c70a858d..1ade08fe35 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1571,6 +1571,135 @@ "litellm_provider": "replicate", "mode": "chat" }, + "openrouter/microsoft/wizardlm-2-8x22b:nitro": { + "max_tokens": 65536, + "input_cost_per_token": 0.000001, + "output_cost_per_token": 0.000001, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/google/gemini-pro-1.5": { + "max_tokens": 8192, + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "input_cost_per_token": 0.0000025, + "output_cost_per_token": 0.0000075, + "input_cost_per_image": 0.00265, + "litellm_provider": "openrouter", + "mode": "chat", + "supports_function_calling": true, + "supports_vision": true + }, + "openrouter/mistralai/mixtral-8x22b-instruct": { + "max_tokens": 65536, + "input_cost_per_token": 0.00000065, + "output_cost_per_token": 0.00000065, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/cohere/command-r-plus": { + "max_tokens": 128000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000015, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/databricks/dbrx-instruct": { + "max_tokens": 32768, + "input_cost_per_token": 0.0000006, + "output_cost_per_token": 0.0000006, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/anthropic/claude-3-haiku": { + "max_tokens": 200000, + "input_cost_per_token": 0.00000025, + "output_cost_per_token": 0.00000125, + "input_cost_per_image": 0.0004, + "litellm_provider": "openrouter", + "mode": "chat", + "supports_function_calling": true, + "supports_vision": true + }, + "openrouter/anthropic/claude-3-sonnet": { + "max_tokens": 200000, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000015, + "input_cost_per_image": 0.0048, + "litellm_provider": "openrouter", + "mode": "chat", + "supports_function_calling": true, + "supports_vision": true + }, + "openrouter/mistralai/mistral-large": { + "max_tokens": 32000, + "input_cost_per_token": 0.000008, + "output_cost_per_token": 0.000024, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/cognitivecomputations/dolphin-mixtral-8x7b": { + "max_tokens": 32769, + "input_cost_per_token": 0.0000005, + "output_cost_per_token": 0.0000005, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/google/gemini-pro-vision": { + "max_tokens": 45875, + "input_cost_per_token": 0.000000125, + "output_cost_per_token": 0.000000375, + "input_cost_per_image": 0.0025, + "litellm_provider": "openrouter", + "mode": "chat", + "supports_function_calling": true, + "supports_vision": true + }, + "openrouter/fireworks/firellava-13b": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000002, + "output_cost_per_token": 0.0000002, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/meta-llama/llama-3-8b-instruct:free": { + "max_tokens": 8192, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/meta-llama/llama-3-8b-instruct:extended": { + "max_tokens": 16384, + "input_cost_per_token": 0.000000225, + "output_cost_per_token": 0.00000225, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/meta-llama/llama-3-70b-instruct:nitro": { + "max_tokens": 8192, + "input_cost_per_token": 0.0000009, + "output_cost_per_token": 0.0000009, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/meta-llama/llama-3-70b-instruct": { + "max_tokens": 8192, + "input_cost_per_token": 0.00000059, + "output_cost_per_token": 0.00000079, + "litellm_provider": "openrouter", + "mode": "chat" + }, + "openrouter/openai/gpt-4-vision-preview": { + "max_tokens": 130000, + "input_cost_per_token": 0.00001, + "output_cost_per_token": 0.00003, + "input_cost_per_image": 0.01445, + "litellm_provider": "openrouter", + "mode": "chat", + "supports_function_calling": true, + "supports_vision": true + }, "openrouter/openai/gpt-3.5-turbo": { "max_tokens": 4095, "input_cost_per_token": 0.0000015, @@ -1621,14 +1750,14 @@ "tool_use_system_prompt_tokens": 395 }, "openrouter/google/palm-2-chat-bison": { - "max_tokens": 8000, + "max_tokens": 25804, "input_cost_per_token": 0.0000005, "output_cost_per_token": 0.0000005, "litellm_provider": "openrouter", "mode": "chat" }, "openrouter/google/palm-2-codechat-bison": { - "max_tokens": 8000, + "max_tokens": 20070, "input_cost_per_token": 0.0000005, "output_cost_per_token": 0.0000005, "litellm_provider": "openrouter", @@ -1711,13 +1840,6 @@ "litellm_provider": "openrouter", "mode": "chat" }, - "openrouter/meta-llama/llama-3-70b-instruct": { - "max_tokens": 8192, - "input_cost_per_token": 0.0000008, - "output_cost_per_token": 0.0000008, - "litellm_provider": "openrouter", - "mode": "chat" - }, "j2-ultra": { "max_tokens": 8192, "max_input_tokens": 8192, @@ -3226,4 +3348,4 @@ "mode": "embedding" } -} +} \ No newline at end of file diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index 448d7cf877..eaf5701355 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

404

This page could not be found.

\ No newline at end of file +404: This page could not be found.LiteLLM Dashboard

404

This page could not be found.

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