diff --git a/.circleci/config.yml b/.circleci/config.yml index 6caed4810..1ab163653 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -61,7 +61,7 @@ jobs: command: | pwd ls - python -m pytest -vv litellm/tests/ -x --junitxml=test-results/junit.xml --durations=5 + python -m pytest -vv litellm/tests/ -x --junitxml=test-results/junit.xml --durations=5 no_output_timeout: 120m # Store test results @@ -78,6 +78,11 @@ jobs: steps: - checkout + + - run: + name: Copy model_prices_and_context_window File to model_prices_and_context_window_backup + command: | + cp model_prices_and_context_window.json litellm/model_prices_and_context_window_backup.json - run: name: Check if litellm dir was updated or if pyproject.toml was modified diff --git a/.gitignore b/.gitignore index 088996ddd..b31366a33 100644 --- a/.gitignore +++ b/.gitignore @@ -19,3 +19,9 @@ litellm/proxy/_secret_config.yaml litellm/tests/aiologs.log litellm/tests/exception_data.txt litellm/tests/config_*.yaml +litellm/tests/langfuse.log +litellm/tests/test_custom_logger.py +litellm/tests/langfuse.log +litellm/tests/dynamo*.log +.vscode/settings.json +litellm/proxy/log.txt diff --git a/Dockerfile b/Dockerfile index 0647ee92a..1a59db16a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,8 +1,11 @@ # Base image -ARG LITELLM_BASE_IMAGE=python:3.9-slim +ARG LITELLM_BUILD_IMAGE=python:3.9 -# allow users to specify, else use python 3.9-slim -FROM $LITELLM_BASE_IMAGE +# Runtime image +ARG LITELLM_RUNTIME_IMAGE=python:3.9-slim + +# allow users to specify, else use python 3.9 +FROM $LITELLM_BUILD_IMAGE as builder # Set the working directory to /app WORKDIR /app @@ -16,7 +19,7 @@ RUN pip install --upgrade pip && \ pip install build # Copy the current directory contents into the container at /app -COPY . /app +COPY requirements.txt . # Build the package RUN rm -rf dist/* && python -m build @@ -25,13 +28,27 @@ RUN rm -rf dist/* && python -m build RUN pip install dist/*.whl # Install any needed packages specified in requirements.txt -RUN pip wheel --no-cache-dir --wheel-dir=wheels -r requirements.txt -RUN pip install --no-cache-dir --find-links=wheels -r requirements.txt +RUN pip install wheel && \ + pip wheel --no-cache-dir --wheel-dir=/app/wheels -r requirements.txt + +############################################################################### +FROM $LITELLM_RUNTIME_IMAGE as runtime + +WORKDIR /app + +# Copy the current directory contents into the container at /app +COPY . . + +COPY --from=builder /app/wheels /app/wheels + +RUN pip install --no-index --find-links=/app/wheels -r requirements.txt + +# Trigger the Prisma CLI to be installed +RUN prisma -v EXPOSE 4000/tcp # Start the litellm proxy, using the `litellm` cli command https://docs.litellm.ai/docs/simple_proxy - # Start the litellm proxy with default options CMD ["--port", "4000"] diff --git a/README.md b/README.md index cc5c1a3a5..583a643f3 100644 --- a/README.md +++ b/README.md @@ -62,6 +62,22 @@ response = completion(model="command-nightly", messages=messages) print(response) ``` +## Async ([Docs](https://docs.litellm.ai/docs/completion/stream#async-completion)) + +```python +from litellm import acompletion +import asyncio + +async def test_get_response(): + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + response = await acompletion(model="gpt-3.5-turbo", messages=messages) + return response + +response = asyncio.run(test_get_response()) +print(response) +``` + ## Streaming ([Docs](https://docs.litellm.ai/docs/completion/stream)) liteLLM supports streaming the model response back, pass `stream=True` to get a streaming iterator in response. Streaming is supported for all models (Bedrock, Huggingface, TogetherAI, Azure, OpenAI, etc.) @@ -140,6 +156,7 @@ response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content | [openrouter](https://docs.litellm.ai/docs/providers/openrouter) | ✅ | ✅ | ✅ | ✅ | | [google - vertex_ai](https://docs.litellm.ai/docs/providers/vertex) | ✅ | ✅ | ✅ | ✅ | | [google - palm](https://docs.litellm.ai/docs/providers/palm) | ✅ | ✅ | ✅ | ✅ | +| [mistral ai api](https://docs.litellm.ai/docs/providers/mistral) | ✅ | ✅ | ✅ | ✅ | | [ai21](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ | ✅ | | [baseten](https://docs.litellm.ai/docs/providers/baseten) | ✅ | ✅ | ✅ | ✅ | | [vllm](https://docs.litellm.ai/docs/providers/vllm) | ✅ | ✅ | ✅ | ✅ | diff --git a/dist/litellm-1.12.5.dev1-py3-none-any.whl b/dist/litellm-1.12.5.dev1-py3-none-any.whl new file mode 100644 index 000000000..395d5c567 Binary files /dev/null and b/dist/litellm-1.12.5.dev1-py3-none-any.whl differ diff --git a/dist/litellm-1.12.5.dev1.tar.gz b/dist/litellm-1.12.5.dev1.tar.gz new file mode 100644 index 000000000..8fdfd9d5e Binary files /dev/null and b/dist/litellm-1.12.5.dev1.tar.gz differ diff --git a/dist/litellm-1.12.6.dev1-py3-none-any.whl b/dist/litellm-1.12.6.dev1-py3-none-any.whl new file mode 100644 index 000000000..95ba50b4f Binary files /dev/null and b/dist/litellm-1.12.6.dev1-py3-none-any.whl differ diff --git a/dist/litellm-1.12.6.dev1.tar.gz b/dist/litellm-1.12.6.dev1.tar.gz new file mode 100644 index 000000000..c18e6d1ce Binary files /dev/null and b/dist/litellm-1.12.6.dev1.tar.gz differ diff --git a/dist/litellm-1.12.6.dev2-py3-none-any.whl b/dist/litellm-1.12.6.dev2-py3-none-any.whl new file mode 100644 index 000000000..bf6d294b9 Binary files /dev/null and b/dist/litellm-1.12.6.dev2-py3-none-any.whl differ diff --git a/dist/litellm-1.12.6.dev2.tar.gz b/dist/litellm-1.12.6.dev2.tar.gz new file mode 100644 index 000000000..6f032ba7b Binary files /dev/null and b/dist/litellm-1.12.6.dev2.tar.gz differ diff --git a/dist/litellm-1.12.6.dev3-py3-none-any.whl b/dist/litellm-1.12.6.dev3-py3-none-any.whl new file mode 100644 index 000000000..81290067d Binary files /dev/null and b/dist/litellm-1.12.6.dev3-py3-none-any.whl differ diff --git a/dist/litellm-1.12.6.dev3.tar.gz b/dist/litellm-1.12.6.dev3.tar.gz new file mode 100644 index 000000000..2dbe390eb Binary files /dev/null and b/dist/litellm-1.12.6.dev3.tar.gz differ diff --git a/dist/litellm-1.12.6.dev4-py3-none-any.whl b/dist/litellm-1.12.6.dev4-py3-none-any.whl new file mode 100644 index 000000000..db89f6f9d Binary files /dev/null and b/dist/litellm-1.12.6.dev4-py3-none-any.whl differ diff --git a/dist/litellm-1.12.6.dev4.tar.gz b/dist/litellm-1.12.6.dev4.tar.gz new file mode 100644 index 000000000..3daae3edd Binary files /dev/null and b/dist/litellm-1.12.6.dev4.tar.gz differ diff --git a/dist/litellm-1.12.6.dev5-py3-none-any.whl b/dist/litellm-1.12.6.dev5-py3-none-any.whl new file mode 100644 index 000000000..d4233ebfb Binary files /dev/null and b/dist/litellm-1.12.6.dev5-py3-none-any.whl differ diff --git a/dist/litellm-1.12.6.dev5.tar.gz b/dist/litellm-1.12.6.dev5.tar.gz new file mode 100644 index 000000000..d2a6280bf Binary files /dev/null and b/dist/litellm-1.12.6.dev5.tar.gz differ diff --git a/dist/litellm-1.14.0.dev1-py3-none-any.whl b/dist/litellm-1.14.0.dev1-py3-none-any.whl new file mode 100644 index 000000000..7428d252d Binary files /dev/null and b/dist/litellm-1.14.0.dev1-py3-none-any.whl differ diff --git a/dist/litellm-1.14.0.dev1.tar.gz b/dist/litellm-1.14.0.dev1.tar.gz new file mode 100644 index 000000000..cd80b0e71 Binary files /dev/null and b/dist/litellm-1.14.0.dev1.tar.gz differ diff --git a/dist/litellm-1.14.5.dev1-py3-none-any.whl b/dist/litellm-1.14.5.dev1-py3-none-any.whl new file mode 100644 index 000000000..1555e6f68 Binary files /dev/null and b/dist/litellm-1.14.5.dev1-py3-none-any.whl differ diff --git a/dist/litellm-1.14.5.dev1.tar.gz b/dist/litellm-1.14.5.dev1.tar.gz new file mode 100644 index 000000000..06f47b0eb Binary files /dev/null and b/dist/litellm-1.14.5.dev1.tar.gz differ diff --git a/docs/my-website/docs/caching/redis_cache.md b/docs/my-website/docs/caching/redis_cache.md index 521c4d00f..979119ad7 100644 --- a/docs/my-website/docs/caching/redis_cache.md +++ b/docs/my-website/docs/caching/redis_cache.md @@ -55,27 +55,76 @@ litellm.cache = cache # set litellm.cache to your cache ``` -### Detecting Cached Responses -For resposes that were returned as cache hit, the response includes a param `cache` = True +## Cache Initialization Parameters -:::info +#### `type` (str, optional) -Only valid for OpenAI <= 0.28.1 [Let us know if you still need this](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=bug&projects=&template=bug_report.yml&title=%5BBug%5D%3A+) -::: +The type of cache to initialize. It can be either "local" or "redis". Defaults to "local". -Example response with cache hit -```python -{ - 'cache': True, - 'id': 'chatcmpl-7wggdzd6OXhgE2YhcLJHJNZsEWzZ2', - 'created': 1694221467, - 'model': 'gpt-3.5-turbo-0613', - 'choices': [ - { - 'index': 0, 'message': {'role': 'assistant', 'content': 'I\'m sorry, but I couldn\'t find any information about "litellm" or how many stars it has. It is possible that you may be referring to a specific product, service, or platform that I am not familiar with. Can you please provide more context or clarify your question?' - }, 'finish_reason': 'stop'} - ], - 'usage': {'prompt_tokens': 17, 'completion_tokens': 59, 'total_tokens': 76}, -} +#### `host` (str, optional) -``` \ No newline at end of file +The host address for the Redis cache. This parameter is required if the `type` is set to "redis". + +#### `port` (int, optional) + +The port number for the Redis cache. This parameter is required if the `type` is set to "redis". + +#### `password` (str, optional) + +The password for the Redis cache. This parameter is required if the `type` is set to "redis". + +#### `supported_call_types` (list, optional) + +A list of call types to cache for. Defaults to caching for all call types. The available call types are: + +- "completion" +- "acompletion" +- "embedding" +- "aembedding" + +#### `**kwargs` (additional keyword arguments) + +Additional keyword arguments are accepted for the initialization of the Redis cache using the `redis.Redis()` constructor. These arguments allow you to fine-tune the Redis cache configuration based on your specific needs. + + +## Logging + +Cache hits are logged in success events as `kwarg["cache_hit"]`. + +Here's an example of accessing it: + + ```python + import litellm +from litellm.integrations.custom_logger import CustomLogger +from litellm import completion, acompletion, Cache + +# create custom callback for success_events +class MyCustomHandler(CustomLogger): + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Success") + print(f"Value of Cache hit: {kwargs['cache_hit']"}) + +async def test_async_completion_azure_caching(): + # set custom callback + customHandler_caching = MyCustomHandler() + litellm.callbacks = [customHandler_caching] + + # init cache + litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) + unique_time = time.time() + response1 = await litellm.acompletion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": f"Hi 👋 - i'm async azure {unique_time}" + }], + caching=True) + await asyncio.sleep(1) + print(f"customHandler_caching.states pre-cache hit: {customHandler_caching.states}") + response2 = await litellm.acompletion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": f"Hi 👋 - i'm async azure {unique_time}" + }], + caching=True) + await asyncio.sleep(1) # success callbacks are done in parallel + ``` diff --git a/docs/my-website/docs/observability/custom_callback.md b/docs/my-website/docs/observability/custom_callback.md index 78b7499a8..319a25e87 100644 --- a/docs/my-website/docs/observability/custom_callback.md +++ b/docs/my-website/docs/observability/custom_callback.md @@ -4,7 +4,9 @@ You can create a custom callback class to precisely log events as they occur in litellm. ```python +import litellm from litellm.integrations.custom_logger import CustomLogger +from litellm import completion, acompletion class MyCustomHandler(CustomLogger): def log_pre_api_call(self, model, messages, kwargs): @@ -21,14 +23,38 @@ class MyCustomHandler(CustomLogger): def log_failure_event(self, kwargs, response_obj, start_time, end_time): print(f"On Failure") + + #### ASYNC #### - for acompletion/aembeddings + + async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Async Streaming") + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Async Success") + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Async Success") customHandler = MyCustomHandler() litellm.callbacks = [customHandler] + +## 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()) ``` ## Callback Functions @@ -87,6 +113,41 @@ print(response) ## Async Callback Functions +We recommend using the Custom Logger class for async. + +```python +from litellm.integrations.custom_logger import CustomLogger +from litellm import acompletion + +class MyCustomHandler(CustomLogger): + #### ASYNC #### + + async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Async Streaming") + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Async Success") + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Async Failure") + +import asyncio +customHandler = MyCustomHandler() + +litellm.callbacks = [customHandler] + +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()) +``` + +**Functions** + +If you just want to pass in an async function for logging. + LiteLLM currently supports just async success callback functions for async completion/embedding calls. ```python @@ -117,9 +178,6 @@ asyncio.run(test_chat_openai()) :::info We're actively trying to expand this to other event types. [Tell us if you need this!](https://github.com/BerriAI/litellm/issues/1007) - - - ::: ## What's in kwargs? @@ -170,6 +228,48 @@ Here's exactly what you can expect in the kwargs dictionary: "end_time" = end_time # datetime object of when call was completed ``` + +### Cache hits + +Cache hits are logged in success events as `kwarg["cache_hit"]`. + +Here's an example of accessing it: + + ```python + import litellm +from litellm.integrations.custom_logger import CustomLogger +from litellm import completion, acompletion, Cache + +class MyCustomHandler(CustomLogger): + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Success") + print(f"Value of Cache hit: {kwargs['cache_hit']"}) + +async def test_async_completion_azure_caching(): + customHandler_caching = MyCustomHandler() + litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) + litellm.callbacks = [customHandler_caching] + unique_time = time.time() + response1 = await litellm.acompletion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": f"Hi 👋 - i'm async azure {unique_time}" + }], + caching=True) + await asyncio.sleep(1) + print(f"customHandler_caching.states pre-cache hit: {customHandler_caching.states}") + response2 = await litellm.acompletion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": f"Hi 👋 - i'm async azure {unique_time}" + }], + caching=True) + await asyncio.sleep(1) # success callbacks are done in parallel + print(f"customHandler_caching.states post-cache hit: {customHandler_caching.states}") + assert len(customHandler_caching.errors) == 0 + assert len(customHandler_caching.states) == 4 # pre, post, success, success + ``` + ### Get complete streaming response LiteLLM will pass you the complete streaming response in the final streaming chunk as part of the kwargs for your custom callback function. diff --git a/docs/my-website/docs/observability/traceloop_integration.md b/docs/my-website/docs/observability/traceloop_integration.md index 9902e58ba..8052420d1 100644 --- a/docs/my-website/docs/observability/traceloop_integration.md +++ b/docs/my-website/docs/observability/traceloop_integration.md @@ -27,8 +27,8 @@ To get better visualizations on how your code behaves, you may want to annotate ## Exporting traces to other systems (e.g. Datadog, New Relic, and others) -Since Traceloop SDK uses OpenTelemetry to send data, you can easily export your traces to other systems, such as Datadog, New Relic, and others. See [Traceloop docs on exporters](https://traceloop.com/docs/python-sdk/exporters) for more information. +Since OpenLLMetry uses OpenTelemetry to send data, you can easily export your traces to other systems, such as Datadog, New Relic, and others. See [OpenLLMetry docs on exporters](https://www.traceloop.com/docs/openllmetry/integrations/introduction) for more information. ## Support -For any question or issue with integration you can reach out to the Traceloop team on [Slack](https://join.slack.com/t/traceloopcommunity/shared_invite/zt-1plpfpm6r-zOHKI028VkpcWdobX65C~g) or via [email](mailto:dev@traceloop.com). +For any question or issue with integration you can reach out to the Traceloop team on [Slack](https://traceloop.com/slack) or via [email](mailto:dev@traceloop.com). diff --git a/docs/my-website/docs/projects/Docq.AI.md b/docs/my-website/docs/projects/Docq.AI.md new file mode 100644 index 000000000..492ce4490 --- /dev/null +++ b/docs/my-website/docs/projects/Docq.AI.md @@ -0,0 +1,21 @@ +**A private and secure ChatGPT alternative that knows your business.** + +Upload docs, ask questions --> get answers. + +Leverage GenAI with your confidential documents to increase efficiency and collaboration. + +OSS core, everything can run in your environment. An extensible platform you can build your GenAI strategy on. Support a variety of popular LLMs including embedded for air gap use cases. + +[![Static Badge][docs-shield]][docs-url] +[![Static Badge][github-shield]][github-url] +[![X (formerly Twitter) Follow][twitter-shield]][twitter-url] + + + + +[docs-shield]: https://img.shields.io/badge/docs-site-black?logo=materialformkdocs +[docs-url]: https://docqai.github.io/docq/ +[github-shield]: https://img.shields.io/badge/Github-repo-black?logo=github +[github-url]: https://github.com/docqai/docq/ +[twitter-shield]: https://img.shields.io/twitter/follow/docqai?logo=x&style=flat +[twitter-url]: https://twitter.com/docqai diff --git a/docs/my-website/docs/providers/mistral.md b/docs/my-website/docs/providers/mistral.md new file mode 100644 index 000000000..a7869415b --- /dev/null +++ b/docs/my-website/docs/providers/mistral.md @@ -0,0 +1,56 @@ +# Mistral AI API +https://docs.mistral.ai/api/ + +## API Key +```python +# env variable +os.environ['MISTRAL_API_KEY'] +``` + +## Sample Usage +```python +from litellm import completion +import os + +os.environ['MISTRAL_API_KEY'] = "" +response = completion( + model="mistral/mistral-tiny"", + messages=[ + {"role": "user", "content": "hello from litellm"} + ], +) +print(response) +``` + +## Sample Usage - Streaming +```python +from litellm import completion +import os + +os.environ['MISTRAL_API_KEY'] = "" +response = completion( + model="mistral/mistral-tiny", + messages=[ + {"role": "user", "content": "hello from litellm"} + ], + stream=True +) + +for chunk in response: + print(chunk) +``` + + +## Supported Models +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/c1b25538277206b9f00de5254d80d6a83bb19a29/model_prices_and_context_window.json). + +| Model Name | Function Call | +|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| mistral-tiny | `completion(model="mistral/mistral-tiny", messages)` | +| mistral-small | `completion(model="mistral/mistral-small", messages)` | +| mistral-medium | `completion(model="mistral/mistral-medium", messages)` | + + + + + diff --git a/docs/my-website/docs/providers/openai_compatible.md b/docs/my-website/docs/providers/openai_compatible.md new file mode 100644 index 000000000..ff68d7a00 --- /dev/null +++ b/docs/my-website/docs/providers/openai_compatible.md @@ -0,0 +1,47 @@ +# OpenAI-Compatible Endpoints + +To call models hosted behind an openai proxy, make 2 changes: + +1. Put `openai/` in front of your model name, so litellm knows you're trying to call an openai-compatible endpoint. + +2. **Do NOT** add anything additional to the base url e.g. `/v1/embedding`. LiteLLM uses the openai-client to make these calls, and that automatically adds the relevant endpoints. + +## Usage + +```python +import litellm +from litellm import embedding +litellm.set_verbose = True +import os + + +litellm_proxy_endpoint = "http://0.0.0.0:8000" +bearer_token = "sk-1234" + +CHOSEN_LITE_LLM_EMBEDDING_MODEL = "openai/GPT-J 6B - Sagemaker Text Embedding (Internal)" + +litellm.set_verbose = False + +print(litellm_proxy_endpoint) + + + +response = embedding( + + model = CHOSEN_LITE_LLM_EMBEDDING_MODEL, # add `openai/` prefix to model so litellm knows to route to OpenAI + + api_key=bearer_token, + + api_base=litellm_proxy_endpoint, # set API Base of your Custom OpenAI Endpoint + + input=["good morning from litellm"], + + api_version='2023-07-01-preview' + +) + +print('================================================') + +print(len(response.data[0]['embedding'])) + +``` \ No newline at end of file diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 1dedd1450..f71aa0ada 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -1,4 +1,4 @@ -# VertexAI - Google +# VertexAI - Google [Gemini] Open In Colab @@ -10,6 +10,16 @@ * run `gcloud auth application-default login` See [Google Cloud Docs](https://cloud.google.com/docs/authentication/external/set-up-adc) * Alternatively you can set `application_default_credentials.json` + +## Sample Usage +```python +import litellm +litellm.vertex_project = "hardy-device-38811" # Your Project ID +litellm.vertex_location = "us-central1" # proj location + +response = completion(model="gemini-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]) +``` + ## Set Vertex Project & Vertex Location All calls using Vertex AI require the following parameters: * Your Project ID @@ -37,13 +47,50 @@ os.environ["VERTEXAI_LOCATION"] = "us-central1 # Your Location litellm.vertex_location = "us-central1 # Your Location ``` -## Sample Usage +## Gemini Pro +| Model Name | Function Call | +|------------------|--------------------------------------| +| gemini-pro | `completion('gemini-pro', messages)` | + +## Gemini Pro Vision +| Model Name | Function Call | +|------------------|--------------------------------------| +| gemini-pro-vision | `completion('gemini-pro-vision', messages)` | + +#### Using Gemini Pro Vision + +Call `gemini-pro-vision` in the same input/output format as OpenAI [`gpt-4-vision`](https://docs.litellm.ai/docs/providers/openai#openai-vision-models) + +LiteLLM Supports the following image types passed in `url` +- Images with Cloud Storage URIs - gs://cloud-samples-data/generative-ai/image/boats.jpeg +- Images with direct links - https://storage.googleapis.com/github-repo/img/gemini/intro/landmark3.jpg +- Videos with Cloud Storage URIs - https://storage.googleapis.com/github-repo/img/gemini/multimodality_usecases_overview/pixel8.mp4 + +**Example Request** ```python import litellm -litellm.vertex_project = "hardy-device-38811" # Your Project ID -litellm.vertex_location = "us-central1" # proj location -response = completion(model="chat-bison", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]) +response = litellm.completion( + model = "vertex_ai/gemini-pro-vision", + messages=[ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Whats in this image?" + }, + { + "type": "image_url", + "image_url": { + "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" + } + } + ] + } + ], +) +print(response) ``` ## Chat Models diff --git a/docs/my-website/docs/proxy/caching.md b/docs/my-website/docs/proxy/caching.md index 37d3658e7..9eb6a1f55 100644 --- a/docs/my-website/docs/proxy/caching.md +++ b/docs/my-website/docs/proxy/caching.md @@ -1,20 +1,24 @@ # Caching Cache LLM Responses +## Quick Start Caching can be enabled by adding the `cache` key in the `config.yaml` -#### Step 1: Add `cache` to the config.yaml +### Step 1: Add `cache` to the config.yaml ```yaml model_list: - model_name: gpt-3.5-turbo litellm_params: model: gpt-3.5-turbo + - model_name: text-embedding-ada-002 + litellm_params: + model: text-embedding-ada-002 litellm_settings: set_verbose: True cache: True # set cache responses to True, litellm defaults to using a redis cache ``` -#### Step 2: Add Redis Credentials to .env +### Step 2: Add Redis Credentials to .env Set either `REDIS_URL` or the `REDIS_HOST` in your os environment, to enable caching. ```shell @@ -32,12 +36,12 @@ REDIS_ = "" ``` [**See how it's read from the environment**](https://github.com/BerriAI/litellm/blob/4d7ff1b33b9991dcf38d821266290631d9bcd2dd/litellm/_redis.py#L40) -#### Step 3: Run proxy with config +### Step 3: Run proxy with config ```shell $ litellm --config /path/to/config.yaml ``` -#### Using Caching +## Using Caching - /chat/completions Send the same request twice: ```shell curl http://0.0.0.0:8000/v1/chat/completions \ @@ -57,9 +61,51 @@ curl http://0.0.0.0:8000/v1/chat/completions \ }' ``` -#### Control caching per completion request +## Using Caching - /embeddings +Send the same request twice: +```shell +curl --location 'http://0.0.0.0:8000/embeddings' \ + --header 'Content-Type: application/json' \ + --data ' { + "model": "text-embedding-ada-002", + "input": ["write a litellm poem"] + }' + +curl --location 'http://0.0.0.0:8000/embeddings' \ + --header 'Content-Type: application/json' \ + --data ' { + "model": "text-embedding-ada-002", + "input": ["write a litellm poem"] + }' +``` + +## Advanced +### Set Cache Params on config.yaml +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + - model_name: text-embedding-ada-002 + litellm_params: + model: text-embedding-ada-002 + +litellm_settings: + set_verbose: True + cache: True # set cache responses to True, litellm defaults to using a redis cache + cache_params: # cache_params are optional + type: "redis" # The type of cache to initialize. Can be "local" or "redis". Defaults to "local". + host: "localhost" # The host address for the Redis cache. Required if type is "redis". + port: 6379 # The port number for the Redis cache. Required if type is "redis". + 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 +``` + +### Override caching per `chat/completions` request Caching can be switched on/off per `/chat/completions` request -- Caching **on** for completion - pass `caching=True`: +- Caching **on** for individual completion - pass `caching=True`: ```shell curl http://0.0.0.0:8000/v1/chat/completions \ -H "Content-Type: application/json" \ @@ -70,7 +116,7 @@ Caching can be switched on/off per `/chat/completions` request "caching": true }' ``` -- Caching **off** for completion - pass `caching=False`: +- Caching **off** for individual completion - pass `caching=False`: ```shell curl http://0.0.0.0:8000/v1/chat/completions \ -H "Content-Type: application/json" \ @@ -80,4 +126,29 @@ Caching can be switched on/off per `/chat/completions` request "temperature": 0.7, "caching": false }' + ``` + + +### Override caching per `/embeddings` request + +Caching can be switched on/off per `/embeddings` request +- Caching **on** for embedding - pass `caching=True`: + ```shell + curl --location 'http://0.0.0.0:8000/embeddings' \ + --header 'Content-Type: application/json' \ + --data ' { + "model": "text-embedding-ada-002", + "input": ["write a litellm poem"], + "caching": true + }' + ``` +- Caching **off** for completion - pass `caching=False`: + ```shell + curl --location 'http://0.0.0.0:8000/embeddings' \ + --header 'Content-Type: application/json' \ + --data ' { + "model": "text-embedding-ada-002", + "input": ["write a litellm poem"], + "caching": false + }' ``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/call_hooks.md b/docs/my-website/docs/proxy/call_hooks.md new file mode 100644 index 000000000..a92b94a86 --- /dev/null +++ b/docs/my-website/docs/proxy/call_hooks.md @@ -0,0 +1,78 @@ +# Modify Incoming Data + +Modify data just before making litellm completion calls call on proxy + +See a complete example with our [parallel request rate limiter](https://github.com/BerriAI/litellm/blob/main/litellm/proxy/hooks/parallel_request_limiter.py) + +## Quick Start + +1. In your Custom Handler add a new `async_pre_call_hook` function + +This function is called just before a litellm completion call is made, and allows you to modify the data going into the litellm call [**See Code**](https://github.com/BerriAI/litellm/blob/589a6ca863000ba8e92c897ba0f776796e7a5904/litellm/proxy/proxy_server.py#L1000) + +```python +from litellm.integrations.custom_logger import CustomLogger +import litellm + +# This file includes the custom callbacks for LiteLLM Proxy +# Once defined, these can be passed in proxy_config.yaml +class MyCustomHandler(CustomLogger): # https://docs.litellm.ai/docs/observability/custom_callback#callback-class + # Class variables or attributes + def __init__(self): + pass + + #### ASYNC #### + + async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + pass + + async def async_log_pre_api_call(self, model, messages, kwargs): + pass + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + pass + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + pass + + #### CALL HOOKS - proxy only #### + + async def async_pre_call_hook(self, user_api_key_dict: UserAPIKeyAuth, cache: DualCache, data: dict, call_type: Literal["completion", "embeddings"]): + data["model"] = "my-new-model" + return data + +proxy_handler_instance = MyCustomHandler() +``` + +2. Add this file to your proxy config + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + +litellm_settings: + callbacks: custom_callbacks.proxy_handler_instance # sets litellm.callbacks = [proxy_handler_instance] +``` + +3. Start the server + test the request + +```shell +$ litellm /path/to/config.yaml +``` +```shell +curl --location 'http://0.0.0.0:8000/chat/completions' \ + --data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "good morning good sir" + } + ], + "user": "ishaan-app", + "temperature": 0.2 + }' +``` + diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md index 65ba90eee..b25bfaa53 100644 --- a/docs/my-website/docs/proxy/deploy.md +++ b/docs/my-website/docs/proxy/deploy.md @@ -1,4 +1,11 @@ -# Deploying LiteLLM Proxy +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# 🐳 Docker, Deploying LiteLLM Proxy + +## Dockerfile + +You can find the Dockerfile to build litellm proxy [here](https://github.com/BerriAI/litellm/blob/main/Dockerfile) ## Quick Start Docker Image: Github Container Registry @@ -7,12 +14,12 @@ See the latest available ghcr docker image here: https://github.com/berriai/litellm/pkgs/container/litellm ```shell -docker pull ghcr.io/berriai/litellm:main-v1.10.1 +docker pull ghcr.io/berriai/litellm:main-v1.12.3 ``` ### Run the Docker Image ```shell -docker run ghcr.io/berriai/litellm:main-v1.10.0 +docker run ghcr.io/berriai/litellm:main-v1.12.3 ``` #### Run the Docker Image with LiteLLM CLI args @@ -21,12 +28,12 @@ See all supported CLI args [here](https://docs.litellm.ai/docs/proxy/cli): Here's how you can run the docker image and pass your config to `litellm` ```shell -docker run ghcr.io/berriai/litellm:main-v1.10.0 --config your_config.yaml +docker run ghcr.io/berriai/litellm:main-v1.12.3 --config your_config.yaml ``` Here's how you can run the docker image and start litellm on port 8002 with `num_workers=8` ```shell -docker run ghcr.io/berriai/litellm:main-v1.10.0 --port 8002 --num_workers 8 +docker run ghcr.io/berriai/litellm:main-v1.12.3 --port 8002 --num_workers 8 ``` #### Run the Docker Image using docker compose @@ -42,6 +49,10 @@ Here's an example `docker-compose.yml` file version: "3.9" services: litellm: + build: + context: . + args: + target: runtime image: ghcr.io/berriai/litellm:main ports: - "8000:8000" # Map the container port to the host, change the host port if necessary @@ -74,6 +85,26 @@ Your LiteLLM container should be running now on the defined port e.g. `8000`. +## Deploy on Google Cloud Run +**Click the button** to deploy to Google Cloud Run + +[![Deploy](https://deploy.cloud.run/button.svg)](https://deploy.cloud.run/?git_repo=https://github.com/BerriAI/litellm) + +#### Testing your deployed proxy +**Assuming the required keys are set as Environment Variables** + +https://litellm-7yjrj3ha2q-uc.a.run.app is our example proxy, substitute it with your deployed cloud run app + +```shell +curl https://litellm-7yjrj3ha2q-uc.a.run.app/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Say this is a test!"}], + "temperature": 0.7 + }' +``` + ## LiteLLM Proxy Performance LiteLLM proxy has been load tested to handle 1500 req/s. diff --git a/docs/my-website/docs/proxy/embedding.md b/docs/my-website/docs/proxy/embedding.md new file mode 100644 index 000000000..e1a7677f9 --- /dev/null +++ b/docs/my-website/docs/proxy/embedding.md @@ -0,0 +1,244 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Embeddings - `/embeddings` + +See supported Embedding Providers & Models [here](https://docs.litellm.ai/docs/embedding/supported_embedding) + + +## Quick start +Here's how to route between GPT-J embedding (sagemaker endpoint), Amazon Titan embedding (Bedrock) and Azure OpenAI embedding on the proxy server: + +1. Set models in your config.yaml +```yaml +model_list: + - model_name: sagemaker-embeddings + litellm_params: + model: "sagemaker/berri-benchmarking-gpt-j-6b-fp16" + - model_name: amazon-embeddings + litellm_params: + model: "bedrock/amazon.titan-embed-text-v1" + - model_name: azure-embeddings + litellm_params: + model: "azure/azure-embedding-model" + api_base: "os.environ/AZURE_API_BASE" # os.getenv("AZURE_API_BASE") + api_key: "os.environ/AZURE_API_KEY" # os.getenv("AZURE_API_KEY") + api_version: "2023-07-01-preview" + +general_settings: + master_key: sk-1234 # [OPTIONAL] if set all calls to proxy will require either this key or a valid generated token +``` + +2. Start the proxy +```shell +$ litellm --config /path/to/config.yaml +``` + +3. Test the embedding call + +```shell +curl --location 'http://0.0.0.0:8000/v1/embeddings' \ +--header 'Authorization: Bearer sk-1234' \ +--header 'Content-Type: application/json' \ +--data '{ + "input": "The food was delicious and the waiter..", + "model": "sagemaker-embeddings", +}' +``` + +## `/embeddings` Request Format +Input, Output and Exceptions are mapped to the OpenAI format for all supported models + + + + +```shell +curl --location 'http://0.0.0.0:8000/embeddings' \ + --header 'Content-Type: application/json' \ + --data ' { + "model": "text-embedding-ada-002", + "input": ["write a litellm poem"] + }' +``` + + + +```python +import openai +from openai import OpenAI + +# set base_url to your proxy server +# set api_key to send to proxy server +client = OpenAI(api_key="", base_url="http://0.0.0.0:8000") + +response = openai.embeddings.create( + input=["hello from litellm"], + model="text-embedding-ada-002" +) + +print(response) + +``` + + + + +```python +from langchain.embeddings import OpenAIEmbeddings + +embeddings = OpenAIEmbeddings(model="sagemaker-embeddings", openai_api_base="http://0.0.0.0:8000", openai_api_key="temp-key") + + +text = "This is a test document." + +query_result = embeddings.embed_query(text) + +print(f"SAGEMAKER EMBEDDINGS") +print(query_result[:5]) + +embeddings = OpenAIEmbeddings(model="bedrock-embeddings", openai_api_base="http://0.0.0.0:8000", openai_api_key="temp-key") + +text = "This is a test document." + +query_result = embeddings.embed_query(text) + +print(f"BEDROCK EMBEDDINGS") +print(query_result[:5]) + +embeddings = OpenAIEmbeddings(model="bedrock-titan-embeddings", openai_api_base="http://0.0.0.0:8000", openai_api_key="temp-key") + +text = "This is a test document." + +query_result = embeddings.embed_query(text) + +print(f"TITAN EMBEDDINGS") +print(query_result[:5]) +``` + + + + + +## `/embeddings` Response Format + +```json +{ + "object": "list", + "data": [ + { + "object": "embedding", + "embedding": [ + 0.0023064255, + -0.009327292, + .... + -0.0028842222, + ], + "index": 0 + } + ], + "model": "text-embedding-ada-002", + "usage": { + "prompt_tokens": 8, + "total_tokens": 8 + } +} + +``` + +## Supported Models + +See supported Embedding Providers & Models [here](https://docs.litellm.ai/docs/embedding/supported_embedding) + +#### Create Config.yaml + + + +LiteLLM Proxy supports all Feature-Extraction Embedding models. + +```yaml +model_list: + - model_name: deployed-codebert-base + litellm_params: + # send request to deployed hugging face inference endpoint + model: huggingface/microsoft/codebert-base # add huggingface prefix so it routes to hugging face + api_key: hf_LdS # api key for hugging face inference endpoint + api_base: https://uysneno1wv2wd4lw.us-east-1.aws.endpoints.huggingface.cloud # your hf inference endpoint + - model_name: codebert-base + litellm_params: + # no api_base set, sends request to hugging face free inference api https://api-inference.huggingface.co/models/ + model: huggingface/microsoft/codebert-base # add huggingface prefix so it routes to hugging face + api_key: hf_LdS # api key for hugging face + +``` + + + + + +```yaml +model_list: + - model_name: azure-embedding-model # model group + litellm_params: + model: azure/azure-embedding-model # model name for litellm.embedding(model=azure/azure-embedding-model) call + api_base: your-azure-api-base + api_key: your-api-key + api_version: 2023-07-01-preview +``` + + + + + +```yaml +model_list: +- model_name: text-embedding-ada-002 # model group + litellm_params: + model: text-embedding-ada-002 # model name for litellm.embedding(model=text-embedding-ada-002) + api_key: your-api-key-1 +- model_name: text-embedding-ada-002 + litellm_params: + model: text-embedding-ada-002 + api_key: your-api-key-2 +``` + + + + + +

Use this for calling /embedding endpoints on OpenAI Compatible Servers.

+ +**Note add `openai/` prefix to `litellm_params`: `model` so litellm knows to route to OpenAI** + +```yaml +model_list: +- model_name: text-embedding-ada-002 # model group + litellm_params: + model: openai/ # model name for litellm.embedding(model=text-embedding-ada-002) + api_base: +``` + + + + +#### Start Proxy +```shell +litellm --config config.yaml +``` + +#### Make Request +Sends Request to `deployed-codebert-base` + +```shell +curl --location 'http://0.0.0.0:8000/embeddings' \ + --header 'Content-Type: application/json' \ + --data ' { + "model": "deployed-codebert-base", + "input": ["write a litellm poem"] + }' +``` + + + + + diff --git a/docs/my-website/docs/proxy/health.md b/docs/my-website/docs/proxy/health.md new file mode 100644 index 000000000..5dffd7100 --- /dev/null +++ b/docs/my-website/docs/proxy/health.md @@ -0,0 +1,62 @@ +# Health Checks +Use this to health check all LLMs defined in your config.yaml + +## Summary + +The proxy exposes: +* a /health endpoint which returns the health of the LLM APIs +* a /test endpoint which makes a ping to the litellm server + +#### Request +Make a GET Request to `/health` on the proxy +```shell +curl --location 'http://0.0.0.0:8000/health' +``` + +You can also run `litellm -health` it makes a `get` request to `http://0.0.0.0:8000/health` for you +``` +litellm --health +``` +#### Response +```shell +{ + "healthy_endpoints": [ + { + "model": "azure/gpt-35-turbo", + "api_base": "https://my-endpoint-canada-berri992.openai.azure.com/" + }, + { + "model": "azure/gpt-35-turbo", + "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com/" + } + ], + "unhealthy_endpoints": [ + { + "model": "azure/gpt-35-turbo", + "api_base": "https://openai-france-1234.openai.azure.com/" + } + ] +} +``` + +## 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://0.0.0.0:8000/health' +``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/load_balancing.md b/docs/my-website/docs/proxy/load_balancing.md index e2e3a7ee6..e223c2d5a 100644 --- a/docs/my-website/docs/proxy/load_balancing.md +++ b/docs/my-website/docs/proxy/load_balancing.md @@ -72,128 +72,28 @@ curl --location 'http://0.0.0.0:8000/chat/completions' \ ' ``` +## Router settings on config - routing_strategy, model_group_alias -## Fallbacks + Cooldowns + Retries + Timeouts +litellm.Router() settings can be set under `router_settings`. You can set `model_group_alias`, `routing_strategy`, `num_retries`,`timeout` . See all Router supported params [here](https://github.com/BerriAI/litellm/blob/1b942568897a48f014fa44618ec3ce54d7570a46/litellm/router.py#L64) -If a call fails after num_retries, fall back to another model group. - -If the error is a context window exceeded error, fall back to a larger model group (if given). - -[**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/router.py) - -**Set via config** -```yaml -model_list: - - model_name: zephyr-beta - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8001 - - model_name: zephyr-beta - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8002 - - model_name: zephyr-beta - litellm_params: - model: huggingface/HuggingFaceH4/zephyr-7b-beta - api_base: http://0.0.0.0:8003 - - model_name: gpt-3.5-turbo - litellm_params: - model: gpt-3.5-turbo - api_key: - - model_name: gpt-3.5-turbo-16k - litellm_params: - model: gpt-3.5-turbo-16k - api_key: - -litellm_settings: - num_retries: 3 # retry call 3 times on each model_name (e.g. zephyr-beta) - request_timeout: 10 # raise Timeout error if call takes longer than 10s. Sets litellm.request_timeout - fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo"]}] # fallback to gpt-3.5-turbo if call fails num_retries - context_window_fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}] # fallback to gpt-3.5-turbo-16k if context window error - allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. -``` - -**Set dynamically** - -```bash -curl --location 'http://0.0.0.0:8000/chat/completions' \ ---header 'Content-Type: application/json' \ ---data ' { - "model": "zephyr-beta", - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - "fallbacks": [{"zephyr-beta": ["gpt-3.5-turbo"]}], - "context_window_fallbacks": [{"zephyr-beta": ["gpt-3.5-turbo"]}], - "num_retries": 2, - "timeout": 10 - } -' -``` - -## Custom Timeouts, Stream Timeouts - Per Model -For each model you can set `timeout` & `stream_timeout` under `litellm_params` +Example config with `router_settings` ```yaml model_list: - model_name: gpt-3.5-turbo litellm_params: - model: azure/gpt-turbo-small-eu - api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ - api_key: - timeout: 0.1 # timeout in (seconds) - stream_timeout: 0.01 # timeout for stream requests (seconds) - max_retries: 5 + model: azure/ + api_base: + api_key: + rpm: 6 # Rate limit for this deployment: in requests per minute (rpm) - model_name: gpt-3.5-turbo litellm_params: model: azure/gpt-turbo-small-ca api_base: https://my-endpoint-canada-berri992.openai.azure.com/ - api_key: - timeout: 0.1 # timeout in (seconds) - stream_timeout: 0.01 # timeout for stream requests (seconds) - max_retries: 5 - -``` - -#### Start Proxy -```shell -$ litellm --config /path/to/config.yaml -``` - - - -## Health Check LLMs on Proxy -Use this to health check all LLMs defined in your config.yaml -#### Request -Make a GET Request to `/health` on the proxy -```shell -curl --location 'http://0.0.0.0:8000/health' -``` - -You can also run `litellm -health` it makes a `get` request to `http://0.0.0.0:8000/health` for you -``` -litellm --health -``` -#### Response -```shell -{ - "healthy_endpoints": [ - { - "model": "azure/gpt-35-turbo", - "api_base": "https://my-endpoint-canada-berri992.openai.azure.com/" - }, - { - "model": "azure/gpt-35-turbo", - "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com/" - } - ], - "unhealthy_endpoints": [ - { - "model": "azure/gpt-35-turbo", - "api_base": "https://openai-france-1234.openai.azure.com/" - } - ] -} + api_key: + rpm: 6 +router_settings: + model_group_alias: {"gpt-4": "gpt-3.5-turbo"} # all requests with `gpt-4` will be routed to models with `gpt-3.5-turbo` + routing_strategy: least-busy # Literal["simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing"] + num_retries: 2 + timeout: 30 # 30 seconds ``` \ 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 c70c8680f..cf14abff3 100644 --- a/docs/my-website/docs/proxy/logging.md +++ b/docs/my-website/docs/proxy/logging.md @@ -1,5 +1,8 @@ -# Logging - Custom Callbacks, OpenTelemetry, Langfuse -Log Proxy Input, Output, Exceptions using Custom Callbacks, Langfuse, OpenTelemetry +import Image from '@theme/IdealImage'; + +# Logging - Custom Callbacks, OpenTelemetry, Langfuse, Sentry + +Log Proxy Input, Output, Exceptions using Custom Callbacks, Langfuse, OpenTelemetry, LangFuse, DynamoDB ## Custom Callback Class [Async] Use this when you want to run custom callbacks in `python` @@ -486,3 +489,166 @@ litellm --test Expected output on Langfuse + +## Logging Proxy Input/Output - DynamoDB + +We will use the `--config` to set +- `litellm.success_callback = ["dynamodb"]` +- `litellm.dynamodb_table_name = "your-table-name"` + +This will log all successfull LLM calls to DynamoDB + +**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: ["dynamodb"] + dynamodb_table_name: your-table-name +``` + +**Step 3**: Start the proxy, make a test request + +Start proxy +```shell +litellm --config config.yaml --debug +``` + +Test Request +```shell +curl --location 'http://0.0.0.0:8000/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 DynamoDB + +#### Data Logged to DynamoDB /chat/completions + +```json +{ + "id": { + "S": "chatcmpl-8W15J4480a3fAQ1yQaMgtsKJAicen" + }, + "call_type": { + "S": "acompletion" + }, + "endTime": { + "S": "2023-12-15 17:25:58.424118" + }, + "messages": { + "S": "[{'role': 'user', 'content': 'This is a test'}]" + }, + "metadata": { + "S": "{}" + }, + "model": { + "S": "gpt-3.5-turbo" + }, + "modelParameters": { + "S": "{'temperature': 0.7, 'max_tokens': 100, 'user': 'ishaan-2'}" + }, + "response": { + "S": "ModelResponse(id='chatcmpl-8W15J4480a3fAQ1yQaMgtsKJAicen', choices=[Choices(finish_reason='stop', index=0, message=Message(content='Great! What can I assist you with?', role='assistant'))], created=1702641357, model='gpt-3.5-turbo-0613', object='chat.completion', system_fingerprint=None, usage=Usage(completion_tokens=9, prompt_tokens=11, total_tokens=20))" + }, + "startTime": { + "S": "2023-12-15 17:25:56.047035" + }, + "usage": { + "S": "Usage(completion_tokens=9, prompt_tokens=11, total_tokens=20)" + }, + "user": { + "S": "ishaan-2" + } +} +``` + +#### Data logged to DynamoDB /embeddings + +```json +{ + "id": { + "S": "4dec8d4d-4817-472d-9fc6-c7a6153eb2ca" + }, + "call_type": { + "S": "aembedding" + }, + "endTime": { + "S": "2023-12-15 17:25:59.890261" + }, + "messages": { + "S": "['hi']" + }, + "metadata": { + "S": "{}" + }, + "model": { + "S": "text-embedding-ada-002" + }, + "modelParameters": { + "S": "{'user': 'ishaan-2'}" + }, + "response": { + "S": "EmbeddingResponse(model='text-embedding-ada-002-v2', data=[{'embedding': [-0.03503197431564331, -0.020601635798811913, -0.015375726856291294, + } +} +``` + + + + +## 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" +``` + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo +litellm_settings: + # other settings + failure_callback: ["sentry"] +general_settings: + database_url: "my-bad-url" # set a fake url to trigger a sentry exception +``` + +**Step 3**: Start the proxy, make a test request + +Start proxy +```shell +litellm --config config.yaml --debug +``` + +Test Request +``` +litellm --test +``` diff --git a/docs/my-website/docs/proxy/reliability.md b/docs/my-website/docs/proxy/reliability.md new file mode 100644 index 000000000..75f43bcdc --- /dev/null +++ b/docs/my-website/docs/proxy/reliability.md @@ -0,0 +1,89 @@ +# Fallbacks, Retries, Timeouts, Cooldowns + +If a call fails after num_retries, fall back to another model group. + +If the error is a context window exceeded error, fall back to a larger model group (if given). + +[**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/router.py) + +**Set via config** +```yaml +model_list: + - model_name: zephyr-beta + litellm_params: + model: huggingface/HuggingFaceH4/zephyr-7b-beta + api_base: http://0.0.0.0:8001 + - model_name: zephyr-beta + litellm_params: + model: huggingface/HuggingFaceH4/zephyr-7b-beta + api_base: http://0.0.0.0:8002 + - model_name: zephyr-beta + litellm_params: + model: huggingface/HuggingFaceH4/zephyr-7b-beta + api_base: http://0.0.0.0:8003 + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + api_key: + - model_name: gpt-3.5-turbo-16k + litellm_params: + model: gpt-3.5-turbo-16k + api_key: + +litellm_settings: + num_retries: 3 # retry call 3 times on each model_name (e.g. zephyr-beta) + request_timeout: 10 # raise Timeout error if call takes longer than 10s. Sets litellm.request_timeout + fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo"]}] # fallback to gpt-3.5-turbo if call fails num_retries + context_window_fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}] # fallback to gpt-3.5-turbo-16k if context window error + allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. +``` + +**Set dynamically** + +```bash +curl --location 'http://0.0.0.0:8000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "zephyr-beta", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + "fallbacks": [{"zephyr-beta": ["gpt-3.5-turbo"]}], + "context_window_fallbacks": [{"zephyr-beta": ["gpt-3.5-turbo"]}], + "num_retries": 2, + "timeout": 10 + } +' +``` + +## Custom Timeouts, Stream Timeouts - Per Model +For each model you can set `timeout` & `stream_timeout` under `litellm_params` +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/gpt-turbo-small-eu + api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ + api_key: + timeout: 0.1 # timeout in (seconds) + stream_timeout: 0.01 # timeout for stream requests (seconds) + max_retries: 5 + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/gpt-turbo-small-ca + api_base: https://my-endpoint-canada-berri992.openai.azure.com/ + api_key: + timeout: 0.1 # timeout in (seconds) + stream_timeout: 0.01 # timeout for stream requests (seconds) + max_retries: 5 + +``` + +#### Start Proxy +```shell +$ litellm --config /path/to/config.yaml +``` + diff --git a/docs/my-website/docs/proxy_server.md b/docs/my-website/docs/proxy_server.md index ec8ca15c5..200a92b95 100644 --- a/docs/my-website/docs/proxy_server.md +++ b/docs/my-website/docs/proxy_server.md @@ -1,13 +1,13 @@ import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; -# [OLD PROXY 👉 [**NEW** proxy here](./simple_proxy.md)] Local OpenAI Proxy Server +# [OLD PROXY 👉 [**NEW** proxy here](./simple_proxy)] Local OpenAI Proxy Server A fast, and lightweight OpenAI-compatible server to call 100+ LLM APIs. :::info -Docs outdated. New docs 👉 [here](./simple_proxy.md) +Docs outdated. New docs 👉 [here](./simple_proxy) ::: diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md index 3f55ae28e..5239f7ab7 100644 --- a/docs/my-website/docs/routing.md +++ b/docs/my-website/docs/routing.md @@ -366,6 +366,63 @@ router = Router(model_list: Optional[list] = None, cache_responses=True) ``` +## Caching across model groups + +If you want to cache across 2 different model groups (e.g. azure deployments, and openai), use caching groups. + +```python +import litellm, asyncio, time +from litellm import Router + +# set os env +os.environ["OPENAI_API_KEY"] = "" +os.environ["AZURE_API_KEY"] = "" +os.environ["AZURE_API_BASE"] = "" +os.environ["AZURE_API_VERSION"] = "" + +async def test_acompletion_caching_on_router_caching_groups(): + # tests acompletion + caching on router + try: + litellm.set_verbose = True + model_list = [ + { + "model_name": "openai-gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + }, + { + "model_name": "azure-gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION") + }, + } + ] + + messages = [ + {"role": "user", "content": f"write a one sentence poem {time.time()}?"} + ] + start_time = time.time() + router = Router(model_list=model_list, + cache_responses=True, + caching_groups=[("openai-gpt-3.5-turbo", "azure-gpt-3.5-turbo")]) + response1 = await router.acompletion(model="openai-gpt-3.5-turbo", messages=messages, temperature=1) + print(f"response1: {response1}") + await asyncio.sleep(1) # add cache is async, async sleep for cache to get set + response2 = await router.acompletion(model="azure-gpt-3.5-turbo", messages=messages, temperature=1) + assert response1.id == response2.id + assert len(response1.choices[0].message.content) > 0 + assert response1.choices[0].message.content == response2.choices[0].message.content + except Exception as e: + traceback.print_exc() + +asyncio.run(test_acompletion_caching_on_router_caching_groups()) +``` + #### Default litellm.completion/embedding params You can also set default params for litellm completion/embedding calls. Here's how to do that: @@ -391,200 +448,3 @@ print(f"response: {response}") ## 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) - -## Queuing (Beta) - -**Never fail a request due to rate limits** - -The LiteLLM Queuing endpoints can handle 100+ req/s. We use Celery workers to process requests. - -:::info - -This is pretty new, and might have bugs. Any contributions to improving our implementation are welcome - -::: - - -[**See Code**](https://github.com/BerriAI/litellm/blob/fbf9cab5b9e35df524e2c9953180c58d92e4cd97/litellm/proxy/proxy_server.py#L589) - - -### Quick Start - -1. Add Redis credentials in a .env file - -```python -REDIS_HOST="my-redis-endpoint" -REDIS_PORT="my-redis-port" -REDIS_PASSWORD="my-redis-password" # [OPTIONAL] if self-hosted -REDIS_USERNAME="default" # [OPTIONAL] if self-hosted -``` - -2. Start litellm server with your model config - -```bash -$ litellm --config /path/to/config.yaml --use_queue -``` - -Here's an example config for `gpt-3.5-turbo` - -**config.yaml** (This will load balance between OpenAI + Azure endpoints) -```yaml -model_list: - - model_name: gpt-3.5-turbo - litellm_params: - model: gpt-3.5-turbo - api_key: - - model_name: gpt-3.5-turbo - litellm_params: - model: azure/chatgpt-v-2 # actual model name - api_key: - api_version: 2023-07-01-preview - api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ -``` - -3. Test (in another window) → sends 100 simultaneous requests to the queue - -```bash -$ litellm --test_async --num_requests 100 -``` - - -### Available Endpoints -- `/queue/request` - Queues a /chat/completions request. Returns a job id. -- `/queue/response/{id}` - Returns the status of a job. If completed, returns the response as well. Potential status's are: `queued` and `finished`. - - -## Hosted Request Queing api.litellm.ai -Queue your LLM API requests to ensure you're under your rate limits -- Step 1: Step 1 Add a config to the proxy, generate a temp key -- Step 2: Queue a request to the proxy, using your generated_key -- Step 3: Poll the request - - -### Step 1 Add a config to the proxy, generate a temp key -```python -import requests -import time -import os - -# Set the base URL as needed -base_url = "https://api.litellm.ai" - -# Step 1 Add a config to the proxy, generate a temp key -# use the same model_name to load balance -config = { - "model_list": [ - { - "model_name": "gpt-3.5-turbo", - "litellm_params": { - "model": "gpt-3.5-turbo", - "api_key": os.environ['OPENAI_API_KEY'], - } - }, - { - "model_name": "gpt-3.5-turbo", - "litellm_params": { - "model": "azure/chatgpt-v-2", - "api_key": "", - "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com/", - "api_version": "2023-07-01-preview" - } - } - ] -} - -response = requests.post( - url=f"{base_url}/key/generate", - json={ - "config": config, - "duration": "30d" # default to 30d, set it to 30m if you want a temp 30 minute key - }, - headers={ - "Authorization": "Bearer sk-hosted-litellm" # this is the key to use api.litellm.ai - } -) - -print("\nresponse from generating key", response.text) -print("\n json response from gen key", response.json()) - -generated_key = response.json()["key"] -print("\ngenerated key for proxy", generated_key) -``` - -#### Output -```shell -response from generating key {"key":"sk-...,"expires":"2023-12-22T03:43:57.615000+00:00"} -``` - -### Step 2: Queue a request to the proxy, using your generated_key -```python -print("Creating a job on the proxy") -job_response = requests.post( - url=f"{base_url}/queue/request", - json={ - 'model': 'gpt-3.5-turbo', - 'messages': [ - {'role': 'system', 'content': f'You are a helpful assistant. What is your name'}, - ], - }, - headers={ - "Authorization": f"Bearer {generated_key}" - } -) -print(job_response.status_code) -print(job_response.text) -print("\nResponse from creating job", job_response.text) -job_response = job_response.json() -job_id = job_response["id"] -polling_url = job_response["url"] -polling_url = f"{base_url}{polling_url}" -print("\nCreated Job, Polling Url", polling_url) -``` - -#### Output -```shell -Response from creating job -{"id":"0e3d9e98-5d56-4d07-9cc8-c34b7e6658d7","url":"/queue/response/0e3d9e98-5d56-4d07-9cc8-c34b7e6658d7","eta":5,"status":"queued"} -``` - -### Step 3: Poll the request -```python -while True: - try: - print("\nPolling URL", polling_url) - polling_response = requests.get( - url=polling_url, - headers={ - "Authorization": f"Bearer {generated_key}" - } - ) - print("\nResponse from polling url", polling_response.text) - polling_response = polling_response.json() - status = polling_response.get("status", None) - if status == "finished": - llm_response = polling_response["result"] - print("LLM Response") - print(llm_response) - break - time.sleep(0.5) - except Exception as e: - print("got exception in polling", e) - break -``` - -#### Output -```shell -Polling URL https://api.litellm.ai/queue/response/0e3d9e98-5d56-4d07-9cc8-c34b7e6658d7 - -Response from polling url {"status":"queued"} - -Polling URL https://api.litellm.ai/queue/response/0e3d9e98-5d56-4d07-9cc8-c34b7e6658d7 - -Response from polling url {"status":"queued"} - -Polling URL https://api.litellm.ai/queue/response/0e3d9e98-5d56-4d07-9cc8-c34b7e6658d7 - -Response from polling url -{"status":"finished","result":{"id":"chatcmpl-8NYRce4IeI4NzYyodT3NNp8fk5cSW","choices":[{"finish_reason":"stop","index":0,"message":{"content":"I am an AI assistant and do not have a physical presence or personal identity. You can simply refer to me as \"Assistant.\" How may I assist you today?","role":"assistant"}}],"created":1700624639,"model":"gpt-3.5-turbo-0613","object":"chat.completion","system_fingerprint":null,"usage":{"completion_tokens":33,"prompt_tokens":17,"total_tokens":50}}} - -``` \ No newline at end of file diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 11f81fa4d..069faa48a 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -61,11 +61,13 @@ const sidebars = { }, items: [ "providers/openai", + "providers/openai_compatible", "providers/azure", "providers/huggingface", "providers/ollama", "providers/vertex", "providers/palm", + "providers/mistral", "providers/anthropic", "providers/aws_sagemaker", "providers/bedrock", @@ -96,10 +98,14 @@ const sidebars = { }, items: [ "proxy/quick_start", - "proxy/configs", + "proxy/configs", + "proxy/embedding", "proxy/load_balancing", "proxy/virtual_keys", "proxy/model_management", + "proxy/reliability", + "proxy/health", + "proxy/call_hooks", "proxy/caching", "proxy/logging", "proxy/cli", @@ -189,6 +195,7 @@ const sidebars = { slug: '/project', }, items: [ + "projects/Docq.AI", "projects/OpenInterpreter", "projects/FastREPL", "projects/PROMPTMETHEUS", diff --git a/docs/my-website/yarn.lock b/docs/my-website/yarn.lock index 5c9fa101d..7e431def2 100644 --- a/docs/my-website/yarn.lock +++ b/docs/my-website/yarn.lock @@ -84,7 +84,7 @@ "@algolia/requester-common" "4.19.1" "@algolia/transporter" "4.19.1" -"@algolia/client-search@>= 4.9.1 < 6", "@algolia/client-search@4.19.1": +"@algolia/client-search@4.19.1": version "4.19.1" resolved "https://registry.npmjs.org/@algolia/client-search/-/client-search-4.19.1.tgz" integrity sha512-mBecfMFS4N+yK/p0ZbK53vrZbL6OtWMk8YmnOv1i0LXx4pelY8TFhqKoTit3NPVPwoSNN0vdSN9dTu1xr1XOVw== @@ -146,6 +146,13 @@ "@jridgewell/gen-mapping" "^0.3.0" "@jridgewell/trace-mapping" "^0.3.9" +"@babel/code-frame@7.10.4", "@babel/code-frame@^7.5.5": + version "7.10.4" + resolved "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.10.4.tgz" + integrity sha512-vG6SvB6oYEhvgisZNFRmRCUkLz11c7rp+tbNTynGqc6mS1d5ATd/sGyV6W0KZZnXRKMTzZDRgQT3Ou9jhpAfUg== + dependencies: + "@babel/highlight" "^7.10.4" + "@babel/code-frame@^7.0.0", "@babel/code-frame@^7.10.4", "@babel/code-frame@^7.16.0", "@babel/code-frame@^7.22.10", "@babel/code-frame@^7.22.13", "@babel/code-frame@^7.8.3": version "7.22.13" resolved "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.22.13.tgz" @@ -154,39 +161,11 @@ "@babel/highlight" "^7.22.13" chalk "^2.4.2" -"@babel/code-frame@^7.5.5", "@babel/code-frame@7.10.4": - version "7.10.4" - resolved "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.10.4.tgz" - integrity sha512-vG6SvB6oYEhvgisZNFRmRCUkLz11c7rp+tbNTynGqc6mS1d5ATd/sGyV6W0KZZnXRKMTzZDRgQT3Ou9jhpAfUg== - dependencies: - "@babel/highlight" "^7.10.4" - "@babel/compat-data@^7.22.5", "@babel/compat-data@^7.22.6", "@babel/compat-data@^7.22.9": version "7.22.9" resolved "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.22.9.tgz" integrity sha512-5UamI7xkUcJ3i9qVDS+KFDEK8/7oJ55/sJMB1Ge7IEapr7KfdfV/HErR+koZwOfd+SgtFKOKRhRakdg++DcJpQ== -"@babel/core@^7.0.0", "@babel/core@^7.0.0-0", "@babel/core@^7.0.0-0 || ^8.0.0-0 <8.0.0", "@babel/core@^7.11.6", "@babel/core@^7.12.0", "@babel/core@^7.12.3", "@babel/core@^7.13.0", "@babel/core@^7.18.6", "@babel/core@^7.19.6", "@babel/core@^7.4.0 || ^8.0.0-0 <8.0.0": - version "7.22.10" - resolved "https://registry.npmjs.org/@babel/core/-/core-7.22.10.tgz" - integrity sha512-fTmqbbUBAwCcre6zPzNngvsI0aNrPZe77AeqvDxWM9Nm+04RrJ3CAmGHA9f7lJQY6ZMhRztNemy4uslDxTX4Qw== - dependencies: - "@ampproject/remapping" "^2.2.0" - "@babel/code-frame" "^7.22.10" - "@babel/generator" "^7.22.10" - "@babel/helper-compilation-targets" "^7.22.10" - "@babel/helper-module-transforms" "^7.22.9" - "@babel/helpers" "^7.22.10" - "@babel/parser" "^7.22.10" - "@babel/template" "^7.22.5" - "@babel/traverse" "^7.22.10" - "@babel/types" "^7.22.10" - convert-source-map "^1.7.0" - debug "^4.1.0" - gensync "^1.0.0-beta.2" - json5 "^2.2.2" - semver "^6.3.1" - "@babel/core@7.12.9": version "7.12.9" resolved "https://registry.npmjs.org/@babel/core/-/core-7.12.9.tgz" @@ -209,6 +188,27 @@ semver "^5.4.1" source-map "^0.5.0" +"@babel/core@^7.12.3", "@babel/core@^7.18.6", "@babel/core@^7.19.6": + version "7.22.10" + resolved "https://registry.npmjs.org/@babel/core/-/core-7.22.10.tgz" + integrity sha512-fTmqbbUBAwCcre6zPzNngvsI0aNrPZe77AeqvDxWM9Nm+04RrJ3CAmGHA9f7lJQY6ZMhRztNemy4uslDxTX4Qw== + dependencies: + "@ampproject/remapping" "^2.2.0" + "@babel/code-frame" "^7.22.10" + "@babel/generator" "^7.22.10" + "@babel/helper-compilation-targets" "^7.22.10" + "@babel/helper-module-transforms" "^7.22.9" + "@babel/helpers" "^7.22.10" + "@babel/parser" "^7.22.10" + "@babel/template" "^7.22.5" + "@babel/traverse" "^7.22.10" + "@babel/types" "^7.22.10" + convert-source-map "^1.7.0" + debug "^4.1.0" + gensync "^1.0.0-beta.2" + json5 "^2.2.2" + semver "^6.3.1" + "@babel/generator@^7.12.5", "@babel/generator@^7.18.7", "@babel/generator@^7.22.10", "@babel/generator@^7.23.3": version "7.23.3" resolved "https://registry.npmjs.org/@babel/generator/-/generator-7.23.3.tgz" @@ -331,16 +331,16 @@ dependencies: "@babel/types" "^7.22.5" -"@babel/helper-plugin-utils@^7.0.0", "@babel/helper-plugin-utils@^7.10.4", "@babel/helper-plugin-utils@^7.12.13", "@babel/helper-plugin-utils@^7.14.5", "@babel/helper-plugin-utils@^7.18.6", "@babel/helper-plugin-utils@^7.22.5", "@babel/helper-plugin-utils@^7.8.0", "@babel/helper-plugin-utils@^7.8.3": - version "7.22.5" - resolved "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.22.5.tgz" - integrity sha512-uLls06UVKgFG9QD4OeFYLEGteMIAa5kpTPcFL28yuCIIzsf6ZyKZMllKVOCZFhiZ5ptnwX4mtKdWCBE/uT4amg== - "@babel/helper-plugin-utils@7.10.4": version "7.10.4" resolved "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.10.4.tgz" integrity sha512-O4KCvQA6lLiMU9l2eawBPMf1xPP8xPfB3iEQw150hOVTqj/rfXz0ThTb4HEzqQfs2Bmo5Ay8BzxfzVtBrr9dVg== +"@babel/helper-plugin-utils@^7.0.0", "@babel/helper-plugin-utils@^7.10.4", "@babel/helper-plugin-utils@^7.12.13", "@babel/helper-plugin-utils@^7.14.5", "@babel/helper-plugin-utils@^7.18.6", "@babel/helper-plugin-utils@^7.22.5", "@babel/helper-plugin-utils@^7.8.0", "@babel/helper-plugin-utils@^7.8.3": + version "7.22.5" + resolved "https://registry.npmjs.org/@babel/helper-plugin-utils/-/helper-plugin-utils-7.22.5.tgz" + integrity sha512-uLls06UVKgFG9QD4OeFYLEGteMIAa5kpTPcFL28yuCIIzsf6ZyKZMllKVOCZFhiZ5ptnwX4mtKdWCBE/uT4amg== + "@babel/helper-remap-async-to-generator@^7.22.5", "@babel/helper-remap-async-to-generator@^7.22.9": version "7.22.9" resolved "https://registry.npmjs.org/@babel/helper-remap-async-to-generator/-/helper-remap-async-to-generator-7.22.9.tgz" @@ -451,7 +451,7 @@ "@babel/helper-create-class-features-plugin" "^7.18.6" "@babel/helper-plugin-utils" "^7.18.6" -"@babel/plugin-proposal-object-rest-spread@^7.12.1", "@babel/plugin-proposal-object-rest-spread@7.12.1": +"@babel/plugin-proposal-object-rest-spread@7.12.1", "@babel/plugin-proposal-object-rest-spread@^7.12.1": version "7.12.1" resolved "https://registry.npmjs.org/@babel/plugin-proposal-object-rest-spread/-/plugin-proposal-object-rest-spread-7.12.1.tgz" integrity sha512-s6SowJIjzlhx8o7lsFx5zmY4At6CTtDvgNQDdPzkBQucle58A6b/TTeEBYtyDgmcXjUTM+vE8YOGHZzzbc/ioA== @@ -528,13 +528,6 @@ dependencies: "@babel/helper-plugin-utils" "^7.8.0" -"@babel/plugin-syntax-jsx@^7.22.5": - version "7.22.5" - resolved "https://registry.npmjs.org/@babel/plugin-syntax-jsx/-/plugin-syntax-jsx-7.22.5.tgz" - integrity sha512-gvyP4hZrgrs/wWMaocvxZ44Hw0b3W8Pe+cMxc8V1ULQ07oh8VNbIRaoD1LRZVTvD+0nieDKjfgKg89sD7rrKrg== - dependencies: - "@babel/helper-plugin-utils" "^7.22.5" - "@babel/plugin-syntax-jsx@7.12.1": version "7.12.1" resolved "https://registry.npmjs.org/@babel/plugin-syntax-jsx/-/plugin-syntax-jsx-7.12.1.tgz" @@ -542,6 +535,13 @@ dependencies: "@babel/helper-plugin-utils" "^7.10.4" +"@babel/plugin-syntax-jsx@^7.22.5": + version "7.22.5" + resolved "https://registry.npmjs.org/@babel/plugin-syntax-jsx/-/plugin-syntax-jsx-7.22.5.tgz" + integrity sha512-gvyP4hZrgrs/wWMaocvxZ44Hw0b3W8Pe+cMxc8V1ULQ07oh8VNbIRaoD1LRZVTvD+0nieDKjfgKg89sD7rrKrg== + dependencies: + "@babel/helper-plugin-utils" "^7.22.5" + "@babel/plugin-syntax-logical-assignment-operators@^7.10.4": version "7.10.4" resolved "https://registry.npmjs.org/@babel/plugin-syntax-logical-assignment-operators/-/plugin-syntax-logical-assignment-operators-7.10.4.tgz" @@ -563,7 +563,7 @@ dependencies: "@babel/helper-plugin-utils" "^7.10.4" -"@babel/plugin-syntax-object-rest-spread@^7.8.0", "@babel/plugin-syntax-object-rest-spread@^7.8.3", "@babel/plugin-syntax-object-rest-spread@7.8.3": +"@babel/plugin-syntax-object-rest-spread@7.8.3", "@babel/plugin-syntax-object-rest-spread@^7.8.0", "@babel/plugin-syntax-object-rest-spread@^7.8.3": version "7.8.3" resolved "https://registry.npmjs.org/@babel/plugin-syntax-object-rest-spread/-/plugin-syntax-object-rest-spread-7.8.3.tgz" integrity sha512-XoqMijGZb9y3y2XskN+P1wUGiVwWZ5JmoDRwx5+3GmEplNyVM2s2Dg8ILFQm8rWM48orGy5YpI5Bl8U1y7ydlA== @@ -1279,7 +1279,7 @@ "@docsearch/css" "3.5.1" algoliasearch "^4.0.0" -"@docusaurus/core@^2.0.0-alpha.60 || ^2.0.0", "@docusaurus/core@2.4.1": +"@docusaurus/core@2.4.1": version "2.4.1" resolved "https://registry.npmjs.org/@docusaurus/core/-/core-2.4.1.tgz" integrity sha512-SNsY7PshK3Ri7vtsLXVeAJGS50nJN3RgF836zkyUfAD01Fq+sAk5EwWgLw+nnm5KVNGDu7PRR2kRGDsWvqpo0g== @@ -1502,7 +1502,7 @@ "@docusaurus/utils-validation" "2.4.1" tslib "^2.4.0" -"@docusaurus/plugin-google-gtag@^2.4.1", "@docusaurus/plugin-google-gtag@2.4.1": +"@docusaurus/plugin-google-gtag@2.4.1", "@docusaurus/plugin-google-gtag@^2.4.1": version "2.4.1" resolved "https://registry.npmjs.org/@docusaurus/plugin-google-gtag/-/plugin-google-gtag-2.4.1.tgz" integrity sha512-mKIefK+2kGTQBYvloNEKtDmnRD7bxHLsBcxgnbt4oZwzi2nxCGjPX6+9SQO2KCN5HZbNrYmGo5GJfMgoRvy6uA== @@ -1573,7 +1573,7 @@ "@docusaurus/theme-search-algolia" "2.4.1" "@docusaurus/types" "2.4.1" -"@docusaurus/react-loadable@5.5.2": +"@docusaurus/react-loadable@5.5.2", "react-loadable@npm:@docusaurus/react-loadable@5.5.2": version "5.5.2" resolved "https://registry.npmjs.org/@docusaurus/react-loadable/-/react-loadable-5.5.2.tgz" integrity sha512-A3dYjdBGuy0IGT+wyLIGIKLRE+sAk1iNk0f1HjNDysO7u8lhL4N3VEm+FAubmJbAztn94F7MxBTPmnixbiyFdQ== @@ -1671,7 +1671,7 @@ fs-extra "^10.1.0" tslib "^2.4.0" -"@docusaurus/types@*", "@docusaurus/types@2.4.1": +"@docusaurus/types@2.4.1": version "2.4.1" resolved "https://registry.npmjs.org/@docusaurus/types/-/types-2.4.1.tgz" integrity sha512-0R+cbhpMkhbRXX138UOc/2XZFF8hiZa6ooZAEEJFp5scytzCw4tC1gChMFXrpa3d2tYE6AX8IrOEpSonLmfQuQ== @@ -1857,16 +1857,16 @@ "@nodelib/fs.stat" "2.0.5" run-parallel "^1.1.9" +"@nodelib/fs.stat@2.0.5", "@nodelib/fs.stat@^2.0.2": + version "2.0.5" + resolved "https://registry.npmjs.org/@nodelib/fs.stat/-/fs.stat-2.0.5.tgz" + integrity sha512-RkhPPp2zrqDAQA/2jNhnztcPAlv64XdhIp7a7454A5ovI7Bukxgt7MX7udwAu3zg1DcpPU0rz3VV1SeaqvY4+A== + "@nodelib/fs.stat@^1.1.2": version "1.1.3" resolved "https://registry.npmjs.org/@nodelib/fs.stat/-/fs.stat-1.1.3.tgz" integrity sha512-shAmDyaQC4H92APFoIaVDHCx5bStIocgvbwQyxPRrbUY20V1EYTbSDchWbuwlMG3V17cprZhA6+78JfB+3DTPw== -"@nodelib/fs.stat@^2.0.2", "@nodelib/fs.stat@2.0.5": - 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version "1.3.2" - resolved "https://registry.npmjs.org/svgo/-/svgo-1.3.2.tgz" - integrity sha512-yhy/sQYxR5BkC98CY7o31VGsg014AKLEPxdfhora76l36hD9Rdy5NZA/Ocn6yayNPgSamYdtX2rFJdcv07AYVw== - dependencies: - chalk "^2.4.1" - coa "^2.0.2" - css-select "^2.0.0" - css-select-base-adapter "^0.1.1" - css-tree "1.0.0-alpha.37" - csso "^4.0.2" - js-yaml "^3.13.1" - mkdirp "~0.5.1" - object.values "^1.1.0" - sax "~1.2.4" - stable "^0.1.8" - unquote "~1.1.1" - util.promisify "~1.0.0" - -svgo@^1.3.2: +svgo@^1.0.0, svgo@^1.3.2: version "1.3.2" resolved "https://registry.npmjs.org/svgo/-/svgo-1.3.2.tgz" integrity sha512-yhy/sQYxR5BkC98CY7o31VGsg014AKLEPxdfhora76l36hD9Rdy5NZA/Ocn6yayNPgSamYdtX2rFJdcv07AYVw== @@ -12070,16 +11664,11 @@ terser@^5.10.0, terser@^5.16.8: commander "^2.20.0" source-map-support "~0.5.20" -text-table@^0.2.0, text-table@0.2.0: +text-table@0.2.0, text-table@^0.2.0: version "0.2.0" resolved "https://registry.npmjs.org/text-table/-/text-table-0.2.0.tgz" integrity sha512-N+8UisAXDGk8PFXP4HAzVR9nbfmVJ3zYLAWiTIoqC5v5isinhr+r5uaO8+7r3BMfuNIufIsA7RdpVgacC2cSpw== -through@^2.3.8: - version "2.3.8" - resolved "https://registry.npmjs.org/through/-/through-2.3.8.tgz" - integrity sha512-w89qg7PI8wAdvX60bMDP+bFoD5Dvhm9oLheFp5O4a2QF0cSBGsBX4qZmadPMvVqlLJBBci+WqGGOAPvcDeNSVg== - through2@^2.0.0: version "2.0.5" resolved "https://registry.npmjs.org/through2/-/through2-2.0.5.tgz" @@ -12088,6 +11677,11 @@ through2@^2.0.0: readable-stream "~2.3.6" xtend "~4.0.1" +through@^2.3.8: + version "2.3.8" + resolved "https://registry.npmjs.org/through/-/through-2.3.8.tgz" + integrity sha512-w89qg7PI8wAdvX60bMDP+bFoD5Dvhm9oLheFp5O4a2QF0cSBGsBX4qZmadPMvVqlLJBBci+WqGGOAPvcDeNSVg== + thunky@^1.0.2: version "1.1.0" resolved "https://registry.npmjs.org/thunky/-/thunky-1.1.0.tgz" @@ -12350,11 +11944,6 @@ typedarray@^0.0.6: resolved "https://registry.npmjs.org/typedarray/-/typedarray-0.0.6.tgz" integrity sha512-/aCDEGatGvZ2BIk+HmLf4ifCJFwvKFNb9/JeZPMulfgFracn9QFcAf5GO8B/mweUjSoblS5In0cWhqpfs/5PQA== -"typescript@>= 2.7": - version "5.1.6" - resolved "https://registry.npmjs.org/typescript/-/typescript-5.1.6.tgz" - integrity sha512-zaWCozRZ6DLEWAWFrVDz1H6FVXzUSfTy5FUMWsQlU8Ym5JP9eO4xkTIROFCQvhQf61z6O/G6ugw3SgAnvvm+HA== - ua-parser-js@^1.0.35: version "1.0.35" resolved "https://registry.npmjs.org/ua-parser-js/-/ua-parser-js-1.0.35.tgz" @@ -12409,10 +11998,10 @@ unicode-property-aliases-ecmascript@^2.0.0: resolved "https://registry.npmjs.org/unicode-property-aliases-ecmascript/-/unicode-property-aliases-ecmascript-2.1.0.tgz" integrity sha512-6t3foTQI9qne+OZoVQB/8x8rk2k1eVy1gRXhV3oFQ5T6R1dqQ1xtin3XqSlx3+ATBkliTaR/hHyJBm+LVPNM8w== -unified@^9.0.0, unified@^9.2.2: - version "9.2.2" - resolved "https://registry.npmjs.org/unified/-/unified-9.2.2.tgz" - integrity sha512-Sg7j110mtefBD+qunSLO1lqOEKdrwBFBrR6Qd8f4uwkhWNlbkaqwHse6e7QvD3AP/MNoJdEDLaf8OxYyoWgorQ== +unified@9.2.0: + version "9.2.0" + resolved "https://registry.npmjs.org/unified/-/unified-9.2.0.tgz" + integrity sha512-vx2Z0vY+a3YoTj8+pttM3tiJHCwY5UFbYdiWrwBEbHmK8pvsPj2rtAX2BFfgXen8T39CJWblWRDT4L5WGXtDdg== dependencies: bail "^1.0.0" extend "^3.0.0" @@ -12421,10 +12010,10 @@ unified@^9.0.0, unified@^9.2.2: trough "^1.0.0" vfile "^4.0.0" -unified@9.2.0: - version "9.2.0" - resolved "https://registry.npmjs.org/unified/-/unified-9.2.0.tgz" - integrity sha512-vx2Z0vY+a3YoTj8+pttM3tiJHCwY5UFbYdiWrwBEbHmK8pvsPj2rtAX2BFfgXen8T39CJWblWRDT4L5WGXtDdg== +unified@^9.0.0, unified@^9.2.2: + version "9.2.2" + resolved "https://registry.npmjs.org/unified/-/unified-9.2.2.tgz" + integrity sha512-Sg7j110mtefBD+qunSLO1lqOEKdrwBFBrR6Qd8f4uwkhWNlbkaqwHse6e7QvD3AP/MNoJdEDLaf8OxYyoWgorQ== dependencies: bail "^1.0.0" extend "^3.0.0" @@ -12460,7 +12049,7 @@ unique-string@^2.0.0: dependencies: crypto-random-string "^2.0.0" -unist-builder@^2.0.0, unist-builder@2.0.3: +unist-builder@2.0.3, unist-builder@^2.0.0: version "2.0.3" resolved "https://registry.npmjs.org/unist-builder/-/unist-builder-2.0.3.tgz" integrity sha512-f98yt5pnlMWlzP539tPc4grGMsFaQQlP/vM396b00jngsiINumNmsY8rkXjfoi1c6QaM8nQ3vaGDuoKWbe/1Uw== @@ -12516,7 +12105,7 @@ unist-util-visit-parents@^3.0.0: "@types/unist" "^2.0.0" unist-util-is "^4.0.0" -unist-util-visit@^2.0.0, unist-util-visit@^2.0.3, unist-util-visit@2.0.3: +unist-util-visit@2.0.3, unist-util-visit@^2.0.0, unist-util-visit@^2.0.3: version "2.0.3" resolved "https://registry.npmjs.org/unist-util-visit/-/unist-util-visit-2.0.3.tgz" integrity sha512-iJ4/RczbJMkD0712mGktuGpm/U4By4FfDonL7N/9tATGIF4imikjOuagyMY53tnZq3NP6BcmlrHhEKAfGWjh7Q== @@ -12530,7 +12119,7 @@ universalify@^2.0.0: resolved "https://registry.npmjs.org/universalify/-/universalify-2.0.0.tgz" integrity sha512-hAZsKq7Yy11Zu1DE0OzWjw7nnLZmJZYTDZZyEFHZdUhV8FkH5MCfoU1XMaxXovpyW5nq5scPqq0ZDP9Zyl04oQ== -unpipe@~1.0.0, unpipe@1.0.0: +unpipe@1.0.0, unpipe@~1.0.0: version "1.0.0" resolved "https://registry.npmjs.org/unpipe/-/unpipe-1.0.0.tgz" integrity sha512-pjy2bYhSsufwWlKwPc+l3cN7+wuJlK6uz0YdJEOlQDbl6jo/YlPi4mb8agUkVC8BF7V8NuzeyPNqRksA3hztKQ== @@ -12689,12 +12278,7 @@ utils-merge@1.0.1: resolved "https://registry.npmjs.org/utils-merge/-/utils-merge-1.0.1.tgz" integrity sha512-pMZTvIkT1d+TFGvDOqodOclx0QWkkgi6Tdoa8gC8ffGAAqz9pzPTZWAybbsHHoED/ztMtkv/VoYTYyShUn81hA== -uuid@^3.0.1: - version "3.4.0" - resolved "https://registry.npmjs.org/uuid/-/uuid-3.4.0.tgz" - integrity sha512-HjSDRw6gZE5JMggctHBcjVak08+KEVhSIiDzFnT9S9aegmp85S/bReBVTb4QTFaRNptJ9kuYaNhnbNEOkbKb/A== - -uuid@^3.3.2: +uuid@^3.0.1, uuid@^3.3.2: version "3.4.0" resolved "https://registry.npmjs.org/uuid/-/uuid-3.4.0.tgz" integrity sha512-HjSDRw6gZE5JMggctHBcjVak08+KEVhSIiDzFnT9S9aegmp85S/bReBVTb4QTFaRNptJ9kuYaNhnbNEOkbKb/A== @@ -12881,7 +12465,7 @@ webpack-sources@^3.2.2, webpack-sources@^3.2.3: resolved "https://registry.npmjs.org/webpack-sources/-/webpack-sources-3.2.3.tgz" integrity sha512-/DyMEOrDgLKKIG0fmvtz+4dUX/3Ghozwgm6iPp8KRhvn+eQf9+Q7GWxVNMk3+uCPWfdXYC4ExGBckIXdFEfH1w== -"webpack@^4.0.0 || ^5.0.0", "webpack@^4.37.0 || ^5.0.0", webpack@^5.0.0, webpack@^5.1.0, webpack@^5.20.0, webpack@^5.73.0, "webpack@>= 4", webpack@>=2, "webpack@>=4.41.1 || 5.x", "webpack@3 || 4 || 5": +webpack@^5.73.0: version "5.88.2" resolved "https://registry.npmjs.org/webpack/-/webpack-5.88.2.tgz" integrity sha512-JmcgNZ1iKj+aiR0OvTYtWQqJwq37Pf683dY9bVORwVbUrDhLhdn/PlO2sHsFHPkj7sHNQF3JwaAkp49V+Sq1tQ== @@ -12921,7 +12505,7 @@ webpackbar@^5.0.2: pretty-time "^1.1.0" std-env "^3.0.1" -websocket-driver@^0.7.4, websocket-driver@>=0.5.1: +websocket-driver@>=0.5.1, websocket-driver@^0.7.4: version "0.7.4" resolved "https://registry.npmjs.org/websocket-driver/-/websocket-driver-0.7.4.tgz" integrity sha512-b17KeDIQVjvb0ssuSDF2cYXSg2iztliJ4B9WdsuB6J952qCPKmnVq4DyW5motImXHDC1cBT/1UezrJVsKw5zjg== @@ -12974,14 +12558,7 @@ which-typed-array@^1.1.11, which-typed-array@^1.1.13: gopd "^1.0.1" has-tostringtag "^1.0.0" -which@^1.2.9: - version "1.3.1" - resolved "https://registry.npmjs.org/which/-/which-1.3.1.tgz" - integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ== - dependencies: - isexe "^2.0.0" - -which@^1.3.1: +which@^1.2.9, which@^1.3.1: version "1.3.1" resolved "https://registry.npmjs.org/which/-/which-1.3.1.tgz" integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ== diff --git a/litellm/__init__.py b/litellm/__init__.py index b7aeeb210..36ce31cfa 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -10,7 +10,7 @@ success_callback: List[Union[str, Callable]] = [] failure_callback: List[Union[str, Callable]] = [] callbacks: List[Callable] = [] _async_input_callback: List[Callable] = [] # internal variable - async custom callbacks are routed here. -_async_success_callback: List[Callable] = [] # internal variable - async custom callbacks are routed here. +_async_success_callback: List[Union[str, Callable]] = [] # internal variable - async custom callbacks are routed here. _async_failure_callback: List[Callable] = [] # internal variable - async custom callbacks are routed here. pre_call_rules: List[Callable] = [] post_call_rules: List[Callable] = [] @@ -48,6 +48,8 @@ cache: Optional[Cache] = None # cache object <- use this - https://docs.litellm. model_alias_map: Dict[str, str] = {} model_group_alias_map: Dict[str, str] = {} max_budget: float = 0.0 # set the max budget across all providers +_openai_completion_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id", "organization", "base_url", "default_headers", "timeout", "response_format", "seed", "tools", "tool_choice", "max_retries"] +_litellm_completion_params = ["metadata", "acompletion", "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", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name", "model_info", "proxy_server_request", "preset_cache_key"] _current_cost = 0 # private variable, used if max budget is set error_logs: Dict = {} add_function_to_prompt: bool = False # if function calling not supported by api, append function call details to system prompt @@ -56,6 +58,7 @@ aclient_session: Optional[httpx.AsyncClient] = None model_fallbacks: Optional[List] = None # Deprecated for 'litellm.fallbacks' model_cost_map_url: str = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json" suppress_debug_info = False +dynamodb_table_name: Optional[str] = None #### RELIABILITY #### request_timeout: Optional[float] = 6000 num_retries: Optional[int] = None @@ -107,6 +110,8 @@ open_ai_text_completion_models: List = [] cohere_models: List = [] anthropic_models: List = [] openrouter_models: List = [] +vertex_language_models: List = [] +vertex_vision_models: List = [] vertex_chat_models: List = [] vertex_code_chat_models: List = [] vertex_text_models: List = [] @@ -133,6 +138,10 @@ for key, value in model_cost.items(): vertex_text_models.append(key) elif value.get('litellm_provider') == 'vertex_ai-code-text-models': vertex_code_text_models.append(key) + elif value.get('litellm_provider') == 'vertex_ai-language-models': + vertex_language_models.append(key) + elif value.get('litellm_provider') == 'vertex_ai-vision-models': + vertex_vision_models.append(key) elif value.get('litellm_provider') == 'vertex_ai-chat-models': vertex_chat_models.append(key) elif value.get('litellm_provider') == 'vertex_ai-code-chat-models': @@ -154,7 +163,16 @@ for key, value in model_cost.items(): openai_compatible_endpoints: List = [ "api.perplexity.ai", "api.endpoints.anyscale.com/v1", - "api.deepinfra.com/v1/openai" + "api.deepinfra.com/v1/openai", + "api.mistral.ai/v1" +] + +# this is maintained for Exception Mapping +openai_compatible_providers: List = [ + "anyscale", + "mistral", + "deepinfra", + "perplexity" ] @@ -266,6 +284,7 @@ model_list = ( provider_list: List = [ "openai", "custom_openai", + "text-completion-openai", "cohere", "anthropic", "replicate", @@ -287,6 +306,7 @@ provider_list: List = [ "deepinfra", "perplexity", "anyscale", + "mistral", "maritalk", "custom", # custom apis ] @@ -396,6 +416,7 @@ from .exceptions import ( AuthenticationError, InvalidRequestError, BadRequestError, + NotFoundError, RateLimitError, ServiceUnavailableError, OpenAIError, @@ -404,7 +425,8 @@ from .exceptions import ( APIError, Timeout, APIConnectionError, - APIResponseValidationError + APIResponseValidationError, + UnprocessableEntityError ) from .budget_manager import BudgetManager from .proxy.proxy_cli import run_server diff --git a/litellm/caching.py b/litellm/caching.py index 1d993927b..73dde7cf9 100644 --- a/litellm/caching.py +++ b/litellm/caching.py @@ -10,19 +10,7 @@ import litellm import time, logging import json, traceback, ast -from typing import Optional - -def get_prompt(*args, **kwargs): - # make this safe checks, it should not throw any exceptions - if len(args) > 1: - messages = args[1] - prompt = " ".join(message["content"] for message in messages) - return prompt - if "messages" in kwargs: - messages = kwargs["messages"] - prompt = " ".join(message["content"] for message in messages) - return prompt - return None +from typing import Optional, Literal, List def print_verbose(print_statement): try: @@ -174,34 +162,36 @@ class DualCache(BaseCache): if self.redis_cache is not None: self.redis_cache.flush_cache() -#### LiteLLM.Completion Cache #### +#### LiteLLM.Completion / Embedding Cache #### class Cache: def __init__( self, - type="local", - host=None, - port=None, - password=None, + type: Optional[Literal["local", "redis"]] = "local", + host: Optional[str] = None, + port: Optional[str] = None, + password: Optional[str] = None, + supported_call_types: Optional[List[Literal["completion", "acompletion", "embedding", "aembedding"]]] = ["completion", "acompletion", "embedding", "aembedding"], **kwargs ): """ Initializes the cache based on the given type. Args: - type (str, optional): The type of cache to initialize. Defaults to "local". + type (str, optional): The type of cache to initialize. Can be "local" or "redis". Defaults to "local". host (str, optional): The host address for the Redis cache. Required if type is "redis". port (int, optional): The port number for the Redis cache. Required if type is "redis". password (str, optional): The password for the Redis cache. Required if type is "redis". + supported_call_types (list, optional): List of call types to cache for. Defaults to cache == on for all call types. **kwargs: Additional keyword arguments for redis.Redis() cache Raises: ValueError: If an invalid cache type is provided. Returns: - None + None. Cache is set as a litellm param """ if type == "redis": - self.cache = RedisCache(host, port, password, **kwargs) + self.cache: BaseCache = RedisCache(host, port, password, **kwargs) if type == "local": self.cache = InMemoryCache() if "cache" not in litellm.input_callback: @@ -210,6 +200,7 @@ class Cache: litellm.success_callback.append("cache") if "cache" not in litellm._async_success_callback: litellm._async_success_callback.append("cache") + self.supported_call_types = supported_call_types # default to ["completion", "acompletion", "embedding", "aembedding"] def get_cache_key(self, *args, **kwargs): """ @@ -222,29 +213,55 @@ class Cache: Returns: str: The cache key generated from the arguments, or None if no cache key could be generated. """ - cache_key ="" + cache_key = "" + print_verbose(f"\nGetting Cache key. Kwargs: {kwargs}") + + # for streaming, we use preset_cache_key. It's created in wrapper(), we do this because optional params like max_tokens, get transformed for bedrock -> max_new_tokens + if kwargs.get("litellm_params", {}).get("preset_cache_key", None) is not None: + print_verbose(f"\nReturning preset cache key: {cache_key}") + return kwargs.get("litellm_params", {}).get("preset_cache_key", None) + # sort kwargs by keys, since model: [gpt-4, temperature: 0.2, max_tokens: 200] == [temperature: 0.2, max_tokens: 200, model: gpt-4] completion_kwargs = ["model", "messages", "temperature", "top_p", "n", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "response_format", "seed", "tools", "tool_choice"] - for param in completion_kwargs: + embedding_only_kwargs = ["input", "encoding_format"] # embedding kwargs = model, input, user, encoding_format. Model, user are checked in completion_kwargs + + # combined_kwargs - NEEDS to be ordered across get_cache_key(). Do not use a set() + combined_kwargs = completion_kwargs + embedding_only_kwargs + for param in combined_kwargs: # ignore litellm params here if param in kwargs: # check if param == model and model_group is passed in, then override model with model_group if param == "model": model_group = None + caching_group = None metadata = kwargs.get("metadata", None) litellm_params = kwargs.get("litellm_params", {}) if metadata is not None: model_group = metadata.get("model_group") + model_group = metadata.get("model_group", None) + caching_groups = metadata.get("caching_groups", None) + if caching_groups: + for group in caching_groups: + if model_group in group: + caching_group = group + break if litellm_params is not None: metadata = litellm_params.get("metadata", None) if metadata is not None: model_group = metadata.get("model_group", None) - param_value = model_group or kwargs[param] # use model_group if it exists, else use kwargs["model"] + caching_groups = metadata.get("caching_groups", None) + if caching_groups: + for group in caching_groups: + if model_group in group: + caching_group = group + break + param_value = caching_group or model_group or kwargs[param] # use caching_group, if set then model_group if it exists, else use kwargs["model"] else: 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}") return cache_key def generate_streaming_content(self, content): @@ -297,4 +314,9 @@ class Cache: result = result.model_dump_json() self.cache.set_cache(cache_key, result, **kwargs) except Exception as e: + print_verbose(f"LiteLLM Cache: Excepton add_cache: {str(e)}") + traceback.print_exc() pass + + async def _async_add_cache(self, result, *args, **kwargs): + self.add_cache(result, *args, **kwargs) \ No newline at end of file diff --git a/litellm/exceptions.py b/litellm/exceptions.py index ec0fe0049..ae714cfed 100644 --- a/litellm/exceptions.py +++ b/litellm/exceptions.py @@ -12,16 +12,19 @@ from openai import ( AuthenticationError, BadRequestError, + NotFoundError, RateLimitError, APIStatusError, OpenAIError, APIError, APITimeoutError, APIConnectionError, - APIResponseValidationError + APIResponseValidationError, + UnprocessableEntityError ) import httpx + class AuthenticationError(AuthenticationError): # type: ignore def __init__(self, message, llm_provider, model, response: httpx.Response): self.status_code = 401 @@ -34,6 +37,20 @@ class AuthenticationError(AuthenticationError): # type: ignore body=None ) # Call the base class constructor with the parameters it needs +# raise when invalid models passed, example gpt-8 +class NotFoundError(NotFoundError): # type: ignore + def __init__(self, message, model, llm_provider, response: httpx.Response): + self.status_code = 404 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + self.message, + response=response, + body=None + ) # Call the base class constructor with the parameters it needs + + class BadRequestError(BadRequestError): # type: ignore def __init__(self, message, model, llm_provider, response: httpx.Response): self.status_code = 400 @@ -46,6 +63,18 @@ class BadRequestError(BadRequestError): # type: ignore body=None ) # Call the base class constructor with the parameters it needs +class UnprocessableEntityError(UnprocessableEntityError): # type: ignore + def __init__(self, message, model, llm_provider, response: httpx.Response): + self.status_code = 422 + self.message = message + self.model = model + self.llm_provider = llm_provider + super().__init__( + self.message, + response=response, + body=None + ) # Call the base class constructor with the parameters it needs + class Timeout(APITimeoutError): # type: ignore def __init__(self, message, model, llm_provider): self.status_code = 408 diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index d0efc2fb0..64c5fc4cf 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -2,8 +2,9 @@ # On success, logs events to Promptlayer import dotenv, os import requests -import requests - +from litellm.proxy._types import UserAPIKeyAuth +from litellm.caching import DualCache +from typing import Literal dotenv.load_dotenv() # Loading env variables using dotenv import traceback @@ -27,7 +28,12 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback def log_failure_event(self, kwargs, response_obj, start_time, end_time): pass + + #### ASYNC #### + async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + pass + async def async_log_pre_api_call(self, model, messages, kwargs): pass @@ -37,6 +43,16 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): pass + #### CALL HOOKS - proxy only #### + """ + Control the modify incoming / outgoung data before calling the model + """ + async def async_pre_call_hook(self, user_api_key_dict: UserAPIKeyAuth, cache: DualCache, data: dict, call_type: Literal["completion", "embeddings"]): + pass + + async def async_post_call_failure_hook(self, original_exception: Exception, user_api_key_dict: UserAPIKeyAuth): + pass + #### SINGLE-USE #### - https://docs.litellm.ai/docs/observability/custom_callback#using-your-custom-callback-function def log_input_event(self, model, messages, kwargs, print_verbose, callback_func): diff --git a/litellm/integrations/dynamodb.py b/litellm/integrations/dynamodb.py new file mode 100644 index 000000000..c025a0edc --- /dev/null +++ b/litellm/integrations/dynamodb.py @@ -0,0 +1,82 @@ +#### What this does #### +# On success + failure, log events to Supabase + +import dotenv, os +import requests + +dotenv.load_dotenv() # Loading env variables using dotenv +import traceback +import datetime, subprocess, sys +import litellm, uuid +from litellm._logging import print_verbose + +class DyanmoDBLogger: + # Class variables or attributes + + def __init__(self): + # Instance variables + import boto3 + self.dynamodb = boto3.resource('dynamodb', region_name=os.environ["AWS_REGION_NAME"]) + if litellm.dynamodb_table_name is None: + raise ValueError("LiteLLM Error, trying to use DynamoDB but not table name passed. Create a table and set `litellm.dynamodb_table_name=`") + self.table_name = litellm.dynamodb_table_name + + async def _async_log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + self.log_event(kwargs, response_obj, start_time, end_time, print_verbose) + def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + try: + print_verbose( + f"DynamoDB Logging - Enters logging function for model {kwargs}" + ) + + # construct payload to send to DynamoDB + # follows the same params as langfuse.py + litellm_params = kwargs.get("litellm_params", {}) + metadata = litellm_params.get("metadata", {}) or {} # if litellm_params['metadata'] == None + messages = kwargs.get("messages") + optional_params = kwargs.get("optional_params", {}) + call_type = kwargs.get("call_type", "litellm.completion") + usage = response_obj["usage"] + id = response_obj.get("id", str(uuid.uuid4())) + + # Build the initial payload + payload = { + "id": id, + "call_type": call_type, + "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": 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 + + + print_verbose(f"\nDynamoDB Logger - Logging payload = {payload}") + + # put data in dyanmo DB + table = self.dynamodb.Table(self.table_name) + # Assuming log_data is a dictionary with log information + response = table.put_item(Item=payload) + + print_verbose(f"Response from DynamoDB:{str(response)}") + + print_verbose( + f"DynamoDB Layer Logging - final response object: {response_obj}" + ) + return response + except: + traceback.print_exc() + print_verbose(f"DynamoDB Layer Error - {traceback.format_exc()}") + pass \ No newline at end of file diff --git a/litellm/integrations/langfuse.py b/litellm/integrations/langfuse.py index ea5276db2..ea32fa9d1 100644 --- a/litellm/integrations/langfuse.py +++ b/litellm/integrations/langfuse.py @@ -58,7 +58,7 @@ class LangFuseLogger: model=kwargs['model'], modelParameters=optional_params, prompt=prompt, - completion=response_obj['choices'][0]['message'], + completion=response_obj['choices'][0]['message'].json(), usage=Usage( prompt_tokens=response_obj['usage']['prompt_tokens'], completion_tokens=response_obj['usage']['completion_tokens'] @@ -70,6 +70,9 @@ class LangFuseLogger: f"Langfuse Layer Logging - final response object: {response_obj}" ) except: - # traceback.print_exc() + traceback.print_exc() print_verbose(f"Langfuse Layer Error - {traceback.format_exc()}") pass + + async def _async_log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): + self.log_event(kwargs, response_obj, start_time, end_time, print_verbose) diff --git a/litellm/integrations/langsmith.py b/litellm/integrations/langsmith.py index 43e9de4d3..de9dd2f71 100644 --- a/litellm/integrations/langsmith.py +++ b/litellm/integrations/langsmith.py @@ -58,7 +58,7 @@ class LangsmithLogger: "inputs": { **new_kwargs }, - "outputs": response_obj, + "outputs": response_obj.json(), "session_name": project_name, "start_time": start_time, "end_time": end_time, diff --git a/litellm/integrations/traceloop.py b/litellm/integrations/traceloop.py index be53de0e9..33b9c92da 100644 --- a/litellm/integrations/traceloop.py +++ b/litellm/integrations/traceloop.py @@ -1,7 +1,8 @@ class TraceloopLogger: def __init__(self): from traceloop.sdk.tracing.tracing import TracerWrapper - + from traceloop.sdk import Traceloop + Traceloop.init(app_name="Litellm-Server", disable_batch=True) self.tracer_wrapper = TracerWrapper() def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose): diff --git a/litellm/llms/azure.py b/litellm/llms/azure.py index f760e9fce..b014667df 100644 --- a/litellm/llms/azure.py +++ b/litellm/llms/azure.py @@ -196,8 +196,19 @@ class AzureChatCompletion(BaseLLM): else: azure_client = client response = azure_client.chat.completions.create(**data) # type: ignore - response.model = "azure/" + str(response.model) - return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response) + stringified_response = response.model_dump_json() + ## LOGGING + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=stringified_response, + additional_args={ + "headers": headers, + "api_version": api_version, + "api_base": api_base, + }, + ) + return convert_to_model_response_object(response_object=json.loads(stringified_response), model_response_object=model_response) except AzureOpenAIError as e: exception_mapping_worked = True raise e @@ -318,7 +329,10 @@ class AzureChatCompletion(BaseLLM): data: dict, model_response: ModelResponse, azure_client_params: dict, + api_key: str, + input: list, client=None, + logging_obj=None ): response = None try: @@ -327,8 +341,23 @@ class AzureChatCompletion(BaseLLM): else: openai_aclient = client response = await openai_aclient.embeddings.create(**data) - return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response, response_type="embedding") + stringified_response = response.model_dump_json() + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=stringified_response, + ) + return convert_to_model_response_object(response_object=json.loads(stringified_response), model_response_object=model_response, response_type="embedding") except Exception as e: + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=str(e), + ) raise e def embedding(self, @@ -372,13 +401,7 @@ class AzureChatCompletion(BaseLLM): azure_client_params["api_key"] = api_key elif azure_ad_token is not None: azure_client_params["azure_ad_token"] = azure_ad_token - if aembedding == True: - response = self.aembedding(data=data, model_response=model_response, azure_client_params=azure_client_params) - return response - if client is None: - azure_client = AzureOpenAI(**azure_client_params) # type: ignore - else: - azure_client = client + ## LOGGING logging_obj.pre_call( input=input, @@ -391,6 +414,14 @@ class AzureChatCompletion(BaseLLM): } }, ) + + if aembedding == True: + response = self.aembedding(data=data, input=input, logging_obj=logging_obj, api_key=api_key, model_response=model_response, azure_client_params=azure_client_params) + return response + if client is None: + azure_client = AzureOpenAI(**azure_client_params) # type: ignore + else: + azure_client = client ## COMPLETION CALL response = azure_client.embeddings.create(**data) # type: ignore ## LOGGING diff --git a/litellm/llms/bedrock.py b/litellm/llms/bedrock.py index 9b6510400..616b9264e 100644 --- a/litellm/llms/bedrock.py +++ b/litellm/llms/bedrock.py @@ -482,7 +482,7 @@ def completion( logging_obj.post_call( input=prompt, api_key="", - original_response=response_body, + original_response=json.dumps(response_body), additional_args={"complete_input_dict": data}, ) print_verbose(f"raw model_response: {response}") @@ -552,6 +552,7 @@ def _embedding_func_single( ## FORMAT EMBEDDING INPUT ## provider = model.split(".")[0] inference_params = copy.deepcopy(optional_params) + inference_params.pop("user", None) # make sure user is not passed in for bedrock call if provider == "amazon": input = input.replace(os.linesep, " ") data = {"inputText": input, **inference_params} @@ -587,7 +588,7 @@ def _embedding_func_single( input=input, api_key="", additional_args={"complete_input_dict": data}, - original_response=response_body, + original_response=json.dumps(response_body), ) if provider == "cohere": response = response_body.get("embeddings") @@ -650,14 +651,5 @@ def embedding( total_tokens=input_tokens + 0 ) model_response.usage = usage - - ## LOGGING - logging_obj.post_call( - input=input, - api_key=api_key, - additional_args={"complete_input_dict": {"model": model, - "texts": input}}, - original_response=embeddings, - ) return model_response diff --git a/litellm/llms/huggingface_restapi.py b/litellm/llms/huggingface_restapi.py index c347910f8..4dae6e88f 100644 --- a/litellm/llms/huggingface_restapi.py +++ b/litellm/llms/huggingface_restapi.py @@ -542,7 +542,7 @@ class Huggingface(BaseLLM): logging_obj.pre_call( input=input, api_key=api_key, - additional_args={"complete_input_dict": data}, + additional_args={"complete_input_dict": data, "headers": headers, "api_base": embed_url}, ) ## COMPLETION CALL response = requests.post( @@ -584,6 +584,14 @@ class Huggingface(BaseLLM): "embedding": embedding # flatten list returned from hf } ) + elif isinstance(embedding, list) and isinstance(embedding[0], float): + output_data.append( + { + "object": "embedding", + "index": idx, + "embedding": embedding # flatten list returned from hf + } + ) else: output_data.append( { diff --git a/litellm/llms/ollama.py b/litellm/llms/ollama.py index a24e47c07..30aaa5381 100644 --- a/litellm/llms/ollama.py +++ b/litellm/llms/ollama.py @@ -1,10 +1,9 @@ -import requests, types +import requests, types, time import json import traceback from typing import Optional import litellm -import httpx - +import httpx, aiohttp, asyncio try: from async_generator import async_generator, yield_ # optional dependency async_generator_imported = True @@ -115,6 +114,9 @@ def get_ollama_response_stream( 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"): url = api_base @@ -136,8 +138,19 @@ def get_ollama_response_stream( logging_obj.pre_call( input=None, api_key=None, - additional_args={"api_base": url, "complete_input_dict": data}, + additional_args={"api_base": url, "complete_input_dict": data, "headers": {}, "acompletion": acompletion,}, ) + if acompletion is True: + if optional_params.get("stream", False): + response = ollama_async_streaming(url=url, data=data, model_response=model_response, encoding=encoding, logging_obj=logging_obj) + else: + response = ollama_acompletion(url=url, data=data, model_response=model_response, encoding=encoding, logging_obj=logging_obj) + return response + + else: + return ollama_completion_stream(url=url, data=data) + +def ollama_completion_stream(url, data): session = requests.Session() with session.post(url, json=data, stream=True) as resp: @@ -169,41 +182,38 @@ def get_ollama_response_stream( traceback.print_exc() session.close() -if async_generator_imported: - # ollama implementation - @async_generator - async def async_get_ollama_response_stream( - api_base="http://localhost:11434", - model="llama2", - prompt="Why is the sky blue?", - optional_params=None, - logging_obj=None, - ): - url = f"{api_base}/api/generate" - - ## Load Config - config=litellm.OllamaConfig.get_config() - for k, v in config.items(): - if k not in optional_params: # completion(top_k=3) > cohere_config(top_k=3) <- allows for dynamic variables to be passed in - optional_params[k] = v - data = { - "model": model, - "prompt": prompt, - **optional_params - } - ## LOGGING - logging_obj.pre_call( - input=None, - api_key=None, - additional_args={"api_base": url, "complete_input_dict": data}, - ) - session = requests.Session() +async def ollama_async_streaming(url, data, model_response, encoding, logging_obj): + try: + client = httpx.AsyncClient() + async with client.stream( + url=f"{url}", + json=data, + method="POST", + timeout=litellm.request_timeout + ) as response: + if response.status_code != 200: + raise OllamaError(status_code=response.status_code, message=response.text) + + streamwrapper = litellm.CustomStreamWrapper(completion_stream=response.aiter_lines(), model=data['model'], custom_llm_provider="ollama",logging_obj=logging_obj) + async for transformed_chunk in streamwrapper: + yield transformed_chunk + except Exception as e: + traceback.print_exc() - with session.post(url, json=data, stream=True) as resp: - if resp.status_code != 200: - raise OllamaError(status_code=resp.status_code, message=resp.text) - for line in resp.iter_lines(): +async def ollama_acompletion(url, data, model_response, encoding, logging_obj): + data["stream"] = False + try: + timeout = aiohttp.ClientTimeout(total=600) # 10 minutes + async with aiohttp.ClientSession(timeout=timeout) as session: + resp = await session.post(url, json=data) + + if resp.status != 200: + text = await resp.text() + raise OllamaError(status_code=resp.status, message=text) + + completion_string = "" + async for line in resp.content.iter_any(): if line: try: json_chunk = line.decode("utf-8") @@ -217,15 +227,24 @@ if async_generator_imported: "content": "", "error": j } - await yield_({"choices": [{"delta": completion_obj}]}) + raise Exception(f"OllamError - {chunk}") if "response" in j: completion_obj = { "role": "assistant", - "content": "", + "content": j["response"], } - completion_obj["content"] = j["response"] - await yield_({"choices": [{"delta": completion_obj}]}) + completion_string = completion_string + completion_obj["content"] except Exception as e: - import logging - logging.debug(f"Error decoding JSON: {e}") - session.close() \ No newline at end of file + traceback.print_exc() + + ## RESPONSE OBJECT + model_response["choices"][0]["finish_reason"] = "stop" + model_response["choices"][0]["message"]["content"] = completion_string + model_response["created"] = int(time.time()) + model_response["model"] = "ollama/" + data['model'] + prompt_tokens = len(encoding.encode(data['prompt'])) # type: ignore + completion_tokens = len(encoding.encode(completion_string)) + 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: + traceback.print_exc() diff --git a/litellm/llms/openai.py b/litellm/llms/openai.py index 29934d130..4fdecce10 100644 --- a/litellm/llms/openai.py +++ b/litellm/llms/openai.py @@ -195,23 +195,23 @@ class OpenAIChatCompletion(BaseLLM): **optional_params } - ## LOGGING - logging_obj.pre_call( - input=messages, - api_key=api_key, - additional_args={"headers": headers, "api_base": api_base, "acompletion": acompletion, "complete_input_dict": data}, - ) - try: max_retries = data.pop("max_retries", 2) if acompletion is True: if optional_params.get("stream", False): - return self.async_streaming(logging_obj=logging_obj, data=data, model=model, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) + return self.async_streaming(logging_obj=logging_obj, headers=headers, data=data, model=model, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) else: - return self.acompletion(data=data, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) + return self.acompletion(data=data, headers=headers, logging_obj=logging_obj, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) elif optional_params.get("stream", False): - return self.streaming(logging_obj=logging_obj, data=data, model=model, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) + return self.streaming(logging_obj=logging_obj, headers=headers, data=data, model=model, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) else: + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={"headers": headers, "api_base": api_base, "acompletion": acompletion, "complete_input_dict": data}, + ) + if not isinstance(max_retries, int): raise OpenAIError(status_code=422, message="max retries must be an int") if client is None: @@ -219,13 +219,14 @@ class OpenAIChatCompletion(BaseLLM): else: openai_client = client response = openai_client.chat.completions.create(**data) # type: ignore + stringified_response = response.model_dump_json() logging_obj.post_call( - input=None, + input=messages, api_key=api_key, - original_response=response, + original_response=stringified_response, additional_args={"complete_input_dict": data}, ) - return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response) + return convert_to_model_response_object(response_object=json.loads(stringified_response), model_response_object=model_response) except Exception as e: if "Conversation roles must alternate user/assistant" in str(e) or "user and assistant roles should be alternating" in str(e): # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility @@ -259,6 +260,8 @@ class OpenAIChatCompletion(BaseLLM): api_base: Optional[str]=None, client=None, max_retries=None, + logging_obj=None, + headers=None ): response = None try: @@ -266,16 +269,23 @@ class OpenAIChatCompletion(BaseLLM): openai_aclient = AsyncOpenAI(api_key=api_key, base_url=api_base, http_client=litellm.aclient_session, timeout=timeout, max_retries=max_retries) else: openai_aclient = 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}, + ) response = await openai_aclient.chat.completions.create(**data) - return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response) + stringified_response = response.model_dump_json() + logging_obj.post_call( + input=data['messages'], + api_key=api_key, + original_response=stringified_response, + additional_args={"complete_input_dict": data}, + ) + return convert_to_model_response_object(response_object=json.loads(stringified_response), model_response_object=model_response) except Exception as e: - if response 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__}") - else: - raise OpenAIError(status_code=500, message=f"{str(e)}") + raise e def streaming(self, logging_obj, @@ -285,12 +295,19 @@ class OpenAIChatCompletion(BaseLLM): api_key: Optional[str]=None, api_base: Optional[str]=None, client = None, - max_retries=None + max_retries=None, + headers=None ): if client is None: openai_client = OpenAI(api_key=api_key, base_url=api_base, http_client=litellm.client_session, timeout=timeout, max_retries=max_retries) else: openai_client = client + ## LOGGING + logging_obj.pre_call( + input=data['messages'], + api_key=api_key, + additional_args={"headers": headers, "api_base": api_base, "acompletion": False, "complete_input_dict": data}, + ) response = openai_client.chat.completions.create(**data) streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="openai",logging_obj=logging_obj) return streamwrapper @@ -304,6 +321,7 @@ class OpenAIChatCompletion(BaseLLM): api_base: Optional[str]=None, client=None, max_retries=None, + headers=None ): response = None try: @@ -311,6 +329,13 @@ class OpenAIChatCompletion(BaseLLM): openai_aclient = AsyncOpenAI(api_key=api_key, base_url=api_base, http_client=litellm.aclient_session, timeout=timeout, max_retries=max_retries) else: openai_aclient = 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}, + ) + response = await openai_aclient.chat.completions.create(**data) streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="openai",logging_obj=logging_obj) async for transformed_chunk in streamwrapper: @@ -325,6 +350,7 @@ class OpenAIChatCompletion(BaseLLM): raise OpenAIError(status_code=500, message=f"{str(e)}") async def aembedding( self, + input: list, data: dict, model_response: ModelResponse, timeout: float, @@ -332,6 +358,7 @@ class OpenAIChatCompletion(BaseLLM): api_base: Optional[str]=None, client=None, max_retries=None, + logging_obj=None ): response = None try: @@ -340,9 +367,24 @@ class OpenAIChatCompletion(BaseLLM): else: openai_aclient = client response = await openai_aclient.embeddings.create(**data) # type: ignore - return response + stringified_response = response.model_dump_json() + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + additional_args={"complete_input_dict": data}, + original_response=stringified_response, + ) + return convert_to_model_response_object(response_object=json.loads(stringified_response), model_response_object=model_response, response_type="embedding") # type: ignore except Exception as e: + ## LOGGING + logging_obj.post_call( + input=input, + api_key=api_key, + original_response=str(e), + ) raise e + def embedding(self, model: str, input: list, @@ -367,13 +409,6 @@ class OpenAIChatCompletion(BaseLLM): max_retries = data.pop("max_retries", 2) if not isinstance(max_retries, int): raise OpenAIError(status_code=422, message="max retries must be an int") - if aembedding == True: - response = self.aembedding(data=data, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) # type: ignore - return response - if client is None: - openai_client = OpenAI(api_key=api_key, base_url=api_base, http_client=litellm.client_session, timeout=timeout, max_retries=max_retries) - else: - openai_client = client ## LOGGING logging_obj.pre_call( input=input, @@ -381,6 +416,14 @@ class OpenAIChatCompletion(BaseLLM): additional_args={"complete_input_dict": data, "api_base": api_base}, ) + if aembedding == True: + response = self.aembedding(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries) # type: ignore + return response + if client is None: + openai_client = OpenAI(api_key=api_key, base_url=api_base, http_client=litellm.client_session, timeout=timeout, max_retries=max_retries) + else: + openai_client = client + ## COMPLETION CALL response = openai_client.embeddings.create(**data) # type: ignore ## LOGGING @@ -472,12 +515,14 @@ class OpenAITextCompletion(BaseLLM): else: prompt = " ".join([message["content"] for message in messages]) # type: ignore + # don't send max retries to the api, if set + optional_params.pop("max_retries", None) + data = { "model": model, "prompt": prompt, **optional_params } - ## LOGGING logging_obj.pre_call( input=messages, diff --git a/litellm/llms/prompt_templates/factory.py b/litellm/llms/prompt_templates/factory.py index 37c96d773..4596e2b62 100644 --- a/litellm/llms/prompt_templates/factory.py +++ b/litellm/llms/prompt_templates/factory.py @@ -73,8 +73,27 @@ def ollama_pt(model, messages): # https://github.com/jmorganca/ollama/blob/af4cf final_prompt_value="### Response:", messages=messages ) + elif "llava" in model: + prompt = "" + images = [] + for message in messages: + if isinstance(message["content"], str): + prompt += message["content"] + elif isinstance(message["content"], list): + # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models + for element in message["content"]: + if isinstance(element, dict): + if element["type"] == "text": + prompt += element["text"] + elif element["type"] == "image_url": + image_url = element["image_url"]["url"] + images.append(image_url) + return { + "prompt": prompt, + "images": images + } else: - prompt = "".join(m["content"] for m in messages) + prompt = "".join(m["content"] if isinstance(m['content'], str) is str else "".join(m['content']) for m in messages) return prompt def mistral_instruct_pt(messages): @@ -161,6 +180,8 @@ def phind_codellama_pt(messages): def hf_chat_template(model: str, messages: list, chat_template: Optional[Any]=None): ## get the tokenizer config from huggingface + bos_token = "" + eos_token = "" if chat_template is None: def _get_tokenizer_config(hf_model_name): url = f"https://huggingface.co/{hf_model_name}/raw/main/tokenizer_config.json" @@ -187,7 +208,10 @@ def hf_chat_template(model: str, messages: list, chat_template: Optional[Any]=No # Create a template object from the template text env = Environment() env.globals['raise_exception'] = raise_exception - template = env.from_string(chat_template) + try: + template = env.from_string(chat_template) + except Exception as e: + raise e def _is_system_in_template(): try: @@ -227,8 +251,8 @@ def hf_chat_template(model: str, messages: list, chat_template: Optional[Any]=No new_messages.append(reformatted_messages[-1]) rendered_text = template.render(bos_token=bos_token, eos_token=eos_token, messages=new_messages) return rendered_text - except: - raise Exception("Error rendering template") + except Exception as e: + raise Exception(f"Error rendering template - {str(e)}") # Anthropic template def claude_2_1_pt(messages: list): # format - https://docs.anthropic.com/claude/docs/how-to-use-system-prompts @@ -266,20 +290,26 @@ def claude_2_1_pt(messages: list): # format - https://docs.anthropic.com/claude/ ### TOGETHER AI def get_model_info(token, model): - headers = { - 'Authorization': f'Bearer {token}' - } - response = requests.get('https://api.together.xyz/models/info', headers=headers) - if response.status_code == 200: - model_info = response.json() - for m in model_info: - if m["name"].lower().strip() == model.strip(): - return m['config'].get('prompt_format', None), m['config'].get('chat_template', None) - return None, None - else: + try: + headers = { + 'Authorization': f'Bearer {token}' + } + response = requests.get('https://api.together.xyz/models/info', headers=headers) + if response.status_code == 200: + model_info = response.json() + for m in model_info: + if m["name"].lower().strip() == model.strip(): + return m['config'].get('prompt_format', None), m['config'].get('chat_template', None) + return None, None + else: + return None, None + except Exception as e: # safely fail a prompt template request return None, None def format_prompt_togetherai(messages, prompt_format, chat_template): + if prompt_format is None: + return default_pt(messages) + human_prompt, assistant_prompt = prompt_format.split('{prompt}') if chat_template is not None: @@ -397,4 +427,4 @@ def prompt_factory(model: str, messages: list, custom_llm_provider: Optional[str return hf_chat_template(original_model_name, messages) except: return default_pt(messages=messages) # default that covers Bloom, T-5, any non-chat tuned model (e.g. base Llama2) - \ No newline at end of file + diff --git a/litellm/llms/replicate.py b/litellm/llms/replicate.py index a4ecbdc1c..b952193e6 100644 --- a/litellm/llms/replicate.py +++ b/litellm/llms/replicate.py @@ -232,7 +232,8 @@ def completion( if system_prompt is not None: input_data = { "prompt": prompt, - "system_prompt": system_prompt + "system_prompt": system_prompt, + **optional_params } # Otherwise, use the prompt as is else: diff --git a/litellm/llms/sagemaker.py b/litellm/llms/sagemaker.py index 96d06bc20..2bfa9f82a 100644 --- a/litellm/llms/sagemaker.py +++ b/litellm/llms/sagemaker.py @@ -158,6 +158,7 @@ def completion( ) except Exception as e: raise SagemakerError(status_code=500, message=f"{str(e)}") + response = response["Body"].read().decode("utf8") ## LOGGING logging_obj.post_call( @@ -171,10 +172,17 @@ def completion( completion_response = json.loads(response) try: completion_response_choices = completion_response[0] + completion_output = "" if "generation" in completion_response_choices: - model_response["choices"][0]["message"]["content"] = completion_response_choices["generation"] + completion_output += completion_response_choices["generation"] elif "generated_text" in completion_response_choices: - model_response["choices"][0]["message"]["content"] = completion_response_choices["generated_text"] + 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) diff --git a/litellm/llms/together_ai.py b/litellm/llms/together_ai.py index 210ed497e..540dbe202 100644 --- a/litellm/llms/together_ai.py +++ b/litellm/llms/together_ai.py @@ -173,10 +173,11 @@ def completion( message=json.dumps(completion_response["output"]), status_code=response.status_code ) - if len(completion_response["output"]["choices"][0]["text"]) > 0: + 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", "")) diff --git a/litellm/llms/vertex_ai.py b/litellm/llms/vertex_ai.py index 11a1e0c6e..5457ee40d 100644 --- a/litellm/llms/vertex_ai.py +++ b/litellm/llms/vertex_ai.py @@ -4,7 +4,7 @@ from enum import Enum import requests import time from typing import Callable, Optional -from litellm.utils import ModelResponse, Usage +from litellm.utils import ModelResponse, Usage, CustomStreamWrapper import litellm import httpx @@ -57,6 +57,108 @@ class VertexAIConfig(): and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod)) and v is not None} +def _get_image_bytes_from_url(image_url: str) -> bytes: + try: + response = requests.get(image_url) + response.raise_for_status() # Raise an error for bad responses (4xx and 5xx) + image_bytes = response.content + return image_bytes + except requests.exceptions.RequestException as e: + # Handle any request exceptions (e.g., connection error, timeout) + return b'' # Return an empty bytes object or handle the error as needed + + +def _load_image_from_url(image_url: str): + """ + Loads an image from a URL. + + Args: + image_url (str): The URL of the image. + + Returns: + Image: The loaded image. + """ + from vertexai.preview.generative_models import GenerativeModel, Part, GenerationConfig, Image + image_bytes = _get_image_bytes_from_url(image_url) + return Image.from_bytes(image_bytes) + +def _gemini_vision_convert_messages( + messages: list +): + """ + Converts given messages for GPT-4 Vision to Gemini format. + + Args: + messages (list): The messages to convert. Each message can be a dictionary with a "content" key. The content can be a string or a list of elements. If it is a string, it will be concatenated to the prompt. If it is a list, each element will be processed based on its type: + - If the element is a dictionary with a "type" key equal to "text", its "text" value will be concatenated to the prompt. + - If the element is a dictionary with a "type" key equal to "image_url", its "image_url" value will be added to the list of images. + + Returns: + tuple: A tuple containing the prompt (a string) and the processed images (a list of objects representing the images). + + Raises: + VertexAIError: If the import of the 'vertexai' module fails, indicating that 'google-cloud-aiplatform' needs to be installed. + Exception: If any other exception occurs during the execution of the function. + + Note: + This function is based on the code from the 'gemini/getting-started/intro_gemini_python.ipynb' notebook in the 'generative-ai' repository on GitHub. + The supported MIME types for images include 'image/png' and 'image/jpeg'. + + Examples: + >>> messages = [ + ... {"content": "Hello, world!"}, + ... {"content": [{"type": "text", "text": "This is a text message."}, {"type": "image_url", "image_url": "example.com/image.png"}]}, + ... ] + >>> _gemini_vision_convert_messages(messages) + ('Hello, world!This is a text message.', [, ]) + """ + try: + import vertexai + except: + raise VertexAIError(status_code=400,message="vertexai import failed please run `pip install google-cloud-aiplatform`") + try: + from vertexai.preview.language_models import ChatModel, CodeChatModel, InputOutputTextPair + from vertexai.language_models import TextGenerationModel, CodeGenerationModel + from vertexai.preview.generative_models import GenerativeModel, Part, GenerationConfig, Image + + # given messages for gpt-4 vision, convert them for gemini + # https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/getting-started/intro_gemini_python.ipynb + prompt = "" + images = [] + for message in messages: + if isinstance(message["content"], str): + prompt += message["content"] + elif isinstance(message["content"], list): + # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models + for element in message["content"]: + if isinstance(element, dict): + if element["type"] == "text": + prompt += element["text"] + elif element["type"] == "image_url": + image_url = element["image_url"]["url"] + images.append(image_url) + # processing images passed to gemini + processed_images = [] + for img in images: + if "gs://" in img: + # Case 1: Images with Cloud Storage URIs + # The supported MIME types for images include image/png and image/jpeg. + part_mime = "image/png" if "png" in img else "image/jpeg" + google_clooud_part = Part.from_uri(img, mime_type=part_mime) + processed_images.append(google_clooud_part) + elif "https:/" in img: + # Case 2: Images with direct links + image = _load_image_from_url(img) + processed_images.append(image) + elif ".mp4" in img and "gs://" in img: + # Case 3: Videos with Cloud Storage URIs + part_mime = "video/mp4" + google_clooud_part = Part.from_uri(img, mime_type=part_mime) + processed_images.append(google_clooud_part) + return prompt, processed_images + except Exception as e: + raise e + def completion( model: str, messages: list, @@ -69,6 +171,7 @@ def completion( optional_params=None, litellm_params=None, logger_fn=None, + acompletion: bool=False ): try: import vertexai @@ -77,6 +180,8 @@ def completion( try: from vertexai.preview.language_models import ChatModel, CodeChatModel, InputOutputTextPair from vertexai.language_models import TextGenerationModel, CodeGenerationModel + from vertexai.preview.generative_models import GenerativeModel, Part, GenerationConfig + vertexai.init( project=vertex_project, location=vertex_location @@ -90,34 +195,94 @@ def completion( # vertexai does not use an API key, it looks for credentials.json in the environment - prompt = " ".join([message["content"] for message in messages]) + prompt = " ".join([message["content"] for message in messages if isinstance(message["content"], str)]) mode = "" request_str = "" - if model in litellm.vertex_chat_models: - chat_model = ChatModel.from_pretrained(model) + response_obj = None + if model in litellm.vertex_language_models: + llm_model = GenerativeModel(model) + mode = "" + request_str += f"llm_model = GenerativeModel({model})\n" + elif model in litellm.vertex_vision_models: + llm_model = GenerativeModel(model) + request_str += f"llm_model = GenerativeModel({model})\n" + mode = "vision" + elif model in litellm.vertex_chat_models: + llm_model = ChatModel.from_pretrained(model) mode = "chat" - request_str += f"chat_model = ChatModel.from_pretrained({model})\n" + request_str += f"llm_model = ChatModel.from_pretrained({model})\n" elif model in litellm.vertex_text_models: - text_model = TextGenerationModel.from_pretrained(model) + llm_model = TextGenerationModel.from_pretrained(model) mode = "text" - request_str += f"text_model = TextGenerationModel.from_pretrained({model})\n" + request_str += f"llm_model = TextGenerationModel.from_pretrained({model})\n" elif model in litellm.vertex_code_text_models: - text_model = CodeGenerationModel.from_pretrained(model) + llm_model = CodeGenerationModel.from_pretrained(model) mode = "text" - request_str += f"text_model = CodeGenerationModel.from_pretrained({model})\n" - else: # vertex_code_chat_models - chat_model = CodeChatModel.from_pretrained(model) + request_str += f"llm_model = CodeGenerationModel.from_pretrained({model})\n" + else: # vertex_code_llm_models + llm_model = CodeChatModel.from_pretrained(model) mode = "chat" - request_str += f"chat_model = CodeChatModel.from_pretrained({model})\n" + request_str += f"llm_model = CodeChatModel.from_pretrained({model})\n" - if mode == "chat": - chat = chat_model.start_chat() - request_str+= f"chat = chat_model.start_chat()\n" + if acompletion == True: # [TODO] expand support to vertex ai chat + text models + if optional_params.get("stream", False) is True: + # async streaming + return async_streaming(llm_model=llm_model, mode=mode, prompt=prompt, logging_obj=logging_obj, request_str=request_str, model=model, model_response=model_response, messages=messages, print_verbose=print_verbose, **optional_params) + return async_completion(llm_model=llm_model, mode=mode, prompt=prompt, logging_obj=logging_obj, request_str=request_str, model=model, model_response=model_response, encoding=encoding, messages=messages,print_verbose=print_verbose,**optional_params) + if mode == "": + chat = llm_model.start_chat() + request_str+= f"chat = llm_model.start_chat()\n" + + if "stream" in optional_params and optional_params["stream"] == True: + stream = optional_params.pop("stream") + request_str += f"chat.send_message({prompt}, generation_config=GenerationConfig(**{optional_params}), stream={stream})\n" + ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + model_response = chat.send_message(prompt, generation_config=GenerationConfig(**optional_params), stream=stream) + optional_params["stream"] = True + return model_response + request_str += f"chat.send_message({prompt}, generation_config=GenerationConfig(**{optional_params})).text\n" ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + response_obj = chat.send_message(prompt, generation_config=GenerationConfig(**optional_params)) + completion_response = response_obj.text + response_obj = response_obj._raw_response + elif mode == "vision": + print_verbose("\nMaking VertexAI Gemini Pro Vision Call") + print_verbose(f"\nProcessing input messages = {messages}") + + prompt, images = _gemini_vision_convert_messages(messages=messages) + content = [prompt] + images + if "stream" in optional_params and optional_params["stream"] == True: + stream = optional_params.pop("stream") + request_str += f"response = llm_model.generate_content({content}, generation_config=GenerationConfig(**{optional_params}), stream={stream})\n" + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + + model_response = llm_model.generate_content( + contents=content, + generation_config=GenerationConfig(**optional_params), + stream=True + ) + optional_params["stream"] = True + return model_response + + request_str += f"response = llm_model.generate_content({content})\n" + ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + ## LLM Call + response = llm_model.generate_content( + contents=content, + generation_config=GenerationConfig(**optional_params) + ) + completion_response = response.text + response_obj = response._raw_response + elif mode == "chat": + chat = llm_model.start_chat() + request_str+= f"chat = llm_model.start_chat()\n" if "stream" in optional_params and optional_params["stream"] == True: # NOTE: VertexAI does not accept stream=True as a param and raises an error, @@ -125,27 +290,30 @@ def completion( # after we get the response we add optional_params["stream"] = True, since main.py needs to know it's a streaming response to then transform it for the OpenAI format optional_params.pop("stream", None) # vertex ai raises an error when passing stream in optional params request_str += f"chat.send_message_streaming({prompt}, **{optional_params})\n" + ## LOGGING logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) model_response = chat.send_message_streaming(prompt, **optional_params) optional_params["stream"] = True return model_response request_str += f"chat.send_message({prompt}, **{optional_params}).text\n" + ## LOGGING logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) completion_response = chat.send_message(prompt, **optional_params).text elif mode == "text": - if "stream" in optional_params and optional_params["stream"] == True: optional_params.pop("stream", None) # See note above on handling streaming for vertex ai - request_str += f"text_model.predict_streaming({prompt}, **{optional_params})\n" + request_str += f"llm_model.predict_streaming({prompt}, **{optional_params})\n" + ## LOGGING logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) - model_response = text_model.predict_streaming(prompt, **optional_params) + model_response = llm_model.predict_streaming(prompt, **optional_params) optional_params["stream"] = True return model_response - request_str += f"text_model.predict({prompt}, **{optional_params}).text\n" + request_str += f"llm_model.predict({prompt}, **{optional_params}).text\n" + ## LOGGING logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) - completion_response = text_model.predict(prompt, **optional_params).text + completion_response = llm_model.predict(prompt, **optional_params).text ## LOGGING logging_obj.post_call( @@ -161,22 +329,162 @@ def completion( model_response["created"] = int(time.time()) model_response["model"] = model ## CALCULATING USAGE - prompt_tokens = len( - encoding.encode(prompt) - ) - completion_tokens = len( - encoding.encode(model_response["choices"][0]["message"].get("content", "")) - ) - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens + if model in litellm.vertex_language_models and response_obj is not None: + model_response["choices"][0].finish_reason = response_obj.candidates[0].finish_reason.name + usage = Usage(prompt_tokens=response_obj.usage_metadata.prompt_token_count, + completion_tokens=response_obj.usage_metadata.candidates_token_count, + total_tokens=response_obj.usage_metadata.total_token_count) + else: + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) ) + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) model_response.usage = usage return model_response except Exception as e: raise VertexAIError(status_code=500, message=str(e)) +async def async_completion(llm_model, mode: str, prompt: str, model: str, model_response: ModelResponse, logging_obj=None, request_str=None, encoding=None, messages = None, print_verbose = None, **optional_params): + """ + Add support for acompletion calls for gemini-pro + """ + try: + from vertexai.preview.generative_models import GenerationConfig + + if mode == "": + # gemini-pro + chat = llm_model.start_chat() + ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + response_obj = await chat.send_message_async(prompt, generation_config=GenerationConfig(**optional_params)) + completion_response = response_obj.text + response_obj = response_obj._raw_response + elif mode == "vision": + print_verbose("\nMaking VertexAI Gemini Pro Vision Call") + print_verbose(f"\nProcessing input messages = {messages}") + + prompt, images = _gemini_vision_convert_messages(messages=messages) + content = [prompt] + images + + request_str += f"response = llm_model.generate_content({content})\n" + ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + + ## LLM Call + response = await llm_model._generate_content_async( + contents=content, + generation_config=GenerationConfig(**optional_params) + ) + completion_response = response.text + response_obj = response._raw_response + elif mode == "chat": + # chat-bison etc. + chat = llm_model.start_chat() + ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + response_obj = await chat.send_message_async(prompt, **optional_params) + completion_response = response_obj.text + elif mode == "text": + # gecko etc. + request_str += f"llm_model.predict({prompt}, **{optional_params}).text\n" + ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + response_obj = await llm_model.predict_async(prompt, **optional_params) + completion_response = response_obj.text + + ## LOGGING + logging_obj.post_call( + input=prompt, api_key=None, original_response=completion_response + ) + + ## RESPONSE OBJECT + if len(str(completion_response)) > 0: + model_response["choices"][0]["message"][ + "content" + ] = str(completion_response) + model_response["choices"][0]["message"]["content"] = str(completion_response) + 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 = response_obj.candidates[0].finish_reason.name + usage = Usage(prompt_tokens=response_obj.usage_metadata.prompt_token_count, + completion_tokens=response_obj.usage_metadata.candidates_token_count, + total_tokens=response_obj.usage_metadata.total_token_count) + else: + prompt_tokens = len( + encoding.encode(prompt) + ) + completion_tokens = len( + encoding.encode(model_response["choices"][0]["message"].get("content", "")) + ) + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens + ) + model_response.usage = usage + return model_response + except Exception as e: + raise VertexAIError(status_code=500, message=str(e)) + +async def async_streaming(llm_model, mode: str, prompt: str, model: str, model_response: ModelResponse, logging_obj=None, request_str=None, messages = None, print_verbose = None, **optional_params): + """ + Add support for async streaming calls for gemini-pro + """ + from vertexai.preview.generative_models import GenerationConfig + if mode == "": + # gemini-pro + chat = llm_model.start_chat() + stream = optional_params.pop("stream") + request_str += f"chat.send_message_async({prompt},generation_config=GenerationConfig(**{optional_params}), stream={stream})\n" + ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + response = await chat.send_message_async(prompt, generation_config=GenerationConfig(**optional_params), stream=stream) + optional_params["stream"] = True + elif mode == "vision": + stream = optional_params.pop("stream") + + print_verbose("\nMaking VertexAI Gemini Pro Vision Call") + print_verbose(f"\nProcessing input messages = {messages}") + + prompt, images = _gemini_vision_convert_messages(messages=messages) + content = [prompt] + images + stream = optional_params.pop("stream") + request_str += f"response = llm_model.generate_content({content}, generation_config=GenerationConfig(**{optional_params}), stream={stream})\n" + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + + response = llm_model._generate_content_streaming_async( + contents=content, + generation_config=GenerationConfig(**optional_params), + stream=True + ) + optional_params["stream"] = True + elif mode == "chat": + chat = llm_model.start_chat() + optional_params.pop("stream", None) # vertex ai raises an error when passing stream in optional params + request_str += f"chat.send_message_streaming_async({prompt}, **{optional_params})\n" + ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + response = chat.send_message_streaming_async(prompt, **optional_params) + optional_params["stream"] = True + elif mode == "text": + optional_params.pop("stream", None) # See note above on handling streaming for vertex ai + request_str += f"llm_model.predict_streaming_async({prompt}, **{optional_params})\n" + ## LOGGING + logging_obj.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params, "request_str": request_str}) + response = llm_model.predict_streaming_async(prompt, **optional_params) + + streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="vertex_ai",logging_obj=logging_obj) + async for transformed_chunk in streamwrapper: + yield transformed_chunk def embedding(): # logic for parsing in - calling - parsing out model embedding calls diff --git a/litellm/main.py b/litellm/main.py index f97659a32..d666cfb2c 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -14,6 +14,7 @@ import dotenv, traceback, random, asyncio, time, contextvars from copy import deepcopy import httpx import litellm + from litellm import ( # type: ignore client, exception_type, @@ -31,7 +32,8 @@ from litellm.utils import ( mock_completion_streaming_obj, convert_to_model_response_object, token_counter, - Usage + Usage, + get_optional_params_embeddings ) from .llms import ( anthropic, @@ -171,11 +173,14 @@ async def acompletion(*args, **kwargs): or custom_llm_provider == "azure" or custom_llm_provider == "custom_openai" or custom_llm_provider == "anyscale" + or custom_llm_provider == "mistral" or custom_llm_provider == "openrouter" or custom_llm_provider == "deepinfra" or custom_llm_provider == "perplexity" or custom_llm_provider == "text-completion-openai" - or custom_llm_provider == "huggingface"): # currently implemented aiohttp calls for just azure and openai, soon all. + or custom_llm_provider == "huggingface" + or custom_llm_provider == "ollama" + or custom_llm_provider == "vertex_ai"): # currently implemented aiohttp calls for just azure and openai, soon all. if kwargs.get("stream", False): response = completion(*args, **kwargs) else: @@ -200,9 +205,12 @@ async def acompletion(*args, **kwargs): async def _async_streaming(response, model, custom_llm_provider, args): try: + print_verbose(f"received response in _async_streaming: {response}") async for line in response: + print_verbose(f"line in async streaming: {line}") yield line except Exception as e: + print_verbose(f"error raised _async_streaming: {traceback.format_exc()}") raise exception_type( model=model, custom_llm_provider=custom_llm_provider, original_exception=e, completion_kwargs=args, ) @@ -278,7 +286,7 @@ def completion( # Optional liteLLM function params **kwargs, -) -> ModelResponse: +) -> Union[ModelResponse, CustomStreamWrapper]: """ Perform a completion() using any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly) Parameters: @@ -319,7 +327,6 @@ def completion( ######### unpacking kwargs ##################### args = locals() api_base = kwargs.get('api_base', None) - return_async = kwargs.get('return_async', False) mock_response = kwargs.get('mock_response', None) force_timeout= kwargs.get('force_timeout', 600) ## deprecated logger_fn = kwargs.get('logger_fn', None) @@ -344,13 +351,14 @@ def completion( final_prompt_value = kwargs.get("final_prompt_value", None) bos_token = kwargs.get("bos_token", None) eos_token = kwargs.get("eos_token", None) + preset_cache_key = kwargs.get("preset_cache_key", None) hf_model_name = kwargs.get("hf_model_name", None) ### ASYNC CALLS ### acompletion = kwargs.get("acompletion", False) client = kwargs.get("client", None) ######## end of unpacking kwargs ########### openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id", "organization", "base_url", "default_headers", "timeout", "response_format", "seed", "tools", "tool_choice", "max_retries"] - litellm_params = ["metadata", "acompletion", "caching", "return_async", "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", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name", "model_info", "proxy_server_request"] + litellm_params = ["metadata", "acompletion", "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", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name", "model_info", "proxy_server_request", "preset_cache_key", "caching_groups"] default_params = openai_params + litellm_params non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider if mock_response: @@ -384,7 +392,6 @@ def completion( model=deployment_id custom_llm_provider="azure" model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base, api_key=api_key) - ### REGISTER CUSTOM MODEL PRICING -- IF GIVEN ### if input_cost_per_token is not None and output_cost_per_token is not None: litellm.register_model({ @@ -448,7 +455,6 @@ def completion( # For logging - save the values of the litellm-specific params passed in litellm_params = get_litellm_params( acompletion=acompletion, - return_async=return_async, api_key=api_key, force_timeout=force_timeout, logger_fn=logger_fn, @@ -460,7 +466,8 @@ def completion( completion_call_id=id, metadata=metadata, model_info=model_info, - proxy_server_request=proxy_server_request + proxy_server_request=proxy_server_request, + preset_cache_key=preset_cache_key ) logging.update_environment_variables(model=model, user=user, optional_params=optional_params, litellm_params=litellm_params) if custom_llm_provider == "azure": @@ -524,23 +531,25 @@ def completion( client=client # pass AsyncAzureOpenAI, AzureOpenAI client ) - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - additional_args={ - "headers": headers, - "api_version": api_version, - "api_base": api_base, - }, - ) + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + additional_args={ + "headers": headers, + "api_version": api_version, + "api_base": api_base, + }, + ) elif ( model in litellm.open_ai_chat_completion_models or custom_llm_provider == "custom_openai" or custom_llm_provider == "deepinfra" or custom_llm_provider == "perplexity" or custom_llm_provider == "anyscale" + or custom_llm_provider == "mistral" or custom_llm_provider == "openai" or "ft:gpt-3.5-turbo" in model # finetune gpt-3.5-turbo ): # allow user to make an openai call with a custom base @@ -604,19 +613,19 @@ def completion( ) raise e - ## LOGGING - logging.post_call( - input=messages, - api_key=api_key, - original_response=response, - additional_args={"headers": headers}, - ) + if optional_params.get("stream", False): + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + additional_args={"headers": headers}, + ) elif ( custom_llm_provider == "text-completion-openai" or "ft:babbage-002" in model or "ft:davinci-002" in model # support for finetuned completion models ): - # print("calling custom openai provider") openai.api_type = "openai" api_base = ( @@ -655,17 +664,6 @@ def completion( prompt = messages[0]["content"] else: prompt = " ".join([message["content"] for message in messages]) # type: ignore - ## LOGGING - logging.pre_call( - input=prompt, - api_key=api_key, - additional_args={ - "openai_organization": litellm.organization, - "headers": headers, - "api_base": api_base, - "api_type": openai.api_type, - }, - ) ## COMPLETION CALL model_response = openai_text_completions.completion( model=model, @@ -681,9 +679,14 @@ def completion( logger_fn=logger_fn ) - # if "stream" in optional_params and optional_params["stream"] == True: - # response = CustomStreamWrapper(model_response, model, custom_llm_provider="text-completion-openai", logging_obj=logging) - # return response + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=model_response, + additional_args={"headers": headers}, + ) response = model_response elif ( "replicate" in model or @@ -728,8 +731,16 @@ def completion( ) if "stream" in optional_params and optional_params["stream"] == True: # don't try to access stream object, - response = CustomStreamWrapper(model_response, model, logging_obj=logging, custom_llm_provider="replicate") - return response + model_response = CustomStreamWrapper(model_response, model, logging_obj=logging, custom_llm_provider="replicate") # type: ignore + + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=replicate_key, + original_response=model_response, + ) + response = model_response elif custom_llm_provider=="anthropic": @@ -749,7 +760,7 @@ def completion( custom_prompt_dict or litellm.custom_prompt_dict ) - model_response = anthropic.completion( + response = anthropic.completion( model=model, messages=messages, api_base=api_base, @@ -765,9 +776,16 @@ def completion( ) if "stream" in optional_params and optional_params["stream"] == True: # don't try to access stream object, - response = CustomStreamWrapper(model_response, model, custom_llm_provider="anthropic", logging_obj=logging) - return response - response = model_response + response = CustomStreamWrapper(response, model, custom_llm_provider="anthropic", logging_obj=logging) + + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + ) + response = response elif custom_llm_provider == "nlp_cloud": nlp_cloud_key = ( api_key or litellm.nlp_cloud_key or get_secret("NLP_CLOUD_API_KEY") or litellm.api_key @@ -780,7 +798,7 @@ def completion( or "https://api.nlpcloud.io/v1/gpu/" ) - model_response = nlp_cloud.completion( + response = nlp_cloud.completion( model=model, messages=messages, api_base=api_base, @@ -796,9 +814,17 @@ def completion( if "stream" in optional_params and optional_params["stream"] == True: # don't try to access stream object, - response = CustomStreamWrapper(model_response, model, custom_llm_provider="nlp_cloud", logging_obj=logging) - return response - response = model_response + response = CustomStreamWrapper(response, model, custom_llm_provider="nlp_cloud", logging_obj=logging) + + if optional_params.get("stream", False) or acompletion == True: + ## LOGGING + logging.post_call( + input=messages, + api_key=api_key, + original_response=response, + ) + + response = response elif custom_llm_provider == "aleph_alpha": aleph_alpha_key = ( api_key or litellm.aleph_alpha_key or get_secret("ALEPH_ALPHA_API_KEY") or get_secret("ALEPHALPHA_API_KEY") or litellm.api_key @@ -1100,7 +1126,7 @@ def completion( ) return response response = model_response - elif model in litellm.vertex_chat_models or model in litellm.vertex_code_chat_models or model in litellm.vertex_text_models or model in litellm.vertex_code_text_models: + elif custom_llm_provider == "vertex_ai": vertex_ai_project = (litellm.vertex_project or get_secret("VERTEXAI_PROJECT")) vertex_ai_location = (litellm.vertex_location @@ -1117,10 +1143,11 @@ def completion( encoding=encoding, vertex_location=vertex_ai_location, vertex_project=vertex_ai_project, - logging_obj=logging + logging_obj=logging, + acompletion=acompletion ) - if "stream" in optional_params and optional_params["stream"] == True: + if "stream" in optional_params and optional_params["stream"] == True and acompletion == False: response = CustomStreamWrapper( model_response, model, custom_llm_provider="vertex_ai", logging_obj=logging ) @@ -1186,6 +1213,7 @@ def completion( # "SageMaker is currently not supporting streaming responses." # fake streaming for sagemaker + print_verbose(f"ENTERS SAGEMAKER CUSTOMSTREAMWRAPPER") resp_string = model_response["choices"][0]["message"]["content"] response = CustomStreamWrapper( resp_string, model, custom_llm_provider="sagemaker", logging_obj=logging @@ -1200,7 +1228,7 @@ def completion( custom_prompt_dict or litellm.custom_prompt_dict ) - model_response = bedrock.completion( + response = bedrock.completion( model=model, messages=messages, custom_prompt_dict=litellm.custom_prompt_dict, @@ -1218,16 +1246,24 @@ def completion( # don't try to access stream object, if "ai21" in model: response = CustomStreamWrapper( - model_response, model, custom_llm_provider="bedrock", logging_obj=logging + response, model, custom_llm_provider="bedrock", logging_obj=logging ) else: response = CustomStreamWrapper( - iter(model_response), model, custom_llm_provider="bedrock", logging_obj=logging + iter(response), model, custom_llm_provider="bedrock", logging_obj=logging ) - return response + + if optional_params.get("stream", False): + ## LOGGING + logging.post_call( + input=messages, + api_key=None, + original_response=response, + ) + ## RESPONSE OBJECT - response = model_response + response = response elif custom_llm_provider == "vllm": model_response = vllm.completion( model=model, @@ -1273,14 +1309,18 @@ def completion( ) else: prompt = prompt_factory(model=model, messages=messages, custom_llm_provider=custom_llm_provider) - ## LOGGING - if kwargs.get('acompletion', False) == True: - if optional_params.get("stream", False) == True: - # assume all ollama responses are streamed - async_generator = ollama.async_get_ollama_response_stream(api_base, model, prompt, optional_params, logging_obj=logging) - return async_generator + if isinstance(prompt, dict): + # for multimode models - ollama/llava prompt_factory returns a dict { + # "prompt": prompt, + # "images": images + # } + prompt, images = prompt["prompt"], prompt["images"] + optional_params["images"] = images - generator = ollama.get_ollama_response_stream(api_base, model, prompt, optional_params, logging_obj=logging) + ## LOGGING + generator = ollama.get_ollama_response_stream(api_base, model, prompt, optional_params, logging_obj=logging, acompletion=acompletion, model_response=model_response, encoding=encoding) + if acompletion is True: + return generator if optional_params.get("stream", False) == True: # assume all ollama responses are streamed response = CustomStreamWrapper( @@ -1716,8 +1756,7 @@ async def aembedding(*args, **kwargs): or custom_llm_provider == "anyscale" or custom_llm_provider == "openrouter" or custom_llm_provider == "deepinfra" - or custom_llm_provider == "perplexity" - or custom_llm_provider == "huggingface"): # currently implemented aiohttp calls for just azure and openai, soon all. + or custom_llm_provider == "perplexity"): # currently implemented aiohttp calls for just azure and openai, soon all. # Await normally init_response = await loop.run_in_executor(None, func_with_context) if isinstance(init_response, dict) or isinstance(init_response, ModelResponse): ## CACHING SCENARIO @@ -1781,22 +1820,21 @@ def embedding( rpm = kwargs.pop("rpm", None) tpm = kwargs.pop("tpm", None) model_info = kwargs.get("model_info", None) + metadata = kwargs.get("metadata", None) + encoding_format = kwargs.get("encoding_format", None) proxy_server_request = kwargs.get("proxy_server_request", None) - aembedding = kwargs.pop("aembedding", None) - openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id", "organization", "base_url", "default_headers", "timeout", "response_format", "seed", "tools", "tool_choice", "max_retries", "encoding_format"] - litellm_params = ["metadata", "acompletion", "caching", "return_async", "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", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name", "proxy_server_request", "model_info"] + aembedding = kwargs.get("aembedding", None) + openai_params = ["user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id", "organization", "base_url", "default_headers", "timeout", "max_retries", "encoding_format"] + litellm_params = ["metadata", "aembedding", "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", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name", "proxy_server_request", "model_info", "preset_cache_key", "caching_groups"] default_params = openai_params + litellm_params non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider - optional_params = {} - for param in non_default_params: - optional_params[param] = kwargs[param] - model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base, api_key=api_key) - + model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base, api_key=api_key) + optional_params = get_optional_params_embeddings(user=user, encoding_format=encoding_format, custom_llm_provider=custom_llm_provider, **non_default_params) try: response = None logging = litellm_logging_obj - logging.update_environment_variables(model=model, user="", optional_params=optional_params, litellm_params={"timeout": timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn, "proxy_server_request": proxy_server_request, "model_info": model_info}) + logging.update_environment_variables(model=model, user=user, optional_params=optional_params, litellm_params={"timeout": timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn, "proxy_server_request": proxy_server_request, "model_info": model_info, "metadata": metadata, "aembedding": aembedding, "preset_cache_key": None, "stream_response": {}}) if azure == True or custom_llm_provider == "azure": # azure configs api_type = get_secret("AZURE_API_TYPE") or "azure" @@ -1936,7 +1974,7 @@ def embedding( ## LOGGING logging.post_call( input=input, - api_key=openai.api_key, + api_key=api_key, original_response=str(e), ) ## Map to OpenAI Exception @@ -1948,6 +1986,59 @@ def embedding( ###### Text Completion ################ +async def atext_completion(*args, **kwargs): + """ + Implemented to handle async streaming for the text completion endpoint + """ + loop = asyncio.get_event_loop() + model = args[0] if len(args) > 0 else kwargs["model"] + ### PASS ARGS TO COMPLETION ### + kwargs["acompletion"] = True + custom_llm_provider = None + try: + # Use a partial function to pass your keyword arguments + func = partial(text_completion, *args, **kwargs) + + # Add the context to the function + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + + _, custom_llm_provider, _, _ = get_llm_provider(model=model, api_base=kwargs.get("api_base", None)) + + if (custom_llm_provider == "openai" + or custom_llm_provider == "azure" + or custom_llm_provider == "custom_openai" + or custom_llm_provider == "anyscale" + or custom_llm_provider == "mistral" + or custom_llm_provider == "openrouter" + or custom_llm_provider == "deepinfra" + or custom_llm_provider == "perplexity" + or custom_llm_provider == "text-completion-openai" + or custom_llm_provider == "huggingface" + or custom_llm_provider == "ollama" + or custom_llm_provider == "vertex_ai"): # currently implemented aiohttp calls for just azure and openai, soon all. + if kwargs.get("stream", False): + response = text_completion(*args, **kwargs) + else: + # Await normally + init_response = await loop.run_in_executor(None, func_with_context) + if isinstance(init_response, dict) or isinstance(init_response, ModelResponse): ## CACHING SCENARIO + response = init_response + elif asyncio.iscoroutine(init_response): + response = await init_response + else: + # Call the synchronous function using run_in_executor + response = await loop.run_in_executor(None, func_with_context) + if kwargs.get("stream", False): # return an async generator + return _async_streaming(response=response, model=model, custom_llm_provider=custom_llm_provider, args=args) + else: + return response + except Exception as e: + custom_llm_provider = custom_llm_provider or "openai" + raise exception_type( + model=model, custom_llm_provider=custom_llm_provider, original_exception=e, completion_kwargs=args, + ) + def text_completion( prompt: Union[str, List[Union[str, List[Union[str, List[int]]]]]], # Required: The prompt(s) to generate completions for. model: Optional[str]=None, # Optional: either `model` or `engine` can be set @@ -2079,7 +2170,7 @@ def text_completion( *args, **all_params, ) - #print(response) + text_completion_response["id"] = response.get("id", None) text_completion_response["object"] = "text_completion" text_completion_response["created"] = response.get("created", None) @@ -2294,6 +2385,8 @@ def stream_chunk_builder(chunks: list, messages: Optional[list]=None): completion_output = combined_content elif len(combined_arguments) > 0: completion_output = combined_arguments + else: + completion_output = "" # # Update usage information if needed try: response["usage"]["prompt_tokens"] = token_counter(model=model, messages=messages) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 2c49dc02d..454b2504a 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -41,6 +41,20 @@ "litellm_provider": "openai", "mode": "chat" }, + "gpt-4-1106-preview": { + "max_tokens": 128000, + "input_cost_per_token": 0.00001, + "output_cost_per_token": 0.00003, + "litellm_provider": "openai", + "mode": "chat" + }, + "gpt-4-vision-preview": { + "max_tokens": 128000, + "input_cost_per_token": 0.00001, + "output_cost_per_token": 0.00003, + "litellm_provider": "openai", + "mode": "chat" + }, "gpt-3.5-turbo": { "max_tokens": 4097, "input_cost_per_token": 0.0000015, @@ -62,6 +76,13 @@ "litellm_provider": "openai", "mode": "chat" }, + "gpt-3.5-turbo-1106": { + "max_tokens": 16385, + "input_cost_per_token": 0.0000010, + "output_cost_per_token": 0.0000020, + "litellm_provider": "openai", + "mode": "chat" + }, "gpt-3.5-turbo-16k": { "max_tokens": 16385, "input_cost_per_token": 0.000003, @@ -76,6 +97,62 @@ "litellm_provider": "openai", "mode": "chat" }, + "ft:gpt-3.5-turbo": { + "max_tokens": 4097, + "input_cost_per_token": 0.000012, + "output_cost_per_token": 0.000016, + "litellm_provider": "openai", + "mode": "chat" + }, + "text-embedding-ada-002": { + "max_tokens": 8191, + "input_cost_per_token": 0.0000001, + "output_cost_per_token": 0.000000, + "litellm_provider": "openai", + "mode": "embedding" + }, + "azure/gpt-4-1106-preview": { + "max_tokens": 128000, + "input_cost_per_token": 0.00001, + "output_cost_per_token": 0.00003, + "litellm_provider": "azure", + "mode": "chat" + }, + "azure/gpt-4-32k": { + "max_tokens": 8192, + "input_cost_per_token": 0.00006, + "output_cost_per_token": 0.00012, + "litellm_provider": "azure", + "mode": "chat" + }, + "azure/gpt-4": { + "max_tokens": 16385, + "input_cost_per_token": 0.00003, + "output_cost_per_token": 0.00006, + "litellm_provider": "azure", + "mode": "chat" + }, + "azure/gpt-3.5-turbo-16k": { + "max_tokens": 16385, + "input_cost_per_token": 0.000003, + "output_cost_per_token": 0.000004, + "litellm_provider": "azure", + "mode": "chat" + }, + "azure/gpt-3.5-turbo": { + "max_tokens": 4097, + "input_cost_per_token": 0.0000015, + "output_cost_per_token": 0.000002, + "litellm_provider": "azure", + "mode": "chat" + }, + "azure/text-embedding-ada-002": { + "max_tokens": 8191, + "input_cost_per_token": 0.0000001, + "output_cost_per_token": 0.000000, + "litellm_provider": "azure", + "mode": "embedding" + }, "text-davinci-003": { "max_tokens": 4097, "input_cost_per_token": 0.000002, @@ -127,6 +204,7 @@ }, "claude-instant-1": { "max_tokens": 100000, + "max_output_tokens": 8191, "input_cost_per_token": 0.00000163, "output_cost_per_token": 0.00000551, "litellm_provider": "anthropic", @@ -134,15 +212,25 @@ }, "claude-instant-1.2": { "max_tokens": 100000, - "input_cost_per_token": 0.00000163, - "output_cost_per_token": 0.00000551, + "max_output_tokens": 8191, + "input_cost_per_token": 0.000000163, + "output_cost_per_token": 0.000000551, "litellm_provider": "anthropic", "mode": "chat" }, "claude-2": { "max_tokens": 100000, - "input_cost_per_token": 0.00001102, - "output_cost_per_token": 0.00003268, + "max_output_tokens": 8191, + "input_cost_per_token": 0.000008, + "output_cost_per_token": 0.000024, + "litellm_provider": "anthropic", + "mode": "chat" + }, + "claude-2.1": { + "max_tokens": 200000, + "max_output_tokens": 8191, + "input_cost_per_token": 0.000008, + "output_cost_per_token": 0.000024, "litellm_provider": "anthropic", "mode": "chat" }, @@ -227,9 +315,51 @@ "max_tokens": 32000, "input_cost_per_token": 0.000000125, "output_cost_per_token": 0.000000125, - "litellm_provider": "vertex_ai-chat-models", + "litellm_provider": "vertex_ai-code-chat-models", "mode": "chat" }, + "palm/chat-bison": { + "max_tokens": 4096, + "input_cost_per_token": 0.000000125, + "output_cost_per_token": 0.000000125, + "litellm_provider": "palm", + "mode": "chat" + }, + "palm/chat-bison-001": { + "max_tokens": 4096, + "input_cost_per_token": 0.000000125, + "output_cost_per_token": 0.000000125, + "litellm_provider": "palm", + "mode": "chat" + }, + "palm/text-bison": { + "max_tokens": 8196, + "input_cost_per_token": 0.000000125, + "output_cost_per_token": 0.000000125, + "litellm_provider": "palm", + "mode": "completion" + }, + "palm/text-bison-001": { + "max_tokens": 8196, + "input_cost_per_token": 0.000000125, + "output_cost_per_token": 0.000000125, + "litellm_provider": "palm", + "mode": "completion" + }, + "palm/text-bison-safety-off": { + "max_tokens": 8196, + "input_cost_per_token": 0.000000125, + "output_cost_per_token": 0.000000125, + "litellm_provider": "palm", + "mode": "completion" + }, + "palm/text-bison-safety-recitation-off": { + "max_tokens": 8196, + "input_cost_per_token": 0.000000125, + "output_cost_per_token": 0.000000125, + "litellm_provider": "palm", + "mode": "completion" + }, "command-nightly": { "max_tokens": 4096, "input_cost_per_token": 0.000015, @@ -267,6 +397,8 @@ }, "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1": { "max_tokens": 4096, + "input_cost_per_token": 0.0000, + "output_cost_per_token": 0.0000, "litellm_provider": "replicate", "mode": "chat" }, @@ -293,6 +425,7 @@ }, "openrouter/anthropic/claude-instant-v1": { "max_tokens": 100000, + "max_output_tokens": 8191, "input_cost_per_token": 0.00000163, "output_cost_per_token": 0.00000551, "litellm_provider": "openrouter", @@ -300,6 +433,7 @@ }, "openrouter/anthropic/claude-2": { "max_tokens": 100000, + "max_output_tokens": 8191, "input_cost_per_token": 0.00001102, "output_cost_per_token": 0.00003268, "litellm_provider": "openrouter", @@ -496,20 +630,31 @@ }, "anthropic.claude-v1": { "max_tokens": 100000, - "input_cost_per_token": 0.00001102, - "output_cost_per_token": 0.00003268, + "max_output_tokens": 8191, + "input_cost_per_token": 0.000008, + "output_cost_per_token": 0.000024, "litellm_provider": "bedrock", "mode": "chat" }, "anthropic.claude-v2": { "max_tokens": 100000, - "input_cost_per_token": 0.00001102, - "output_cost_per_token": 0.00003268, + "max_output_tokens": 8191, + "input_cost_per_token": 0.000008, + "output_cost_per_token": 0.000024, + "litellm_provider": "bedrock", + "mode": "chat" + }, + "anthropic.claude-v2:1": { + "max_tokens": 200000, + "max_output_tokens": 8191, + "input_cost_per_token": 0.000008, + "output_cost_per_token": 0.000024, "litellm_provider": "bedrock", "mode": "chat" }, "anthropic.claude-instant-v1": { "max_tokens": 100000, + "max_output_tokens": 8191, "input_cost_per_token": 0.00000163, "output_cost_per_token": 0.00000551, "litellm_provider": "bedrock", @@ -529,26 +674,80 @@ "litellm_provider": "bedrock", "mode": "chat" }, + "meta.llama2-70b-chat-v1": { + "max_tokens": 4096, + "input_cost_per_token": 0.00000195, + "output_cost_per_token": 0.00000256, + "litellm_provider": "bedrock", + "mode": "chat" + }, + "sagemaker/meta-textgeneration-llama-2-7b": { + "max_tokens": 4096, + "input_cost_per_token": 0.000, + "output_cost_per_token": 0.000, + "litellm_provider": "sagemaker", + "mode": "completion" + }, + "sagemaker/meta-textgeneration-llama-2-7b-f": { + "max_tokens": 4096, + "input_cost_per_token": 0.000, + "output_cost_per_token": 0.000, + "litellm_provider": "sagemaker", + "mode": "chat" + }, + "sagemaker/meta-textgeneration-llama-2-13b": { + "max_tokens": 4096, + "input_cost_per_token": 0.000, + "output_cost_per_token": 0.000, + "litellm_provider": "sagemaker", + "mode": "completion" + }, + "sagemaker/meta-textgeneration-llama-2-13b-f": { + "max_tokens": 4096, + "input_cost_per_token": 0.000, + "output_cost_per_token": 0.000, + "litellm_provider": "sagemaker", + "mode": "chat" + }, + "sagemaker/meta-textgeneration-llama-2-70b": { + "max_tokens": 4096, + "input_cost_per_token": 0.000, + "output_cost_per_token": 0.000, + "litellm_provider": "sagemaker", + "mode": "completion" + }, + "sagemaker/meta-textgeneration-llama-2-70b-b-f": { + "max_tokens": 4096, + "input_cost_per_token": 0.000, + "output_cost_per_token": 0.000, + "litellm_provider": "sagemaker", + "mode": "chat" + }, "together-ai-up-to-3b": { "input_cost_per_token": 0.0000001, - "output_cost_per_token": 0.0000001 + "output_cost_per_token": 0.0000001, + "litellm_provider": "together_ai" }, "together-ai-3.1b-7b": { "input_cost_per_token": 0.0000002, - "output_cost_per_token": 0.0000002 + "output_cost_per_token": 0.0000002, + "litellm_provider": "together_ai" }, "together-ai-7.1b-20b": { "max_tokens": 1000, "input_cost_per_token": 0.0000004, - "output_cost_per_token": 0.0000004 + "output_cost_per_token": 0.0000004, + "litellm_provider": "together_ai" }, "together-ai-20.1b-40b": { - "input_cost_per_token": 0.000001, - "output_cost_per_token": 0.000001 + "input_cost_per_token": 0.0000008, + "output_cost_per_token": 0.0000008, + "litellm_provider": "together_ai" }, "together-ai-40.1b-70b": { - "input_cost_per_token": 0.000003, - "output_cost_per_token": 0.000003 + "input_cost_per_token": 0.0000009, + "output_cost_per_token": 0.0000009, + "litellm_provider": "together_ai" }, "ollama/llama2": { "max_tokens": 4096, @@ -578,10 +777,38 @@ "litellm_provider": "ollama", "mode": "completion" }, + "ollama/mistral": { + "max_tokens": 8192, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "completion" + }, + "ollama/codellama": { + "max_tokens": 4096, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "completion" + }, + "ollama/orca-mini": { + "max_tokens": 4096, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "completion" + }, + "ollama/vicuna": { + "max_tokens": 2048, + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "ollama", + "mode": "completion" + }, "deepinfra/meta-llama/Llama-2-70b-chat-hf": { - "max_tokens": 6144, - "input_cost_per_token": 0.000001875, - "output_cost_per_token": 0.000001875, + "max_tokens": 4096, + "input_cost_per_token": 0.000000700, + "output_cost_per_token": 0.000000950, "litellm_provider": "deepinfra", "mode": "chat" }, @@ -619,5 +846,103 @@ "output_cost_per_token": 0.00000095, "litellm_provider": "deepinfra", "mode": "chat" + }, + "perplexity/pplx-7b-chat": { + "max_tokens": 8192, + "input_cost_per_token": 0.0000000, + "output_cost_per_token": 0.000000, + "litellm_provider": "perplexity", + "mode": "chat" + }, + "perplexity/pplx-70b-chat": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000000, + "output_cost_per_token": 0.000000, + "litellm_provider": "perplexity", + "mode": "chat" + }, + "perplexity/pplx-7b-online": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000000, + "output_cost_per_token": 0.0005, + "litellm_provider": "perplexity", + "mode": "chat" + }, + "perplexity/pplx-70b-online": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000000, + "output_cost_per_token": 0.0005, + "litellm_provider": "perplexity", + "mode": "chat" + }, + "perplexity/llama-2-13b-chat": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000000, + "output_cost_per_token": 0.000000, + "litellm_provider": "perplexity", + "mode": "chat" + }, + "perplexity/llama-2-70b-chat": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000000, + "output_cost_per_token": 0.000000, + "litellm_provider": "perplexity", + "mode": "chat" + }, + "perplexity/mistral-7b-instruct": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000000, + "output_cost_per_token": 0.000000, + "litellm_provider": "perplexity", + "mode": "chat" + }, + "perplexity/replit-code-v1.5-3b": { + "max_tokens": 4096, + "input_cost_per_token": 0.0000000, + "output_cost_per_token": 0.000000, + "litellm_provider": "perplexity", + "mode": "chat" + }, + "anyscale/mistralai/Mistral-7B-Instruct-v0.1": { + "max_tokens": 16384, + "input_cost_per_token": 0.00000015, + "output_cost_per_token": 0.00000015, + "litellm_provider": "anyscale", + "mode": "chat" + }, + "anyscale/HuggingFaceH4/zephyr-7b-beta": { + "max_tokens": 16384, + "input_cost_per_token": 0.00000015, + "output_cost_per_token": 0.00000015, + "litellm_provider": "anyscale", + "mode": "chat" + }, + "anyscale/meta-llama/Llama-2-7b-chat-hf": { + "max_tokens": 4096, + "input_cost_per_token": 0.00000015, + "output_cost_per_token": 0.00000015, + "litellm_provider": "anyscale", + "mode": "chat" + }, + "anyscale/meta-llama/Llama-2-13b-chat-hf": { + "max_tokens": 4096, + "input_cost_per_token": 0.00000025, + "output_cost_per_token": 0.00000025, + "litellm_provider": "anyscale", + "mode": "chat" + }, + "anyscale/meta-llama/Llama-2-70b-chat-hf": { + "max_tokens": 4096, + "input_cost_per_token": 0.000001, + "output_cost_per_token": 0.000001, + "litellm_provider": "anyscale", + "mode": "chat" + }, + "anyscale/codellama/CodeLlama-34b-Instruct-hf": { + "max_tokens": 16384, + "input_cost_per_token": 0.000001, + "output_cost_per_token": 0.000001, + "litellm_provider": "anyscale", + "mode": "chat" } } diff --git a/litellm/proxy/_experimental/post_call_rules.py b/litellm/proxy/_experimental/post_call_rules.py new file mode 100644 index 000000000..12caa5513 --- /dev/null +++ b/litellm/proxy/_experimental/post_call_rules.py @@ -0,0 +1,4 @@ +def my_custom_rule(input): # receives the model response + # if len(input) < 5: # trigger fallback if the model response is too short + return False + return True \ No newline at end of file diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 68709f34d..cb04f32a5 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -2,8 +2,21 @@ from pydantic import BaseModel, Extra, Field, root_validator from typing import Optional, List, Union, Dict, Literal from datetime import datetime import uuid, json + +class LiteLLMBase(BaseModel): + """ + Implements default functions, all pydantic objects should have. + """ + def json(self, **kwargs): + try: + return self.model_dump() # noqa + except: + # if using pydantic v1 + return self.dict() + + ######### Request Class Definition ###### -class ProxyChatCompletionRequest(BaseModel): +class ProxyChatCompletionRequest(LiteLLMBase): model: str messages: List[Dict[str, str]] temperature: Optional[float] = None @@ -38,16 +51,16 @@ class ProxyChatCompletionRequest(BaseModel): class Config: extra='allow' # allow params not defined here, these fall in litellm.completion(**kwargs) -class ModelInfoDelete(BaseModel): +class ModelInfoDelete(LiteLLMBase): id: Optional[str] -class ModelInfo(BaseModel): +class ModelInfo(LiteLLMBase): id: Optional[str] mode: Optional[Literal['embedding', 'chat', 'completion']] - input_cost_per_token: Optional[float] - output_cost_per_token: Optional[float] - max_tokens: Optional[int] + input_cost_per_token: Optional[float] = 0.0 + output_cost_per_token: Optional[float] = 0.0 + max_tokens: Optional[int] = 2048 # assume 2048 if not set # for azure models we need users to specify the base model, one azure you can call deployments - azure/my-random-model # we look up the base model in model_prices_and_context_window.json @@ -65,38 +78,41 @@ class ModelInfo(BaseModel): class Config: extra = Extra.allow # Allow extra fields protected_namespaces = () + - # @root_validator(pre=True) - # def set_model_info(cls, values): - # if values.get("id") is None: - # values.update({"id": str(uuid.uuid4())}) - # if values.get("mode") is None: - # values.update({"mode": str(uuid.uuid4())}) - # return values + @root_validator(pre=True) + def set_model_info(cls, values): + if values.get("id") is None: + values.update({"id": str(uuid.uuid4())}) + if values.get("mode") is None: + values.update({"mode": None}) + if values.get("input_cost_per_token") is None: + values.update({"input_cost_per_token": None}) + if values.get("output_cost_per_token") is None: + values.update({"output_cost_per_token": None}) + if values.get("max_tokens") is None: + values.update({"max_tokens": None}) + if values.get("base_model") is None: + values.update({"base_model": None}) + return values -class ModelParams(BaseModel): +class ModelParams(LiteLLMBase): model_name: str litellm_params: dict - model_info: Optional[ModelInfo]=None + model_info: ModelInfo - # def __init__(self, model_name: str, litellm_params: dict, model_info: Optional[ModelInfo] = None): - # self.model_name = model_name - # self.litellm_params = litellm_params - # self.model_info = model_info if model_info else ModelInfo() - # super.__init__(model_name=self.model_name, litellm_params=self.litellm_params, model_info=self.model_info) - class Config: protected_namespaces = () - # @root_validator(pre=True) - # def set_model_info(cls, values): - # if values.get("model_info") is None: - # values.update({"model_info": ModelInfo()}) - # return values + @root_validator(pre=True) + def set_model_info(cls, values): + if values.get("model_info") is None: + values.update({"model_info": ModelInfo()}) + return values -class GenerateKeyRequest(BaseModel): +class GenerateKeyRequest(LiteLLMBase): duration: Optional[str] = "1h" models: Optional[list] = [] aliases: Optional[dict] = {} @@ -105,26 +121,32 @@ class GenerateKeyRequest(BaseModel): user_id: Optional[str] = None max_parallel_requests: Optional[int] = None - def json(self, **kwargs): - try: - return self.model_dump() # noqa - except: - # if using pydantic v1 - return self.dict() +class UpdateKeyRequest(LiteLLMBase): + key: str + duration: Optional[str] = None + models: Optional[list] = None + aliases: Optional[dict] = None + config: Optional[dict] = None + spend: Optional[float] = None + user_id: Optional[str] = None + max_parallel_requests: Optional[int] = None -class GenerateKeyResponse(BaseModel): +class GenerateKeyResponse(LiteLLMBase): key: str expires: datetime user_id: str -class _DeleteKeyObject(BaseModel): + + + +class _DeleteKeyObject(LiteLLMBase): key: str -class DeleteKeyRequest(BaseModel): +class DeleteKeyRequest(LiteLLMBase): keys: List[_DeleteKeyObject] -class UserAPIKeyAuth(BaseModel): # the expected response object for user api key auth +class UserAPIKeyAuth(LiteLLMBase): # the expected response object for user api key auth """ Return the row in the db """ @@ -137,7 +159,7 @@ class UserAPIKeyAuth(BaseModel): # the expected response object for user api key max_parallel_requests: Optional[int] = None duration: str = "1h" -class ConfigGeneralSettings(BaseModel): +class ConfigGeneralSettings(LiteLLMBase): """ Documents all the fields supported by `general_settings` in config.yaml """ @@ -153,10 +175,12 @@ class ConfigGeneralSettings(BaseModel): health_check_interval: int = Field(300, description="background health check interval in seconds") -class ConfigYAML(BaseModel): +class ConfigYAML(LiteLLMBase): """ Documents all the fields supported by the config.yaml """ model_list: Optional[List[ModelParams]] = Field(None, description="List of supported models on the server, with model-specific configs") litellm_settings: Optional[dict] = Field(None, description="litellm Module settings. See __init__.py for all, example litellm.drop_params=True, litellm.set_verbose=True, litellm.api_base, litellm.cache") general_settings: Optional[ConfigGeneralSettings] = None + class Config: + protected_namespaces = () diff --git a/litellm/proxy/custom_callbacks.py b/litellm/proxy/custom_callbacks.py index c04916344..dfcd55520 100644 --- a/litellm/proxy/custom_callbacks.py +++ b/litellm/proxy/custom_callbacks.py @@ -1,3 +1,11 @@ +import sys, os, traceback + +# this file is to test litellm/proxy + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path + from litellm.integrations.custom_logger import CustomLogger import litellm import inspect @@ -36,9 +44,12 @@ class MyCustomHandler(CustomLogger): def log_success_event(self, kwargs, response_obj, start_time, end_time): print_verbose("On Success!") + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): print_verbose(f"On Async Success!") + response_cost = litellm.completion_cost(completion_response=response_obj) + assert response_cost > 0.0 return async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): diff --git a/litellm/proxy/health_check.py b/litellm/proxy/health_check.py index db07ae20a..f8e56c059 100644 --- a/litellm/proxy/health_check.py +++ b/litellm/proxy/health_check.py @@ -69,7 +69,6 @@ async def _perform_health_check(model_list: list): for model in model_list: litellm_params = model["litellm_params"] model_info = model.get("model_info", {}) - litellm_params["model"] = litellm.utils.remove_model_id(litellm_params["model"]) litellm_params["messages"] = _get_random_llm_message() prepped_params.append(litellm_params) diff --git a/litellm/proxy/hooks/parallel_request_limiter.py b/litellm/proxy/hooks/parallel_request_limiter.py index 4a321d009..d42a5739a 100644 --- a/litellm/proxy/hooks/parallel_request_limiter.py +++ b/litellm/proxy/hooks/parallel_request_limiter.py @@ -1,6 +1,7 @@ from typing import Optional import litellm from litellm.caching import DualCache +from litellm.proxy._types import UserAPIKeyAuth from litellm.integrations.custom_logger import CustomLogger from fastapi import HTTPException @@ -14,24 +15,28 @@ class MaxParallelRequestsHandler(CustomLogger): print(print_statement) # noqa - async def max_parallel_request_allow_request(self, max_parallel_requests: Optional[int], api_key: Optional[str], user_api_key_cache: DualCache): + async def async_pre_call_hook(self, user_api_key_dict: UserAPIKeyAuth, cache: DualCache, data: dict, call_type: str): + self.print_verbose(f"Inside Max Parallel Request Pre-Call Hook") + api_key = user_api_key_dict.api_key + max_parallel_requests = user_api_key_dict.max_parallel_requests + if api_key is None: return if max_parallel_requests is None: return - self.user_api_key_cache = user_api_key_cache # save the api key cache for updating the value + self.user_api_key_cache = cache # save the api key cache for updating the value # CHECK IF REQUEST ALLOWED request_count_api_key = f"{api_key}_request_count" - current = user_api_key_cache.get_cache(key=request_count_api_key) + current = cache.get_cache(key=request_count_api_key) self.print_verbose(f"current: {current}") if current is None: - user_api_key_cache.set_cache(request_count_api_key, 1) + cache.set_cache(request_count_api_key, 1) elif int(current) < max_parallel_requests: # Increase count for this token - user_api_key_cache.set_cache(request_count_api_key, int(current) + 1) + cache.set_cache(request_count_api_key, int(current) + 1) else: raise HTTPException(status_code=429, detail="Max parallel request limit reached.") @@ -55,16 +60,24 @@ class MaxParallelRequestsHandler(CustomLogger): except Exception as e: self.print_verbose(e) # noqa - async def async_log_failure_call(self, api_key, user_api_key_cache): + async def async_log_failure_call(self, user_api_key_dict: UserAPIKeyAuth, original_exception: Exception): try: + self.print_verbose(f"Inside Max Parallel Request Failure Hook") + api_key = user_api_key_dict.api_key if api_key is None: return - request_count_api_key = f"{api_key}_request_count" - # Decrease count for this token - current = self.user_api_key_cache.get_cache(key=request_count_api_key) or 1 - new_val = current - 1 - self.print_verbose(f"updated_value in failure call: {new_val}") - self.user_api_key_cache.set_cache(request_count_api_key, new_val) + ## decrement call count if call failed + if (hasattr(original_exception, "status_code") + and original_exception.status_code == 429 + and "Max parallel request limit reached" in str(original_exception)): + pass # ignore failed calls due to max limit being reached + else: + request_count_api_key = f"{api_key}_request_count" + # Decrease count for this token + current = self.user_api_key_cache.get_cache(key=request_count_api_key) or 1 + new_val = current - 1 + self.print_verbose(f"updated_value in failure call: {new_val}") + self.user_api_key_cache.set_cache(request_count_api_key, new_val) except Exception as e: self.print_verbose(f"An exception occurred - {str(e)}") # noqa \ No newline at end of file diff --git a/litellm/proxy/proxy_cli.py b/litellm/proxy/proxy_cli.py index 9a03bca52..761319d15 100644 --- a/litellm/proxy/proxy_cli.py +++ b/litellm/proxy/proxy_cli.py @@ -3,6 +3,7 @@ import subprocess, traceback, json import os, sys import random, appdirs from datetime import datetime +import importlib from dotenv import load_dotenv import operator sys.path.append(os.getcwd()) @@ -76,13 +77,14 @@ def is_port_in_use(port): @click.option('--config', '-c', default=None, help='Path to the proxy configuration file (e.g. config.yaml). Usage `litellm --config config.yaml`') @click.option('--max_budget', default=None, type=float, help='Set max budget for API calls - works for hosted models like OpenAI, TogetherAI, Anthropic, etc.`') @click.option('--telemetry', default=True, type=bool, help='Helps us know if people are using this feature. Turn this off by doing `--telemetry False`') +@click.option('--version', '-v', default=False, is_flag=True, type=bool, help='Print LiteLLM version') @click.option('--logs', flag_value=False, type=int, help='Gets the "n" most recent logs. By default gets most recent log.') @click.option('--health', flag_value=True, help='Make a chat/completions request to all llms in config.yaml') @click.option('--test', flag_value=True, help='proxy chat completions url to make a test request to') @click.option('--test_async', default=False, is_flag=True, help='Calls async endpoints /queue/requests and /queue/response') @click.option('--num_requests', default=10, type=int, help='Number of requests to hit async endpoint with') @click.option('--local', is_flag=True, default=False, help='for local debugging') -def run_server(host, port, api_base, api_version, model, alias, add_key, headers, save, debug, temperature, max_tokens, request_timeout, drop_params, add_function_to_prompt, config, max_budget, telemetry, logs, test, local, num_workers, test_async, num_requests, use_queue, health): +def run_server(host, port, api_base, api_version, model, alias, add_key, headers, save, debug, temperature, max_tokens, request_timeout, drop_params, add_function_to_prompt, config, max_budget, telemetry, logs, test, local, num_workers, test_async, num_requests, use_queue, health, version): global feature_telemetry args = locals() if local: @@ -113,6 +115,10 @@ def run_server(host, port, api_base, api_version, model, alias, add_key, headers except: raise Exception("LiteLLM: No logs saved!") return + if version == True: + pkg_version = importlib.metadata.version("litellm") + click.echo(f'\nLiteLLM: Current Version = {pkg_version}\n') + return if model and "ollama" in model and api_base is None: run_ollama_serve() if test_async is True: diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 1c096aac7..0180d232e 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -11,8 +11,10 @@ model_list: output_cost_per_token: 0.00003 max_tokens: 4096 base_model: gpt-3.5-turbo - - - model_name: openai-gpt-3.5 + - model_name: BEDROCK_GROUP + litellm_params: + model: bedrock/cohere.command-text-v14 + - model_name: Azure OpenAI GPT-4 Canada-East (External) litellm_params: model: gpt-3.5-turbo api_key: os.environ/OPENAI_API_KEY @@ -41,11 +43,12 @@ model_list: mode: completion litellm_settings: + # cache: True # setting callback class # callbacks: custom_callbacks.proxy_handler_instance # sets litellm.callbacks = [proxy_handler_instance] - model_group_alias_map: {"gpt-4": "openai-gpt-3.5"} # all requests with gpt-4 model_name, get sent to openai-gpt-3.5 - general_settings: + +environment_variables: # otel: True # OpenTelemetry Logger # master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index a82723051..e942d4d41 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -195,8 +195,10 @@ prisma_client: Optional[PrismaClient] = None user_api_key_cache = DualCache() user_custom_auth = None use_background_health_checks = None +use_queue = False health_check_interval = None health_check_results = {} +queue: List = [] ### INITIALIZE GLOBAL LOGGING OBJECT ### proxy_logging_obj = ProxyLogging(user_api_key_cache=user_api_key_cache) ### REDIS QUEUE ### @@ -252,51 +254,58 @@ async def user_api_key_auth(request: Request, api_key: str = fastapi.Security(ap if api_key is None: # only require api key if master key is set raise Exception(f"No api key passed in.") - route = request.url.path + route: str = request.url.path # note: never string compare api keys, this is vulenerable to a time attack. Use secrets.compare_digest instead is_master_key_valid = secrets.compare_digest(api_key, master_key) if is_master_key_valid: return UserAPIKeyAuth(api_key=master_key) - if (route == "/key/generate" or route == "/key/delete" or route == "/key/info") and not is_master_key_valid: - raise Exception(f"If master key is set, only master key can be used to generate, delete or get info for new keys") + if route.startswith("/key/") and not is_master_key_valid: + raise Exception(f"If master key is set, only master key can be used to generate, delete, update or get info for new keys") - if prisma_client: - ## check for cache hit (In-Memory Cache) - valid_token = user_api_key_cache.get_cache(key=api_key) - print(f"valid_token from cache: {valid_token}") - if valid_token is None: - ## check db - valid_token = await prisma_client.get_data(token=api_key, expires=datetime.utcnow()) - user_api_key_cache.set_cache(key=api_key, value=valid_token, ttl=60) - elif valid_token is not None: - print(f"API Key Cache Hit!") - if valid_token: - litellm.model_alias_map = valid_token.aliases - config = valid_token.config - if config != {}: - model_list = config.get("model_list", []) - llm_model_list = model_list - print("\n new llm router model list", llm_model_list) - if len(valid_token.models) == 0: # assume an empty model list means all models are allowed to be called - api_key = valid_token.token - valid_token_dict = _get_pydantic_json_dict(valid_token) - valid_token_dict.pop("token", None) - return UserAPIKeyAuth(api_key=api_key, **valid_token_dict) - else: - data = await request.json() - model = data.get("model", None) - if model in litellm.model_alias_map: - model = litellm.model_alias_map[model] - if model and model not in valid_token.models: - raise Exception(f"Token not allowed to access model") + if prisma_client is None: # if both master key + user key submitted, and user key != master key, and no db connected, raise an error + raise Exception("No connected db.") + + ## check for cache hit (In-Memory Cache) + valid_token = user_api_key_cache.get_cache(key=api_key) + print(f"valid_token from cache: {valid_token}") + if valid_token is None: + ## check db + print(f"api key: {api_key}") + valid_token = await prisma_client.get_data(token=api_key, expires=datetime.utcnow()) + print(f"valid token from prisma: {valid_token}") + user_api_key_cache.set_cache(key=api_key, value=valid_token, ttl=60) + elif valid_token is not None: + print(f"API Key Cache Hit!") + if valid_token: + litellm.model_alias_map = valid_token.aliases + config = valid_token.config + if config != {}: + model_list = config.get("model_list", []) + llm_model_list = model_list + print("\n new llm router model list", llm_model_list) + if len(valid_token.models) == 0: # assume an empty model list means all models are allowed to be called api_key = valid_token.token valid_token_dict = _get_pydantic_json_dict(valid_token) valid_token_dict.pop("token", None) return UserAPIKeyAuth(api_key=api_key, **valid_token_dict) else: - raise Exception(f"Invalid token") + try: + data = await request.json() + except json.JSONDecodeError: + data = {} # Provide a default value, such as an empty dictionary + model = data.get("model", None) + if model in litellm.model_alias_map: + model = litellm.model_alias_map[model] + if model and model not in valid_token.models: + raise Exception(f"Token not allowed to access model") + api_key = valid_token.token + valid_token_dict = _get_pydantic_json_dict(valid_token) + valid_token_dict.pop("token", None) + return UserAPIKeyAuth(api_key=api_key, **valid_token_dict) + else: + raise Exception(f"Invalid token") except Exception as e: print(f"An exception occurred - {traceback.format_exc()}") if isinstance(e, HTTPException): @@ -310,24 +319,12 @@ async def user_api_key_auth(request: Request, api_key: str = fastapi.Security(ap def prisma_setup(database_url: Optional[str]): global prisma_client, proxy_logging_obj, user_api_key_cache - proxy_logging_obj._init_litellm_callbacks() if database_url is not None: try: prisma_client = PrismaClient(database_url=database_url, proxy_logging_obj=proxy_logging_obj) except Exception as e: print("Error when initializing prisma, Ensure you run pip install prisma", e) -def celery_setup(use_queue: bool): - global celery_fn, celery_app_conn, async_result - if use_queue: - from litellm.proxy.queue.celery_worker import start_worker - from litellm.proxy.queue.celery_app import celery_app, process_job - from celery.result import AsyncResult - start_worker(os.getcwd()) - celery_fn = process_job - async_result = AsyncResult - celery_app_conn = celery_app - def load_from_azure_key_vault(use_azure_key_vault: bool = False): if use_azure_key_vault is False: return @@ -380,30 +377,14 @@ async def track_cost_callback( if "complete_streaming_response" in kwargs: # for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost completion_response=kwargs["complete_streaming_response"] - input_text = kwargs["messages"] - output_text = completion_response["choices"][0]["message"]["content"] - response_cost = litellm.completion_cost( - model = kwargs["model"], - messages = input_text, - completion=output_text - ) + response_cost = litellm.completion_cost(completion_response=completion_response) print("streaming response_cost", response_cost) user_api_key = kwargs["litellm_params"]["metadata"].get("user_api_key", None) - print(f"user_api_key - {user_api_key}; prisma_client - {prisma_client}") if user_api_key and prisma_client: await update_prisma_database(token=user_api_key, response_cost=response_cost) elif kwargs["stream"] == False: # for non streaming responses - input_text = kwargs.get("messages", "") - print(f"type of input_text: {type(input_text)}") - if isinstance(input_text, list): - response_cost = litellm.completion_cost(completion_response=completion_response, messages=input_text) - elif isinstance(input_text, str): - response_cost = litellm.completion_cost(completion_response=completion_response, prompt=input_text) - print(f"received completion response: {completion_response}") - - print(f"regular response_cost: {response_cost}") + response_cost = litellm.completion_cost(completion_response=completion_response) user_api_key = kwargs["litellm_params"]["metadata"].get("user_api_key", None) - print(f"user_api_key - {user_api_key}; prisma_client - {prisma_client}") if user_api_key and prisma_client: await update_prisma_database(token=user_api_key, response_cost=response_cost) except Exception as e: @@ -459,7 +440,7 @@ async def _run_background_health_check(): await asyncio.sleep(health_check_interval) def load_router_config(router: Optional[litellm.Router], config_file_path: str): - global master_key, user_config_file_path, otel_logging, user_custom_auth, user_custom_auth_path, use_background_health_checks, health_check_interval + global master_key, user_config_file_path, otel_logging, user_custom_auth, user_custom_auth_path, use_background_health_checks, health_check_interval, use_queue config = {} try: if os.path.exists(config_file_path): @@ -504,6 +485,18 @@ def load_router_config(router: Optional[litellm.Router], config_file_path: str): cache_port = litellm.get_secret("REDIS_PORT", None) cache_password = litellm.get_secret("REDIS_PASSWORD", None) + cache_params = { + "type": cache_type, + "host": cache_host, + "port": cache_port, + "password": cache_password + } + + if "cache_params" in litellm_settings: + cache_params_in_config = litellm_settings["cache_params"] + # overwrie cache_params with cache_params_in_config + cache_params.update(cache_params_in_config) + # Assuming cache_type, cache_host, cache_port, and cache_password are strings print(f"{blue_color_code}Cache Type:{reset_color_code} {cache_type}") print(f"{blue_color_code}Cache Host:{reset_color_code} {cache_host}") @@ -513,15 +506,15 @@ def load_router_config(router: Optional[litellm.Router], config_file_path: str): ## to pass a complete url, or set ssl=True, etc. just set it as `os.environ[REDIS_URL] = `, _redis.py checks for REDIS specific environment variables litellm.cache = Cache( - type=cache_type, - host=cache_host, - port=cache_port, - password=cache_password + **cache_params ) print(f"{blue_color_code}Set Cache on LiteLLM Proxy: {litellm.cache.cache}{reset_color_code} {cache_password}") elif key == "callbacks": litellm.callbacks = [get_instance_fn(value=value, config_file_path=config_file_path)] print_verbose(f"{blue_color_code} Initialized Callbacks - {litellm.callbacks} {reset_color_code}") + elif key == "post_call_rules": + litellm.post_call_rules = [get_instance_fn(value=value, config_file_path=config_file_path)] + print(f"litellm.post_call_rules: {litellm.post_call_rules}") elif key == "success_callback": litellm.success_callback = [] @@ -533,10 +526,6 @@ def load_router_config(router: Optional[litellm.Router], config_file_path: str): # these are litellm callbacks - "langfuse", "sentry", "wandb" else: litellm.success_callback.append(callback) - if callback == "traceloop": - from traceloop.sdk import Traceloop - print_verbose(f"{blue_color_code} Initializing Traceloop SDK - \nRunning:`Traceloop.init(app_name='Litellm-Server', disable_batch=True)`") - Traceloop.init(app_name="Litellm-Server", disable_batch=True) print_verbose(f"{blue_color_code} Initialized Success Callbacks - {litellm.success_callback} {reset_color_code}") elif key == "failure_callback": litellm.failure_callback = [] @@ -550,6 +539,10 @@ def load_router_config(router: Optional[litellm.Router], config_file_path: str): else: litellm.failure_callback.append(callback) print_verbose(f"{blue_color_code} Initialized Success Callbacks - {litellm.failure_callback} {reset_color_code}") + elif key == "cache_params": + # this is set in the cache branch + # see usage here: https://docs.litellm.ai/docs/proxy/caching + pass else: setattr(litellm, key, value) @@ -572,7 +565,6 @@ def load_router_config(router: Optional[litellm.Router], config_file_path: str): cost_tracking() ### START REDIS QUEUE ### use_queue = general_settings.get("use_queue", False) - celery_setup(use_queue=use_queue) ### MASTER KEY ### master_key = general_settings.get("master_key", None) if master_key and master_key.startswith("os.environ/"): @@ -683,6 +675,8 @@ async def generate_key_helper_fn(duration: Optional[str], models: list, aliases: raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR) return {"token": token, "expires": new_verification_token.expires, "user_id": user_id} + + async def delete_verification_token(tokens: List): global prisma_client try: @@ -761,8 +755,6 @@ def initialize( if max_budget: # litellm-specific param litellm.max_budget = max_budget dynamic_config["general"]["max_budget"] = max_budget - if use_queue: - celery_setup(use_queue=use_queue) if experimental: pass user_telemetry = telemetry @@ -798,48 +790,12 @@ def data_generator(response): async def async_data_generator(response, user_api_key_dict): print_verbose("inside generator") async for chunk in response: - # try: - # await proxy_logging_obj.pre_call_hook(user_api_key_dict=user_api_key_dict, data=None, call_type="completion") - # except Exception as e: - # print(f"An exception occurred - {str(e)}") - print_verbose(f"returned chunk: {chunk}") try: yield f"data: {json.dumps(chunk.dict())}\n\n" except: yield f"data: {json.dumps(chunk)}\n\n" -def litellm_completion(*args, **kwargs): - global user_temperature, user_request_timeout, user_max_tokens, user_api_base - call_type = kwargs.pop("call_type") - # override with user settings, these are params passed via cli - if user_temperature: - kwargs["temperature"] = user_temperature - if user_request_timeout: - kwargs["request_timeout"] = user_request_timeout - if user_max_tokens: - kwargs["max_tokens"] = user_max_tokens - if user_api_base: - kwargs["api_base"] = user_api_base - ## ROUTE TO CORRECT ENDPOINT ## - router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else [] - try: - if llm_router is not None and kwargs["model"] in router_model_names: # model in router model list - if call_type == "chat_completion": - response = llm_router.completion(*args, **kwargs) - elif call_type == "text_completion": - response = llm_router.text_completion(*args, **kwargs) - else: - if call_type == "chat_completion": - response = litellm.completion(*args, **kwargs) - elif call_type == "text_completion": - response = litellm.text_completion(*args, **kwargs) - except Exception as e: - raise e - if 'stream' in kwargs and kwargs['stream'] == True: # use generate_responses to stream responses - return StreamingResponse(data_generator(response), media_type='text/event-stream') - return response - def get_litellm_model_info(model: dict = {}): model_info = model.get("model_info", {}) model_to_lookup = model.get("litellm_params", {}).get("model", None) @@ -870,6 +826,8 @@ async def startup_event(): initialize(**worker_config) + proxy_logging_obj._init_litellm_callbacks() # INITIALIZE LITELLM CALLBACKS ON SERVER STARTUP <- do this to catch any logging errors on startup, not when calls are being made + if use_background_health_checks: asyncio.create_task(_run_background_health_check()) # start the background health check coroutine. @@ -881,16 +839,6 @@ async def startup_event(): # add master key to db await generate_key_helper_fn(duration=None, models=[], aliases={}, config={}, spend=0, token=master_key) -@router.on_event("shutdown") -async def shutdown_event(): - global prisma_client, master_key, user_custom_auth - if prisma_client: - print("Disconnecting from Prisma") - await prisma_client.disconnect() - - ## RESET CUSTOM VARIABLES ## - master_key = None - user_custom_auth = None #### API ENDPOINTS #### @router.get("/v1/models", dependencies=[Depends(user_api_key_auth)]) @@ -929,7 +877,8 @@ def model_list(): @router.post("/v1/completions", dependencies=[Depends(user_api_key_auth)]) @router.post("/completions", dependencies=[Depends(user_api_key_auth)]) @router.post("/engines/{model:path}/completions", dependencies=[Depends(user_api_key_auth)]) -async def completion(request: Request, model: Optional[str] = None, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth)): +async def completion(request: Request, model: Optional[str] = None, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), background_tasks: BackgroundTasks = BackgroundTasks()): + global user_temperature, user_request_timeout, user_max_tokens, user_api_base try: body = await request.body() body_str = body.decode() @@ -938,7 +887,7 @@ async def completion(request: Request, model: Optional[str] = None, user_api_key except: data = json.loads(body_str) - data["user"] = user_api_key_dict.user_id + data["user"] = data.get("user", user_api_key_dict.user_id) data["model"] = ( general_settings.get("completion_model", None) # server default or user_model # model name passed via cli args @@ -947,17 +896,44 @@ async def completion(request: Request, model: Optional[str] = None, user_api_key ) if user_model: data["model"] = user_model - data["call_type"] = "text_completion" if "metadata" in data: data["metadata"]["user_api_key"] = user_api_key_dict.api_key else: data["metadata"] = {"user_api_key": user_api_key_dict.api_key} - return litellm_completion( - **data - ) + # override with user settings, these are params passed via cli + if user_temperature: + data["temperature"] = user_temperature + if user_request_timeout: + data["request_timeout"] = user_request_timeout + if user_max_tokens: + data["max_tokens"] = user_max_tokens + if user_api_base: + data["api_base"] = user_api_base + + ### CALL HOOKS ### - modify incoming data before calling the model + data = await proxy_logging_obj.pre_call_hook(user_api_key_dict=user_api_key_dict, data=data, call_type="completion") + + ### ROUTE THE REQUEST ### + router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else [] + if llm_router is not None and data["model"] in router_model_names: # model in router model list + response = await llm_router.atext_completion(**data) + elif llm_router is not None and data["model"] in llm_router.deployment_names: # model in router deployments, calling a specific deployment on the router + response = await llm_router.atext_completion(**data, specific_deployment = True) + elif llm_router is not None and llm_router.model_group_alias is not None and data["model"] in llm_router.model_group_alias: # model set in model_group_alias + response = await llm_router.atext_completion(**data) + else: # router is not set + response = await litellm.atext_completion(**data) + + print(f"final response: {response}") + if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses + return StreamingResponse(async_data_generator(user_api_key_dict=user_api_key_dict, response=response), media_type='text/event-stream') + + background_tasks.add_task(log_input_output, request, response) # background task for logging to OTEL + return response except Exception as e: print(f"\033[1;31mAn error occurred: {e}\n\n Debug this by setting `--debug`, e.g. `litellm --model gpt-3.5-turbo --debug`") + traceback.print_exc() error_traceback = traceback.format_exc() error_msg = f"{str(e)}\n\n{error_traceback}" try: @@ -995,7 +971,7 @@ async def chat_completion(request: Request, model: Optional[str] = None, user_ap ) # users can pass in 'user' param to /chat/completions. Don't override it - if data.get("user", None) is None: + if data.get("user", None) is None and user_api_key_dict.user_id is not None: # if users are using user_api_key_auth, set `user` in `data` data["user"] = user_api_key_dict.user_id @@ -1027,7 +1003,7 @@ async def chat_completion(request: Request, model: Optional[str] = None, user_ap response = await llm_router.acompletion(**data) elif llm_router is not None and data["model"] in llm_router.deployment_names: # model in router deployments, calling a specific deployment on the router response = await llm_router.acompletion(**data, specific_deployment = True) - elif llm_router is not None and litellm.model_group_alias_map is not None and data["model"] in litellm.model_group_alias_map: # model set in model_group_alias_map + elif llm_router is not None and llm_router.model_group_alias is not None and data["model"] in llm_router.model_group_alias: # model set in model_group_alias response = await llm_router.acompletion(**data) else: # router is not set response = await litellm.acompletion(**data) @@ -1088,7 +1064,9 @@ async def embeddings(request: Request, user_api_key_dict: UserAPIKeyAuth = Depen "body": copy.copy(data) # use copy instead of deepcopy } - data["user"] = user_api_key_dict.user_id + if data.get("user", None) is None and user_api_key_dict.user_id is not None: + data["user"] = user_api_key_dict.user_id + data["model"] = ( general_settings.get("embedding_model", None) # server default or user_model # model name passed via cli args @@ -1098,10 +1076,11 @@ async def embeddings(request: Request, user_api_key_dict: UserAPIKeyAuth = Depen data["model"] = user_model if "metadata" in data: data["metadata"]["user_api_key"] = user_api_key_dict.api_key + data["metadata"]["headers"] = dict(request.headers) else: data["metadata"] = {"user_api_key": user_api_key_dict.api_key} + data["metadata"]["headers"] = dict(request.headers) router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else [] - print(f"received data: {data['input']}") if "input" in data and isinstance(data['input'], list) and isinstance(data['input'][0], list) and isinstance(data['input'][0][0], int): # check if array of tokens passed in # check if non-openai/azure model called - e.g. for langchain integration if llm_model_list is not None and data["model"] in router_model_names: @@ -1119,12 +1098,13 @@ async def embeddings(request: Request, user_api_key_dict: UserAPIKeyAuth = Depen ### CALL HOOKS ### - modify incoming data / reject request before calling the model data = await proxy_logging_obj.pre_call_hook(user_api_key_dict=user_api_key_dict, data=data, call_type="embeddings") - ## ROUTE TO CORRECT ENDPOINT ## if llm_router is not None and data["model"] in router_model_names: # model in router model list response = await llm_router.aembedding(**data) elif llm_router is not None and data["model"] in llm_router.deployment_names: # model in router deployments, calling a specific deployment on the router response = await llm_router.aembedding(**data, specific_deployment = True) + elif llm_router is not None and llm_router.model_group_alias is not None and data["model"] in llm_router.model_group_alias: # model set in model_group_alias + response = await llm_router.aembedding(**data) # ensure this goes the llm_router, router will do the correct alias mapping else: response = await litellm.aembedding(**data) background_tasks.add_task(log_input_output, request, response) # background task for logging to OTEL @@ -1133,7 +1113,19 @@ async def embeddings(request: Request, user_api_key_dict: UserAPIKeyAuth = Depen except Exception as e: await proxy_logging_obj.post_call_failure_hook(user_api_key_dict=user_api_key_dict, original_exception=e) traceback.print_exc() - raise e + if isinstance(e, HTTPException): + raise e + else: + error_traceback = traceback.format_exc() + error_msg = f"{str(e)}\n\n{error_traceback}" + try: + status = e.status_code # type: ignore + except: + status = 500 + raise HTTPException( + status_code=status, + detail=error_msg + ) #### KEY MANAGEMENT #### @@ -1162,6 +1154,30 @@ async def generate_key_fn(request: Request, data: GenerateKeyRequest, Authorizat response = await generate_key_helper_fn(**data_json) return GenerateKeyResponse(key=response["token"], expires=response["expires"], user_id=response["user_id"]) +@router.post("/key/update", tags=["key management"], dependencies=[Depends(user_api_key_auth)]) +async def update_key_fn(request: Request, data: UpdateKeyRequest): + """ + Update an existing key + """ + global prisma_client + try: + data_json: dict = data.json() + key = data_json.pop("key") + # get the row from db + if prisma_client is None: + raise Exception("Not connected to DB!") + + non_default_values = {k: v for k, v in data_json.items() if v is not None} + print(f"non_default_values: {non_default_values}") + response = await prisma_client.update_data(token=key, data={**non_default_values, "token": key}) + return {"key": key, **non_default_values} + # update based on remaining passed in values + except Exception as e: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail={"error": str(e)}, + ) + @router.post("/key/delete", tags=["key management"], dependencies=[Depends(user_api_key_auth)]) async def delete_key_fn(request: Request, data: DeleteKeyRequest): try: @@ -1207,10 +1223,12 @@ async def add_new_model(model_params: ModelParams): print_verbose(f"Loaded config: {config}") # Add the new model to the config + model_info = model_params.model_info.json() + model_info = {k: v for k, v in model_info.items() if v is not None} config['model_list'].append({ 'model_name': model_params.model_name, 'litellm_params': model_params.litellm_params, - 'model_info': model_params.model_info + 'model_info': model_info }) # Save the updated config @@ -1228,7 +1246,7 @@ async def add_new_model(model_params: ModelParams): raise HTTPException(status_code=500, detail=f"Internal Server Error: {str(e)}") #### [BETA] - This is a beta endpoint, format might change based on user feedback https://github.com/BerriAI/litellm/issues/933. If you need a stable endpoint use /model/info -@router.get("/v1/model/info", description="Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)", tags=["model management"], dependencies=[Depends(user_api_key_auth)]) +@router.get("/model/info", description="Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)", tags=["model management"], dependencies=[Depends(user_api_key_auth)]) async def model_info_v1(request: Request): global llm_model_list, general_settings, user_config_file_path # Load existing config @@ -1256,7 +1274,7 @@ async def model_info_v1(request: Request): #### [BETA] - This is a beta endpoint, format might change based on user feedback. - https://github.com/BerriAI/litellm/issues/933 -@router.get("/model/info", description="Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)", tags=["model management"], dependencies=[Depends(user_api_key_auth)]) +@router.get("/v1/model/info", description="Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)", tags=["model management"], dependencies=[Depends(user_api_key_auth)]) async def model_info(request: Request): global llm_model_list, general_settings, user_config_file_path # Load existing config @@ -1341,47 +1359,108 @@ async def delete_model(model_info: ModelInfoDelete): raise HTTPException(status_code=500, detail=f"Internal Server Error: {str(e)}") #### EXPERIMENTAL QUEUING #### -@router.post("/queue/request", dependencies=[Depends(user_api_key_auth)]) -async def async_queue_request(request: Request): - global celery_fn, llm_model_list - if celery_fn is not None: - body = await request.body() - body_str = body.decode() - try: - data = ast.literal_eval(body_str) - except: - data = json.loads(body_str) +async def _litellm_chat_completions_worker(data, user_api_key_dict): + """ + worker to make litellm completions calls + """ + while True: + try: + ### CALL HOOKS ### - modify incoming data before calling the model + data = await proxy_logging_obj.pre_call_hook(user_api_key_dict=user_api_key_dict, data=data, call_type="completion") + + print(f"_litellm_chat_completions_worker started") + ### ROUTE THE REQUEST ### + router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else [] + if llm_router is not None and data["model"] in router_model_names: # model in router model list + response = await llm_router.acompletion(**data) + elif llm_router is not None and data["model"] in llm_router.deployment_names: # model in router deployments, calling a specific deployment on the router + response = await llm_router.acompletion(**data, specific_deployment = True) + elif llm_router is not None and llm_router.model_group_alias is not None and data["model"] in llm_router.model_group_alias: # model set in model_group_alias + response = await llm_router.acompletion(**data) + else: # router is not set + response = await litellm.acompletion(**data) + + print(f"final response: {response}") + return response + except HTTPException as e: + print(f"EXCEPTION RAISED IN _litellm_chat_completions_worker - {e.status_code}; {e.detail}") + if e.status_code == 429 and "Max parallel request limit reached" in e.detail: + print(f"Max parallel request limit reached!") + timeout = litellm._calculate_retry_after(remaining_retries=3, max_retries=3, min_timeout=1) + await asyncio.sleep(timeout) + else: + raise e + + +@router.post("/queue/chat/completions", tags=["experimental"], dependencies=[Depends(user_api_key_auth)]) +async def async_queue_request(request: Request, model: Optional[str] = None, user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), background_tasks: BackgroundTasks = BackgroundTasks()): + global general_settings, user_debug, proxy_logging_obj + """ + v2 attempt at a background worker to handle queuing. + + Just supports /chat/completion calls currently. + + Now using a FastAPI background task + /chat/completions compatible endpoint + """ + try: + data = {} + data = await request.json() # type: ignore + + # Include original request and headers in the data + data["proxy_server_request"] = { + "url": str(request.url), + "method": request.method, + "headers": dict(request.headers), + "body": copy.copy(data) # use copy instead of deepcopy + } + + print_verbose(f"receiving data: {data}") data["model"] = ( general_settings.get("completion_model", None) # server default or user_model # model name passed via cli args + or model # for azure deployments or data["model"] # default passed in http request ) - data["llm_model_list"] = llm_model_list - print(f"data: {data}") - job = celery_fn.apply_async(kwargs=data) - return {"id": job.id, "url": f"/queue/response/{job.id}", "eta": 5, "status": "queued"} - else: + + # users can pass in 'user' param to /chat/completions. Don't override it + if data.get("user", None) is None and user_api_key_dict.user_id is not None: + # if users are using user_api_key_auth, set `user` in `data` + data["user"] = user_api_key_dict.user_id + + if "metadata" in data: + print(f'received metadata: {data["metadata"]}') + data["metadata"]["user_api_key"] = user_api_key_dict.api_key + data["metadata"]["headers"] = dict(request.headers) + else: + data["metadata"] = {"user_api_key": user_api_key_dict.api_key} + data["metadata"]["headers"] = dict(request.headers) + + global user_temperature, user_request_timeout, user_max_tokens, user_api_base + # override with user settings, these are params passed via cli + if user_temperature: + data["temperature"] = user_temperature + if user_request_timeout: + data["request_timeout"] = user_request_timeout + if user_max_tokens: + data["max_tokens"] = user_max_tokens + if user_api_base: + data["api_base"] = user_api_base + + response = await asyncio.wait_for(_litellm_chat_completions_worker(data=data, user_api_key_dict=user_api_key_dict), timeout=litellm.request_timeout) + + if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses + return StreamingResponse(async_data_generator(user_api_key_dict=user_api_key_dict, response=response), media_type='text/event-stream') + + background_tasks.add_task(log_input_output, request, response) # background task for logging to OTEL + return response + except Exception as e: + await proxy_logging_obj.post_call_failure_hook(user_api_key_dict=user_api_key_dict, original_exception=e) raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail={"error": "Queue not initialized"}, + detail={"error": str(e)}, ) - -@router.get("/queue/response/{task_id}", dependencies=[Depends(user_api_key_auth)]) -async def async_queue_response(request: Request, task_id: str): - global celery_app_conn, async_result - try: - if celery_app_conn is not None and async_result is not None: - job = async_result(task_id, app=celery_app_conn) - if job.ready(): - return {"status": "finished", "result": job.result} - else: - return {'status': 'queued'} - else: - raise Exception() - except Exception as e: - return {"status": "finished", "result": str(e)} - - + + @router.get("/ollama_logs", dependencies=[Depends(user_api_key_auth)]) async def retrieve_server_log(request: Request): filepath = os.path.expanduser("~/.ollama/logs/server.log") @@ -1411,8 +1490,18 @@ async def config_yaml_endpoint(config_info: ConfigYAML): return {"hello": "world"} -@router.get("/test") +@router.get("/test", tags=["health"]) async def test_endpoint(request: Request): + """ + A test endpoint that pings the proxy server to check if it's healthy. + + Parameters: + request (Request): The incoming request. + + Returns: + dict: A dictionary containing the route of the request URL. + """ + # ping the proxy server to check if its healthy return {"route": request.url.path} @router.get("/health", tags=["health"], dependencies=[Depends(user_api_key_auth)]) @@ -1470,4 +1559,27 @@ async def get_routes(): return {"routes": routes} +@router.on_event("shutdown") +async def shutdown_event(): + global prisma_client, master_key, user_custom_auth + if prisma_client: + print("Disconnecting from Prisma") + await prisma_client.disconnect() + + ## RESET CUSTOM VARIABLES ## + cleanup_router_config_variables() + +def cleanup_router_config_variables(): + global master_key, user_config_file_path, otel_logging, user_custom_auth, user_custom_auth_path, use_background_health_checks, health_check_interval + + # Set all variables to None + master_key = None + user_config_file_path = None + otel_logging = None + user_custom_auth = None + user_custom_auth_path = None + use_background_health_checks = None + health_check_interval = None + + app.include_router(router) diff --git a/litellm/proxy/tests/test_proxy_exception_mapping.py b/litellm/proxy/tests/test_proxy_exception_mapping.py deleted file mode 100644 index 7fb5bedbe..000000000 --- a/litellm/proxy/tests/test_proxy_exception_mapping.py +++ /dev/null @@ -1,23 +0,0 @@ -import openai -client = openai.OpenAI( - api_key="anything", - # base_url="http://0.0.0.0:8000", -) - -try: - # 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) -# except openai.APITimeoutError: -# print("Got openai Timeout Exception. Good job. The proxy mapped to OpenAI exceptions") -except Exception as e: - print("\n the proxy did not map to OpenAI exception. Instead got", e) - print(e.type) # type: ignore - print(e.message) # type: ignore - print(e.code) # type: ignore \ No newline at end of file diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index e972eff4d..938508bcc 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -1,13 +1,13 @@ from typing import Optional, List, Any, Literal -import os, subprocess, hashlib, importlib, asyncio +import os, subprocess, hashlib, importlib, asyncio, copy import litellm, backoff from litellm.proxy._types import UserAPIKeyAuth from litellm.caching import DualCache from litellm.proxy.hooks.parallel_request_limiter import MaxParallelRequestsHandler - +from litellm.integrations.custom_logger import CustomLogger def print_verbose(print_statement): if litellm.set_verbose: - print(print_statement) # noqa + print(f"LiteLLM Proxy: {print_statement}") # noqa ### LOGGING ### class ProxyLogging: """ @@ -26,7 +26,7 @@ class ProxyLogging: pass def _init_litellm_callbacks(self): - + print_verbose(f"INITIALIZING LITELLM CALLBACKS!") litellm.callbacks.append(self.max_parallel_request_limiter) for callback in litellm.callbacks: if callback not in litellm.input_callback: @@ -64,18 +64,14 @@ class ProxyLogging: 1. /chat/completions 2. /embeddings """ - try: - self.call_details["data"] = data - self.call_details["call_type"] = call_type + try: + for callback in litellm.callbacks: + if isinstance(callback, CustomLogger) and 'async_pre_call_hook' in vars(callback.__class__): + response = await callback.async_pre_call_hook(user_api_key_dict=user_api_key_dict, cache=self.call_details["user_api_key_cache"], data=data, call_type=call_type) + if response is not None: + data = response - ## check if max parallel requests set - if user_api_key_dict.max_parallel_requests is not None: - ## if set, check if request allowed - await self.max_parallel_request_limiter.max_parallel_request_allow_request( - max_parallel_requests=user_api_key_dict.max_parallel_requests, - api_key=user_api_key_dict.api_key, - user_api_key_cache=self.call_details["user_api_key_cache"]) - + print_verbose(f'final data being sent to {call_type} call: {data}') return data except Exception as e: raise e @@ -103,17 +99,13 @@ class ProxyLogging: 1. /chat/completions 2. /embeddings """ - # check if max parallel requests set - if user_api_key_dict.max_parallel_requests is not None: - ## decrement call count if call failed - if (hasattr(original_exception, "status_code") - and original_exception.status_code == 429 - and "Max parallel request limit reached" in str(original_exception)): - pass # ignore failed calls due to max limit being reached - else: - await self.max_parallel_request_limiter.async_log_failure_call( - api_key=user_api_key_dict.api_key, - user_api_key_cache=self.call_details["user_api_key_cache"]) + + for callback in litellm.callbacks: + try: + if isinstance(callback, CustomLogger): + await callback.async_post_call_failure_hook(user_api_key_dict=user_api_key_dict, original_exception=original_exception) + except Exception as e: + raise e return @@ -165,19 +157,20 @@ class PrismaClient: async def get_data(self, token: str, expires: Optional[Any]=None): try: # check if plain text or hash + hashed_token = token if token.startswith("sk-"): - token = self.hash_token(token=token) + hashed_token = self.hash_token(token=token) if expires: response = await self.db.litellm_verificationtoken.find_first( where={ - "token": token, + "token": hashed_token, "expires": {"gte": expires} # Check if the token is not expired } ) else: response = await self.db.litellm_verificationtoken.find_unique( where={ - "token": token + "token": hashed_token } ) return response @@ -200,18 +193,18 @@ class PrismaClient: try: token = data["token"] hashed_token = self.hash_token(token=token) - data["token"] = hashed_token + db_data = copy.deepcopy(data) + db_data["token"] = hashed_token new_verification_token = await self.db.litellm_verificationtoken.upsert( # type: ignore where={ 'token': hashed_token, }, data={ - "create": {**data}, #type: ignore + "create": {**db_data}, #type: ignore "update": {} # don't do anything if it already exists } ) - return new_verification_token except Exception as e: asyncio.create_task(self.proxy_logging_obj.failure_handler(original_exception=e)) @@ -235,15 +228,16 @@ class PrismaClient: if token.startswith("sk-"): token = self.hash_token(token=token) - data["token"] = token + db_data = copy.deepcopy(data) + db_data["token"] = token response = await self.db.litellm_verificationtoken.update( where={ "token": token }, - data={**data} # type: ignore + data={**db_data} # type: ignore ) print_verbose("\033[91m" + f"DB write succeeded {response}" + "\033[0m") - return {"token": token, "data": data} + return {"token": token, "data": db_data} except Exception as e: asyncio.create_task(self.proxy_logging_obj.failure_handler(original_exception=e)) print_verbose("\033[91m" + f"DB write failed: {e}" + "\033[0m") diff --git a/litellm/router.py b/litellm/router.py index 1721a381b..1b8dc1172 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -7,6 +7,7 @@ # # Thank you ! We ❤️ you! - Krrish & Ishaan +import copy from datetime import datetime from typing import Dict, List, Optional, Union, Literal, Any import random, threading, time, traceback, uuid @@ -17,6 +18,7 @@ import inspect, concurrent from openai import AsyncOpenAI from collections import defaultdict from litellm.router_strategy.least_busy import LeastBusyLoggingHandler +import copy class Router: """ Example usage: @@ -68,6 +70,7 @@ class Router: redis_password: Optional[str] = None, cache_responses: Optional[bool] = False, cache_kwargs: dict = {}, # additional kwargs to pass to RedisCache (see caching.py) + caching_groups: Optional[List[tuple]] = None, # if you want to cache across model groups ## RELIABILITY ## num_retries: int = 0, timeout: Optional[float] = None, @@ -76,11 +79,13 @@ class Router: fallbacks: List = [], allowed_fails: Optional[int] = None, context_window_fallbacks: List = [], + model_group_alias: Optional[dict] = {}, routing_strategy: Literal["simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing"] = "simple-shuffle") -> None: self.set_verbose = set_verbose self.deployment_names: List = [] # names of models under litellm_params. ex. azure/chatgpt-v-2 if model_list: + model_list = copy.deepcopy(model_list) self.set_model_list(model_list) self.healthy_deployments: List = self.model_list self.deployment_latency_map = {} @@ -99,6 +104,7 @@ class Router: self.fail_calls: defaultdict = defaultdict(int) # dict to store fail_calls made to each model self.success_calls: defaultdict = defaultdict(int) # dict to store success_calls made to each model self.previous_models: List = [] # list to store failed calls (passed in as metadata to next call) + self.model_group_alias: dict = model_group_alias or {} # dict to store aliases for router, ex. {"gpt-4": "gpt-3.5-turbo"}, all requests with gpt-4 -> get routed to gpt-3.5-turbo group # make Router.chat.completions.create compatible for openai.chat.completions.create self.chat = litellm.Chat(params=default_litellm_params) @@ -107,9 +113,10 @@ class Router: self.default_litellm_params = default_litellm_params self.default_litellm_params.setdefault("timeout", timeout) self.default_litellm_params.setdefault("max_retries", 0) + self.default_litellm_params.setdefault("metadata", {}).update({"caching_groups": caching_groups}) ### CACHING ### - cache_type = "local" # default to an in-memory cache + cache_type: Literal["local", "redis"] = "local" # default to an in-memory cache redis_cache = None cache_config = {} if redis_url is not None or (redis_host is not None and redis_port is not None and redis_password is not None): @@ -133,7 +140,7 @@ class Router: if cache_responses: if litellm.cache is None: # the cache can be initialized on the proxy server. We should not overwrite it - litellm.cache = litellm.Cache(type=cache_type, **cache_config) + litellm.cache = litellm.Cache(type=cache_type, **cache_config) # type: ignore self.cache_responses = cache_responses self.cache = DualCache(redis_cache=redis_cache, in_memory_cache=InMemoryCache()) # use a dual cache (Redis+In-Memory) for tracking cooldowns, usage, etc. ### ROUTING SETUP ### @@ -198,19 +205,10 @@ class Router: data = deployment["litellm_params"].copy() kwargs["model_info"] = deployment.get("model_info", {}) for k, v in self.default_litellm_params.items(): - if k not in data: # prioritize model-specific params > default router params - data[k] = v - - ########## remove -ModelID-XXXX from model ############## - original_model_string = data["model"] - # Find the index of "ModelID" in the string - self.print_verbose(f"completion model: {original_model_string}") - index_of_model_id = original_model_string.find("-ModelID") - # Remove everything after "-ModelID" if it exists - if index_of_model_id != -1: - data["model"] = original_model_string[:index_of_model_id] - else: - data["model"] = original_model_string + if k not in kwargs: # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) model_client = self._get_client(deployment=deployment, kwargs=kwargs) return litellm.completion(**{**data, "messages": messages, "caching": self.cache_responses, "client": model_client, **kwargs}) except Exception as e: @@ -241,31 +239,25 @@ class Router: **kwargs): try: self.print_verbose(f"Inside _acompletion()- model: {model}; kwargs: {kwargs}") - original_model_string = None # set a default for this variable deployment = self.get_available_deployment(model=model, messages=messages, specific_deployment=kwargs.pop("specific_deployment", None)) kwargs.setdefault("metadata", {}).update({"deployment": deployment["litellm_params"]["model"]}) kwargs["model_info"] = deployment.get("model_info", {}) data = deployment["litellm_params"].copy() + model_name = data["model"] for k, v in self.default_litellm_params.items(): - if k not in data: # prioritize model-specific params > default router params - data[k] = v - ########## remove -ModelID-XXXX from model ############## - original_model_string = data["model"] - # Find the index of "ModelID" in the string - index_of_model_id = original_model_string.find("-ModelID") - # Remove everything after "-ModelID" if it exists - if index_of_model_id != -1: - data["model"] = original_model_string[:index_of_model_id] - else: - data["model"] = original_model_string + if k not in kwargs: # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + model_client = self._get_client(deployment=deployment, kwargs=kwargs, client_type="async") - self.total_calls[original_model_string] +=1 + self.total_calls[model_name] +=1 response = await litellm.acompletion(**{**data, "messages": messages, "caching": self.cache_responses, "client": model_client, **kwargs}) - self.success_calls[original_model_string] +=1 + self.success_calls[model_name] +=1 return response except Exception as e: - if original_model_string is not None: - self.fail_calls[original_model_string] +=1 + if model_name is not None: + self.fail_calls[model_name] +=1 raise e def text_completion(self, @@ -283,8 +275,43 @@ class Router: data = deployment["litellm_params"].copy() for k, v in self.default_litellm_params.items(): - if k not in data: # prioritize model-specific params > default router params - data[k] = v + if k not in kwargs: # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + + # call via litellm.completion() + return litellm.text_completion(**{**data, "prompt": prompt, "caching": self.cache_responses, **kwargs}) # type: ignore + except Exception as e: + if self.num_retries > 0: + kwargs["model"] = model + kwargs["messages"] = messages + kwargs["original_exception"] = e + kwargs["original_function"] = self.completion + return self.function_with_retries(**kwargs) + else: + raise e + + async def atext_completion(self, + model: str, + prompt: str, + is_retry: Optional[bool] = False, + is_fallback: Optional[bool] = False, + is_async: Optional[bool] = False, + **kwargs): + try: + kwargs.setdefault("metadata", {}).update({"model_group": model}) + messages=[{"role": "user", "content": prompt}] + # pick the one that is available (lowest TPM/RPM) + deployment = self.get_available_deployment(model=model, messages=messages, specific_deployment=kwargs.pop("specific_deployment", None)) + + data = deployment["litellm_params"].copy() + for k, v in self.default_litellm_params.items(): + if k not in kwargs: # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + ########## remove -ModelID-XXXX from model ############## original_model_string = data["model"] # Find the index of "ModelID" in the string @@ -294,8 +321,9 @@ class Router: data["model"] = original_model_string[:index_of_model_id] else: data["model"] = original_model_string - # call via litellm.completion() - return litellm.text_completion(**{**data, "prompt": prompt, "caching": self.cache_responses, **kwargs}) # type: ignore + # call via litellm.atext_completion() + response = await litellm.atext_completion(**{**data, "prompt": prompt, "caching": self.cache_responses, **kwargs}) # type: ignore + return response except Exception as e: if self.num_retries > 0: kwargs["model"] = model @@ -313,21 +341,14 @@ class Router: **kwargs) -> Union[List[float], None]: # pick the one that is available (lowest TPM/RPM) deployment = self.get_available_deployment(model=model, input=input, specific_deployment=kwargs.pop("specific_deployment", None)) - kwargs.setdefault("metadata", {}).update({"deployment": deployment["litellm_params"]["model"]}) - kwargs["model_info"] = deployment.get("model_info", {}) + kwargs.setdefault("model_info", {}) + kwargs.setdefault("metadata", {}).update({"model_group": model, "deployment": deployment["litellm_params"]["model"]}) # [TODO]: move to using async_function_with_fallbacks data = deployment["litellm_params"].copy() for k, v in self.default_litellm_params.items(): - if k not in data: # prioritize model-specific params > default router params - data[k] = v - ########## remove -ModelID-XXXX from model ############## - original_model_string = data["model"] - # Find the index of "ModelID" in the string - index_of_model_id = original_model_string.find("-ModelID") - # Remove everything after "-ModelID" if it exists - if index_of_model_id != -1: - data["model"] = original_model_string[:index_of_model_id] - else: - data["model"] = original_model_string + if k not in kwargs: # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) model_client = self._get_client(deployment=deployment, kwargs=kwargs) # call via litellm.embedding() return litellm.embedding(**{**data, "input": input, "caching": self.cache_responses, "client": model_client, **kwargs}) @@ -339,21 +360,15 @@ class Router: **kwargs) -> Union[List[float], None]: # pick the one that is available (lowest TPM/RPM) deployment = self.get_available_deployment(model=model, input=input, specific_deployment=kwargs.pop("specific_deployment", None)) - kwargs.setdefault("metadata", {}).update({"deployment": deployment["litellm_params"]["model"]}) + kwargs.setdefault("metadata", {}).update({"model_group": model, "deployment": deployment["litellm_params"]["model"]}) data = deployment["litellm_params"].copy() kwargs["model_info"] = deployment.get("model_info", {}) for k, v in self.default_litellm_params.items(): - if k not in data: # prioritize model-specific params > default router params - data[k] = v - ########## remove -ModelID-XXXX from model ############## - original_model_string = data["model"] - # Find the index of "ModelID" in the string - index_of_model_id = original_model_string.find("-ModelID") - # Remove everything after "-ModelID" if it exists - if index_of_model_id != -1: - data["model"] = original_model_string[:index_of_model_id] - else: - data["model"] = original_model_string + if k not in kwargs: # prioritize model-specific params > default router params + kwargs[k] = v + elif k == "metadata": + kwargs[k].update(v) + model_client = self._get_client(deployment=deployment, kwargs=kwargs, client_type="async") return await litellm.aembedding(**{**data, "input": input, "caching": self.cache_responses, "client": model_client, **kwargs}) @@ -371,7 +386,7 @@ class Router: self.print_verbose(f'Async Response: {response}') return response except Exception as e: - self.print_verbose(f"An exception occurs: {e}") + self.print_verbose(f"An exception occurs: {e}\n\n Traceback{traceback.format_exc()}") original_exception = e try: self.print_verbose(f"Trying to fallback b/w models") @@ -637,9 +652,10 @@ class Router: model_name = kwargs.get('model', None) # i.e. gpt35turbo custom_llm_provider = kwargs.get("litellm_params", {}).get('custom_llm_provider', None) # i.e. azure metadata = kwargs.get("litellm_params", {}).get('metadata', None) + deployment_id = kwargs.get("litellm_params", {}).get("model_info").get("id") + self._set_cooldown_deployments(deployment_id) # setting deployment_id in cooldown deployments if metadata: deployment = metadata.get("deployment", None) - self._set_cooldown_deployments(deployment) deployment_exceptions = self.model_exception_map.get(deployment, []) deployment_exceptions.append(exception_str) self.model_exception_map[deployment] = deployment_exceptions @@ -877,7 +893,7 @@ class Router: return chosen_item def set_model_list(self, model_list: list): - self.model_list = model_list + self.model_list = copy.deepcopy(model_list) # we add api_base/api_key each model so load balancing between azure/gpt on api_base1 and api_base2 works import os for model in self.model_list: @@ -889,23 +905,26 @@ class Router: model["model_info"] = model_info #### for OpenAI / Azure we need to initalize the Client for High Traffic ######## custom_llm_provider = litellm_params.get("custom_llm_provider") - if custom_llm_provider is None: - custom_llm_provider = model_name.split("/",1)[0] + custom_llm_provider = custom_llm_provider or model_name.split("/",1)[0] or "" + default_api_base = None + default_api_key = None + if custom_llm_provider in litellm.openai_compatible_providers: + _, custom_llm_provider, api_key, api_base = litellm.get_llm_provider(model=model_name) + default_api_base = api_base + default_api_key = api_key if ( model_name in litellm.open_ai_chat_completion_models - or custom_llm_provider == "custom_openai" - or custom_llm_provider == "deepinfra" - or custom_llm_provider == "perplexity" - or custom_llm_provider == "anyscale" - or custom_llm_provider == "openai" + or custom_llm_provider in litellm.openai_compatible_providers or custom_llm_provider == "azure" + or custom_llm_provider == "custom_openai" + or custom_llm_provider == "openai" or "ft:gpt-3.5-turbo" in model_name or model_name in litellm.open_ai_embedding_models ): # glorified / complicated reading of configs # user can pass vars directly or they can pas os.environ/AZURE_API_KEY, in which case we will read the env # we do this here because we init clients for Azure, OpenAI and we need to set the right key - api_key = litellm_params.get("api_key") + api_key = litellm_params.get("api_key") or default_api_key if api_key and api_key.startswith("os.environ/"): api_key_env_name = api_key.replace("os.environ/", "") api_key = litellm.get_secret(api_key_env_name) @@ -913,7 +932,7 @@ class Router: api_base = litellm_params.get("api_base") base_url = litellm_params.get("base_url") - api_base = api_base or base_url # allow users to pass in `api_base` or `base_url` for azure + api_base = api_base or base_url or default_api_base # allow users to pass in `api_base` or `base_url` for azure if api_base and api_base.startswith("os.environ/"): api_base_env_name = api_base.replace("os.environ/", "") api_base = litellm.get_secret(api_base_env_name) @@ -1049,12 +1068,6 @@ class Router: ############ End of initializing Clients for OpenAI/Azure ################### self.deployment_names.append(model["litellm_params"]["model"]) - model_id = "" - for key in model["litellm_params"]: - if key != "api_key" and key != "metadata": - model_id+= str(model["litellm_params"][key]) - model["litellm_params"]["model"] += "-ModelID-" + model_id - self.print_verbose(f"\n Initialized Model List {self.model_list}") ############ Users can either pass tpm/rpm as a litellm_param or a router param ########### @@ -1115,38 +1128,41 @@ class Router: if specific_deployment == True: # users can also specify a specific deployment name. At this point we should check if they are just trying to call a specific deployment for deployment in self.model_list: - cleaned_model = litellm.utils.remove_model_id(deployment.get("litellm_params").get("model")) - if cleaned_model == model: + deployment_model = deployment.get("litellm_params").get("model") + if deployment_model == model: # User Passed a specific deployment name on their config.yaml, example azure/chat-gpt-v-2 # return the first deployment where the `model` matches the specificed deployment name return deployment raise ValueError(f"LiteLLM Router: Trying to call specific deployment, but Model:{model} does not exist in Model List: {self.model_list}") # check if aliases set on litellm model alias map - if model in litellm.model_group_alias_map: - self.print_verbose(f"Using a model alias. Got Request for {model}, sending requests to {litellm.model_group_alias_map.get(model)}") - model = litellm.model_group_alias_map[model] + if model in self.model_group_alias: + self.print_verbose(f"Using a model alias. Got Request for {model}, sending requests to {self.model_group_alias.get(model)}") + model = self.model_group_alias[model] ## get healthy deployments ### get all deployments - ### filter out the deployments currently cooling down healthy_deployments = [m for m in self.model_list if m["model_name"] == model] if len(healthy_deployments) == 0: # check if the user sent in a deployment name instead healthy_deployments = [m for m in self.model_list if m["litellm_params"]["model"] == model] self.print_verbose(f"initial list of deployments: {healthy_deployments}") + + # filter out the deployments currently cooling down deployments_to_remove = [] - cooldown_deployments = self._get_cooldown_deployments() + # cooldown_deployments is a list of model_id's cooling down, cooldown_deployments = ["16700539-b3cd-42f4-b426-6a12a1bb706a", "16700539-b3cd-42f4-b426-7899"] + cooldown_deployments = self._get_cooldown_deployments() self.print_verbose(f"cooldown deployments: {cooldown_deployments}") - ### FIND UNHEALTHY DEPLOYMENTS + # Find deployments in model_list whose model_id is cooling down for deployment in healthy_deployments: - deployment_name = deployment["litellm_params"]["model"] - if deployment_name in cooldown_deployments: + deployment_id = deployment["model_info"]["id"] + if deployment_id in cooldown_deployments: deployments_to_remove.append(deployment) - ### FILTER OUT UNHEALTHY DEPLOYMENTS + # remove unhealthy deployments from healthy deployments for deployment in deployments_to_remove: healthy_deployments.remove(deployment) + self.print_verbose(f"healthy deployments: length {len(healthy_deployments)} {healthy_deployments}") if len(healthy_deployments) == 0: raise ValueError("No models available") @@ -1222,11 +1238,14 @@ class Router: raise ValueError("No models available.") def flush_cache(self): + litellm.cache = None self.cache.flush_cache() def reset(self): ## clean up on close litellm.success_callback = [] + litellm.__async_success_callback = [] litellm.failure_callback = [] + litellm._async_failure_callback = [] self.flush_cache() \ No newline at end of file diff --git a/litellm/tests/conftest.py b/litellm/tests/conftest.py new file mode 100644 index 000000000..411da8023 --- /dev/null +++ b/litellm/tests/conftest.py @@ -0,0 +1,34 @@ +# conftest.py + +import pytest, sys, os +import importlib +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm + + +@pytest.fixture(scope="function", autouse=True) +def setup_and_teardown(): + """ + This fixture reloads litellm before every function. To speed up testing by removing callbacks being chained. + """ + curr_dir = os.getcwd() # Get the current working directory + sys.path.insert(0, os.path.abspath("../..")) # Adds the project directory to the system path + import litellm + importlib.reload(litellm) + print(litellm) + # from litellm import Router, completion, aembedding, acompletion, embedding + yield + +def pytest_collection_modifyitems(config, items): + # Separate tests in 'test_amazing_proxy_custom_logger.py' and other tests + custom_logger_tests = [item for item in items if 'custom_logger' in item.parent.name] + other_tests = [item for item in items if 'custom_logger' not in item.parent.name] + + # Sort tests based on their names + custom_logger_tests.sort(key=lambda x: x.name) + other_tests.sort(key=lambda x: x.name) + + # Reorder the items list + items[:] = custom_logger_tests + other_tests \ No newline at end of file diff --git a/litellm/tests/example_config_yaml/cache_no_params.yaml b/litellm/tests/example_config_yaml/cache_no_params.yaml new file mode 100644 index 000000000..20ed919dd --- /dev/null +++ b/litellm/tests/example_config_yaml/cache_no_params.yaml @@ -0,0 +1,7 @@ +model_list: + - model_name: "openai-model" + litellm_params: + model: "gpt-3.5-turbo" + +litellm_settings: + cache: True diff --git a/litellm/tests/example_config_yaml/cache_with_params.yaml b/litellm/tests/example_config_yaml/cache_with_params.yaml new file mode 100644 index 000000000..372151d0c --- /dev/null +++ b/litellm/tests/example_config_yaml/cache_with_params.yaml @@ -0,0 +1,10 @@ +model_list: + - model_name: "openai-model" + litellm_params: + model: "gpt-3.5-turbo" + +litellm_settings: + cache: True + cache_params: + supported_call_types: ["embedding", "aembedding"] + host: "localhost" \ No newline at end of file diff --git a/litellm/tests/langfuse.log b/litellm/tests/langfuse.log new file mode 100644 index 000000000..58cdb5267 --- /dev/null +++ b/litellm/tests/langfuse.log @@ -0,0 +1,4 @@ +uploading batch of 2 items +successfully uploaded batch of 2 items +uploading batch of 2 items +successfully uploaded batch of 2 items diff --git a/litellm/tests/test_acooldowns_router.py b/litellm/tests/test_acooldowns_router.py index afab5944c..d1a33d10b 100644 --- a/litellm/tests/test_acooldowns_router.py +++ b/litellm/tests/test_acooldowns_router.py @@ -118,6 +118,7 @@ def test_cooldown_same_model_name(): "api_key": os.getenv("AZURE_API_KEY"), "api_version": os.getenv("AZURE_API_VERSION"), "api_base": "BAD_API_BASE", + "tpm": 90 }, }, { @@ -126,7 +127,8 @@ def test_cooldown_same_model_name(): "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") + "api_base": os.getenv("AZURE_API_BASE"), + "tpm": 0.000001 }, }, ] @@ -151,13 +153,14 @@ def test_cooldown_same_model_name(): ] ) print(router.model_list) - litellm_model_names = [] + model_ids = [] for model in router.model_list: - litellm_model_names.append(model["litellm_params"]["model"]) - print("\n litellm model names ", litellm_model_names) + model_ids.append(model["model_info"]["id"]) + print("\n litellm model ids ", model_ids) # example litellm_model_names ['azure/chatgpt-v-2-ModelID-64321', 'azure/chatgpt-v-2-ModelID-63960'] - assert litellm_model_names[0] != litellm_model_names[1] # ensure both models have a uuid added, and they have different names + assert model_ids[0] != model_ids[1] # ensure both models have a uuid added, and they have different names + print("\ngot response\n", response) except Exception as e: pytest.fail(f"Got unexpected exception on router! - {e}") diff --git a/litellm/tests/test_amazing_vertex_completion.py b/litellm/tests/test_amazing_vertex_completion.py index 8b6821caa..6506f0a41 100644 --- a/litellm/tests/test_amazing_vertex_completion.py +++ b/litellm/tests/test_amazing_vertex_completion.py @@ -9,9 +9,9 @@ import os, io sys.path.insert( 0, os.path.abspath("../..") ) # Adds the parent directory to the system path -import pytest +import pytest, asyncio import litellm -from litellm import embedding, completion, completion_cost, Timeout +from litellm import embedding, completion, completion_cost, Timeout, acompletion from litellm import RateLimitError import json import os @@ -63,6 +63,27 @@ def load_vertex_ai_credentials(): # Export the temporary file as GOOGLE_APPLICATION_CREDENTIALS os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = os.path.abspath(temp_file.name) +@pytest.mark.asyncio +async def get_response(): + load_vertex_ai_credentials() + prompt = '\ndef count_nums(arr):\n """\n Write a function count_nums which takes an array of integers and returns\n the number of elements which has a sum of digits > 0.\n If a number is negative, then its first signed digit will be negative:\n e.g. -123 has signed digits -1, 2, and 3.\n >>> count_nums([]) == 0\n >>> count_nums([-1, 11, -11]) == 1\n >>> count_nums([1, 1, 2]) == 3\n """\n' + try: + response = await acompletion( + model="gemini-pro", + messages=[ + { + "role": "system", + "content": "Complete the given code with no more explanation. Remember that there is a 4-space indent before the first line of your generated code.", + }, + {"role": "user", "content": prompt}, + ], + ) + return response + except litellm.UnprocessableEntityError as e: + pass + except Exception as e: + pytest.fail(f"An error occurred - {str(e)}") + def test_vertex_ai(): import random @@ -72,14 +93,15 @@ def test_vertex_ai(): litellm.set_verbose=False litellm.vertex_project = "hardy-device-386718" - test_models = random.sample(test_models, 4) + test_models = random.sample(test_models, 1) + test_models += litellm.vertex_language_models # always test gemini-pro for model in test_models: try: - if model in ["code-gecko@001", "code-gecko@latest", "code-bison@001", "text-bison@001"]: + if model in ["code-gecko", "code-gecko@001", "code-gecko@002", "code-gecko@latest", "code-bison@001", "text-bison@001"]: # our account does not have access to this model continue print("making request", model) - response = completion(model=model, messages=[{'role': 'user', 'content': 'hi'}]) + response = completion(model=model, messages=[{'role': 'user', 'content': 'hi'}], temperature=0.7) print("\nModel Response", response) print(response) assert type(response.choices[0].message.content) == str @@ -94,11 +116,12 @@ def test_vertex_ai_stream(): litellm.vertex_project = "hardy-device-386718" import random - test_models = litellm.vertex_chat_models + litellm.vertex_code_chat_models + litellm.vertex_text_models + litellm.vertex_code_text_models - test_models = random.sample(test_models, 4) + test_models = litellm.vertex_chat_models + litellm.vertex_code_chat_models + litellm.vertex_text_models + litellm.vertex_code_text_models + test_models = random.sample(test_models, 1) + test_models += litellm.vertex_language_models # always test gemini-pro for model in test_models: try: - if model in ["code-gecko@001", "code-gecko@latest", "code-bison@001", "text-bison@001"]: + if model in ["code-gecko", "code-gecko@001", "code-gecko@002", "code-gecko@latest", "code-bison@001", "text-bison@001"]: # our account does not have access to this model continue print("making request", model) @@ -115,3 +138,199 @@ def test_vertex_ai_stream(): except Exception as e: pytest.fail(f"Error occurred: {e}") # test_vertex_ai_stream() + +@pytest.mark.asyncio +async def test_async_vertexai_response(): + import random + load_vertex_ai_credentials() + test_models = litellm.vertex_chat_models + litellm.vertex_code_chat_models + litellm.vertex_text_models + litellm.vertex_code_text_models + test_models = random.sample(test_models, 1) + test_models += litellm.vertex_language_models # always test gemini-pro + for model in test_models: + print(f'model being tested in async call: {model}') + if model in ["code-gecko", "code-gecko@001", "code-gecko@002", "code-gecko@latest", "code-bison@001", "text-bison@001"]: + # our account does not have access to this model + continue + try: + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + response = await acompletion(model=model, messages=messages, temperature=0.7, timeout=5) + print(f"response: {response}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + +# asyncio.run(test_async_vertexai_response()) + +@pytest.mark.asyncio +async def test_async_vertexai_streaming_response(): + import random + load_vertex_ai_credentials() + test_models = litellm.vertex_chat_models + litellm.vertex_code_chat_models + litellm.vertex_text_models + litellm.vertex_code_text_models + test_models = random.sample(test_models, 1) + test_models += litellm.vertex_language_models # always test gemini-pro + for model in test_models: + if model in ["code-gecko", "code-gecko@001", "code-gecko@002", "code-gecko@latest", "code-bison@001", "text-bison@001"]: + # our account does not have access to this model + continue + try: + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + response = await acompletion(model="gemini-pro", messages=messages, temperature=0.7, timeout=5, stream=True) + print(f"response: {response}") + complete_response = "" + async for chunk in response: + print(f"chunk: {chunk}") + complete_response += chunk.choices[0].delta.content + print(f"complete_response: {complete_response}") + assert len(complete_response) > 0 + except litellm.Timeout as e: + pass + except Exception as e: + print(e) + pytest.fail(f"An exception occurred: {e}") + +# asyncio.run(test_async_vertexai_streaming_response()) + +def test_gemini_pro_vision(): + try: + load_vertex_ai_credentials() + litellm.set_verbose = True + litellm.num_retries=0 + resp = litellm.completion( + model = "vertex_ai/gemini-pro-vision", + messages=[ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Whats in this image?" + }, + { + "type": "image_url", + "image_url": { + "url": "gs://cloud-samples-data/generative-ai/image/boats.jpeg" + } + } + ] + } + ], + ) + print(resp) + except Exception as e: + import traceback + traceback.print_exc() + raise e +# test_gemini_pro_vision() + + +# Extra gemini Vision tests for completion + stream, async, async + stream +# if we run into issues with gemini, we will also add these to our ci/cd pipeline +# def test_gemini_pro_vision_stream(): +# try: +# litellm.set_verbose = False +# litellm.num_retries=0 +# print("streaming response from gemini-pro-vision") +# resp = litellm.completion( +# model = "vertex_ai/gemini-pro-vision", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "Whats in this image?" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" +# } +# } +# ] +# } +# ], +# stream=True +# ) +# print(resp) +# for chunk in resp: +# print(chunk) +# except Exception as e: +# import traceback +# traceback.print_exc() +# raise e +# test_gemini_pro_vision_stream() + +# def test_gemini_pro_vision_async(): +# try: +# litellm.set_verbose = True +# litellm.num_retries=0 +# async def test(): +# resp = await litellm.acompletion( +# model = "vertex_ai/gemini-pro-vision", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "Whats in this image?" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" +# } +# } +# ] +# } +# ], +# ) +# print("async response gemini pro vision") +# print(resp) +# asyncio.run(test()) +# except Exception as e: +# import traceback +# traceback.print_exc() +# raise e +# test_gemini_pro_vision_async() + + +# def test_gemini_pro_vision_async_stream(): +# try: +# litellm.set_verbose = True +# litellm.num_retries=0 +# async def test(): +# resp = await litellm.acompletion( +# model = "vertex_ai/gemini-pro-vision", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "Whats in this image?" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" +# } +# } +# ] +# } +# ], +# stream=True +# ) +# print("async response gemini pro vision") +# print(resp) +# for chunk in resp: +# print(chunk) +# asyncio.run(test()) +# except Exception as e: +# import traceback +# traceback.print_exc() +# raise e +# test_gemini_pro_vision_async() \ No newline at end of file diff --git a/litellm/tests/test_caching.py b/litellm/tests/test_caching.py index cce923b6c..24b7f37a8 100644 --- a/litellm/tests/test_caching.py +++ b/litellm/tests/test_caching.py @@ -29,16 +29,19 @@ def generate_random_word(length=4): messages = [{"role": "user", "content": "who is ishaan 5222"}] def test_caching_v2(): # test in memory cache try: + litellm.set_verbose=True litellm.cache = Cache() response1 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) response2 = completion(model="gpt-3.5-turbo", messages=messages, caching=True) print(f"response1: {response1}") print(f"response2: {response2}") litellm.cache = None # disable cache + litellm.success_callback = [] + litellm._async_success_callback = [] if response2['choices'][0]['message']['content'] != response1['choices'][0]['message']['content']: print(f"response1: {response1}") print(f"response2: {response2}") - pytest.fail(f"Error occurred: {e}") + pytest.fail(f"Error occurred:") except Exception as e: print(f"error occurred: {traceback.format_exc()}") pytest.fail(f"Error occurred: {e}") @@ -58,6 +61,8 @@ def test_caching_with_models_v2(): print(f"response2: {response2}") print(f"response3: {response3}") litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] if response3['choices'][0]['message']['content'] == response2['choices'][0]['message']['content']: # if models are different, it should not return cached response print(f"response2: {response2}") @@ -91,6 +96,8 @@ def test_embedding_caching(): print(f"Embedding 2 response time: {end_time - start_time} seconds") litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] assert end_time - start_time <= 0.1 # ensure 2nd response comes in in under 0.1 s if embedding2['data'][0]['embedding'] != embedding1['data'][0]['embedding']: print(f"embedding1: {embedding1}") @@ -145,6 +152,8 @@ def test_embedding_caching_azure(): print(f"Embedding 2 response time: {end_time - start_time} seconds") litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] assert end_time - start_time <= 0.1 # ensure 2nd response comes in in under 0.1 s if embedding2['data'][0]['embedding'] != embedding1['data'][0]['embedding']: print(f"embedding1: {embedding1}") @@ -175,6 +184,8 @@ def test_redis_cache_completion(): print("\nresponse 3", response3) print("\nresponse 4", response4) litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] """ 1 & 2 should be exactly the same @@ -226,6 +237,8 @@ def test_redis_cache_completion_stream(): assert response_1_content == response_2_content, f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" litellm.success_callback = [] litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] except Exception as e: print(e) litellm.success_callback = [] @@ -271,11 +284,53 @@ def test_redis_cache_acompletion_stream(): print("\nresponse 2", response_2_content) assert response_1_content == response_2_content, f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] except Exception as e: print(e) raise e # test_redis_cache_acompletion_stream() +def test_redis_cache_acompletion_stream_bedrock(): + import asyncio + try: + litellm.set_verbose = True + random_word = generate_random_word() + messages = [{"role": "user", "content": f"write a one sentence poem about: {random_word}"}] + litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) + print("test for caching, streaming + completion") + response_1_content = "" + response_2_content = "" + + async def call1(): + nonlocal response_1_content + response1 = await litellm.acompletion(model="bedrock/anthropic.claude-v1", messages=messages, max_tokens=40, temperature=1, stream=True) + async for chunk in response1: + print(chunk) + response_1_content += chunk.choices[0].delta.content or "" + print(response_1_content) + asyncio.run(call1()) + time.sleep(0.5) + print("\n\n Response 1 content: ", response_1_content, "\n\n") + + async def call2(): + nonlocal response_2_content + response2 = await litellm.acompletion(model="bedrock/anthropic.claude-v1", messages=messages, max_tokens=40, temperature=1, stream=True) + async for chunk in response2: + print(chunk) + response_2_content += chunk.choices[0].delta.content or "" + print(response_2_content) + asyncio.run(call2()) + print("\nresponse 1", response_1_content) + print("\nresponse 2", response_2_content) + assert response_1_content == response_2_content, f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" + litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] + except Exception as e: + print(e) + raise e +# test_redis_cache_acompletion_stream_bedrock() # redis cache with custom keys def custom_get_cache_key(*args, **kwargs): # return key to use for your cache: @@ -312,9 +367,44 @@ def test_custom_redis_cache_with_key(): if response3['choices'][0]['message']['content'] == response2['choices'][0]['message']['content']: pytest.fail(f"Error occurred:") litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] # test_custom_redis_cache_with_key() +def test_cache_override(): + # test if we can override the cache, when `caching=False` but litellm.cache = Cache() is set + # in this case it should not return cached responses + litellm.cache = Cache() + print("Testing cache override") + litellm.set_verbose=True + + # test embedding + response1 = embedding( + model = "text-embedding-ada-002", + input=[ + "hello who are you" + ], + caching = False + ) + + + start_time = time.time() + + response2 = embedding( + model = "text-embedding-ada-002", + input=[ + "hello who are you" + ], + caching = False + ) + + end_time = time.time() + print(f"Embedding 2 response time: {end_time - start_time} seconds") + + assert end_time - start_time > 0.1 # ensure 2nd response comes in over 0.1s. This should not be cached. +# test_cache_override() + def test_custom_redis_cache_params(): # test if we can init redis with **kwargs @@ -333,6 +423,8 @@ def test_custom_redis_cache_params(): print(litellm.cache.cache.redis_client) litellm.cache = None + litellm.success_callback = [] + litellm._async_success_callback = [] except Exception as e: pytest.fail(f"Error occurred:", e) @@ -340,15 +432,58 @@ def test_custom_redis_cache_params(): def test_get_cache_key(): from litellm.caching import Cache try: + print("Testing get_cache_key") cache_instance = Cache() cache_key = cache_instance.get_cache_key(**{'model': 'gpt-3.5-turbo', 'messages': [{'role': 'user', 'content': 'write a one sentence poem about: 7510'}], 'max_tokens': 40, 'temperature': 0.2, 'stream': True, 'litellm_call_id': 'ffe75e7e-8a07-431f-9a74-71a5b9f35f0b', 'litellm_logging_obj': {}} ) + cache_key_2 = cache_instance.get_cache_key(**{'model': 'gpt-3.5-turbo', 'messages': [{'role': 'user', 'content': 'write a one sentence poem about: 7510'}], 'max_tokens': 40, 'temperature': 0.2, 'stream': True, 'litellm_call_id': 'ffe75e7e-8a07-431f-9a74-71a5b9f35f0b', 'litellm_logging_obj': {}} + ) assert cache_key == "model: gpt-3.5-turbomessages: [{'role': 'user', 'content': 'write a one sentence poem about: 7510'}]temperature: 0.2max_tokens: 40" + assert cache_key == cache_key_2, f"{cache_key} != {cache_key_2}. The same kwargs should have the same cache key across runs" + + embedding_cache_key = cache_instance.get_cache_key( + **{'model': 'azure/azure-embedding-model', 'api_base': 'https://openai-gpt-4-test-v-1.openai.azure.com/', + 'api_key': '', 'api_version': '2023-07-01-preview', + 'timeout': None, 'max_retries': 0, 'input': ['hi who is ishaan'], + 'caching': True, + 'client': "" + } + ) + + print(embedding_cache_key) + + assert embedding_cache_key == "model: azure/azure-embedding-modelinput: ['hi who is ishaan']", f"{embedding_cache_key} != 'model: azure/azure-embedding-modelinput: ['hi who is ishaan']'. The same kwargs should have the same cache key across runs" + + # Proxy - embedding cache, test if embedding key, gets model_group and not model + embedding_cache_key_2 = cache_instance.get_cache_key( + **{'model': 'azure/azure-embedding-model', 'api_base': 'https://openai-gpt-4-test-v-1.openai.azure.com/', + 'api_key': '', 'api_version': '2023-07-01-preview', + 'timeout': None, 'max_retries': 0, 'input': ['hi who is ishaan'], + 'caching': True, + 'client': "", + 'proxy_server_request': {'url': 'http://0.0.0.0:8000/embeddings', + 'method': 'POST', + 'headers': + {'host': '0.0.0.0:8000', 'user-agent': 'curl/7.88.1', 'accept': '*/*', 'content-type': 'application/json', + 'content-length': '80'}, + 'body': {'model': 'azure-embedding-model', 'input': ['hi who is ishaan']}}, + 'user': None, + 'metadata': {'user_api_key': None, + 'headers': {'host': '0.0.0.0:8000', 'user-agent': 'curl/7.88.1', 'accept': '*/*', 'content-type': 'application/json', 'content-length': '80'}, + 'model_group': 'EMBEDDING_MODEL_GROUP', + 'deployment': 'azure/azure-embedding-model-ModelID-azure/azure-embedding-modelhttps://openai-gpt-4-test-v-1.openai.azure.com/2023-07-01-preview'}, + 'model_info': {'mode': 'embedding', 'base_model': 'text-embedding-ada-002', 'id': '20b2b515-f151-4dd5-a74f-2231e2f54e29'}, + 'litellm_call_id': '2642e009-b3cd-443d-b5dd-bb7d56123b0e', 'litellm_logging_obj': ''} + ) + + print(embedding_cache_key_2) + assert embedding_cache_key_2 == "model: EMBEDDING_MODEL_GROUPinput: ['hi who is ishaan']" + print("passed!") except Exception as e: traceback.print_exc() pytest.fail(f"Error occurred:", e) -# test_get_cache_key() +test_get_cache_key() # test_custom_redis_cache_params() diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py index 3d8b9a1b2..d8babc0ca 100644 --- a/litellm/tests/test_completion.py +++ b/litellm/tests/test_completion.py @@ -21,6 +21,13 @@ messages = [{"content": user_message, "role": "user"}] def logger_fn(user_model_dict): print(f"user_model_dict: {user_model_dict}") +@pytest.fixture(autouse=True) +def reset_callbacks(): + print("\npytest fixture - resetting callbacks") + litellm.success_callback = [] + litellm._async_success_callback = [] + litellm.failure_callback = [] + litellm.callbacks = [] def test_completion_custom_provider_model_name(): try: @@ -54,13 +61,32 @@ def test_completion_claude(): print(response) print(response.usage) print(response.usage.completion_tokens) - print(response["usage"]["completion_tokens"]) + print(response["usage"]["completion_tokens"]) # print("new cost tracking") except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_claude() +def test_completion_mistral_api(): + try: + litellm.set_verbose=True + response = completion( + model="mistral/mistral-tiny", + messages=[ + { + "role": "user", + "content": "Hey, how's it going?", + } + ], + safe_mode = True + ) + # Add any assertions here to check the response + print(response) + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_mistral_api() + def test_completion_claude2_1(): try: print("claude2.1 test request") @@ -287,7 +313,7 @@ def hf_test_completion_tgi(): print(response) except Exception as e: pytest.fail(f"Error occurred: {e}") -hf_test_completion_tgi() +# hf_test_completion_tgi() # ################### Hugging Face Conversational models ######################## # def hf_test_completion_conv(): @@ -611,7 +637,7 @@ def test_completion_azure_key_completion_arg(): os.environ.pop("AZURE_API_KEY", None) try: print("azure gpt-3.5 test\n\n") - litellm.set_verbose=False + litellm.set_verbose=True ## Test azure call response = completion( model="azure/chatgpt-v-2", @@ -696,6 +722,7 @@ def test_completion_azure(): print(response) cost = completion_cost(completion_response=response) + assert cost > 0.0 print("Cost for azure completion request", cost) except Exception as e: pytest.fail(f"Error occurred: {e}") @@ -1013,15 +1040,56 @@ def test_completion_together_ai(): # Add any assertions here to check the response print(response) cost = completion_cost(completion_response=response) + assert cost > 0.0 print("Cost for completion call together-computer/llama-2-70b: ", f"${float(cost):.10f}") except Exception as e: pytest.fail(f"Error occurred: {e}") +def test_completion_together_ai_mixtral(): + model_name = "together_ai/DiscoResearch/DiscoLM-mixtral-8x7b-v2" + try: + messages =[ + {"role": "user", "content": "Who are you"}, + {"role": "assistant", "content": "I am your helpful assistant."}, + {"role": "user", "content": "Tell me a joke"}, + ] + response = completion(model=model_name, messages=messages, max_tokens=256, n=1, logger_fn=logger_fn) + # Add any assertions here to check the response + print(response) + cost = completion_cost(completion_response=response) + assert cost > 0.0 + print("Cost for completion call together-computer/llama-2-70b: ", f"${float(cost):.10f}") + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"Error occurred: {e}") + +test_completion_together_ai_mixtral() + +def test_completion_together_ai_yi_chat(): + model_name = "together_ai/zero-one-ai/Yi-34B-Chat" + try: + messages =[ + {"role": "user", "content": "What llm are you?"}, + ] + response = completion(model=model_name, messages=messages) + # Add any assertions here to check the response + print(response) + cost = completion_cost(completion_response=response) + assert cost > 0.0 + print("Cost for completion call together-computer/llama-2-70b: ", f"${float(cost):.10f}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_together_ai_yi_chat() + # test_completion_together_ai() def test_customprompt_together_ai(): try: litellm.set_verbose = False litellm.num_retries = 0 + print("in test_customprompt_together_ai") + print(litellm.success_callback) + print(litellm._async_success_callback) response = completion( model="together_ai/mistralai/Mistral-7B-Instruct-v0.1", messages=messages, @@ -1030,7 +1098,6 @@ def test_customprompt_together_ai(): print(response) except litellm.exceptions.Timeout as e: print(f"Timeout Error") - litellm.num_retries = 3 # reset retries pass except Exception as e: print(f"ERROR TYPE {type(e)}") @@ -1065,7 +1132,7 @@ def test_completion_chat_sagemaker(): temperature=0.7, stream=True, ) - # Add any assertions here to check the response + # Add any assertions here to check the response complete_response = "" for chunk in response: complete_response += chunk.choices[0].delta.content or "" diff --git a/litellm/tests/test_config.py b/litellm/tests/test_config.py index 73e719cad..ceecaf181 100644 --- a/litellm/tests/test_config.py +++ b/litellm/tests/test_config.py @@ -47,7 +47,7 @@ def test_config_context_moderation(): print(f"Exception: {e}") pytest.fail(f"An exception occurred: {e}") -# test_config_context_moderation() +# test_config_context_moderation() def test_config_context_default_fallback(): try: diff --git a/litellm/tests/test_configs/custom_callbacks.py b/litellm/tests/test_configs/custom_callbacks.py index 94b89e589..7aa1577f6 100644 --- a/litellm/tests/test_configs/custom_callbacks.py +++ b/litellm/tests/test_configs/custom_callbacks.py @@ -2,7 +2,7 @@ from litellm.integrations.custom_logger import CustomLogger import inspect import litellm -class MyCustomHandler(CustomLogger): +class testCustomCallbackProxy(CustomLogger): def __init__(self): self.success: bool = False # type: ignore self.failure: bool = False # type: ignore @@ -55,8 +55,11 @@ class MyCustomHandler(CustomLogger): self.async_success = True print("Value of async success: ", self.async_success) print("\n kwargs: ", kwargs) - if kwargs.get("model") == "azure-embedding-model": + if kwargs.get("model") == "azure-embedding-model" or kwargs.get("model") == "ada": + print("Got an embedding model", kwargs.get("model")) + print("Setting embedding success to True") self.async_success_embedding = True + print("Value of async success embedding: ", self.async_success_embedding) self.async_embedding_kwargs = kwargs self.async_embedding_response = response_obj if kwargs.get("stream") == True: @@ -79,6 +82,9 @@ class MyCustomHandler(CustomLogger): # tokens used in response usage = response_obj["usage"] + print("\n\n in custom callback vars my custom logger, ", vars(my_custom_logger)) + + print( f""" Model: {model}, @@ -104,4 +110,4 @@ class MyCustomHandler(CustomLogger): self.async_completion_kwargs_fail = kwargs -my_custom_logger = MyCustomHandler() \ No newline at end of file +my_custom_logger = testCustomCallbackProxy() \ No newline at end of file diff --git a/litellm/tests/test_configs/test_bad_config.yaml b/litellm/tests/test_configs/test_bad_config.yaml new file mode 100644 index 000000000..9899af2b7 --- /dev/null +++ b/litellm/tests/test_configs/test_bad_config.yaml @@ -0,0 +1,16 @@ +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + api_key: bad-key + model: gpt-3.5-turbo + - model_name: azure-gpt-3.5-turbo + litellm_params: + model: azure/chatgpt-v-2 + api_base: os.environ/AZURE_API_BASE + api_key: bad-key + - model_name: azure-embedding + litellm_params: + model: azure/azure-embedding-model + api_base: os.environ/AZURE_API_BASE + api_key: bad-key + \ No newline at end of file diff --git a/litellm/tests/test_configs/test_config_no_auth.yaml b/litellm/tests/test_configs/test_config_no_auth.yaml index 1af6e6e6d..2fd9ef203 100644 --- a/litellm/tests/test_configs/test_config_no_auth.yaml +++ b/litellm/tests/test_configs/test_config_no_auth.yaml @@ -19,3 +19,63 @@ model_list: model_info: description: this is a test openai model model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 56f1bd94-3b54-4b67-9ea2-7c70e9a3a709 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 4d1ee26c-abca-450c-8744-8e87fd6755e9 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 00e19c0f-b63d-42bb-88e9-016fb0c60764 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 79fc75bf-8e1b-47d5-8d24-9365a854af03 + model_name: test_openai_models +- litellm_params: + api_base: os.environ/AZURE_API_BASE + api_key: os.environ/AZURE_API_KEY + api_version: 2023-07-01-preview + model: azure/azure-embedding-model + model_name: azure-embedding-model +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 55848c55-4162-40f9-a6e2-9a722b9ef404 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 34339b1e-e030-4bcc-a531-c48559f10ce4 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: f6f74e14-ac64-4403-9365-319e584dcdc5 + model_name: test_openai_models +- litellm_params: + model: gpt-3.5-turbo + model_info: + description: this is a test openai model + id: 9b1ef341-322c-410a-8992-903987fef439 + model_name: test_openai_models +- model_name: amazon-embeddings + litellm_params: + model: "bedrock/amazon.titan-embed-text-v1" +- model_name: "GPT-J 6B - Sagemaker Text Embedding (Internal)" + litellm_params: + model: "sagemaker/berri-benchmarking-gpt-j-6b-fp16" \ No newline at end of file diff --git a/litellm/tests/test_custom_callback_input.py b/litellm/tests/test_custom_callback_input.py new file mode 100644 index 000000000..d193c53b6 --- /dev/null +++ b/litellm/tests/test_custom_callback_input.py @@ -0,0 +1,631 @@ +### What this tests #### +## This test asserts the type of data passed into each method of the custom callback handler +import sys, os, time, inspect, asyncio, traceback +from datetime import datetime +import pytest +sys.path.insert(0, os.path.abspath('../..')) +from typing import Optional, Literal, List, Union +from litellm import completion, embedding, Cache +import litellm +from litellm.integrations.custom_logger import CustomLogger + +# Test Scenarios (test across completion, streaming, embedding) +## 1: Pre-API-Call +## 2: Post-API-Call +## 3: On LiteLLM Call success +## 4: On LiteLLM Call failure +## 5. Caching + +# Test models +## 1. OpenAI +## 2. Azure OpenAI +## 3. Non-OpenAI/Azure - e.g. Bedrock + +# Test interfaces +## 1. litellm.completion() + litellm.embeddings() +## refer to test_custom_callback_input_router.py for the router + proxy tests + +class CompletionCustomHandler(CustomLogger): # https://docs.litellm.ai/docs/observability/custom_callback#callback-class + """ + The set of expected inputs to a custom handler for a + """ + # Class variables or attributes + def __init__(self): + self.errors = [] + self.states: Optional[List[Literal["sync_pre_api_call", "async_pre_api_call", "post_api_call", "sync_stream", "async_stream", "sync_success", "async_success", "sync_failure", "async_failure"]]] = [] + + def log_pre_api_call(self, model, messages, kwargs): + try: + self.states.append("sync_pre_api_call") + ## MODEL + assert isinstance(model, str) + ## MESSAGES + assert isinstance(messages, list) + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("post_api_call") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert end_time == None + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert isinstance(kwargs['input'], (list, dict, str)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) or inspect.iscoroutine(kwargs['original_response']) or inspect.isasyncgen(kwargs['original_response']) + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("async_stream") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance(response_obj, litellm.ModelResponse) + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) and isinstance(kwargs['messages'][0], dict) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert (isinstance(kwargs['input'], list) and isinstance(kwargs['input'][0], dict)) or isinstance(kwargs['input'], (dict, str)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) or inspect.isasyncgen(kwargs['original_response']) or inspect.iscoroutine(kwargs['original_response']) + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("sync_success") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance(response_obj, litellm.ModelResponse) + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) and isinstance(kwargs['messages'][0], dict) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert (isinstance(kwargs['input'], list) and isinstance(kwargs['input'][0], dict)) or isinstance(kwargs['input'], (dict, str)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("sync_failure") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) and isinstance(kwargs['messages'][0], dict) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert (isinstance(kwargs['input'], list) and isinstance(kwargs['input'][0], dict)) or isinstance(kwargs['input'], (dict, str)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) or kwargs["original_response"] == None + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_pre_api_call(self, model, messages, kwargs): + try: + self.states.append("async_pre_api_call") + ## MODEL + assert isinstance(model, str) + ## MESSAGES + assert isinstance(messages, list) and isinstance(messages[0], dict) + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) and isinstance(kwargs['messages'][0], dict) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("async_success") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance(response_obj, (litellm.ModelResponse, litellm.EmbeddingResponse)) + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert isinstance(kwargs['input'], (list, dict, str)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) or inspect.isasyncgen(kwargs['original_response']) or inspect.iscoroutine(kwargs['original_response']) + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + assert kwargs["cache_hit"] is None or isinstance(kwargs["cache_hit"], bool) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("async_failure") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert isinstance(kwargs['input'], (list, str, dict)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) or inspect.isasyncgen(kwargs['original_response']) or kwargs['original_response'] == None + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + +# COMPLETION +## Test OpenAI + sync +def test_chat_openai_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = litellm.completion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm sync openai" + }]) + ## test streaming + response = litellm.completion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }], + stream=True) + for chunk in response: + continue + ## test failure callback + try: + response = litellm.completion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }], + api_key="my-bad-key", + stream=True) + for chunk in response: + continue + except: + pass + time.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + +# test_chat_openai_stream() + +## Test OpenAI + Async +@pytest.mark.asyncio +async def test_async_chat_openai_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = await litellm.acompletion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }]) + ## test streaming + response = await litellm.acompletion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }], + stream=True) + async for chunk in response: + continue + ## test failure callback + try: + response = await litellm.acompletion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }], + api_key="my-bad-key", + stream=True) + async for chunk in response: + continue + except: + pass + time.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + +# asyncio.run(test_async_chat_openai_stream()) + +## Test Azure + sync +def test_chat_azure_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = litellm.completion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm sync azure" + }]) + # test streaming + response = litellm.completion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm sync azure" + }], + stream=True) + for chunk in response: + continue + # test failure callback + try: + response = litellm.completion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm sync azure" + }], + api_key="my-bad-key", + stream=True) + for chunk in response: + continue + except: + pass + time.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + +# test_chat_azure_stream() + +## Test Azure + Async +@pytest.mark.asyncio +async def test_async_chat_azure_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = await litellm.acompletion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm async azure" + }]) + ## test streaming + response = await litellm.acompletion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm async azure" + }], + stream=True) + async for chunk in response: + continue + ## test failure callback + try: + response = await litellm.acompletion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm async azure" + }], + api_key="my-bad-key", + stream=True) + async for chunk in response: + continue + except: + pass + await asyncio.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + +# asyncio.run(test_async_chat_azure_stream()) + +## Test Bedrock + sync +def test_chat_bedrock_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = litellm.completion(model="bedrock/anthropic.claude-v1", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm sync bedrock" + }]) + # test streaming + response = litellm.completion(model="bedrock/anthropic.claude-v1", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm sync bedrock" + }], + stream=True) + for chunk in response: + continue + # test failure callback + try: + response = litellm.completion(model="bedrock/anthropic.claude-v1", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm sync bedrock" + }], + aws_region_name="my-bad-region", + stream=True) + for chunk in response: + continue + except: + pass + time.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + +# test_chat_bedrock_stream() + +## Test Bedrock + Async +@pytest.mark.asyncio +async def test_async_chat_bedrock_stream(): + try: + customHandler = CompletionCustomHandler() + litellm.callbacks = [customHandler] + response = await litellm.acompletion(model="bedrock/anthropic.claude-v1", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm async bedrock" + }]) + # test streaming + response = await litellm.acompletion(model="bedrock/anthropic.claude-v1", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm async bedrock" + }], + stream=True) + print(f"response: {response}") + async for chunk in response: + print(f"chunk: {chunk}") + continue + ## test failure callback + try: + response = await litellm.acompletion(model="bedrock/anthropic.claude-v1", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm async bedrock" + }], + aws_region_name="my-bad-key", + stream=True) + async for chunk in response: + continue + except: + pass + time.sleep(1) + print(f"customHandler.errors: {customHandler.errors}") + assert len(customHandler.errors) == 0 + litellm.callbacks = [] + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + +# asyncio.run(test_async_chat_bedrock_stream()) + +# EMBEDDING +## Test OpenAI + Async +@pytest.mark.asyncio +async def test_async_embedding_openai(): + try: + customHandler_success = CompletionCustomHandler() + customHandler_failure = CompletionCustomHandler() + litellm.callbacks = [customHandler_success] + response = await litellm.aembedding(model="azure/azure-embedding-model", + input=["good morning from litellm"]) + await asyncio.sleep(1) + print(f"customHandler_success.errors: {customHandler_success.errors}") + print(f"customHandler_success.states: {customHandler_success.states}") + assert len(customHandler_success.errors) == 0 + assert len(customHandler_success.states) == 3 # pre, post, success + # test failure callback + litellm.callbacks = [customHandler_failure] + try: + response = await litellm.aembedding(model="text-embedding-ada-002", + input=["good morning from litellm"], + api_key="my-bad-key") + except: + pass + await asyncio.sleep(1) + print(f"customHandler_failure.errors: {customHandler_failure.errors}") + print(f"customHandler_failure.states: {customHandler_failure.states}") + assert len(customHandler_failure.errors) == 0 + assert len(customHandler_failure.states) == 3 # pre, post, failure + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + +# asyncio.run(test_async_embedding_openai()) + +## Test Azure + Async +@pytest.mark.asyncio +async def test_async_embedding_azure(): + try: + customHandler_success = CompletionCustomHandler() + customHandler_failure = CompletionCustomHandler() + litellm.callbacks = [customHandler_success] + response = await litellm.aembedding(model="azure/azure-embedding-model", + input=["good morning from litellm"]) + await asyncio.sleep(1) + print(f"customHandler_success.errors: {customHandler_success.errors}") + print(f"customHandler_success.states: {customHandler_success.states}") + assert len(customHandler_success.errors) == 0 + assert len(customHandler_success.states) == 3 # pre, post, success + # test failure callback + litellm.callbacks = [customHandler_failure] + try: + response = await litellm.aembedding(model="azure/azure-embedding-model", + input=["good morning from litellm"], + api_key="my-bad-key") + except: + pass + await asyncio.sleep(1) + print(f"customHandler_failure.errors: {customHandler_failure.errors}") + print(f"customHandler_failure.states: {customHandler_failure.states}") + assert len(customHandler_failure.errors) == 0 + assert len(customHandler_failure.states) == 3 # pre, post, success + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + +# asyncio.run(test_async_embedding_azure()) + +## Test Bedrock + Async +@pytest.mark.asyncio +async def test_async_embedding_bedrock(): + try: + customHandler_success = CompletionCustomHandler() + customHandler_failure = CompletionCustomHandler() + litellm.callbacks = [customHandler_success] + litellm.set_verbose = True + response = await litellm.aembedding(model="bedrock/cohere.embed-multilingual-v3", + input=["good morning from litellm"], aws_region_name="os.environ/AWS_REGION_NAME_2") + await asyncio.sleep(1) + print(f"customHandler_success.errors: {customHandler_success.errors}") + print(f"customHandler_success.states: {customHandler_success.states}") + assert len(customHandler_success.errors) == 0 + assert len(customHandler_success.states) == 3 # pre, post, success + # test failure callback + litellm.callbacks = [customHandler_failure] + try: + response = await litellm.aembedding(model="bedrock/cohere.embed-multilingual-v3", + input=["good morning from litellm"], + aws_region_name="my-bad-region") + except: + pass + await asyncio.sleep(1) + print(f"customHandler_failure.errors: {customHandler_failure.errors}") + print(f"customHandler_failure.states: {customHandler_failure.states}") + assert len(customHandler_failure.errors) == 0 + assert len(customHandler_failure.states) == 3 # pre, post, success + except Exception as e: + pytest.fail(f"An exception occurred: {str(e)}") + +# asyncio.run(test_async_embedding_bedrock()) + +# CACHING +## Test Azure - completion, embedding +@pytest.mark.asyncio +async def test_async_completion_azure_caching(): + customHandler_caching = CompletionCustomHandler() + litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) + litellm.callbacks = [customHandler_caching] + unique_time = time.time() + response1 = await litellm.acompletion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": f"Hi 👋 - i'm async azure {unique_time}" + }], + caching=True) + await asyncio.sleep(1) + print(f"customHandler_caching.states pre-cache hit: {customHandler_caching.states}") + response2 = await litellm.acompletion(model="azure/chatgpt-v-2", + messages=[{ + "role": "user", + "content": f"Hi 👋 - i'm async azure {unique_time}" + }], + caching=True) + await asyncio.sleep(1) # success callbacks are done in parallel + print(f"customHandler_caching.states post-cache hit: {customHandler_caching.states}") + assert len(customHandler_caching.errors) == 0 + assert len(customHandler_caching.states) == 4 # pre, post, success, success + +@pytest.mark.asyncio +async def test_async_embedding_azure_caching(): + print("Testing custom callback input - Azure Caching") + customHandler_caching = CompletionCustomHandler() + litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) + litellm.callbacks = [customHandler_caching] + unique_time = time.time() + response1 = await litellm.aembedding(model="azure/azure-embedding-model", + input=[f"good morning from litellm1 {unique_time}"], + caching=True) + await asyncio.sleep(1) # set cache is async for aembedding() + response2 = await litellm.aembedding(model="azure/azure-embedding-model", + input=[f"good morning from litellm1 {unique_time}"], + caching=True) + await asyncio.sleep(1) # success callbacks are done in parallel + print(customHandler_caching.states) + assert len(customHandler_caching.errors) == 0 + assert len(customHandler_caching.states) == 4 # pre, post, success, success + +# asyncio.run( +# test_async_embedding_azure_caching() +# ) \ No newline at end of file diff --git a/litellm/tests/test_custom_callback_router.py b/litellm/tests/test_custom_callback_router.py new file mode 100644 index 000000000..43d532521 --- /dev/null +++ b/litellm/tests/test_custom_callback_router.py @@ -0,0 +1,488 @@ +### What this tests #### +## This test asserts the type of data passed into each method of the custom callback handler +import sys, os, time, inspect, asyncio, traceback +from datetime import datetime +import pytest +sys.path.insert(0, os.path.abspath('../..')) +from typing import Optional, Literal, List +from litellm import Router, Cache +import litellm +from litellm.integrations.custom_logger import CustomLogger + +# Test Scenarios (test across completion, streaming, embedding) +## 1: Pre-API-Call +## 2: Post-API-Call +## 3: On LiteLLM Call success +## 4: On LiteLLM Call failure +## fallbacks +## retries + +# Test cases +## 1. Simple Azure OpenAI acompletion + streaming call +## 2. Simple Azure OpenAI aembedding call +## 3. Azure OpenAI acompletion + streaming call with retries +## 4. Azure OpenAI aembedding call with retries +## 5. Azure OpenAI acompletion + streaming call with fallbacks +## 6. Azure OpenAI aembedding call with fallbacks + +# Test interfaces +## 1. router.completion() + router.embeddings() +## 2. proxy.completions + proxy.embeddings + +class CompletionCustomHandler(CustomLogger): # https://docs.litellm.ai/docs/observability/custom_callback#callback-class + """ + The set of expected inputs to a custom handler for a + """ + # Class variables or attributes + def __init__(self): + self.errors = [] + self.states: Optional[List[Literal["sync_pre_api_call", "async_pre_api_call", "post_api_call", "sync_stream", "async_stream", "sync_success", "async_success", "sync_failure", "async_failure"]]] = [] + + def log_pre_api_call(self, model, messages, kwargs): + try: + print(f'received kwargs in pre-input: {kwargs}') + self.states.append("sync_pre_api_call") + ## MODEL + assert isinstance(model, str) + ## MESSAGES + assert isinstance(messages, list) + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + ### ROUTER-SPECIFIC KWARGS + assert isinstance(kwargs["litellm_params"]["metadata"], dict) + assert isinstance(kwargs["litellm_params"]["metadata"]["model_group"], str) + assert isinstance(kwargs["litellm_params"]["metadata"]["deployment"], str) + assert isinstance(kwargs["litellm_params"]["model_info"], dict) + assert isinstance(kwargs["litellm_params"]["model_info"]["id"], str) + assert isinstance(kwargs["litellm_params"]["proxy_server_request"], (str, type(None))) + assert isinstance(kwargs["litellm_params"]["preset_cache_key"], (str, type(None))) + assert isinstance(kwargs["litellm_params"]["stream_response"], dict) + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("post_api_call") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert end_time == None + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert isinstance(kwargs['input'], (list, dict, str)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) or inspect.iscoroutine(kwargs['original_response']) or inspect.isasyncgen(kwargs['original_response']) + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + ### ROUTER-SPECIFIC KWARGS + assert isinstance(kwargs["litellm_params"]["metadata"], dict) + assert isinstance(kwargs["litellm_params"]["metadata"]["model_group"], str) + assert isinstance(kwargs["litellm_params"]["metadata"]["deployment"], str) + assert isinstance(kwargs["litellm_params"]["model_info"], dict) + assert isinstance(kwargs["litellm_params"]["model_info"]["id"], str) + assert isinstance(kwargs["litellm_params"]["proxy_server_request"], (str, type(None))) + assert isinstance(kwargs["litellm_params"]["preset_cache_key"], (str, type(None))) + assert isinstance(kwargs["litellm_params"]["stream_response"], dict) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("async_stream") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance(response_obj, litellm.ModelResponse) + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) and isinstance(kwargs['messages'][0], dict) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert (isinstance(kwargs['input'], list) and isinstance(kwargs['input'][0], dict)) or isinstance(kwargs['input'], (dict, str)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) or inspect.isasyncgen(kwargs['original_response']) or inspect.iscoroutine(kwargs['original_response']) + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("sync_success") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance(response_obj, litellm.ModelResponse) + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) and isinstance(kwargs['messages'][0], dict) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert (isinstance(kwargs['input'], list) and isinstance(kwargs['input'][0], dict)) or isinstance(kwargs['input'], (dict, str)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + assert kwargs["cache_hit"] is None or isinstance(kwargs["cache_hit"], bool) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("sync_failure") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) and isinstance(kwargs['messages'][0], dict) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert (isinstance(kwargs['input'], list) and isinstance(kwargs['input'][0], dict)) or isinstance(kwargs['input'], (dict, str)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) or kwargs["original_response"] == None + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_pre_api_call(self, model, messages, kwargs): + try: + """ + No-op. + Not implemented yet. + """ + pass + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + try: + self.states.append("async_success") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert isinstance(response_obj, (litellm.ModelResponse, litellm.EmbeddingResponse)) + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert isinstance(kwargs['input'], (list, dict, str)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) or inspect.isasyncgen(kwargs['original_response']) or inspect.iscoroutine(kwargs['original_response']) + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + assert kwargs["cache_hit"] is None or isinstance(kwargs["cache_hit"], bool) + ### ROUTER-SPECIFIC KWARGS + assert isinstance(kwargs["litellm_params"]["metadata"], dict) + assert isinstance(kwargs["litellm_params"]["metadata"]["model_group"], str) + assert isinstance(kwargs["litellm_params"]["metadata"]["deployment"], str) + assert isinstance(kwargs["litellm_params"]["model_info"], dict) + assert isinstance(kwargs["litellm_params"]["model_info"]["id"], str) + assert isinstance(kwargs["litellm_params"]["proxy_server_request"], (str, type(None))) + assert isinstance(kwargs["litellm_params"]["preset_cache_key"], (str, type(None))) + assert isinstance(kwargs["litellm_params"]["stream_response"], dict) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + + async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + try: + print(f"received original response: {kwargs['original_response']}") + self.states.append("async_failure") + ## START TIME + assert isinstance(start_time, datetime) + ## END TIME + assert isinstance(end_time, datetime) + ## RESPONSE OBJECT + assert response_obj == None + ## KWARGS + assert isinstance(kwargs['model'], str) + assert isinstance(kwargs['messages'], list) + assert isinstance(kwargs['optional_params'], dict) + assert isinstance(kwargs['litellm_params'], dict) + assert isinstance(kwargs['start_time'], (datetime, type(None))) + assert isinstance(kwargs['stream'], bool) + assert isinstance(kwargs['user'], (str, type(None))) + assert isinstance(kwargs['input'], (list, str, dict)) + assert isinstance(kwargs['api_key'], (str, type(None))) + assert isinstance(kwargs['original_response'], (str, litellm.CustomStreamWrapper)) or inspect.isasyncgen(kwargs['original_response']) or inspect.iscoroutine(kwargs['original_response']) or kwargs['original_response'] == None + assert isinstance(kwargs['additional_args'], (dict, type(None))) + assert isinstance(kwargs['log_event_type'], str) + except: + print(f"Assertion Error: {traceback.format_exc()}") + self.errors.append(traceback.format_exc()) + +# Simple Azure OpenAI call +## COMPLETION +@pytest.mark.asyncio +async def test_async_chat_azure(): + try: + customHandler_completion_azure_router = CompletionCustomHandler() + customHandler_streaming_azure_router = CompletionCustomHandler() + customHandler_failure = CompletionCustomHandler() + litellm.callbacks = [customHandler_completion_azure_router] + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model 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") + }, + "tpm": 240000, + "rpm": 1800 + }, + ] + router = Router(model_list=model_list) # type: ignore + response = await router.acompletion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }]) + await asyncio.sleep(2) + assert len(customHandler_completion_azure_router.errors) == 0 + assert len(customHandler_completion_azure_router.states) == 3 # pre, post, success + # streaming + litellm.callbacks = [customHandler_streaming_azure_router] + router2 = Router(model_list=model_list) # type: ignore + response = await router2.acompletion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }], + stream=True) + async for chunk in response: + print(f"async azure router chunk: {chunk}") + continue + await asyncio.sleep(1) + print(f"customHandler.states: {customHandler_streaming_azure_router.states}") + assert len(customHandler_streaming_azure_router.errors) == 0 + assert len(customHandler_streaming_azure_router.states) >= 4 # pre, post, stream (multiple times), success + # failure + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "my-bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + ] + litellm.callbacks = [customHandler_failure] + router3 = Router(model_list=model_list) # type: ignore + try: + response = await router3.acompletion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }]) + print(f"response in router3 acompletion: {response}") + except: + pass + await asyncio.sleep(1) + print(f"customHandler.states: {customHandler_failure.states}") + assert len(customHandler_failure.errors) == 0 + assert len(customHandler_failure.states) == 3 # pre, post, failure + assert "async_failure" in customHandler_failure.states + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + pytest.fail(f"An exception occurred - {str(e)}") +# asyncio.run(test_async_chat_azure()) +## EMBEDDING +@pytest.mark.asyncio +async def test_async_embedding_azure(): + try: + customHandler = CompletionCustomHandler() + customHandler_failure = CompletionCustomHandler() + litellm.callbacks = [customHandler] + model_list = [ + { + "model_name": "azure-embedding-model", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/azure-embedding-model", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + ] + router = Router(model_list=model_list) # type: ignore + response = await router.aembedding(model="azure-embedding-model", + input=["hello from litellm!"]) + await asyncio.sleep(2) + assert len(customHandler.errors) == 0 + assert len(customHandler.states) == 3 # pre, post, success + # failure + model_list = [ + { + "model_name": "azure-embedding-model", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/azure-embedding-model", + "api_key": "my-bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + ] + litellm.callbacks = [customHandler_failure] + router3 = Router(model_list=model_list) # type: ignore + try: + response = await router3.aembedding(model="azure-embedding-model", + input=["hello from litellm!"]) + print(f"response in router3 aembedding: {response}") + except: + pass + await asyncio.sleep(1) + print(f"customHandler.states: {customHandler_failure.states}") + assert len(customHandler_failure.errors) == 0 + assert len(customHandler_failure.states) == 3 # pre, post, failure + assert "async_failure" in customHandler_failure.states + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + pytest.fail(f"An exception occurred - {str(e)}") +# asyncio.run(test_async_embedding_azure()) +# Azure OpenAI call w/ Fallbacks +## COMPLETION +@pytest.mark.asyncio +async def test_async_chat_azure_with_fallbacks(): + try: + customHandler_fallbacks = CompletionCustomHandler() + litellm.callbacks = [customHandler_fallbacks] + # with fallbacks + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "my-bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "gpt-3.5-turbo-16k", + "litellm_params": { + "model": "gpt-3.5-turbo-16k", + }, + "tpm": 240000, + "rpm": 1800 + } + ] + router = Router(model_list=model_list, fallbacks=[{"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}]) # type: ignore + response = await router.acompletion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }]) + await asyncio.sleep(2) + print(f"customHandler_fallbacks.states: {customHandler_fallbacks.states}") + assert len(customHandler_fallbacks.errors) == 0 + assert len(customHandler_fallbacks.states) == 6 # pre, post, failure, pre, post, success + litellm.callbacks = [] + except Exception as e: + print(f"Assertion Error: {traceback.format_exc()}") + pytest.fail(f"An exception occurred - {str(e)}") +# asyncio.run(test_async_chat_azure_with_fallbacks()) + +# CACHING +## Test Azure - completion, embedding +@pytest.mark.asyncio +async def test_async_completion_azure_caching(): + customHandler_caching = CompletionCustomHandler() + litellm.cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) + litellm.callbacks = [customHandler_caching] + unique_time = time.time() + model_list = [ + { + "model_name": "gpt-3.5-turbo", # openai model 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") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "gpt-3.5-turbo-16k", + "litellm_params": { + "model": "gpt-3.5-turbo-16k", + }, + "tpm": 240000, + "rpm": 1800 + } + ] + router = Router(model_list=model_list) # type: ignore + response1 = await router.acompletion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": f"Hi 👋 - i'm async azure {unique_time}" + }], + caching=True) + await asyncio.sleep(1) + print(f"customHandler_caching.states pre-cache hit: {customHandler_caching.states}") + response2 = await router.acompletion(model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": f"Hi 👋 - i'm async azure {unique_time}" + }], + caching=True) + await asyncio.sleep(1) # success callbacks are done in parallel + print(f"customHandler_caching.states post-cache hit: {customHandler_caching.states}") + assert len(customHandler_caching.errors) == 0 + assert len(customHandler_caching.states) == 4 # pre, post, success, success diff --git a/litellm/tests/test_custom_logger.py b/litellm/tests/test_custom_logger.py index 2df5e0f76..533168388 100644 --- a/litellm/tests/test_custom_logger.py +++ b/litellm/tests/test_custom_logger.py @@ -1,15 +1,14 @@ ### What this tests #### -import sys, os, time, inspect, asyncio +import sys, os, time, inspect, asyncio, traceback import pytest sys.path.insert(0, os.path.abspath('../..')) from litellm import completion, embedding import litellm from litellm.integrations.custom_logger import CustomLogger - -async_success = False -complete_streaming_response_in_callback = "" + class MyCustomHandler(CustomLogger): + complete_streaming_response_in_callback = "" def __init__(self): self.success: bool = False # type: ignore self.failure: bool = False # type: ignore @@ -27,9 +26,12 @@ class MyCustomHandler(CustomLogger): self.stream_collected_response = None # type: ignore self.sync_stream_collected_response = None # type: ignore + self.user = None # type: ignore + self.data_sent_to_api: dict = {} def log_pre_api_call(self, model, messages, kwargs): print(f"Pre-API Call") + self.data_sent_to_api = kwargs["additional_args"].get("complete_input_dict", {}) def log_post_api_call(self, kwargs, response_obj, start_time, end_time): print(f"Post-API Call") @@ -50,9 +52,8 @@ class MyCustomHandler(CustomLogger): async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): print(f"On Async success") + print(f"received kwargs user: {kwargs['user']}") self.async_success = True - print("Value of async success: ", self.async_success) - print("\n kwargs: ", kwargs) if kwargs.get("model") == "text-embedding-ada-002": self.async_success_embedding = True self.async_embedding_kwargs = kwargs @@ -60,31 +61,32 @@ class MyCustomHandler(CustomLogger): if kwargs.get("stream") == True: self.stream_collected_response = response_obj self.async_completion_kwargs = kwargs + self.user = kwargs.get("user", None) async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): print(f"On Async Failure") self.async_failure = True - print("Value of async failure: ", self.async_failure) - print("\n kwargs: ", kwargs) if kwargs.get("model") == "text-embedding-ada-002": self.async_failure_embedding = True self.async_embedding_kwargs_fail = kwargs self.async_completion_kwargs_fail = kwargs -async def async_test_logging_fn(kwargs, completion_obj, start_time, end_time): - global async_success, complete_streaming_response_in_callback - print(f"ON ASYNC LOGGING") - async_success = True - print("\nKWARGS", kwargs) - complete_streaming_response_in_callback = kwargs.get("complete_streaming_response") +class TmpFunction: + complete_streaming_response_in_callback = "" + async_success: bool = False + async def async_test_logging_fn(self, kwargs, completion_obj, start_time, end_time): + print(f"ON ASYNC LOGGING") + self.async_success = True + print(f'kwargs.get("complete_streaming_response"): {kwargs.get("complete_streaming_response")}') + self.complete_streaming_response_in_callback = kwargs.get("complete_streaming_response") def test_async_chat_openai_stream(): try: - global complete_streaming_response_in_callback + tmp_function = TmpFunction() # litellm.set_verbose = True - litellm.success_callback = [async_test_logging_fn] + litellm.success_callback = [tmp_function.async_test_logging_fn] complete_streaming_response = "" async def call_gpt(): nonlocal complete_streaming_response @@ -98,12 +100,16 @@ def test_async_chat_openai_stream(): complete_streaming_response += chunk["choices"][0]["delta"]["content"] or "" print(complete_streaming_response) asyncio.run(call_gpt()) - assert complete_streaming_response_in_callback["choices"][0]["message"]["content"] == complete_streaming_response - assert async_success == True + complete_streaming_response = complete_streaming_response.strip("'") + response1 = tmp_function.complete_streaming_response_in_callback["choices"][0]["message"]["content"] + response2 = complete_streaming_response + # assert [ord(c) for c in response1] == [ord(c) for c in response2] + assert response1 == response2 + assert tmp_function.async_success == True except Exception as e: print(e) pytest.fail(f"An error occurred - {str(e)}") -test_async_chat_openai_stream() +# test_async_chat_openai_stream() def test_completion_azure_stream_moderation_failure(): try: @@ -205,13 +211,27 @@ def test_azure_completion_stream(): assert response_in_success_handler == complete_streaming_response except Exception as e: pytest.fail(f"Error occurred: {e}") -test_azure_completion_stream() -def test_async_custom_handler(): - try: - customHandler2 = MyCustomHandler() - litellm.callbacks = [customHandler2] - litellm.set_verbose = True +@pytest.mark.asyncio +async def test_async_custom_handler_completion(): + try: + customHandler_success = MyCustomHandler() + customHandler_failure = MyCustomHandler() + # success + assert customHandler_success.async_success == False + litellm.callbacks = [customHandler_success] + response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{ + "role": "user", + "content": "hello from litellm test", + }] + ) + await asyncio.sleep(1) + assert customHandler_success.async_success == True, "async success is not set to True even after success" + assert customHandler_success.async_completion_kwargs.get("model") == "gpt-3.5-turbo" + # failure + litellm.callbacks = [customHandler_failure] messages = [ {"role": "system", "content": "You are a helpful assistant."}, { @@ -219,77 +239,101 @@ def test_async_custom_handler(): "content": "how do i kill someone", }, ] - async def test_1(): - try: - response = await litellm.acompletion( - model="gpt-3.5-turbo", - messages=messages, - api_key="test", - ) - except: - pass - assert customHandler2.async_failure == False - asyncio.run(test_1()) - assert customHandler2.async_failure == True, "async failure is not set to True even after failure" - assert customHandler2.async_completion_kwargs_fail.get("model") == "gpt-3.5-turbo" - assert len(str(customHandler2.async_completion_kwargs_fail.get("exception"))) > 10 # exppect APIError("OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: test. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}"), 'traceback_exception': 'Traceback (most recent call last):\n File "/Users/ishaanjaffer/Github/litellm/litellm/llms/openai.py", line 269, in acompletion\n response = await openai_aclient.chat.completions.create(**data)\n File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/openai/resources/chat/completions.py", line 119 - print("Passed setting async failure") - - async def test_2(): + assert customHandler_failure.async_failure == False + try: response = await litellm.acompletion( - model="gpt-3.5-turbo", - messages=[{ - "role": "user", - "content": "hello from litellm test", - }] - ) - print("\n response", response) - assert customHandler2.async_success == False - asyncio.run(test_2()) - assert customHandler2.async_success == True, "async success is not set to True even after success" - assert customHandler2.async_completion_kwargs.get("model") == "gpt-3.5-turbo" + model="gpt-3.5-turbo", + messages=messages, + api_key="my-bad-key", + ) + except: + pass + assert customHandler_failure.async_failure == True, "async failure is not set to True even after failure" + assert customHandler_failure.async_completion_kwargs_fail.get("model") == "gpt-3.5-turbo" + assert len(str(customHandler_failure.async_completion_kwargs_fail.get("exception"))) > 10 # expect APIError("OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: test. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}"), 'traceback_exception': 'Traceback (most recent call last):\n File "/Users/ishaanjaffer/Github/litellm/litellm/llms/openai.py", line 269, in acompletion\n response = await openai_aclient.chat.completions.create(**data)\n File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/openai/resources/chat/completions.py", line 119 + litellm.callbacks = [] + print("Passed setting async failure") + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") +# asyncio.run(test_async_custom_handler_completion()) - - async def test_3(): - response = await litellm.aembedding( +@pytest.mark.asyncio +async def test_async_custom_handler_embedding(): + try: + customHandler_embedding = MyCustomHandler() + litellm.callbacks = [customHandler_embedding] + # success + assert customHandler_embedding.async_success_embedding == False + response = await litellm.aembedding( model="text-embedding-ada-002", input = ["hello world"], ) - print("\n response", response) - assert customHandler2.async_success_embedding == False - asyncio.run(test_3()) - assert customHandler2.async_success_embedding == True, "async_success_embedding is not set to True even after success" - assert customHandler2.async_embedding_kwargs.get("model") == "text-embedding-ada-002" - assert customHandler2.async_embedding_response["usage"]["prompt_tokens"] ==2 + await asyncio.sleep(1) + assert customHandler_embedding.async_success_embedding == True, "async_success_embedding is not set to True even after success" + assert customHandler_embedding.async_embedding_kwargs.get("model") == "text-embedding-ada-002" + assert customHandler_embedding.async_embedding_response["usage"]["prompt_tokens"] ==2 print("Passed setting async success: Embedding") - - - print("Testing custom failure callback for embedding") - - async def test_4(): - try: - response = await litellm.aembedding( - model="text-embedding-ada-002", - input = ["hello world"], - api_key="test", - ) - except: - pass - - assert customHandler2.async_failure_embedding == False - asyncio.run(test_4()) - assert customHandler2.async_failure_embedding == True, "async failure embedding is not set to True even after failure" - assert customHandler2.async_embedding_kwargs_fail.get("model") == "text-embedding-ada-002" - assert len(str(customHandler2.async_embedding_kwargs_fail.get("exception"))) > 10 # exppect APIError("OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: test. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}"), 'traceback_exception': 'Traceback (most recent call last):\n File "/Users/ishaanjaffer/Github/litellm/litellm/llms/openai.py", line 269, in acompletion\n response = await openai_aclient.chat.completions.create(**data)\n File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/openai/resources/chat/completions.py", line 119 - print("Passed setting async failure") - + # failure + assert customHandler_embedding.async_failure_embedding == False + try: + response = await litellm.aembedding( + model="text-embedding-ada-002", + input = ["hello world"], + api_key="my-bad-key", + ) + except: + pass + assert customHandler_embedding.async_failure_embedding == True, "async failure embedding is not set to True even after failure" + assert customHandler_embedding.async_embedding_kwargs_fail.get("model") == "text-embedding-ada-002" + assert len(str(customHandler_embedding.async_embedding_kwargs_fail.get("exception"))) > 10 # exppect APIError("OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: test. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}"), 'traceback_exception': 'Traceback (most recent call last):\n File "/Users/ishaanjaffer/Github/litellm/litellm/llms/openai.py", line 269, in acompletion\n response = await openai_aclient.chat.completions.create(**data)\n File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/openai/resources/chat/completions.py", line 119 except Exception as e: - pytest.fail(f"Error occurred: {e}") -# test_async_custom_handler() + pytest.fail(f"An exception occurred - {str(e)}") +# asyncio.run(test_async_custom_handler_embedding()) + +@pytest.mark.asyncio +async def test_async_custom_handler_embedding_optional_param(): + """ + Tests if the openai optional params for embedding - user + encoding_format, + are logged + """ + customHandler_optional_params = MyCustomHandler() + litellm.callbacks = [customHandler_optional_params] + response = await litellm.aembedding( + model="azure/azure-embedding-model", + input = ["hello world"], + user = "John" + ) + await asyncio.sleep(1) # success callback is async + assert customHandler_optional_params.user == "John" + assert customHandler_optional_params.user == customHandler_optional_params.data_sent_to_api["user"] + +# asyncio.run(test_async_custom_handler_embedding_optional_param()) + +@pytest.mark.asyncio +async def test_async_custom_handler_embedding_optional_param_bedrock(): + """ + Tests if the openai optional params for embedding - user + encoding_format, + are logged + + but makes sure these are not sent to the non-openai/azure endpoint (raises errors). + """ + litellm.drop_params = True + litellm.set_verbose = True + customHandler_optional_params = MyCustomHandler() + litellm.callbacks = [customHandler_optional_params] + response = await litellm.aembedding( + model="bedrock/amazon.titan-embed-text-v1", + input = ["hello world"], + user = "John" + ) + await asyncio.sleep(1) # success callback is async + assert customHandler_optional_params.user == "John" + assert "user" not in customHandler_optional_params.data_sent_to_api + -from litellm import Cache def test_redis_cache_completion_stream(): + from litellm import Cache # Important Test - This tests if we can add to streaming cache, when custom callbacks are set import random try: @@ -316,13 +360,10 @@ def test_redis_cache_completion_stream(): print("\nresponse 2", response_2_content) assert response_1_content == response_2_content, f"Response 1 != Response 2. Same params, Response 1{response_1_content} != Response 2{response_2_content}" litellm.success_callback = [] + litellm._async_success_callback = [] litellm.cache = None except Exception as e: print(e) litellm.success_callback = [] raise e - """ - - 1 & 2 should be exactly the same - """ # test_redis_cache_completion_stream() \ No newline at end of file diff --git a/litellm/tests/test_dynamodb_logs.py b/litellm/tests/test_dynamodb_logs.py new file mode 100644 index 000000000..6e40c9512 --- /dev/null +++ b/litellm/tests/test_dynamodb_logs.py @@ -0,0 +1,120 @@ +import sys +import os +import io, asyncio +# import logging +# logging.basicConfig(level=logging.DEBUG) +sys.path.insert(0, os.path.abspath('../..')) + +from litellm import completion +import litellm +litellm.num_retries = 3 + +import time, random +import pytest + + +def pre_request(): + file_name = f"dynamo.log" + log_file = open(file_name, "a+") + + # Clear the contents of the file by truncating it + log_file.truncate(0) + + # Save the original stdout so that we can restore it later + original_stdout = sys.stdout + # Redirect stdout to the file + sys.stdout = log_file + + return original_stdout, log_file, file_name + + +import re +def verify_log_file(log_file_path): + + with open(log_file_path, 'r') as log_file: + log_content = log_file.read() + print(f"\nVerifying DynamoDB file = {log_file_path}. File content=", log_content) + + # Define the pattern to search for in the log file + pattern = r"Response from DynamoDB:{.*?}" + + # Find all matches in the log content + matches = re.findall(pattern, log_content) + + # Print the DynamoDB success log matches + print("DynamoDB Success Log Matches:") + for match in matches: + print(match) + + # Print the total count of lines containing the specified response + print(f"Total occurrences of specified response: {len(matches)}") + + # Count the occurrences of successful responses (status code 200 or 201) + success_count = sum(1 for match in matches if "'HTTPStatusCode': 200" in match or "'HTTPStatusCode': 201" in match) + + # Print the count of successful responses + print(f"Count of successful responses from DynamoDB: {success_count}") + assert success_count == 3 # Expect 3 success logs from dynamoDB + + +def test_dynamo_logging(): + # all dynamodb requests need to be in one test function + # since we are modifying stdout, and pytests runs tests in parallel + try: + # pre + # redirect stdout to log_file + + litellm.success_callback = ["dynamodb"] + litellm.dynamodb_table_name = "litellm-logs-1" + litellm.set_verbose = True + original_stdout, log_file, file_name = pre_request() + + + print("Testing async dynamoDB logging") + async def _test(): + return await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content":"This is a test"}], + max_tokens=100, + temperature=0.7, + user = "ishaan-2" + ) + response = asyncio.run(_test()) + print(f"response: {response}") + + + # streaming + async + async def _test2(): + response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content":"This is a test"}], + max_tokens=10, + temperature=0.7, + user = "ishaan-2", + stream=True + ) + async for chunk in response: + pass + asyncio.run(_test2()) + + # aembedding() + async def _test3(): + return await litellm.aembedding( + model="text-embedding-ada-002", + input = ["hi"], + user = "ishaan-2" + ) + response = asyncio.run(_test3()) + time.sleep(1) + except Exception as e: + pytest.fail(f"An exception occurred - {e}") + finally: + # post, close log file and verify + # Reset stdout to the original value + sys.stdout = original_stdout + # Close the file + log_file.close() + verify_log_file(file_name) + print("Passed! Testing async dynamoDB logging") + +# test_dynamo_logging_async() diff --git a/litellm/tests/test_embedding.py b/litellm/tests/test_embedding.py index 71e59819f..9a2a5951a 100644 --- a/litellm/tests/test_embedding.py +++ b/litellm/tests/test_embedding.py @@ -164,7 +164,7 @@ def test_bedrock_embedding_titan(): assert all(isinstance(x, float) for x in response['data'][0]['embedding']), "Expected response to be a list of floats" except Exception as e: pytest.fail(f"Error occurred: {e}") -# test_bedrock_embedding_titan() +test_bedrock_embedding_titan() def test_bedrock_embedding_cohere(): try: diff --git a/litellm/tests/test_exceptions.py b/litellm/tests/test_exceptions.py index 67298f019..f6a0ba25b 100644 --- a/litellm/tests/test_exceptions.py +++ b/litellm/tests/test_exceptions.py @@ -21,6 +21,7 @@ from concurrent.futures import ThreadPoolExecutor import pytest litellm.vertex_project = "pathrise-convert-1606954137718" litellm.vertex_location = "us-central1" +litellm.num_retries=0 # litellm.failure_callback = ["sentry"] #### What this tests #### @@ -38,10 +39,11 @@ models = ["command-nightly"] # Test 1: Context Window Errors @pytest.mark.parametrize("model", models) def test_context_window(model): + print("Testing context window error") sample_text = "Say error 50 times" * 1000000 messages = [{"content": sample_text, "role": "user"}] try: - litellm.set_verbose = False + litellm.set_verbose = True response = completion(model=model, messages=messages) print(f"response: {response}") print("FAILED!") @@ -176,7 +178,7 @@ def test_completion_azure_exception(): try: import openai print("azure gpt-3.5 test\n\n") - litellm.set_verbose=False + litellm.set_verbose=True ## Test azure call old_azure_key = os.environ["AZURE_API_KEY"] os.environ["AZURE_API_KEY"] = "good morning" @@ -189,6 +191,7 @@ def test_completion_azure_exception(): } ], ) + os.environ["AZURE_API_KEY"] = old_azure_key print(f"response: {response}") print(response) except openai.AuthenticationError as e: @@ -196,14 +199,14 @@ def test_completion_azure_exception(): print("good job got the correct error for azure when key not set") except Exception as e: pytest.fail(f"Error occurred: {e}") -test_completion_azure_exception() +# test_completion_azure_exception() async def asynctest_completion_azure_exception(): try: import openai import litellm print("azure gpt-3.5 test\n\n") - litellm.set_verbose=False + litellm.set_verbose=True ## Test azure call old_azure_key = os.environ["AZURE_API_KEY"] os.environ["AZURE_API_KEY"] = "good morning" @@ -226,19 +229,75 @@ async def asynctest_completion_azure_exception(): print("Got wrong exception") print("exception", e) pytest.fail(f"Error occurred: {e}") - # import asyncio # asyncio.run( # asynctest_completion_azure_exception() # ) +def asynctest_completion_openai_exception_bad_model(): + try: + import openai + import litellm, asyncio + print("azure exception bad model\n\n") + litellm.set_verbose=True + ## Test azure call + async def test(): + response = await litellm.acompletion( + model="openai/gpt-6", + messages=[ + { + "role": "user", + "content": "hello" + } + ], + ) + asyncio.run(test()) + except openai.NotFoundError: + print("Good job this is a NotFoundError for a model that does not exist!") + print("Passed") + except Exception as e: + print("Raised wrong type of exception", type(e)) + assert isinstance(e, openai.BadRequestError) + pytest.fail(f"Error occurred: {e}") + +# asynctest_completion_openai_exception_bad_model() + + + +def asynctest_completion_azure_exception_bad_model(): + try: + import openai + import litellm, asyncio + print("azure exception bad model\n\n") + litellm.set_verbose=True + ## Test azure call + async def test(): + response = await litellm.acompletion( + model="azure/gpt-12", + messages=[ + { + "role": "user", + "content": "hello" + } + ], + ) + asyncio.run(test()) + except openai.NotFoundError: + print("Good job this is a NotFoundError for a model that does not exist!") + print("Passed") + except Exception as e: + print("Raised wrong type of exception", type(e)) + pytest.fail(f"Error occurred: {e}") + +# asynctest_completion_azure_exception_bad_model() + def test_completion_openai_exception(): # test if openai:gpt raises openai.AuthenticationError try: import openai print("openai gpt-3.5 test\n\n") - litellm.set_verbose=False + litellm.set_verbose=True ## Test azure call old_azure_key = os.environ["OPENAI_API_KEY"] os.environ["OPENAI_API_KEY"] = "good morning" @@ -255,11 +314,38 @@ def test_completion_openai_exception(): print(response) except openai.AuthenticationError as e: os.environ["OPENAI_API_KEY"] = old_azure_key - print("good job got the correct error for openai when key not set") + print("OpenAI: good job got the correct error for openai when key not set") except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_openai_exception() +def test_completion_mistral_exception(): + # test if mistral/mistral-tiny raises openai.AuthenticationError + try: + import openai + print("Testing mistral ai exception mapping") + litellm.set_verbose=True + ## Test azure call + old_azure_key = os.environ["MISTRAL_API_KEY"] + os.environ["MISTRAL_API_KEY"] = "good morning" + response = completion( + model="mistral/mistral-tiny", + messages=[ + { + "role": "user", + "content": "hello" + } + ], + ) + print(f"response: {response}") + print(response) + except openai.AuthenticationError as e: + os.environ["MISTRAL_API_KEY"] = old_azure_key + print("good job got the correct error for openai when key not set") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_mistral_exception() + @@ -301,4 +387,4 @@ def test_completion_openai_exception(): # counts[result] += 1 # accuracy_score = counts[True]/(counts[True] + counts[False]) -# print(f"accuracy_score: {accuracy_score}") +# print(f"accuracy_score: {accuracy_score}") \ No newline at end of file diff --git a/litellm/tests/test_langfuse.py b/litellm/tests/test_langfuse.py index 017d7e1e2..02abbf656 100644 --- a/litellm/tests/test_langfuse.py +++ b/litellm/tests/test_langfuse.py @@ -9,33 +9,107 @@ from litellm import completion import litellm litellm.num_retries = 3 litellm.success_callback = ["langfuse"] -# litellm.set_verbose = True +os.environ["LANGFUSE_DEBUG"] = "True" import time import pytest +def search_logs(log_file_path): + """ + Searches the given log file for logs containing the "/api/public" string. + + Parameters: + - log_file_path (str): The path to the log file to be searched. + + Returns: + - None + + Raises: + - Exception: If there are any bad logs found in the log file. + """ + import re + print("\n searching logs") + bad_logs = [] + good_logs = [] + all_logs = [] + try: + with open(log_file_path, 'r') as log_file: + lines = log_file.readlines() + print(f"searching logslines: {lines}") + for line in lines: + all_logs.append(line.strip()) + if "/api/public" in line: + print("Found log with /api/public:") + print(line.strip()) + print("\n\n") + match = re.search(r'receive_response_headers.complete return_value=\(b\'HTTP/1.1\', (\d+),', line) + if match: + status_code = int(match.group(1)) + if status_code != 200 and status_code != 201: + print("got a BAD log") + bad_logs.append(line.strip()) + else: + + good_logs.append(line.strip()) + print("\nBad Logs") + print(bad_logs) + if len(bad_logs)>0: + raise Exception(f"bad logs, Bad logs = {bad_logs}") + + print("\nGood Logs") + print(good_logs) + if len(good_logs) <= 0: + raise Exception(f"There were no Good Logs from Langfuse. No logs with /api/public status 200. \nAll logs:{all_logs}") + + except Exception as e: + raise e + +def pre_langfuse_setup(): + """ + Set up the logging for the 'pre_langfuse_setup' function. + """ + # sends logs to langfuse.log + import logging + # Configure the logging to write to a file + logging.basicConfig(filename="langfuse.log", level=logging.DEBUG) + logger = logging.getLogger() + + # Add a FileHandler to the logger + file_handler = logging.FileHandler("langfuse.log", mode='w') + file_handler.setLevel(logging.DEBUG) + logger.addHandler(file_handler) + return + +@pytest.mark.skip(reason="beta test - checking langfuse output") def test_langfuse_logging_async(): try: + pre_langfuse_setup() litellm.set_verbose = True async def _test_langfuse(): return await litellm.acompletion( model="gpt-3.5-turbo", messages=[{"role": "user", "content":"This is a test"}], - max_tokens=1000, + max_tokens=100, temperature=0.7, timeout=5, ) response = asyncio.run(_test_langfuse()) print(f"response: {response}") + + # time.sleep(2) + # # check langfuse.log to see if there was a failed response + # search_logs("langfuse.log") except litellm.Timeout as e: pass except Exception as e: pytest.fail(f"An exception occurred - {e}") -# test_langfuse_logging_async() +test_langfuse_logging_async() +@pytest.mark.skip(reason="beta test - checking langfuse output") def test_langfuse_logging(): try: - # litellm.set_verbose = True + pre_langfuse_setup() + litellm.set_verbose = True response = completion(model="claude-instant-1.2", messages=[{ "role": "user", @@ -43,17 +117,20 @@ def test_langfuse_logging(): }], max_tokens=10, temperature=0.2, - metadata={"langfuse/key": "foo"} ) print(response) + # time.sleep(5) + # # check langfuse.log to see if there was a failed response + # search_logs("langfuse.log") + except litellm.Timeout as e: pass except Exception as e: - print(e) + pytest.fail(f"An exception occurred - {e}") test_langfuse_logging() - +@pytest.mark.skip(reason="beta test - checking langfuse output") def test_langfuse_logging_stream(): try: litellm.set_verbose=True @@ -77,6 +154,7 @@ def test_langfuse_logging_stream(): # test_langfuse_logging_stream() +@pytest.mark.skip(reason="beta test - checking langfuse output") def test_langfuse_logging_custom_generation_name(): try: litellm.set_verbose=True @@ -99,8 +177,8 @@ def test_langfuse_logging_custom_generation_name(): pytest.fail(f"An exception occurred - {e}") print(e) -test_langfuse_logging_custom_generation_name() - +# test_langfuse_logging_custom_generation_name() +@pytest.mark.skip(reason="beta test - checking langfuse output") def test_langfuse_logging_function_calling(): function1 = [ { diff --git a/litellm/tests/test_model_alias_map.py b/litellm/tests/test_model_alias_map.py index f4647fe7c..b99a626e3 100644 --- a/litellm/tests/test_model_alias_map.py +++ b/litellm/tests/test_model_alias_map.py @@ -17,10 +17,10 @@ model_alias_map = { "good-model": "anyscale/meta-llama/Llama-2-7b-chat-hf" } -litellm.model_alias_map = model_alias_map def test_model_alias_map(): try: + litellm.model_alias_map = model_alias_map response = completion( "good-model", messages=[{"role": "user", "content": "Hey, how's it going?"}], diff --git a/litellm/tests/test_ollama.py b/litellm/tests/test_ollama.py index b3635b6a9..4a602fe64 100644 --- a/litellm/tests/test_ollama.py +++ b/litellm/tests/test_ollama.py @@ -1,100 +1,37 @@ -##### THESE TESTS CAN ONLY RUN LOCALLY WITH THE OLLAMA SERVER RUNNING ###### -# import aiohttp -# import json -# import asyncio -# import requests -# -# async def get_ollama_response_stream(api_base="http://localhost:11434", model="llama2", prompt="Why is the sky blue?"): -# session = aiohttp.ClientSession() -# url = f'{api_base}/api/generate' -# data = { -# "model": model, -# "prompt": prompt, -# } +import sys, os +import traceback +from dotenv import load_dotenv -# response = "" +load_dotenv() +import os, io -# try: -# async with session.post(url, json=data) as resp: -# async for line in resp.content.iter_any(): -# if line: -# try: -# json_chunk = line.decode("utf-8") -# chunks = json_chunk.split("\n") -# for chunk in chunks: -# if chunk.strip() != "": -# j = json.loads(chunk) -# if "response" in j: -# print(j["response"]) -# yield { -# "role": "assistant", -# "content": j["response"] -# } -# # self.responses.append(j["response"]) -# # yield "blank" -# except Exception as e: -# print(f"Error decoding JSON: {e}") -# finally: -# await session.close() - -# async def get_ollama_response_no_stream(api_base="http://localhost:11434", model="llama2", prompt="Why is the sky blue?"): -# generator = get_ollama_response_stream(api_base="http://localhost:11434", model="llama2", prompt="Why is the sky blue?") -# response = "" -# async for elem in generator: -# print(elem) -# response += elem["content"] -# return response - -# #generator = get_ollama_response_stream() - -# result = asyncio.run(get_ollama_response_no_stream()) -# print(result) - -# # return this generator to the client for streaming requests +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm -# async def get_response(): -# global generator -# async for elem in generator: -# print(elem) +## for ollama we can't test making the completion call +from litellm.utils import get_optional_params, get_llm_provider -# asyncio.run(get_response()) +def test_get_ollama_params(): + try: + converted_params = get_optional_params(custom_llm_provider="ollama", model="llama2", max_tokens=20, temperature=0.5, stream=True) + print("Converted params", converted_params) + assert converted_params == {'num_predict': 20, 'stream': True, 'temperature': 0.5}, f"{converted_params} != {'num_predict': 20, 'stream': True, 'temperature': 0.5}" + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_get_ollama_params() +def test_get_ollama_model(): + try: + model, custom_llm_provider, _, _ = get_llm_provider("ollama/code-llama-22") + print("Model", "custom_llm_provider", model, custom_llm_provider) + assert custom_llm_provider == "ollama", f"{custom_llm_provider} != ollama" + assert model == "code-llama-22", f"{model} != code-llama-22" + except Exception as e: + pytest.fail(f"Error occurred: {e}") -##### latest implementation of making raw http post requests to local ollama server - -# import requests -# import json -# def get_ollama_response_stream(api_base="http://localhost:11434", model="llama2", prompt="Why is the sky blue?"): -# url = f"{api_base}/api/generate" -# data = { -# "model": model, -# "prompt": prompt, -# } -# session = requests.Session() - -# with session.post(url, json=data, stream=True) as resp: -# for line in resp.iter_lines(): -# if line: -# try: -# json_chunk = line.decode("utf-8") -# chunks = json_chunk.split("\n") -# for chunk in chunks: -# if chunk.strip() != "": -# j = json.loads(chunk) -# if "response" in j: -# completion_obj = { -# "role": "assistant", -# "content": "", -# } -# completion_obj["content"] = j["response"] -# yield {"choices": [{"delta": completion_obj}]} -# except Exception as e: -# print(f"Error decoding JSON: {e}") -# session.close() - -# response = get_ollama_response_stream() - -# for chunk in response: -# print(chunk['choices'][0]['delta']) +# test_get_ollama_model() \ No newline at end of file diff --git a/litellm/tests/test_ollama_local.py b/litellm/tests/test_ollama_local.py index 05dd9c646..b5be561ad 100644 --- a/litellm/tests/test_ollama_local.py +++ b/litellm/tests/test_ollama_local.py @@ -16,6 +16,19 @@ # user_message = "respond in 20 words. who are you?" # messages = [{ "content": user_message,"role": "user"}] +# async def test_async_ollama_streaming(): +# try: +# litellm.set_verbose = True +# response = await litellm.acompletion(model="ollama/mistral-openorca", +# messages=[{"role": "user", "content": "Hey, how's it going?"}], +# stream=True) +# async for chunk in response: +# print(chunk) +# except Exception as e: +# print(e) + +# asyncio.run(test_async_ollama_streaming()) + # def test_completion_ollama(): # try: # response = completion( @@ -29,7 +42,7 @@ # except Exception as e: # pytest.fail(f"Error occurred: {e}") -# test_completion_ollama() +# # test_completion_ollama() # def test_completion_ollama_with_api_base(): # try: @@ -42,7 +55,7 @@ # except Exception as e: # pytest.fail(f"Error occurred: {e}") -# test_completion_ollama_with_api_base() +# # test_completion_ollama_with_api_base() # def test_completion_ollama_custom_prompt_template(): @@ -72,7 +85,7 @@ # traceback.print_exc() # pytest.fail(f"Error occurred: {e}") -# test_completion_ollama_custom_prompt_template() +# # test_completion_ollama_custom_prompt_template() # async def test_completion_ollama_async_stream(): # user_message = "what is the weather" @@ -98,8 +111,8 @@ # except Exception as e: # pytest.fail(f"Error occurred: {e}") -# import asyncio -# asyncio.run(test_completion_ollama_async_stream()) +# # import asyncio +# # asyncio.run(test_completion_ollama_async_stream()) @@ -154,8 +167,35 @@ # pass # pytest.fail(f"Error occurred: {e}") -# test_completion_expect_error() +# # test_completion_expect_error() -# if __name__ == "__main__": -# import asyncio -# asyncio.run(main()) + +# def test_ollama_llava(): +# litellm.set_verbose=True +# # same params as gpt-4 vision +# response = completion( +# model = "ollama/llava", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "What is in this picture" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": "iVBORw0KGgoAAAANSUhEUgAAAG0AAABmCAYAAADBPx+VAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAA3VSURBVHgB7Z27r0zdG8fX743i1bi1ikMoFMQloXRpKFFIqI7LH4BEQ+NWIkjQuSWCRIEoULk0gsK1kCBI0IhrQVT7tz/7zZo888yz1r7MnDl7z5xvsjkzs2fP3uu71nNfa7lkAsm7d++Sffv2JbNmzUqcc8m0adOSzZs3Z+/XES4ZckAWJEGWPiCxjsQNLWmQsWjRIpMseaxcuTKpG/7HP27I8P79e7dq1ars/yL4/v27S0ejqwv+cUOGEGGpKHR37tzJCEpHV9tnT58+dXXCJDdECBE2Ojrqjh071hpNECjx4cMHVycM1Uhbv359B2F79+51586daxN/+pyRkRFXKyRDAqxEp4yMlDDzXG1NPnnyJKkThoK0VFd1ELZu3TrzXKxKfW7dMBQ6bcuWLW2v0VlHjx41z717927ba22U9APcw7Nnz1oGEPeL3m3p2mTAYYnFmMOMXybPPXv2bNIPpFZr1NHn4HMw0KRBjg9NuRw95s8PEcz/6DZELQd/09C9QGq5RsmSRybqkwHGjh07OsJSsYYm3ijPpyHzoiacg35MLdDSIS/O1yM778jOTwYUkKNHWUzUWaOsylE00MyI0fcnOwIdjvtNdW/HZwNLGg+sR1kMepSNJXmIwxBZiG8tDTpEZzKg0GItNsosY8USkxDhD0Rinuiko2gfL/RbiD2LZAjU9zKQJj8RDR0vJBR1/Phx9+PHj9Z7REF4nTZkxzX4LCXHrV271qXkBAPGfP/atWvu/PnzHe4C97F48eIsRLZ9+3a3f/9+87dwP1JxaF7/3r17ba+5l4EcaVo0lj3SBq5kGTJSQmLWMjgYNei2GPT1MuMqGTDEFHzeQSP2wi/jGnkmPJ/nhccs44jvDAxpVcxnq0F6eT8h4ni/iIWpR5lPyA6ETkNXoSukvpJAD3AsXLiwpZs49+fPn5ke4j10TqYvegSfn0OnafC+Tv9ooA/JPkgQysqQNBzagXY55nO/oa1F7qvIPWkRL12WRpMWUvpVDYmxAPehxWSe8ZEXL20sadYIozfmNch4QJPAfeJgW3rNsnzphBKNJM2KKODo1rVOMRYik5ETy3ix4qWNI81qAAirizgMIc+yhTytx0JWZuNI03qsrgWlGtwjoS9XwgUhWGyhUaRZZQNNIEwCiXD16tXcAHUs79co0vSD8rrJCIW98pzvxpAWyyo3HYwqS0+H0BjStClcZJT5coMm6D2LOF8TolGJtK9fvyZpyiC5ePFi9nc/oJU4eiEP0jVoAnHa9wyJycITMP78+eMeP37sXrx44d6+fdt6f82aNdkx1pg9e3Zb5W+RSRE+n+VjksQWifvVaTKFhn5O8my63K8Qabdv33b379/PiAP//vuvW7BggZszZ072/+TJk91YgkafPn166zXB1rQHFvouAWHq9z3SEevSUerqCn2/dDCeta2jxYbr69evk4MHDyY7d+7MjhMnTiTPnz9Pfv/+nfQT2ggpO2dMF8cghuoM7Ygj5iWCqRlGFml0QC/ftGmTmzt3rmsaKDsgBSPh0/8yPeLLBihLkOKJc0jp8H8vUzcxIA1k6QJ/c78tWEyj5P3o4u9+jywNPdJi5rAH9x0KHcl4Hg570eQp3+vHXGyrmEeigzQsQsjavXt38ujRo44LQuDDhw+TW7duRS1HGgMxhNXHgflaNTOsHyKvHK5Ijo2jbFjJBQK9YwFd6RVMzfgRBmEfP37suBBm/p49e1qjEP2mwTViNRo0VJWH1deMXcNK08uUjVUu7s/zRaL+oLNxz1bpANco4npUgX4G2eFbpDFyQoQxojBCpEGSytmOH8qrH5Q9vuzD6ofQylkCUmh8DBAr+q8JCyVNtWQIidKQE9wNtLSQnS4jDSsxNHogzFuQBw4cyM61UKVsjfr3ooBkPSqqQHesUPWVtzi9/vQi1T+rJj7WiTz4Pt/l3LxUkr5P2VYZaZ4URpsE+st/dujQoaBBYokbrz/8TJNQYLSonrPS9kUaSkPeZyj1AWSj+d+VBoy1pIWVNed8P0Ll/ee5HdGRhrHhR5GGN0r4LGZBaj8oFDJitBTJzIZgFcmU0Y8ytWMZMzJOaXUSrUs5RxKnrxmbb5YXO9VGUhtpXldhEUogFr3IzIsvlpmdosVcGVGXFWp2oU9kLFL3dEkSz6NHEY1sjSRdIuDFWEhd8KxFqsRi1uM/nz9/zpxnwlESONdg6dKlbsaMGS4EHFHtjFIDHwKOo46l4TxSuxgDzi+rE2jg+BaFruOX4HXa0Nnf1lwAPufZeF8/r6zD97WK2qFnGjBxTw5qNGPxT+5T/r7/7RawFC3j4vTp09koCxkeHjqbHJqArmH5UrFKKksnxrK7FuRIs8STfBZv+luugXZ2pR/pP9Ois4z+TiMzUUkUjD0iEi1fzX8GmXyuxUBRcaUfykV0YZnlJGKQpOiGB76x5GeWkWWJc3mOrK6S7xdND+W5N6XyaRgtWJFe13GkaZnKOsYqGdOVVVbGupsyA/l7emTLHi7vwTdirNEt0qxnzAvBFcnQF16xh/TMpUuXHDowhlA9vQVraQhkudRdzOnK+04ZSP3DUhVSP61YsaLtd/ks7ZgtPcXqPqEafHkdqa84X6aCeL7YWlv6edGFHb+ZFICPlljHhg0bKuk0CSvVznWsotRu433alNdFrqG45ejoaPCaUkWERpLXjzFL2Rpllp7PJU2a/v7Ab8N05/9t27Z16KUqoFGsxnI9EosS2niSYg9SpU6B4JgTrvVW1flt1sT+0ADIJU2maXzcUTraGCRaL1Wp9rUMk16PMom8QhruxzvZIegJjFU7LLCePfS8uaQdPny4jTTL0dbee5mYokQsXTIWNY46kuMbnt8Kmec+LGWtOVIl9cT1rCB0V8WqkjAsRwta93TbwNYoGKsUSChN44lgBNCoHLHzquYKrU6qZ8lolCIN0Rh6cP0Q3U6I6IXILYOQI513hJaSKAorFpuHXJNfVlpRtmYBk1Su1obZr5dnKAO+L10Hrj3WZW+E3qh6IszE37F6EB+68mGpvKm4eb9bFrlzrok7fvr0Kfv727dvWRmdVTJHw0qiiCUSZ6wCK+7XL/AcsgNyL74DQQ730sv78Su7+t/A36MdY0sW5o40ahslXr58aZ5HtZB8GH64m9EmMZ7FpYw4T6QnrZfgenrhFxaSiSGXtPnz57e9TkNZLvTjeqhr734CNtrK41L40sUQckmj1lGKQ0rC37x544r8eNXRpnVE3ZZY7zXo8NomiO0ZUCj2uHz58rbXoZ6gc0uA+F6ZeKS/jhRDUq8MKrTho9fEkihMmhxtBI1DxKFY9XLpVcSkfoi8JGnToZO5sU5aiDQIW716ddt7ZLYtMQlhECdBGXZZMWldY5BHm5xgAroWj4C0hbYkSc/jBmggIrXJWlZM6pSETsEPGqZOndr2uuuR5rF169a2HoHPdurUKZM4CO1WTPqaDaAd+GFGKdIQkxAn9RuEWcTRyN2KSUgiSgF5aWzPTeA/lN5rZubMmR2bE4SIC4nJoltgAV/dVefZm72AtctUCJU2CMJ327hxY9t7EHbkyJFseq+EJSY16RPo3Dkq1kkr7+q0bNmyDuLQcZBEPYmHVdOBiJyIlrRDq41YPWfXOxUysi5fvtyaj+2BpcnsUV/oSoEMOk2CQGlr4ckhBwaetBhjCwH0ZHtJROPJkyc7UjcYLDjmrH7ADTEBXFfOYmB0k9oYBOjJ8b4aOYSe7QkKcYhFlq3QYLQhSidNmtS2RATwy8YOM3EQJsUjKiaWZ+vZToUQgzhkHXudb/PW5YMHD9yZM2faPsMwoc7RciYJXbGuBqJ1UIGKKLv915jsvgtJxCZDubdXr165mzdvtr1Hz5LONA8jrUwKPqsmVesKa49S3Q4WxmRPUEYdTjgiUcfUwLx589ySJUva3oMkP6IYddq6HMS4o55xBJBUeRjzfa4Zdeg56QZ43LhxoyPo7Lf1kNt7oO8wWAbNwaYjIv5lhyS7kRf96dvm5Jah8vfvX3flyhX35cuX6HfzFHOToS1H4BenCaHvO8pr8iDuwoUL7tevX+b5ZdbBair0xkFIlFDlW4ZknEClsp/TzXyAKVOmmHWFVSbDNw1l1+4f90U6IY/q4V27dpnE9bJ+v87QEydjqx/UamVVPRG+mwkNTYN+9tjkwzEx+atCm/X9WvWtDtAb68Wy9LXa1UmvCDDIpPkyOQ5ZwSzJ4jMrvFcr0rSjOUh+GcT4LSg5ugkW1Io0/SCDQBojh0hPlaJdah+tkVYrnTZowP8iq1F1TgMBBauufyB33x1v+NWFYmT5KmppgHC+NkAgbmRkpD3yn9QIseXymoTQFGQmIOKTxiZIWpvAatenVqRVXf2nTrAWMsPnKrMZHz6bJq5jvce6QK8J1cQNgKxlJapMPdZSR64/UivS9NztpkVEdKcrs5alhhWP9NeqlfWopzhZScI6QxseegZRGeg5a8C3Re1Mfl1ScP36ddcUaMuv24iOJtz7sbUjTS4qBvKmstYJoUauiuD3k5qhyr7QdUHMeCgLa1Ear9NquemdXgmum4fvJ6w1lqsuDhNrg1qSpleJK7K3TF0Q2jSd94uSZ60kK1e3qyVpQK6PVWXp2/FC3mp6jBhKKOiY2h3gtUV64TWM6wDETRPLDfSakXmH3w8g9Jlug8ZtTt4kVF0kLUYYmCCtD/DrQ5YhMGbA9L3ucdjh0y8kOHW5gU/VEEmJTcL4Pz/f7mgoAbYkAAAAAElFTkSuQmCC" +# } +# } +# ] +# } +# ], +# ) +# print("Response from ollama/llava") +# print(response) +# test_ollama_llava() + + +# # PROCESSED CHUNK PRE CHUNK CREATOR diff --git a/litellm/tests/test_optional_params.py b/litellm/tests/test_optional_params.py new file mode 100644 index 000000000..0ce917afe --- /dev/null +++ b/litellm/tests/test_optional_params.py @@ -0,0 +1,27 @@ +#### What this tests #### +# This tests if get_optional_params works as expected +import sys, os, time, inspect, asyncio, traceback +import pytest +sys.path.insert(0, os.path.abspath('../..')) +import litellm +from litellm.utils import get_optional_params_embeddings +## get_optional_params_embeddings +### Models: OpenAI, Azure, Bedrock +### Scenarios: w/ optional params + litellm.drop_params = True + +def test_bedrock_optional_params_embeddings(): + litellm.drop_params = True + optional_params = get_optional_params_embeddings(user="John", encoding_format=None, custom_llm_provider="bedrock") + assert len(optional_params) == 0 + +def test_openai_optional_params_embeddings(): + litellm.drop_params = True + optional_params = get_optional_params_embeddings(user="John", encoding_format=None, custom_llm_provider="openai") + assert len(optional_params) == 1 + assert optional_params["user"] == "John" + +def test_azure_optional_params_embeddings(): + litellm.drop_params = True + optional_params = get_optional_params_embeddings(user="John", encoding_format=None, custom_llm_provider="azure") + assert len(optional_params) == 1 + assert optional_params["user"] == "John" diff --git a/litellm/tests/test_proxy_custom_auth.py b/litellm/tests/test_proxy_custom_auth.py index 5708b1c41..c96acb816 100644 --- a/litellm/tests/test_proxy_custom_auth.py +++ b/litellm/tests/test_proxy_custom_auth.py @@ -19,21 +19,23 @@ from litellm import RateLimitError from fastapi.testclient import TestClient from fastapi import FastAPI from litellm.proxy.proxy_server import router, save_worker_config, initialize # Replace with the actual module where your FastAPI router is defined -filepath = os.path.dirname(os.path.abspath(__file__)) -config_fp = f"{filepath}/test_configs/test_config_custom_auth.yaml" -save_worker_config(config=config_fp, model=None, alias=None, api_base=None, api_version=None, debug=False, temperature=None, max_tokens=None, request_timeout=600, max_budget=None, telemetry=False, drop_params=True, add_function_to_prompt=False, headers=None, save=False, use_queue=False) -app = FastAPI() -app.include_router(router) # Include your router in the test app -@app.on_event("startup") -async def wrapper_startup_event(): - initialize(config=config_fp, model=None, alias=None, api_base=None, api_version=None, debug=False, temperature=None, max_tokens=None, request_timeout=600, max_budget=None, telemetry=False, drop_params=True, add_function_to_prompt=False, headers=None, save=False, use_queue=False) + # Here you create a fixture that will be used by your tests # Make sure the fixture returns TestClient(app) -@pytest.fixture(autouse=True) +@pytest.fixture(scope="function") def client(): - with TestClient(app) as client: - yield client + from litellm.proxy.proxy_server import cleanup_router_config_variables + cleanup_router_config_variables() + filepath = os.path.dirname(os.path.abspath(__file__)) + config_fp = f"{filepath}/test_configs/test_config_custom_auth.yaml" + # initialize can get run in parallel, it sets specific variables for the fast api app, sinc eit gets run in parallel different tests use the wrong variables + app = FastAPI() + initialize(config=config_fp) + + app.include_router(router) # Include your router in the test app + return TestClient(app) + def test_custom_auth(client): try: diff --git a/litellm/tests/test_amazing_proxy_custom_logger.py b/litellm/tests/test_proxy_custom_logger.py similarity index 65% rename from litellm/tests/test_amazing_proxy_custom_logger.py rename to litellm/tests/test_proxy_custom_logger.py index 0c4463cc7..6ddc9caac 100644 --- a/litellm/tests/test_amazing_proxy_custom_logger.py +++ b/litellm/tests/test_proxy_custom_logger.py @@ -3,7 +3,7 @@ import traceback from dotenv import load_dotenv load_dotenv() -import os, io +import os, io, asyncio # this file is to test litellm/proxy @@ -21,21 +21,24 @@ from fastapi.testclient import TestClient from fastapi import FastAPI from litellm.proxy.proxy_server import router, save_worker_config, initialize # Replace with the actual module where your FastAPI router is defined filepath = os.path.dirname(os.path.abspath(__file__)) -config_fp = f"{filepath}/test_configs/test_custom_logger.yaml" python_file_path = f"{filepath}/test_configs/custom_callbacks.py" -save_worker_config(config=config_fp, model=None, alias=None, api_base=None, api_version=None, debug=False, temperature=None, max_tokens=None, request_timeout=600, max_budget=None, telemetry=False, drop_params=True, add_function_to_prompt=False, headers=None, save=False, use_queue=False) -app = FastAPI() -app.include_router(router) # Include your router in the test app -@app.on_event("startup") -async def wrapper_startup_event(): - initialize(config=config_fp, model=None, alias=None, api_base=None, api_version=None, debug=True, temperature=None, max_tokens=None, request_timeout=600, max_budget=None, telemetry=False, drop_params=True, add_function_to_prompt=False, headers=None, save=False, use_queue=False) -# Here you create a fixture that will be used by your tests -# Make sure the fixture returns TestClient(app) -@pytest.fixture(autouse=True) +# @app.on_event("startup") +# async def wrapper_startup_event(): + # initialize(config=config_fp) + +# Use the app fixture in your client fixture + +@pytest.fixture def client(): - with TestClient(app) as client: - yield client + filepath = os.path.dirname(os.path.abspath(__file__)) + config_fp = f"{filepath}/test_configs/test_custom_logger.yaml" + initialize(config=config_fp) + app = FastAPI() + app.include_router(router) # Include your router in the test app + return TestClient(app) + + # Your bearer token token = os.getenv("PROXY_MASTER_KEY") @@ -45,15 +48,76 @@ headers = { } -def test_chat_completion(client): +print("Testing proxy custom logger") + +def test_embedding(client): try: - # Your test data + litellm.set_verbose=False + from litellm.proxy.utils import get_instance_fn + my_custom_logger = get_instance_fn( + value = "custom_callbacks.my_custom_logger", + config_file_path=python_file_path + ) + print("id of initialized custom logger", id(my_custom_logger)) + litellm.callbacks = [my_custom_logger] + # Your test data print("initialized proxy") # import the initialized custom logger print(litellm.callbacks) - assert len(litellm.callbacks) == 1 # assert litellm is initialized with 1 callback - my_custom_logger = litellm.callbacks[0] + # assert len(litellm.callbacks) == 1 # assert litellm is initialized with 1 callback + print("my_custom_logger", my_custom_logger) + assert my_custom_logger.async_success_embedding == False + + test_data = { + "model": "azure-embedding-model", + "input": ["hello"] + } + response = client.post("/embeddings", json=test_data, headers=headers) + print("made request", response.status_code, response.text) + print("vars my custom logger /embeddings", vars(my_custom_logger), "id", id(my_custom_logger)) + assert my_custom_logger.async_success_embedding == True # checks if the status of async_success is True, only the async_log_success_event can set this to true + assert my_custom_logger.async_embedding_kwargs["model"] == "azure-embedding-model" # checks if kwargs passed to async_log_success_event are correct + kwargs = my_custom_logger.async_embedding_kwargs + litellm_params = kwargs.get("litellm_params") + metadata = litellm_params.get("metadata", None) + print("\n\n Metadata in custom logger kwargs", litellm_params.get("metadata")) + assert metadata is not None + assert "user_api_key" in metadata + assert "headers" in metadata + proxy_server_request = litellm_params.get("proxy_server_request") + model_info = litellm_params.get("model_info") + assert proxy_server_request == {'url': 'http://testserver/embeddings', 'method': 'POST', 'headers': {'host': 'testserver', 'accept': '*/*', 'accept-encoding': 'gzip, deflate', 'connection': 'keep-alive', 'user-agent': 'testclient', 'authorization': 'Bearer sk-1234', 'content-length': '54', 'content-type': 'application/json'}, 'body': {'model': 'azure-embedding-model', 'input': ['hello']}} + assert model_info == {'input_cost_per_token': 0.002, 'mode': 'embedding', 'id': 'hello'} + result = response.json() + print(f"Received response: {result}") + print("Passed Embedding custom logger on proxy!") + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + +def test_chat_completion(client): + try: + # Your test data + + print("initialized proxy") + litellm.set_verbose=False + from litellm.proxy.utils import get_instance_fn + my_custom_logger = get_instance_fn( + value = "custom_callbacks.my_custom_logger", + config_file_path=python_file_path + ) + + print("id of initialized custom logger", id(my_custom_logger)) + + litellm.callbacks = [my_custom_logger] + # import the initialized custom logger + print(litellm.callbacks) + + # assert len(litellm.callbacks) == 1 # assert litellm is initialized with 1 callback + + print("LiteLLM Callbacks", litellm.callbacks) + print("my_custom_logger", my_custom_logger) assert my_custom_logger.async_success == False test_data = { @@ -61,7 +125,7 @@ def test_chat_completion(client): "messages": [ { "role": "user", - "content": "hi" + "content": "write a litellm poem" }, ], "max_tokens": 10, @@ -70,33 +134,53 @@ def test_chat_completion(client): response = client.post("/chat/completions", json=test_data, headers=headers) print("made request", response.status_code, response.text) + print("LiteLLM Callbacks", litellm.callbacks) + asyncio.sleep(1) # sleep while waiting for callback to run + + print("my_custom_logger in /chat/completions", my_custom_logger, "id", id(my_custom_logger)) + print("vars my custom logger, ", vars(my_custom_logger)) assert my_custom_logger.async_success == True # checks if the status of async_success is True, only the async_log_success_event can set this to true assert my_custom_logger.async_completion_kwargs["model"] == "chatgpt-v-2" # checks if kwargs passed to async_log_success_event are correct print("\n\n Custom Logger Async Completion args", my_custom_logger.async_completion_kwargs) - litellm_params = my_custom_logger.async_completion_kwargs.get("litellm_params") + metadata = litellm_params.get("metadata", None) + print("\n\n Metadata in custom logger kwargs", litellm_params.get("metadata")) + assert metadata is not None + assert "user_api_key" in metadata + assert "headers" in metadata config_model_info = litellm_params.get("model_info") proxy_server_request_object = litellm_params.get("proxy_server_request") assert config_model_info == {'id': 'gm', 'input_cost_per_token': 0.0002, 'mode': 'chat'} - assert proxy_server_request_object == {'url': 'http://testserver/chat/completions', 'method': 'POST', 'headers': {'host': 'testserver', 'accept': '*/*', 'accept-encoding': 'gzip, deflate', 'connection': 'keep-alive', 'user-agent': 'testclient', 'authorization': 'Bearer sk-1234', 'content-length': '105', 'content-type': 'application/json'}, 'body': {'model': 'Azure OpenAI GPT-4 Canada', 'messages': [{'role': 'user', 'content': 'hi'}], 'max_tokens': 10}} + assert proxy_server_request_object == {'url': 'http://testserver/chat/completions', 'method': 'POST', 'headers': {'host': 'testserver', 'accept': '*/*', 'accept-encoding': 'gzip, deflate', 'connection': 'keep-alive', 'user-agent': 'testclient', 'authorization': 'Bearer sk-1234', 'content-length': '123', 'content-type': 'application/json'}, 'body': {'model': 'Azure OpenAI GPT-4 Canada', 'messages': [{'role': 'user', 'content': 'write a litellm poem'}], 'max_tokens': 10}} result = response.json() print(f"Received response: {result}") print("\nPassed /chat/completions with Custom Logger!") except Exception as e: - pytest.fail("LiteLLM Proxy test failed. Exception", e) + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") def test_chat_completion_stream(client): try: # Your test data + litellm.set_verbose=False + from litellm.proxy.utils import get_instance_fn + my_custom_logger = get_instance_fn( + value = "custom_callbacks.my_custom_logger", + config_file_path=python_file_path + ) + + print("id of initialized custom logger", id(my_custom_logger)) + + litellm.callbacks = [my_custom_logger] import json print("initialized proxy") # import the initialized custom logger print(litellm.callbacks) + - assert len(litellm.callbacks) == 1 # assert litellm is initialized with 1 callback - my_custom_logger = litellm.callbacks[0] + print("LiteLLM Callbacks", litellm.callbacks) + print("my_custom_logger", my_custom_logger) assert my_custom_logger.streaming_response_obj == None # no streaming response obj is set pre call @@ -148,37 +232,5 @@ def test_chat_completion_stream(client): assert complete_response == streamed_response["choices"][0]["message"]["content"] except Exception as e: - pytest.fail("LiteLLM Proxy test failed. Exception", e) + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") - - -def test_embedding(client): - try: - # Your test data - print("initialized proxy") - # import the initialized custom logger - print(litellm.callbacks) - - assert len(litellm.callbacks) == 1 # assert litellm is initialized with 1 callback - my_custom_logger = litellm.callbacks[0] - assert my_custom_logger.async_success_embedding == False - - test_data = { - "model": "azure-embedding-model", - "input": ["hello"] - } - response = client.post("/embeddings", json=test_data, headers=headers) - print("made request", response.status_code, response.text) - assert my_custom_logger.async_success_embedding == True # checks if the status of async_success is True, only the async_log_success_event can set this to true - assert my_custom_logger.async_embedding_kwargs["model"] == "azure-embedding-model" # checks if kwargs passed to async_log_success_event are correct - - kwargs = my_custom_logger.async_embedding_kwargs - litellm_params = kwargs.get("litellm_params") - proxy_server_request = litellm_params.get("proxy_server_request") - model_info = litellm_params.get("model_info") - assert proxy_server_request == {'url': 'http://testserver/embeddings', 'method': 'POST', 'headers': {'host': 'testserver', 'accept': '*/*', 'accept-encoding': 'gzip, deflate', 'connection': 'keep-alive', 'user-agent': 'testclient', 'authorization': 'Bearer sk-1234', 'content-length': '54', 'content-type': 'application/json'}, 'body': {'model': 'azure-embedding-model', 'input': ['hello']}} - assert model_info == {'input_cost_per_token': 0.002, 'mode': 'embedding', 'id': 'hello'} - result = response.json() - print(f"Received response: {result}") - except Exception as e: - pytest.fail("LiteLLM Proxy test failed. Exception", e) \ No newline at end of file diff --git a/litellm/tests/test_proxy_exception_mapping.py b/litellm/tests/test_proxy_exception_mapping.py new file mode 100644 index 000000000..5dcb782c4 --- /dev/null +++ b/litellm/tests/test_proxy_exception_mapping.py @@ -0,0 +1,177 @@ +# test that the proxy actually does exception mapping to the OpenAI format + +import sys, os +from dotenv import load_dotenv + +load_dotenv() +import os, io, asyncio +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import pytest +import litellm, openai +from fastapi.testclient import TestClient +from fastapi import FastAPI +from litellm.proxy.proxy_server import router, save_worker_config, initialize # Replace with the actual module where your FastAPI router is defined + +@pytest.fixture +def client(): + filepath = os.path.dirname(os.path.abspath(__file__)) + config_fp = f"{filepath}/test_configs/test_bad_config.yaml" + initialize(config=config_fp) + app = FastAPI() + app.include_router(router) # Include your router in the test app + return TestClient(app) + +# raise openai.AuthenticationError +def test_chat_completion_exception(client): + try: + # Your test data + test_data = { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "hi" + }, + ], + "max_tokens": 10, + } + + response = client.post("/chat/completions", json=test_data) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response(response=response) + assert isinstance(openai_exception, openai.AuthenticationError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + +# raise openai.AuthenticationError +def test_chat_completion_exception_azure(client): + try: + # Your test data + test_data = { + "model": "azure-gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "hi" + }, + ], + "max_tokens": 10, + } + + response = client.post("/chat/completions", json=test_data) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response(response=response) + assert isinstance(openai_exception, openai.AuthenticationError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + +# raise openai.AuthenticationError +def test_embedding_auth_exception_azure(client): + try: + # Your test data + test_data = { + "model": "azure-embedding", + "input": ["hi"] + } + + response = client.post("/embeddings", json=test_data) + print("Response from proxy=", response) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response(response=response) + print("Exception raised=", openai_exception) + assert isinstance(openai_exception, openai.AuthenticationError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + + + +# raise openai.BadRequestError +# chat/completions openai +def test_exception_openai_bad_model(client): + try: + # Your test data + test_data = { + "model": "azure/GPT-12", + "messages": [ + { + "role": "user", + "content": "hi" + }, + ], + "max_tokens": 10, + } + + response = client.post("/chat/completions", json=test_data) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response(response=response) + print("Type of exception=", type(openai_exception)) + assert isinstance(openai_exception, openai.NotFoundError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + +# chat/completions any model +def test_chat_completion_exception_any_model(client): + try: + # Your test data + test_data = { + "model": "Lite-GPT-12", + "messages": [ + { + "role": "user", + "content": "hi" + }, + ], + "max_tokens": 10, + } + + response = client.post("/chat/completions", json=test_data) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response(response=response) + print("Exception raised=", openai_exception) + assert isinstance(openai_exception, openai.NotFoundError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + + +# embeddings any model +def test_embedding_exception_any_model(client): + try: + # Your test data + test_data = { + "model": "Lite-GPT-12", + "input": ["hi"] + } + + response = client.post("/embeddings", json=test_data) + print("Response from proxy=", response) + + # make an openai client to call _make_status_error_from_response + openai_client = openai.OpenAI(api_key="anything") + openai_exception = openai_client._make_status_error_from_response(response=response) + print("Exception raised=", openai_exception) + assert isinstance(openai_exception, openai.NotFoundError) + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") + + diff --git a/litellm/tests/test_proxy_server.py b/litellm/tests/test_proxy_server.py index bc22c384f..5e9854f43 100644 --- a/litellm/tests/test_proxy_server.py +++ b/litellm/tests/test_proxy_server.py @@ -24,30 +24,29 @@ logging.basicConfig( from fastapi.testclient import TestClient from fastapi import FastAPI from litellm.proxy.proxy_server import router, save_worker_config, initialize # Replace with the actual module where your FastAPI router is defined -filepath = os.path.dirname(os.path.abspath(__file__)) -config_fp = f"{filepath}/test_configs/test_config_no_auth.yaml" -save_worker_config(config=config_fp, model=None, alias=None, api_base=None, api_version=None, debug=False, temperature=None, max_tokens=None, request_timeout=600, max_budget=None, telemetry=False, drop_params=True, add_function_to_prompt=False, headers=None, save=False, use_queue=False) -app = FastAPI() -app.include_router(router) # Include your router in the test app -@app.on_event("startup") -async def wrapper_startup_event(): - initialize(config=config_fp) # Your bearer token -token = os.getenv("PROXY_MASTER_KEY") +token = "" headers = { "Authorization": f"Bearer {token}" } -# Here you create a fixture that will be used by your tests -# Make sure the fixture returns TestClient(app) -@pytest.fixture(autouse=True) -def client(): - with TestClient(app) as client: - yield client +@pytest.fixture(scope="function") +def client_no_auth(): + # Assuming litellm.proxy.proxy_server is an object + from litellm.proxy.proxy_server import cleanup_router_config_variables + cleanup_router_config_variables() + filepath = os.path.dirname(os.path.abspath(__file__)) + config_fp = f"{filepath}/test_configs/test_config_no_auth.yaml" + # initialize can get run in parallel, it sets specific variables for the fast api app, sinc eit gets run in parallel different tests use the wrong variables + initialize(config=config_fp) + app = FastAPI() + app.include_router(router) # Include your router in the test app -def test_chat_completion(client): + return TestClient(app) + +def test_chat_completion(client_no_auth): global headers try: # Your test data @@ -62,8 +61,8 @@ def test_chat_completion(client): "max_tokens": 10, } - print("testing proxy server") - response = client.post("/v1/chat/completions", json=test_data, headers=headers) + print("testing proxy server with chat completions") + response = client_no_auth.post("/v1/chat/completions", json=test_data) print(f"response - {response.text}") assert response.status_code == 200 result = response.json() @@ -73,7 +72,8 @@ def test_chat_completion(client): # Run the test -def test_chat_completion_azure(client): +def test_chat_completion_azure(client_no_auth): + global headers try: # Your test data @@ -88,8 +88,8 @@ def test_chat_completion_azure(client): "max_tokens": 10, } - print("testing proxy server with Azure Request") - response = client.post("/v1/chat/completions", json=test_data, headers=headers) + print("testing proxy server with Azure Request /chat/completions") + response = client_no_auth.post("/v1/chat/completions", json=test_data) assert response.status_code == 200 result = response.json() @@ -102,15 +102,55 @@ def test_chat_completion_azure(client): # test_chat_completion_azure() -def test_embedding(client): +def test_embedding(client_no_auth): global headers + from litellm.proxy.proxy_server import user_custom_auth + try: test_data = { "model": "azure/azure-embedding-model", "input": ["good morning from litellm"], } - print("testing proxy server with OpenAI embedding") - response = client.post("/v1/embeddings", json=test_data, headers=headers) + + response = client_no_auth.post("/v1/embeddings", json=test_data) + + assert response.status_code == 200 + result = response.json() + print(len(result["data"][0]["embedding"])) + assert len(result["data"][0]["embedding"]) > 10 # this usually has len==1536 so + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + +def test_bedrock_embedding(client_no_auth): + global headers + from litellm.proxy.proxy_server import user_custom_auth + + try: + test_data = { + "model": "amazon-embeddings", + "input": ["good morning from litellm"], + } + + response = client_no_auth.post("/v1/embeddings", json=test_data) + + assert response.status_code == 200 + result = response.json() + print(len(result["data"][0]["embedding"])) + assert len(result["data"][0]["embedding"]) > 10 # this usually has len==1536 so + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + +def test_sagemaker_embedding(client_no_auth): + global headers + from litellm.proxy.proxy_server import user_custom_auth + + try: + test_data = { + "model": "GPT-J 6B - Sagemaker Text Embedding (Internal)", + "input": ["good morning from litellm"], + } + + response = client_no_auth.post("/v1/embeddings", json=test_data) assert response.status_code == 200 result = response.json() @@ -122,8 +162,8 @@ def test_embedding(client): # Run the test # test_embedding() -@pytest.mark.skip(reason="hitting yaml load issues on circle-ci") -def test_add_new_model(client): +# @pytest.mark.skip(reason="hitting yaml load issues on circle-ci") +def test_add_new_model(client_no_auth): global headers try: test_data = { @@ -135,15 +175,15 @@ def test_add_new_model(client): "description": "this is a test openai model" } } - client.post("/model/new", json=test_data, headers=headers) - response = client.get("/model/info", headers=headers) + client_no_auth.post("/model/new", json=test_data, headers=headers) + response = client_no_auth.get("/model/info", headers=headers) assert response.status_code == 200 result = response.json() print(f"response: {result}") model_info = None for m in result["data"]: - if m["id"]["model_name"] == "test_openai_models": - model_info = m["id"]["model_info"] + if m["model_name"] == "test_openai_models": + model_info = m["model_info"] assert model_info["description"] == "this is a test openai model" except Exception as e: pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") @@ -164,10 +204,9 @@ class MyCustomHandler(CustomLogger): customHandler = MyCustomHandler() -def test_chat_completion_optional_params(client): +def test_chat_completion_optional_params(client_no_auth): # [PROXY: PROD TEST] - DO NOT DELETE # This tests if all the /chat/completion params are passed to litellm - try: # Your test data litellm.set_verbose=True @@ -185,7 +224,7 @@ def test_chat_completion_optional_params(client): litellm.callbacks = [customHandler] print("testing proxy server: optional params") - response = client.post("/v1/chat/completions", json=test_data, headers=headers) + response = client_no_auth.post("/v1/chat/completions", json=test_data) assert response.status_code == 200 result = response.json() print(f"Received response: {result}") @@ -217,6 +256,29 @@ def test_load_router_config(): print(result) assert len(result[1]) == 2 + # tests for litellm.cache set from config + print("testing reading proxy config for cache") + litellm.cache = None + load_router_config( + router=None, + config_file_path=f"{filepath}/example_config_yaml/cache_no_params.yaml" + ) + assert litellm.cache is not None + assert "redis_client" in vars(litellm.cache.cache) # it should default to redis on proxy + assert litellm.cache.supported_call_types == ['completion', 'acompletion', 'embedding', 'aembedding'] # init with all call types + + print("testing reading proxy config for cache with params") + load_router_config( + router=None, + config_file_path=f"{filepath}/example_config_yaml/cache_with_params.yaml" + ) + assert litellm.cache is not None + print(litellm.cache) + print(litellm.cache.supported_call_types) + print(vars(litellm.cache.cache)) + assert "redis_client" in vars(litellm.cache.cache) # it should default to redis on proxy + assert litellm.cache.supported_call_types == ['embedding', 'aembedding'] # init with all call types + except Exception as e: pytest.fail("Proxy: Got exception reading config", e) -# test_load_router_config() +# test_load_router_config() \ No newline at end of file diff --git a/litellm/tests/test_proxy_server_keys.py b/litellm/tests/test_proxy_server_keys.py index db083c30c..14b239ae1 100644 --- a/litellm/tests/test_proxy_server_keys.py +++ b/litellm/tests/test_proxy_server_keys.py @@ -37,6 +37,8 @@ async def wrapper_startup_event(): # Make sure the fixture returns TestClient(app) @pytest.fixture(autouse=True) def client(): + from litellm.proxy.proxy_server import cleanup_router_config_variables + cleanup_router_config_variables() with TestClient(app) as client: yield client @@ -69,6 +71,38 @@ def test_add_new_key(client): except Exception as e: pytest.fail(f"LiteLLM Proxy test failed. Exception: {str(e)}") + +def test_update_new_key(client): + try: + # Your test data + test_data = { + "models": ["gpt-3.5-turbo", "gpt-4", "claude-2", "azure-model"], + "aliases": {"mistral-7b": "gpt-3.5-turbo"}, + "duration": "20m" + } + print("testing proxy server") + # Your bearer token + token = os.getenv("PROXY_MASTER_KEY") + + headers = { + "Authorization": f"Bearer {token}" + } + response = client.post("/key/generate", json=test_data, headers=headers) + print(f"response: {response.text}") + assert response.status_code == 200 + result = response.json() + assert result["key"].startswith("sk-") + def _post_data(): + json_data = {'models': ['bedrock-models'], "key": result["key"]} + response = client.post("/key/update", json=json_data, headers=headers) + print(f"response text: {response.text}") + assert response.status_code == 200 + return response + _post_data() + print(f"Received response: {result}") + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception: {str(e)}") + # # Run the test - only runs via pytest diff --git a/litellm/tests/test_router.py b/litellm/tests/test_router.py index 4ec91ec0d..403c8dc2a 100644 --- a/litellm/tests/test_router.py +++ b/litellm/tests/test_router.py @@ -366,69 +366,12 @@ def test_function_calling(): } ] - router = Router(model_list=model_list, routing_strategy="latency-based-routing") + router = Router(model_list=model_list) response = router.completion(model="gpt-3.5-turbo-0613", messages=messages, functions=functions) router.reset() print(response) -def test_acompletion_on_router(): - # tests acompletion + caching on router - try: - litellm.set_verbose = True - model_list = [ - { - "model_name": "gpt-3.5-turbo", - "litellm_params": { - "model": "gpt-3.5-turbo-0613", - "api_key": os.getenv("OPENAI_API_KEY"), - }, - "tpm": 100000, - "rpm": 10000, - }, - { - "model_name": "gpt-3.5-turbo", - "litellm_params": { - "model": "azure/chatgpt-v-2", - "api_key": os.getenv("AZURE_API_KEY"), - "api_base": os.getenv("AZURE_API_BASE"), - "api_version": os.getenv("AZURE_API_VERSION") - }, - "tpm": 100000, - "rpm": 10000, - } - ] - - messages = [ - {"role": "user", "content": f"write a one sentence poem {time.time()}?"} - ] - start_time = time.time() - router = Router(model_list=model_list, - redis_host=os.environ["REDIS_HOST"], - redis_password=os.environ["REDIS_PASSWORD"], - redis_port=os.environ["REDIS_PORT"], - cache_responses=True, - timeout=30, - routing_strategy="simple-shuffle") - async def get_response(): - print("Testing acompletion + caching on router") - response1 = await router.acompletion(model="gpt-3.5-turbo", messages=messages, temperature=1) - print(f"response1: {response1}") - response2 = await router.acompletion(model="gpt-3.5-turbo", messages=messages, temperature=1) - print(f"response2: {response2}") - assert response1.id == response2.id - assert len(response1.choices[0].message.content) > 0 - assert response1.choices[0].message.content == response2.choices[0].message.content - asyncio.run(get_response()) - router.reset() - except litellm.Timeout as e: - end_time = time.time() - print(f"timeout error occurred: {end_time - start_time}") - pass - except Exception as e: - traceback.print_exc() - pytest.fail(f"Error occurred: {e}") - -test_acompletion_on_router() +# test_acompletion_on_router() def test_function_calling_on_router(): try: @@ -507,7 +450,6 @@ def test_aembedding_on_router(): model="text-embedding-ada-002", input=["good morning from litellm 2"], ) - print("sync embedding response: ", response) router.reset() except Exception as e: traceback.print_exc() @@ -591,6 +533,30 @@ def test_bedrock_on_router(): pytest.fail(f"Error occurred: {e}") # test_bedrock_on_router() +# test openai-compatible endpoint +@pytest.mark.asyncio +async def test_mistral_on_router(): + litellm.set_verbose = True + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "mistral/mistral-medium", + }, + }, + ] + router = Router(model_list=model_list) + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "hello from litellm test", + } + ] + ) + print(response) +asyncio.run(test_mistral_on_router()) def test_openai_completion_on_router(): # [PROD Use Case] - Makes an acompletion call + async acompletion call, and sync acompletion call, sync completion + stream diff --git a/litellm/tests/test_router_caching.py b/litellm/tests/test_router_caching.py new file mode 100644 index 000000000..27191c8d2 --- /dev/null +++ b/litellm/tests/test_router_caching.py @@ -0,0 +1,127 @@ +#### What this tests #### +# This tests caching on the router +import sys, os, time +import traceback, asyncio +import pytest +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path +import litellm +from litellm import Router + +## Scenarios +## 1. 2 models - openai + azure - 1 model group "gpt-3.5-turbo", +## 2. 2 models - openai, azure - 2 diff model groups, 1 caching group + +@pytest.mark.asyncio +async def test_acompletion_caching_on_router(): + # tests acompletion + caching on router + try: + litellm.set_verbose = True + model_list = [ + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 100000, + "rpm": 10000, + }, + { + "model_name": "gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION") + }, + "tpm": 100000, + "rpm": 10000, + } + ] + + messages = [ + {"role": "user", "content": f"write a one sentence poem {time.time()}?"} + ] + start_time = time.time() + router = Router(model_list=model_list, + redis_host=os.environ["REDIS_HOST"], + redis_password=os.environ["REDIS_PASSWORD"], + redis_port=os.environ["REDIS_PORT"], + cache_responses=True, + timeout=30, + routing_strategy="simple-shuffle") + response1 = await router.acompletion(model="gpt-3.5-turbo", messages=messages, temperature=1) + print(f"response1: {response1}") + await asyncio.sleep(1) # add cache is async, async sleep for cache to get set + response2 = await router.acompletion(model="gpt-3.5-turbo", messages=messages, temperature=1) + print(f"response2: {response2}") + assert response1.id == response2.id + assert len(response1.choices[0].message.content) > 0 + assert response1.choices[0].message.content == response2.choices[0].message.content + router.reset() + except litellm.Timeout as e: + end_time = time.time() + print(f"timeout error occurred: {end_time - start_time}") + pass + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") + +@pytest.mark.asyncio +async def test_acompletion_caching_on_router_caching_groups(): + # tests acompletion + caching on router + try: + litellm.set_verbose = True + model_list = [ + { + "model_name": "openai-gpt-3.5-turbo", + "litellm_params": { + "model": "gpt-3.5-turbo-0613", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 100000, + "rpm": 10000, + }, + { + "model_name": "azure-gpt-3.5-turbo", + "litellm_params": { + "model": "azure/chatgpt-v-2", + "api_key": os.getenv("AZURE_API_KEY"), + "api_base": os.getenv("AZURE_API_BASE"), + "api_version": os.getenv("AZURE_API_VERSION") + }, + "tpm": 100000, + "rpm": 10000, + } + ] + + messages = [ + {"role": "user", "content": f"write a one sentence poem {time.time()}?"} + ] + start_time = time.time() + router = Router(model_list=model_list, + redis_host=os.environ["REDIS_HOST"], + redis_password=os.environ["REDIS_PASSWORD"], + redis_port=os.environ["REDIS_PORT"], + cache_responses=True, + timeout=30, + routing_strategy="simple-shuffle", + caching_groups=[("openai-gpt-3.5-turbo", "azure-gpt-3.5-turbo")]) + response1 = await router.acompletion(model="openai-gpt-3.5-turbo", messages=messages, temperature=1) + print(f"response1: {response1}") + await asyncio.sleep(1) # add cache is async, async sleep for cache to get set + response2 = await router.acompletion(model="azure-gpt-3.5-turbo", messages=messages, temperature=1) + print(f"response2: {response2}") + assert response1.id == response2.id + assert len(response1.choices[0].message.content) > 0 + assert response1.choices[0].message.content == response2.choices[0].message.content + router.reset() + except litellm.Timeout as e: + end_time = time.time() + print(f"timeout error occurred: {end_time - start_time}") + pass + except Exception as e: + traceback.print_exc() + pytest.fail(f"Error occurred: {e}") \ No newline at end of file diff --git a/litellm/tests/test_router_fallbacks.py b/litellm/tests/test_router_fallbacks.py index 3779dc09a..22b5f121e 100644 --- a/litellm/tests/test_router_fallbacks.py +++ b/litellm/tests/test_router_fallbacks.py @@ -21,80 +21,89 @@ class MyCustomHandler(CustomLogger): print(f"Pre-API Call") def log_post_api_call(self, kwargs, response_obj, start_time, end_time): - print(f"Post-API Call") + print(f"Post-API Call - response object: {response_obj}; model: {kwargs['model']}") + def log_stream_event(self, kwargs, response_obj, start_time, end_time): print(f"On Stream") + + def async_log_stream_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Stream") def log_success_event(self, kwargs, response_obj, start_time, end_time): print(f"previous_models: {kwargs['litellm_params']['metadata']['previous_models']}") self.previous_models += len(kwargs["litellm_params"]["metadata"]["previous_models"]) # {"previous_models": [{"model": litellm_model_name, "exception_type": AuthenticationError, "exception_string": }]} print(f"self.previous_models: {self.previous_models}") print(f"On Success") + + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + print(f"previous_models: {kwargs['litellm_params']['metadata']['previous_models']}") + self.previous_models += len(kwargs["litellm_params"]["metadata"]["previous_models"]) # {"previous_models": [{"model": litellm_model_name, "exception_type": AuthenticationError, "exception_string": }]} + print(f"self.previous_models: {self.previous_models}") + print(f"On Success") def log_failure_event(self, kwargs, response_obj, start_time, end_time): print(f"On Failure") -model_list = [ - { # list of model deployments - "model_name": "azure/gpt-3.5-turbo", # openai model name - "litellm_params": { # params for litellm completion/embedding call - "model": "azure/chatgpt-v-2", - "api_key": "bad-key", - "api_version": os.getenv("AZURE_API_VERSION"), - "api_base": os.getenv("AZURE_API_BASE") - }, - "tpm": 240000, - "rpm": 1800 - }, - { # list of model deployments - "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model 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") - }, - "tpm": 240000, - "rpm": 1800 - }, - { - "model_name": "azure/gpt-3.5-turbo", # openai model name - "litellm_params": { # params for litellm completion/embedding call - "model": "azure/chatgpt-functioncalling", - "api_key": "bad-key", - "api_version": os.getenv("AZURE_API_VERSION"), - "api_base": os.getenv("AZURE_API_BASE") - }, - "tpm": 240000, - "rpm": 1800 - }, - { - "model_name": "gpt-3.5-turbo", # openai model name - "litellm_params": { # params for litellm completion/embedding call - "model": "gpt-3.5-turbo", - "api_key": os.getenv("OPENAI_API_KEY"), - }, - "tpm": 1000000, - "rpm": 9000 - }, - { - "model_name": "gpt-3.5-turbo-16k", # openai model name - "litellm_params": { # params for litellm completion/embedding call - "model": "gpt-3.5-turbo-16k", - "api_key": os.getenv("OPENAI_API_KEY"), - }, - "tpm": 1000000, - "rpm": 9000 - } -] - - kwargs = {"model": "azure/gpt-3.5-turbo", "messages": [{"role": "user", "content":"Hey, how's it going?"}]} def test_sync_fallbacks(): try: + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model 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") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000 + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000 + } + ] + litellm.set_verbose = True customHandler = MyCustomHandler() litellm.callbacks = [customHandler] @@ -106,62 +115,93 @@ def test_sync_fallbacks(): print(f"response: {response}") time.sleep(0.05) # allow a delay as success_callbacks are on a separate thread assert customHandler.previous_models == 1 # 0 retries, 1 fallback + + print("Passed ! Test router_fallbacks: test_sync_fallbacks()") router.reset() except Exception as e: print(e) # test_sync_fallbacks() -def test_async_fallbacks(): +@pytest.mark.asyncio +async def test_async_fallbacks(): litellm.set_verbose = False + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model 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") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000 + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000 + } + ] + router = Router(model_list=model_list, fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}], context_window_fallbacks=[{"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}], set_verbose=False) - async def test_get_response(): - customHandler = MyCustomHandler() - litellm.callbacks = [customHandler] - user_message = "Hello, how are you?" - messages = [{"content": user_message, "role": "user"}] - try: - response = await router.acompletion(**kwargs) - print(f"customHandler.previous_models: {customHandler.previous_models}") - time.sleep(0.05) # allow a delay as success_callbacks are on a separate thread - assert customHandler.previous_models == 1 # 0 retries, 1 fallback - router.reset() - except litellm.Timeout as e: - pass - except Exception as e: - pytest.fail(f"An exception occurred: {e}") - finally: - router.reset() - asyncio.run(test_get_response()) + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + user_message = "Hello, how are you?" + messages = [{"content": user_message, "role": "user"}] + try: + response = await router.acompletion(**kwargs) + print(f"customHandler.previous_models: {customHandler.previous_models}") + await asyncio.sleep(0.05) # allow a delay as success_callbacks are on a separate thread + assert customHandler.previous_models == 1 # 0 retries, 1 fallback + router.reset() + except litellm.Timeout as e: + pass + except Exception as e: + pytest.fail(f"An exception occurred: {e}") + finally: + router.reset() # test_async_fallbacks() -## COMMENTING OUT as the context size exceeds both gpt-3.5-turbo and gpt-3.5-turbo-16k, need a better message here -# def test_sync_context_window_fallbacks(): -# try: -# customHandler = MyCustomHandler() -# litellm.callbacks = [customHandler] -# sample_text = "Say error 50 times" * 10000 -# kwargs["model"] = "azure/gpt-3.5-turbo-context-fallback" -# kwargs["messages"] = [{"role": "user", "content": sample_text}] -# router = Router(model_list=model_list, -# fallbacks=[{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}], -# context_window_fallbacks=[{"azure/gpt-3.5-turbo-context-fallback": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}], -# set_verbose=False) -# response = router.completion(**kwargs) -# print(f"response: {response}") -# time.sleep(0.05) # allow a delay as success_callbacks are on a separate thread -# assert customHandler.previous_models == 1 # 0 retries, 1 fallback -# router.reset() -# except Exception as e: -# print(f"An exception occurred - {e}") -# finally: -# router.reset() - -# test_sync_context_window_fallbacks() - def test_dynamic_fallbacks_sync(): """ Allow setting the fallback in the router.completion() call. @@ -169,6 +209,60 @@ def test_dynamic_fallbacks_sync(): try: customHandler = MyCustomHandler() litellm.callbacks = [customHandler] + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model 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") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000 + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000 + } + ] + router = Router(model_list=model_list, set_verbose=True) kwargs = {} kwargs["model"] = "azure/gpt-3.5-turbo" @@ -184,26 +278,83 @@ def test_dynamic_fallbacks_sync(): # test_dynamic_fallbacks_sync() -def test_dynamic_fallbacks_async(): +@pytest.mark.asyncio +async def test_dynamic_fallbacks_async(): """ Allow setting the fallback in the router.completion() call. """ - async def test_get_response(): - try: - customHandler = MyCustomHandler() - litellm.callbacks = [customHandler] - router = Router(model_list=model_list, set_verbose=True) - kwargs = {} - kwargs["model"] = "azure/gpt-3.5-turbo" - kwargs["messages"] = [{"role": "user", "content": "Hey, how's it going?"}] - kwargs["fallbacks"] = [{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}] - response = await router.acompletion(**kwargs) - print(f"response: {response}") - time.sleep(0.05) # allow a delay as success_callbacks are on a separate thread - assert customHandler.previous_models == 1 # 0 retries, 1 fallback - router.reset() - except Exception as e: - pytest.fail(f"An exception occurred - {e}") - asyncio.run(test_get_response()) + try: + model_list = [ + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { # list of model deployments + "model_name": "azure/gpt-3.5-turbo-context-fallback", # openai model 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") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "azure/gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": "bad-key", + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 240000, + "rpm": 1800 + }, + { + "model_name": "gpt-3.5-turbo", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000 + }, + { + "model_name": "gpt-3.5-turbo-16k", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo-16k", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 1000000, + "rpm": 9000 + } + ] -# test_dynamic_fallbacks_async() \ No newline at end of file + print() + print() + print() + print() + print(f"STARTING DYNAMIC ASYNC") + customHandler = MyCustomHandler() + litellm.callbacks = [customHandler] + router = Router(model_list=model_list, set_verbose=True) + kwargs = {} + kwargs["model"] = "azure/gpt-3.5-turbo" + kwargs["messages"] = [{"role": "user", "content": "Hey, how's it going?"}] + kwargs["fallbacks"] = [{"azure/gpt-3.5-turbo": ["gpt-3.5-turbo"]}] + response = await router.acompletion(**kwargs) + print(f"RESPONSE: {response}") + await asyncio.sleep(0.05) # allow a delay as success_callbacks are on a separate thread + assert customHandler.previous_models == 1 # 0 retries, 1 fallback + router.reset() + except Exception as e: + pytest.fail(f"An exception occurred - {e}") +# asyncio.run(test_dynamic_fallbacks_async()) \ No newline at end of file diff --git a/litellm/tests/test_router_get_deployments.py b/litellm/tests/test_router_get_deployments.py index 3e6bbd2b6..a71fc3823 100644 --- a/litellm/tests/test_router_get_deployments.py +++ b/litellm/tests/test_router_get_deployments.py @@ -49,7 +49,7 @@ def test_weighted_selection_router(): for _ in range(1000): selected_model = router.get_available_deployment("gpt-3.5-turbo") selected_model_id = selected_model["litellm_params"]["model"] - selected_model_name = litellm.utils.remove_model_id(selected_model_id) + selected_model_name = selected_model_id selection_counts[selected_model_name] +=1 print(selection_counts) @@ -101,7 +101,7 @@ def test_weighted_selection_router_tpm(): for _ in range(1000): selected_model = router.get_available_deployment("gpt-3.5-turbo") selected_model_id = selected_model["litellm_params"]["model"] - selected_model_name = litellm.utils.remove_model_id(selected_model_id) + selected_model_name = selected_model_id selection_counts[selected_model_name] +=1 print(selection_counts) @@ -154,7 +154,7 @@ def test_weighted_selection_router_tpm_as_router_param(): for _ in range(1000): selected_model = router.get_available_deployment("gpt-3.5-turbo") selected_model_id = selected_model["litellm_params"]["model"] - selected_model_name = litellm.utils.remove_model_id(selected_model_id) + selected_model_name = selected_model_id selection_counts[selected_model_name] +=1 print(selection_counts) @@ -210,7 +210,7 @@ def test_weighted_selection_router_rpm_as_router_param(): for _ in range(1000): selected_model = router.get_available_deployment("gpt-3.5-turbo") selected_model_id = selected_model["litellm_params"]["model"] - selected_model_name = litellm.utils.remove_model_id(selected_model_id) + selected_model_name = selected_model_id selection_counts[selected_model_name] +=1 print(selection_counts) @@ -270,7 +270,7 @@ def test_weighted_selection_router_no_rpm_set(): for _ in range(1000): selected_model = router.get_available_deployment("claude-1") selected_model_id = selected_model["litellm_params"]["model"] - selected_model_name = litellm.utils.remove_model_id(selected_model_id) + selected_model_name = selected_model_id selection_counts[selected_model_name] +=1 print(selection_counts) @@ -291,14 +291,13 @@ def test_weighted_selection_router_no_rpm_set(): def test_model_group_aliases(): try: litellm.set_verbose = False - litellm.model_group_alias_map = {"gpt-4": "gpt-3.5-turbo"} model_list = [ { "model_name": "gpt-3.5-turbo", "litellm_params": { "model": "gpt-3.5-turbo-0613", "api_key": os.getenv("OPENAI_API_KEY"), - "rpm": 6, + "tpm": 1, }, }, { @@ -308,29 +307,46 @@ def test_model_group_aliases(): "api_key": os.getenv("AZURE_API_KEY"), "api_base": os.getenv("AZURE_API_BASE"), "api_version": os.getenv("AZURE_API_VERSION"), - "rpm": 1440, + "tpm": 99, }, }, { "model_name": "claude-1", "litellm_params": { "model": "bedrock/claude1.2", - "rpm": 1440, + "tpm": 1, }, } ] router = Router( model_list=model_list, + model_group_alias={"gpt-4": "gpt-3.5-turbo"} # gpt-4 requests sent to gpt-3.5-turbo ) + + # test that gpt-4 requests are sent to gpt-3.5-turbo for _ in range(20): selected_model = router.get_available_deployment("gpt-4") print("\n selected model", selected_model) selected_model_name = selected_model.get("model_name") - if selected_model_name is not "gpt-3.5-turbo": + if selected_model_name != "gpt-3.5-turbo": pytest.fail(f"Selected model {selected_model_name} is not gpt-3.5-turbo") - + + # test that + # call get_available_deployment 1k times, it should pick azure/chatgpt-v-2 about 90% of the time + selection_counts = defaultdict(int) + for _ in range(1000): + selected_model = router.get_available_deployment("gpt-3.5-turbo") + selected_model_id = selected_model["litellm_params"]["model"] + selected_model_name = selected_model_id + selection_counts[selected_model_name] +=1 + print(selection_counts) + + total_requests = sum(selection_counts.values()) + + # Assert that 'azure/chatgpt-v-2' has about 90% of the total requests + assert selection_counts['azure/chatgpt-v-2'] / total_requests > 0.89, f"Assertion failed: 'azure/chatgpt-v-2' does not have about 90% of the total requests in the weighted load balancer. Selection counts {selection_counts}" + router.reset() - litellm.model_group_alias_map = {} except Exception as e: traceback.print_exc() pytest.fail(f"Error occurred: {e}") diff --git a/litellm/tests/test_streaming.py b/litellm/tests/test_streaming.py index 38af89631..8e6bd346f 100644 --- a/litellm/tests/test_streaming.py +++ b/litellm/tests/test_streaming.py @@ -58,6 +58,7 @@ def validate_first_format(chunk): for choice in chunk['choices']: assert isinstance(choice['index'], int), "'index' should be an integer." assert isinstance(choice['delta']['role'], str), "'role' should be a string." + assert "messages" not in choice # openai v1.0.0 returns content as None assert (choice['finish_reason'] is None) or isinstance(choice['finish_reason'], str), "'finish_reason' should be None or a string." @@ -230,7 +231,7 @@ def test_completion_cohere_stream_bad_key(): def test_completion_azure_stream(): try: - litellm.set_verbose = True + litellm.set_verbose = False messages = [ {"role": "system", "content": "You are a helpful assistant."}, { @@ -243,14 +244,14 @@ def test_completion_azure_stream(): ) complete_response = "" # Add any assertions here to check the response - for idx, chunk in enumerate(response): - chunk, finished = streaming_format_tests(idx, chunk) - if finished: - break + for idx, init_chunk in enumerate(response): + chunk, finished = streaming_format_tests(idx, init_chunk) complete_response += chunk + if finished: + assert isinstance(init_chunk.choices[0], litellm.utils.StreamingChoices) + break if complete_response.strip() == "": raise Exception("Empty response received") - print(f"completion_response: {complete_response}") except Exception as e: pytest.fail(f"Error occurred: {e}") # test_completion_azure_stream() @@ -334,6 +335,37 @@ def test_completion_palm_stream(): pytest.fail(f"Error occurred: {e}") # test_completion_palm_stream() + +def test_completion_mistral_api_stream(): + try: + litellm.set_verbose = True + print("Testing streaming mistral api response") + response = completion( + model="mistral/mistral-medium", + messages=[ + { + "role": "user", + "content": "Hey, how's it going?", + } + ], + max_tokens=10, + stream=True + ) + complete_response = "" + for idx, chunk in enumerate(response): + print(chunk) + # print(chunk.choices[0].delta) + chunk, finished = streaming_format_tests(idx, chunk) + if finished: + break + complete_response += chunk + if complete_response.strip() == "": + raise Exception("Empty response received") + print(f"completion_response: {complete_response}") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +# test_completion_mistral_api_stream() + def test_completion_deep_infra_stream(): # deep infra currently includes role in the 2nd chunk # waiting for them to make a fix on this @@ -364,6 +396,7 @@ def test_completion_deep_infra_stream(): pytest.fail(f"Error occurred: {e}") # test_completion_deep_infra_stream() +@pytest.mark.skip() def test_completion_nlp_cloud_stream(): try: messages = [ @@ -633,6 +666,47 @@ def test_completion_bedrock_ai21_stream(): # test_completion_bedrock_ai21_stream() +def test_sagemaker_weird_response(): + """ + When the stream ends, flush any remaining holding chunks. + """ + try: + chunk = """[INST] Hey, how's it going? [/INST] + + I'm doing well, thanks for asking! How about you? Is there anything you'd like to chat about or ask? I'm here to help with any questions you might have.""" + + logging_obj = litellm.Logging(model="berri-benchmarking-Llama-2-70b-chat-hf-4", messages=messages, stream=True, litellm_call_id="1234", function_id="function_id", call_type="acompletion", start_time=time.time()) + response = litellm.CustomStreamWrapper(completion_stream=chunk, model="berri-benchmarking-Llama-2-70b-chat-hf-4", custom_llm_provider="sagemaker", logging_obj=logging_obj) + complete_response = "" + for chunk in response: + complete_response += chunk["choices"][0]["delta"]["content"] + assert len(complete_response) > 0 + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") +# test_sagemaker_weird_response() + +@pytest.mark.asyncio +async def test_sagemaker_streaming_async(): + try: + messages = [{"role": "user", "content": "Hey, how's it going?"}] + litellm.set_verbose=True + response = await litellm.acompletion( + model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4", + messages=messages, + max_tokens=100, + temperature=0.7, + stream=True, + ) + + # Add any assertions here to check the response + complete_response = "" + async for chunk in response: + complete_response += chunk.choices[0].delta.content or "" + print(f"complete_response: {complete_response}") + assert len(complete_response) > 0 + except Exception as e: + pytest.fail(f"An exception occurred - {str(e)}") + # def test_completion_sagemaker_stream(): # try: # response = completion( diff --git a/litellm/tests/test_text_completion.py b/litellm/tests/test_text_completion.py index 660367651..9257a07f3 100644 --- a/litellm/tests/test_text_completion.py +++ b/litellm/tests/test_text_completion.py @@ -1,4 +1,4 @@ -import sys, os +import sys, os, asyncio import traceback from dotenv import load_dotenv @@ -10,7 +10,7 @@ sys.path.insert( ) # Adds the parent directory to the system path import pytest import litellm -from litellm import embedding, completion, text_completion, completion_cost +from litellm import embedding, completion, text_completion, completion_cost, atext_completion from litellm import RateLimitError @@ -61,7 +61,7 @@ def test_completion_openai_engine(): #print(response.choices[0].text) except Exception as e: pytest.fail(f"Error occurred: {e}") -test_completion_openai_engine() +# test_completion_openai_engine() def test_completion_chatgpt_prompt(): @@ -142,7 +142,7 @@ def test_completion_hf_prompt_array(): prompt=token_prompt, # token prompt is a 2d list, max_tokens=0, temperature=0.0, - echo=True, + # echo=True, # hugging face inference api is currently raising errors for this, looks like they have a regression on their side ) print("\n\n response") @@ -163,8 +163,23 @@ def test_text_completion_stream(): max_tokens=10, ) for chunk in response: - print(chunk) + print(f"chunk: {chunk}") except Exception as e: pytest.fail(f"GOT exception for HF In streaming{e}") -test_text_completion_stream() +# test_text_completion_stream() + +async def test_text_completion_async_stream(): + try: + response = await atext_completion( + model="text-completion-openai/text-davinci-003", + prompt="good morning", + stream=True, + max_tokens=10, + ) + async for chunk in response: + print(f"chunk: {chunk}") + except Exception as e: + pytest.fail(f"GOT exception for HF In streaming{e}") + +asyncio.run(test_text_completion_async_stream()) \ No newline at end of file diff --git a/litellm/utils.py b/litellm/utils.py index 2b23d6a78..bdd935119 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -8,6 +8,7 @@ # Thank you users! We ❤️ you! - Krrish & Ishaan import sys, re +import litellm import dotenv, json, traceback, threading import subprocess, os import litellm, openai @@ -19,6 +20,7 @@ import uuid import aiohttp import logging import asyncio, httpx, inspect +from inspect import iscoroutine import copy from tokenizers import Tokenizer from dataclasses import ( @@ -38,12 +40,14 @@ from .integrations.langsmith import LangsmithLogger from .integrations.weights_biases import WeightsBiasesLogger from .integrations.custom_logger import CustomLogger from .integrations.langfuse import LangFuseLogger +from .integrations.dynamodb import DyanmoDBLogger from .integrations.litedebugger import LiteDebugger from openai import OpenAIError as OriginalError from openai._models import BaseModel as OpenAIObject from .exceptions import ( AuthenticationError, BadRequestError, + NotFoundError, RateLimitError, ServiceUnavailableError, OpenAIError, @@ -51,7 +55,8 @@ from .exceptions import ( Timeout, APIConnectionError, APIError, - BudgetExceededError + BudgetExceededError, + UnprocessableEntityError ) from typing import cast, List, Dict, Union, Optional, Literal from .caching import Cache @@ -75,6 +80,7 @@ langsmithLogger = None weightsBiasesLogger = None customLogger = None langFuseLogger = None +dynamoLogger = None llmonitorLogger = None aispendLogger = None berrispendLogger = None @@ -126,7 +132,7 @@ def map_finish_reason(finish_reason: str): # openai supports 5 stop sequences - # cohere mapping - https://docs.cohere.com/reference/generate elif finish_reason == "COMPLETE": return "stop" - elif finish_reason == "MAX_TOKENS": + elif finish_reason == "MAX_TOKENS": # cohere + vertex ai return "length" elif finish_reason == "ERROR_TOXIC": return "content_filter" @@ -135,6 +141,10 @@ def map_finish_reason(finish_reason: str): # openai supports 5 stop sequences - # huggingface mapping https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/generate_stream elif finish_reason == "eos_token" or finish_reason == "stop_sequence": return "stop" + elif finish_reason == "FINISH_REASON_UNSPECIFIED" or finish_reason == "STOP": # vertex ai - got from running `print(dir(response_obj.candidates[0].finish_reason))`: ['FINISH_REASON_UNSPECIFIED', 'MAX_TOKENS', 'OTHER', 'RECITATION', 'SAFETY', 'STOP',] + return "stop" + elif finish_reason == "SAFETY": # vertex ai + return "content_filter" return finish_reason class FunctionCall(OpenAIObject): @@ -178,6 +188,14 @@ class Message(OpenAIObject): # Allow dictionary-style assignment of attributes setattr(self, key, value) + def json(self, **kwargs): + try: + return self.model_dump() # noqa + except: + # if using pydantic v1 + return self.dict() + + class Delta(OpenAIObject): def __init__(self, content=None, role=None, **params): super(Delta, self).__init__(**params) @@ -352,6 +370,13 @@ class ModelResponse(OpenAIObject): def __setitem__(self, key, value): # Allow dictionary-style assignment of attributes setattr(self, key, value) + + def json(self, **kwargs): + try: + return self.model_dump() # noqa + except: + # if using pydantic v1 + return self.dict() class Embedding(OpenAIObject): embedding: list = [] @@ -417,6 +442,13 @@ class EmbeddingResponse(OpenAIObject): def __setitem__(self, key, value): # Allow dictionary-style assignment of attributes setattr(self, key, value) + + def json(self, **kwargs): + try: + return self.model_dump() # noqa + except: + # if using pydantic v1 + return self.dict() class TextChoices(OpenAIObject): def __init__(self, finish_reason=None, index=0, text=None, logprobs=None, **params): @@ -546,8 +578,9 @@ class Logging: self.litellm_call_id = litellm_call_id self.function_id = function_id self.streaming_chunks = [] # for generating complete stream response + self.model_call_details = {} - def update_environment_variables(self, model, user, optional_params, litellm_params): + def update_environment_variables(self, model, user, optional_params, litellm_params, **additional_params): self.optional_params = optional_params self.model = model self.user = user @@ -562,7 +595,9 @@ class Logging: "start_time": self.start_time, "stream": self.stream, "user": user, - **self.optional_params + "call_type": str(self.call_type), + **self.optional_params, + **additional_params } def _pre_call(self, input, api_key, model=None, additional_args={}): @@ -793,7 +828,7 @@ class Logging: ) pass - def _success_handler_helper_fn(self, result=None, start_time=None, end_time=None): + def _success_handler_helper_fn(self, result=None, start_time=None, end_time=None, cache_hit=None): try: if start_time is None: start_time = self.start_time @@ -801,9 +836,7 @@ class Logging: end_time = datetime.datetime.now() self.model_call_details["log_event_type"] = "successful_api_call" self.model_call_details["end_time"] = end_time - - if isinstance(result, OpenAIObject): - result = result.model_dump() + self.model_call_details["cache_hit"] = cache_hit if litellm.max_budget and self.stream: time_diff = (end_time - start_time).total_seconds() @@ -811,36 +844,33 @@ class Logging: litellm._current_cost += litellm.completion_cost(model=self.model, prompt="", completion=result["content"], total_time=float_diff) return start_time, end_time, result - except: - pass + except Exception as e: + print_verbose(f"[Non-Blocking] LiteLLM.Success_Call Error: {str(e)}") - def success_handler(self, result=None, start_time=None, end_time=None, **kwargs): + def success_handler(self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs): print_verbose( f"Logging Details LiteLLM-Success Call" ) + # print(f"original response in success handler: {self.model_call_details['original_response']}") try: - print_verbose(f"success callbacks: {litellm.success_callback}") + print_verbose(f"success callbacks: {litellm.success_callback}") ## BUILD COMPLETE STREAMED RESPONSE complete_streaming_response = None - if self.stream == True and self.model_call_details.get("litellm_params", {}).get("acompletion", False) == True: - # if it's acompletion == True, chunks are built/appended in async_success_handler + if self.stream and self.model_call_details.get("litellm_params", {}).get("acompletion", False) == False: # only call stream chunk builder if it's not acompletion() if result.choices[0].finish_reason is not None: # if it's the last chunk - complete_streaming_response = litellm.stream_chunk_builder(self.streaming_chunks, messages=self.model_call_details.get("messages", None)) - else: - # this is a completion() call - if self.stream: - print_verbose("success callback - assembling complete streaming response") - if result.choices[0].finish_reason is not None: # if it's the last chunk - print_verbose(f"success callback - Got the very Last chunk. Assembling {self.streaming_chunks}") - self.streaming_chunks.append(result) + self.streaming_chunks.append(result) + # print_verbose(f"final set of received chunks: {self.streaming_chunks}") + try: complete_streaming_response = litellm.stream_chunk_builder(self.streaming_chunks, messages=self.model_call_details.get("messages", None)) - print_verbose(f"success callback - complete streamign response{complete_streaming_response}") - else: - self.streaming_chunks.append(result) + except: + complete_streaming_response = None + else: + self.streaming_chunks.append(result) + if complete_streaming_response: self.model_call_details["complete_streaming_response"] = complete_streaming_response - start_time, end_time, result = self._success_handler_helper_fn(start_time=start_time, end_time=end_time, result=result) + start_time, end_time, result = self._success_handler_helper_fn(start_time=start_time, end_time=end_time, result=result, cache_hit=cache_hit) for callback in litellm.success_callback: try: if callback == "lite_debugger": @@ -857,9 +887,6 @@ class Logging: call_type = self.call_type, stream = self.stream, ) - if callback == "api_manager": - print_verbose("reaches api manager for updating model cost") - litellm.apiManager.update_cost(completion_obj=result, user=self.user) if callback == "promptlayer": print_verbose("reaches promptlayer for logging!") promptLayerLogger.log_event( @@ -947,6 +974,7 @@ class Logging: print_verbose=print_verbose, ) if callback == "langfuse": + global langFuseLogger print_verbose("reaches langfuse for logging!") kwargs = {} for k, v in self.model_call_details.items(): @@ -959,7 +987,8 @@ class Logging: else: print_verbose("reaches langfuse for streaming logging!") result = kwargs["complete_streaming_response"] - + if langFuseLogger is None: + langFuseLogger = LangFuseLogger() langFuseLogger.log_event( kwargs=kwargs, response_obj=result, @@ -967,7 +996,7 @@ class Logging: end_time=end_time, print_verbose=print_verbose, ) - if callback == "cache": + if callback == "cache" and litellm.cache is not None: # this only logs streaming once, complete_streaming_response exists i.e when stream ends print_verbose("success_callback: reaches cache for logging!") kwargs = self.model_call_details @@ -992,7 +1021,7 @@ class Logging: end_time=end_time, print_verbose=print_verbose, ) - if isinstance(callback, CustomLogger): # custom logger class + elif isinstance(callback, CustomLogger) and self.model_call_details.get("litellm_params", {}).get("acompletion", False) == False and self.model_call_details.get("litellm_params", {}).get("aembedding", False) == False: # custom logger class print_verbose(f"success callbacks: Running Custom Logger Class") if self.stream and complete_streaming_response is None: callback.log_stream_event( @@ -1037,26 +1066,31 @@ class Logging: ) pass - async def async_success_handler(self, result=None, start_time=None, end_time=None, **kwargs): + async def async_success_handler(self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs): """ Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. """ print_verbose(f"Async success callbacks: {litellm._async_success_callback}") - ## BUILD COMPLETE STREAMED RESPONSE complete_streaming_response = None if self.stream: if result.choices[0].finish_reason is not None: # if it's the last chunk self.streaming_chunks.append(result) - complete_streaming_response = litellm.stream_chunk_builder(self.streaming_chunks, messages=self.model_call_details.get("messages", None)) + # print_verbose(f"final set of received chunks: {self.streaming_chunks}") + try: + complete_streaming_response = litellm.stream_chunk_builder(self.streaming_chunks, messages=self.model_call_details.get("messages", None)) + except Exception as e: + print_verbose(f"Error occurred building stream chunk: {traceback.format_exc()}") + complete_streaming_response = None else: self.streaming_chunks.append(result) if complete_streaming_response: + print_verbose("Async success callbacks: Got a complete streaming response") self.model_call_details["complete_streaming_response"] = complete_streaming_response - start_time, end_time, result = self._success_handler_helper_fn(start_time=start_time, end_time=end_time, result=result) + start_time, end_time, result = self._success_handler_helper_fn(start_time=start_time, end_time=end_time, result=result, cache_hit=cache_hit) for callback in litellm._async_success_callback: try: - if callback == "cache": + if callback == "cache" and litellm.cache is not None: # set_cache once complete streaming response is built print_verbose("async success_callback: reaches cache for logging!") kwargs = self.model_call_details @@ -1079,6 +1113,13 @@ class Logging: start_time=start_time, end_time=end_time, ) + else: + await callback.async_log_stream_event( # [TODO]: move this to being an async log stream event function + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time + ) else: await callback.async_log_success_event( kwargs=self.model_call_details, @@ -1096,29 +1137,82 @@ class Logging: print_verbose=print_verbose, callback_func=callback ) + if callback == "dynamodb": + global dynamoLogger + if dynamoLogger is None: + dynamoLogger = DyanmoDBLogger() + if self.stream: + if "complete_streaming_response" in self.model_call_details: + print_verbose("DynamoDB Logger: Got Stream Event - Completed Stream Response") + await dynamoLogger._async_log_event( + kwargs=self.model_call_details, + response_obj=self.model_call_details["complete_streaming_response"], + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose + ) + else: + print_verbose("DynamoDB Logger: Got Stream Event - No complete stream response as yet") + else: + await dynamoLogger._async_log_event( + kwargs=self.model_call_details, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) + if callback == "langfuse": + global langFuseLogger + print_verbose("reaches langfuse for 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: + if "complete_streaming_response" not in kwargs: + return + else: + print_verbose("reaches langfuse for streaming logging!") + result = kwargs["complete_streaming_response"] + if langFuseLogger is None: + langFuseLogger = LangFuseLogger() + await langFuseLogger._async_log_event( + kwargs=kwargs, + response_obj=result, + start_time=start_time, + end_time=end_time, + print_verbose=print_verbose, + ) except: print_verbose( f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}" ) + pass + + def _failure_handler_helper_fn(self, exception, traceback_exception, start_time=None, end_time=None): + if start_time is None: + start_time = self.start_time + if end_time is None: + end_time = datetime.datetime.now() + + # on some exceptions, model_call_details is not always initialized, this ensures that we still log those exceptions + if not hasattr(self, "model_call_details"): + self.model_call_details = {} + + self.model_call_details["log_event_type"] = "failed_api_call" + self.model_call_details["exception"] = exception + self.model_call_details["traceback_exception"] = traceback_exception + self.model_call_details["end_time"] = end_time + self.model_call_details.setdefault("original_response", None) + return start_time, end_time def failure_handler(self, exception, traceback_exception, start_time=None, end_time=None): print_verbose( f"Logging Details LiteLLM-Failure Call" ) try: - if start_time is None: - start_time = self.start_time - if end_time is None: - end_time = datetime.datetime.now() - - # on some exceptions, model_call_details is not always initialized, this ensures that we still log those exceptions - if not hasattr(self, "model_call_details"): - self.model_call_details = {} - - self.model_call_details["log_event_type"] = "failed_api_call" - self.model_call_details["exception"] = exception - self.model_call_details["traceback_exception"] = traceback_exception - self.model_call_details["end_time"] = end_time + start_time, end_time = self._failure_handler_helper_fn(exception=exception, traceback_exception=traceback_exception, start_time=start_time, end_time=end_time) result = None # result sent to all loggers, init this to None incase it's not created for callback in litellm.failure_callback: try: @@ -1184,7 +1278,7 @@ class Logging: print_verbose=print_verbose, callback_func=callback ) - elif isinstance(callback, CustomLogger): # custom logger class + elif isinstance(callback, CustomLogger) and self.model_call_details.get("litellm_params", {}).get("acompletion", False) == False and self.model_call_details.get("litellm_params", {}).get("aembedding", False) == False: # custom logger class callback.log_failure_event( start_time=start_time, end_time=end_time, @@ -1210,16 +1304,8 @@ class Logging: """ Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions. """ - # on some exceptions, model_call_details is not always initialized, this ensures that we still log those exceptions - if not hasattr(self, "model_call_details"): - self.model_call_details = {} - - self.model_call_details["log_event_type"] = "failed_api_call" - self.model_call_details["exception"] = exception - self.model_call_details["traceback_exception"] = traceback_exception - self.model_call_details["end_time"] = end_time - result = {} # result sent to all loggers, init this to None incase it's not created - + start_time, end_time = self._failure_handler_helper_fn(exception=exception, traceback_exception=traceback_exception, start_time=start_time, end_time=end_time) + result = None # result sent to all loggers, init this to None incase it's not created for callback in litellm._async_failure_callback: try: if isinstance(callback, CustomLogger): # custom logger class @@ -1238,7 +1324,7 @@ class Logging: print_verbose=print_verbose, callback_func=callback ) - except: + except Exception as e: print_verbose( f"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while success logging {traceback.format_exc()}" ) @@ -1378,6 +1464,15 @@ def client(original_function): if inspect.iscoroutinefunction(callback): litellm._async_success_callback.append(callback) removed_async_items.append(index) + elif callback == "dynamodb": + # dynamo is an async callback, it's used for the proxy and needs to be async + # we only support async dynamo db logging for acompletion/aembedding since that's used on proxy + litellm._async_success_callback.append(callback) + removed_async_items.append(index) + elif callback == "langfuse" and inspect.iscoroutinefunction(original_function): + # use async success callback for langfuse if this is litellm.acompletion(). Streaming logging does not work otherwise + litellm._async_success_callback.append(callback) + removed_async_items.append(index) # Pop the async items from success_callback in reverse order to avoid index issues for index in reversed(removed_async_items): @@ -1407,6 +1502,7 @@ def client(original_function): model = args[0] if len(args) > 0 else kwargs["model"] call_type = original_function.__name__ if call_type == CallTypes.completion.value or call_type == CallTypes.acompletion.value: + messages = None if len(args) > 1: messages = args[1] elif kwargs.get("messages", None): @@ -1477,16 +1573,15 @@ def client(original_function): raise BudgetExceededError(current_cost=litellm._current_cost, max_budget=litellm.max_budget) # [OPTIONAL] CHECK CACHE - # remove this after deprecating litellm.caching - if (litellm.caching or litellm.caching_with_models) and litellm.cache is None: - litellm.cache = Cache() - print_verbose(f"kwargs[caching]: {kwargs.get('caching', False)}; litellm.cache: {litellm.cache}") # if caching is false, don't run this if (kwargs.get("caching", None) is None and litellm.cache is not None) or kwargs.get("caching", False) == True: # allow users to control returning cached responses from the completion function # checking cache - if (litellm.cache != None or litellm.caching or litellm.caching_with_models): + print_verbose(f"INSIDE CHECKING CACHE") + if litellm.cache is not None and str(original_function.__name__) in litellm.cache.supported_call_types: print_verbose(f"Checking Cache") + preset_cache_key = litellm.cache.get_cache_key(*args, **kwargs) + kwargs["preset_cache_key"] = preset_cache_key # for streaming calls, we need to pass the preset_cache_key cached_result = litellm.cache.get_cache(*args, **kwargs) if cached_result != None: if "detail" in cached_result: @@ -1522,17 +1617,12 @@ def client(original_function): post_call_processing(original_response=result, model=model) # [OPTIONAL] ADD TO CACHE - if litellm.caching or litellm.caching_with_models or litellm.cache != None: # user init a cache object + if litellm.cache is not None and str(original_function.__name__) in litellm.cache.supported_call_types: litellm.cache.add_cache(result, *args, **kwargs) # LOG SUCCESS - handle streaming success logging in the _next_ object, remove `handle_success` once it's deprecated print_verbose(f"Wrapper: Completed Call, calling success_handler") threading.Thread(target=logging_obj.success_handler, args=(result, start_time, end_time)).start() - # threading.Thread(target=logging_obj.success_handler, args=(result, start_time, end_time)).start() - my_thread = threading.Thread( - target=handle_success, args=(args, kwargs, result, start_time, end_time) - ) # don't interrupt execution of main thread - my_thread.start() # RETURN RESULT result._response_ms = (end_time - start_time).total_seconds() * 1000 # return response latency in ms like openai return result @@ -1605,7 +1695,7 @@ def client(original_function): if (kwargs.get("caching", None) is None and litellm.cache is not None) or kwargs.get("caching", False) == True: # allow users to control returning cached responses from the completion function # checking cache print_verbose(f"INSIDE CHECKING CACHE") - if litellm.cache is not None: + if litellm.cache is not None and str(original_function.__name__) in litellm.cache.supported_call_types: print_verbose(f"Checking Cache") cached_result = litellm.cache.get_cache(*args, **kwargs) if cached_result != None: @@ -1613,13 +1703,22 @@ def client(original_function): call_type = original_function.__name__ if call_type == CallTypes.acompletion.value and isinstance(cached_result, dict): if kwargs.get("stream", False) == True: - return convert_to_streaming_response_async( + cached_result = convert_to_streaming_response_async( response_object=cached_result, ) else: - return convert_to_model_response_object(response_object=cached_result, model_response_object=ModelResponse()) - else: - return cached_result + cached_result = convert_to_model_response_object(response_object=cached_result, model_response_object=ModelResponse()) + elif call_type == CallTypes.aembedding.value and isinstance(cached_result, dict): + cached_result = convert_to_model_response_object(response_object=cached_result, model_response_object=EmbeddingResponse(), response_type="embedding") + # LOG SUCCESS + cache_hit = True + end_time = datetime.datetime.now() + model, custom_llm_provider, dynamic_api_key, api_base = litellm.get_llm_provider(model=model, custom_llm_provider=kwargs.get('custom_llm_provider', None), api_base=kwargs.get('api_base', None), api_key=kwargs.get('api_key', None)) + print_verbose(f"Async Wrapper: Completed Call, calling async_success_handler: {logging_obj.async_success_handler}") + logging_obj.update_environment_variables(model=model, user=kwargs.get('user', None), optional_params={}, litellm_params={"logger_fn": kwargs.get('logger_fn', None), "acompletion": True, "metadata": kwargs.get("metadata", {}), "model_info": kwargs.get("model_info", {}), "proxy_server_request": kwargs.get("proxy_server_request", None), "preset_cache_key": kwargs.get("preset_cache_key", None), "stream_response": kwargs.get("stream_response", {})}, input=kwargs.get('messages', ""), api_key=kwargs.get('api_key', None), original_response=str(cached_result), additional_args=None, stream=kwargs.get('stream', False)) + asyncio.create_task(logging_obj.async_success_handler(cached_result, start_time, end_time, cache_hit)) + threading.Thread(target=logging_obj.success_handler, args=(cached_result, start_time, end_time, cache_hit)).start() + return cached_result # MODEL CALL result = await original_function(*args, **kwargs) end_time = datetime.datetime.now() @@ -1636,10 +1735,13 @@ def client(original_function): post_call_processing(original_response=result, model=model) # [OPTIONAL] ADD TO CACHE - if litellm.caching or litellm.caching_with_models or litellm.cache != None: # user init a cache object - litellm.cache.add_cache(result, *args, **kwargs) + if litellm.cache is not None and str(original_function.__name__) in litellm.cache.supported_call_types: + if isinstance(result, litellm.ModelResponse) or isinstance(result, litellm.EmbeddingResponse): + asyncio.create_task(litellm.cache._async_add_cache(result.json(), *args, **kwargs)) + else: + asyncio.create_task(litellm.cache._async_add_cache(result, *args, **kwargs)) # LOG SUCCESS - handle streaming success logging in the _next_ object - print_verbose(f"Async Wrapper: Completed Call, calling async_success_handler") + print_verbose(f"Async Wrapper: Completed Call, calling async_success_handler: {logging_obj.async_success_handler}") asyncio.create_task(logging_obj.async_success_handler(result, start_time, end_time)) threading.Thread(target=logging_obj.success_handler, args=(result, start_time, end_time)).start() # RETURN RESULT @@ -1647,6 +1749,19 @@ def client(original_function): result._response_ms = (end_time - start_time).total_seconds() * 1000 # return response latency in ms like openai return result except Exception as e: + traceback_exception = traceback.format_exc() + crash_reporting(*args, **kwargs, exception=traceback_exception) + end_time = datetime.datetime.now() + if logging_obj: + try: + logging_obj.failure_handler(e, traceback_exception, start_time, end_time) # DO NOT MAKE THREADED - router retry fallback relies on this! + except Exception as e: + raise e + try: + await logging_obj.async_failure_handler(e, traceback_exception, start_time, end_time) + except Exception as e: + raise e + call_type = original_function.__name__ if call_type == CallTypes.acompletion.value: num_retries = ( @@ -1656,27 +1771,24 @@ def client(original_function): ) litellm.num_retries = None # set retries to None to prevent infinite loops context_window_fallback_dict = kwargs.get("context_window_fallback_dict", {}) - + if num_retries: - kwargs["num_retries"] = num_retries - kwargs["original_function"] = original_function - if (isinstance(e, openai.RateLimitError)): # rate limiting specific error - kwargs["retry_strategy"] = "exponential_backoff_retry" - elif (isinstance(e, openai.APIError)): # generic api error - kwargs["retry_strategy"] = "constant_retry" - return await litellm.acompletion_with_retries(*args, **kwargs) + try: + kwargs["num_retries"] = num_retries + kwargs["original_function"] = original_function + if (isinstance(e, openai.RateLimitError)): # rate limiting specific error + kwargs["retry_strategy"] = "exponential_backoff_retry" + elif (isinstance(e, openai.APIError)): # generic api error + kwargs["retry_strategy"] = "constant_retry" + return await litellm.acompletion_with_retries(*args, **kwargs) + except: + pass elif isinstance(e, litellm.exceptions.ContextWindowExceededError) and context_window_fallback_dict and model in context_window_fallback_dict: if len(args) > 0: args[0] = context_window_fallback_dict[model] else: kwargs["model"] = context_window_fallback_dict[model] return await original_function(*args, **kwargs) - traceback_exception = traceback.format_exc() - crash_reporting(*args, **kwargs, exception=traceback_exception) - end_time = datetime.datetime.now() - if logging_obj: - logging_obj.failure_handler(e, traceback_exception, start_time, end_time) # DO NOT MAKE THREADED - router retry fallback relies on this! - asyncio.create_task(logging_obj.async_failure_handler(e, traceback_exception, start_time, end_time)) raise e is_coroutine = inspect.iscoroutinefunction(original_function) @@ -2056,7 +2168,6 @@ def register_model(model_cost: Union[str, dict]): return model_cost def get_litellm_params( - return_async=False, api_key=None, force_timeout=600, azure=False, @@ -2074,10 +2185,10 @@ def get_litellm_params( model_info=None, proxy_server_request=None, acompletion=None, + preset_cache_key = None ): litellm_params = { "acompletion": acompletion, - "return_async": return_async, "api_key": api_key, "force_timeout": force_timeout, "logger_fn": logger_fn, @@ -2090,12 +2201,46 @@ def get_litellm_params( "metadata": metadata, "model_info": model_info, "proxy_server_request": proxy_server_request, + "preset_cache_key": preset_cache_key, "stream_response": {} # litellm_call_id: ModelResponse Dict } return litellm_params +def get_optional_params_embeddings( + # 2 optional params + user=None, + encoding_format=None, + custom_llm_provider="", + **kwargs +): + # retrieve all parameters passed to the function + passed_params = locals() + custom_llm_provider = passed_params.pop("custom_llm_provider", None) + special_params = passed_params.pop("kwargs") + for k, v in special_params.items(): + passed_params[k] = v + + default_params = { + "user": None, + "encoding_format": None + } + + non_default_params = {k: v for k, v in passed_params.items() if (k in default_params and v != default_params[k])} + ## raise exception if non-default value passed for non-openai/azure embedding calls + if custom_llm_provider != "openai" and custom_llm_provider != "azure": + if len(non_default_params.keys()) > 0: + if litellm.drop_params is True: # drop the unsupported non-default values + keys = list(non_default_params.keys()) + for k in keys: + non_default_params.pop(k, None) + return non_default_params + raise UnsupportedParamsError(status_code=500, message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.") + + final_params = {**non_default_params, **kwargs} + return final_params + def get_optional_params( # use the openai defaults # 12 optional params functions=[], @@ -2107,7 +2252,7 @@ def get_optional_params( # use the openai defaults stop=None, max_tokens=None, presence_penalty=None, - frequency_penalty=0, + frequency_penalty=None, logit_bias=None, user=None, model=None, @@ -2591,6 +2736,28 @@ def get_optional_params( # use the openai defaults optional_params["stream"] = stream if max_tokens: optional_params["max_tokens"] = max_tokens + elif custom_llm_provider == "mistral": + supported_params = ["temperature", "top_p", "stream", "max_tokens"] + _check_valid_arg(supported_params=supported_params) + optional_params = non_default_params + if temperature is not None: + optional_params["temperature"] = temperature + if top_p is not None: + optional_params["top_p"] = top_p + if stream is not None: + optional_params["stream"] = stream + if max_tokens is not None: + optional_params["max_tokens"] = max_tokens + + # check safe_mode, random_seed: https://docs.mistral.ai/api/#operation/createChatCompletion + safe_mode = passed_params.pop("safe_mode", None) + random_seed = passed_params.pop("random_seed", None) + extra_body = {} + if safe_mode is not None: + extra_body["safe_mode"] = safe_mode + if random_seed is not None: + extra_body["random_seed"] = random_seed + optional_params["extra_body"] = extra_body # openai client supports `extra_body` param else: # assume passing in params for openai/azure openai supported_params = ["functions", "function_call", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "response_format", "seed", "tools", "tool_choice", "max_retries"] _check_valid_arg(supported_params=supported_params) @@ -2661,6 +2828,10 @@ def get_llm_provider(model: str, custom_llm_provider: Optional[str] = None, api_ # deepinfra is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.endpoints.anyscale.com/v1 api_base = "https://api.deepinfra.com/v1/openai" dynamic_api_key = get_secret("DEEPINFRA_API_KEY") + elif custom_llm_provider == "mistral": + # mistral is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.mistral.ai + api_base = "https://api.mistral.ai/v1" + dynamic_api_key = get_secret("MISTRAL_API_KEY") return model, custom_llm_provider, dynamic_api_key, api_base # check if api base is a known openai compatible endpoint @@ -2676,6 +2847,9 @@ def get_llm_provider(model: str, custom_llm_provider: Optional[str] = None, api_ elif endpoint == "api.deepinfra.com/v1/openai": custom_llm_provider = "deepinfra" dynamic_api_key = get_secret("DEEPINFRA_API_KEY") + elif endpoint == "api.mistral.ai/v1": + custom_llm_provider = "mistral" + dynamic_api_key = get_secret("MISTRAL_API_KEY") return model, custom_llm_provider, dynamic_api_key, api_base # check if model in known model provider list -> for huggingface models, raise exception as they don't have a fixed provider (can be togetherai, anyscale, baseten, runpod, et.) @@ -2703,12 +2877,13 @@ def get_llm_provider(model: str, custom_llm_provider: Optional[str] = None, api_ ## openrouter elif model in litellm.maritalk_models: custom_llm_provider = "maritalk" - ## vertex - text + chat models + ## vertex - text + chat + language (gemini) models elif( model in litellm.vertex_chat_models or model in litellm.vertex_code_chat_models or model in litellm.vertex_text_models or - model in litellm.vertex_code_text_models + model in litellm.vertex_code_text_models or + model in litellm.vertex_language_models ): custom_llm_provider = "vertex_ai" ## ai21 @@ -2736,7 +2911,18 @@ def get_llm_provider(model: str, custom_llm_provider: Optional[str] = None, api_ print() # noqa print("\033[1;31mProvider List: https://docs.litellm.ai/docs/providers\033[0m") # noqa print() # noqa - raise ValueError(f"LLM Provider NOT provided. Pass in the LLM provider you are trying to call. E.g. For 'Huggingface' inference endpoints pass in `completion(model='huggingface/{model}',..)` Learn more: https://docs.litellm.ai/docs/providers") + error_str = f"LLM Provider NOT provided. Pass in the LLM provider you are trying to call. You passed model={model}\n Pass model as E.g. For 'Huggingface' inference endpoints pass in `completion(model='huggingface/starcoder',..)` Learn more: https://docs.litellm.ai/docs/providers" + # maps to openai.NotFoundError, this is raised when openai does not recognize the llm + raise litellm.exceptions.NotFoundError( # type: ignore + message=error_str, + model=model, + response=httpx.Response( + status_code=404, + content=error_str, + request=httpx.request(method="completion", url="https://github.com/BerriAI/litellm") # type: ignore + ), + llm_provider="" + ) return model, custom_llm_provider, dynamic_api_key, api_base except Exception as e: raise e @@ -3280,7 +3466,7 @@ def validate_environment(model: Optional[str]=None) -> dict: return {"keys_in_environment": keys_in_environment, "missing_keys": missing_keys} def set_callbacks(callback_list, function_id=None): - global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, heliconeLogger, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, llmonitorLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, langsmithLogger + global sentry_sdk_instance, capture_exception, add_breadcrumb, posthog, slack_app, alerts_channel, traceloopLogger, heliconeLogger, aispendLogger, berrispendLogger, supabaseClient, liteDebuggerClient, llmonitorLogger, promptLayerLogger, langFuseLogger, customLogger, weightsBiasesLogger, langsmithLogger, dynamoLogger try: for callback in callback_list: print_verbose(f"callback: {callback}") @@ -3343,6 +3529,8 @@ def set_callbacks(callback_list, function_id=None): promptLayerLogger = PromptLayerLogger() elif callback == "langfuse": langFuseLogger = LangFuseLogger() + elif callback == "dynamodb": + dynamoLogger = DyanmoDBLogger() elif callback == "wandb": weightsBiasesLogger = WeightsBiasesLogger() elif callback == "langsmith": @@ -4043,7 +4231,7 @@ def exception_type( llm_provider=custom_llm_provider ) - if custom_llm_provider == "openai" or custom_llm_provider == "text-completion-openai" or custom_llm_provider == "custom_openai": + if custom_llm_provider == "openai" or custom_llm_provider == "text-completion-openai" or custom_llm_provider == "custom_openai" or custom_llm_provider in litellm.openai_compatible_providers: if "This model's maximum context length is" in error_str or "Request too large" in error_str: exception_mapping_worked = True raise ContextWindowExceededError( @@ -4052,6 +4240,14 @@ def exception_type( model=model, response=original_exception.response ) + elif "invalid_request_error" in error_str and "model_not_found" in error_str: + exception_mapping_worked = True + raise NotFoundError( + message=f"OpenAIException - {original_exception.message}", + llm_provider="openai", + model=model, + response=original_exception.response + ) elif "invalid_request_error" in error_str and "Incorrect API key provided" not in error_str: exception_mapping_worked = True raise BadRequestError( @@ -4070,6 +4266,14 @@ def exception_type( model=model, response=original_exception.response ) + elif original_exception.status_code == 404: + exception_mapping_worked = True + raise NotFoundError( + message=f"OpenAIException - {original_exception.message}", + model=model, + llm_provider="openai", + response=original_exception.response + ) elif original_exception.status_code == 408: exception_mapping_worked = True raise Timeout( @@ -4077,7 +4281,7 @@ def exception_type( model=model, llm_provider="openai", ) - if original_exception.status_code == 422: + elif original_exception.status_code == 422: exception_mapping_worked = True raise BadRequestError( message=f"OpenAIException - {original_exception.message}", @@ -4354,7 +4558,15 @@ def exception_type( ) elif "403" in error_str: exception_mapping_worked = True - raise AuthenticationError( + raise U( + message=f"VertexAIException - {error_str}", + model=model, + llm_provider="vertex_ai", + response=original_exception.response + ) + elif "The response was blocked." in error_str: + exception_mapping_worked = True + raise UnprocessableEntityError( message=f"VertexAIException - {error_str}", model=model, llm_provider="vertex_ai", @@ -4733,29 +4945,30 @@ def exception_type( llm_provider="together_ai", response=original_exception.response ) - elif original_exception.status_code == 408: + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 408: + exception_mapping_worked = True + raise Timeout( + message=f"TogetherAIException - {original_exception.message}", + model=model, + llm_provider="together_ai", + request=original_exception.request + ) + elif original_exception.status_code == 429: + exception_mapping_worked = True + raise RateLimitError( + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + response=original_exception.response + ) + elif original_exception.status_code == 524: exception_mapping_worked = True raise Timeout( - message=f"TogetherAIException - {original_exception.message}", - model=model, - llm_provider="together_ai", - request=original_exception.request - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( message=f"TogetherAIException - {original_exception.message}", llm_provider="together_ai", model=model, - response=original_exception.response ) - elif original_exception.status_code == 524: - exception_mapping_worked = True - raise Timeout( - message=f"TogetherAIException - {original_exception.message}", - llm_provider="together_ai", - model=model, - ) else: exception_mapping_worked = True raise APIError( @@ -4868,6 +5081,14 @@ def exception_type( model=model, response=original_exception.response ) + elif "DeploymentNotFound" in error_str: + exception_mapping_worked = True + raise NotFoundError( + message=f"AzureException - {original_exception.message}", + llm_provider="azure", + model=model, + response=original_exception.response + ) elif "invalid_request_error" in error_str: exception_mapping_worked = True raise BadRequestError( @@ -5088,9 +5309,6 @@ class CustomStreamWrapper: self.special_tokens = ["<|assistant|>", "<|system|>", "<|user|>", "", ""] self.holding_chunk = "" self.complete_response = "" - if self.logging_obj: - # Log the type of the received item - self.logging_obj.post_call(str(type(completion_stream))) def __iter__(self): return self @@ -5115,10 +5333,6 @@ class CustomStreamWrapper: except Exception as e: raise e - def logging(self, text): - if self.logging_obj: - self.logging_obj.post_call(text) - def check_special_tokens(self, chunk: str, finish_reason: Optional[str]): hold = False if finish_reason: @@ -5413,6 +5627,30 @@ class CustomStreamWrapper: traceback.print_exc() return "" + def handle_ollama_stream(self, chunk): + try: + json_chunk = json.loads(chunk) + if "error" in json_chunk: + raise Exception(f"Ollama Error - {json_chunk}") + + text = "" + is_finished = False + finish_reason = None + if json_chunk["done"] == True: + text = "" + is_finished = True + finish_reason = "stop" + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + elif json_chunk["response"]: + print_verbose(f"delta content: {json_chunk}") + text = json_chunk["response"] + return {"text": text, "is_finished": is_finished, "finish_reason": finish_reason} + else: + raise Exception(f"Ollama Error - {json_chunk}") + except Exception as e: + raise e + + def handle_bedrock_stream(self, chunk): if hasattr(chunk, "get"): chunk = chunk.get('chunk') @@ -5458,6 +5696,7 @@ class CustomStreamWrapper: def chunk_creator(self, chunk): model_response = ModelResponse(stream=True, model=self.model) + model_response.choices = [StreamingChoices()] model_response.choices[0].finish_reason = None response_obj = {} try: @@ -5548,6 +5787,7 @@ class CustomStreamWrapper: model_response.choices[0].finish_reason = response_obj["finish_reason"] self.sent_last_chunk = True elif self.custom_llm_provider == "sagemaker": + print_verbose(f"ENTERS SAGEMAKER STREAMING") if len(self.completion_stream)==0: if self.sent_last_chunk: raise StopIteration @@ -5555,6 +5795,7 @@ class CustomStreamWrapper: model_response.choices[0].finish_reason = "stop" self.sent_last_chunk = True new_chunk = self.completion_stream + print_verbose(f"sagemaker chunk: {new_chunk}") completion_obj["content"] = new_chunk self.completion_stream = self.completion_stream[len(self.completion_stream):] elif self.custom_llm_provider == "petals": @@ -5584,9 +5825,11 @@ class CustomStreamWrapper: self.completion_stream = self.completion_stream[chunk_size:] time.sleep(0.05) elif self.custom_llm_provider == "ollama": - if "error" in chunk: - exception_type(model=self.model, custom_llm_provider=self.custom_llm_provider, original_exception=chunk["error"]) - completion_obj = chunk + response_obj = self.handle_ollama_stream(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + model_response.choices[0].finish_reason = response_obj["finish_reason"] elif self.custom_llm_provider == "text-completion-openai": response_obj = self.handle_openai_text_completion_chunk(chunk) completion_obj["content"] = response_obj["text"] @@ -5632,16 +5875,19 @@ class CustomStreamWrapper: completion_obj["role"] = "assistant" self.sent_first_chunk = True model_response.choices[0].delta = Delta(**completion_obj) - # LOGGING - threading.Thread(target=self.logging_obj.success_handler, args=(model_response,)).start() print_verbose(f"model_response: {model_response}") return model_response else: return elif model_response.choices[0].finish_reason: + # flush any remaining holding chunk + if len(self.holding_chunk) > 0: + if model_response.choices[0].delta.content is None: + model_response.choices[0].delta.content = self.holding_chunk + else: + model_response.choices[0].delta.content = self.holding_chunk + model_response.choices[0].delta.content + self.holding_chunk = "" model_response.choices[0].finish_reason = map_finish_reason(model_response.choices[0].finish_reason) # ensure consistent output to openai - # LOGGING - threading.Thread(target=self.logging_obj.success_handler, args=(model_response,)).start() return model_response elif response_obj is not None and response_obj.get("original_chunk", None) is not None: # function / tool calling branch - only set for openai/azure compatible endpoints # enter this branch when no content has been passed in response @@ -5662,8 +5908,6 @@ class CustomStreamWrapper: if self.sent_first_chunk == False: model_response.choices[0].delta["role"] = "assistant" self.sent_first_chunk = True - # LOGGING - threading.Thread(target=self.logging_obj.success_handler, args=(model_response,)).start() # log response return model_response else: return @@ -5672,26 +5916,33 @@ class CustomStreamWrapper: except Exception as e: traceback_exception = traceback.format_exc() e.message = str(e) - # LOG FAILURE - handle streaming failure logging in the _next_ object, remove `handle_failure` once it's deprecated - threading.Thread(target=self.logging_obj.failure_handler, args=(e, traceback_exception)).start() raise exception_type(model=self.model, custom_llm_provider=self.custom_llm_provider, original_exception=e) ## needs to handle the empty string case (even starting chunk can be an empty string) def __next__(self): try: - while True: + while True: if isinstance(self.completion_stream, str) or isinstance(self.completion_stream, bytes): chunk = self.completion_stream else: chunk = next(self.completion_stream) + print_verbose(f"value of chunk: {chunk} ") if chunk is not None and chunk != b'': + print_verbose(f"PROCESSED CHUNK PRE CHUNK CREATOR: {chunk}") response = self.chunk_creator(chunk=chunk) - if response is not None: - return response + print_verbose(f"PROCESSED CHUNK POST CHUNK CREATOR: {response}") + if response is None: + continue + ## LOGGING + threading.Thread(target=self.logging_obj.success_handler, args=(response,)).start() # log response + return response except StopIteration: raise # Re-raise StopIteration except Exception as e: - # Handle other exceptions if needed + print_verbose(f"HITS AN ERROR: {str(e)}\n\n {traceback.format_exc()}") + traceback_exception = traceback.format_exc() + # LOG FAILURE - handle streaming failure logging in the _next_ object, remove `handle_failure` once it's deprecated + threading.Thread(target=self.logging_obj.failure_handler, args=(e, traceback_exception)).start() raise e @@ -5702,24 +5953,41 @@ class CustomStreamWrapper: or self.custom_llm_provider == "azure" or self.custom_llm_provider == "custom_openai" or self.custom_llm_provider == "text-completion-openai" - or self.custom_llm_provider == "huggingface"): + or self.custom_llm_provider == "huggingface" + or self.custom_llm_provider == "ollama" + or self.custom_llm_provider == "vertex_ai"): + print_verbose(f"INSIDE ASYNC STREAMING!!!") + print_verbose(f"value of async completion stream: {self.completion_stream}") async for chunk in self.completion_stream: + print_verbose(f"value of async chunk: {chunk}") if chunk == "None" or chunk is None: raise Exception # chunk_creator() does logging/stream chunk building. We need to let it know its being called in_async_func, so we don't double add chunks. # __anext__ also calls async_success_handler, which does logging + print_verbose(f"PROCESSED ASYNC CHUNK PRE CHUNK CREATOR: {chunk}") processed_chunk = self.chunk_creator(chunk=chunk) + print_verbose(f"PROCESSED ASYNC CHUNK POST CHUNK CREATOR: {processed_chunk}") if processed_chunk is None: continue ## LOGGING + threading.Thread(target=self.logging_obj.success_handler, args=(processed_chunk,)).start() # log response asyncio.create_task(self.logging_obj.async_success_handler(processed_chunk,)) return processed_chunk raise StopAsyncIteration else: # temporary patch for non-aiohttp async calls - return next(self) + # example - boto3 bedrock llms + processed_chunk = next(self) + asyncio.create_task(self.logging_obj.async_success_handler(processed_chunk,)) + return processed_chunk + except StopAsyncIteration: + raise + except StopIteration: + raise StopAsyncIteration # Re-raise StopIteration except Exception as e: + traceback_exception = traceback.format_exc() # Handle any exceptions that might occur during streaming + asyncio.create_task(self.logging_obj.async_failure_handler(e, traceback_exception)) raise StopAsyncIteration class TextCompletionStreamWrapper: @@ -5732,31 +6000,48 @@ class TextCompletionStreamWrapper: def __aiter__(self): return self + + def convert_to_text_completion_object(self, chunk: ModelResponse): + try: + response = TextCompletionResponse() + response["id"] = chunk.get("id", None) + response["object"] = "text_completion" + response["created"] = response.get("created", None) + response["model"] = response.get("model", None) + text_choices = TextChoices() + if isinstance(chunk, Choices): # chunk should always be of type StreamingChoices + raise Exception + text_choices["text"] = chunk["choices"][0]["delta"]["content"] + text_choices["index"] = response["choices"][0]["index"] + text_choices["finish_reason"] = response["choices"][0]["finish_reason"] + response["choices"] = [text_choices] + return response + except Exception as e: + raise Exception(f"Error occurred converting to text completion object - chunk: {chunk}; Error: {str(e)}") def __next__(self): # model_response = ModelResponse(stream=True, model=self.model) response = TextCompletionResponse() try: - while True: # loop until a non-empty string is found - # return this for all models - chunk = next(self.completion_stream) - response["id"] = chunk.get("id", None) - response["object"] = "text_completion" - response["created"] = response.get("created", None) - response["model"] = response.get("model", None) - text_choices = TextChoices() - text_choices["text"] = chunk["choices"][0]["delta"]["content"] - text_choices["index"] = response["choices"][0]["index"] - text_choices["finish_reason"] = response["choices"][0]["finish_reason"] - response["choices"] = [text_choices] - return response + for chunk in self.completion_stream: + if chunk == "None" or chunk is None: + raise Exception + processed_chunk = self.convert_to_text_completion_object(chunk=chunk) + return processed_chunk + raise StopIteration except StopIteration: raise StopIteration except Exception as e: print(f"got exception {e}") # noqa + async def __anext__(self): try: - return next(self) + async for chunk in self.completion_stream: + if chunk == "None" or chunk is None: + raise Exception + processed_chunk = self.convert_to_text_completion_object(chunk=chunk) + return processed_chunk + raise StopIteration except StopIteration: raise StopAsyncIteration @@ -6228,12 +6513,3 @@ def transform_logprobs(hf_response): transformed_logprobs = token_info return transformed_logprobs - -# used in LiteLLM Router -def remove_model_id(original_model_string): - # Find the index of "ModelID" in the string - index_of_model_id = original_model_string.find("-ModelID") - # Remove everything after "-ModelID" if it exists - if index_of_model_id != -1: - return original_model_string[:index_of_model_id] - return original_model_string \ No newline at end of file diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 454b2504a..32d28e675 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -210,6 +210,27 @@ "litellm_provider": "anthropic", "mode": "chat" }, + "mistral/mistral-tiny": { + "max_tokens": 8192, + "input_cost_per_token": 0.00000015, + "output_cost_per_token": 0.00000046, + "litellm_provider": "mistral", + "mode": "chat" + }, + "mistral/mistral-small": { + "max_tokens": 8192, + "input_cost_per_token": 0.00000066, + "output_cost_per_token": 0.00000197, + "litellm_provider": "mistral", + "mode": "chat" + }, + "mistral/mistral-medium": { + "max_tokens": 8192, + "input_cost_per_token": 0.00000273, + "output_cost_per_token": 0.00000820, + "litellm_provider": "mistral", + "mode": "chat" + }, "claude-instant-1.2": { "max_tokens": 100000, "max_output_tokens": 8191, @@ -262,6 +283,13 @@ "litellm_provider": "vertex_ai-chat-models", "mode": "chat" }, + "chat-bison@002": { + "max_tokens": 4096, + "input_cost_per_token": 0.000000125, + "output_cost_per_token": 0.000000125, + "litellm_provider": "vertex_ai-chat-models", + "mode": "chat" + }, "chat-bison-32k": { "max_tokens": 32000, "input_cost_per_token": 0.000000125, @@ -287,14 +315,21 @@ "max_tokens": 2048, "input_cost_per_token": 0.000000125, "output_cost_per_token": 0.000000125, - "litellm_provider": "vertex_ai-chat-models", + "litellm_provider": "vertex_ai-code-text-models", "mode": "completion" }, - "code-gecko@latest": { + "code-gecko@002": { "max_tokens": 2048, "input_cost_per_token": 0.000000125, "output_cost_per_token": 0.000000125, - "litellm_provider": "vertex_ai-chat-models", + "litellm_provider": "vertex_ai-code-text-models", + "mode": "completion" + }, + "code-gecko": { + "max_tokens": 2048, + "input_cost_per_token": 0.000000125, + "output_cost_per_token": 0.000000125, + "litellm_provider": "vertex_ai-code-text-models", "mode": "completion" }, "codechat-bison": { @@ -318,6 +353,22 @@ "litellm_provider": "vertex_ai-code-chat-models", "mode": "chat" }, + "gemini-pro": { + "max_tokens": 30720, + "max_output_tokens": 2048, + "input_cost_per_token": 0.00000025, + "output_cost_per_token": 0.0000005, + "litellm_provider": "vertex_ai-language-models", + "mode": "chat" + }, + "gemini-pro-vision": { + "max_tokens": 30720, + "max_output_tokens": 2048, + "input_cost_per_token": 0.00000025, + "output_cost_per_token": 0.0000005, + "litellm_provider": "vertex_ai-vision-models", + "mode": "chat" + }, "palm/chat-bison": { "max_tokens": 4096, "input_cost_per_token": 0.000000125, diff --git a/poetry.lock b/poetry.lock index 58dd309df..1d8765bfd 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,112 +1,100 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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