diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..ed04868620b75782741e60b0e852924beb6826e0 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +docs/my-website/img/alt_dashboard.png filter=lfs diff=lfs merge=lfs -text +docs/my-website/img/dashboard_log.png filter=lfs diff=lfs merge=lfs -text diff --git a/docs/my-website/.gitignore b/docs/my-website/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..b2d6de30624f651a6c584d4faefafceac6302be1 --- /dev/null +++ b/docs/my-website/.gitignore @@ -0,0 +1,20 @@ +# Dependencies +/node_modules + +# Production +/build + +# Generated files +.docusaurus +.cache-loader + +# Misc +.DS_Store +.env.local +.env.development.local +.env.test.local +.env.production.local + +npm-debug.log* +yarn-debug.log* +yarn-error.log* diff --git a/docs/my-website/Dockerfile b/docs/my-website/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..cb259dbbee02e8bb8dbba5b9c3f3d8549214f7c6 --- /dev/null +++ b/docs/my-website/Dockerfile @@ -0,0 +1,9 @@ +FROM python:3.10 + +COPY . /app +WORKDIR /app +RUN pip install -r requirements.txt + +EXPOSE $PORT + +CMD litellm --host 0.0.0.0 --port $PORT --workers 10 --config config.yaml \ No newline at end of file diff --git a/docs/my-website/README.md b/docs/my-website/README.md new file mode 100644 index 0000000000000000000000000000000000000000..aaba2fa1e16eebb0ff68df9127e1afc6395c74d8 --- /dev/null +++ b/docs/my-website/README.md @@ -0,0 +1,41 @@ +# Website + +This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator. + +### Installation + +``` +$ yarn +``` + +### Local Development + +``` +$ yarn start +``` + +This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. + +### Build + +``` +$ yarn build +``` + +This command generates static content into the `build` directory and can be served using any static contents hosting service. + +### Deployment + +Using SSH: + +``` +$ USE_SSH=true yarn deploy +``` + +Not using SSH: + +``` +$ GIT_USER= yarn deploy +``` + +If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch. diff --git a/docs/my-website/babel.config.js b/docs/my-website/babel.config.js new file mode 100644 index 0000000000000000000000000000000000000000..e00595dae7d69190e2a9d07202616c2ea932e487 --- /dev/null +++ b/docs/my-website/babel.config.js @@ -0,0 +1,3 @@ +module.exports = { + presets: [require.resolve('@docusaurus/core/lib/babel/preset')], +}; diff --git a/docs/my-website/blog/2021-08-26-welcome/index.md b/docs/my-website/blog/2021-08-26-welcome/index.md new file mode 100644 index 0000000000000000000000000000000000000000..9022806cc48677b79864fe79311b054f94bf3f16 --- /dev/null +++ b/docs/my-website/blog/2021-08-26-welcome/index.md @@ -0,0 +1,43 @@ +# 🚅 litellm +A light 100 line package to simplify calling OpenAI, Azure, Cohere, Anthropic APIs + +###### litellm manages: +* Calling all LLM APIs using the OpenAI format - `completion(model, messages)` +* Consistent output for all LLM APIs, text responses will always be available at `['choices'][0]['message']['content']` +* Consistent Exceptions for all LLM APIs, we map RateLimit, Context Window, and Authentication Error exceptions across all providers to their OpenAI equivalents. [see Code](https://github.com/BerriAI/litellm/blob/ba1079ff6698ef238c5c7f771dd2b698ec76f8d9/litellm/utils.py#L250) + +###### observability: +* Logging - see exactly what the raw model request/response is by plugging in your own function `completion(.., logger_fn=your_logging_fn)` and/or print statements from the package `litellm.set_verbose=True` +* Callbacks - automatically send your data to Helicone, Sentry, Posthog, Slack - `litellm.success_callbacks`, `litellm.failure_callbacks` [see Callbacks](https://litellm.readthedocs.io/en/latest/advanced/) + +## Quick Start +Go directly to code: [Getting Started Notebook](https://colab.research.google.com/drive/1gR3pY-JzDZahzpVdbGBtrNGDBmzUNJaJ?usp=sharing) +### Installation +``` +pip install litellm +``` + +### Usage +```python +from litellm import completion + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "openai key" +os.environ["COHERE_API_KEY"] = "cohere key" + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# openai call +response = completion(model="gpt-3.5-turbo", messages=messages) + +# cohere call +response = completion("command-nightly", messages) +``` +Need Help / Support : [see troubleshooting](https://litellm.readthedocs.io/en/latest/troubleshoot) + +## Why did we build liteLLM +- **Need for simplicity**: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI, Cohere + +## Support +* [Meet with us 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +* Contact us at ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/budget_manager.md b/docs/my-website/docs/budget_manager.md new file mode 100644 index 0000000000000000000000000000000000000000..2a3b25b8e2139eeb4ffe0ce44cb8ac5ac666d340 --- /dev/null +++ b/docs/my-website/docs/budget_manager.md @@ -0,0 +1,248 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Budget Manager + +Don't want to get crazy bills because either while you're calling LLM APIs **or** while your users are calling them? use this. + +LiteLLM exposes: +* `litellm.max_budget`: a global variable you can use to set the max budget (in USD) across all your litellm calls. If this budget is exceeded, it will raise a BudgetExceededError +* `BudgetManager`: A class to help set budgets per user. BudgetManager creates a dictionary to manage the user budgets, where the key is user and the object is their current cost + model-specific costs. + +## quick start + +```python +import litellm, os +from litellm import completion + +# set env variable +os.environ["OPENAI_API_KEY"] = "your-api-key" + +litellm.max_budget = 0.001 # sets a max budget of $0.001 + +messages = [{"role": "user", "content": "Hey, how's it going"}] +completion(model="gpt-4", messages=messages) +print(litellm._current_cost) +completion(model="gpt-4", messages=messages) +``` + +## User-based rate limiting + + Open In Colab + + +```python +from litellm import BudgetManager, completion + +budget_manager = BudgetManager(project_name="test_project") + +user = "1234" + +# create a budget if new user user +if not budget_manager.is_valid_user(user): + budget_manager.create_budget(total_budget=10, user=user) + +# check if a given call can be made +if budget_manager.get_current_cost(user=user) <= budget_manager.get_total_budget(user): + response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}]) + budget_manager.update_cost(completion_obj=response, user=user) +else: + response = "Sorry - no budget!" +``` + +[**Implementation Code**](https://github.com/BerriAI/litellm/blob/main/litellm/budget_manager.py) + +## use with Text Input / Output + +Update cost by just passing in the text input / output and model name. + +```python +from litellm import BudgetManager + +budget_manager = BudgetManager(project_name="test_project") +user = "12345" +budget_manager.create_budget(total_budget=10, user=user, duration="daily") + +input_text = "hello world" +output_text = "it's a sunny day in san francisco" +model = "gpt-3.5-turbo" + +budget_manager.update_cost(user=user, model=model, input_text=input_text, output_text=output_text) # 👈 +print(budget_manager.get_current_cost(user)) +``` + +## advanced usage +In production, we will need to +* store user budgets in a database +* reset user budgets based on a set duration + + + +### LiteLLM API + +The LiteLLM API provides both. It stores the user object in a hosted db, and runs a cron job daily to reset user-budgets based on the set duration (e.g. reset budget daily/weekly/monthly/etc.). + +**Usage** +```python +budget_manager = BudgetManager(project_name="", client_type="hosted") +``` + +**Complete Code** +```python +from litellm import BudgetManager, completion + +budget_manager = BudgetManager(project_name="", client_type="hosted") + +user = "1234" + +# create a budget if new user user +if not budget_manager.is_valid_user(user): + budget_manager.create_budget(total_budget=10, user=user, duration="monthly") # 👈 duration = 'daily'/'weekly'/'monthly'/'yearly' + +# check if a given call can be made +if budget_manager.get_current_cost(user=user) <= budget_manager.get_total_budget(user): + response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}]) + budget_manager.update_cost(completion_obj=response, user=user) +else: + response = "Sorry - no budget!" +``` + +### Self-hosted + +To use your own db, set the BudgetManager client type to `hosted` **and** set the api_base. + +Your api is expected to expose `/get_budget` and `/set_budget` endpoints. [See code for details](https://github.com/BerriAI/litellm/blob/27f1051792176a7eb1fe3b72b72bccd6378d24e9/litellm/budget_manager.py#L7) + +**Usage** +```python +budget_manager = BudgetManager(project_name="", client_type="hosted", api_base="your_custom_api") +``` +**Complete Code** +```python +from litellm import BudgetManager, completion + +budget_manager = BudgetManager(project_name="", client_type="hosted", api_base="your_custom_api") + +user = "1234" + +# create a budget if new user user +if not budget_manager.is_valid_user(user): + budget_manager.create_budget(total_budget=10, user=user, duration="monthly") # 👈 duration = 'daily'/'weekly'/'monthly'/'yearly' + +# check if a given call can be made +if budget_manager.get_current_cost(user=user) <= budget_manager.get_total_budget(user): + response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}]) + budget_manager.update_cost(completion_obj=response, user=user) +else: + response = "Sorry - no budget!" +``` + +## Budget Manager Class +The `BudgetManager` class is used to manage budgets for different users. It provides various functions to create, update, and retrieve budget information. + +Below is a list of public functions exposed by the Budget Manager class and their input/outputs. + +### __init__ +```python +def __init__(self, project_name: str, client_type: str = "local", api_base: Optional[str] = None) +``` +- `project_name` (str): The name of the project. +- `client_type` (str): The client type ("local" or "hosted"). Defaults to "local". +- `api_base` (Optional[str]): The base URL of the API. Defaults to None. + + +### create_budget +```python +def create_budget(self, total_budget: float, user: str, duration: Literal["daily", "weekly", "monthly", "yearly"], created_at: float = time.time()) +``` +Creates a budget for a user. + +- `total_budget` (float): The total budget of the user. +- `user` (str): The user id. +- `duration` (Literal["daily", "weekly", "monthly", "yearly"]): The budget duration. +- `created_at` (float): The creation time. Default is the current time. + +### projected_cost +```python +def projected_cost(self, model: str, messages: list, user: str) +``` +Computes the projected cost for a session. + +- `model` (str): The name of the model. +- `messages` (list): The list of messages. +- `user` (str): The user id. + +### get_total_budget +```python +def get_total_budget(self, user: str) +``` +Returns the total budget of a user. + +- `user` (str): user id. + +### update_cost +```python +def update_cost(self, completion_obj: ModelResponse, user: str) +``` +Updates the user's cost. + +- `completion_obj` (ModelResponse): The completion object received from the model. +- `user` (str): The user id. + +### get_current_cost +```python +def get_current_cost(self, user: str) +``` +Returns the current cost of a user. + +- `user` (str): The user id. + +### get_model_cost +```python +def get_model_cost(self, user: str) +``` +Returns the model cost of a user. + +- `user` (str): The user id. + +### is_valid_user +```python +def is_valid_user(self, user: str) -> bool +``` +Checks if a user is valid. + +- `user` (str): The user id. + +### get_users +```python +def get_users(self) +``` +Returns a list of all users. + +### reset_cost +```python +def reset_cost(self, user: str) +``` +Resets the cost of a user. + +- `user` (str): The user id. + +### reset_on_duration +```python +def reset_on_duration(self, user: str) +``` +Resets the cost of a user based on the duration. + +- `user` (str): The user id. + +### update_budget_all_users +```python +def update_budget_all_users(self) +``` +Updates the budget for all users. + +### save_data +```python +def save_data(self) +``` +Stores the user dictionary. \ No newline at end of file diff --git a/docs/my-website/docs/caching/caching_api.md b/docs/my-website/docs/caching/caching_api.md new file mode 100644 index 0000000000000000000000000000000000000000..969de441f30eb781719a10fe3aaa51a4b921b6af --- /dev/null +++ b/docs/my-website/docs/caching/caching_api.md @@ -0,0 +1,75 @@ +# Hosted Cache - api.litellm.ai + +Use api.litellm.ai for caching `completion()` and `embedding()` responses + +## Quick Start Usage - Completion +```python +import litellm +from litellm import completion +from litellm.caching import Cache +litellm.cache = Cache(type="hosted") # init cache to use api.litellm.ai + +# Make completion calls +response1 = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Tell me a joke."}] + caching=True +) + +response2 = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Tell me a joke."}], + caching=True +) +# response1 == response2, response 1 is cached +``` + + +## Usage - Embedding() + +```python +import time +import litellm +from litellm import completion, embedding +from litellm.caching import Cache +litellm.cache = Cache(type="hosted") + +start_time = time.time() +embedding1 = embedding(model="text-embedding-ada-002", input=["hello from litellm"*5], caching=True) +end_time = time.time() +print(f"Embedding 1 response time: {end_time - start_time} seconds") + +start_time = time.time() +embedding2 = embedding(model="text-embedding-ada-002", input=["hello from litellm"*5], caching=True) +end_time = time.time() +print(f"Embedding 2 response time: {end_time - start_time} seconds") +``` + +## Caching with Streaming +LiteLLM can cache your streamed responses for you + +### Usage +```python +import litellm +from litellm import completion +from litellm.caching import Cache +litellm.cache = Cache(type="hosted") + +# Make completion calls +response1 = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Tell me a joke."}], + stream=True, + caching=True) +for chunk in response1: + print(chunk) + + +response2 = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Tell me a joke."}], + stream=True, + caching=True) +for chunk in response2: + print(chunk) +``` \ No newline at end of file diff --git a/docs/my-website/docs/caching/local_caching.md b/docs/my-website/docs/caching/local_caching.md new file mode 100644 index 0000000000000000000000000000000000000000..d0e26e4bf97817cc3b29f8a658f0c003fede02f9 --- /dev/null +++ b/docs/my-website/docs/caching/local_caching.md @@ -0,0 +1,92 @@ +# LiteLLM - Local Caching + +## Caching `completion()` and `embedding()` calls when switched on + +liteLLM implements exact match caching and supports the following Caching: +* In-Memory Caching [Default] +* Redis Caching Local +* Redis Caching Hosted + +## Quick Start Usage - Completion +Caching - cache +Keys in the cache are `model`, the following example will lead to a cache hit +```python +import litellm +from litellm import completion +from litellm.caching import Cache +litellm.cache = Cache() + +# Make completion calls +response1 = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Tell me a joke."}] + caching=True +) +response2 = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Tell me a joke."}], + caching=True +) + +# response1 == response2, response 1 is cached +``` + +## Custom Key-Value Pairs +Add custom key-value pairs to your cache. + +```python +from litellm.caching import Cache +cache = Cache() + +cache.add_cache(cache_key="test-key", result="1234") + +cache.get_cache(cache_key="test-key) +``` + +## Caching with Streaming +LiteLLM can cache your streamed responses for you + +### Usage +```python +import litellm +from litellm import completion +from litellm.caching import Cache +litellm.cache = Cache() + +# Make completion calls +response1 = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Tell me a joke."}], + stream=True, + caching=True) +for chunk in response1: + print(chunk) +response2 = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Tell me a joke."}], + stream=True, + caching=True) +for chunk in response2: + print(chunk) +``` + +## Usage - Embedding() +1. Caching - cache +Keys in the cache are `model`, the following example will lead to a cache hit +```python +import time +import litellm +from litellm import embedding +from litellm.caching import Cache +litellm.cache = Cache() + +start_time = time.time() +embedding1 = embedding(model="text-embedding-ada-002", input=["hello from litellm"*5], caching=True) +end_time = time.time() +print(f"Embedding 1 response time: {end_time - start_time} seconds") + +start_time = time.time() +embedding2 = embedding(model="text-embedding-ada-002", input=["hello from litellm"*5], caching=True) +end_time = time.time() +print(f"Embedding 2 response time: {end_time - start_time} seconds") +``` \ No newline at end of file diff --git a/docs/my-website/docs/caching/redis_cache.md b/docs/my-website/docs/caching/redis_cache.md new file mode 100644 index 0000000000000000000000000000000000000000..f0fcc6952c17223c546fbef94e39a872b3e01058 --- /dev/null +++ b/docs/my-website/docs/caching/redis_cache.md @@ -0,0 +1,73 @@ +# Redis Cache +### Pre-requisites +Install redis +``` +pip install redis +``` +For the hosted version you can setup your own Redis DB here: https://app.redislabs.com/ +### Usage +```python +import litellm +from litellm import completion +from litellm.caching import Cache +litellm.cache = Cache(type="redis", host=, port=, password=) + +# Make completion calls +response1 = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Tell me a joke."}], + caching=True +) +response2 = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Tell me a joke."}], + caching=True +) + +# response1 == response2, response 1 is cached +``` + +### Custom Cache Keys: + +Define function to return cache key +```python +# this function takes in *args, **kwargs and returns the key you want to use for caching +def custom_get_cache_key(*args, **kwargs): + # return key to use for your cache: + key = kwargs.get("model", "") + str(kwargs.get("messages", "")) + str(kwargs.get("temperature", "")) + str(kwargs.get("logit_bias", "")) + print("key for cache", key) + return key + +``` + +Set your function as litellm.cache.get_cache_key +```python +from litellm.caching import Cache + +cache = Cache(type="redis", host=os.environ['REDIS_HOST'], port=os.environ['REDIS_PORT'], password=os.environ['REDIS_PASSWORD']) + +cache.get_cache_key = custom_get_cache_key # set get_cache_key function for your cache + +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 + +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}, +} + +``` \ No newline at end of file diff --git a/docs/my-website/docs/completion/batching.md b/docs/my-website/docs/completion/batching.md new file mode 100644 index 0000000000000000000000000000000000000000..05683b3ddbf8998ce884a653ab03a8600d8b2ac8 --- /dev/null +++ b/docs/my-website/docs/completion/batching.md @@ -0,0 +1,182 @@ +# Batching Completion() +LiteLLM allows you to: +* Send many completion calls to 1 model +* Send 1 completion call to many models: Return Fastest Response +* Send 1 completion call to many models: Return All Responses + +## Send multiple completion calls to 1 model + +In the batch_completion method, you provide a list of `messages` where each sub-list of messages is passed to `litellm.completion()`, allowing you to process multiple prompts efficiently in a single API call. + + + Open In Colab + + +### Example Code +```python +import litellm +import os +from litellm import batch_completion + +os.environ['ANTHROPIC_API_KEY'] = "" + + +responses = batch_completion( + model="claude-2", + messages = [ + [ + { + "role": "user", + "content": "good morning? " + } + ], + [ + { + "role": "user", + "content": "what's the time? " + } + ] + ] +) +``` + +## Send 1 completion call to many models: Return Fastest Response +This makes parallel calls to the specified `models` and returns the first response + +Use this to reduce latency + +### Example Code +```python +import litellm +import os +from litellm import batch_completion_models + +os.environ['ANTHROPIC_API_KEY'] = "" +os.environ['OPENAI_API_KEY'] = "" +os.environ['COHERE_API_KEY'] = "" + +response = batch_completion_models( + models=["gpt-3.5-turbo", "claude-instant-1.2", "command-nightly"], + messages=[{"role": "user", "content": "Hey, how's it going"}] +) +print(result) +``` + +### Output +Returns the first response +```json +{ + "object": "chat.completion", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": " I'm doing well, thanks for asking! I'm an AI assistant created by Anthropic to be helpful, harmless, and honest.", + "role": "assistant", + "logprobs": null + } + } + ], + "id": "chatcmpl-23273eed-e351-41be-a492-bafcf5cf3274", + "created": 1695154628.2076092, + "model": "command-nightly", + "usage": { + "prompt_tokens": 6, + "completion_tokens": 14, + "total_tokens": 20 + } +} +``` + +## Send 1 completion call to many models: Return All Responses +This makes parallel calls to the specified models and returns all responses + +Use this to process requests concurrently and get responses from multiple models. + +### Example Code +```python +import litellm +import os +from litellm import batch_completion_models_all_responses + +os.environ['ANTHROPIC_API_KEY'] = "" +os.environ['OPENAI_API_KEY'] = "" +os.environ['COHERE_API_KEY'] = "" + +responses = batch_completion_models_all_responses( + models=["gpt-3.5-turbo", "claude-instant-1.2", "command-nightly"], + messages=[{"role": "user", "content": "Hey, how's it going"}] +) +print(responses) + +``` + +### Output + +```json +[ JSON: { + "object": "chat.completion", + "choices": [ + { + "finish_reason": "stop_sequence", + "index": 0, + "message": { + "content": " It's going well, thank you for asking! How about you?", + "role": "assistant", + "logprobs": null + } + } + ], + "id": "chatcmpl-e673ec8e-4e8f-4c9e-bf26-bf9fa7ee52b9", + "created": 1695222060.917964, + "model": "claude-instant-1.2", + "usage": { + "prompt_tokens": 14, + "completion_tokens": 9, + "total_tokens": 23 + } +}, JSON: { + "object": "chat.completion", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": " It's going well, thank you for asking! How about you?", + "role": "assistant", + "logprobs": null + } + } + ], + "id": "chatcmpl-ab6c5bd3-b5d9-4711-9697-e28d9fb8a53c", + "created": 1695222061.0445492, + "model": "command-nightly", + "usage": { + "prompt_tokens": 6, + "completion_tokens": 14, + "total_tokens": 20 + } +}, JSON: { + "id": "chatcmpl-80szFnKHzCxObW0RqCMw1hWW1Icrq", + "object": "chat.completion", + "created": 1695222061, + "model": "gpt-3.5-turbo-0613", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "Hello! I'm an AI language model, so I don't have feelings, but I'm here to assist you with any questions or tasks you might have. How can I help you today?" + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 13, + "completion_tokens": 39, + "total_tokens": 52 + } +}] + +``` diff --git a/docs/my-website/docs/completion/config.md b/docs/my-website/docs/completion/config.md new file mode 100644 index 0000000000000000000000000000000000000000..44ff6e8a70fa9642eaf808a5fb7daa1db6b7485d --- /dev/null +++ b/docs/my-website/docs/completion/config.md @@ -0,0 +1,49 @@ +# Model Config + +Model-specific changes can make our code complicated, making it harder to debug errors. Use model configs to simplify this. + +### usage + +Handling prompt logic. Different models have different context windows. Use `adapt_to_prompt_size` to select the right model for the prompt (in case the current model is too small). + + +```python +from litellm import completion_with_config +import os + +config = { + "available_models": ["gpt-3.5-turbo", "claude-instant-1", "gpt-3.5-turbo-16k"], + "adapt_to_prompt_size": True, # 👈 key change +} + +# set env var +os.environ["OPENAI_API_KEY"] = "your-api-key" +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + + +sample_text = "how does a court case get to the Supreme Court?" * 1000 +messages = [{"content": sample_text, "role": "user"}] +response = completion_with_config(model="gpt-3.5-turbo", messages=messages, config=config) +``` + +[**See Code**](https://github.com/BerriAI/litellm/blob/30724d9e51cdc2c3e0eb063271b4f171bc01b382/litellm/utils.py#L2783) + +### Complete Config Structure + +```python +config = { + "default_fallback_models": # [Optional] List of model names to try if a call fails + "available_models": # [Optional] List of all possible models you could call + "adapt_to_prompt_size": # [Optional] True/False - if you want to select model based on prompt size (will pick from available_models) + "model": { + "model-name": { + "needs_moderation": # [Optional] True/False - if you want to call openai moderations endpoint before making completion call. Will raise exception, if flagged. + "error_handling": { + "error-type": { # One of the errors listed here - https://docs.litellm.ai/docs/exception_mapping#custom-mapping-list + "fallback_model": "" # str, name of the model it should try instead, when that error occurs + } + } + } + } +} +``` \ No newline at end of file diff --git a/docs/my-website/docs/completion/function_call.md b/docs/my-website/docs/completion/function_call.md new file mode 100644 index 0000000000000000000000000000000000000000..8004a55d12b75ecd859dd080d9fcf05e363e6016 --- /dev/null +++ b/docs/my-website/docs/completion/function_call.md @@ -0,0 +1,545 @@ +# Function Calling +Function calling is supported with the following models on OpenAI, Azure OpenAI + +- gpt-4 +- gpt-4-1106-preview +- gpt-4-0613 +- gpt-3.5-turbo +- gpt-3.5-turbo-1106 +- gpt-3.5-turbo-0613 +- Non OpenAI LLMs (litellm adds the function call to the prompt for these llms) + +In addition, parallel function calls is supported on the following models: +- gpt-4-1106-preview +- gpt-3.5-turbo-1106 + +## Parallel Function calling +Parallel function calling is the model's ability to perform multiple function calls together, allowing the effects and results of these function calls to be resolved in parallel + +## Quick Start - gpt-3.5-turbo-1106 + + Open In Colab + + +In this example we define a single function `get_current_weather`. + +- Step 1: Send the model the `get_current_weather` with the user question +- Step 2: Parse the output from the model response - Execute the `get_current_weather` with the model provided args +- Step 3: Send the model the output from running the `get_current_weather` function + + +### Full Code - Parallel function calling with `gpt-3.5-turbo-1106` + +```python +import litellm +import json +# set openai api key +import os +os.environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from .env and sends the request + +# Example dummy function hard coded to return the same weather +# In production, this could be your backend API or an external API +def get_current_weather(location, unit="fahrenheit"): + """Get the current weather in a given location""" + if "tokyo" in location.lower(): + return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"}) + elif "san francisco" in location.lower(): + return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}) + elif "paris" in location.lower(): + return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"}) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + + +def test_parallel_function_call(): + try: + # Step 1: send the conversation and available functions to the model + messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}] + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } + ] + response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, + tools=tools, + tool_choice="auto", # auto is default, but we'll be explicit + ) + print("\nFirst LLM Response:\n", response) + response_message = response.choices[0].message + tool_calls = response_message.tool_calls + + print("\nLength of tool calls", len(tool_calls)) + + # Step 2: check if the model wanted to call a function + if tool_calls: + # Step 3: call the function + # Note: the JSON response may not always be valid; be sure to handle errors + available_functions = { + "get_current_weather": get_current_weather, + } # only one function in this example, but you can have multiple + messages.append(response_message) # extend conversation with assistant's reply + + # Step 4: send the info for each function call and function response to the model + for tool_call in tool_calls: + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + messages.append( + { + "tool_call_id": tool_call.id, + "role": "tool", + "name": function_name, + "content": function_response, + } + ) # extend conversation with function response + second_response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, + ) # get a new response from the model where it can see the function response + print("\nSecond LLM response:\n", second_response) + return second_response + except Exception as e: + print(f"Error occurred: {e}") + +test_parallel_function_call() +``` + +### Explanation - Parallel function calling +Below is an explanation of what is happening in the code snippet above for Parallel function calling with `gpt-3.5-turbo-1106` +### Step1: litellm.completion() with `tools` set to `get_current_weather` +```python +import litellm +import json +# set openai api key +import os +os.environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from .env and sends the request +# Example dummy function hard coded to return the same weather +# In production, this could be your backend API or an external API +def get_current_weather(location, unit="fahrenheit"): + """Get the current weather in a given location""" + if "tokyo" in location.lower(): + return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"}) + elif "san francisco" in location.lower(): + return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}) + elif "paris" in location.lower(): + return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"}) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + +messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}] +tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } +] + +response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, + tools=tools, + tool_choice="auto", # auto is default, but we'll be explicit +) +print("\nLLM Response1:\n", response) +response_message = response.choices[0].message +tool_calls = response.choices[0].message.tool_calls +``` + +##### Expected output +In the output you can see the model calls the function multiple times - for San Francisco, Tokyo, Paris +```json +ModelResponse( + id='chatcmpl-8MHBKZ9t6bXuhBvUMzoKsfmmlv7xq', + choices=[ + Choices(finish_reason='tool_calls', + index=0, + message=Message(content=None, role='assistant', + tool_calls=[ + ChatCompletionMessageToolCall(id='call_DN6IiLULWZw7sobV6puCji1O', function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), type='function'), + + ChatCompletionMessageToolCall(id='call_ERm1JfYO9AFo2oEWRmWUd40c', function=Function(arguments='{"location": "Tokyo", "unit": "celsius"}', name='get_current_weather'), type='function'), + + ChatCompletionMessageToolCall(id='call_2lvUVB1y4wKunSxTenR0zClP', function=Function(arguments='{"location": "Paris", "unit": "celsius"}', name='get_current_weather'), type='function') + ])) + ], + created=1700319953, + model='gpt-3.5-turbo-1106', + object='chat.completion', + system_fingerprint='fp_eeff13170a', + usage={'completion_tokens': 77, 'prompt_tokens': 88, 'total_tokens': 165}, + _response_ms=1177.372 +) +``` + +### Step 2 - Parse the Model Response and Execute Functions +After sending the initial request, parse the model response to identify the function calls it wants to make. In this example, we expect three tool calls, each corresponding to a location (San Francisco, Tokyo, and Paris). + +```python +# Check if the model wants to call a function +if tool_calls: + # Execute the functions and prepare responses + available_functions = { + "get_current_weather": get_current_weather, + } + + messages.append(response_message) # Extend conversation with assistant's reply + + for tool_call in tool_calls: + print(f"\nExecuting tool call\n{tool_call}") + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + # calling the get_current_weather() function + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + print(f"Result from tool call\n{function_response}\n") + + # Extend conversation with function response + messages.append( + { + "tool_call_id": tool_call.id, + "role": "tool", + "name": function_name, + "content": function_response, + } + ) + +``` + +### Step 3 - Second litellm.completion() call +Once the functions are executed, send the model the information for each function call and its response. This allows the model to generate a new response considering the effects of the function calls. +```python +second_response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, +) +print("Second Response\n", second_response) +``` + +#### Expected output +```json +ModelResponse( + id='chatcmpl-8MHBLh1ldADBP71OrifKap6YfAd4w', + choices=[ + Choices(finish_reason='stop', index=0, + message=Message(content="The current weather in San Francisco is 72°F, in Tokyo it's 10°C, and in Paris it's 22°C.", role='assistant')) + ], + created=1700319955, + model='gpt-3.5-turbo-1106', + object='chat.completion', + system_fingerprint='fp_eeff13170a', + usage={'completion_tokens': 28, 'prompt_tokens': 169, 'total_tokens': 197}, + _response_ms=1032.431 +) +``` + +## Parallel Function Calling - Azure OpenAI +```python +# set Azure env variables +import os +os.environ['AZURE_API_KEY'] = "" # litellm reads AZURE_API_KEY from .env and sends the request +os.environ['AZURE_API_BASE'] = "https://openai-gpt-4-test-v-1.openai.azure.com/" +os.environ['AZURE_API_VERSION'] = "2023-07-01-preview" + +import litellm +import json +# Example dummy function hard coded to return the same weather +# In production, this could be your backend API or an external API +def get_current_weather(location, unit="fahrenheit"): + """Get the current weather in a given location""" + if "tokyo" in location.lower(): + return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"}) + elif "san francisco" in location.lower(): + return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}) + elif "paris" in location.lower(): + return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"}) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + +## Step 1: send the conversation and available functions to the model +messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}] +tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } +] + +response = litellm.completion( + model="azure/chatgpt-functioncalling", # model = azure/ + messages=messages, + tools=tools, + tool_choice="auto", # auto is default, but we'll be explicit +) +print("\nLLM Response1:\n", response) +response_message = response.choices[0].message +tool_calls = response.choices[0].message.tool_calls +print("\nTool Choice:\n", tool_calls) + +## Step 2 - Parse the Model Response and Execute Functions +# Check if the model wants to call a function +if tool_calls: + # Execute the functions and prepare responses + available_functions = { + "get_current_weather": get_current_weather, + } + + messages.append(response_message) # Extend conversation with assistant's reply + + for tool_call in tool_calls: + print(f"\nExecuting tool call\n{tool_call}") + function_name = tool_call.function.name + function_to_call = available_functions[function_name] + function_args = json.loads(tool_call.function.arguments) + # calling the get_current_weather() function + function_response = function_to_call( + location=function_args.get("location"), + unit=function_args.get("unit"), + ) + print(f"Result from tool call\n{function_response}\n") + + # Extend conversation with function response + messages.append( + { + "tool_call_id": tool_call.id, + "role": "tool", + "name": function_name, + "content": function_response, + } + ) + +## Step 3 - Second litellm.completion() call +second_response = litellm.completion( + model="azure/chatgpt-functioncalling", + messages=messages, +) +print("Second Response\n", second_response) +print("Second Response Message\n", second_response.choices[0].message.content) + +``` + +## Deprecated - Function Calling with `completion(functions=functions)` +```python +import os, litellm +from litellm import completion + +os.environ['OPENAI_API_KEY'] = "" + +messages = [ + {"role": "user", "content": "What is the weather like in Boston?"} +] + +# python function that will get executed +def get_current_weather(location): + if location == "Boston, MA": + return "The weather is 12F" + +# JSON Schema to pass to OpenAI +functions = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + } + } + ] + +response = completion(model="gpt-3.5-turbo-0613", messages=messages, functions=functions) +print(response) +``` + +## litellm.function_to_dict - Convert Functions to dictionary for OpenAI function calling +`function_to_dict` allows you to pass a function docstring and produce a dictionary usable for OpenAI function calling + +### Using `function_to_dict` +1. Define your function `get_current_weather` +2. Add a docstring to your function `get_current_weather` +3. Pass the function to `litellm.utils.function_to_dict` to get the dictionary for OpenAI function calling + +```python +# function with docstring +def get_current_weather(location: str, unit: str): + """Get the current weather in a given location + + Parameters + ---------- + location : str + The city and state, e.g. San Francisco, CA + unit : {'celsius', 'fahrenheit'} + Temperature unit + + Returns + ------- + str + a sentence indicating the weather + """ + if location == "Boston, MA": + return "The weather is 12F" + +# use litellm.utils.function_to_dict to convert function to dict +function_json = litellm.utils.function_to_dict(get_current_weather) +print(function_json) +``` + +#### Output from function_to_dict +```json +{ + 'name': 'get_current_weather', + 'description': 'Get the current weather in a given location', + 'parameters': { + 'type': 'object', + 'properties': { + 'location': {'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA'}, + 'unit': {'type': 'string', 'description': 'Temperature unit', 'enum': "['fahrenheit', 'celsius']"} + }, + 'required': ['location', 'unit'] + } +} +``` + +### Using function_to_dict with Function calling +```python +import os, litellm +from litellm import completion + +os.environ['OPENAI_API_KEY'] = "" + +messages = [ + {"role": "user", "content": "What is the weather like in Boston?"} +] + +def get_current_weather(location: str, unit: str): + """Get the current weather in a given location + + Parameters + ---------- + location : str + The city and state, e.g. San Francisco, CA + unit : str {'celsius', 'fahrenheit'} + Temperature unit + + Returns + ------- + str + a sentence indicating the weather + """ + if location == "Boston, MA": + return "The weather is 12F" + +functions = [litellm.utils.function_to_dict(get_current_weather)] + +response = completion(model="gpt-3.5-turbo-0613", messages=messages, functions=functions) +print(response) +``` + +## Function calling for Non-OpenAI LLMs + +### Adding Function to prompt +For Non OpenAI LLMs LiteLLM allows you to add the function to the prompt set: `litellm.add_function_to_prompt = True` + +#### Usage +```python +import os, litellm +from litellm import completion + +# IMPORTANT - Set this to TRUE to add the function to the prompt for Non OpenAI LLMs +litellm.add_function_to_prompt = True # set add_function_to_prompt for Non OpenAI LLMs + +os.environ['ANTHROPIC_API_KEY'] = "" + +messages = [ + {"role": "user", "content": "What is the weather like in Boston?"} +] + +def get_current_weather(location): + if location == "Boston, MA": + return "The weather is 12F" + +functions = [ + { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + } + } + ] + +response = completion(model="claude-2", messages=messages, functions=functions) +print(response) +``` + diff --git a/docs/my-website/docs/completion/input.md b/docs/my-website/docs/completion/input.md new file mode 100644 index 0000000000000000000000000000000000000000..7902275aba1f5c4abbe52fcfd94eb83f7b51df83 --- /dev/null +++ b/docs/my-website/docs/completion/input.md @@ -0,0 +1,582 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Input Params + +## Common Params +LiteLLM accepts and translates the [OpenAI Chat Completion params](https://platform.openai.com/docs/api-reference/chat/create) across all providers. + +### Usage +```python +import litellm + +# set env variables +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +## SET MAX TOKENS - via completion() +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + +print(response) +``` + +### Translated OpenAI params +This is a list of openai params we translate across providers. + +This list is constantly being updated. + +| Provider | temperature | max_tokens | top_p | stream | stop | n | presence_penalty | frequency_penalty | functions | function_call | +|---|---|---|---|---|---|---|---|---|---|---| +|Anthropic| ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | +|OpenAI| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | +|Replicate | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | +|Anyscale | ✅ | ✅ | ✅ | ✅ | +|Cohere| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | +|Huggingface| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | +|Openrouter| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | +|AI21| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | +|VertexAI| ✅ | ✅ | | ✅ | | | | | | | +|Bedrock| ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | +|Sagemaker| ✅ | ✅ (only `jumpstart llama2`) | | ✅ | | | | | | | +|TogetherAI| ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | +|AlephAlpha| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | +|Palm| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | +|NLP Cloud| ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | +|Petals| ✅ | ✅ | | ✅ | | | | | | | +|Ollama| ✅ | ✅ | ✅ | ✅ | ✅ | | | ✅ | | | + +:::note + +By default, LiteLLM raises an exception if the openai param being passed in isn't supported. + +To drop the param instead, set `litellm.drop_params = True`. + +**For function calling:** + +Add to prompt for non-openai models, set: `litellm.add_function_to_prompt = True`. +::: + +## Input Params + +```python +def completion( + model: str, + messages: List = [], + # Optional OpenAI params + temperature: Optional[float] = None, + top_p: Optional[float] = None, + n: Optional[int] = None, + stream: Optional[bool] = None, + stop=None, + max_tokens: Optional[float] = None, + presence_penalty: Optional[float] = None, + frequency_penalty: Optional[float]=None, + logit_bias: dict = {}, + user: str = "", + deployment_id = None, + request_timeout: Optional[int] = None, + response_format: Optional[dict] = None, + seed: Optional[int] = None, + tools: Optional[List] = None, + tool_choice: Optional[str] = None, + functions: List = [], # soon to be deprecated + function_call: str = "", # soon to be deprecated + + # Optional LiteLLM params + api_base: Optional[str] = None, + api_version: Optional[str] = None, + api_key: Optional[str] = None, + num_retries: Optional[int] = None, # set to retry a model if an APIError, TimeoutError, or ServiceUnavailableError occurs + context_window_fallback_dict: Optional[dict] = None, # mapping of model to use if call fails due to context window error + fallbacks: Optional[list] = None, # pass in a list of api_base,keys, etc. + metadata: Optional[dict] = None # additional call metadata, passed to logging integrations / custom callbacks + + + **kwargs, +) -> ModelResponse: +``` +### Required Fields + +- `model`: *string* - ID of the model to use. Refer to the model endpoint compatibility table for details on which models work with the Chat API. + +- `messages`: *array* - A list of messages comprising the conversation so far. + +#### Properties of `messages` +*Note* - Each message in the array contains the following properties: + +- `role`: *string* - The role of the message's author. Roles can be: system, user, assistant, or function. + +- `content`: *string or null* - The contents of the message. It is required for all messages, but may be null for assistant messages with function calls. + +- `name`: *string (optional)* - The name of the author of the message. It is required if the role is "function". The name should match the name of the function represented in the content. It can contain characters (a-z, A-Z, 0-9), and underscores, with a maximum length of 64 characters. + +- `function_call`: *object (optional)* - The name and arguments of a function that should be called, as generated by the model. + + + +## Optional Fields + +`temperature`: *number or null (optional)* - The sampling temperature to be used, between 0 and 2. Higher values like 0.8 produce more random outputs, while lower values like 0.2 make outputs more focused and deterministic. + +- `top_p`: *number or null (optional)* - An alternative to sampling with temperature. It instructs the model to consider the results of the tokens with top_p probability. For example, 0.1 means only the tokens comprising the top 10% probability mass are considered. + +- `n`: *integer or null (optional)* - The number of chat completion choices to generate for each input message. + +- `stream`: *boolean or null (optional)* - If set to true, it sends partial message deltas. Tokens will be sent as they become available, with the stream terminated by a [DONE] message. + +- `stop`: *string/ array/ null (optional)* - Up to 4 sequences where the API will stop generating further tokens. + +- `max_tokens`: *integer (optional)* - The maximum number of tokens to generate in the chat completion. + +- `presence_penalty`: *number or null (optional)* - It is used to penalize new tokens based on their existence in the text so far. + +- `response_format`: *object (optional)* - An object specifying the format that the model must output. + + - Setting to `{ "type": "json_object" }` enables JSON mode, which guarantees the message the model generates is valid JSON. + + - Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly "stuck" request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length. + +- `seed`: *integer or null (optional)* - This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the `system_fingerprint` response parameter to monitor changes in the backend. + +- `tools`: *array (optional)* - A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. + + - `type`: *string* - The type of the tool. Currently, only function is supported. + + - `function`: *object* - Required. + +- `tool_choice`: *string or object (optional)* - Controls which (if any) function is called by the model. none means the model will not call a function and instead generates a message. auto means the model can pick between generating a message or calling a function. Specifying a particular function via {"type: "function", "function": {"name": "my_function"}} forces the model to call that function. + + - `none` is the default when no functions are present. `auto` is the default if functions are present. + +- `frequency_penalty`: *number or null (optional)* - It is used to penalize new tokens based on their frequency in the text so far. + +- `logit_bias`: *map (optional)* - Used to modify the probability of specific tokens appearing in the completion. + +- `user`: *string (optional)* - A unique identifier representing your end-user. This can help OpenAI to monitor and detect abuse. + +- `timeout`: *int (optional)* - Timeout in seconds for completion requests (Defaults to 600 seconds) + +#### Deprecated Params +- `functions`: *array* - A list of functions that the model may use to generate JSON inputs. Each function should have the following properties: + + - `name`: *string* - The name of the function to be called. It should contain a-z, A-Z, 0-9, underscores and dashes, with a maximum length of 64 characters. + + - `description`: *string (optional)* - A description explaining what the function does. It helps the model to decide when and how to call the function. + + - `parameters`: *object* - The parameters that the function accepts, described as a JSON Schema object. + +- `function_call`: *string or object (optional)* - Controls how the model responds to function calls. + + +#### litellm-specific params + +- `api_base`: *string (optional)* - The api endpoint you want to call the model with + +- `api_version`: *string (optional)* - (Azure-specific) the api version for the call + +- `num_retries`: *int (optional)* - The number of times to retry the API call if an APIError, TimeoutError or ServiceUnavailableError occurs + +- `context_window_fallback_dict`: *dict (optional)* - A mapping of model to use if call fails due to context window error + +- `fallbacks`: *list (optional)* - A list of model names + params to be used, in case the initial call fails + +- `metadata`: *dict (optional)* - Any additional data you want to be logged when the call is made (sent to logging integrations, eg. promptlayer and accessible via custom callback function) + +## Provider-specific Params +Providers might offer params not supported by OpenAI (e.g. top_k). You can pass those in 2 ways: +- via completion(): We'll pass the non-openai param, straight to the provider as part of the request body. + - e.g. `completion(model="claude-instant-1", top_k=3)` +- via provider-specific config variable (e.g. `litellm.OpenAIConfig()`). + + + + +```python +import litellm, os + +# set env variables +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="gpt-3.5-turbo", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.OpenAIConfig(max_tokens=10) + +response_2 = litellm.completion( + model="gpt-3.5-turbo", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + +```python +import litellm, os + +# set env variables +os.environ["OPENAI_API_KEY"] = "your-openai-key" + + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="text-davinci-003", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.OpenAITextCompletionConfig(max_tokens=10) +response_2 = litellm.completion( + model="text-davinci-003", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + +```python +import litellm, os + +# set env variables +os.environ["AZURE_API_BASE"] = "your-azure-api-base" +os.environ["AZURE_API_TYPE"] = "azure" # [OPTIONAL] +os.environ["AZURE_API_VERSION"] = "2023-07-01-preview" # [OPTIONAL] + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="azure/chatgpt-v-2", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.AzureOpenAIConfig(max_tokens=10) +response_2 = litellm.completion( + model="azure/chatgpt-v-2", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + +```python +import litellm, os + +# set env variables +os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key" + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="claude-instant-1", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.AnthropicConfig(max_tokens_to_sample=200) +response_2 = litellm.completion( + model="claude-instant-1", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + + +```python +import litellm, os + +# set env variables +os.environ["HUGGINGFACE_API_KEY"] = "your-huggingface-key" #[OPTIONAL] + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="huggingface/mistralai/Mistral-7B-Instruct-v0.1", + messages=[{ "content": "Hello, how are you?","role": "user"}], + api_base="https://your-huggingface-api-endpoint", + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.HuggingfaceConfig(max_new_tokens=200) +response_2 = litellm.completion( + model="huggingface/mistralai/Mistral-7B-Instruct-v0.1", + messages=[{ "content": "Hello, how are you?","role": "user"}], + api_base="https://your-huggingface-api-endpoint" + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + + + +```python +import litellm, os + +# set env variables +os.environ["TOGETHERAI_API_KEY"] = "your-togetherai-key" + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="together_ai/togethercomputer/llama-2-70b-chat", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.TogetherAIConfig(max_tokens_to_sample=200) +response_2 = litellm.completion( + model="together_ai/togethercomputer/llama-2-70b-chat", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + + +```python +import litellm, os + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="ollama/llama2", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.OllamConfig(num_predict=200) +response_2 = litellm.completion( + model="ollama/llama2", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + + +```python +import litellm, os + +# set env variables +os.environ["REPLICATE_API_KEY"] = "your-replicate-key" + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.ReplicateConfig(max_new_tokens=200) +response_2 = litellm.completion( + model="replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + + + +```python +import litellm + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="petals/petals-team/StableBeluga2", + messages=[{ "content": "Hello, how are you?","role": "user"}], + api_base="https://chat.petals.dev/api/v1/generate", + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.PetalsConfig(max_new_tokens=10) +response_2 = litellm.completion( + model="petals/petals-team/StableBeluga2", + messages=[{ "content": "Hello, how are you?","role": "user"}], + api_base="https://chat.petals.dev/api/v1/generate", + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + + +```python +import litellm, os + +# set env variables +os.environ["PALM_API_KEY"] = "your-palm-key" + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="palm/chat-bison", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.PalmConfig(maxOutputTokens=10) +response_2 = litellm.completion( + model="palm/chat-bison", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + +```python +import litellm, os + +# set env variables +os.environ["AI21_API_KEY"] = "your-ai21-key" + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="j2-mid", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.AI21Config(maxOutputTokens=10) +response_2 = litellm.completion( + model="j2-mid", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + + +```python +import litellm, os + +# set env variables +os.environ["COHERE_API_KEY"] = "your-cohere-key" + +## SET MAX TOKENS - via completion() +response_1 = litellm.completion( + model="command-nightly", + messages=[{ "content": "Hello, how are you?","role": "user"}], + max_tokens=10 + ) + +response_1_text = response_1.choices[0].message.content + +## SET MAX TOKENS - via config +litellm.CohereConfig(max_tokens=200) +response_2 = litellm.completion( + model="command-nightly", + messages=[{ "content": "Hello, how are you?","role": "user"}], + ) + +response_2_text = response_2.choices[0].message.content + +## TEST OUTPUT +assert len(response_2_text) > len(response_1_text) +``` + + + + + + +[**Check out the tutorial!**](../tutorials/provider_specific_params.md) diff --git a/docs/my-website/docs/completion/message_trimming.md b/docs/my-website/docs/completion/message_trimming.md new file mode 100644 index 0000000000000000000000000000000000000000..abb203095879325abec4b38b802c96fe7f80bdf8 --- /dev/null +++ b/docs/my-website/docs/completion/message_trimming.md @@ -0,0 +1,36 @@ +# Trimming Input Messages +**Use litellm.trim_messages() to ensure messages does not exceed a model's token limit or specified `max_tokens`** + +## Usage +```python +from litellm import completion +from litellm.utils import trim_messages + +response = completion( + model=model, + messages=trim_messages(messages, model) # trim_messages ensures tokens(messages) < max_tokens(model) +) +``` + +## Usage - set max_tokens +```python +from litellm import completion +from litellm.utils import trim_messages + +response = completion( + model=model, + messages=trim_messages(messages, model, max_tokens=10), # trim_messages ensures tokens(messages) < max_tokens +) +``` + +## Parameters + +The function uses the following parameters: + +- `messages`:[Required] This should be a list of input messages + +- `model`:[Optional] This is the LiteLLM model being used. This parameter is optional, as you can alternatively specify the `max_tokens` parameter. + +- `max_tokens`:[Optional] This is an int, manually set upper limit on messages + +- `trim_ratio`:[Optional] This represents the target ratio of tokens to use following trimming. It's default value is 0.75, which implies that messages will be trimmed to utilise about 75% \ No newline at end of file diff --git a/docs/my-website/docs/completion/mock_requests.md b/docs/my-website/docs/completion/mock_requests.md new file mode 100644 index 0000000000000000000000000000000000000000..fc357b0d7d741213e3f3e1326a798d21254acc2d --- /dev/null +++ b/docs/my-website/docs/completion/mock_requests.md @@ -0,0 +1,72 @@ +# Mock Completion() Responses - Save Testing Costs 💰 + +For testing purposes, you can use `completion()` with `mock_response` to mock calling the completion endpoint. + +This will return a response object with a default response (works for streaming as well), without calling the LLM APIs. + +## quick start +```python +from litellm import completion + +model = "gpt-3.5-turbo" +messages = [{"role":"user", "content":"This is a test request"}] + +completion(model=model, messages=messages, mock_response="It's simple to use and easy to get started") +``` + +## streaming + +```python +from litellm import completion +model = "gpt-3.5-turbo" +messages = [{"role": "user", "content": "Hey, I'm a mock request"}] +response = completion(model=model, messages=messages, stream=True, mock_response="It's simple to use and easy to get started") +for chunk in response: + print(chunk) # {'choices': [{'delta': {'role': 'assistant', 'content': 'Thi'}, 'finish_reason': None}]} + complete_response += chunk["choices"][0]["delta"]["content"] +``` + +## (Non-streaming) Mock Response Object + +```json +{ + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "This is a mock request", + "role": "assistant", + "logprobs": null + } + } + ], + "created": 1694459929.4496052, + "model": "MockResponse", + "usage": { + "prompt_tokens": null, + "completion_tokens": null, + "total_tokens": null + } +} +``` + +## Building a pytest function using `completion` with `mock_response` + +```python +from litellm import completion +import pytest + +def test_completion_openai(): + try: + response = completion( + model="gpt-3.5-turbo", + messages=[{"role":"user", "content":"Why is LiteLLM amazing?"}], + mock_response="LiteLLM is awesome" + ) + # Add any assertions here to check the response + print(response) + assert(response['choices'][0]['message']['content'] == "LiteLLM is awesome") + except Exception as e: + pytest.fail(f"Error occurred: {e}") +``` \ No newline at end of file diff --git a/docs/my-website/docs/completion/model_alias.md b/docs/my-website/docs/completion/model_alias.md new file mode 100644 index 0000000000000000000000000000000000000000..5fa8326499317eda014dfbd2659ef76f99bdb210 --- /dev/null +++ b/docs/my-website/docs/completion/model_alias.md @@ -0,0 +1,53 @@ +# Model Alias + +The model name you show an end-user might be different from the one you pass to LiteLLM - e.g. Displaying `GPT-3.5` while calling `gpt-3.5-turbo-16k` on the backend. + +LiteLLM simplifies this by letting you pass in a model alias mapping. + +# expected format + +```python +litellm.model_alias_map = { + # a dictionary containing a mapping of the alias string to the actual litellm model name string + "model_alias": "litellm_model_name" +} +``` + +# usage + +### Relevant Code +```python +model_alias_map = { + "GPT-3.5": "gpt-3.5-turbo-16k", + "llama2": "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf" +} + +litellm.model_alias_map = model_alias_map +``` + +### Complete Code +```python +import litellm +from litellm import completion + + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "openai key" +os.environ["REPLICATE_API_KEY"] = "cohere key" + +## set model alias map +model_alias_map = { + "GPT-3.5": "gpt-3.5-turbo-16k", + "llama2": "replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf" +} + +litellm.model_alias_map = model_alias_map + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# call "gpt-3.5-turbo-16k" +response = completion(model="GPT-3.5", messages=messages) + +# call replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca1... +response = completion("llama2", messages) +``` diff --git a/docs/my-website/docs/completion/multiple_deployments.md b/docs/my-website/docs/completion/multiple_deployments.md new file mode 100644 index 0000000000000000000000000000000000000000..7337906dbbf352dbbc5610a9800bd4d4eac7c7ee --- /dev/null +++ b/docs/my-website/docs/completion/multiple_deployments.md @@ -0,0 +1,53 @@ +# Multiple Deployments + +If you have multiple deployments of the same model, you can pass the list of deployments, and LiteLLM will return the first result. + +## Quick Start + +Multiple providers offer Mistral-7B-Instruct. + +Here's how you can use litellm to return the first result: + +```python +from litellm import completion + +messages=[{"role": "user", "content": "Hey, how's it going?"}] + +## All your mistral deployments ## +model_list = [{ + "model_name": "mistral-7b-instruct", + "litellm_params": { # params for litellm completion/embedding call + "model": "replicate/mistralai/mistral-7b-instruct-v0.1:83b6a56e7c828e667f21fd596c338fd4f0039b46bcfa18d973e8e70e455fda70", + "api_key": "replicate_api_key", + } +}, { + "model_name": "mistral-7b-instruct", + "litellm_params": { # params for litellm completion/embedding call + "model": "together_ai/mistralai/Mistral-7B-Instruct-v0.1", + "api_key": "togetherai_api_key", + } +}, { + "model_name": "mistral-7b-instruct", + "litellm_params": { # params for litellm completion/embedding call + "model": "together_ai/mistralai/Mistral-7B-Instruct-v0.1", + "api_key": "togetherai_api_key", + } +}, { + "model_name": "mistral-7b-instruct", + "litellm_params": { # params for litellm completion/embedding call + "model": "perplexity/mistral-7b-instruct", + "api_key": "perplexity_api_key" + } +}, { + "model_name": "mistral-7b-instruct", + "litellm_params": { + "model": "deepinfra/mistralai/Mistral-7B-Instruct-v0.1", + "api_key": "deepinfra_api_key" + } +}] + +## LiteLLM completion call ## returns first response +response = completion(model="mistral-7b-instruct", messages=messages, model_list=model_list) + +print(response) +``` \ No newline at end of file diff --git a/docs/my-website/docs/completion/output.md b/docs/my-website/docs/completion/output.md new file mode 100644 index 0000000000000000000000000000000000000000..f705bc9f311657f891538bdd240349474e2011fe --- /dev/null +++ b/docs/my-website/docs/completion/output.md @@ -0,0 +1,68 @@ +# Output + +## Format +Here's the exact json output and type you can expect from all litellm `completion` calls for all models + +```python +{ + 'choices': [ + { + 'finish_reason': str, # String: 'stop' + 'index': int, # Integer: 0 + 'message': { # Dictionary [str, str] + 'role': str, # String: 'assistant' + 'content': str # String: "default message" + } + } + ], + 'created': str, # String: None + 'model': str, # String: None + 'usage': { # Dictionary [str, int] + 'prompt_tokens': int, # Integer + 'completion_tokens': int, # Integer + 'total_tokens': int # Integer + } +} + +``` + +You can access the response as a dictionary or as a class object, just as OpenAI allows you +```python +print(response.choices[0].message.content) +print(response['choices'][0]['message']['content']) +``` + +Here's what an example response looks like +```python +{ + 'choices': [ + { + 'finish_reason': 'stop', + 'index': 0, + 'message': { + 'role': 'assistant', + 'content': " I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic." + } + } + ], + 'created': 1691429984.3852863, + 'model': 'claude-instant-1', + 'usage': {'prompt_tokens': 18, 'completion_tokens': 23, 'total_tokens': 41} +} +``` + +## Additional Attributes + +You can also access information like latency. + +```python +from litellm import completion +import os +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +messages=[{"role": "user", "content": "Hey!"}] + +response = completion(model="claude-2", messages=messages) + +print(response.response_ms) # 616.25# 616.25 +``` \ No newline at end of file diff --git a/docs/my-website/docs/completion/prompt_formatting.md b/docs/my-website/docs/completion/prompt_formatting.md new file mode 100644 index 0000000000000000000000000000000000000000..0681de7395477c81f1dec8fc0ec58b9fdf1c3f14 --- /dev/null +++ b/docs/my-website/docs/completion/prompt_formatting.md @@ -0,0 +1,86 @@ +# Prompt Formatting + +LiteLLM automatically translates the OpenAI ChatCompletions prompt format, to other models. You can control this by setting a custom prompt template for a model as well. + +## Huggingface Models + +LiteLLM supports [Huggingface Chat Templates](https://huggingface.co/docs/transformers/main/chat_templating), and will automatically check if your huggingface model has a registered chat template (e.g. [Mistral-7b](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json#L32)). + +For popular models (e.g. meta-llama/llama2), we have their templates saved as part of the package. + +**Stored Templates** + +| Model Name | Works for Models | Completion Call +| -------- | -------- | -------- | +| mistralai/Mistral-7B-Instruct-v0.1 | mistralai/Mistral-7B-Instruct-v0.1| `completion(model='huggingface/mistralai/Mistral-7B-Instruct-v0.1', messages=messages, api_base="your_api_endpoint")` | +| meta-llama/Llama-2-7b-chat | All meta-llama llama2 chat models| `completion(model='huggingface/meta-llama/Llama-2-7b', messages=messages, api_base="your_api_endpoint")` | +| tiiuae/falcon-7b-instruct | All falcon instruct models | `completion(model='huggingface/tiiuae/falcon-7b-instruct', messages=messages, api_base="your_api_endpoint")` | +| mosaicml/mpt-7b-chat | All mpt chat models | `completion(model='huggingface/mosaicml/mpt-7b-chat', messages=messages, api_base="your_api_endpoint")` | +| codellama/CodeLlama-34b-Instruct-hf | All codellama instruct models | `completion(model='huggingface/codellama/CodeLlama-34b-Instruct-hf', messages=messages, api_base="your_api_endpoint")` | +| WizardLM/WizardCoder-Python-34B-V1.0 | All wizardcoder models | `completion(model='huggingface/WizardLM/WizardCoder-Python-34B-V1.0', messages=messages, api_base="your_api_endpoint")` | +| Phind/Phind-CodeLlama-34B-v2 | All phind-codellama models | `completion(model='huggingface/Phind/Phind-CodeLlama-34B-v2', messages=messages, api_base="your_api_endpoint")` | + +[**Jump to code**](https://github.com/BerriAI/litellm/blob/main/litellm/llms/prompt_templates/factory.py) + +## Format Prompt Yourself + +You can also format the prompt yourself. Here's how: + +```python +import litellm +# Create your own custom prompt template +litellm.register_prompt_template( + model="togethercomputer/LLaMA-2-7B-32K", + initial_prompt_value="You are a good assistant" # [OPTIONAL] + roles={ + "system": { + "pre_message": "[INST] <>\n", # [OPTIONAL] + "post_message": "\n<>\n [/INST]\n" # [OPTIONAL] + }, + "user": { + "pre_message": "[INST] ", # [OPTIONAL] + "post_message": " [/INST]" # [OPTIONAL] + }, + "assistant": { + "pre_message": "\n" # [OPTIONAL] + "post_message": "\n" # [OPTIONAL] + } + } + final_prompt_value="Now answer as best you can:" # [OPTIONAL] +) + +def test_huggingface_custom_model(): + model = "huggingface/togethercomputer/LLaMA-2-7B-32K" + response = completion(model=model, messages=messages, api_base="https://my-huggingface-endpoint") + print(response['choices'][0]['message']['content']) + return response + +test_huggingface_custom_model() +``` + +This is currently supported for Huggingface, TogetherAI, Ollama, and Petals. + +Other providers either have fixed prompt templates (e.g. Anthropic), or format it themselves (e.g. Replicate). If there's a provider we're missing coverage for, let us know! + +## All Providers + +Here's the code for how we format all providers. Let us know how we can improve this further + + +| Provider | Model Name | Code | +| -------- | -------- | -------- | +| Anthropic | `claude-instant-1`, `claude-instant-1.2`, `claude-2` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/anthropic.py#L84) +| OpenAI Text Completion | `text-davinci-003`, `text-curie-001`, `text-babbage-001`, `text-ada-001`, `babbage-002`, `davinci-002`, | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/main.py#L442) +| Replicate | all model names starting with `replicate/` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/replicate.py#L180) +| Cohere | `command-nightly`, `command`, `command-light`, `command-medium-beta`, `command-xlarge-beta` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/cohere.py#L115) +| Huggingface | all model names starting with `huggingface/` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/huggingface_restapi.py#L186) +| OpenRouter | all model names starting with `openrouter/` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/main.py#L611) +| AI21 | `j2-mid`, `j2-light`, `j2-ultra` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/ai21.py#L107) +| VertexAI | `text-bison`, `text-bison@001`, `chat-bison`, `chat-bison@001`, `chat-bison-32k`, `code-bison`, `code-bison@001`, `code-gecko@001`, `code-gecko@latest`, `codechat-bison`, `codechat-bison@001`, `codechat-bison-32k` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/vertex_ai.py#L89) +| Bedrock | all model names starting with `bedrock/` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/bedrock.py#L183) +| Sagemaker | `sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/sagemaker.py#L89) +| TogetherAI | all model names starting with `together_ai/` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/together_ai.py#L101) +| AlephAlpha | all model names starting with `aleph_alpha/` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/aleph_alpha.py#L184) +| Palm | all model names starting with `palm/` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/palm.py#L95) +| NLP Cloud | all model names starting with `palm/` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/nlp_cloud.py#L120) +| Petals | all model names starting with `petals/` | [Code](https://github.com/BerriAI/litellm/blob/721564c63999a43f96ee9167d0530759d51f8d45/litellm/llms/petals.py#L87) \ No newline at end of file diff --git a/docs/my-website/docs/completion/reliable_completions.md b/docs/my-website/docs/completion/reliable_completions.md new file mode 100644 index 0000000000000000000000000000000000000000..2656f9a4fb436e3722f2e817bf86bc6d80577b0b --- /dev/null +++ b/docs/my-website/docs/completion/reliable_completions.md @@ -0,0 +1,196 @@ +# Reliability - Retries, Fallbacks + +LiteLLM helps prevent failed requests in 2 ways: +- Retries +- Fallbacks: Context Window + General + +## Helper utils +LiteLLM supports the following functions for reliability: +* `litellm.longer_context_model_fallback_dict`: Dictionary which has a mapping for those models which have larger equivalents +* `num_retries`: use tenacity retries +* `completion()` with fallbacks: switch between models/keys/api bases in case of errors. + +## Retry failed requests + +Call it in completion like this `completion(..num_retries=2)`. + + +Here's a quick look at how you can use it: + +```python +from litellm import completion + +user_message = "Hello, whats the weather in San Francisco??" +messages = [{"content": user_message, "role": "user"}] + +# normal call +response = completion( + model="gpt-3.5-turbo", + messages=messages, + num_retries=2 + ) +``` + +## Fallbacks + +### Context Window Fallbacks +```python +from litellm import completion + +fallback_dict = {"gpt-3.5-turbo": "gpt-3.5-turbo-16k"} +messages = [{"content": "how does a court case get to the Supreme Court?" * 500, "role": "user"}] + +completion(model="gpt-3.5-turbo", messages=messages, context_window_fallback_dict=ctx_window_fallback_dict) +``` + +### Fallbacks - Switch Models/API Keys/API Bases + +LLM APIs can be unstable, completion() with fallbacks ensures you'll always get a response from your calls + +#### Usage +To use fallback models with `completion()`, specify a list of models in the `fallbacks` parameter. + +The `fallbacks` list should include the primary model you want to use, followed by additional models that can be used as backups in case the primary model fails to provide a response. + +#### switch models +```python +response = completion(model="bad-model", messages=messages, + fallbacks=["gpt-3.5-turbo" "command-nightly"]) +``` + +#### switch api keys/bases (E.g. azure deployment) +Switch between different keys for the same azure deployment, or use another deployment as well. + +```python +api_key="bad-key" +response = completion(model="azure/gpt-4", messages=messages, api_key=api_key, + fallbacks=[{"api_key": "good-key-1"}, {"api_key": "good-key-2", "api_base": "good-api-base-2"}]) +``` + +[Check out this section for implementation details](#fallbacks-1) + +## Implementation Details + +### Fallbacks +#### Output from calls +``` +Completion with 'bad-model': got exception Unable to map your input to a model. Check your input - {'model': 'bad-model' + + + +completion call gpt-3.5-turbo +{ + "id": "chatcmpl-7qTmVRuO3m3gIBg4aTmAumV1TmQhB", + "object": "chat.completion", + "created": 1692741891, + "model": "gpt-3.5-turbo-0613", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "I apologize, but as an AI, I do not have the capability to provide real-time weather updates. However, you can easily check the current weather in San Francisco by using a search engine or checking a weather website or app." + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 16, + "completion_tokens": 46, + "total_tokens": 62 + } +} + +``` + +#### How does fallbacks work + +When you pass `fallbacks` to `completion`, it makes the first `completion` call using the primary model specified as `model` in `completion(model=model)`. If the primary model fails or encounters an error, it automatically tries the `fallbacks` models in the specified order. This ensures a response even if the primary model is unavailable. + + +#### Key components of Model Fallbacks implementation: +* Looping through `fallbacks` +* Cool-Downs for rate-limited models + +#### Looping through `fallbacks` +Allow `45seconds` for each request. In the 45s this function tries calling the primary model set as `model`. If model fails it loops through the backup `fallbacks` models and attempts to get a response in the allocated `45s` time set here: +```python +while response == None and time.time() - start_time < 45: + for model in fallbacks: +``` + +#### Cool-Downs for rate-limited models +If a model API call leads to an error - allow it to cooldown for `60s` +```python +except Exception as e: + print(f"got exception {e} for model {model}") + rate_limited_models.add(model) + model_expiration_times[model] = ( + time.time() + 60 + ) # cool down this selected model + pass +``` + +Before making an LLM API call we check if the selected model is in `rate_limited_models`, if so skip making the API call +```python +if ( + model in rate_limited_models +): # check if model is currently cooling down + if ( + model_expiration_times.get(model) + and time.time() >= model_expiration_times[model] + ): + rate_limited_models.remove( + model + ) # check if it's been 60s of cool down and remove model + else: + continue # skip model + +``` + +#### Full code of completion with fallbacks() +```python + + response = None + rate_limited_models = set() + model_expiration_times = {} + start_time = time.time() + fallbacks = [kwargs["model"]] + kwargs["fallbacks"] + del kwargs["fallbacks"] # remove fallbacks so it's not recursive + + while response == None and time.time() - start_time < 45: + for model in fallbacks: + # loop thru all models + try: + if ( + model in rate_limited_models + ): # check if model is currently cooling down + if ( + model_expiration_times.get(model) + and time.time() >= model_expiration_times[model] + ): + rate_limited_models.remove( + model + ) # check if it's been 60s of cool down and remove model + else: + continue # skip model + + # delete model from kwargs if it exists + if kwargs.get("model"): + del kwargs["model"] + + print("making completion call", model) + response = litellm.completion(**kwargs, model=model) + + if response != None: + return response + + except Exception as e: + print(f"got exception {e} for model {model}") + rate_limited_models.add(model) + model_expiration_times[model] = ( + time.time() + 60 + ) # cool down this selected model + pass + return response +``` diff --git a/docs/my-website/docs/completion/stream.md b/docs/my-website/docs/completion/stream.md new file mode 100644 index 0000000000000000000000000000000000000000..f563c987b5b8eb002fa5b46724974ffc6e898a8e --- /dev/null +++ b/docs/my-website/docs/completion/stream.md @@ -0,0 +1,76 @@ +# Streaming + Async + +- [Streaming Responses](#streaming-responses) +- [Async Completion](#async-completion) +- [Async + Streaming Completion](#async-streaming) + +## Streaming Responses +LiteLLM supports streaming the model response back by passing `stream=True` as an argument to the completion function +### Usage +```python +from litellm import completion +messages = [{"role": "user", "content": "Hey, how's it going?"}] +response = completion(model="gpt-3.5-turbo", messages=messages, stream=True) +for part in response: + print(part.choices[0].delta.content or "") +``` + +### Helper function + +LiteLLM also exposes a helper function to rebuild the complete streaming response from the list of chunks. + +```python +from litellm import completion +messages = [{"role": "user", "content": "Hey, how's it going?"}] +response = completion(model="gpt-3.5-turbo", messages=messages, stream=True) + +for chunk in response: + chunks.append(chunk) + +print(litellm.stream_chunk_builder(chunks, messages=messages)) +``` + +## Async Completion +Asynchronous Completion with LiteLLM. LiteLLM provides an asynchronous version of the completion function called `acompletion` +### Usage +```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) + +``` + +## Async Streaming +We've implemented an `__anext__()` function in the streaming object returned. This enables async iteration over the streaming object. + +### Usage +Here's an example of using it with openai. +```python +from litellm import acompletion +import asyncio, os, traceback + +async def completion_call(): + try: + print("test acompletion + streaming") + response = await acompletion( + model="gpt-3.5-turbo", + messages=[{"content": "Hello, how are you?", "role": "user"}], + stream=True + ) + print(f"response: {response}") + async for chunk in response: + print(chunk) + except: + print(f"error occurred: {traceback.format_exc()}") + pass + +asyncio.run(completion_call()) +``` \ No newline at end of file diff --git a/docs/my-website/docs/completion/token_usage.md b/docs/my-website/docs/completion/token_usage.md new file mode 100644 index 0000000000000000000000000000000000000000..3760c3311bfe49b6e2dc339b08d0d5f4c5c0cf08 --- /dev/null +++ b/docs/my-website/docs/completion/token_usage.md @@ -0,0 +1,154 @@ +# Completion Token Usage & Cost +By default LiteLLM returns token usage in all completion requests ([See here](https://litellm.readthedocs.io/en/latest/output/)) + +However, we also expose 5 helper functions + **[NEW]** an API to calculate token usage across providers: + +- `encode`: This encodes the text passed in, using the model-specific tokenizer. [**Jump to code**](#1-encode) + +- `decode`: This decodes the tokens passed in, using the model-specific tokenizer. [**Jump to code**](#2-decode) + +- `token_counter`: This returns the number of tokens for a given input - it uses the tokenizer based on the model, and defaults to tiktoken if no model-specific tokenizer is available. [**Jump to code**](#3-token_counter) + +- `cost_per_token`: This returns the cost (in USD) for prompt (input) and completion (output) tokens. Uses the live list from `api.litellm.ai`. [**Jump to code**](#4-cost_per_token) + +- `completion_cost`: This returns the overall cost (in USD) for a given LLM API Call. It combines `token_counter` and `cost_per_token` to return the cost for that query (counting both cost of input and output). [**Jump to code**](#5-completion_cost) + +- `get_max_tokens`: This returns the maximum number of tokens allowed for the given model. [**Jump to code**](#6-get_max_tokens) + +- `model_cost`: This returns a dictionary for all models, with their max_tokens, input_cost_per_token and output_cost_per_token. It uses the `api.litellm.ai` call shown below. [**Jump to code**](#7-model_cost) + +- `register_model`: This registers new / overrides existing models (and their pricing details) in the model cost dictionary. [**Jump to code**](#8-register_model) + +- `api.litellm.ai`: Live token + price count across [all supported models](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). [**Jump to code**](#9-apilitellmai) + +📣 This is a community maintained list. Contributions are welcome! ❤️ + +## Example Usage + +### 1. `encode` +Encoding has model-specific tokenizers for anthropic, cohere, llama2 and openai. If an unsupported model is passed in, it'll default to using tiktoken (openai's tokenizer). + +```python +from litellm import encode, decode + +sample_text = "Hellö World, this is my input string!" +# openai encoding + decoding +openai_tokens = encode(model="gpt-3.5-turbo", text=sample_text) +print(openai_tokens) +``` + +### 2. `decode` + +Decoding is supported for anthropic, cohere, llama2 and openai. + +```python +from litellm import encode, decode + +sample_text = "Hellö World, this is my input string!" +# openai encoding + decoding +openai_tokens = encode(model="gpt-3.5-turbo", text=sample_text) +openai_text = decode(model="gpt-3.5-turbo", tokens=openai_tokens) +print(openai_text) +``` + +### 3. `token_counter` + +```python +from litellm import token_counter + +messages = [{"user": "role", "content": "Hey, how's it going"}] +print(token_counter(model="gpt-3.5-turbo", messages=messages)) +``` + +### 4. `cost_per_token` + +```python +from litellm import cost_per_token + +prompt_tokens = 5 +completion_tokens = 10 +prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = cost_per_token(model="gpt-3.5-turbo", prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)) + +print(prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar) +``` + +### 5. `completion_cost` + +* Input: Accepts a `litellm.completion()` response **OR** prompt + completion strings +* Output: Returns a `float` of cost for the `completion` call + +**litellm.completion()** +```python +from litellm import completion, completion_cost + +response = completion( + model="bedrock/anthropic.claude-v2", + messages=messages, + request_timeout=200, + ) +# pass your response from completion to completion_cost +cost = completion_cost(completion_response=response) +formatted_string = f"${float(cost):.10f}" +print(formatted_string) +``` + +**prompt + completion string** +```python +from litellm import completion_cost +cost = completion_cost(model="bedrock/anthropic.claude-v2", prompt="Hey!", completion="How's it going?") +formatted_string = f"${float(cost):.10f}" +print(formatted_string) +``` +### 6. `get_max_tokens` + +Input: Accepts a model name - e.g., gpt-3.5-turbo (to get a complete list, call litellm.model_list). +Output: Returns the maximum number of tokens allowed for the given model + +```python +from litellm import get_max_tokens + +model = "gpt-3.5-turbo" + +print(get_max_tokens(model)) # Output: 4097 +``` + +### 7. `model_cost` + +* Output: Returns a dict object containing the max_tokens, input_cost_per_token, output_cost_per_token for all models on [community-maintained list](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json) + +```python +from litellm import model_cost + +print(model_cost) # {'gpt-3.5-turbo': {'max_tokens': 4000, 'input_cost_per_token': 1.5e-06, 'output_cost_per_token': 2e-06}, ...} +``` + +### 8. `register_model` + +* Input: Provide EITHER a model cost dictionary or a url to a hosted json blob +* Output: Returns updated model_cost dictionary + updates litellm.model_cost with model details. + +**Dictionary** +```python +from litellm import register_model + +litellm.register_model({ + "gpt-4": { + "max_tokens": 8192, + "input_cost_per_token": 0.00002, + "output_cost_per_token": 0.00006, + "litellm_provider": "openai", + "mode": "chat" + }, +}) +``` + +**URL for json blob** +```python +import litellm + +litellm.register_model(model_cost= +"https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json") +``` + + + diff --git a/docs/my-website/docs/contact.md b/docs/my-website/docs/contact.md new file mode 100644 index 0000000000000000000000000000000000000000..d5309cd7373e07351b1935de5e8503a9ddb7a90b --- /dev/null +++ b/docs/my-website/docs/contact.md @@ -0,0 +1,6 @@ +# Contact Us + +[![](https://dcbadge.vercel.app/api/server/wuPM9dRgDw)](https://discord.gg/wuPM9dRgDw) + +* [Meet with us 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +* Contact us at ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/debugging/hosted_debugging.md b/docs/my-website/docs/debugging/hosted_debugging.md new file mode 100644 index 0000000000000000000000000000000000000000..7e894e7eeddf2c3065d6fd3fccd7b1a69b365888 --- /dev/null +++ b/docs/my-website/docs/debugging/hosted_debugging.md @@ -0,0 +1,91 @@ +import Image from '@theme/IdealImage'; +import QueryParamReader from '../../src/components/queryParamReader.js' + +# [Beta] Monitor Logs in Production + +:::note + +This is in beta. Expect frequent updates, as we improve based on your feedback. + +::: + +LiteLLM provides an integration to let you monitor logs in production. + +👉 Jump to our sample LiteLLM Dashboard: https://admin.litellm.ai/ + + +Dashboard + +## Debug your first logs + + Open In Colab + + + +### 1. Get your LiteLLM Token + +Go to [admin.litellm.ai](https://admin.litellm.ai/) and copy the code snippet with your unique token + +Usage + +### 2. Set up your environment + +**Add it to your .env** + +```python +import os + +os.env["LITELLM_TOKEN"] = "e24c4c06-d027-4c30-9e78-18bc3a50aebb" # replace with your unique token + +``` + +**Turn on LiteLLM Client** +```python +import litellm +litellm.client = True +``` + +### 3. Make a normal `completion()` call +```python +import litellm +from litellm import completion +import os + +# set env variables +os.environ["LITELLM_TOKEN"] = "e24c4c06-d027-4c30-9e78-18bc3a50aebb" # replace with your unique token +os.environ["OPENAI_API_KEY"] = "openai key" + +litellm.use_client = True # enable logging dashboard +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# openai call +response = completion(model="gpt-3.5-turbo", messages=messages) +``` + +Your `completion()` call print with a link to your session dashboard (https://admin.litellm.ai/) + +In the above case it would be: [`admin.litellm.ai/e24c4c06-d027-4c30-9e78-18bc3a50aebb`](https://admin.litellm.ai/e24c4c06-d027-4c30-9e78-18bc3a50aebb) + +Click on your personal dashboard link. Here's how you can find it 👇 + +Dashboard + +[👋 Tell us if you need better privacy controls](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version?month=2023-08) + +### 3. Review request log + +Oh! Looks like our request was made successfully. Let's click on it and see exactly what got sent to the LLM provider. + +Dashboard Log Row + + + +Ah! So we can see that this request was made to a **Baseten** (see litellm_params > custom_llm_provider) for a model with ID - **7qQNLDB** (see model). The message sent was - `"Hey, how's it going?"` and the response received was - `"As an AI language model, I don't have feelings or emotions, but I can assist you with your queries. How can I assist you today?"` + +Dashboard Log Row + +:::info + +🎉 Congratulations! You've successfully debugger your first log! + +::: \ No newline at end of file diff --git a/docs/my-website/docs/debugging/local_debugging.md b/docs/my-website/docs/debugging/local_debugging.md new file mode 100644 index 0000000000000000000000000000000000000000..87faef73e4bb400d5da331a5ad1f0d8d2511ae2f --- /dev/null +++ b/docs/my-website/docs/debugging/local_debugging.md @@ -0,0 +1,64 @@ +# Local Debugging +There's 2 ways to do local debugging - `litellm.set_verbose=True` and by passing in a custom function `completion(...logger_fn=)`. Warning: Make sure to not use `set_verbose` in production. It logs API keys, which might end up in log files. + +## Set Verbose + +This is good for getting print statements for everything litellm is doing. +```python +import litellm +from litellm import completion + +litellm.set_verbose=True # 👈 this is the 1-line change you need to make + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "openai key" +os.environ["COHERE_API_KEY"] = "cohere key" + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# openai call +response = completion(model="gpt-3.5-turbo", messages=messages) + +# cohere call +response = completion("command-nightly", messages) +``` + +## Logger Function +But sometimes all you care about is seeing exactly what's getting sent to your api call and what's being returned - e.g. if the api call is failing, why is that happening? what are the exact params being set? + +In that case, LiteLLM allows you to pass in a custom logging function to see / modify the model call Input/Outputs. + +**Note**: We expect you to accept a dict object. + +Your custom function + +```python +def my_custom_logging_fn(model_call_dict): + print(f"model call details: {model_call_dict}") +``` + +### Complete Example +```python +from litellm import completion + +def my_custom_logging_fn(model_call_dict): + print(f"model call details: {model_call_dict}") + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "openai key" +os.environ["COHERE_API_KEY"] = "cohere key" + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# openai call +response = completion(model="gpt-3.5-turbo", messages=messages, logger_fn=my_custom_logging_fn) + +# cohere call +response = completion("command-nightly", messages, logger_fn=my_custom_logging_fn) +``` + +## Still Seeing Issues? + +Text us @ +17708783106 or Join the [Discord](https://discord.com/invite/wuPM9dRgDw). + +We promise to help you in `lite`ning speed ❤️ diff --git a/docs/my-website/docs/default_code_snippet.md b/docs/my-website/docs/default_code_snippet.md new file mode 100644 index 0000000000000000000000000000000000000000..0921c316685ea8369acd22805cf889b803ef4189 --- /dev/null +++ b/docs/my-website/docs/default_code_snippet.md @@ -0,0 +1,22 @@ +--- +displayed_sidebar: tutorialSidebar +--- +# Get Started + +import QueryParamReader from '../src/components/queryParamReader.js' +import TokenComponent from '../src/components/queryParamToken.js' + +:::info + +This section assumes you've already added your API keys in + +If you want to use the non-hosted version, [go here](https://docs.litellm.ai/docs/#quick-start) + +::: + + +``` +pip install litellm +``` + + \ No newline at end of file diff --git a/docs/my-website/docs/embedding/async_embedding.md b/docs/my-website/docs/embedding/async_embedding.md new file mode 100644 index 0000000000000000000000000000000000000000..a78e993174c34faf0f66d39ca9176faddced0115 --- /dev/null +++ b/docs/my-website/docs/embedding/async_embedding.md @@ -0,0 +1,15 @@ +# Async Embedding + +LiteLLM provides an asynchronous version of the `embedding` function called `aembedding` +### Usage +```python +from litellm import aembedding +import asyncio + +async def test_get_response(): + response = await aembedding('text-embedding-ada-002', input=["good morning from litellm"]) + return response + +response = asyncio.run(test_get_response()) +print(response) +``` \ No newline at end of file diff --git a/docs/my-website/docs/embedding/moderation.md b/docs/my-website/docs/embedding/moderation.md new file mode 100644 index 0000000000000000000000000000000000000000..321548979a7edbe69549d66a1fc99e66a21e4dbe --- /dev/null +++ b/docs/my-website/docs/embedding/moderation.md @@ -0,0 +1,10 @@ +# Moderation +LiteLLM supports the moderation endpoint for OpenAI + +## Usage +```python +import os +from litellm import moderation +os.environ['OPENAI_API_KEY'] = "" +response = moderation(input="i'm ishaan cto of litellm") +``` diff --git a/docs/my-website/docs/embedding/supported_embedding.md b/docs/my-website/docs/embedding/supported_embedding.md new file mode 100644 index 0000000000000000000000000000000000000000..3c13c57324397d72c2ef6d88de7e48042aab056d --- /dev/null +++ b/docs/my-website/docs/embedding/supported_embedding.md @@ -0,0 +1,201 @@ +# Embedding Models + +## Quick Start +```python +from litellm import embedding +import os +os.environ['OPENAI_API_KEY'] = "" +response = embedding(model='text-embedding-ada-002', input=["good morning from litellm"]) +``` + +### Input Params for `litellm.embedding()` +### Required Fields + +- `model`: *string* - ID of the model to use. `model='text-embedding-ada-002'` + +- `input`: *array* - Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for text-embedding-ada-002), cannot be an empty string, and any array must be 2048 dimensions or less. +``` +input=["good morning from litellm"] +``` + +### Optional LiteLLM Fields + +- `user`: *string (optional)* A unique identifier representing your end-user, + +- `timeout`: *integer* - The maximum time, in seconds, to wait for the API to respond. Defaults to 600 seconds (10 minutes). + +- `api_base`: *string (optional)* - The api endpoint you want to call the model with + +- `api_version`: *string (optional)* - (Azure-specific) the api version for the call + +- `api_key`: *string (optional)* - The API key to authenticate and authorize requests. If not provided, the default API key is used. + +- `api_type`: *string (optional)* - The type of API to use. + +### Output from `litellm.embedding()` + +```json +{ + "object": "list", + "data": [ + { + "object": "embedding", + "index": 0, + "embedding": [ + -0.0022326677571982145, + 0.010749882087111473, + ... + ... + ... + + ] + } + ], + "model": "text-embedding-ada-002-v2", + "usage": { + "prompt_tokens": 10, + "total_tokens": 10 + } +} +``` + +## OpenAI Embedding Models + +### Usage +```python +from litellm import embedding +import os +os.environ['OPENAI_API_KEY'] = "" +response = embedding('text-embedding-ada-002', input=["good morning from litellm"]) +``` + +| Model Name | Function Call | Required OS Variables | +|----------------------|---------------------------------------------|--------------------------------------| +| text-embedding-ada-002 | `embedding('text-embedding-ada-002', input)` | `os.environ['OPENAI_API_KEY']` | + +## Azure OpenAI Embedding Models + +### API keys +This can be set as env variables or passed as **params to litellm.embedding()** +```python +import os +os.environ['AZURE_API_KEY'] = +os.environ['AZURE_API_BASE'] = +os.environ['AZURE_API_VERSION'] = +``` + +### Usage +```python +from litellm import embedding +response = embedding( + model="azure/", + input=["good morning from litellm"], + api_key=api_key, + api_base=api_base, + api_version=api_version, +) +print(response) +``` + +| Model Name | Function Call | +|----------------------|---------------------------------------------| +| text-embedding-ada-002 | `embedding(model="azure/", input=input)` | + +h/t to [Mikko](https://www.linkedin.com/in/mikkolehtimaki/) for this integration + +## OpenAI Compatible Embedding Models +Use this for calling `/embedding` endpoints on OpenAI Compatible Servers, example https://github.com/xorbitsai/inference + +**Note add `openai/` prefix to model so litellm knows to route to OpenAI** + +### Usage +```python +from litellm import embedding +response = embedding( + model = "openai/", # add `openai/` prefix to model so litellm knows to route to OpenAI + api_base="http://0.0.0.0:8000/" # set API Base of your Custom OpenAI Endpoint + input=["good morning from litellm"] +) +``` + +## Bedrock Embedding + +### API keys +This can be set as env variables or passed as **params to litellm.embedding()** +```python +import os +os.environ["AWS_ACCESS_KEY_ID"] = "" # Access key +os.environ["AWS_SECRET_ACCESS_KEY"] = "" # Secret access key +os.environ["AWS_REGION_NAME"] = "" # us-east-1, us-east-2, us-west-1, us-west-2 +``` + +### Usage +```python +from litellm import embedding +response = embedding( + model="amazon.titan-embed-text-v1", + input=["good morning from litellm"], +) +print(response) +``` + +| Model Name | Function Call | +|----------------------|---------------------------------------------| +| Titan Embeddings - G1 | `embedding(model="amazon.titan-embed-text-v1", input=input)` | + + +## Cohere Embedding Models +https://docs.cohere.com/reference/embed + +### Usage +```python +from litellm import embedding +os.environ["COHERE_API_KEY"] = "cohere key" + +# cohere call +response = embedding( + model="embed-english-v3.0", + input=["good morning from litellm", "this is another item"], + input_type="search_document" # optional param for v3 llms +) +``` +| Model Name | Function Call | +|--------------------------|--------------------------------------------------------------| +| embed-english-v3.0 | `embedding(model="embed-english-v3.0", input=["good morning from litellm", "this is another item"])` | +| embed-english-light-v3.0 | `embedding(model="embed-english-light-v3.0", input=["good morning from litellm", "this is another item"])` | +| embed-multilingual-v3.0 | `embedding(model="embed-multilingual-v3.0", input=["good morning from litellm", "this is another item"])` | +| embed-multilingual-light-v3.0 | `embedding(model="embed-multilingual-light-v3.0", input=["good morning from litellm", "this is another item"])` | +| embed-english-v2.0 | `embedding(model="embed-english-v2.0", input=["good morning from litellm", "this is another item"])` | +| embed-english-light-v2.0 | `embedding(model="embed-english-light-v2.0", input=["good morning from litellm", "this is another item"])` | +| embed-multilingual-v2.0 | `embedding(model="embed-multilingual-v2.0", input=["good morning from litellm", "this is another item"])` | + +## HuggingFace Embedding Models +LiteLLM supports all Feature-Extraction Embedding models: https://huggingface.co/models?pipeline_tag=feature-extraction + +### Usage +```python +from litellm import embedding +import os +os.environ['HUGGINGFACE_API_KEY'] = "" +response = embedding( + model='huggingface/microsoft/codebert-base', + input=["good morning from litellm"] +) +``` +### Usage - Custom API Base +```python +from litellm import embedding +import os +os.environ['HUGGINGFACE_API_KEY'] = "" +response = embedding( + model='huggingface/microsoft/codebert-base', + input=["good morning from litellm"], + api_base = "https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud" +) +``` + +| Model Name | Function Call | Required OS Variables | +|-----------------------|--------------------------------------------------------------|-------------------------------------------------| +| microsoft/codebert-base | `embedding('huggingface/microsoft/codebert-base', input=input)` | `os.environ['HUGGINGFACE_API_KEY']` | +| BAAI/bge-large-zh | `embedding('huggingface/BAAI/bge-large-zh', input=input)` | `os.environ['HUGGINGFACE_API_KEY']` | +| any-hf-embedding-model | `embedding('huggingface/hf-embedding-model', input=input)` | `os.environ['HUGGINGFACE_API_KEY']` | diff --git a/docs/my-website/docs/exception_mapping.md b/docs/my-website/docs/exception_mapping.md new file mode 100644 index 0000000000000000000000000000000000000000..2dcc584dc3b5dedf91f95827428476daf05e6435 --- /dev/null +++ b/docs/my-website/docs/exception_mapping.md @@ -0,0 +1,102 @@ +# Exception Mapping + +LiteLLM maps exceptions across all providers to their OpenAI counterparts. +- Rate Limit Errors +- Invalid Request Errors +- Authentication Errors +- Timeout Errors `openai.APITimeoutError` +- ServiceUnavailableError +- APIError +- APIConnectionError + +Base case we return APIConnectionError + +All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM. + +For all cases, the exception returned inherits from the original OpenAI Exception but contains 3 additional attributes: +* status_code - the http status code of the exception +* message - the error message +* llm_provider - the provider raising the exception + +## Usage + +```python +import litellm +import openai + +try: + response = litellm.completion( + model="gpt-4", + messages=[ + { + "role": "user", + "content": "hello, write a 20 pageg essay" + } + ], + timeout=0.01, # this will raise a timeout exception + ) +except openai.APITimeoutError as e: + print("Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e) + print(type(e)) + pass +``` + +## Usage - Catching Streaming Exceptions +```python +import litellm +try: + response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "hello, write a 20 pg essay" + } + ], + timeout=0.0001, # this will raise an exception + stream=True, + ) + for chunk in response: + print(chunk) +except openai.APITimeoutError as e: + print("Passed: Raised correct exception. Got openai.APITimeoutError\nGood Job", e) + print(type(e)) + pass +except Exception as e: + print(f"Did not raise error `openai.APITimeoutError`. Instead raised error type: {type(e)}, Error: {e}") + +``` + +## Details + +To see how it's implemented - [check out the code](https://github.com/BerriAI/litellm/blob/a42c197e5a6de56ea576c73715e6c7c6b19fa249/litellm/utils.py#L1217) + +[Create an issue](https://github.com/BerriAI/litellm/issues/new) **or** [make a PR](https://github.com/BerriAI/litellm/pulls) if you want to improve the exception mapping. + +**Note** For OpenAI and Azure we return the original exception (since they're of the OpenAI Error type). But we add the 'llm_provider' attribute to them. [See code](https://github.com/BerriAI/litellm/blob/a42c197e5a6de56ea576c73715e6c7c6b19fa249/litellm/utils.py#L1221) + +## Custom mapping list + +Base case - we return the original exception. + +| | ContextWindowExceededError | AuthenticationError | InvalidRequestError | RateLimitError | ServiceUnavailableError | +|---------------|----------------------------|---------------------|---------------------|---------------|-------------------------| +| Anthropic | ✅ | ✅ | ✅ | ✅ | | +| OpenAI | ✅ | ✅ |✅ |✅ |✅| +| Replicate | ✅ | ✅ | ✅ | ✅ | ✅ | +| Cohere | ✅ | ✅ | ✅ | ✅ | ✅ | +| Huggingface | ✅ | ✅ | ✅ | ✅ | | +| Openrouter | ✅ | ✅ | ✅ | ✅ | | +| AI21 | ✅ | ✅ | ✅ | ✅ | | +| VertexAI | | |✅ | | | +| Bedrock | | |✅ | | | +| Sagemaker | | |✅ | | | +| TogetherAI | ✅ | ✅ | ✅ | ✅ | | +| AlephAlpha | ✅ | ✅ | ✅ | ✅ | ✅ | + + +> For a deeper understanding of these exceptions, you can check out [this](https://github.com/BerriAI/litellm/blob/d7e58d13bf9ba9edbab2ab2f096f3de7547f35fa/litellm/utils.py#L1544) implementation for additional insights. + +The `ContextWindowExceededError` is a sub-class of `InvalidRequestError`. It was introduced to provide more granularity for exception-handling scenarios. Please refer to [this issue to learn more](https://github.com/BerriAI/litellm/issues/228). + +Contributions to improve exception mapping are [welcome](https://github.com/BerriAI/litellm#contributing) diff --git a/docs/my-website/docs/extras/contributing.md b/docs/my-website/docs/extras/contributing.md new file mode 100644 index 0000000000000000000000000000000000000000..f470515e3f59cb2d23e9d4b94a8786dd77c86210 --- /dev/null +++ b/docs/my-website/docs/extras/contributing.md @@ -0,0 +1,49 @@ +# Contributing to Documentation + +This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator. + +Clone litellm +``` +git clone https://github.com/BerriAI/litellm.git +``` + +### Local setup for locally running docs + +#### Installation +``` +npm install --global yarn +``` + + +### Local Development + +``` +cd docs/my-website +``` + +Let's Install requirement + +``` +yarn +``` +Run website + +``` +yarn start +``` +Open docs here: [http://localhost:3000/](http://localhost:3000/) + +``` + +This command builds your Markdown files into HTML and starts a development server to browse your documentation. Open up [http://127.0.0.1:8000/](http://127.0.0.1:8000/) in your web browser to see your documentation. You can make changes to your Markdown files and your docs will automatically rebuild. + +[Full tutorial here](https://docs.readthedocs.io/en/stable/intro/getting-started-with-mkdocs.html) + +### Making changes to Docs +- All the docs are placed under the `docs` directory +- If you are adding a new `.md` file or editing the hierarchy edit `mkdocs.yml` in the root of the project +- After testing your changes, make a change to the `main` branch of [github.com/BerriAI/litellm](https://github.com/BerriAI/litellm) + + + + diff --git a/docs/my-website/docs/getting_started.md b/docs/my-website/docs/getting_started.md new file mode 100644 index 0000000000000000000000000000000000000000..607b8694370e89dcf9cabcb13b9bef838a2a2c8b --- /dev/null +++ b/docs/my-website/docs/getting_started.md @@ -0,0 +1,100 @@ +# Getting Started + +import QuickStart from '../src/components/QuickStart.js' + +LiteLLM simplifies LLM API calls by mapping them all to the [OpenAI ChatCompletion format](https://platform.openai.com/docs/api-reference/chat). + +## basic usage + +By default we provide a free $10 community-key to try all providers supported on LiteLLM. + +```python +from litellm import completion + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "your-api-key" +os.environ["COHERE_API_KEY"] = "your-api-key" + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# openai call +response = completion(model="gpt-3.5-turbo", messages=messages) + +# cohere call +response = completion("command-nightly", messages) +``` + +**Need a dedicated key?** +Email us @ krrish@berri.ai + +Next Steps 👉 [Call all supported models - e.g. Claude-2, Llama2-70b, etc.](./proxy_api.md#supported-models) + +More details 👉 +* [Completion() function details](./completion/) +* [All supported models / providers on LiteLLM](./providers/) +* [Build your own OpenAI proxy](https://github.com/BerriAI/liteLLM-proxy/tree/main) + +## streaming + +Same example from before. Just pass in `stream=True` in the completion args. +```python +from litellm import completion + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "openai key" +os.environ["COHERE_API_KEY"] = "cohere key" + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# openai call +response = completion(model="gpt-3.5-turbo", messages=messages, stream=True) + +# cohere call +response = completion("command-nightly", messages, stream=True) + +print(response) +``` + +More details 👉 +* [streaming + async](./completion/stream.md) +* [tutorial for streaming Llama2 on TogetherAI](./tutorials/TogetherAI_liteLLM.md) + +## exception handling + +LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM. + +```python +from openai.error import OpenAIError +from litellm import completion + +os.environ["ANTHROPIC_API_KEY"] = "bad-key" +try: + # some code + completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}]) +except OpenAIError as e: + print(e) +``` + +## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks)) +LiteLLM exposes pre defined callbacks to send data to Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack +```python +from litellm import completion + +## set env variables for logging tools +os.environ["LANGFUSE_PUBLIC_KEY"] = "" +os.environ["LANGFUSE_SECRET_KEY"] = "" +os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id" + +os.environ["OPENAI_API_KEY"] + +# set callbacks +litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langfuse, llmonitor, supabase + +#openai call +response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) +``` + +More details 👉 +* [exception mapping](./exception_mapping.md) +* [retries + model fallbacks for completion()](./completion/reliable_completions.md) +* [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md) \ No newline at end of file diff --git a/docs/my-website/docs/index.md b/docs/my-website/docs/index.md new file mode 100644 index 0000000000000000000000000000000000000000..f2329be1e673492df771619fa827dd0fd92a7f03 --- /dev/null +++ b/docs/my-website/docs/index.md @@ -0,0 +1,402 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# LiteLLM - Getting Started + +https://github.com/BerriAI/litellm + +import QuickStart from '../src/components/QuickStart.js' + +## **Call 100+ LLMs using the same Input/Output Format** + +## Basic usage + + Open In Colab + + +```shell +pip install litellm +``` + + + +```python +from litellm import completion +import os + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "your-api-key" + +response = completion( + model="gpt-3.5-turbo", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` + + + + +```python +from litellm import completion +import os + +## set ENV variables +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +response = completion( + model="claude-2", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` + + + + + +```python +from litellm import completion +import os + +# auth: run 'gcloud auth application-default' +os.environ["VERTEX_PROJECT"] = "hardy-device-386718" +os.environ["VERTEX_LOCATION"] = "us-central1" + +response = completion( + model="chat-bison", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` + + + + + +```python +from litellm import completion +import os + +os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" + +# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints +response = completion( + model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0", + messages=[{ "content": "Hello, how are you?","role": "user"}], + api_base="https://my-endpoint.huggingface.cloud" +) + +print(response) +``` + + + + + +```python +from litellm import completion +import os + +## set ENV variables +os.environ["AZURE_API_KEY"] = "" +os.environ["AZURE_API_BASE"] = "" +os.environ["AZURE_API_VERSION"] = "" + +# azure call +response = completion( + "azure/", + messages = [{ "content": "Hello, how are you?","role": "user"}] +) +``` + + + + + + +```python +from litellm import completion + +response = completion( + model="ollama/llama2", + messages = [{ "content": "Hello, how are you?","role": "user"}], + api_base="http://localhost:11434" +) +``` + + + +```python +from litellm import completion +import os + +## set ENV variables +os.environ["OPENROUTER_API_KEY"] = "openrouter_api_key" + +response = completion( + model="openrouter/google/palm-2-chat-bison", + messages = [{ "content": "Hello, how are you?","role": "user"}], +) +``` + + + + +## Streaming +Set `stream=True` in the `completion` args. + + + +```python +from litellm import completion +import os + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "your-api-key" + +response = completion( + model="gpt-3.5-turbo", + messages=[{ "content": "Hello, how are you?","role": "user"}], + stream=True, +) + +for chunk in response: + print(chunk) +``` + + + + +```python +from litellm import completion +import os + +## set ENV variables +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +response = completion( + model="claude-2", + messages=[{ "content": "Hello, how are you?","role": "user"}], + stream=True, +) + +for chunk in response: + print(chunk) +``` + + + + + +```python +from litellm import completion +import os + +# auth: run 'gcloud auth application-default' +os.environ["VERTEX_PROJECT"] = "hardy-device-386718" +os.environ["VERTEX_LOCATION"] = "us-central1" + +response = completion( + model="chat-bison", + messages=[{ "content": "Hello, how are you?","role": "user"}], + stream=True, +) + +for chunk in response: + print(chunk) +``` + + + + + +```python +from litellm import completion +import os + +os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" + +# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints +response = completion( + model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0", + messages=[{ "content": "Hello, how are you?","role": "user"}], + api_base="https://my-endpoint.huggingface.cloud", + stream=True, +) + + +for chunk in response: + print(chunk) +``` + + + + + +```python +from litellm import completion +import os + +## set ENV variables +os.environ["AZURE_API_KEY"] = "" +os.environ["AZURE_API_BASE"] = "" +os.environ["AZURE_API_VERSION"] = "" + +# azure call +response = completion( + "azure/", + messages = [{ "content": "Hello, how are you?","role": "user"}], + stream=True, +) + +for chunk in response: + print(chunk) +``` + + + + + + +```python +from litellm import completion + +response = completion( + model="ollama/llama2", + messages = [{ "content": "Hello, how are you?","role": "user"}], + api_base="http://localhost:11434", + stream=True, +) + +for chunk in response: + print(chunk) +``` + + + +```python +from litellm import completion +import os + +## set ENV variables +os.environ["OPENROUTER_API_KEY"] = "openrouter_api_key" + +response = completion( + model="openrouter/google/palm-2-chat-bison", + messages = [{ "content": "Hello, how are you?","role": "user"}], + stream=True, +) + +for chunk in response: + print(chunk) +``` + + + + +## Exception handling + +LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM. + +```python +from openai.error import OpenAIError +from litellm import completion + +os.environ["ANTHROPIC_API_KEY"] = "bad-key" +try: + # some code + completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}]) +except OpenAIError as e: + print(e) +``` + +## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks)) +LiteLLM exposes pre defined callbacks to send data to Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack +```python +from litellm import completion + +## set env variables for logging tools +os.environ["LANGFUSE_PUBLIC_KEY"] = "" +os.environ["LANGFUSE_SECRET_KEY"] = "" +os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id" + +os.environ["OPENAI_API_KEY"] + +# set callbacks +litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langfuse, llmonitor, supabase + +#openai call +response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) +``` + +## Calculate Costs, Usage, Latency + +Pass the completion response to `litellm.completion_cost(completion_response=response)` and get the cost + +```python +from litellm import completion, completion_cost +import os +os.environ["OPENAI_API_KEY"] = "your-api-key" + +response = completion( + model="gpt-3.5-turbo", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) + +cost = completion_cost(completion_response=response) +print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}") +``` + +**Output** +```shell +Cost for completion call with gpt-3.5-turbo: $0.0000775000 +``` + +### Track Costs, Usage, Latency for streaming +We use a custom callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback +- We define a callback function to calculate cost `def track_cost_callback()` +- In `def track_cost_callback()` we check if the stream is complete - `if "complete_streaming_response" in kwargs` +- Use `litellm.completion_cost()` to calculate cost, once the stream is complete + +```python +import litellm + +# track_cost_callback +def track_cost_callback( + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, end_time # start/end time +): + try: + # check if it has collected an entire stream response + 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 + ) + print("streaming response_cost", response_cost) + except: + pass +# set callback +litellm.success_callback = [track_cost_callback] # set custom callback function + +# litellm.completion() call +response = completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "Hi 👋 - i'm openai" + } + ], + stream=True +) +``` + +## More details +* [exception mapping](./exception_mapping.md) +* [retries + model fallbacks for completion()](./completion/reliable_completions.md) +* [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md) \ No newline at end of file diff --git a/docs/my-website/docs/langchain/langchain.md b/docs/my-website/docs/langchain/langchain.md new file mode 100644 index 0000000000000000000000000000000000000000..fa5a0a96b58d0665b8f1430a966f94e20e4aa2ca --- /dev/null +++ b/docs/my-website/docs/langchain/langchain.md @@ -0,0 +1,135 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Using ChatLiteLLM() - Langchain + +## Pre-Requisites +```shell +!pip install litellm langchain +``` +## Quick Start + + + + +```python +import os +from langchain.chat_models import ChatLiteLLM +from langchain.prompts.chat import ( + ChatPromptTemplate, + SystemMessagePromptTemplate, + AIMessagePromptTemplate, + HumanMessagePromptTemplate, +) +from langchain.schema import AIMessage, HumanMessage, SystemMessage + +os.environ['OPENAI_API_KEY'] = "" +chat = ChatLiteLLM(model="gpt-3.5-turbo") +messages = [ + HumanMessage( + content="what model are you" + ) +] +chat(messages) +``` + + + + + +```python +import os +from langchain.chat_models import ChatLiteLLM +from langchain.prompts.chat import ( + ChatPromptTemplate, + SystemMessagePromptTemplate, + AIMessagePromptTemplate, + HumanMessagePromptTemplate, +) +from langchain.schema import AIMessage, HumanMessage, SystemMessage + +os.environ['ANTHROPIC_API_KEY'] = "" +chat = ChatLiteLLM(model="claude-2", temperature=0.3) +messages = [ + HumanMessage( + content="what model are you" + ) +] +chat(messages) +``` + + + + + +```python +import os +from langchain.chat_models import ChatLiteLLM +from langchain.prompts.chat import ( + ChatPromptTemplate, + SystemMessagePromptTemplate, + AIMessagePromptTemplate, + HumanMessagePromptTemplate, +) +from langchain.schema import AIMessage, HumanMessage, SystemMessage + +os.environ['REPLICATE_API_TOKEN'] = "" +chat = ChatLiteLLM(model="replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1") +messages = [ + HumanMessage( + content="what model are you?" + ) +] +chat(messages) +``` + + + + + +```python +import os +from langchain.chat_models import ChatLiteLLM +from langchain.prompts.chat import ( + ChatPromptTemplate, + SystemMessagePromptTemplate, + AIMessagePromptTemplate, + HumanMessagePromptTemplate, +) +from langchain.schema import AIMessage, HumanMessage, SystemMessage + +os.environ['COHERE_API_KEY'] = "" +chat = ChatLiteLLM(model="command-nightly") +messages = [ + HumanMessage( + content="what model are you?" + ) +] +chat(messages) +``` + + + + +```python +import os +from langchain.chat_models import ChatLiteLLM +from langchain.prompts.chat import ( + ChatPromptTemplate, + SystemMessagePromptTemplate, + AIMessagePromptTemplate, + HumanMessagePromptTemplate, +) +from langchain.schema import AIMessage, HumanMessage, SystemMessage + +os.environ['PALM_API_KEY'] = "" +chat = ChatLiteLLM(model="palm/chat-bison") +messages = [ + HumanMessage( + content="what model are you?" + ) +] +chat(messages) +``` + + diff --git a/docs/my-website/docs/migration.md b/docs/my-website/docs/migration.md new file mode 100644 index 0000000000000000000000000000000000000000..e1af07d4684373991dcf1d851d2b943aad82c858 --- /dev/null +++ b/docs/my-website/docs/migration.md @@ -0,0 +1,35 @@ +# Migration Guide - LiteLLM v1.0.0+ + +When we have breaking changes (i.e. going from 1.x.x to 2.x.x), we will document those changes here. + + +## `1.0.0` + +**Last Release before breaking change**: 0.14.0 + +**What changed?** + +- Requires `openai>=1.0.0` +- `openai.InvalidRequestError` → `openai.BadRequestError` +- `openai.ServiceUnavailableError` → `openai.APIStatusError` +- *NEW* litellm client, allow users to pass api_key + - `litellm.Litellm(api_key="sk-123")` +- response objects now inherit from `BaseModel` (prev. `OpenAIObject`) +- *NEW* default exception - `APIConnectionError` (prev. `APIError`) +- litellm.get_max_tokens() now returns an int not a dict + ```python + max_tokens = litellm.get_max_tokens("gpt-3.5-turbo") # returns an int not a dict + assert max_tokens==4097 + ``` +- Streaming - OpenAI Chunks now return `None` for empty stream chunks. This is how to process stream chunks with content + ```python + response = litellm.completion(model="gpt-3.5-turbo", messages=messages, stream=True) + for part in response: + print(part.choices[0].delta.content or "") + ``` + +**How can we communicate changes better?** +Tell us +- [Discord](https://discord.com/invite/wuPM9dRgDw) +- Email (krrish@berri.ai/ishaan@berri.ai) +- Text us (+17708783106) diff --git a/docs/my-website/docs/observability/callbacks.md b/docs/my-website/docs/observability/callbacks.md new file mode 100644 index 0000000000000000000000000000000000000000..892be932262421bbebc1c716e813b6bef1e84d9d --- /dev/null +++ b/docs/my-website/docs/observability/callbacks.md @@ -0,0 +1,35 @@ +# Callbacks + +## Use Callbacks to send Output Data to Posthog, Sentry etc + +liteLLM provides `input_callbacks`, `success_callbacks` and `failure_callbacks`, making it easy for you to send data to a particular provider depending on the status of your responses. + +liteLLM supports: + +- [Custom Callback Functions](https://docs.litellm.ai/docs/observability/custom_callback) +- [LLMonitor](https://llmonitor.com/docs) +- [Helicone](https://docs.helicone.ai/introduction) +- [Traceloop](https://traceloop.com/docs) +- [Sentry](https://docs.sentry.io/platforms/python/) +- [PostHog](https://posthog.com/docs/libraries/python) +- [Slack](https://slack.dev/bolt-python/concepts) + +### Quick Start + +```python +from litellm import completion + +# set callbacks +litellm.input_callback=["sentry"] # for sentry breadcrumbing - logs the input being sent to the api +litellm.success_callback=["posthog", "helicone", "llmonitor"] +litellm.failure_callback=["sentry", "llmonitor"] + +## set env variables +os.environ['SENTRY_DSN'], os.environ['SENTRY_API_TRACE_RATE']= "" +os.environ['POSTHOG_API_KEY'], os.environ['POSTHOG_API_URL'] = "api-key", "api-url" +os.environ["HELICONE_API_KEY"] = "" +os.environ["TRACELOOP_API_KEY"] = "" +os.environ["LLMONITOR_APP_ID"] = "" + +response = completion(model="gpt-3.5-turbo", messages=messages) +``` diff --git a/docs/my-website/docs/observability/custom_callback.md b/docs/my-website/docs/observability/custom_callback.md new file mode 100644 index 0000000000000000000000000000000000000000..78b7499a8b570cb50a100daa639a2633905dacf2 --- /dev/null +++ b/docs/my-website/docs/observability/custom_callback.md @@ -0,0 +1,358 @@ +# Custom Callbacks + +## Callback Class +You can create a custom callback class to precisely log events as they occur in litellm. + +```python +from litellm.integrations.custom_logger import CustomLogger + +class MyCustomHandler(CustomLogger): + def log_pre_api_call(self, model, messages, kwargs): + print(f"Pre-API Call") + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + print(f"Post-API Call") + + def 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"On Success") + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Failure") + +customHandler = MyCustomHandler() + +litellm.callbacks = [customHandler] +response = completion(model="gpt-3.5-turbo", messages=[{ "role": "user", "content": "Hi 👋 - i'm openai"}], + stream=True) +for chunk in response: + continue +``` + +## Callback Functions +If you just want to log on a specific event (e.g. on input) - you can use callback functions. + +You can set custom callbacks to trigger for: +- `litellm.input_callback` - Track inputs/transformed inputs before making the LLM API call +- `litellm.success_callback` - Track inputs/outputs after making LLM API call +- `litellm.failure_callback` - Track inputs/outputs + exceptions for litellm calls + +## Defining a Custom Callback Function +Create a custom callback function that takes specific arguments: + +```python +def custom_callback( + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, end_time # start/end time +): + # Your custom code here + print("LITELLM: in custom callback function") + print("kwargs", kwargs) + print("completion_response", completion_response) + print("start_time", start_time) + print("end_time", end_time) +``` + +### Setting the custom callback function +```python +import litellm +litellm.success_callback = [custom_callback] +``` + +## Using Your Custom Callback Function + +```python +import litellm +from litellm import completion + +# Assign the custom callback function +litellm.success_callback = [custom_callback] + +response = completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "Hi 👋 - i'm openai" + } + ] +) + +print(response) + +``` + +## Async Callback Functions + +LiteLLM currently supports just async success callback functions for async completion/embedding calls. + +```python +import asyncio, litellm + +async def async_test_logging_fn(kwargs, completion_obj, start_time, end_time): + print(f"On Async Success!") + +async def test_chat_openai(): + try: + # litellm.set_verbose = True + litellm.success_callback = [async_test_logging_fn] + 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 + except Exception as e: + print(e) + pytest.fail(f"An error occurred - {str(e)}") + +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? + +Notice we pass in a kwargs argument to custom callback. +```python +def custom_callback( + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, end_time # start/end time +): + # Your custom code here + print("LITELLM: in custom callback function") + print("kwargs", kwargs) + print("completion_response", completion_response) + print("start_time", start_time) + print("end_time", end_time) +``` + +This is a dictionary containing all the model-call details (the params we receive, the values we send to the http endpoint, the response we receive, stacktrace in case of errors, etc.). + +This is all logged in the [model_call_details via our Logger](https://github.com/BerriAI/litellm/blob/fc757dc1b47d2eb9d0ea47d6ad224955b705059d/litellm/utils.py#L246). + +Here's exactly what you can expect in the kwargs dictionary: +```shell +### DEFAULT PARAMS ### +"model": self.model, +"messages": self.messages, +"optional_params": self.optional_params, # model-specific params passed in +"litellm_params": self.litellm_params, # litellm-specific params passed in (e.g. metadata passed to completion call) +"start_time": self.start_time, # datetime object of when call was started + +### PRE-API CALL PARAMS ### (check via kwargs["log_event_type"]="pre_api_call") +"input" = input # the exact prompt sent to the LLM API +"api_key" = api_key # the api key used for that LLM API +"additional_args" = additional_args # any additional details for that API call (e.g. contains optional params sent) + +### POST-API CALL PARAMS ### (check via kwargs["log_event_type"]="post_api_call") +"original_response" = original_response # the original http response received (saved via response.text) + +### ON-SUCCESS PARAMS ### (check via kwargs["log_event_type"]="successful_api_call") +"complete_streaming_response" = complete_streaming_response # the complete streamed response (only set if `completion(..stream=True)`) +"end_time" = end_time # datetime object of when call was completed + +### ON-FAILURE PARAMS ### (check via kwargs["log_event_type"]="failed_api_call") +"exception" = exception # the Exception raised +"traceback_exception" = traceback_exception # the traceback generated via `traceback.format_exc()` +"end_time" = end_time # datetime object of when call was completed +``` + +### 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. + +```python +# litellm.set_verbose = False + def custom_callback( + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, end_time # start/end time + ): + # print(f"streaming response: {completion_response}") + if "complete_streaming_response" in kwargs: + print(f"Complete Streaming Response: {kwargs['complete_streaming_response']}") + + # Assign the custom callback function + litellm.success_callback = [custom_callback] + + response = completion(model="claude-instant-1", messages=messages, stream=True) + for idx, chunk in enumerate(response): + pass +``` + + +### Log additional metadata + +LiteLLM accepts a metadata dictionary in the completion call. You can pass additional metadata into your completion call via `completion(..., metadata={"key": "value"})`. + +Since this is a [litellm-specific param](https://github.com/BerriAI/litellm/blob/b6a015404eed8a0fa701e98f4581604629300ee3/litellm/main.py#L235), it's accessible via kwargs["litellm_params"] + +```python +from litellm import completion +import os, litellm + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "your-api-key" + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +def custom_callback( + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, end_time # start/end time +): + print(kwargs["litellm_params"]["metadata"]) + + +# Assign the custom callback function +litellm.success_callback = [custom_callback] + +response = litellm.completion(model="gpt-3.5-turbo", messages=messages, metadata={"hello": "world"}) +``` + +## Examples + +### Custom Callback to track costs for Streaming + Non-Streaming +```python + +def track_cost_callback( + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, end_time # start/end time +): + try: + # init logging config + logging.basicConfig( + filename='cost.log', + level=logging.INFO, + format='%(asctime)s - %(message)s', + datefmt='%Y-%m-%d %H:%M:%S' + ) + + # check if it has collected an entire stream response + 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 + ) + print("streaming response_cost", response_cost) + logging.info(f"Model {kwargs['model']} Cost: ${response_cost:.8f}") + + # for non streaming responses + else: + # we pass the completion_response obj + if kwargs["stream"] != True: + response_cost = litellm.completion_cost(completion_response=completion_response) + print("regular response_cost", response_cost) + logging.info(f"Model {completion_response.model} Cost: ${response_cost:.8f}") + except: + pass + +# Assign the custom callback function +litellm.success_callback = [track_cost_callback] + +response = completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "Hi 👋 - i'm openai" + } + ] +) + +print(response) +``` + +### Custom Callback to log transformed Input to LLMs +```python +def get_transformed_inputs( + kwargs, +): + params_to_model = kwargs["additional_args"]["complete_input_dict"] + print("params to model", params_to_model) + +litellm.input_callback = [get_transformed_inputs] + +def test_chat_openai(): + try: + response = completion(model="claude-2", + messages=[{ + "role": "user", + "content": "Hi 👋 - i'm openai" + }]) + + print(response) + + except Exception as e: + print(e) + pass +``` + +#### Output +```shell +params to model {'model': 'claude-2', 'prompt': "\n\nHuman: Hi 👋 - i'm openai\n\nAssistant: ", 'max_tokens_to_sample': 256} +``` + +### Custom Callback to write to Mixpanel + +```python +import mixpanel +import litellm +from litellm import completion + +def custom_callback( + kwargs, # kwargs to completion + completion_response, # response from completion + start_time, end_time # start/end time +): + # Your custom code here + mixpanel.track("LLM Response", {"llm_response": completion_response}) + + +# Assign the custom callback function +litellm.success_callback = [custom_callback] + +response = completion( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": "Hi 👋 - i'm openai" + } + ] +) + +print(response) + +``` + + + + + + + + + + + + diff --git a/docs/my-website/docs/observability/helicone_integration.md b/docs/my-website/docs/observability/helicone_integration.md new file mode 100644 index 0000000000000000000000000000000000000000..de89ba8da8c03a4e9c17071a6344498e7ca5527a --- /dev/null +++ b/docs/my-website/docs/observability/helicone_integration.md @@ -0,0 +1,55 @@ +# Helicone Tutorial +[Helicone](https://helicone.ai/) is an open source observability platform that proxies your OpenAI traffic and provides you key insights into your spend, latency and usage. + +## Use Helicone to log requests across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM) +liteLLM provides `success_callbacks` and `failure_callbacks`, making it easy for you to send data to a particular provider depending on the status of your responses. + +In this case, we want to log requests to Helicone when a request succeeds. + +### Approach 1: Use Callbacks +Use just 1 line of code, to instantly log your responses **across all providers** with helicone: +```python +litellm.success_callback=["helicone"] +``` + +Complete code +```python +from litellm import completion + +## set env variables +os.environ["HELICONE_API_KEY"] = "your-helicone-key" +os.environ["OPENAI_API_KEY"], os.environ["COHERE_API_KEY"] = "", "" + +# set callbacks +litellm.success_callback=["helicone"] + +#openai call +response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) + +#cohere call +response = completion(model="command-nightly", messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}]) +``` + +### Approach 2: [OpenAI + Azure only] Use Helicone as a proxy +Helicone provides advanced functionality like caching, etc. Helicone currently supports this for Azure and OpenAI. + +If you want to use Helicone to proxy your OpenAI/Azure requests, then you can - + +- Set helicone as your base url via: `litellm.api_url` +- Pass in helicone request headers via: `litellm.headers` + +Complete Code +```python +import litellm +from litellm import completion + +litellm.api_base = "https://oai.hconeai.com/v1" +litellm.headers = {"Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}"} + +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "how does a court case get to the Supreme Court?"}] +) + +print(response) +``` diff --git a/docs/my-website/docs/observability/langfuse_integration.md b/docs/my-website/docs/observability/langfuse_integration.md new file mode 100644 index 0000000000000000000000000000000000000000..3e5f5603d0b1d08f2be852bb09053d5cf0d7fb44 --- /dev/null +++ b/docs/my-website/docs/observability/langfuse_integration.md @@ -0,0 +1,105 @@ +import Image from '@theme/IdealImage'; + +# Langfuse - Logging LLM Input/Output + +LangFuse is open Source Observability & Analytics for LLM Apps +Detailed production traces and a granular view on quality, cost and latency + + + +:::info +We want to learn how we can make the callbacks better! Meet the LiteLLM [founders](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) or +join our [discord](https://discord.gg/wuPM9dRgDw) +::: + +## Pre-Requisites +Ensure you have run `pip install langfuse` for this integration +```shell +pip install langfuse litellm +``` + +## Quick Start +Use just 2 lines of code, to instantly log your responses **across all providers** with Langfuse + + Open In Colab + + +Get your Langfuse API Keys from https://cloud.langfuse.com/ +```python +litellm.success_callback = ["langfuse"] +``` +```python +# pip install langfuse +import litellm +import os + +# from https://cloud.langfuse.com/ +os.environ["LANGFUSE_PUBLIC_KEY"] = "" +os.environ["LANGFUSE_SECRET_KEY"] = "" +# Optional, defaults to https://cloud.langfuse.com +os.environ["LANGFUSE_HOST"] # optional + +# LLM API Keys +os.environ['OPENAI_API_KEY']="" + +# set langfuse as a callback, litellm will send the data to langfuse +litellm.success_callback = ["langfuse"] + +# openai call +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hi 👋 - i'm openai"} + ] +) +``` + +## Advanced +### Set Custom Generation names, pass metadata + +```python +import litellm +from litellm import completion +import os + +# from https://cloud.langfuse.com/ +os.environ["LANGFUSE_PUBLIC_KEY"] = "" +os.environ["LANGFUSE_SECRET_KEY"] = "" + + +# OpenAI and Cohere keys +# You can use any of the litellm supported providers: https://docs.litellm.ai/docs/providers +os.environ['OPENAI_API_KEY']="" + +# set langfuse as a callback, litellm will send the data to langfuse +litellm.success_callback = ["langfuse"] + +# openai call +response = completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hi 👋 - i'm openai"} + ], + metadata = { + "generation_name": "litellm-ishaan-gen", # set langfuse generation name + # custom metadata fields + "project": "litellm-proxy" + } +) + +print(response) + +``` + + + +## Troubleshooting & Errors +### Data not getting logged to Langfuse ? +- Ensure you're on the latest version of langfuse `pip install langfuse -U`. The latest version allows litellm to log JSON input/outputs to langfuse + +## Support & Talk to Founders + +- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +- [Community Discord 💭](https://discord.gg/wuPM9dRgDw) +- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ +- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai \ No newline at end of file diff --git a/docs/my-website/docs/observability/langsmith_integration.md b/docs/my-website/docs/observability/langsmith_integration.md new file mode 100644 index 0000000000000000000000000000000000000000..247cb8015a3f116e1393387769796cd242c4b0e4 --- /dev/null +++ b/docs/my-website/docs/observability/langsmith_integration.md @@ -0,0 +1,77 @@ +import Image from '@theme/IdealImage'; + +# Langsmith - Logging LLM Input/Output +An all-in-one developer platform for every step of the application lifecycle +https://smith.langchain.com/ + + + +:::info +We want to learn how we can make the callbacks better! Meet the LiteLLM [founders](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) or +join our [discord](https://discord.gg/wuPM9dRgDw) +::: + +## Pre-Requisites +```shell +pip install litellm +``` + +## Quick Start +Use just 2 lines of code, to instantly log your responses **across all providers** with Langsmith + + +```python +litellm.success_callback = ["langsmith"] +``` +```python +import litellm +import os + +os.environ["LANGSMITH_API_KEY"] = "" +# LLM API Keys +os.environ['OPENAI_API_KEY']="" + +# set langsmith as a callback, litellm will send the data to langsmith +litellm.success_callback = ["langsmith"] + +# openai call +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hi 👋 - i'm openai"} + ] +) +``` + +## Advanced +### Set Custom Project & Run names + +```python +import litellm +import os + +os.environ["LANGSMITH_API_KEY"] = "" +# LLM API Keys +os.environ['OPENAI_API_KEY']="" + +# set langfuse as a callback, litellm will send the data to langfuse +litellm.success_callback = ["langfuse"] + +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hi 👋 - i'm openai"} + ], + metadata={ + "run_name": "litellmRUN", # langsmith run name + "project_name": "litellm-completion", # langsmith project name + } +) +print(response) +``` +## Support & Talk to Founders + +- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +- [Community Discord 💭](https://discord.gg/wuPM9dRgDw) +- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ +- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai \ No newline at end of file diff --git a/docs/my-website/docs/observability/llmonitor_integration.md b/docs/my-website/docs/observability/llmonitor_integration.md new file mode 100644 index 0000000000000000000000000000000000000000..06ac44a84156b9823a164e7686dde358ee07541f --- /dev/null +++ b/docs/my-website/docs/observability/llmonitor_integration.md @@ -0,0 +1,65 @@ +# LLMonitor Tutorial + +[LLMonitor](https://llmonitor.com/) is an open-source observability platform that provides cost tracking, user tracking and powerful agent tracing. + + + +## Use LLMonitor to log requests across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM) + +liteLLM provides `callbacks`, making it easy for you to log data depending on the status of your responses. + +:::info +We want to learn how we can make the callbacks better! Meet the [founders](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) or +join our [discord](https://discord.gg/wuPM9dRgDw) +::: + +### Using Callbacks + +First, sign up to get an app ID on the [LLMonitor dashboard](https://llmonitor.com). + +Use just 2 lines of code, to instantly log your responses **across all providers** with llmonitor: + +```python +litellm.success_callback = ["llmonitor"] +litellm.failure_callback = ["llmonitor"] +``` + +Complete code + +```python +from litellm import completion + +## set env variables +os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id" +# Optional: os.environ["LLMONITOR_API_URL"] = "self-hosting-url" + +os.environ["OPENAI_API_KEY"], os.environ["COHERE_API_KEY"] = "", "" + +# set callbacks +litellm.success_callback = ["llmonitor"] +litellm.failure_callback = ["llmonitor"] + +#openai call +response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + user="ishaan_litellm" +) + +#cohere call +response = completion( + model="command-nightly", + messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}], + user="ishaan_litellm" +) +``` + +## Support & Talk to Founders + +- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +- [Community Discord 💭](https://discord.gg/wuPM9dRgDw) +- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ +- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai +- Meet the LLMonitor team on [Discord](http://discord.com/invite/8PafSG58kK) or via [email](mailto:vince@llmonitor.com). \ No newline at end of file diff --git a/docs/my-website/docs/observability/promptlayer_integration.md b/docs/my-website/docs/observability/promptlayer_integration.md new file mode 100644 index 0000000000000000000000000000000000000000..a48dd91057dc849ad6b6311ca17fc0e1a87a6eef --- /dev/null +++ b/docs/my-website/docs/observability/promptlayer_integration.md @@ -0,0 +1,77 @@ +# Promptlayer Tutorial + +Promptlayer is a platform for prompt engineers. Log OpenAI requests. Search usage history. Track performance. Visually manage prompt templates. + + + +## Use Promptlayer to log requests across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM) + +liteLLM provides `callbacks`, making it easy for you to log data depending on the status of your responses. + +### Using Callbacks + +Get your PromptLayer API Key from https://promptlayer.com/ + +Use just 2 lines of code, to instantly log your responses **across all providers** with promptlayer: + +```python +litellm.success_callback = ["promptlayer"] + +``` + +Complete code + +```python +from litellm import completion + +## set env variables +os.environ["PROMPTLAYER_API_KEY"] = "your-promptlayer-key" + +os.environ["OPENAI_API_KEY"], os.environ["COHERE_API_KEY"] = "", "" + +# set callbacks +litellm.success_callback = ["promptlayer"] + +#openai call +response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) + +#cohere call +response = completion(model="command-nightly", messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}]) +``` + +### Logging Metadata + +You can also log completion call metadata to Promptlayer. + +You can add metadata to a completion call through the metadata param: +```python +completion(model,messages, metadata={"model": "ai21"}) +``` + +**Complete Code** +```python +from litellm import completion + +## set env variables +os.environ["PROMPTLAYER_API_KEY"] = "your-promptlayer-key" + +os.environ["OPENAI_API_KEY"], os.environ["COHERE_API_KEY"] = "", "" + +# set callbacks +litellm.success_callback = ["promptlayer"] + +#openai call - log llm provider is openai +response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], metadata={"provider": "openai"}) + +#cohere call - log llm provider is cohere +response = completion(model="command-nightly", messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}], metadata={"provider": "cohere"}) +``` + +Credits to [Nick Bradford](https://github.com/nsbradford), from [Vim-GPT](https://github.com/nsbradford/VimGPT), for the suggestion. + +## Support & Talk to Founders + +- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +- [Community Discord 💭](https://discord.gg/wuPM9dRgDw) +- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ +- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai \ No newline at end of file diff --git a/docs/my-website/docs/observability/sentry.md b/docs/my-website/docs/observability/sentry.md new file mode 100644 index 0000000000000000000000000000000000000000..255dd55cfeccdab2215343d006b38faebaf167ca --- /dev/null +++ b/docs/my-website/docs/observability/sentry.md @@ -0,0 +1,44 @@ +import Image from '@theme/IdealImage'; + +# Sentry - Log LLM Exceptions +[Sentry](https://sentry.io/) provides error monitoring for production. LiteLLM can add breadcrumbs and send exceptions to Sentry with this integration + +Track exceptions for: +- litellm.completion() - completion()for 100+ LLMs +- litellm.acompletion() - async completion() +- Streaming completion() & acompletion() calls + + + + +## Usage + +### Set SENTRY_DSN & callback + +```python +import litellm, os +os.environ["SENTRY_DSN"] = "your-sentry-url" +litellm.failure_callback=["sentry"] +``` + +### Sentry callback with completion +```python +import litellm +from litellm import completion + +litellm.input_callback=["sentry"] # adds sentry breadcrumbing +litellm.failure_callback=["sentry"] # [OPTIONAL] if you want litellm to capture -> send exception to sentry + +import os +os.environ["SENTRY_DSN"] = "your-sentry-url" +os.environ["OPENAI_API_KEY"] = "your-openai-key" + +# set bad key to trigger error +api_key="bad-key" +response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey!"}], stream=True, api_key=api_key) + +print(response) +``` + +[Let us know](https://github.com/BerriAI/litellm/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.yml&title=%5BFeature%5D%3A+) if you need any additional options from Sentry. + diff --git a/docs/my-website/docs/observability/slack_integration.md b/docs/my-website/docs/observability/slack_integration.md new file mode 100644 index 0000000000000000000000000000000000000000..162ceb450f9fdcab2f6fc7d0bf40c7888629f690 --- /dev/null +++ b/docs/my-website/docs/observability/slack_integration.md @@ -0,0 +1,93 @@ +import Image from '@theme/IdealImage'; + +# Slack - Logging LLM Input/Output, Exceptions + + + +:::info +We want to learn how we can make the callbacks better! Meet the LiteLLM [founders](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) or +join our [discord](https://discord.gg/wuPM9dRgDw) +::: + +## Pre-Requisites + +### Step 1 +```shell +pip install litellm +``` + +### Step 2 +Get a slack webhook url from https://api.slack.com/messaging/webhooks + + + +## Quick Start +### Create a custom Callback to log to slack +We create a custom callback, to log to slack webhooks, see [custom callbacks on litellm](https://docs.litellm.ai/docs/observability/custom_callback) +```python +def send_slack_alert( + kwargs, + completion_response, + start_time, + end_time, +): + print( + "in custom slack callback func" + ) + import requests + import json + + # Define the Slack webhook URL + # get it from https://api.slack.com/messaging/webhooks + slack_webhook_url = os.environ['SLACK_WEBHOOK_URL'] # "https://hooks.slack.com/services/<>/<>/<>" + + # Define the text payload, send data available in litellm custom_callbacks + text_payload = f"""LiteLLM Logging: kwargs: {str(kwargs)}\n\n, response: {str(completion_response)}\n\n, start time{str(start_time)} end time: {str(end_time)} + """ + payload = { + "text": text_payload + } + + # Set the headers + headers = { + "Content-type": "application/json" + } + + # Make the POST request + response = requests.post(slack_webhook_url, json=payload, headers=headers) + + # Check the response status + if response.status_code == 200: + print("Message sent successfully to Slack!") + else: + print(f"Failed to send message to Slack. Status code: {response.status_code}") + print(response.json()) +``` + +### Pass callback to LiteLLM +```python +litellm.success_callback = [send_slack_alert] +``` + +```python +import litellm +litellm.success_callback = [send_slack_alert] # log success +litellm.failure_callback = [send_slack_alert] # log exceptions + +# this will raise an exception +response = litellm.completion( + model="gpt-2", + messages=[ + { + "role": "user", + "content": "Hi 👋 - i'm openai" + } + ] +) +``` +## Support & Talk to Founders + +- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +- [Community Discord 💭](https://discord.gg/wuPM9dRgDw) +- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ +- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai \ No newline at end of file diff --git a/docs/my-website/docs/observability/supabase_integration.md b/docs/my-website/docs/observability/supabase_integration.md new file mode 100644 index 0000000000000000000000000000000000000000..a0897a27300a1804c180f9d973018581b76277f2 --- /dev/null +++ b/docs/my-website/docs/observability/supabase_integration.md @@ -0,0 +1,101 @@ +# Supabase Tutorial +[Supabase](https://supabase.com/) is an open source Firebase alternative. +Start your project with a Postgres database, Authentication, instant APIs, Edge Functions, Realtime subscriptions, Storage, and Vector embeddings. + +## Use Supabase to log requests and see total spend across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM) +liteLLM provides `success_callbacks` and `failure_callbacks`, making it easy for you to send data to a particular provider depending on the status of your responses. + +In this case, we want to log requests to Supabase in both scenarios - when it succeeds and fails. + +### Create a supabase table + +Go to your Supabase project > go to the [Supabase SQL Editor](https://supabase.com/dashboard/projects) and create a new table with this configuration. + +Note: You can change the table name. Just don't change the column names. + +```sql +create table + public.request_logs ( + id bigint generated by default as identity, + created_at timestamp with time zone null default now(), + model text null default ''::text, + messages json null default '{}'::json, + response json null default '{}'::json, + end_user text null default ''::text, + status text null default ''::text, + error json null default '{}'::json, + response_time real null default '0'::real, + total_cost real null, + additional_details json null default '{}'::json, + litellm_call_id text unique, + primary key (id) + ) tablespace pg_default; +``` + +### Use Callbacks +Use just 2 lines of code, to instantly see costs and log your responses **across all providers** with Supabase: + +```python +litellm.success_callback=["supabase"] +litellm.failure_callback=["supabase"] +``` + +Complete code +```python +from litellm import completion + +## set env variables +### SUPABASE +os.environ["SUPABASE_URL"] = "your-supabase-url" +os.environ["SUPABASE_KEY"] = "your-supabase-key" + +## LLM API KEY +os.environ["OPENAI_API_KEY"] = "" + +# set callbacks +litellm.success_callback=["supabase"] +litellm.failure_callback=["supabase"] + +# openai call +response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + user="ishaan22" # identify users +) + +# bad call, expect this call to fail and get logged +response = completion( + model="chatgpt-test", + messages=[{"role": "user", "content": "Hi 👋 - i'm a bad call to test error logging"}] +) + +``` + +### Additional Controls + +**Identify end-user** + +Pass `user` to `litellm.completion` to map your llm call to an end-user + +```python +response = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}], + user="ishaan22" # identify users +) +``` + +**Different Table name** + +If you modified your table name, here's how to pass the new name. + +```python +litellm.modify_integration("supabase",{"table_name": "litellm_logs"}) +``` + +## Support & Talk to Founders + +- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +- [Community Discord 💭](https://discord.gg/wuPM9dRgDw) +- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ +- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/observability/telemetry.md b/docs/my-website/docs/observability/telemetry.md new file mode 100644 index 0000000000000000000000000000000000000000..78267b9c569f91cf5ef651905962e55bd89b5991 --- /dev/null +++ b/docs/my-website/docs/observability/telemetry.md @@ -0,0 +1,13 @@ +# Telemetry + +LiteLLM contains a telemetry feature that tells us what models are used, and what errors are hit. + +## What is logged? + +Only the model name and exception raised is logged. + +## Why? +We use this information to help us understand how LiteLLM is used, and improve stability. + +## Opting out +If you prefer to opt out of telemetry, you can do this by setting `litellm.telemetry = False`. \ No newline at end of file diff --git a/docs/my-website/docs/observability/traceloop_integration.md b/docs/my-website/docs/observability/traceloop_integration.md new file mode 100644 index 0000000000000000000000000000000000000000..9902e58bab8e99e5cff0f33de552456c94aa2c37 --- /dev/null +++ b/docs/my-website/docs/observability/traceloop_integration.md @@ -0,0 +1,34 @@ +import Image from '@theme/IdealImage'; + +# Traceloop (OpenLLMetry) - Tracing LLMs with OpenTelemetry + +[Traceloop](https://traceloop.com) is a platform for monitoring and debugging the quality of your LLM outputs. +It provides you with a way to track the performance of your LLM application; rollout changes with confidence; and debug issues in production. +It is based on [OpenTelemetry](https://opentelemetry.io), so it can provide full visibility to your LLM requests, as well vector DB usage, and other infra in your stack. + + + +## Getting Started + +Install the Traceloop SDK: + +``` +pip install traceloop-sdk +``` + +Use just 2 lines of code, to instantly log your LLM responses with OpenTelemetry: + +```python +Traceloop.init(app_name=, disable_batch=True) +litellm.success_callback = ["traceloop"] +``` + +To get better visualizations on how your code behaves, you may want to annotate specific parts of your LLM chain. See [Traceloop docs on decorators](https://traceloop.com/docs/python-sdk/decorators) for more information. + +## 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. + +## 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). diff --git a/docs/my-website/docs/observability/wandb_integration.md b/docs/my-website/docs/observability/wandb_integration.md new file mode 100644 index 0000000000000000000000000000000000000000..dfa7b46dd35dbc94206d1192b962ac5609796308 --- /dev/null +++ b/docs/my-website/docs/observability/wandb_integration.md @@ -0,0 +1,51 @@ +import Image from '@theme/IdealImage'; + +# Weights & Biases - Logging LLM Input/Output +Weights & Biases helps AI developers build better models faster https://wandb.ai + + + +:::info +We want to learn how we can make the callbacks better! Meet the LiteLLM [founders](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) or +join our [discord](https://discord.gg/wuPM9dRgDw) +::: + +## Pre-Requisites +Ensure you have run `pip install wandb` for this integration +```shell +pip install wandb litellm +``` + +## Quick Start +Use just 2 lines of code, to instantly log your responses **across all providers** with Weights & Biases + +```python +litellm.success_callback = ["wandb"] +``` +```python +# pip install wandb +import litellm +import os + +os.environ["WANDB_API_KEY"] = "" +# LLM API Keys +os.environ['OPENAI_API_KEY']="" + +# set wandb as a callback, litellm will send the data to Weights & Biases +litellm.success_callback = ["wandb"] + +# openai call +response = litellm.completion( + model="gpt-3.5-turbo", + messages=[ + {"role": "user", "content": "Hi 👋 - i'm openai"} + ] +) +``` + +## Support & Talk to Founders + +- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +- [Community Discord 💭](https://discord.gg/wuPM9dRgDw) +- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ +- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai \ No newline at end of file diff --git a/docs/my-website/docs/projects.md b/docs/my-website/docs/projects.md new file mode 100644 index 0000000000000000000000000000000000000000..3abc32eadfbc2c568e2849a82a7d10a20dd8de26 --- /dev/null +++ b/docs/my-website/docs/projects.md @@ -0,0 +1,19 @@ +# Projects Built on LiteLLM + + + +### EntoAI +Chat and Ask on your own data. +[Github](https://github.com/akshata29/entaoai) + +### GPT-Migrate +Easily migrate your codebase from one framework or language to another. +[Github](https://github.com/0xpayne/gpt-migrate) + +### Otter +Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability. +[Github](https://github.com/Luodian/Otter) + + + + diff --git a/docs/my-website/docs/projects/Codium PR Agent.md b/docs/my-website/docs/projects/Codium PR Agent.md new file mode 100644 index 0000000000000000000000000000000000000000..72451912318a234516ce96730e297a106a1cafea --- /dev/null +++ b/docs/my-website/docs/projects/Codium PR Agent.md @@ -0,0 +1,3 @@ +An AI-Powered 🤖 Tool for Automated Pull Request Analysis, +Feedback, Suggestions 💻🔍 +[Github](https://github.com/Codium-ai/pr-agent) \ No newline at end of file diff --git a/docs/my-website/docs/projects/FastREPL.md b/docs/my-website/docs/projects/FastREPL.md new file mode 100644 index 0000000000000000000000000000000000000000..8ba43325ca4dc85919109e0d9a445eb8bd3cff86 --- /dev/null +++ b/docs/my-website/docs/projects/FastREPL.md @@ -0,0 +1,4 @@ +⚡Fast Run-Eval-Polish Loop for LLM Applications + +Core: https://github.com/fastrepl/fastrepl +Proxy: https://github.com/fastrepl/proxy diff --git a/docs/my-website/docs/projects/GPT Migrate.md b/docs/my-website/docs/projects/GPT Migrate.md new file mode 100644 index 0000000000000000000000000000000000000000..e5f8832f0b8896be2db31c26402e4f70a60db7cb --- /dev/null +++ b/docs/my-website/docs/projects/GPT Migrate.md @@ -0,0 +1 @@ +Easily migrate your codebase from one framework or language to another. \ No newline at end of file diff --git a/docs/my-website/docs/projects/Langstream.md b/docs/my-website/docs/projects/Langstream.md new file mode 100644 index 0000000000000000000000000000000000000000..2e9e45611d4cdad1d9e0e7f8e0f559d2f56dda25 --- /dev/null +++ b/docs/my-website/docs/projects/Langstream.md @@ -0,0 +1,3 @@ +Build robust LLM applications with true composability 🔗 +[Github](https://github.com/rogeriochaves/langstream) +[Docs](https://rogeriochaves.github.io/langstream/) \ No newline at end of file diff --git a/docs/my-website/docs/projects/LiteLLM Proxy.md b/docs/my-website/docs/projects/LiteLLM Proxy.md new file mode 100644 index 0000000000000000000000000000000000000000..8dbef44b9805a94f1b1ee7e3c1cbeceee406023b --- /dev/null +++ b/docs/my-website/docs/projects/LiteLLM Proxy.md @@ -0,0 +1,3 @@ +### LiteLLM Proxy +liteLLM Proxy Server: 50+ LLM Models, Error Handling, Caching +[Github](https://github.com/BerriAI/litellm/tree/main/proxy-server) \ No newline at end of file diff --git a/docs/my-website/docs/projects/OpenInterpreter.md b/docs/my-website/docs/projects/OpenInterpreter.md new file mode 100644 index 0000000000000000000000000000000000000000..7ec1f738eaf8371df484bb5641ca730aa30e9c29 --- /dev/null +++ b/docs/my-website/docs/projects/OpenInterpreter.md @@ -0,0 +1,2 @@ +Open Interpreter lets LLMs run code on your computer to complete tasks. +[Github](https://github.com/KillianLucas/open-interpreter/) \ No newline at end of file diff --git a/docs/my-website/docs/projects/Otter.md b/docs/my-website/docs/projects/Otter.md new file mode 100644 index 0000000000000000000000000000000000000000..63fb131aadf7c4db370f1bac07ba8abd541302d9 --- /dev/null +++ b/docs/my-website/docs/projects/Otter.md @@ -0,0 +1,2 @@ +🦦 Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability. +[Github](https://github.com/Luodian/Otter) \ No newline at end of file diff --git a/docs/my-website/docs/projects/PROMPTMETHEUS.md b/docs/my-website/docs/projects/PROMPTMETHEUS.md new file mode 100644 index 0000000000000000000000000000000000000000..8a1423ad6e15604cfd9a2c5a6150506c39baf53a --- /dev/null +++ b/docs/my-website/docs/projects/PROMPTMETHEUS.md @@ -0,0 +1,9 @@ +🔥 PROMPTMETHEUS – Prompt Engineering IDE + +Compose, test, optimize, and deploy reliable prompts for large language models. + +PROMPTMETHEUS is a Prompt Engineering IDE, designed to help you automate repetitive tasks and augment your apps and workflows with the mighty capabilities of all the LLMs in the LiteLLM quiver. + +Website → [www.promptmetheus.com](https://promptmetheus.com) +FORGE → [forge.promptmetheus.com](https://forge.promptmetheus.com) +ARCHERY → [archery.promptmetheus.com](https://archery.promptmetheus.com) diff --git a/docs/my-website/docs/projects/Prompt2Model.md b/docs/my-website/docs/projects/Prompt2Model.md new file mode 100644 index 0000000000000000000000000000000000000000..8b319a7c1ed33b2da002279a1daadd6f4c753286 --- /dev/null +++ b/docs/my-website/docs/projects/Prompt2Model.md @@ -0,0 +1,5 @@ +Prompt2Model - Generate Deployable Models from Instructions + +Github: https://github.com/neulab/prompt2model + +Prompt2Model is a system that takes a natural language task description (like the prompts used for LLMs such as ChatGPT) to train a small special-purpose model that is conducive for deployment. \ No newline at end of file diff --git a/docs/my-website/docs/projects/Quivr.md b/docs/my-website/docs/projects/Quivr.md new file mode 100644 index 0000000000000000000000000000000000000000..fbdf63690094ea2b9f72b42b737b36c89203484e --- /dev/null +++ b/docs/my-website/docs/projects/Quivr.md @@ -0,0 +1 @@ +🧠 Your Second Brain supercharged by Generative AI 🧠 Dump all your files and chat with your personal assistant on your files & more using GPT 3.5/4, Private, Anthropic, VertexAI, LLMs... \ No newline at end of file diff --git a/docs/my-website/docs/projects/SalesGPT.md b/docs/my-website/docs/projects/SalesGPT.md new file mode 100644 index 0000000000000000000000000000000000000000..f08fb078a115aa0269ce7331ed5f5d3832eb2323 --- /dev/null +++ b/docs/my-website/docs/projects/SalesGPT.md @@ -0,0 +1,3 @@ +🤖 SalesGPT - Your Context-Aware AI Sales Assistant + +Github: https://github.com/filip-michalsky/SalesGPT \ No newline at end of file diff --git a/docs/my-website/docs/projects/YiVal.md b/docs/my-website/docs/projects/YiVal.md new file mode 100644 index 0000000000000000000000000000000000000000..2e416e2f1147bca50794f9ef80139cac17abde28 --- /dev/null +++ b/docs/my-website/docs/projects/YiVal.md @@ -0,0 +1,5 @@ +🚀 Evaluate and Evolve.🚀 YiVal is an open source GenAI-Ops framework that allows you to manually or automatically tune and evaluate your AIGC prompts, retrieval configs and fine-tune the model params all at once with your preferred choices of test dataset generation, evaluation algorithms and improvement strategies. + +Github: https://github.com/YiVal/YiVal + +Docs: https://yival.github.io/YiVal/ \ No newline at end of file diff --git a/docs/my-website/docs/providers/ai21.md b/docs/my-website/docs/providers/ai21.md new file mode 100644 index 0000000000000000000000000000000000000000..c0987b31266c6a54ef727c308eca0ca42ac930e1 --- /dev/null +++ b/docs/my-website/docs/providers/ai21.md @@ -0,0 +1,32 @@ +# AI21 + +LiteLLM supports j2-light, j2-mid and j2-ultra from [AI21](https://www.ai21.com/studio/pricing). + +They're available to use without a waitlist. + +### API KEYS +```python +import os +os.environ["AI21_API_KEY"] = "your-api-key" +``` + +### Sample Usage + +```python +from litellm import completion + +# set env variable +os.environ["AI21_API_KEY"] = "your-api-key" + +messages = [{"role": "user", "content": "Write me a poem about the blue sky"}] + +completion(model="j2-light", messages=messages) +``` + +### AI21 Models + +| Model Name | Function Call | Required OS Variables | +|------------------|--------------------------------------------|--------------------------------------| +| j2-light | `completion('j2-light', messages)` | `os.environ['AI21_API_KEY']` | +| j2-mid | `completion('j2-mid', messages)` | `os.environ['AI21_API_KEY']` | +| j2-ultra | `completion('j2-ultra', messages)` | `os.environ['AI21_API_KEY']` | \ No newline at end of file diff --git a/docs/my-website/docs/providers/aleph_alpha.md b/docs/my-website/docs/providers/aleph_alpha.md new file mode 100644 index 0000000000000000000000000000000000000000..4cdb521f3b3c2f43f7fb9d451615fc0a2ed0af9b --- /dev/null +++ b/docs/my-website/docs/providers/aleph_alpha.md @@ -0,0 +1,23 @@ +# Aleph Alpha + +LiteLLM supports all models from [Aleph Alpha](https://www.aleph-alpha.com/). + +Like AI21 and Cohere, you can use these models without a waitlist. + +### API KEYS +```python +import os +os.environ["ALEPHALPHA_API_KEY"] = "" +``` + +### Aleph Alpha Models +https://www.aleph-alpha.com/ + +| Model Name | Function Call | Required OS Variables | +|------------------|--------------------------------------------|------------------------------------| +| luminous-base | `completion(model='luminous-base', messages=messages)` | `os.environ['ALEPHALPHA_API_KEY']` | +| luminous-base-control | `completion(model='luminous-base-control', messages=messages)` | `os.environ['ALEPHALPHA_API_KEY']` | +| luminous-extended | `completion(model='luminous-extended', messages=messages)` | `os.environ['ALEPHALPHA_API_KEY']` | +| luminous-extended-control | `completion(model='luminous-extended-control', messages=messages)` | `os.environ['ALEPHALPHA_API_KEY']` | +| luminous-supreme | `completion(model='luminous-supreme', messages=messages)` | `os.environ['ALEPHALPHA_API_KEY']` | +| luminous-supreme-control | `completion(model='luminous-supreme-control', messages=messages)` | `os.environ['ALEPHALPHA_API_KEY']` | diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md new file mode 100644 index 0000000000000000000000000000000000000000..2a20a58d90330dbe6cad05c67be89927262a3802 --- /dev/null +++ b/docs/my-website/docs/providers/anthropic.md @@ -0,0 +1,55 @@ +# Anthropic +LiteLLM supports + +- `claude-2.1` +- `claude-2.1` +- `claude-instant-1` +- `claude-instant-1.2` + +## API Keys + +```python +import os + +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" +``` + +## Sample Usage + +```python +import os +from litellm import completion + +# set env - [OPTIONAL] replace with your anthropic key +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +messages = [{"role": "user", "content": "Hey! how's it going?"}] +response = completion(model="claude-instant-1", messages=messages) +print(response) +``` + +## streaming +Just set `stream=True` when calling completion. + +```python +import os +from litellm import completion + +# set env +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +messages = [{"role": "user", "content": "Hey! how's it going?"}] +response = completion(model="claude-instant-1", messages=messages, stream=True) +for chunk in response: + print(chunk["choices"][0]["delta"]["content"]) # same as openai format +``` + + +### Model Details + +| Model Name | Function Call | Required OS Variables | +|------------------|--------------------------------------------|--------------------------------------| +| claude-2.1 | `completion('claude-2.1', messages)` | `os.environ['ANTHROPIC_API_KEY']` | +| claude-2 | `completion('claude-2', messages)` | `os.environ['ANTHROPIC_API_KEY']` | +| claude-instant-1 | `completion('claude-instant-1', messages)` | `os.environ['ANTHROPIC_API_KEY']` | +| claude-instant-1.2 | `completion('claude-instant-1.2', messages)` | `os.environ['ANTHROPIC_API_KEY']` | diff --git a/docs/my-website/docs/providers/anyscale.md b/docs/my-website/docs/providers/anyscale.md new file mode 100644 index 0000000000000000000000000000000000000000..ba75e5ba18cf0777e67a34e61fb63db9def7826b --- /dev/null +++ b/docs/my-website/docs/providers/anyscale.md @@ -0,0 +1,54 @@ +# Anyscale +https://app.endpoints.anyscale.com/ + +## API Key +```python +# env variable +os.environ['ANYSCALE_API_KEY'] +``` + +## Sample Usage +```python +from litellm import completion +import os + +os.environ['ANYSCALE_API_KEY'] = "" +response = completion( + model="anyscale/mistralai/Mistral-7B-Instruct-v0.1", + messages=messages +) +print(response) +``` + +## Sample Usage - Streaming +```python +from litellm import completion +import os + +os.environ['ANYSCALE_API_KEY'] = "" +response = completion( + model="anyscale/mistralai/Mistral-7B-Instruct-v0.1", + messages=messages, + stream=True +) + +for chunk in response: + print(chunk) +``` + + +## Supported Models +All models listed here https://app.endpoints.anyscale.com/ 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#L659). + +| Model Name | Function Call | +|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| llama2-7b-chat | `completion(model="anyscale/meta-llama/Llama-2-7b-chat-hf", messages)` | +| llama-2-13b-chat | `completion(model="anyscale/meta-llama/Llama-2-13b-chat-hf", messages)` | +| llama-2-70b-chat | `completion(model="anyscale/meta-llama/Llama-2-70b-chat-hf", messages)` | +| mistral-7b-instruct | `completion(model="anyscale/mistralai/Mistral-7B-Instruct-v0.1", messages)` | +| CodeLlama-34b-Instruct | `completion(model="anyscale/codellama/CodeLlama-34b-Instruct-hf", messages)` | + + + + + diff --git a/docs/my-website/docs/providers/aws_sagemaker.md b/docs/my-website/docs/providers/aws_sagemaker.md new file mode 100644 index 0000000000000000000000000000000000000000..264d6d9f2546b77e3833accfc39922f0fe103656 --- /dev/null +++ b/docs/my-website/docs/providers/aws_sagemaker.md @@ -0,0 +1,77 @@ +# AWS Sagemaker +LiteLLM supports Llama2 on Sagemaker + +### API KEYS +```python +!pip install boto3 + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" +``` + +### Usage +```python +import os +from litellm import completion + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +response = completion( + model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", + messages=[{ "content": "Hello, how are you?","role": "user"}], + temperature=0.2, + max_tokens=80 + ) +``` + +### Passing credentials as parameters - Completion() +Pass AWS credentials as parameters to litellm.completion +```python +import os +from litellm import completion + +response = completion( + model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", + messages=[{ "content": "Hello, how are you?","role": "user"}], + aws_access_key_id="", + aws_secret_access_key="", + aws_region_name="", +) +``` + +### Usage - Streaming +Sagemaker currently does not support streaming - LiteLLM fakes streaming by returning chunks of the response string + +```python +import os +from litellm import completion + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +response = completion( + model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", + messages=[{ "content": "Hello, how are you?","role": "user"}], + temperature=0.2, + max_tokens=80, + stream=True, + ) +for chunk in response: + print(chunk) +``` + +### AWS Sagemaker Models +Here's an example of using a sagemaker model with LiteLLM + +| Model Name | Function Call | +|-------------------------------|-------------------------------------------------------------------------------------------| +| Meta Llama 2 7B | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| Meta Llama 2 7B (Chat/Fine-tuned) | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b-f', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| Meta Llama 2 13B | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-13b', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| Meta Llama 2 13B (Chat/Fine-tuned) | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-13b-f', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| Meta Llama 2 70B | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| Meta Llama 2 70B (Chat/Fine-tuned) | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b-b-f', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | diff --git a/docs/my-website/docs/providers/azure.md b/docs/my-website/docs/providers/azure.md new file mode 100644 index 0000000000000000000000000000000000000000..074301791224e2151d12199fdc49ec4d6e8d6672 --- /dev/null +++ b/docs/my-website/docs/providers/azure.md @@ -0,0 +1,255 @@ +# Azure OpenAI +## API Keys, Params +api_key, api_base, api_version etc can be passed directly to `litellm.completion` - see here or set as `litellm.api_key` params see here +```python +import os +os.environ["AZURE_API_KEY"] = "" +os.environ["AZURE_API_BASE"] = "" +os.environ["AZURE_API_VERSION"] = "" + +# optional +os.environ["AZURE_AD_TOKEN"] = "" +os.environ["AZURE_API_TYPE"] = "" +``` + +## Usage + + Open In Colab + + +### Completion - using .env variables + +```python +from litellm import completion + +## set ENV variables +os.environ["AZURE_API_KEY"] = "" +os.environ["AZURE_API_BASE"] = "" +os.environ["AZURE_API_VERSION"] = "" + +# azure call +response = completion( + model = "azure/", + messages = [{ "content": "Hello, how are you?","role": "user"}] +) +``` + +### Completion - using api_key, api_base, api_version + +```python +import litellm + +# azure call +response = litellm.completion( + model = "azure/", # model = azure/ + api_base = "", # azure api base + api_version = "", # azure api version + api_key = "", # azure api key + messages = [{"role": "user", "content": "good morning"}], +) +``` + +### Completion - using azure_ad_token, api_base, api_version + +```python +import litellm + +# azure call +response = litellm.completion( + model = "azure/", # model = azure/ + api_base = "", # azure api base + api_version = "", # azure api version + azure_ad_token="", # azure_ad_token + messages = [{"role": "user", "content": "good morning"}], +) +``` + +## Azure OpenAI Chat Completion Models + +| Model Name | Function Call | +|------------------|----------------------------------------| +| gpt-4 | `completion('azure/', messages)` | +| gpt-4-0314 | `completion('azure/', messages)` | +| gpt-4-0613 | `completion('azure/', messages)` | +| gpt-4-32k | `completion('azure/', messages)` | +| gpt-4-32k-0314 | `completion('azure/', messages)` | +| gpt-4-32k-0613 | `completion('azure/', messages)` | +| gpt-3.5-turbo | `completion('azure/', messages)` | +| gpt-3.5-turbo-0301 | `completion('azure/', messages)` | +| gpt-3.5-turbo-0613 | `completion('azure/', messages)` | +| gpt-3.5-turbo-16k | `completion('azure/', messages)` | +| gpt-3.5-turbo-16k-0613 | `completion('azure/', messages)` + +## Advanced +### Azure API Load-Balancing + +Use this if you're trying to load-balance across multiple Azure/OpenAI deployments. + +`Router` prevents failed requests, by picking the deployment which is below rate-limit and has the least amount of tokens used. + +In production, [Router connects to a Redis Cache](#redis-queue) to track usage across multiple deployments. + +#### Quick Start + +```python +pip install litellm +``` + +```python +from litellm import Router + +model_list = [{ # list of model deployments + "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", # openai model name + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "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", # 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 +}] + +router = Router(model_list=model_list) + +# openai.chat.completions.create replacement +response = router.completion(model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hey, how's it going?"}] + +print(response) +``` + +#### Redis Queue + +```python +router = Router(model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=os.getenv("REDIS_PORT")) + +print(response) +``` + + +### Parallel Function calling +See a detailed walthrough of parallel function calling with litellm [here](https://docs.litellm.ai/docs/completion/function_call) +```python +# set Azure env variables +import os +os.environ['AZURE_API_KEY'] = "" # litellm reads AZURE_API_KEY from .env and sends the request +os.environ['AZURE_API_BASE'] = "https://openai-gpt-4-test-v-1.openai.azure.com/" +os.environ['AZURE_API_VERSION'] = "2023-07-01-preview" + +import litellm +import json +# Example dummy function hard coded to return the same weather +# In production, this could be your backend API or an external API +def get_current_weather(location, unit="fahrenheit"): + """Get the current weather in a given location""" + if "tokyo" in location.lower(): + return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"}) + elif "san francisco" in location.lower(): + return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}) + elif "paris" in location.lower(): + return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"}) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + +## Step 1: send the conversation and available functions to the model +messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}] +tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } +] + +response = litellm.completion( + model="azure/chatgpt-functioncalling", # model = azure/ + messages=messages, + tools=tools, + tool_choice="auto", # auto is default, but we'll be explicit +) +print("\nLLM Response1:\n", response) +response_message = response.choices[0].message +tool_calls = response.choices[0].message.tool_calls +print("\nTool Choice:\n", tool_calls) +``` + + +### Authentication with Azure Active Directory Tokens (Microsoft Entra ID) +This is a walkthrough on how to use Azure Active Directory Tokens - Microsoft Entra ID to make `litellm.completion()` calls + +Step 1 - Download Azure CLI +Installation instructons: https://learn.microsoft.com/en-us/cli/azure/install-azure-cli +```shell +brew update && brew install azure-cli +``` +Step 2 - Sign in using `az` +```shell +az login --output table +``` + +Step 3 - Generate azure ad token +```shell +az account get-access-token --resource https://cognitiveservices.azure.com +``` + +In this step you should see an `accessToken` generated +```shell +{ + "accessToken": "eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiIsIng1dCI6IjlHbW55RlBraGMzaE91UjIybXZTdmduTG83WSIsImtpZCI6IjlHbW55RlBraGMzaE91UjIybXZTdmduTG83WSJ9", + "expiresOn": "2023-11-14 15:50:46.000000", + "expires_on": 1700005846, + "subscription": "db38de1f-4bb3..", + "tenant": "bdfd79b3-8401-47..", + "tokenType": "Bearer" +} +``` + +Step 4 - Make litellm.completion call with Azure AD token + +Set `azure_ad_token` = `accessToken` from step 3 or set `os.environ['AZURE_AD_TOKEN']` + +```python +response = litellm.completion( + model = "azure/", # model = azure/ + api_base = "", # azure api base + api_version = "", # azure api version + azure_ad_token="", # your accessToken from step 3 + messages = [{"role": "user", "content": "good morning"}], +) + +``` diff --git a/docs/my-website/docs/providers/baseten.md b/docs/my-website/docs/providers/baseten.md new file mode 100644 index 0000000000000000000000000000000000000000..902b1548faa3ac04aa7eb95860ab6237be1ce763 --- /dev/null +++ b/docs/my-website/docs/providers/baseten.md @@ -0,0 +1,23 @@ +# Baseten +LiteLLM supports any Text-Gen-Interface models on Baseten. + +[Here's a tutorial on deploying a huggingface TGI model (Llama2, CodeLlama, WizardCoder, Falcon, etc.) on Baseten](https://truss.baseten.co/examples/performance/tgi-server) + +### API KEYS +```python +import os +os.environ["BASETEN_API_KEY"] = "" +``` + +### Baseten Models +Baseten provides infrastructure to deploy and serve ML models https://www.baseten.co/. Use liteLLM to easily call models deployed on Baseten. + +Example Baseten Usage - Note: liteLLM supports all models deployed on Baseten + +Usage: Pass `model=baseten/` + +| Model Name | Function Call | Required OS Variables | +|------------------|--------------------------------------------|------------------------------------| +| Falcon 7B | `completion(model='baseten/qvv0xeq', messages=messages)` | `os.environ['BASETEN_API_KEY']` | +| Wizard LM | `completion(model='baseten/q841o8w', messages=messages)` | `os.environ['BASETEN_API_KEY']` | +| MPT 7B Base | `completion(model='baseten/31dxrj3', messages=messages)` | `os.environ['BASETEN_API_KEY']` | diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md new file mode 100644 index 0000000000000000000000000000000000000000..2b2934c4e7a3f084cbf6a80ece0a2cd901ad361f --- /dev/null +++ b/docs/my-website/docs/providers/bedrock.md @@ -0,0 +1,176 @@ +# AWS Bedrock +Anthropic, Amazon Titan, A121 LLMs are Supported on Bedrock + +## Pre-Requisites +LiteLLM requires `boto3` to be installed on your system for Bedrock requests +```shell +pip install boto3>=1.28.57 +``` + +## Required Environment Variables +```python +os.environ["AWS_ACCESS_KEY_ID"] = "" # Access key +os.environ["AWS_SECRET_ACCESS_KEY"] = "" # Secret access key +os.environ["AWS_REGION_NAME"] = "" # us-east-1, us-east-2, us-west-1, us-west-2 +``` + +## Usage + + + Open In Colab + + +```python +import os +from litellm import completion + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +response = completion( + model="anthropic.claude-instant-v1", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` + +## Usage - Streaming +```python +import os +from litellm import completion + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +response = completion( + model="anthropic.claude-instant-v1", + messages=[{ "content": "Hello, how are you?","role": "user"}], + stream=True +) +for chunk in response: + print(chunk) +``` + +#### Example Streaming Output Chunk +```json +{ + "choices": [ + { + "finish_reason": null, + "index": 0, + "delta": { + "content": "ase can appeal the case to a higher federal court. If a higher federal court rules in a way that conflicts with a ruling from a lower federal court or conflicts with a ruling from a higher state court, the parties involved in the case can appeal the case to the Supreme Court. In order to appeal a case to the Sup" + } + } + ], + "created": null, + "model": "anthropic.claude-instant-v1", + "usage": { + "prompt_tokens": null, + "completion_tokens": null, + "total_tokens": null + } +} +``` + +## Boto3 - Authentication + +### Passing credentials as parameters - Completion() +Pass AWS credentials as parameters to litellm.completion +```python +import os +from litellm import completion + +response = completion( + model="anthropic.claude-instant-v1", + messages=[{ "content": "Hello, how are you?","role": "user"}], + aws_access_key_id="", + aws_secret_access_key="", + aws_region_name="", +) +``` + +### Passing an external BedrockRuntime.Client as a parameter - Completion() +Pass an external BedrockRuntime.Client object as a parameter to litellm.completion. Useful when using an AWS credentials profile, SSO session, assumed role session, or if environment variables are not available for auth. + +Create a client from session credentials: +```python +import boto3 +from litellm import completion + +bedrock = boto3.client( + service_name="bedrock-runtime", + region_name="us-east-1", + aws_access_key_id="", + aws_secret_access_key="", + aws_session_token="", +) + +response = completion( + model="anthropic.claude-instant-v1", + messages=[{ "content": "Hello, how are you?","role": "user"}], + aws_bedrock_client=bedrock, +) +``` + +Create a client from AWS profile in `~/.aws/config`: +```python +import boto3 +from litellm import completion + +dev_session = boto3.Session(profile_name="dev-profile") +bedrock = dev_session.client( + service_name="bedrock-runtime", + region_name="us-east-1", +) + +response = completion( + model="anthropic.claude-instant-v1", + messages=[{ "content": "Hello, how are you?","role": "user"}], + aws_bedrock_client=bedrock, +) +``` + +## Supported AWS Bedrock Models +Here's an example of using a bedrock model with LiteLLM + +| Model Name | Command | +|--------------------------|------------------------------------------------------------------| +| Anthropic Claude-V2 | `completion(model='anthropic.claude-v2', messages=messages)` | `os.environ['ANTHROPIC_ACCESS_KEY_ID']`, `os.environ['ANTHROPIC_SECRET_ACCESS_KEY']` | +| Anthropic Claude-Instant V1 | `completion(model='anthropic.claude-instant-v1', messages=messages)` | `os.environ['ANTHROPIC_ACCESS_KEY_ID']`, `os.environ['ANTHROPIC_SECRET_ACCESS_KEY']` | +| Anthropic Claude-V1 | `completion(model='anthropic.claude-v1', messages=messages)` | `os.environ['ANTHROPIC_ACCESS_KEY_ID']`, `os.environ['ANTHROPIC_SECRET_ACCESS_KEY']` | +| Amazon Titan Lite | `completion(model='amazon.titan-text-lite-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| Amazon Titan Express | `completion(model='amazon.titan-text-express-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| Cohere Command | `completion(model='cohere.command-text-v14', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| AI21 J2-Mid | `completion(model='ai21.j2-mid-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| AI21 J2-Ultra | `completion(model='ai21.j2-ultra-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| Meta Llama 2 Chat 13b | `completion(model='meta.llama2-13b-chat-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| Meta Llama 2 Chat 70b | `completion(model='meta.llama2-70b-chat-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | + +## Bedrock Embedding + +### API keys +This can be set as env variables or passed as **params to litellm.embedding()** +```python +import os +os.environ["AWS_ACCESS_KEY_ID"] = "" # Access key +os.environ["AWS_SECRET_ACCESS_KEY"] = "" # Secret access key +os.environ["AWS_REGION_NAME"] = "" # us-east-1, us-east-2, us-west-1, us-west-2 +``` + +### Usage +```python +from litellm import embedding +response = embedding( + model="amazon.titan-embed-text-v1", + input=["good morning from litellm"], +) +print(response) +``` + +## Supported AWS Bedrock Embedding Models + +| Model Name | Function Call | +|----------------------|---------------------------------------------| +| Titan Embeddings - G1 | `embedding(model="amazon.titan-embed-text-v1", input=input)` | diff --git a/docs/my-website/docs/providers/cohere.md b/docs/my-website/docs/providers/cohere.md new file mode 100644 index 0000000000000000000000000000000000000000..98014377069a8353e0e05743342820e8829c0990 --- /dev/null +++ b/docs/my-website/docs/providers/cohere.md @@ -0,0 +1,90 @@ +# Cohere + +## API KEYS + +```python +import os +os.environ["COHERE_API_KEY"] = "" +``` + +## Usage + +```python +from litellm import completion + +## set ENV variables +os.environ["COHERE_API_KEY"] = "cohere key" + +# cohere call +response = completion( + model="command-nightly", + messages = [{ "content": "Hello, how are you?","role": "user"}] +) +``` + +## Usage - Streaming + +```python +from litellm import completion + +## set ENV variables +os.environ["COHERE_API_KEY"] = "cohere key" + +# cohere call +response = completion( + model="command-nightly", + messages = [{ "content": "Hello, how are you?","role": "user"}], + stream=True +) + +for chunk in response: + print(chunk) +``` + +LiteLLM supports 'command', 'command-light', 'command-medium', 'command-medium-beta', 'command-xlarge-beta', 'command-nightly' models from [Cohere](https://cohere.com/). + +## Embedding + +```python +from litellm import embedding +os.environ["COHERE_API_KEY"] = "cohere key" + +# cohere call +response = embedding( + model="embed-english-v3.0", + input=["good morning from litellm", "this is another item"], +) +``` + +### Setting - Input Type for v3 models +v3 Models have a required parameter: `input_type`, it can be one of the following four values: + +- `input_type="search_document"`: (default) Use this for texts (documents) you want to store in your vector database +- `input_type="search_query"`: Use this for search queries to find the most relevant documents in your vector database +- `input_type="classification"`: Use this if you use the embeddings as an input for a classification system +- `input_type="clustering"`: Use this if you use the embeddings for text clustering + +https://txt.cohere.com/introducing-embed-v3/ +```python +from litellm import embedding +os.environ["COHERE_API_KEY"] = "cohere key" + +# cohere call +response = embedding( + model="embed-english-v3.0", + input=["good morning from litellm", "this is another item"], + input_type="search_document" +) +``` + +### Supported Embedding Models +| Model Name | Function Call | +|--------------------------|--------------------------------------------------------------| +| embed-english-v3.0 | `embedding(model="embed-english-v3.0", input=["good morning from litellm", "this is another item"])` | +| embed-english-light-v3.0 | `embedding(model="embed-english-light-v3.0", input=["good morning from litellm", "this is another item"])` | +| embed-multilingual-v3.0 | `embedding(model="embed-multilingual-v3.0", input=["good morning from litellm", "this is another item"])` | +| embed-multilingual-light-v3.0 | `embedding(model="embed-multilingual-light-v3.0", input=["good morning from litellm", "this is another item"])` | +| embed-english-v2.0 | `embedding(model="embed-english-v2.0", input=["good morning from litellm", "this is another item"])` | +| embed-english-light-v2.0 | `embedding(model="embed-english-light-v2.0", input=["good morning from litellm", "this is another item"])` | +| embed-multilingual-v2.0 | `embedding(model="embed-multilingual-v2.0", input=["good morning from litellm", "this is another item"])` | + diff --git a/docs/my-website/docs/providers/custom.md b/docs/my-website/docs/providers/custom.md new file mode 100644 index 0000000000000000000000000000000000000000..81b92f0a0310c50d363054fcd07569f1da480a12 --- /dev/null +++ b/docs/my-website/docs/providers/custom.md @@ -0,0 +1,69 @@ +# Custom LLM API-Endpoints +LiteLLM supports Custom deploy api endpoints + +LiteLLM Expects the following input and output for custom LLM API endpoints + +### Model Details + +For calls to your custom API base ensure: +* Set `api_base="your-api-base"` +* Add `custom/` as a prefix to the `model` param. If your API expects `meta-llama/Llama-2-13b-hf` set `model=custom/meta-llama/Llama-2-13b-hf` + +| Model Name | Function Call | +|------------------|--------------------------------------------| +| meta-llama/Llama-2-13b-hf | `response = completion(model="custom/meta-llama/Llama-2-13b-hf", messages=messages, api_base="https://your-custom-inference-endpoint")` | +| meta-llama/Llama-2-13b-hf | `response = completion(model="custom/meta-llama/Llama-2-13b-hf", messages=messages, api_base="https://api.autoai.dev/inference")` | + +### Example Call to Custom LLM API using LiteLLM +```python +from litellm import completion +response = completion( + model="custom/meta-llama/Llama-2-13b-hf", + messages= [{"content": "what is custom llama?", "role": "user"}], + temperature=0.2, + max_tokens=10, + api_base="https://api.autoai.dev/inference", + request_timeout=300, +) +print("got response\n", response) +``` + +#### Setting your Custom API endpoint + +Inputs to your custom LLM api bases should follow this format: + +```python +resp = requests.post( + your-api_base, + json={ + 'model': 'meta-llama/Llama-2-13b-hf', # model name + 'params': { + 'prompt': ["The capital of France is P"], + 'max_tokens': 32, + 'temperature': 0.7, + 'top_p': 1.0, + 'top_k': 40, + } + } +) +``` + +Outputs from your custom LLM api bases should follow this format: +```python +{ + 'data': [ + { + 'prompt': 'The capital of France is P', + 'output': [ + 'The capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France' + ], + 'params': { + 'temperature': 0.7, + 'top_k': 40, + 'top_p': 1 + } + } + ], + 'message': 'ok' +} +``` \ No newline at end of file diff --git a/docs/my-website/docs/providers/custom_openai_proxy.md b/docs/my-website/docs/providers/custom_openai_proxy.md new file mode 100644 index 0000000000000000000000000000000000000000..b6f2eccac585dff41af7ef09efeeace0b2f46319 --- /dev/null +++ b/docs/my-website/docs/providers/custom_openai_proxy.md @@ -0,0 +1,129 @@ +# Custom API Server (OpenAI Format) + +LiteLLM allows you to call your custom endpoint in the OpenAI ChatCompletion format + +## API KEYS +No api keys required + +## Set up your Custom API Server +Your server should have the following Endpoints: + +Here's an example OpenAI proxy server with routes: https://replit.com/@BerriAI/openai-proxy#main.py + +### Required Endpoints +- POST `/chat/completions` - chat completions endpoint + +### Optional Endpoints +- POST `/completions` - completions endpoint +- Get `/models` - available models on server +- POST `/embeddings` - creates an embedding vector representing the input text. + + +## Example Usage + +### Call `/chat/completions` +In order to use your custom OpenAI Chat Completion proxy with LiteLLM, ensure you set + +* `api_base` to your proxy url, example "https://openai-proxy.berriai.repl.co" +* `custom_llm_provider` to `openai` this ensures litellm uses the `openai.ChatCompletion` to your api_base + +```python +import os +from litellm import completion + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "anything" #key is not used for proxy + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +response = completion( + model="command-nightly", + messages=[{ "content": "Hello, how are you?","role": "user"}], + api_base="https://openai-proxy.berriai.repl.co", + custom_llm_provider="openai" # litellm will use the openai.ChatCompletion to make the request + +) +print(response) +``` + +#### Response +```json +{ + "object": + "chat.completion", + "choices": [{ + "finish_reason": "stop", + "index": 0, + "message": { + "content": + "The sky, a canvas of blue,\nA work of art, pure and true,\nA", + "role": "assistant" + } + }], + "id": + "chatcmpl-7fbd6077-de10-4cb4-a8a4-3ef11a98b7c8", + "created": + 1699290237.408061, + "model": + "togethercomputer/llama-2-70b-chat", + "usage": { + "completion_tokens": 18, + "prompt_tokens": 14, + "total_tokens": 32 + } + } +``` + + +### Call `/completions` +In order to use your custom OpenAI Completion proxy with LiteLLM, ensure you set + +* `api_base` to your proxy url, example "https://openai-proxy.berriai.repl.co" +* `custom_llm_provider` to `text-completion-openai` this ensures litellm uses the `openai.Completion` to your api_base + +```python +import os +from litellm import completion + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "anything" #key is not used for proxy + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +response = completion( + model="command-nightly", + messages=[{ "content": "Hello, how are you?","role": "user"}], + api_base="https://openai-proxy.berriai.repl.co", + custom_llm_provider="text-completion-openai" # litellm will use the openai.Completion to make the request + +) +print(response) +``` + +#### Response +```json +{ + "warning": + "This model version is deprecated. Migrate before January 4, 2024 to avoid disruption of service. Learn more https://platform.openai.com/docs/deprecations", + "id": + "cmpl-8HxHqF5dymQdALmLplS0dWKZVFe3r", + "object": + "text_completion", + "created": + 1699290166, + "model": + "text-davinci-003", + "choices": [{ + "text": + "\n\nThe weather in San Francisco varies depending on what time of year and time", + "index": 0, + "logprobs": None, + "finish_reason": "length" + }], + "usage": { + "prompt_tokens": 7, + "completion_tokens": 16, + "total_tokens": 23 + } + } +``` \ No newline at end of file diff --git a/docs/my-website/docs/providers/deepinfra.md b/docs/my-website/docs/providers/deepinfra.md new file mode 100644 index 0000000000000000000000000000000000000000..3c6506308cf0dbb999880c81f9041ed5033afbda --- /dev/null +++ b/docs/my-website/docs/providers/deepinfra.md @@ -0,0 +1,50 @@ +# DeepInfra +https://deepinfra.com/ + +## API Key +```python +# env variable +os.environ['DEEPINFRA_API_KEY'] +``` + +## Sample Usage +```python +from litellm import completion +import os + +os.environ['DEEPINFRA_API_KEY'] = "" +response = completion( + model="deepinfra/meta-llama/Llama-2-70b-chat-hf", + messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}] +) +``` + +## Sample Usage - Streaming +```python +from litellm import completion +import os + +os.environ['DEEPINFRA_API_KEY'] = "" +response = completion( + model="deepinfra/meta-llama/Llama-2-70b-chat-hf", + messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}], + stream=True +) + +for chunk in response: + print(chunk) +``` + +## Chat Models +| Model Name | Function Call | +|------------------|--------------------------------------| +| meta-llama/Llama-2-70b-chat-hf | `completion(model="deepinfra/meta-llama/Llama-2-70b-chat-hf", messages)` | +| meta-llama/Llama-2-7b-chat-hf | `completion(model="deepinfra/meta-llama/Llama-2-7b-chat-hf", messages)` | +| meta-llama/Llama-2-13b-chat-hf | `completion(model="deepinfra/meta-llama/Llama-2-13b-chat-hf", messages)` | +| codellama/CodeLlama-34b-Instruct-hf | `completion(model="deepinfra/codellama/CodeLlama-34b-Instruct-hf", messages)` | +| mistralai/Mistral-7B-Instruct-v0.1 | `completion(model="deepinfra/mistralai/Mistral-7B-Instruct-v0.1", messages)` | +| jondurbin/airoboros-l2-70b-gpt4-1.4.1 | `completion(model="deepinfra/jondurbin/airoboros-l2-70b-gpt4-1.4.1", messages)` | + + + + diff --git a/docs/my-website/docs/providers/huggingface.md b/docs/my-website/docs/providers/huggingface.md new file mode 100644 index 0000000000000000000000000000000000000000..f8ebadfcfa7d76bf234fbaa1242f9bb924e0d1f9 --- /dev/null +++ b/docs/my-website/docs/providers/huggingface.md @@ -0,0 +1,318 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Huggingface + +LiteLLM supports the following types of Huggingface models: +* Text-generation-interface: [Here's all the models that use this format](https://huggingface.co/models?other=text-generation-inference). +* Conversational task: [Here's all the models that use this format](https://huggingface.co/models?pipeline_tag=conversational). +* Non TGI/Conversational-task LLMs + +## Usage + + + Open In Colab + + +You need to tell LiteLLM when you're calling Huggingface. +This is done by adding the "huggingface/" prefix to `model`, example `completion(model="huggingface/",...)`. + + + + +```python +import os +from litellm import completion + +# [OPTIONAL] set env var +os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" + +messages = [{ "content": "There's a llama in my garden 😱 What should I do?","role": "user"}] + +# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints +response = completion( + model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0", + messages=messages, + api_base="https://my-endpoint.huggingface.cloud" +) + +print(response) +``` + + + + +```python +import os +from litellm import completion + +# [OPTIONAL] set env var +os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" + +messages = [{ "content": "There's a llama in my garden 😱 What should I do?","role": "user"}] + +# e.g. Call 'facebook/blenderbot-400M-distill' hosted on HF Inference endpoints +response = completion( + model="huggingface/facebook/blenderbot-400M-distill", + messages=messages, + api_base="https://my-endpoint.huggingface.cloud" +) + +print(response) +``` + + + +```python +import os +from litellm import completion + +# [OPTIONAL] set env var +os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" + +messages = [{ "content": "There's a llama in my garden 😱 What should I do?","role": "user"}] + +# e.g. Call 'roneneldan/TinyStories-3M' hosted on HF Inference endpoints +response = completion( + model="huggingface/roneneldan/TinyStories-3M", + messages=messages, + api_base="https://p69xlsj6rpno5drq.us-east-1.aws.endpoints.huggingface.cloud", +) + +print(response) +``` + + + +## Streaming + + + Open In Colab + + +You need to tell LiteLLM when you're calling Huggingface. +This is done by adding the "huggingface/" prefix to `model`, example `completion(model="huggingface/",...)`. + +```python +import os +from litellm import completion + +# [OPTIONAL] set env var +os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" + +messages = [{ "content": "There's a llama in my garden 😱 What should I do?","role": "user"}] + +# e.g. Call 'facebook/blenderbot-400M-distill' hosted on HF Inference endpoints +response = completion( + model="huggingface/facebook/blenderbot-400M-distill", + messages=messages, + api_base="https://my-endpoint.huggingface.cloud", + stream=True +) + +print(response) +for chunk in response: + print(chunk) +``` + +## Embedding +LiteLLM supports Huggingface's [text-embedding-inference](https://github.com/huggingface/text-embeddings-inference) format. +```python +from litellm import embedding +import os +os.environ['HUGGINGFACE_API_KEY'] = "" +response = embedding( + model='huggingface/microsoft/codebert-base', + input=["good morning from litellm"] +) +``` + +## Advanced + +### Setting API KEYS + API BASE +If required, you can set the api key + api base, set it in your os environment. [Code for how it's sent](https://github.com/BerriAI/litellm/blob/0100ab2382a0e720c7978fbf662cc6e6920e7e03/litellm/llms/huggingface_restapi.py#L25) + +```python +import os +os.environ["HUGGINGFACE_API_KEY"] = "" +os.environ["HUGGINGFACE_API_BASE"] = "" +``` + +### Viewing Log probs + +#### Using `decoder_input_details` - OpenAI `echo` +The `echo` param is supported by OpenAI Completions - Use `litellm.text_completion()` for this +```python +from litellm import text_completion +response = text_completion( + model="huggingface/bigcode/starcoder", + prompt="good morning", + max_tokens=10, logprobs=10, + echo=True +) + ``` + +#### Output + ```json +{ + "id":"chatcmpl-3fc71792-c442-4ba1-a611-19dd0ac371ad", + "object":"text_completion", + "created":1698801125.936519, + "model":"bigcode/starcoder", + "choices":[ + { + "text":", I'm going to make you a sand", + "index":0, + "logprobs":{ + "tokens":[ + "good", + " morning", + ",", + " I", + "'m", + " going", + " to", + " make", + " you", + " a", + " s", + "and" + ], + "token_logprobs":[ + "None", + -14.96875, + -2.2285156, + -2.734375, + -2.0957031, + -2.0917969, + -0.09429932, + -3.1132812, + -1.3203125, + -1.2304688, + -1.6201172, + -0.010292053 + ] + }, + "finish_reason":"length" + } + ], + "usage":{ + "completion_tokens":9, + "prompt_tokens":2, + "total_tokens":11 + } +} +``` + +### Models with Prompt Formatting +For models with special prompt templates (e.g. Llama2), we format the prompt to fit their template. + +#### Models with natively Supported Prompt Templates + +| Model Name | Works for Models | Function Call | Required OS Variables | +| -------- | -------- | -------- | -------- | +| mistralai/Mistral-7B-Instruct-v0.1 | mistralai/Mistral-7B-Instruct-v0.1| `completion(model='huggingface/mistralai/Mistral-7B-Instruct-v0.1', messages=messages, api_base="your_api_endpoint")` | `os.environ['HUGGINGFACE_API_KEY']` | +| meta-llama/Llama-2-7b-chat | All meta-llama llama2 chat models| `completion(model='huggingface/meta-llama/Llama-2-7b', messages=messages, api_base="your_api_endpoint")` | `os.environ['HUGGINGFACE_API_KEY']` | +| tiiuae/falcon-7b-instruct | All falcon instruct models | `completion(model='huggingface/tiiuae/falcon-7b-instruct', messages=messages, api_base="your_api_endpoint")` | `os.environ['HUGGINGFACE_API_KEY']` | +| mosaicml/mpt-7b-chat | All mpt chat models | `completion(model='huggingface/mosaicml/mpt-7b-chat', messages=messages, api_base="your_api_endpoint")` | `os.environ['HUGGINGFACE_API_KEY']` | +| codellama/CodeLlama-34b-Instruct-hf | All codellama instruct models | `completion(model='huggingface/codellama/CodeLlama-34b-Instruct-hf', messages=messages, api_base="your_api_endpoint")` | `os.environ['HUGGINGFACE_API_KEY']` | +| WizardLM/WizardCoder-Python-34B-V1.0 | All wizardcoder models | `completion(model='huggingface/WizardLM/WizardCoder-Python-34B-V1.0', messages=messages, api_base="your_api_endpoint")` | `os.environ['HUGGINGFACE_API_KEY']` | +| Phind/Phind-CodeLlama-34B-v2 | All phind-codellama models | `completion(model='huggingface/Phind/Phind-CodeLlama-34B-v2', messages=messages, api_base="your_api_endpoint")` | `os.environ['HUGGINGFACE_API_KEY']` | + + +**What if we don't support a model you need?** +You can also specify you're own custom prompt formatting, in case we don't have your model covered yet. + +**Does this mean you have to specify a prompt for all models?** +No. By default we'll concatenate your message content to make a prompt. + +**Default Prompt Template** +```python +def default_pt(messages): + return " ".join(message["content"] for message in messages) +``` + +[Code for how prompt formats work in LiteLLM](https://github.com/BerriAI/litellm/blob/main/litellm/llms/prompt_templates/factory.py) + +#### Custom prompt templates +```python +# Create your own custom prompt template works +litellm.register_prompt_template( + model="togethercomputer/LLaMA-2-7B-32K", + roles={ + "system": { + "pre_message": "[INST] <>\n", + "post_message": "\n<>\n [/INST]\n" + }, + "user": { + "pre_message": "[INST] ", + "post_message": " [/INST]\n" + }, + "assistant": { + "post_message": "\n" + } + } + ) + +def test_huggingface_custom_model(): + model = "huggingface/togethercomputer/LLaMA-2-7B-32K" + response = completion(model=model, messages=messages, api_base="https://ecd4sb5n09bo4ei2.us-east-1.aws.endpoints.huggingface.cloud") + print(response['choices'][0]['message']['content']) + return response + +test_huggingface_custom_model() +``` + +[Implementation Code](https://github.com/BerriAI/litellm/blob/c0b3da2c14c791a0b755f0b1e5a9ef065951ecbf/litellm/llms/huggingface_restapi.py#L52) + +### Deploying a model on huggingface +You can use any chat/text model from Hugging Face with the following steps: + +* Copy your model id/url from Huggingface Inference Endpoints + - [ ] Go to https://ui.endpoints.huggingface.co/ + - [ ] Copy the url of the specific model you'd like to use + HF_Dashboard +* Set it as your model name +* Set your HUGGINGFACE_API_KEY as an environment variable + +Need help deploying a model on huggingface? [Check out this guide.](https://huggingface.co/docs/inference-endpoints/guides/create_endpoint) + + +# output + +Same as the OpenAI format, but also includes logprobs. [See the code](https://github.com/BerriAI/litellm/blob/b4b2dbf005142e0a483d46a07a88a19814899403/litellm/llms/huggingface_restapi.py#L115) + +```json +{ + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": "\ud83d\ude31\n\nComment: @SarahSzabo I'm", + "role": "assistant", + "logprobs": -22.697942825499993 + } + } + ], + "created": 1693436637.38206, + "model": "https://ji16r2iys9a8rjk2.us-east-1.aws.endpoints.huggingface.cloud", + "usage": { + "prompt_tokens": 14, + "completion_tokens": 11, + "total_tokens": 25 + } +} +``` + +# FAQ +**Does this support stop sequences?** + +Yes, we support stop sequences - and you can pass as many as allowed by Huggingface (or any provider!) + +**How do you deal with repetition penalty?** + +We map the presence penalty parameter in openai to the repetition penalty parameter on Huggingface. [See code](https://github.com/BerriAI/litellm/blob/b4b2dbf005142e0a483d46a07a88a19814899403/litellm/utils.py#L757). + +We welcome any suggestions for improving our Huggingface integration - Create an [issue](https://github.com/BerriAI/litellm/issues/new/choose)/[Join the Discord](https://discord.com/invite/wuPM9dRgDw)! \ No newline at end of file diff --git a/docs/my-website/docs/providers/nlp_cloud.md b/docs/my-website/docs/providers/nlp_cloud.md new file mode 100644 index 0000000000000000000000000000000000000000..3d74fb7e160c6c789d0ed3b8e9843d4788f3ed1d --- /dev/null +++ b/docs/my-website/docs/providers/nlp_cloud.md @@ -0,0 +1,63 @@ +# NLP Cloud + +LiteLLM supports all LLMs on NLP Cloud. + +## API Keys + +```python +import os + +os.environ["NLP_CLOUD_API_KEY"] = "your-api-key" +``` + +## Sample Usage + +```python +import os +from litellm import completion + +# set env +os.environ["NLP_CLOUD_API_KEY"] = "your-api-key" + +messages = [{"role": "user", "content": "Hey! how's it going?"}] +response = completion(model="dolphin", messages=messages) +print(response) +``` + +## streaming +Just set `stream=True` when calling completion. + +```python +import os +from litellm import completion + +# set env +os.environ["NLP_CLOUD_API_KEY"] = "your-api-key" + +messages = [{"role": "user", "content": "Hey! how's it going?"}] +response = completion(model="dolphin", messages=messages, stream=True) +for chunk in response: + print(chunk["choices"][0]["delta"]["content"]) # same as openai format +``` + +## non-dolphin models + +By default, LiteLLM will map `dolphin` and `chatdolphin` to nlp cloud. + +If you're trying to call any other model (e.g. GPT-J, Llama-2, etc.) with nlp cloud, just set it as your custom llm provider. + + +```python +import os +from litellm import completion + +# set env - [OPTIONAL] replace with your nlp cloud key +os.environ["NLP_CLOUD_API_KEY"] = "your-api-key" + +messages = [{"role": "user", "content": "Hey! how's it going?"}] + +# e.g. to call Llama2 on NLP Cloud +response = completion(model="nlp_cloud/finetuned-llama-2-70b", messages=messages, stream=True) +for chunk in response: + print(chunk["choices"][0]["delta"]["content"]) # same as openai format +``` diff --git a/docs/my-website/docs/providers/ollama.md b/docs/my-website/docs/providers/ollama.md new file mode 100644 index 0000000000000000000000000000000000000000..f5a1b0d6d070fdc2cd0b4027e4dcc2716a472116 --- /dev/null +++ b/docs/my-website/docs/providers/ollama.md @@ -0,0 +1,163 @@ +# Ollama +LiteLLM supports all models from [Ollama](https://github.com/jmorganca/ollama) + + + Open In Colab + + +## Pre-requisites +Ensure you have your ollama server running + +## Example usage +```python +from litellm import completion + +response = completion( + model="ollama/llama2", + messages=[{ "content": "respond in 20 words. who are you?","role": "user"}], + api_base="http://localhost:11434" +) +print(response) + +``` + +## Example usage - Streaming +```python +from litellm import completion + +response = completion( + model="ollama/llama2", + messages=[{ "content": "respond in 20 words. who are you?","role": "user"}], + api_base="http://localhost:11434", + stream=True +) +print(response) +for chunk in response: + print(chunk['choices'][0]['delta']) + +``` + +## Example usage - Streaming + Acompletion +Ensure you have async_generator installed for using ollama acompletion with streaming +```shell +pip install async_generator +``` + +```python +async def async_ollama(): + response = await litellm.acompletion( + model="ollama/llama2", + messages=[{ "content": "what's the weather" ,"role": "user"}], + api_base="http://localhost:11434", + stream=True + ) + async for chunk in response: + print(chunk) + +# call async_ollama +import asyncio +asyncio.run(async_ollama()) + +``` +## Ollama Models +Ollama supported models: https://github.com/jmorganca/ollama + +| Model Name | Function Call | +|----------------------|----------------------------------------------------------------------------------- +| Mistral | `completion(model='ollama/mistral', messages, api_base="http://localhost:11434", stream=True)` | +| Llama2 7B | `completion(model='ollama/llama2', messages, api_base="http://localhost:11434", stream=True)` | +| Llama2 13B | `completion(model='ollama/llama2:13b', messages, api_base="http://localhost:11434", stream=True)` | +| Llama2 70B | `completion(model='ollama/llama2:70b', messages, api_base="http://localhost:11434", stream=True)` | +| Llama2 Uncensored | `completion(model='ollama/llama2-uncensored', messages, api_base="http://localhost:11434", stream=True)` | +| Code Llama | `completion(model='ollama/codellama', messages, api_base="http://localhost:11434", stream=True)` | +| Llama2 Uncensored | `completion(model='ollama/llama2-uncensored', messages, api_base="http://localhost:11434", stream=True)` | +| Orca Mini | `completion(model='ollama/orca-mini', messages, api_base="http://localhost:11434", stream=True)` | +| Vicuna | `completion(model='ollama/vicuna', messages, api_base="http://localhost:11434", stream=True)` | +| Nous-Hermes | `completion(model='ollama/nous-hermes', messages, api_base="http://localhost:11434", stream=True)` | +| Nous-Hermes 13B | `completion(model='ollama/nous-hermes:13b', messages, api_base="http://localhost:11434", stream=True)` | +| Wizard Vicuna Uncensored | `completion(model='ollama/wizard-vicuna', messages, api_base="http://localhost:11434", stream=True)` | + + + +## LiteLLM/Ollama Docker Image + +For Ollama LiteLLM Provides a Docker Image for an OpenAI API compatible server for local LLMs - llama2, mistral, codellama + + +[![Chat on WhatsApp](https://img.shields.io/static/v1?label=Chat%20on&message=WhatsApp&color=success&logo=WhatsApp&style=flat-square)](https://wa.link/huol9n) [![Chat on Discord](https://img.shields.io/static/v1?label=Chat%20on&message=Discord&color=blue&logo=Discord&style=flat-square)](https://discord.gg/wuPM9dRgDw) +### An OpenAI API compatible server for local LLMs - llama2, mistral, codellama + +### Quick Start: +Docker Hub: +For ARM Processors: https://hub.docker.com/repository/docker/litellm/ollama/general +For Intel/AMD Processors: to be added +```shell +docker pull litellm/ollama +``` + +```shell +docker run --name ollama litellm/ollama +``` + +#### Test the server container +On the docker container run the `test.py` file using `python3 test.py` + + +### Making a request to this server +```python +import openai + +api_base = f"http://0.0.0.0:8000" # base url for server + +openai.api_base = api_base +openai.api_key = "temp-key" +print(openai.api_base) + + +print(f'LiteLLM: response from proxy with streaming') +response = openai.chat.completions.create( + model="ollama/llama2", + messages = [ + { + "role": "user", + "content": "this is a test request, acknowledge that you got it" + } + ], + stream=True +) + +for chunk in response: + print(f'LiteLLM: streaming response from proxy {chunk}') +``` + +### Responses from this server +```json +{ + "object": "chat.completion", + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": " Hello! I acknowledge receipt of your test request. Please let me know if there's anything else I can assist you with.", + "role": "assistant", + "logprobs": null + } + } + ], + "id": "chatcmpl-403d5a85-2631-4233-92cb-01e6dffc3c39", + "created": 1696992706.619709, + "model": "ollama/llama2", + "usage": { + "prompt_tokens": 18, + "completion_tokens": 25, + "total_tokens": 43 + } +} +``` + +## Support / talk with founders +- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +- [Community Discord 💭](https://discord.gg/wuPM9dRgDw) +- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ +- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai diff --git a/docs/my-website/docs/providers/openai.md b/docs/my-website/docs/providers/openai.md new file mode 100644 index 0000000000000000000000000000000000000000..1a515dea3aa6099e6dd63b398ee4f6bffae5d42f --- /dev/null +++ b/docs/my-website/docs/providers/openai.md @@ -0,0 +1,219 @@ +# OpenAI +LiteLLM supports OpenAI Chat + Text completion and embedding calls. + +### Required API Keys + +```python +import os +os.environ["OPENAI_API_KEY"] = "your-api-key" +``` + +### Usage +```python +import os +from litellm import completion + +os.environ["OPENAI_API_KEY"] = "your-api-key" + +# openai call +response = completion( + model = "gpt-3.5-turbo", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` + +### Optional Keys - OpenAI Organization, OpenAI API Base + +```python +import os +os.environ["OPENAI_ORGANIZATION"] = "your-org-id" # OPTIONAL +os.environ["OPENAI_API_BASE"] = "openaiai-api-base" # OPTIONAL +``` + +### OpenAI Chat Completion Models + +| Model Name | Function Call | +|-----------------------|-----------------------------------------------------------------| +| gpt-4-1106-preview | `response = completion(model="gpt-4-1106-preview", messages=messages)` | +| gpt-3.5-turbo-1106 | `response = completion(model="gpt-3.5-turbo-1106", messages=messages)` | +| gpt-3.5-turbo | `response = completion(model="gpt-3.5-turbo", messages=messages)` | +| gpt-3.5-turbo-0301 | `response = completion(model="gpt-3.5-turbo-0301", messages=messages)` | +| gpt-3.5-turbo-0613 | `response = completion(model="gpt-3.5-turbo-0613", messages=messages)` | +| gpt-3.5-turbo-16k | `response = completion(model="gpt-3.5-turbo-16k", messages=messages)` | +| gpt-3.5-turbo-16k-0613| `response = completion(model="gpt-3.5-turbo-16k-0613", messages=messages)` | +| gpt-4 | `response = completion(model="gpt-4", messages=messages)` | +| gpt-4-0314 | `response = completion(model="gpt-4-0314", messages=messages)` | +| gpt-4-0613 | `response = completion(model="gpt-4-0613", messages=messages)` | +| gpt-4-32k | `response = completion(model="gpt-4-32k", messages=messages)` | +| gpt-4-32k-0314 | `response = completion(model="gpt-4-32k-0314", messages=messages)` | +| gpt-4-32k-0613 | `response = completion(model="gpt-4-32k-0613", messages=messages)` | + + +These also support the `OPENAI_API_BASE` environment variable, which can be used to specify a custom API endpoint. + +## OpenAI Vision Models +| Model Name | Function Call | +|-----------------------|-----------------------------------------------------------------| +| gpt-4-vision-preview | `response = completion(model="gpt-4-vision-preview", messages=messages)` | + +#### Usage +```python +import os +from litellm import completion + +os.environ["OPENAI_API_KEY"] = "your-api-key" + +# openai call +response = completion( + model = "gpt-4-vision-preview", + messages=[ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What’s 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" + } + } + ] + } + ], +) + +``` + +## OpenAI Text Completion Models / Instruct Models + +| Model Name | Function Call | +|---------------------|----------------------------------------------------| +| gpt-3.5-turbo-instruct | `response = completion(model="gpt-3.5-turbo-instruct", messages=messages)` | +| text-davinci-003 | `response = completion(model="text-davinci-003", messages=messages)` | +| ada-001 | `response = completion(model="ada-001", messages=messages)` | +| curie-001 | `response = completion(model="curie-001", messages=messages)` | +| babbage-001 | `response = completion(model="babbage-001", messages=messages)` | +| babbage-002 | `response = completion(model="babbage-002", messages=messages)` | +| davinci-002 | `response = completion(model="davinci-002", messages=messages)` | + +## Advanced + +### Parallel Function calling +See a detailed walthrough of parallel function calling with litellm [here](https://docs.litellm.ai/docs/completion/function_call) +```python +import litellm +import json +# set openai api key +import os +os.environ['OPENAI_API_KEY'] = "" # litellm reads OPENAI_API_KEY from .env and sends the request +# Example dummy function hard coded to return the same weather +# In production, this could be your backend API or an external API +def get_current_weather(location, unit="fahrenheit"): + """Get the current weather in a given location""" + if "tokyo" in location.lower(): + return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"}) + elif "san francisco" in location.lower(): + return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}) + elif "paris" in location.lower(): + return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"}) + else: + return json.dumps({"location": location, "temperature": "unknown"}) + +messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}] +tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, + }, + "required": ["location"], + }, + }, + } +] + +response = litellm.completion( + model="gpt-3.5-turbo-1106", + messages=messages, + tools=tools, + tool_choice="auto", # auto is default, but we'll be explicit +) +print("\nLLM Response1:\n", response) +response_message = response.choices[0].message +tool_calls = response.choices[0].message.tool_calls +``` + + +### Setting Organization-ID for completion calls +This can be set in one of the following ways: +- Environment Variable `OPENAI_ORGANIZATION` +- Params to `litellm.completion(model=model, organization="your-organization-id")` +- Set as `litellm.organization="your-organization-id"` +```python +import os +from litellm import completion + +os.environ["OPENAI_API_KEY"] = "your-api-key" +os.environ["OPENAI_ORGANIZATION"] = "your-org-id" # OPTIONAL + +response = completion( + model = "gpt-3.5-turbo", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` +### Using Helicone Proxy with LiteLLM +```python +import os +import litellm +from litellm import completion + +os.environ["OPENAI_API_KEY"] = "" + +# os.environ["OPENAI_API_BASE"] = "" +litellm.api_base = "https://oai.hconeai.com/v1" +litellm.headers = { + "Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}", + "Helicone-Cache-Enabled": "true", +} + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# openai call +response = completion("gpt-3.5-turbo", messages) +``` + +### Using OpenAI Proxy with LiteLLM +```python +import os +import litellm +from litellm import completion + +os.environ["OPENAI_API_KEY"] = "" + +# set custom api base to your proxy +# either set .env or litellm.api_base +# os.environ["OPENAI_API_BASE"] = "" +litellm.api_base = "your-openai-proxy-url" + + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# openai call +response = completion("openai/your-model-name", messages) +``` + +If you need to set api_base dynamically, just pass it in completions instead - `completions(...,api_base="your-proxy-api-base")` + +For more check out [setting API Base/Keys](../set_keys.md) \ No newline at end of file diff --git a/docs/my-website/docs/providers/openrouter.md b/docs/my-website/docs/providers/openrouter.md new file mode 100644 index 0000000000000000000000000000000000000000..f4888005d0799d9dee0516d49087110fc56d32c2 --- /dev/null +++ b/docs/my-website/docs/providers/openrouter.md @@ -0,0 +1,37 @@ +# OpenRouter +LiteLLM supports all the text models from [OpenRouter](https://openrouter.ai/docs) + + + Open In Colab + + +## Usage +```python +import os +from litellm import completion +os.environ["OPENROUTER_API_KEY"] = "" + +os.environ["OR_SITE_URL"] = "" # optional +os.environ["OR_APP_NAME"] = "" # optional + +response = completion( + model="openrouter/google/palm-2-chat-bison", + messages=messages, + ) +``` + +### OpenRouter Completion Models + +| Model Name | Function Call | Required OS Variables | +|---------------------------|-----------------------------------------------------|--------------------------------------------------------------| +| openrouter/openai/gpt-3.5-turbo | `completion('openrouter/openai/gpt-3.5-turbo', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OPENROUTER_API_KEY']` | +| openrouter/openai/gpt-3.5-turbo-16k | `completion('openrouter/openai/gpt-3.5-turbo-16k', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OPENROUTER_API_KEY']` | +| openrouter/openai/gpt-4 | `completion('openrouter/openai/gpt-4', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OPENROUTER_API_KEY']` | +| openrouter/openai/gpt-4-32k | `completion('openrouter/openai/gpt-4-32k', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OPENROUTER_API_KEY']` | +| openrouter/anthropic/claude-2 | `completion('openrouter/anthropic/claude-2', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OPENROUTER_API_KEY']` | +| openrouter/anthropic/claude-instant-v1 | `completion('openrouter/anthropic/claude-instant-v1', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OPENROUTER_API_KEY']` | +| openrouter/google/palm-2-chat-bison | `completion('openrouter/google/palm-2-chat-bison', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OPENROUTER_API_KEY']` | +| openrouter/google/palm-2-codechat-bison | `completion('openrouter/google/palm-2-codechat-bison', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OPENROUTER_API_KEY']` | +| openrouter/meta-llama/llama-2-13b-chat | `completion('openrouter/meta-llama/llama-2-13b-chat', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OPENROUTER_API_KEY']` | +| openrouter/meta-llama/llama-2-70b-chat | `completion('openrouter/meta-llama/llama-2-70b-chat', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OPENROUTER_API_KEY']` | + diff --git a/docs/my-website/docs/providers/palm.md b/docs/my-website/docs/providers/palm.md new file mode 100644 index 0000000000000000000000000000000000000000..39fcc207c2e8ccbb431de4b348c0d6e601bdca4b --- /dev/null +++ b/docs/my-website/docs/providers/palm.md @@ -0,0 +1,37 @@ +# PaLM API - Google + +## Pre-requisites +* `pip install -q google-generativeai` + +## Sample Usage +```python +import litellm +import os + +os.environ['PALM_API_KEY'] = "" +response = completion( + model="palm/chat-bison", + messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}] +) +``` + +## Sample Usage - Streaming +```python +import litellm +import os + +os.environ['PALM_API_KEY'] = "" +response = completion( + model="palm/chat-bison", + messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}], + stream=True +) + +for chunk in response: + print(chunk) +``` + +## Chat Models +| Model Name | Function Call | Required OS Variables | +|------------------|--------------------------------------|-------------------------| +| chat-bison | `completion('palm/chat-bison', messages)` | `os.environ['PALM_API_KEY']` | diff --git a/docs/my-website/docs/providers/perplexity.md b/docs/my-website/docs/providers/perplexity.md new file mode 100644 index 0000000000000000000000000000000000000000..7cfc4c8619f70168b42945e33d6b9c2d84285eaa --- /dev/null +++ b/docs/my-website/docs/providers/perplexity.md @@ -0,0 +1,62 @@ +# Perplexity AI (pplx-api) +https://www.perplexity.ai + +## API Key +```python +# env variable +os.environ['PERPLEXITYAI_API_KEY'] +``` + +## Sample Usage +```python +from litellm import completion +import os + +os.environ['PERPLEXITYAI_API_KEY'] = "" +response = completion( + model="perplexity/mistral-7b-instruct", + messages=messages +) +print(response) +``` + +## Sample Usage - Streaming +```python +from litellm import completion +import os + +os.environ['PERPLEXITYAI_API_KEY'] = "" +response = completion( + model="perplexity/mistral-7b-instruct", + messages=messages, + stream=True +) + +for chunk in response: + print(chunk) +``` + + +## Supported Models +All models listed here https://docs.perplexity.ai/docs/model-cards are supported + +| Model Name | Function Call | +|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| pplx-7b-chat | `completion(model="perplexity/pplx-7b-chat", messages)` | +| pplx-70b-chat | `completion(model="perplexity/pplx-70b-chat", messages)` | +| pplx-7b-online | `completion(model="perplexity/pplx-7b-online", messages)` | +| pplx-70b-online | `completion(model="perplexity/pplx-70b-online", messages)` | +| codellama-34b-instruct | `completion(model="perplexity/codellama-34b-instruct", messages)` | +| llama-2-13b-chat | `completion(model="perplexity/llama-2-13b-chat", messages)` | +| llama-2-70b-chat | `completion(model="perplexity/llama-2-70b-chat", messages)` | +| mistral-7b-instruct | `completion(model="perplexity/mistral-7b-instruct", messages)` | +| openhermes-2-mistral-7b | `completion(model="perplexity/openhermes-2-mistral-7b", messages)` | +| openhermes-2.5-mistral-7b | `completion(model="perplexity/openhermes-2.5-mistral-7b", messages)` | +| pplx-7b-chat-alpha | `completion(model="perplexity/pplx-7b-chat-alpha", messages)` | +| pplx-70b-chat-alpha | `completion(model="perplexity/pplx-70b-chat-alpha", messages)` | + + + + + + diff --git a/docs/my-website/docs/providers/petals.md b/docs/my-website/docs/providers/petals.md new file mode 100644 index 0000000000000000000000000000000000000000..b5dd1705b431373356bc83e54fe5de4650a014fd --- /dev/null +++ b/docs/my-website/docs/providers/petals.md @@ -0,0 +1,49 @@ +# Petals +Petals: https://github.com/bigscience-workshop/petals + + + Open In Colab + + +## Pre-Requisites +Ensure you have `petals` installed +```shell +pip install git+https://github.com/bigscience-workshop/petals +``` + +## Usage +Ensure you add `petals/` as a prefix for all petals LLMs. This sets the custom_llm_provider to petals + +```python +from litellm import completion + +response = completion( + model="petals/petals-team/StableBeluga2", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) + +print(response) +``` + +## Usage with Streaming + +```python +response = completion( + model="petals/petals-team/StableBeluga2", + messages=[{ "content": "Hello, how are you?","role": "user"}], + stream=True +) + +print(response) +for chunk in response: + print(chunk) +``` + +### Model Details + +| Model Name | Function Call | +|------------------|--------------------------------------------| +| petals-team/StableBeluga | `completion('petals/petals-team/StableBeluga2', messages)` | +| huggyllama/llama-65b | `completion('petals/huggyllama/llama-65b', messages)` | + + diff --git a/docs/my-website/docs/providers/replicate.md b/docs/my-website/docs/providers/replicate.md new file mode 100644 index 0000000000000000000000000000000000000000..3384ba35c283b02cbfd6f1b2fd646a35a668c02c --- /dev/null +++ b/docs/my-website/docs/providers/replicate.md @@ -0,0 +1,100 @@ +# Replicate + +LiteLLM supports all models on Replicate + +### API KEYS +```python +import os +os.environ["REPLICATE_API_KEY"] = "" +``` + +### Example Call + +```python +from litellm import completion +import os +## set ENV variables +os.environ["REPLICATE_API_KEY"] = "replicate key" + +# replicate llama-2 call +response = completion( + model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", + messages = [{ "content": "Hello, how are you?","role": "user"}] +) +``` + +### Example - Calling Replicate Deployments +Calling a [deployed replicate LLM](https://replicate.com/deployments) +Add the `replicate/deployments/` prefix to your model, so litellm will call the `deployments` endpoint. This will call `ishaan-jaff/ishaan-mistral` deployment on replicate + +```python +response = completion( + model="replicate/deployments/ishaan-jaff/ishaan-mistral", + messages= [{ "content": "Hello, how are you?","role": "user"}] +) +``` + +:::warning Replicate Cold Boots + +Replicate responses can take 3-5 mins due to replicate cold boots, if you're trying to debug try making the request with `litellm.set_verbose=True`. [More info on replicate cold boots](https://replicate.com/docs/how-does-replicate-work#cold-boots) + +::: + +### Replicate Models +liteLLM supports all replicate LLMs + +For replicate models ensure to add a `replicate/` prefix to the `model` arg. liteLLM detects it using this arg. + +Below are examples on how to call replicate LLMs using liteLLM + +Model Name | Function Call | Required OS Variables | +-----------------------------|----------------------------------------------------------------|--------------------------------------| + replicate/llama-2-70b-chat | `completion(model='replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf', messages, supports_system_prompt=True)` | `os.environ['REPLICATE_API_KEY']` | + a16z-infra/llama-2-13b-chat| `completion(model='replicate/a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52', messages, supports_system_prompt=True)`| `os.environ['REPLICATE_API_KEY']` | + replicate/vicuna-13b | `completion(model='replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b', messages)` | `os.environ['REPLICATE_API_KEY']` | + daanelson/flan-t5-large | `completion(model='replicate/daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f', messages)` | `os.environ['REPLICATE_API_KEY']` | + custom-llm | `completion(model='replicate/custom-llm-version-id', messages)` | `os.environ['REPLICATE_API_KEY']` | + replicate deployment | `completion(model='replicate/deployments/ishaan-jaff/ishaan-mistral', messages)` | `os.environ['REPLICATE_API_KEY']` | + + +### Passing additional params - max_tokens, temperature +See all litellm.completion supported params [here](https://docs.litellm.ai/docs/completion/input) + +```python +# !pip install litellm +from litellm import completion +import os +## set ENV variables +os.environ["REPLICATE_API_KEY"] = "replicate key" + +# replicate llama-2 call +response = completion( + model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", + messages = [{ "content": "Hello, how are you?","role": "user"}], + max_tokens=20, + temperature=0.5 + +) +``` + +### Passings Replicate specific params +Send params [not supported by `litellm.completion()`](https://docs.litellm.ai/docs/completion/input) but supported by Replicate by passing them to `litellm.completion` + +Example `seed`, `min_tokens` are Replicate specific param + +```python +# !pip install litellm +from litellm import completion +import os +## set ENV variables +os.environ["REPLICATE_API_KEY"] = "replicate key" + +# replicate llama-2 call +response = completion( + model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", + messages = [{ "content": "Hello, how are you?","role": "user"}], + seed=-1, + min_tokens=2, + top_k=20, +) +``` diff --git a/docs/my-website/docs/providers/togetherai.md b/docs/my-website/docs/providers/togetherai.md new file mode 100644 index 0000000000000000000000000000000000000000..d718619f04f00d02fc162a68f4882b38eb505fec --- /dev/null +++ b/docs/my-website/docs/providers/togetherai.md @@ -0,0 +1,224 @@ +# Together AI +LiteLLM supports all models on Together AI. + +## API Keys + +```python +import os +os.environ["TOGETHERAI_API_KEY"] = "your-api-key" +``` +## Sample Usage + +```python +from litellm import completion + +os.environ["TOGETHERAI_API_KEY"] = "your-api-key" + +messages = [{"role": "user", "content": "Write me a poem about the blue sky"}] + +completion(model="together_ai/togethercomputer/Llama-2-7B-32K-Instruct", messages=messages) +``` + +## Together AI Models +liteLLM supports `non-streaming` and `streaming` requests to all models on https://api.together.xyz/ + +Example TogetherAI Usage - Note: liteLLM supports all models deployed on TogetherAI + + +### Llama LLMs - Chat +| Model Name | Function Call | Required OS Variables | +|-----------------------------------|------------------------------------------------------------------------|---------------------------------| +| togethercomputer/llama-2-70b-chat | `completion('together_ai/togethercomputer/llama-2-70b-chat', messages)` | `os.environ['TOGETHERAI_API_KEY']` | + +### Llama LLMs - Language / Instruct +| Model Name | Function Call | Required OS Variables | +|-----------------------------------|------------------------------------------------------------------------|---------------------------------| +| togethercomputer/llama-2-70b | `completion('together_ai/togethercomputer/llama-2-70b', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| togethercomputer/LLaMA-2-7B-32K | `completion('together_ai/togethercomputer/LLaMA-2-7B-32K', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| togethercomputer/Llama-2-7B-32K-Instruct | `completion('together_ai/togethercomputer/Llama-2-7B-32K-Instruct', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| togethercomputer/llama-2-7b | `completion('together_ai/togethercomputer/llama-2-7b', messages)` | `os.environ['TOGETHERAI_API_KEY']` | + +### Falcon LLMs +| Model Name | Function Call | Required OS Variables | +|-----------------------------------|------------------------------------------------------------------------|---------------------------------| +| togethercomputer/falcon-40b-instruct | `completion('together_ai/togethercomputer/falcon-40b-instruct', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| togethercomputer/falcon-7b-instruct | `completion('together_ai/togethercomputer/falcon-7b-instruct', messages)` | `os.environ['TOGETHERAI_API_KEY']` | + +### Alpaca LLMs +| Model Name | Function Call | Required OS Variables | +|-----------------------------------|------------------------------------------------------------------------|---------------------------------| +| togethercomputer/alpaca-7b | `completion('together_ai/togethercomputer/alpaca-7b', messages)` | `os.environ['TOGETHERAI_API_KEY']` | + +### Other Chat LLMs +| Model Name | Function Call | Required OS Variables | +|-----------------------------------|------------------------------------------------------------------------|---------------------------------| +| HuggingFaceH4/starchat-alpha | `completion('together_ai/HuggingFaceH4/starchat-alpha', messages)` | `os.environ['TOGETHERAI_API_KEY']` | + +### Code LLMs +| Model Name | Function Call | Required OS Variables | +|-----------------------------------|------------------------------------------------------------------------|---------------------------------| +| togethercomputer/CodeLlama-34b | `completion('together_ai/togethercomputer/CodeLlama-34b', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| togethercomputer/CodeLlama-34b-Instruct | `completion('together_ai/togethercomputer/CodeLlama-34b-Instruct', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| togethercomputer/CodeLlama-34b-Python | `completion('together_ai/togethercomputer/CodeLlama-34b-Python', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| defog/sqlcoder | `completion('together_ai/defog/sqlcoder', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| NumbersStation/nsql-llama-2-7B | `completion('together_ai/NumbersStation/nsql-llama-2-7B', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| WizardLM/WizardCoder-15B-V1.0 | `completion('together_ai/WizardLM/WizardCoder-15B-V1.0', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| WizardLM/WizardCoder-Python-34B-V1.0 | `completion('together_ai/WizardLM/WizardCoder-Python-34B-V1.0', messages)` | `os.environ['TOGETHERAI_API_KEY']` | + +### Language LLMs +| Model Name | Function Call | Required OS Variables | +|-----------------------------------|------------------------------------------------------------------------|---------------------------------| +| NousResearch/Nous-Hermes-Llama2-13b | `completion('together_ai/NousResearch/Nous-Hermes-Llama2-13b', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| Austism/chronos-hermes-13b | `completion('together_ai/Austism/chronos-hermes-13b', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| upstage/SOLAR-0-70b-16bit | `completion('together_ai/upstage/SOLAR-0-70b-16bit', messages)` | `os.environ['TOGETHERAI_API_KEY']` | +| WizardLM/WizardLM-70B-V1.0 | `completion('together_ai/WizardLM/WizardLM-70B-V1.0', messages)` | `os.environ['TOGETHERAI_API_KEY']` | + + +## Prompt Templates + +Using a chat model on Together AI with it's own prompt format? + +### Using Llama2 Instruct models +If you're using Together AI's Llama2 variants( `model=togethercomputer/llama-2..-instruct`), LiteLLM can automatically translate between the OpenAI prompt format and the TogetherAI Llama2 one (`[INST]..[/INST]`). + +```python +from litellm import completion + +# set env variable +os.environ["TOGETHERAI_API_KEY"] = "" + +messages = [{"role": "user", "content": "Write me a poem about the blue sky"}] + +completion(model="together_ai/togethercomputer/Llama-2-7B-32K-Instruct", messages=messages) +``` + +### Using another model + +You can create a custom prompt template on LiteLLM (and we [welcome PRs](https://github.com/BerriAI/litellm) to add them to the main repo 🤗) + +Let's make one for `OpenAssistant/llama2-70b-oasst-sft-v10`! + +The accepted template format is: [Reference](https://huggingface.co/OpenAssistant/llama2-70b-oasst-sft-v10-) +``` +""" +<|im_start|>system +{system_message}<|im_end|> +<|im_start|>user +{prompt}<|im_end|> +<|im_start|>assistant +""" +``` + +Let's register our custom prompt template: [Implementation Code](https://github.com/BerriAI/litellm/blob/64f3d3c56ef02ac5544983efc78293de31c1c201/litellm/llms/prompt_templates/factory.py#L77) +```python +import litellm + +litellm.register_prompt_template( + model="OpenAssistant/llama2-70b-oasst-sft-v10", + roles={ + "system": { + "pre_message": "[<|im_start|>system", + "post_message": "\n" + }, + "user": { + "pre_message": "<|im_start|>user", + "post_message": "\n" + }, + "assistant": { + "pre_message": "<|im_start|>assistant", + "post_message": "\n" + } + } + ) +``` + +Let's use it! + +```python +from litellm import completion + +# set env variable +os.environ["TOGETHERAI_API_KEY"] = "" + +messages=[{"role":"user", "content": "Write me a poem about the blue sky"}] + +completion(model="together_ai/OpenAssistant/llama2-70b-oasst-sft-v10", messages=messages) +``` + +**Complete Code** + +```python +import litellm +from litellm import completion + +# set env variable +os.environ["TOGETHERAI_API_KEY"] = "" + +litellm.register_prompt_template( + model="OpenAssistant/llama2-70b-oasst-sft-v10", + roles={ + "system": { + "pre_message": "[<|im_start|>system", + "post_message": "\n" + }, + "user": { + "pre_message": "<|im_start|>user", + "post_message": "\n" + }, + "assistant": { + "pre_message": "<|im_start|>assistant", + "post_message": "\n" + } + } + ) + +messages=[{"role":"user", "content": "Write me a poem about the blue sky"}] + +response = completion(model="together_ai/OpenAssistant/llama2-70b-oasst-sft-v10", messages=messages) + +print(response) +``` + +**Output** +```json +{ + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": ".\n\nThe sky is a canvas of blue,\nWith clouds that drift and move,", + "role": "assistant", + "logprobs": null + } + } + ], + "created": 1693941410.482018, + "model": "OpenAssistant/llama2-70b-oasst-sft-v10", + "usage": { + "prompt_tokens": 7, + "completion_tokens": 16, + "total_tokens": 23 + }, + "litellm_call_id": "f21315db-afd6-4c1e-b43a-0b5682de4b06" +} +``` + +## Advanced Usage + +Instead of using the `custom_llm_provider` arg to specify which provider you're using (e.g. together ai), you can just pass the provider name as part of the model name, and LiteLLM will parse it out. + +Expected format: / + +e.g. completion(model="together_ai/togethercomputer/Llama-2-7B-32K-Instruct", ...) + +```python +from litellm import completion + +# set env variable +os.environ["TOGETHERAI_API_KEY"] = "" + +messages = [{"role": "user", "content": "Write me a poem about the blue sky"}] + +completion(model="together_ai/togethercomputer/Llama-2-7B-32K-Instruct", messages=messages) +``` \ No newline at end of file diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md new file mode 100644 index 0000000000000000000000000000000000000000..1dedd14502882abb91e536ed0031303a8cf86d11 --- /dev/null +++ b/docs/my-website/docs/providers/vertex.md @@ -0,0 +1,75 @@ +# VertexAI - Google + + + Open In Colab + + +## Pre-requisites +* `pip install google-cloud-aiplatform` +* Authentication: + * 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` + +## Set Vertex Project & Vertex Location +All calls using Vertex AI require the following parameters: +* Your Project ID +```python +import os, litellm + +# set via env var +os.environ["VERTEXAI_PROJECT"] = "hardy-device-38811" # Your Project ID` + +### OR ### + +# set directly on module +litellm.vertex_project = "hardy-device-38811" # Your Project ID` +``` +* Your Project Location +```python +import os, litellm + +# set via env var +os.environ["VERTEXAI_LOCATION"] = "us-central1 # Your Location + +### OR ### + +# set directly on module +litellm.vertex_location = "us-central1 # Your Location +``` + +## Sample Usage +```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"}]) +``` + +## Chat Models +| Model Name | Function Call | +|------------------|--------------------------------------| +| chat-bison-32k | `completion('chat-bison-32k', messages)` | +| chat-bison | `completion('chat-bison', messages)` | +| chat-bison@001 | `completion('chat-bison@001', messages)` | + +## Code Chat Models +| Model Name | Function Call | +|----------------------|--------------------------------------------| +| codechat-bison | `completion('codechat-bison', messages)` | +| codechat-bison-32k | `completion('codechat-bison-32k', messages)` | +| codechat-bison@001 | `completion('codechat-bison@001', messages)` | + +## Text Models +| Model Name | Function Call | +|------------------|--------------------------------------| +| text-bison | `completion('text-bison', messages)` | +| text-bison@001 | `completion('text-bison@001', messages)` | + +## Code Text Models +| Model Name | Function Call | +|------------------|--------------------------------------| +| code-bison | `completion('code-bison', messages)` | +| code-bison@001 | `completion('code-bison@001', messages)` | +| code-gecko@001 | `completion('code-gecko@001', messages)` | +| code-gecko@latest| `completion('code-gecko@latest', messages)` | diff --git a/docs/my-website/docs/providers/vllm.md b/docs/my-website/docs/providers/vllm.md new file mode 100644 index 0000000000000000000000000000000000000000..df9e07ef7b7e692be6263f44bd0049f7682ca68c --- /dev/null +++ b/docs/my-website/docs/providers/vllm.md @@ -0,0 +1,123 @@ +# VLLM + +LiteLLM supports all models on VLLM. + +🚀[Code Tutorial](https://github.com/BerriAI/litellm/blob/main/cookbook/VLLM_Model_Testing.ipynb) + +### Quick Start +``` +pip install litellm vllm +``` +```python +import litellm + +response = completion( + model="vllm/facebook/opt-125m", # add a vllm prefix so litellm knows the custom_llm_provider==vllm + messages=messages, + temperature=0.2, + max_tokens=80) + +print(response) +``` + +### Calling hosted VLLM Server +In order to use litellm to call a hosted vllm server add the following to your completion call + +* `custom_llm_provider == "openai"` +* `api_base = "your-hosted-vllm-server"` + +```python +import litellm + +response = completion( + model="openai/facebook/opt-125m", # pass the vllm model name + messages=messages, + api_base="https://hosted-vllm-api.co", + temperature=0.2, + max_tokens=80) + +print(response) +``` + + +### Batch Completion + +```python +from litellm import batch_completion + +model_name = "facebook/opt-125m" +provider = "vllm" +messages = [[{"role": "user", "content": "Hey, how's it going"}] for _ in range(5)] + +response_list = batch_completion( + model=model_name, + custom_llm_provider=provider, # can easily switch to huggingface, replicate, together ai, sagemaker, etc. + messages=messages, + temperature=0.2, + max_tokens=80, + ) +print(response_list) +``` +### Prompt Templates + +For models with special prompt templates (e.g. Llama2), we format the prompt to fit their template. + +**What if we don't support a model you need?** +You can also specify you're own custom prompt formatting, in case we don't have your model covered yet. + +**Does this mean you have to specify a prompt for all models?** +No. By default we'll concatenate your message content to make a prompt (expected format for Bloom, T-5, Llama-2 base models, etc.) + +**Default Prompt Template** +```python +def default_pt(messages): + return " ".join(message["content"] for message in messages) +``` + +[Code for how prompt templates work in LiteLLM](https://github.com/BerriAI/litellm/blob/main/litellm/llms/prompt_templates/factory.py) + + +#### Models we already have Prompt Templates for + +| Model Name | Works for Models | Function Call | +| -------- | -------- | -------- | +| meta-llama/Llama-2-7b-chat | All meta-llama llama2 chat models| `completion(model='vllm/meta-llama/Llama-2-7b', messages=messages, api_base="your_api_endpoint")` | +| tiiuae/falcon-7b-instruct | All falcon instruct models | `completion(model='vllm/tiiuae/falcon-7b-instruct', messages=messages, api_base="your_api_endpoint")` | +| mosaicml/mpt-7b-chat | All mpt chat models | `completion(model='vllm/mosaicml/mpt-7b-chat', messages=messages, api_base="your_api_endpoint")` | +| codellama/CodeLlama-34b-Instruct-hf | All codellama instruct models | `completion(model='vllm/codellama/CodeLlama-34b-Instruct-hf', messages=messages, api_base="your_api_endpoint")` | +| WizardLM/WizardCoder-Python-34B-V1.0 | All wizardcoder models | `completion(model='vllm/WizardLM/WizardCoder-Python-34B-V1.0', messages=messages, api_base="your_api_endpoint")` | +| Phind/Phind-CodeLlama-34B-v2 | All phind-codellama models | `completion(model='vllm/Phind/Phind-CodeLlama-34B-v2', messages=messages, api_base="your_api_endpoint")` | + +#### Custom prompt templates + +```python +# Create your own custom prompt template works +litellm.register_prompt_template( + model="togethercomputer/LLaMA-2-7B-32K", + roles={ + "system": { + "pre_message": "[INST] <>\n", + "post_message": "\n<>\n [/INST]\n" + }, + "user": { + "pre_message": "[INST] ", + "post_message": " [/INST]\n" + }, + "assistant": { + "pre_message": "\n", + "post_message": "\n", + } + } # tell LiteLLM how you want to map the openai messages to this model +) + +def test_vllm_custom_model(): + model = "vllm/togethercomputer/LLaMA-2-7B-32K" + response = completion(model=model, messages=messages) + print(response['choices'][0]['message']['content']) + return response + +test_vllm_custom_model() +``` + +[Implementation Code](https://github.com/BerriAI/litellm/blob/6b3cb1898382f2e4e80fd372308ea232868c78d1/litellm/utils.py#L1414) + diff --git a/docs/my-website/docs/proxy/caching.md b/docs/my-website/docs/proxy/caching.md new file mode 100644 index 0000000000000000000000000000000000000000..d052102db9ba9121164770a8611a86e9ec6b75df --- /dev/null +++ b/docs/my-website/docs/proxy/caching.md @@ -0,0 +1,74 @@ +# Caching +Cache LLM Responses + +Caching can be enabled by adding the `cache` key in 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 + +litellm_settings: + set_verbose: True + cache: # init cache + type: redis # tell litellm to use redis caching +``` + +#### Step 2: Add Redis Credentials to .env +LiteLLM requires the following REDIS credentials in your env to enable caching + + ```shell + REDIS_HOST = "" # REDIS_HOST='redis-18841.c274.us-east-1-3.ec2.cloud.redislabs.com' + REDIS_PORT = "" # REDIS_PORT='18841' + REDIS_PASSWORD = "" # REDIS_PASSWORD='liteLlmIsAmazing' + ``` +#### Step 3: Run proxy with config +```shell +$ litellm --config /path/to/config.yaml +``` + +#### Using Caching +Send the same request twice: +```shell +curl http://0.0.0.0:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "write a poem about litellm!"}], + "temperature": 0.7 + }' + +curl http://0.0.0.0:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "write a poem about litellm!"}], + "temperature": 0.7 + }' +``` + +#### Control caching per completion request +Caching can be switched on/off per `/chat/completions` request +- Caching **on** for completion - pass `caching=True`: + ```shell + curl http://0.0.0.0:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "write a poem about litellm!"}], + "temperature": 0.7, + "caching": true + }' + ``` +- Caching **off** for completion - pass `caching=False`: + ```shell + curl http://0.0.0.0:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "write a poem about litellm!"}], + "temperature": 0.7, + "caching": false + }' + ``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/cli.md b/docs/my-website/docs/proxy/cli.md new file mode 100644 index 0000000000000000000000000000000000000000..c9425b40252cfb7d3e830fca4c94bcaffc529539 --- /dev/null +++ b/docs/my-website/docs/proxy/cli.md @@ -0,0 +1,144 @@ +# CLI Arguments +Cli arguments, --host, --port, --num_workers + +#### --host + - **Default:** `'0.0.0.0'` + - The host for the server to listen on. + - **Usage:** + ```shell + litellm --host 127.0.0.1 + ``` + +#### --port + - **Default:** `8000` + - The port to bind the server to. + - **Usage:** + ```shell + litellm --port 8080 + ``` + +#### --num_workers + - **Default:** `1` + - The number of uvicorn workers to spin up. + - **Usage:** + ```shell + litellm --num_workers 4 + ``` + +#### --api_base + - **Default:** `None` + - The API base for the model litellm should call. + - **Usage:** + ```shell + litellm --model huggingface/tinyllama --api_base https://k58ory32yinf1ly0.us-east-1.aws.endpoints.huggingface.cloud + ``` + +#### --api_version + - **Default:** `None` + - For Azure services, specify the API version. + - **Usage:** + ```shell + litellm --model azure/gpt-deployment --api_version 2023-08-01 --api_base https://" + ``` + +#### --model or -m + - **Default:** `None` + - The model name to pass to Litellm. + - **Usage:** + ```shell + litellm --model gpt-3.5-turbo + ``` + +#### --test + - **Type:** `bool` (Flag) + - Proxy chat completions URL to make a test request. + - **Usage:** + ```shell + litellm --test + ``` + +#### --health + - **Type:** `bool` (Flag) + - Runs a health check on all models in config.yaml + - **Usage:** + ```shell + litellm --health + ``` + +#### --alias + - **Default:** `None` + - An alias for the model, for user-friendly reference. + - **Usage:** + ```shell + litellm --alias my-gpt-model + ``` + +#### --debug + - **Default:** `False` + - **Type:** `bool` (Flag) + - Enable debugging mode for the input. + - **Usage:** + ```shell + litellm --debug + ``` + +#### --temperature + - **Default:** `None` + - **Type:** `float` + - Set the temperature for the model. + - **Usage:** + ```shell + litellm --temperature 0.7 + ``` + +#### --max_tokens + - **Default:** `None` + - **Type:** `int` + - Set the maximum number of tokens for the model output. + - **Usage:** + ```shell + litellm --max_tokens 50 + ``` + +#### --request_timeout + - **Default:** `600` + - **Type:** `int` + - Set the timeout in seconds for completion calls. + - **Usage:** + ```shell + litellm --request_timeout 300 + ``` + +#### --drop_params + - **Type:** `bool` (Flag) + - Drop any unmapped params. + - **Usage:** + ```shell + litellm --drop_params + ``` + +#### --add_function_to_prompt + - **Type:** `bool` (Flag) + - If a function passed but unsupported, pass it as a part of the prompt. + - **Usage:** + ```shell + litellm --add_function_to_prompt + ``` + +#### --config + - Configure Litellm by providing a configuration file path. + - **Usage:** + ```shell + litellm --config path/to/config.yaml + ``` + +#### --telemetry + - **Default:** `True` + - **Type:** `bool` + - Help track usage of this feature. + - **Usage:** + ```shell + litellm --telemetry False + ``` + + diff --git a/docs/my-website/docs/proxy/configs.md b/docs/my-website/docs/proxy/configs.md new file mode 100644 index 0000000000000000000000000000000000000000..71ce7de02ff9dd15e5db422796beefb8af1113f3 --- /dev/null +++ b/docs/my-website/docs/proxy/configs.md @@ -0,0 +1,191 @@ +# Proxy Config.yaml +Set model list, `api_base`, `api_key`, `temperature` & proxy server settings (`master-key`) on the config.yaml. + +| Param Name | Description | +|----------------------|---------------------------------------------------------------| +| `model_list` | List of supported models on the server, with model-specific configs | +| `litellm_settings` | litellm Module settings, example `litellm.drop_params=True`, `litellm.set_verbose=True`, `litellm.api_base`, `litellm.cache` | +| `general_settings` | Server settings, example setting `master_key: sk-my_special_key` | +| `environment_variables` | Environment Variables example, `REDIS_HOST`, `REDIS_PORT` | + +## Quick Start + +Set a model alias for your deployments. + +In the `config.yaml` the model_name parameter is the user-facing name to use for your deployment. + +In the config below requests with: +- `model=vllm-models` will route to `openai/facebook/opt-125m`. +- `model=gpt-3.5-turbo` will load balance between `azure/gpt-turbo-small-eu` and `azure/gpt-turbo-small-ca` + +```yaml +model_list: + - model_name: gpt-3.5-turbo # user-facing model alias + litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input + model: azure/gpt-turbo-small-eu + api_base: https://my-endpoint-europe-berri-992.openai.azure.com/ + api_key: "os.environ/AZURE_API_KEY_EU" # does os.getenv("AZURE_API_KEY_EU") + rpm: 6 # 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: "os.environ/AZURE_API_KEY_CA" + rpm: 6 + - model_name: vllm-models + litellm_params: + model: openai/facebook/opt-125m # the `openai/` prefix tells litellm it's openai compatible + api_base: http://0.0.0.0:8000 + rpm: 1440 + model_info: + version: 2 + +litellm_settings: # module level litellm settings - https://github.com/BerriAI/litellm/blob/main/litellm/__init__.py + drop_params: True + set_verbose: True + +general_settings: + master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234) +``` + +#### Step 2: Start Proxy with config + +```shell +$ litellm --config /path/to/config.yaml +``` + +#### Step 3: Use proxy +Curl Command +```shell +curl --location 'http://0.0.0.0:8000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "zephyr-alpha", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + } +' +``` + +## Save Model-specific params (API Base, API Keys, Temperature, Headers etc.) +You can use the config to save model-specific information like api_base, api_key, temperature, max_tokens, etc. + +[**All input params**](https://docs.litellm.ai/docs/completion/input#input-params-1) + +**Step 1**: Create a `config.yaml` file +```yaml +model_list: + - model_name: gpt-4-team1 + litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body + model: azure/chatgpt-v-2 + api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ + api_version: "2023-05-15" + azure_ad_token: eyJ0eXAiOiJ + - model_name: gpt-4-team2 + litellm_params: + model: azure/gpt-4 + api_key: sk-123 + api_base: https://openai-gpt-4-test-v-2.openai.azure.com/ + - model_name: mistral-7b + litellm_params: + model: ollama/mistral + api_base: your_ollama_api_base + headers: { + "HTTP-Referer": "litellm.ai", + "X-Title": "LiteLLM Server" + } +``` + +**Step 2**: Start server with config + +```shell +$ litellm --config /path/to/config.yaml +``` + +## Load API Keys + +### Load API Keys from Environment + +If you have secrets saved in your environment, and don't want to expose them in the config.yaml, here's how to load model-specific keys from the environment. + +```python +os.environ["AZURE_NORTH_AMERICA_API_KEY"] = "your-azure-api-key" +``` + +```yaml +model_list: + - model_name: gpt-4-team1 + litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body + model: azure/chatgpt-v-2 + api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ + api_version: "2023-05-15" + api_key: os.environ/AZURE_NORTH_AMERICA_API_KEY +``` + +[**See Code**](https://github.com/BerriAI/litellm/blob/c12d6c3fe80e1b5e704d9846b246c059defadce7/litellm/utils.py#L2366) + +s/o to [@David Manouchehri](https://www.linkedin.com/in/davidmanouchehri/) for helping with this. + +### Load API Keys from Azure Vault + +1. Install Proxy dependencies +```bash +$ pip install litellm[proxy] litellm[extra_proxy] +``` + +2. Save Azure details in your environment +```bash +export["AZURE_CLIENT_ID"]="your-azure-app-client-id" +export["AZURE_CLIENT_SECRET"]="your-azure-app-client-secret" +export["AZURE_TENANT_ID"]="your-azure-tenant-id" +export["AZURE_KEY_VAULT_URI"]="your-azure-key-vault-uri" +``` + +3. Add to proxy config.yaml +```yaml +model_list: + - model_name: "my-azure-models" # model alias + litellm_params: + model: "azure/" + api_key: "os.environ/AZURE-API-KEY" # reads from key vault - get_secret("AZURE_API_KEY") + api_base: "os.environ/AZURE-API-BASE" # reads from key vault - get_secret("AZURE_API_BASE") + +general_settings: + use_azure_key_vault: True +``` + +You can now test this by starting your proxy: +```bash +litellm --config /path/to/config.yaml +``` + +### Set Custom Prompt Templates + +LiteLLM by default checks if a model has a [prompt template and applies it](../completion/prompt_formatting.md) (e.g. if a huggingface model has a saved chat template in it's tokenizer_config.json). However, you can also set a custom prompt template on your proxy in the `config.yaml`: + +**Step 1**: Save your prompt template in a `config.yaml` +```yaml +# Model-specific parameters +model_list: + - model_name: mistral-7b # model alias + litellm_params: # actual params for litellm.completion() + model: "huggingface/mistralai/Mistral-7B-Instruct-v0.1" + api_base: "" + api_key: "" # [OPTIONAL] for hf inference endpoints + initial_prompt_value: "\n" + roles: {"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}} + final_prompt_value: "\n" + bos_token: "" + eos_token: "" + max_tokens: 4096 +``` + +**Step 2**: Start server with config + +```shell +$ litellm --config /path/to/config.yaml +``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/cost_tracking.md b/docs/my-website/docs/proxy/cost_tracking.md new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md new file mode 100644 index 0000000000000000000000000000000000000000..5f9c4cb5f4472aea00ad99bd4f4a7b8ad4590fc2 --- /dev/null +++ b/docs/my-website/docs/proxy/deploy.md @@ -0,0 +1,5 @@ +# Deploying LiteLLM Proxy + +### Deploy on Render https://render.com/ + + diff --git a/docs/my-website/docs/proxy/load_balancing.md b/docs/my-website/docs/proxy/load_balancing.md new file mode 100644 index 0000000000000000000000000000000000000000..e2e3a7ee66dd962edf82968590e82b7ecf25f144 --- /dev/null +++ b/docs/my-website/docs/proxy/load_balancing.md @@ -0,0 +1,199 @@ + +# Load Balancing - Multiple Instances of 1 model +Load balance multiple instances of the same model + +The proxy will handle routing requests (using LiteLLM's Router). **Set `rpm` in the config if you want maximize throughput** +## Quick Start - Load Balancing +### Step 1 - Set deployments on config + +**Example config below**. Here requests with `model=gpt-3.5-turbo` will be routed across multiple instances of `azure/gpt-3.5-turbo` +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + 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: + rpm: 6 + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/gpt-turbo-large + api_base: https://openai-france-1234.openai.azure.com/ + api_key: + rpm: 1440 +``` + +### Step 2: Start Proxy with config + +```shell +$ litellm --config /path/to/config.yaml +``` + +### Step 3: Use proxy - Call a model group [Load Balancing] +Curl Command +```shell +curl --location 'http://0.0.0.0:8000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + } +' +``` + +### Usage - Call a specific model deployment +If you want to call a specific model defined in the `config.yaml`, you can call the `litellm_params: model` + +In this example it will call `azure/gpt-turbo-small-ca`. Defined in the config on Step 1 + +```bash +curl --location 'http://0.0.0.0:8000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "azure/gpt-turbo-small-ca", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + } +' +``` + + +## Fallbacks + Cooldowns + Retries + Timeouts + +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 +``` + + + +## 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/" + } + ] +} +``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md new file mode 100644 index 0000000000000000000000000000000000000000..95f6a83a9d012768f4d23a95383515b65943361d --- /dev/null +++ b/docs/my-website/docs/proxy/logging.md @@ -0,0 +1,312 @@ +# Logging - Custom Callbacks, OpenTelemetry, Langfuse +Log Proxy Input, Output, Exceptions using Custom Callbacks, Langfuse, OpenTelemetry + +## Custom Callbacks +Use this when you want to run custom callbacks in `python` + +### Step 1 - Create your custom `litellm` callback class +We use `litellm.integrations.custom_logger` for this, **more details about litellm custom callbacks [here](https://docs.litellm.ai/docs/observability/custom_callback)** + +Define your custom callback class in a python file. + +Here's an example custom logger for tracking `key, user, model, prompt, response, tokens, cost`. We create a file called `custom_callbacks.py` and initialize `proxy_handler_instance` + +```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): + def log_pre_api_call(self, model, messages, kwargs): + print(f"Pre-API Call") + + def log_post_api_call(self, kwargs, response_obj, start_time, end_time): + print(f"Post-API Call") + + def 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): + # Logging key details: key, user, model, prompt, response, tokens, cost + print("\nOn Success") + # Access kwargs passed to litellm.completion() + model = kwargs.get("model", None) + messages = kwargs.get("messages", None) + user = kwargs.get("user", None) + + # Access litellm_params passed to litellm.completion(), example access `metadata` + litellm_params = kwargs.get("litellm_params", {}) + metadata = litellm_params.get("metadata", {}) # Headers passed to LiteLLM proxy + + # Calculate cost using litellm.completion_cost() + cost = litellm.completion_cost(completion_response=response_obj) + usage = response_obj["usage"] # Tokens used in response + + print( + f""" + Model: {model}, + Messages: {messages}, + User: {user}, + Usage: {usage}, + Cost: {cost}, + Response: {response} + Proxy Metadata: {metadata} + """ + ) + return + + def log_failure_event(self, kwargs, response_obj, start_time, end_time): + print(f"On Failure") + +proxy_handler_instance = MyCustomHandler() + +# Set litellm.callbacks = [proxy_handler_instance] on the proxy +# need to set litellm.callbacks = [proxy_handler_instance] # on the proxy +``` + +### Step 2 - Pass your custom callback class in `config.yaml` +We pass the custom callback class defined in **Step1** to the config.yaml. +Set `callbacks` to `python_filename.logger_instance_name` + +In the config below, we pass +- python_filename: `custom_callbacks.py` +- logger_instance_name: `proxy_handler_instance`. This is defined in Step 1 + +`callbacks: custom_callbacks.proxy_handler_instance` + + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + +litellm_settings: + callbacks: custom_callbacks.proxy_handler_instance # sets litellm.callbacks = [proxy_handler_instance] + +``` + +### Step 3 - Start proxy + test request +```shell +litellm --config proxy_config.yaml +``` + +```shell +curl --location 'http://0.0.0.0:8000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "good morning good sir" + } + ], + "user": "ishaan-app", + "temperature": 0.2 + }' +``` + +#### Resulting Log on Proxy +```shell +On Success + Model: gpt-3.5-turbo, + Messages: [{'role': 'user', 'content': 'good morning good sir'}], + User: ishaan-app, + Usage: {'completion_tokens': 10, 'prompt_tokens': 11, 'total_tokens': 21}, + Cost: 3.65e-05, + Response: {'id': 'chatcmpl-8S8avKJ1aVBg941y5xzGMSKrYCMvN', 'choices': [{'finish_reason': 'stop', 'index': 0, 'message': {'content': 'Good morning! How can I assist you today?', 'role': 'assistant'}}], 'created': 1701716913, 'model': 'gpt-3.5-turbo-0613', 'object': 'chat.completion', 'system_fingerprint': None, 'usage': {'completion_tokens': 10, 'prompt_tokens': 11, 'total_tokens': 21}} + Proxy Metadata: {'user_api_key': None, 'headers': Headers({'host': '0.0.0.0:8000', 'user-agent': 'curl/7.88.1', 'accept': '*/*', 'authorization': 'Bearer sk-1234', 'content-length': '199', 'content-type': 'application/x-www-form-urlencoded'}), 'model_group': 'gpt-3.5-turbo', 'deployment': 'gpt-3.5-turbo-ModelID-gpt-3.5-turbo'} +``` + +## OpenTelemetry, ElasticSearch + +### Step 1 Start OpenTelemetry Collecter Docker Container +This container sends logs to your selected destination + +#### Install OpenTelemetry Collecter Docker Image +```shell +docker pull otel/opentelemetry-collector:0.90.0 +docker run -p 127.0.0.1:4317:4317 -p 127.0.0.1:55679:55679 otel/opentelemetry-collector:0.90.0 +``` + +#### Set Destination paths on OpenTelemetry Collecter + +Here's the OpenTelemetry yaml config to use with Elastic Search +```yaml +receivers: + otlp: + protocols: + grpc: + endpoint: 0.0.0.0:4317 + +processors: + batch: + timeout: 1s + send_batch_size: 1024 + +exporters: + logging: + loglevel: debug + otlphttp/elastic: + endpoint: "" + headers: + Authorization: "Bearer " + +service: + pipelines: + metrics: + receivers: [otlp] + exporters: [logging, otlphttp/elastic] + traces: + receivers: [otlp] + exporters: [logging, otlphttp/elastic] + logs: + receivers: [otlp] + exporters: [logging,otlphttp/elastic] +``` + +#### Start the OpenTelemetry container with config +Run the following command to start your docker container. We pass `otel_config.yaml` from the previous step + +```shell +docker run -p 4317:4317 \ + -v $(pwd)/otel_config.yaml:/etc/otel-collector-config.yaml \ + otel/opentelemetry-collector:latest \ + --config=/etc/otel-collector-config.yaml +``` + +### Step 2 Configure LiteLLM proxy to log on OpenTelemetry + +#### Pip install opentelemetry +```shell +pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp -U +``` + +#### Set (OpenTelemetry) `otel=True` on the proxy `config.yaml` +**Example config.yaml** + +```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: + rpm: 6 # Rate limit for this deployment: in requests per minute (rpm) + +general_settings: + otel: True # set OpenTelemetry=True, on litellm Proxy + +``` + +#### Set OTEL collector endpoint +LiteLLM will read the `OTEL_ENDPOINT` environment variable to send data to your OTEL collector + +```python +os.environ['OTEL_ENDPOINT'] # defauls to 127.0.0.1:4317 if not provided +``` + +#### Start LiteLLM Proxy +```shell +litellm -config config.yaml +``` + +#### Run a test request to Proxy +```shell +curl --location 'http://0.0.0.0:8000/chat/completions' \ + --header 'Authorization: Bearer sk-1244' \ + --data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "request from LiteLLM testing" + } + ] + }' +``` + + +#### Test & View Logs on OpenTelemetry Collecter +On successfull logging you should be able to see this log on your `OpenTelemetry Collecter` Docker Container +```shell +Events: +SpanEvent #0 + -> Name: LiteLLM: Request Input + -> Timestamp: 2023-12-02 05:05:53.71063 +0000 UTC + -> DroppedAttributesCount: 0 + -> Attributes:: + -> type: Str(http) + -> asgi: Str({'version': '3.0', 'spec_version': '2.3'}) + -> http_version: Str(1.1) + -> server: Str(('127.0.0.1', 8000)) + -> client: Str(('127.0.0.1', 62796)) + -> scheme: Str(http) + -> method: Str(POST) + -> root_path: Str() + -> path: Str(/chat/completions) + -> raw_path: Str(b'/chat/completions') + -> query_string: Str(b'') + -> headers: Str([(b'host', b'0.0.0.0:8000'), (b'user-agent', b'curl/7.88.1'), (b'accept', b'*/*'), (b'authorization', b'Bearer sk-1244'), (b'content-length', b'147'), (b'content-type', b'application/x-www-form-urlencoded')]) + -> state: Str({}) + -> app: Str() + -> fastapi_astack: Str() + -> router: Str() + -> endpoint: Str() + -> path_params: Str({}) + -> route: Str(APIRoute(path='/chat/completions', name='chat_completion', methods=['POST'])) +SpanEvent #1 + -> Name: LiteLLM: Request Headers + -> Timestamp: 2023-12-02 05:05:53.710652 +0000 UTC + -> DroppedAttributesCount: 0 + -> Attributes:: + -> host: Str(0.0.0.0:8000) + -> user-agent: Str(curl/7.88.1) + -> accept: Str(*/*) + -> authorization: Str(Bearer sk-1244) + -> content-length: Str(147) + -> content-type: Str(application/x-www-form-urlencoded) +SpanEvent #2 +``` + +### View Log on Elastic Search +Here's the log view on Elastic Search. You can see the request `input`, `output` and `headers` + + + +## Logging Proxy Input/Output - Langfuse +We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successfull LLM calls to langfuse + +**Step 1** Install langfuse + +```shell +pip install langfuse +``` + +**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: ["langfuse"] +``` + +**Step 3**: Start the proxy, make a test request + +Start proxy +```shell +litellm --config config.yaml --debug +``` + +Test Request +``` +litellm --test +``` + +Expected output on Langfuse + + diff --git a/docs/my-website/docs/proxy/model_management.md b/docs/my-website/docs/proxy/model_management.md new file mode 100644 index 0000000000000000000000000000000000000000..0cd4ab829cd63ff45cdb34df49b65c30ad39fc31 --- /dev/null +++ b/docs/my-website/docs/proxy/model_management.md @@ -0,0 +1,74 @@ +# Model Management +Add new models + Get model info without restarting proxy. + +## Get Model Information + +Retrieve detailed information about each model listed in the `/models` endpoint, including descriptions from the `config.yaml` file, and additional model info (e.g. max tokens, cost per input token, etc.) pulled the model_info you set and the litellm model cost map. Sensitive details like API keys are excluded for security purposes. + + + + +```bash +curl -X GET "http://0.0.0.0:8000/model/info" \ + -H "accept: application/json" \ +``` + + + +## Add a New Model + +Add a new model to the list in the `config.yaml` by providing the model parameters. This allows you to update the model list without restarting the proxy. + + + + +```bash +curl -X POST "http://0.0.0.0:8000/model/new" \ + -H "accept: application/json" \ + -H "Content-Type: application/json" \ + -d '{ "model_name": "azure-gpt-turbo", "litellm_params": {"model": "azure/gpt-3.5-turbo", "api_key": "os.environ/AZURE_API_KEY", "api_base": "my-azure-api-base"} }' +``` + + + + +### Model Parameters Structure + +When adding a new model, your JSON payload should conform to the following structure: + +- `model_name`: The name of the new model (required). +- `litellm_params`: A dictionary containing parameters specific to the Litellm setup (required). +- `model_info`: An optional dictionary to provide additional information about the model. + +Here's an example of how to structure your `ModelParams`: + +```json +{ + "model_name": "my_awesome_model", + "litellm_params": { + "some_parameter": "some_value", + "another_parameter": "another_value" + }, + "model_info": { + "author": "Your Name", + "version": "1.0", + "description": "A brief description of the model." + } +} +``` +--- + +Keep in mind that as both endpoints are in [BETA], you may need to visit the associated GitHub issues linked in the API descriptions to check for updates or provide feedback: + +- Get Model Information: [Issue #933](https://github.com/BerriAI/litellm/issues/933) +- Add a New Model: [Issue #964](https://github.com/BerriAI/litellm/issues/964) + +Feedback on the beta endpoints is valuable and helps improve the API for all users. \ No newline at end of file diff --git a/docs/my-website/docs/proxy/perf.md b/docs/my-website/docs/proxy/perf.md new file mode 100644 index 0000000000000000000000000000000000000000..947ff0462db13d336f15dc22d34db1753c413772 --- /dev/null +++ b/docs/my-website/docs/proxy/perf.md @@ -0,0 +1,9 @@ +# LiteLLM Proxy Performance + +### Throughput - 30% Increase +LiteLLM proxy + Load Balancer gives **30% increase** in throughput compared to Raw OpenAI API + + +### Latency Added - 0.00325 seconds +LiteLLM proxy adds **0.00325 seconds** latency as compared to using the Raw OpenAI API + \ No newline at end of file diff --git a/docs/my-website/docs/proxy/quick_start.md b/docs/my-website/docs/proxy/quick_start.md new file mode 100644 index 0000000000000000000000000000000000000000..f1749bc50958d98c28af66a8480a43f7437fe5f3 --- /dev/null +++ b/docs/my-website/docs/proxy/quick_start.md @@ -0,0 +1,510 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Quick Start +Quick start CLI, Config, Docker + +LiteLLM Server manages: + +* **Unified Interface**: Calling 100+ LLMs [Huggingface/Bedrock/TogetherAI/etc.](#other-supported-models) in the OpenAI `ChatCompletions` & `Completions` format +* **Load Balancing**: between [Multiple Models](#multiple-models---quick-start) + [Deployments of the same model](#multiple-instances-of-1-model) - LiteLLM proxy can handle 1.5k+ requests/second during load tests. +* **Cost tracking**: Authentication & Spend Tracking [Virtual Keys](#managing-auth---virtual-keys) + +[**See LiteLLM Proxy code**](https://github.com/BerriAI/litellm/tree/main/litellm/proxy) + + +View all the supported args for the Proxy CLI [here](https://docs.litellm.ai/docs/simple_proxy#proxy-cli-arguments) + +```shell +$ pip install litellm[proxy] +``` + +If this fails try running + +```shell +$ pip install 'litellm[proxy]' +``` + +## Quick Start - LiteLLM Proxy CLI + +Run the following command to start the litellm proxy +```shell +$ litellm --model huggingface/bigcode/starcoder + +#INFO: Proxy running on http://0.0.0.0:8000 +``` + +### Test +In a new shell, run, this will make an `openai.chat.completions` request. Ensure you're using openai v1.0.0+ +```shell +litellm --test +``` + +This will now automatically route any requests for gpt-3.5-turbo to bigcode starcoder, hosted on huggingface inference endpoints. + +### Using LiteLLM Proxy - Curl Request, OpenAI Package + + + + +```shell +curl --location 'http://0.0.0.0:8000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + } +' +``` + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:8000" +) + +# 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) + +``` + + + + + +### Supported LLMs +All LiteLLM supported LLMs are supported on the Proxy. Seel all [supported llms](https://docs.litellm.ai/docs/providers) + + + +```shell +$ export AWS_ACCESS_KEY_ID= +$ export AWS_REGION_NAME= +$ export AWS_SECRET_ACCESS_KEY= +``` + +```shell +$ litellm --model bedrock/anthropic.claude-v2 +``` + + + +```shell +$ export AZURE_API_KEY=my-api-key +$ export AZURE_API_BASE=my-api-base +``` +``` +$ litellm --model azure/my-deployment-name +``` + + + + +```shell +$ export OPENAI_API_KEY=my-api-key +``` + +```shell +$ litellm --model gpt-3.5-turbo +``` + + + +```shell +$ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL] +``` +```shell +$ litellm --model huggingface/ --api_base https://k58ory32yinf1ly0.us-east-1.aws.endpoints.huggingface.cloud +``` + + + + +```shell +$ litellm --model huggingface/ --api_base http://0.0.0.0:8001 +``` + + + + +```shell +export AWS_ACCESS_KEY_ID= +export AWS_REGION_NAME= +export AWS_SECRET_ACCESS_KEY= +``` + +```shell +$ litellm --model sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b +``` + + + + +```shell +$ export ANTHROPIC_API_KEY=my-api-key +``` +```shell +$ litellm --model claude-instant-1 +``` + + + +Assuming you're running vllm locally + +```shell +$ litellm --model vllm/facebook/opt-125m +``` + + + +```shell +$ export TOGETHERAI_API_KEY=my-api-key +``` +```shell +$ litellm --model together_ai/lmsys/vicuna-13b-v1.5-16k +``` + + + + + +```shell +$ export REPLICATE_API_KEY=my-api-key +``` +```shell +$ litellm \ + --model replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3 +``` + + + + + +```shell +$ litellm --model petals/meta-llama/Llama-2-70b-chat-hf +``` + + + + + +```shell +$ export PALM_API_KEY=my-palm-key +``` +```shell +$ litellm --model palm/chat-bison +``` + + + + + +```shell +$ export AI21_API_KEY=my-api-key +``` + +```shell +$ litellm --model j2-light +``` + + + + + +```shell +$ export COHERE_API_KEY=my-api-key +``` + +```shell +$ litellm --model command-nightly +``` + + + + + + +## Quick Start - LiteLLM Proxy + Config.yaml +The config allows you to create a model list and set `api_base`, `max_tokens` (all litellm params). See more details about the config [here](https://docs.litellm.ai/docs/proxy/configs) + +### Create a Config for LiteLLM Proxy +Example config + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/ + api_base: + api_key: + - 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: +``` + +### Run proxy with config + +```shell +litellm --config your_config.yaml +``` + +## Quick Start Docker Image: Github Container Registry + +### Pull the litellm ghcr docker image +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 +``` + +### Run the Docker Image +```shell +docker run ghcr.io/berriai/litellm:main-v1.10.0 +``` + +#### Run the Docker Image with LiteLLM CLI args + +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 +``` + +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 +``` + +## Server Endpoints +- POST `/chat/completions` - chat completions endpoint to call 100+ LLMs +- POST `/completions` - completions endpoint +- POST `/embeddings` - embedding endpoint for Azure, OpenAI, Huggingface endpoints +- GET `/models` - available models on server +- POST `/key/generate` - generate a key to access the proxy + +## Using with OpenAI compatible projects +Set `base_url` to the LiteLLM Proxy server + + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:8000" +) + +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +]) + +print(response) + +``` + + + +#### Start the LiteLLM proxy +```shell +litellm --model gpt-3.5-turbo + +#INFO: Proxy running on http://0.0.0.0:8000 +``` + +#### 1. Clone the repo + +```shell +git clone https://github.com/danny-avila/LibreChat.git +``` + + +#### 2. Modify Librechat's `docker-compose.yml` +LiteLLM Proxy is running on port `8000`, set `8000` as the proxy below +```yaml +OPENAI_REVERSE_PROXY=http://host.docker.internal:8000/v1/chat/completions +``` + +#### 3. Save fake OpenAI key in Librechat's `.env` + +Copy Librechat's `.env.example` to `.env` and overwrite the default OPENAI_API_KEY (by default it requires the user to pass a key). +```env +OPENAI_API_KEY=sk-1234 +``` + +#### 4. Run LibreChat: +```shell +docker compose up +``` + + + + +Continue-Dev brings ChatGPT to VSCode. See how to [install it here](https://continue.dev/docs/quickstart). + +In the [config.py](https://continue.dev/docs/reference/Models/openai) set this as your default model. +```python + default=OpenAI( + api_key="IGNORED", + model="fake-model-name", + context_length=2048, # customize if needed for your model + api_base="http://localhost:8000" # your proxy server url + ), +``` + +Credits [@vividfog](https://github.com/jmorganca/ollama/issues/305#issuecomment-1751848077) for this tutorial. + + + + +```shell +$ pip install aider + +$ aider --openai-api-base http://0.0.0.0:8000 --openai-api-key fake-key +``` + + + +```python +pip install pyautogen +``` + +```python +from autogen import AssistantAgent, UserProxyAgent, oai +config_list=[ + { + "model": "my-fake-model", + "api_base": "http://localhost:8000", #litellm compatible endpoint + "api_type": "open_ai", + "api_key": "NULL", # just a placeholder + } +] + +response = oai.Completion.create(config_list=config_list, prompt="Hi") +print(response) # works fine + +llm_config={ + "config_list": config_list, +} + +assistant = AssistantAgent("assistant", llm_config=llm_config) +user_proxy = UserProxyAgent("user_proxy") +user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list) +``` + +Credits [@victordibia](https://github.com/microsoft/autogen/issues/45#issuecomment-1749921972) for this tutorial. + + + +A guidance language for controlling large language models. +https://github.com/guidance-ai/guidance + +**NOTE:** Guidance sends additional params like `stop_sequences` which can cause some models to fail if they don't support it. + +**Fix**: Start your proxy using the `--drop_params` flag + +```shell +litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048 --drop_params +``` + +```python +import guidance + +# set api_base to your proxy +# set api_key to anything +gpt4 = guidance.llms.OpenAI("gpt-4", api_base="http://0.0.0.0:8000", api_key="anything") + +experts = guidance(''' +{{#system~}} +You are a helpful and terse assistant. +{{~/system}} + +{{#user~}} +I want a response to the following question: +{{query}} +Name 3 world-class experts (past or present) who would be great at answering this? +Don't answer the question yet. +{{~/user}} + +{{#assistant~}} +{{gen 'expert_names' temperature=0 max_tokens=300}} +{{~/assistant}} +''', llm=gpt4) + +result = experts(query='How can I be more productive?') +print(result) +``` + + + +## Debugging Proxy +Run the proxy with `--debug` to easily view debug logs +```shell +litellm --model gpt-3.5-turbo --debug +``` + +When making requests you should see the POST request sent by LiteLLM to the LLM on the Terminal output +```shell +POST Request Sent from LiteLLM: +curl -X POST \ +https://api.openai.com/v1/chat/completions \ +-H 'content-type: application/json' -H 'Authorization: Bearer sk-qnWGUIW9****************************************' \ +-d '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "this is a test request, write a short poem"}]}' +``` + +## Health Check LLMs on Proxy +Use this to health check all LLMs defined in your config.yaml +#### Request +```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/" + } + ] +} +``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/virtual_keys.md b/docs/my-website/docs/proxy/virtual_keys.md new file mode 100644 index 0000000000000000000000000000000000000000..ac1cf54deba505661e90b1598b7e3ff9c4ea684e --- /dev/null +++ b/docs/my-website/docs/proxy/virtual_keys.md @@ -0,0 +1,138 @@ +# Key Management +Track Spend and create virtual keys for the proxy + +Grant other's temporary access to your proxy, with keys that expire after a set duration. + +Requirements: + +- Need to a postgres database (e.g. [Supabase](https://supabase.com/), [Neon](https://neon.tech/), etc) + +You can then generate temporary keys by hitting the `/key/generate` endpoint. + +[**See code**](https://github.com/BerriAI/litellm/blob/7a669a36d2689c7f7890bc9c93e04ff3c2641299/litellm/proxy/proxy_server.py#L672) + +**Step 1: Save postgres db url** + +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: ollama/llama2 + - model_name: gpt-3.5-turbo + litellm_params: + model: ollama/llama2 + +general_settings: + master_key: sk-1234 # [OPTIONAL] if set all calls to proxy will require either this key or a valid generated token + database_url: "postgresql://:@:/" +``` + +**Step 2: Start litellm** + +```shell +litellm --config /path/to/config.yaml +``` + +**Step 3: Generate temporary keys** + +```shell +curl 'http://0.0.0.0:8000/key/generate' \ +--h 'Authorization: Bearer sk-1234' \ +--d '{"models": ["gpt-3.5-turbo", "gpt-4", "claude-2"], "duration": "20m"}' +``` + +- `models`: *list or null (optional)* - Specify the models a token has access too. If null, then token has access to all models on server. + +- `duration`: *str or null (optional)* Specify the length of time the token is valid for. If null, default is set to 1 hour. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d"). + +Expected response: + +```python +{ + "key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token + "expires": "2023-11-19T01:38:25.838000+00:00" # datetime object +} +``` + +### Managing Auth - Upgrade/Downgrade Models + +If a user is expected to use a given model (i.e. gpt3-5), and you want to: + +- try to upgrade the request (i.e. GPT4) +- or downgrade it (i.e. Mistral) +- OR rotate the API KEY (i.e. open AI) +- OR access the same model through different end points (i.e. openAI vs openrouter vs Azure) + +Here's how you can do that: + +**Step 1: Create a model group in config.yaml (save model name, api keys, etc.)** + +```yaml +model_list: + - model_name: my-free-tier + litellm_params: + model: huggingface/HuggingFaceH4/zephyr-7b-beta + api_base: http://0.0.0.0:8001 + - model_name: my-free-tier + litellm_params: + model: huggingface/HuggingFaceH4/zephyr-7b-beta + api_base: http://0.0.0.0:8002 + - model_name: my-free-tier + litellm_params: + model: huggingface/HuggingFaceH4/zephyr-7b-beta + api_base: http://0.0.0.0:8003 + - model_name: my-paid-tier + litellm_params: + model: gpt-4 + api_key: my-api-key +``` + +**Step 2: Generate a user key - enabling them access to specific models, custom model aliases, etc.** + +```bash +curl -X POST "https://0.0.0.0:8000/key/generate" \ +-H "Authorization: Bearer sk-1234" \ +-H "Content-Type: application/json" \ +-d '{ + "models": ["my-free-tier"], + "aliases": {"gpt-3.5-turbo": "my-free-tier"}, + "duration": "30min" +}' +``` + +- **How to upgrade / downgrade request?** Change the alias mapping +- **How are routing between diff keys/api bases done?** litellm handles this by shuffling between different models in the model list with the same model_name. [**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/router.py) + +### Managing Auth - Tracking Spend + +You can get spend for a key by using the `/key/info` endpoint. + +```bash +curl 'http://0.0.0.0:8000/key/info?key=' \ + -X GET \ + -H 'Authorization: Bearer ' +``` + +This is automatically updated (in USD) when calls are made to /completions, /chat/completions, /embeddings using litellm's completion_cost() function. [**See Code**](https://github.com/BerriAI/litellm/blob/1a6ea20a0bb66491968907c2bfaabb7fe45fc064/litellm/utils.py#L1654). + +**Sample response** + +```python +{ + "key": "sk-tXL0wt5-lOOVK9sfY2UacA", + "info": { + "token": "sk-tXL0wt5-lOOVK9sfY2UacA", + "spend": 0.0001065, + "expires": "2023-11-24T23:19:11.131000Z", + "models": [ + "gpt-3.5-turbo", + "gpt-4", + "claude-2" + ], + "aliases": { + "mistral-7b": "gpt-3.5-turbo" + }, + "config": {} + } +} +``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy_api.md b/docs/my-website/docs/proxy_api.md new file mode 100644 index 0000000000000000000000000000000000000000..89bfacbe19f30ec0f1270c809b60deb5f4943f51 --- /dev/null +++ b/docs/my-website/docs/proxy_api.md @@ -0,0 +1,86 @@ +# 🔑 LiteLLM Keys (Access Claude-2, Llama2-70b, etc.) + +Use this if you're trying to add support for new LLMs and need access for testing. We provide a free $10 community-key for testing all providers on LiteLLM: + +## usage (community-key) + +```python +import os +from litellm import completion + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "your-api-key" +os.environ["COHERE_API_KEY"] = "your-api-key" + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# openai call +response = completion(model="gpt-3.5-turbo", messages=messages) + +# cohere call +response = completion("command-nightly", messages) +``` + +**Need a dedicated key?** +Email us @ krrish@berri.ai + +## Supported Models for LiteLLM Key +These are the models that currently work with the "sk-litellm-.." keys. + +For a complete list of models/providers that you can call with LiteLLM, [check out our provider list](./providers/) + +* OpenAI models - [OpenAI docs](./providers/openai.md) + * gpt-4 + * gpt-3.5-turbo + * gpt-3.5-turbo-16k +* Llama2 models - [TogetherAI docs](./providers/togetherai.md) + * togethercomputer/llama-2-70b-chat + * togethercomputer/llama-2-70b + * togethercomputer/LLaMA-2-7B-32K + * togethercomputer/Llama-2-7B-32K-Instruct + * togethercomputer/llama-2-7b + * togethercomputer/CodeLlama-34b + * WizardLM/WizardCoder-Python-34B-V1.0 + * NousResearch/Nous-Hermes-Llama2-13b +* Falcon models - [TogetherAI docs](./providers/togetherai.md) + * togethercomputer/falcon-40b-instruct + * togethercomputer/falcon-7b-instruct +* Jurassic/AI21 models - [AI21 docs](./providers/ai21.md) + * j2-ultra + * j2-mid + * j2-light +* NLP Cloud models - [NLPCloud docs](./providers/nlp_cloud.md) + * dolpin + * chatdolphin +* Anthropic models - [Anthropic docs](./providers/anthropic.md) + * claude-2 + * claude-instant-v1 + + +## For OpenInterpreter +This was initially built for the Open Interpreter community. If you're trying to use this feature in there, here's how you can do it: +**Note**: You will need to clone and modify the Github repo, until [this PR is merged.](https://github.com/KillianLucas/open-interpreter/pull/288) + +``` +git clone https://github.com/krrishdholakia/open-interpreter-litellm-fork +``` +To run it do: +``` +poetry build + +# call gpt-4 - always add 'litellm_proxy/' in front of the model name +poetry run interpreter --model litellm_proxy/gpt-4 + +# call llama-70b - always add 'litellm_proxy/' in front of the model name +poetry run interpreter --model litellm_proxy/togethercomputer/llama-2-70b-chat + +# call claude-2 - always add 'litellm_proxy/' in front of the model name +poetry run interpreter --model litellm_proxy/claude-2 +``` + +And that's it! + +Now you can call any model you like! + + +Want us to add more models? [Let us know!](https://github.com/BerriAI/litellm/issues/new/choose) \ No newline at end of file diff --git a/docs/my-website/docs/proxy_server.md b/docs/my-website/docs/proxy_server.md new file mode 100644 index 0000000000000000000000000000000000000000..ec8ca15c5b6cb00ca389c846e1ce7d7645d65455 --- /dev/null +++ b/docs/my-website/docs/proxy_server.md @@ -0,0 +1,818 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# [OLD PROXY 👉 [**NEW** proxy here](./simple_proxy.md)] 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) + +::: + +## Usage +```shell +pip install litellm[proxy] +``` +```shell +$ litellm --model ollama/codellama + +#INFO: Ollama running on http://0.0.0.0:8000 +``` + +### Test +In a new shell, run: +```shell +$ litellm --test +``` + +### Replace openai base + +```python +import openai + +openai.api_base = "http://0.0.0.0:8000" + +print(openai.ChatCompletion.create(model="test", messages=[{"role":"user", "content":"Hey!"}])) +``` + +#### Other supported models: + + +Assuming you're running vllm locally + +```shell +$ litellm --model vllm/facebook/opt-125m +``` + + + +```shell +$ litellm --model openai/ --api_base +``` + + + +```shell +$ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL] +$ litellm --model claude-instant-1 +``` + + + + +```shell +$ export ANTHROPIC_API_KEY=my-api-key +$ litellm --model claude-instant-1 +``` + + + + + +```shell +$ export TOGETHERAI_API_KEY=my-api-key +$ litellm --model together_ai/lmsys/vicuna-13b-v1.5-16k +``` + + + + + +```shell +$ export REPLICATE_API_KEY=my-api-key +$ litellm \ + --model replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3 +``` + + + + + +```shell +$ litellm --model petals/meta-llama/Llama-2-70b-chat-hf +``` + + + + + +```shell +$ export PALM_API_KEY=my-palm-key +$ litellm --model palm/chat-bison +``` + + + + + +```shell +$ export AZURE_API_KEY=my-api-key +$ export AZURE_API_BASE=my-api-base + +$ litellm --model azure/my-deployment-name +``` + + + + + +```shell +$ export AI21_API_KEY=my-api-key +$ litellm --model j2-light +``` + + + + + +```shell +$ export COHERE_API_KEY=my-api-key +$ litellm --model command-nightly +``` + + + + + +### Tutorial: Use with Multiple LLMs + LibreChat/Chatbot-UI/Auto-Gen/ChatDev/Langroid,etc. + + + +Replace openai base: +```python +import openai + +openai.api_key = "any-string-here" +openai.api_base = "http://0.0.0.0:8080" # your proxy url + +# call openai +response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey"}]) + +print(response) + +# call cohere +response = openai.ChatCompletion.create(model="command-nightly", messages=[{"role": "user", "content": "Hey"}]) + +print(response) +``` + + + +#### 1. Clone the repo + +```shell +git clone https://github.com/danny-avila/LibreChat.git +``` + + +#### 2. Modify `docker-compose.yml` +```yaml +OPENAI_REVERSE_PROXY=http://host.docker.internal:8000/v1/chat/completions +``` + +#### 3. Save fake OpenAI key in `.env` +```env +OPENAI_API_KEY=sk-1234 +``` + +#### 4. Run LibreChat: +```shell +docker compose up +``` + + + +#### 1. Clone the repo +```shell +git clone https://github.com/dotneet/smart-chatbot-ui.git +``` + +#### 2. Install Dependencies +```shell +npm i +``` + +#### 3. Create your env +```shell +cp .env.local.example .env.local +``` + +#### 4. Set the API Key and Base +```env +OPENAI_API_KEY="my-fake-key" +OPENAI_API_HOST="http://0.0.0.0:8000 +``` + +#### 5. Run with docker compose +```shell +docker compose up -d +``` + + + +```python +pip install pyautogen +``` + +```python +from autogen import AssistantAgent, UserProxyAgent, oai +config_list=[ + { + "model": "my-fake-model", + "api_base": "http://0.0.0.0:8000", #litellm compatible endpoint + "api_type": "open_ai", + "api_key": "NULL", # just a placeholder + } +] + +response = oai.Completion.create(config_list=config_list, prompt="Hi") +print(response) # works fine + +llm_config={ + "config_list": config_list, +} + +assistant = AssistantAgent("assistant", llm_config=llm_config) +user_proxy = UserProxyAgent("user_proxy") +user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list) +``` + +Credits [@victordibia](https://github.com/microsoft/autogen/issues/45#issuecomment-1749921972) for this tutorial. + + + + +```python +from autogen import AssistantAgent, GroupChatManager, UserProxyAgent +from autogen.agentchat import GroupChat +config_list = [ + { + "model": "ollama/mistralorca", + "api_base": "http://0.0.0.0:8000", # litellm compatible endpoint + "api_type": "open_ai", + "api_key": "NULL", # just a placeholder + } +] +llm_config = {"config_list": config_list, "seed": 42} + +code_config_list = [ + { + "model": "ollama/phind-code", + "api_base": "http://0.0.0.0:8000", # litellm compatible endpoint + "api_type": "open_ai", + "api_key": "NULL", # just a placeholder + } +] + +code_config = {"config_list": code_config_list, "seed": 42} + +admin = UserProxyAgent( + name="Admin", + system_message="A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved by this admin.", + llm_config=llm_config, + code_execution_config=False, +) + + +engineer = AssistantAgent( + name="Engineer", + llm_config=code_config, + system_message="""Engineer. You follow an approved plan. You write python/shell code to solve tasks. Wrap the code in a code block that specifies the script type. The user can't modify your code. So do not suggest incomplete code which requires others to modify. Don't use a code block if it's not intended to be executed by the executor. +Don't include multiple code blocks in one response. Do not ask others to copy and paste the result. Check the execution result returned by the executor. +If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try. +""", +) +planner = AssistantAgent( + name="Planner", + system_message="""Planner. Suggest a plan. Revise the plan based on feedback from admin and critic, until admin approval. +The plan may involve an engineer who can write code and a scientist who doesn't write code. +Explain the plan first. Be clear which step is performed by an engineer, and which step is performed by a scientist. +""", + llm_config=llm_config, +) +executor = UserProxyAgent( + name="Executor", + system_message="Executor. Execute the code written by the engineer and report the result.", + human_input_mode="NEVER", + llm_config=llm_config, + code_execution_config={"last_n_messages": 3, "work_dir": "paper"}, +) +critic = AssistantAgent( + name="Critic", + system_message="Critic. Double check plan, claims, code from other agents and provide feedback. Check whether the plan includes adding verifiable info such as source URL.", + llm_config=llm_config, +) +groupchat = GroupChat( + agents=[admin, engineer, planner, executor, critic], + messages=[], + max_round=50, +) +manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config) + + +admin.initiate_chat( + manager, + message=""" +""", +) +``` + +Credits [@Nathan](https://gist.github.com/CUexter) for this tutorial. + + + +### Setup ChatDev ([Docs](https://github.com/OpenBMB/ChatDev#%EF%B8%8F-quickstart)) +```shell +git clone https://github.com/OpenBMB/ChatDev.git +cd ChatDev +conda create -n ChatDev_conda_env python=3.9 -y +conda activate ChatDev_conda_env +pip install -r requirements.txt +``` +### Run ChatDev w/ Proxy +```shell +export OPENAI_API_KEY="sk-1234" +``` + +```shell +export OPENAI_API_BASE="http://0.0.0.0:8000" +``` +```shell +python3 run.py --task "a script that says hello world" --name "hello world" +``` + + + +```python +pip install langroid +``` + +```python +from langroid.language_models.openai_gpt import OpenAIGPTConfig, OpenAIGPT + +# configure the LLM +my_llm_config = OpenAIGPTConfig( + # where proxy server is listening + api_base="http://0.0.0.0:8000", +) + +# create llm, one-off interaction +llm = OpenAIGPT(my_llm_config) +response = mdl.chat("What is the capital of China?", max_tokens=50) + +# Create an Agent with this LLM, wrap it in a Task, and +# run it as an interactive chat app: +from langroid.agent.base import ChatAgent, ChatAgentConfig +from langroid.agent.task import Task + +agent_config = ChatAgentConfig(llm=my_llm_config, name="my-llm-agent") +agent = ChatAgent(agent_config) + +task = Task(agent, name="my-llm-task") +task.run() +``` + +Credits [@pchalasani](https://github.com/pchalasani) and [Langroid](https://github.com/langroid/langroid) for this tutorial. + + + +## Local Proxy + +Here's how to use the local proxy to test codellama/mistral/etc. models for different github repos + +```shell +pip install litellm +``` + +```shell +$ ollama pull codellama # OUR Local CodeLlama + +$ litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048 +``` + +### Tutorial: Use with Multiple LLMs + Aider/AutoGen/Langroid/etc. + + + +```shell +$ litellm + +#INFO: litellm proxy running on http://0.0.0.0:8000 +``` + +#### Send a request to your proxy +```python +import openai + +openai.api_key = "any-string-here" +openai.api_base = "http://0.0.0.0:8080" # your proxy url + +# call gpt-3.5-turbo +response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey"}]) + +print(response) + +# call ollama/llama2 +response = openai.ChatCompletion.create(model="ollama/llama2", messages=[{"role": "user", "content": "Hey"}]) + +print(response) +``` + + + + +Continue-Dev brings ChatGPT to VSCode. See how to [install it here](https://continue.dev/docs/quickstart). + +In the [config.py](https://continue.dev/docs/reference/Models/openai) set this as your default model. +```python + default=OpenAI( + api_key="IGNORED", + model="fake-model-name", + context_length=2048, # customize if needed for your model + api_base="http://localhost:8000" # your proxy server url + ), +``` + +Credits [@vividfog](https://github.com/jmorganca/ollama/issues/305#issuecomment-1751848077) for this tutorial. + + + +```shell +$ pip install aider + +$ aider --openai-api-base http://0.0.0.0:8000 --openai-api-key fake-key +``` + + + +```python +pip install pyautogen +``` + +```python +from autogen import AssistantAgent, UserProxyAgent, oai +config_list=[ + { + "model": "my-fake-model", + "api_base": "http://localhost:8000", #litellm compatible endpoint + "api_type": "open_ai", + "api_key": "NULL", # just a placeholder + } +] + +response = oai.Completion.create(config_list=config_list, prompt="Hi") +print(response) # works fine + +llm_config={ + "config_list": config_list, +} + +assistant = AssistantAgent("assistant", llm_config=llm_config) +user_proxy = UserProxyAgent("user_proxy") +user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list) +``` + +Credits [@victordibia](https://github.com/microsoft/autogen/issues/45#issuecomment-1749921972) for this tutorial. + + + + +```python +from autogen import AssistantAgent, GroupChatManager, UserProxyAgent +from autogen.agentchat import GroupChat +config_list = [ + { + "model": "ollama/mistralorca", + "api_base": "http://localhost:8000", # litellm compatible endpoint + "api_type": "open_ai", + "api_key": "NULL", # just a placeholder + } +] +llm_config = {"config_list": config_list, "seed": 42} + +code_config_list = [ + { + "model": "ollama/phind-code", + "api_base": "http://localhost:8000", # litellm compatible endpoint + "api_type": "open_ai", + "api_key": "NULL", # just a placeholder + } +] + +code_config = {"config_list": code_config_list, "seed": 42} + +admin = UserProxyAgent( + name="Admin", + system_message="A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved by this admin.", + llm_config=llm_config, + code_execution_config=False, +) + + +engineer = AssistantAgent( + name="Engineer", + llm_config=code_config, + system_message="""Engineer. You follow an approved plan. You write python/shell code to solve tasks. Wrap the code in a code block that specifies the script type. The user can't modify your code. So do not suggest incomplete code which requires others to modify. Don't use a code block if it's not intended to be executed by the executor. +Don't include multiple code blocks in one response. Do not ask others to copy and paste the result. Check the execution result returned by the executor. +If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try. +""", +) +planner = AssistantAgent( + name="Planner", + system_message="""Planner. Suggest a plan. Revise the plan based on feedback from admin and critic, until admin approval. +The plan may involve an engineer who can write code and a scientist who doesn't write code. +Explain the plan first. Be clear which step is performed by an engineer, and which step is performed by a scientist. +""", + llm_config=llm_config, +) +executor = UserProxyAgent( + name="Executor", + system_message="Executor. Execute the code written by the engineer and report the result.", + human_input_mode="NEVER", + llm_config=llm_config, + code_execution_config={"last_n_messages": 3, "work_dir": "paper"}, +) +critic = AssistantAgent( + name="Critic", + system_message="Critic. Double check plan, claims, code from other agents and provide feedback. Check whether the plan includes adding verifiable info such as source URL.", + llm_config=llm_config, +) +groupchat = GroupChat( + agents=[admin, engineer, planner, executor, critic], + messages=[], + max_round=50, +) +manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config) + + +admin.initiate_chat( + manager, + message=""" +""", +) +``` + +Credits [@Nathan](https://gist.github.com/CUexter) for this tutorial. + + + +### Setup ChatDev ([Docs](https://github.com/OpenBMB/ChatDev#%EF%B8%8F-quickstart)) +```shell +git clone https://github.com/OpenBMB/ChatDev.git +cd ChatDev +conda create -n ChatDev_conda_env python=3.9 -y +conda activate ChatDev_conda_env +pip install -r requirements.txt +``` +### Run ChatDev w/ Proxy +```shell +export OPENAI_API_KEY="sk-1234" +``` + +```shell +export OPENAI_API_BASE="http://0.0.0.0:8000" +``` +```shell +python3 run.py --task "a script that says hello world" --name "hello world" +``` + + + +```python +pip install langroid +``` + +```python +from langroid.language_models.openai_gpt import OpenAIGPTConfig, OpenAIGPT + +# configure the LLM +my_llm_config = OpenAIGPTConfig( + #format: "local/[URL where LiteLLM proxy is listening] + chat_model="local/localhost:8000", + chat_context_length=2048, # adjust based on model +) + +# create llm, one-off interaction +llm = OpenAIGPT(my_llm_config) +response = mdl.chat("What is the capital of China?", max_tokens=50) + +# Create an Agent with this LLM, wrap it in a Task, and +# run it as an interactive chat app: +from langroid.agent.base import ChatAgent, ChatAgentConfig +from langroid.agent.task import Task + +agent_config = ChatAgentConfig(llm=my_llm_config, name="my-llm-agent") +agent = ChatAgent(agent_config) + +task = Task(agent, name="my-llm-task") +task.run() +``` + +Credits [@pchalasani](https://github.com/pchalasani) and [Langroid](https://github.com/langroid/langroid) for this tutorial. + + +GPT-Pilot helps you build apps with AI Agents. [For more](https://github.com/Pythagora-io/gpt-pilot) + +In your .env set the openai endpoint to your local server. + +``` +OPENAI_ENDPOINT=http://0.0.0.0:8000 +OPENAI_API_KEY=my-fake-key +``` + + +A guidance language for controlling large language models. +https://github.com/guidance-ai/guidance + +**NOTE:** Guidance sends additional params like `stop_sequences` which can cause some models to fail if they don't support it. + +**Fix**: Start your proxy using the `--drop_params` flag + +```shell +litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048 --drop_params +``` + +```python +import guidance + +# set api_base to your proxy +# set api_key to anything +gpt4 = guidance.llms.OpenAI("gpt-4", api_base="http://0.0.0.0:8000", api_key="anything") + +experts = guidance(''' +{{#system~}} +You are a helpful and terse assistant. +{{~/system}} + +{{#user~}} +I want a response to the following question: +{{query}} +Name 3 world-class experts (past or present) who would be great at answering this? +Don't answer the question yet. +{{~/user}} + +{{#assistant~}} +{{gen 'expert_names' temperature=0 max_tokens=300}} +{{~/assistant}} +''', llm=gpt4) + +result = experts(query='How can I be more productive?') +print(result) +``` + + + +:::note +**Contribute** Using this server with a project? Contribute your tutorial [here!](https://github.com/BerriAI/litellm) + +::: + +## Advanced + +### Logs + +```shell +$ litellm --logs +``` + +This will return the most recent log (the call that went to the LLM API + the received response). + +All logs are saved to a file called `api_logs.json` in the current directory. + +### Configure Proxy + +If you need to: +* save API keys +* set litellm params (e.g. drop unmapped params, set fallback models, etc.) +* set model-specific params (max tokens, temperature, api base, prompt template) + +You can do set these just for that session (via cli), or persist these across restarts (via config file). + +#### Save API Keys +```shell +$ litellm --api_key OPENAI_API_KEY=sk-... +``` +LiteLLM will save this to a locally stored config file, and persist this across sessions. + +LiteLLM Proxy supports all litellm supported api keys. To add keys for a specific provider, check this list: + + + + +```shell +$ litellm --add_key HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL] +``` + + + + +```shell +$ litellm --add_key ANTHROPIC_API_KEY=my-api-key +``` + + + + +```shell +$ litellm --add_key PERPLEXITYAI_API_KEY=my-api-key +``` + + + + + +```shell +$ litellm --add_key TOGETHERAI_API_KEY=my-api-key +``` + + + + + +```shell +$ litellm --add_key REPLICATE_API_KEY=my-api-key +``` + + + + + +```shell +$ litellm --add_key AWS_ACCESS_KEY_ID=my-key-id +$ litellm --add_key AWS_SECRET_ACCESS_KEY=my-secret-access-key +``` + + + + + +```shell +$ litellm --add_key PALM_API_KEY=my-palm-key +``` + + + + + +```shell +$ litellm --add_key AZURE_API_KEY=my-api-key +$ litellm --add_key AZURE_API_BASE=my-api-base + +``` + + + + + +```shell +$ litellm --add_key AI21_API_KEY=my-api-key +``` + + + + + +```shell +$ litellm --add_key COHERE_API_KEY=my-api-key +``` + + + + + +E.g.: Set api base, max tokens and temperature. + +**For that session**: +```shell +litellm --model ollama/llama2 \ + --api_base http://localhost:11434 \ + --max_tokens 250 \ + --temperature 0.5 + +# OpenAI-compatible server running on http://0.0.0.0:8000 +``` + +### Performance + +We load-tested 500,000 HTTP connections on the FastAPI server for 1 minute, using [wrk](https://github.com/wg/wrk). + +There are our results: + +```shell +Thread Stats Avg Stdev Max +/- Stdev + Latency 156.38ms 25.52ms 361.91ms 84.73% + Req/Sec 13.61 5.13 40.00 57.50% + 383625 requests in 1.00m, 391.10MB read + Socket errors: connect 0, read 1632, write 1, timeout 0 +``` + + +## Support/ talk with founders + +- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) +- [Community Discord 💭](https://discord.gg/wuPM9dRgDw) +- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ +- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai + diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md new file mode 100644 index 0000000000000000000000000000000000000000..c0ca93b252b63a0e84a7454e05308c78b7b979c0 --- /dev/null +++ b/docs/my-website/docs/routing.md @@ -0,0 +1,580 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + +# Router - Load Balancing, Fallbacks + +LiteLLM manages: +- Load-balance across multiple deployments (e.g. Azure/OpenAI) +- Prioritizing important requests to ensure they don't fail (i.e. Queueing) +- Basic reliability logic - cooldowns, fallbacks, timeouts and retries (fixed + exponential backoff) across multiple deployments/providers. + +In production, litellm supports using Redis as a way to track cooldown server and usage (managing tpm/rpm limits). + +:::info + +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) + +::: + + +## Load Balancing +(s/o [@paulpierre](https://www.linkedin.com/in/paulpierre/) and [sweep proxy](https://docs.sweep.dev/blogs/openai-proxy) for their contributions to this implementation) +[**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/router.py) + +### Quick Start + +```python +from litellm import Router + +model_list = [{ # list of model deployments + "model_name": "gpt-3.5-turbo", # model alias + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", # actual model name + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + } +}, { + "model_name": "gpt-3.5-turbo", + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + } +}, { + "model_name": "gpt-3.5-turbo", + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + } +}] + +router = Router(model_list=model_list) + +# openai.ChatCompletion.create replacement +response = await router.acompletion(model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hey, how's it going?"}]) + +print(response) +``` + +### Available Endpoints +- `router.completion()` - chat completions endpoint to call 100+ LLMs +- `router.acompletion()` - async chat completion calls +- `router.embeddings()` - embedding endpoint for Azure, OpenAI, Huggingface endpoints +- `router.aembeddings()` - async embeddings endpoint +- `router.text_completion()` - completion calls in the old OpenAI `/v1/completions` endpoint format + +### Advanced +#### Routing Strategies - Weighted Pick, Rate Limit Aware + +Router provides 2 strategies for routing your calls across multiple deployments: + + + + +**Default** Picks a deployment based on the provided **Requests per minute (rpm) or Tokens per minute (tpm)** + +If `rpm` or `tpm` is not provided, it randomly picks a deployment + +```python +from litellm import Router +import asyncio + +model_list = [{ # list of model deployments + "model_name": "gpt-3.5-turbo", # model alias + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", # actual model name + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "rpm": 900, # requests per minute for this API + } +}, { + "model_name": "gpt-3.5-turbo", + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE"), + "rpm": 10, + } +}, { + "model_name": "gpt-3.5-turbo", + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + "rpm": 10, + } +}] + +# init router +router = Router(model_list=model_list, routing_strategy="simple-shuffle") +async def router_acompletion(): + response = await router.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hey, how's it going?"}] + ) + print(response) + return response + +asyncio.run(router_acompletion()) +``` + + + +This will route to the deployment with the lowest TPM usage for that minute. + +In production, we use Redis to track usage (TPM/RPM) across multiple deployments. + +If you pass in the deployment's tpm/rpm limits, this will also check against that, and filter out any who's limits would be exceeded. + +For Azure, your RPM = TPM/6. + + +```python +from litellm import Router + + +model_list = [{ # list of model deployments + "model_name": "gpt-3.5-turbo", # model alias + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-v-2", # actual model name + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 100000, + "rpm": 10000, +}, { + "model_name": "gpt-3.5-turbo", + "litellm_params": { # params for litellm completion/embedding call + "model": "azure/chatgpt-functioncalling", + "api_key": os.getenv("AZURE_API_KEY"), + "api_version": os.getenv("AZURE_API_VERSION"), + "api_base": os.getenv("AZURE_API_BASE") + }, + "tpm": 100000, + "rpm": 1000, +}, { + "model_name": "gpt-3.5-turbo", + "litellm_params": { # params for litellm completion/embedding call + "model": "gpt-3.5-turbo", + "api_key": os.getenv("OPENAI_API_KEY"), + }, + "tpm": 100000, + "rpm": 1000, +}] +router = Router(model_list=model_list, + redis_host=os.environ["REDIS_HOST"], + redis_password=os.environ["REDIS_PASSWORD"], + redis_port=os.environ["REDIS_PORT"], + routing_strategy="simple-shuffle") + + +response = await router.acompletion(model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hey, how's it going?"}] + +print(response) +``` + + + + + +## Basic Reliability + +### Timeouts + +The timeout set in router is for the entire length of the call, and is passed down to the completion() call level as well. + +```python +from litellm import Router + +model_list = [{...}] + +router = Router(model_list=model_list, + timeout=30) # raise timeout error if call takes > 30s + +print(response) +``` + +### Cooldowns + +Set the limit for how many calls a model is allowed to fail in a minute, before being cooled down for a minute. + +```python +from litellm import Router + +model_list = [{...}] + +router = Router(model_list=model_list, + allowed_fails=1) # cooldown model if it fails > 1 call in a minute. + +user_message = "Hello, whats the weather in San Francisco??" +messages = [{"content": user_message, "role": "user"}] + +# normal call +response = router.completion(model="gpt-3.5-turbo", messages=messages) + +print(f"response: {response}") + +``` + +### Retries + +For both async + sync functions, we support retrying failed requests. + +For RateLimitError we implement exponential backoffs + +For generic errors, we retry immediately + +Here's a quick look at how we can set `num_retries = 3`: + +```python +from litellm import Router + +model_list = [{...}] + +router = Router(model_list=model_list, + num_retries=3) + +user_message = "Hello, whats the weather in San Francisco??" +messages = [{"content": user_message, "role": "user"}] + +# normal call +response = router.completion(model="gpt-3.5-turbo", messages=messages) + +print(f"response: {response}") +``` + +### Fallbacks + +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). + +```python +from litellm import Router + +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": "bad-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=True) + + +user_message = "Hello, whats the weather in San Francisco??" +messages = [{"content": user_message, "role": "user"}] + +# normal fallback call +response = router.completion(model="azure/gpt-3.5-turbo", messages=messages) + +# context window fallback call +response = router.completion(model="azure/gpt-3.5-turbo-context-fallback", messages=messages) + +print(f"response: {response}") +``` + +### Caching + +In production, we recommend using a Redis cache. For quickly testing things locally, we also support simple in-memory caching. + +**In-memory Cache** + +```python +router = Router(model_list=model_list, + cache_responses=True) + +print(response) +``` + +**Redis Cache** +```python +router = Router(model_list=model_list, + redis_host=os.getenv("REDIS_HOST"), + redis_password=os.getenv("REDIS_PASSWORD"), + redis_port=os.getenv("REDIS_PORT"), + cache_responses=True) + +print(response) +``` +#### Default litellm.completion/embedding params + +You can also set default params for litellm completion/embedding calls. Here's how to do that: + +```python +from litellm import Router + +fallback_dict = {"gpt-3.5-turbo": "gpt-3.5-turbo-16k"} + +router = Router(model_list=model_list, + default_litellm_params={"context_window_fallback_dict": fallback_dict}) + +user_message = "Hello, whats the weather in San Francisco??" +messages = [{"content": user_message, "role": "user"}] + +# normal call +response = router.completion(model="gpt-3.5-turbo", messages=messages) + +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/docs/rules.md b/docs/my-website/docs/rules.md new file mode 100644 index 0000000000000000000000000000000000000000..97da9096db493d12166479a36cac63957ab20a5e --- /dev/null +++ b/docs/my-website/docs/rules.md @@ -0,0 +1,89 @@ +# Rules + +Use this to fail a request based on the input or output of an llm api call. + + +```python +import litellm +import os + +# set env vars +os.environ["OPENAI_API_KEY"] = "your-api-key" +os.environ["OPENROUTER_API_KEY"] = "your-api-key" + +def my_custom_rule(input): # receives the model response + if "i don't think i can answer" in input: # trigger fallback if the model refuses to answer + return False + return True + +litellm.post_call_rules = [my_custom_rule] # have these be functions that can be called to fail a call + +response = litellm.completion(model="gpt-3.5-turbo", messages=[{"role": "user", +"content": "Hey, how's it going?"}], fallbacks=["openrouter/gryphe/mythomax-l2-13b"]) +``` + +## Available Endpoints + +* `litellm.pre_call_rules = []` - A list of functions to iterate over before making the api call. Each function is expected to return either True (allow call) or False (fail call). + +* `litellm.post_call_rules = []` - List of functions to iterate over before making the api call. Each function is expected to return either True (allow call) or False (fail call). + + +## Expected format of rule + +```python +def my_custom_rule(input: str) -> bool: # receives the model response + if "i don't think i can answer" in input: # trigger fallback if the model refuses to answer + return False + return True +``` + +#### Inputs +* `input`: *str*: The user input or llm response. + +#### Outputs +* `bool`: Return True (allow call) or False (fail call) + + +## Example Rules + +### Example 1: Fail if user input is too long + +```python +import litellm +import os + +# set env vars +os.environ["OPENAI_API_KEY"] = "your-api-key" + +def my_custom_rule(input): # receives the model response + if len(input) > 10: # fail call if too long + return False + return True + +litellm.pre_call_rules = [my_custom_rule] # have these be functions that can be called to fail a call + +response = litellm.completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}]) +``` + +### Example 2: Fallback to uncensored model if llm refuses to answer + + +```python +import litellm +import os + +# set env vars +os.environ["OPENAI_API_KEY"] = "your-api-key" +os.environ["OPENROUTER_API_KEY"] = "your-api-key" + +def my_custom_rule(input): # receives the model response + if "i don't think i can answer" in input: # trigger fallback if the model refuses to answer + return False + return True + +litellm.post_call_rules = [my_custom_rule] # have these be functions that can be called to fail a call + +response = litellm.completion(model="gpt-3.5-turbo", messages=[{"role": "user", +"content": "Hey, how's it going?"}], fallbacks=["openrouter/gryphe/mythomax-l2-13b"]) +``` \ No newline at end of file diff --git a/docs/my-website/docs/secret.md b/docs/my-website/docs/secret.md new file mode 100644 index 0000000000000000000000000000000000000000..39ee09854074693ddf5afa98cf4385cc362944aa --- /dev/null +++ b/docs/my-website/docs/secret.md @@ -0,0 +1,97 @@ +# Secret Manager +LiteLLM supports reading secrets from Azure Key Vault and Infisical + +- [Azure Key Vault](#azure-key-vault) +- [Infisical Secret Manager](#infisical-secret-manager) +- [.env Files](#env-files) + +## Azure Key Vault + +### Quick Start + +```python +### Instantiate Azure Key Vault Client ### +from azure.keyvault.secrets import SecretClient +from azure.identity import ClientSecretCredential + +# Set your Azure Key Vault URI +KVUri = os.getenv("AZURE_KEY_VAULT_URI") + +# Set your Azure AD application/client ID, client secret, and tenant ID - create an application with permission to call your key vault +client_id = os.getenv("AZURE_CLIENT_ID") +client_secret = os.getenv("AZURE_CLIENT_SECRET") +tenant_id = os.getenv("AZURE_TENANT_ID") + +# Initialize the ClientSecretCredential +credential = ClientSecretCredential(client_id=client_id, client_secret=client_secret, tenant_id=tenant_id) + +# Create the SecretClient using the credential +client = SecretClient(vault_url=KVUri, credential=credential) + +### Connect to LiteLLM ### +import litellm +litellm.secret_manager = client + +litellm.get_secret("your-test-key") +``` + +### Usage with OpenAI Proxy Server + +1. Install Proxy dependencies +```bash +pip install litellm[proxy] litellm[extra_proxy] +``` + +2. Save Azure details in your environment +```bash +export["AZURE_CLIENT_ID"]="your-azure-app-client-id" +export["AZURE_CLIENT_SECRET"]="your-azure-app-client-secret" +export["AZURE_TENANT_ID"]="your-azure-tenant-id" +export["AZURE_KEY_VAULT_URI"]="your-azure-key-vault-uri" +``` + +3. Add to proxy config.yaml +```yaml +model_list: + - model_name: "my-azure-models" # model alias + litellm_params: + model: "azure/" + api_key: "os.environ/AZURE-API-KEY" # reads from key vault - get_secret("AZURE_API_KEY") + api_base: "os.environ/AZURE-API-BASE" # reads from key vault - get_secret("AZURE_API_BASE") + +general_settings: + use_azure_key_vault: True +``` + +You can now test this by starting your proxy: +```bash +litellm --config /path/to/config.yaml +``` + +[Quick Test Proxy](./simple_proxy.md#using-litellm-proxy---curl-request-openai-package) + +## Infisical Secret Manager +Integrates with [Infisical's Secret Manager](https://infisical.com/) for secure storage and retrieval of API keys and sensitive data. + +### Usage +liteLLM manages reading in your LLM API secrets/env variables from Infisical for you + +```python +import litellm +from infisical import InfisicalClient + +litellm.secret_manager = InfisicalClient(token="your-token") + +messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What's the weather like today?"}, +] + +response = litellm.completion(model="gpt-3.5-turbo", messages=messages) + +print(response) +``` + + +## .env Files +If no secret manager client is specified, Litellm automatically uses the `.env` file to manage sensitive data. diff --git a/docs/my-website/docs/set_keys.md b/docs/my-website/docs/set_keys.md new file mode 100644 index 0000000000000000000000000000000000000000..4c8cc42fe79858da25167ae4b631c89e485d8e6a --- /dev/null +++ b/docs/my-website/docs/set_keys.md @@ -0,0 +1,169 @@ +# Setting API Keys, Base, Version + +LiteLLM allows you to specify the following: +* API Key +* API Base +* API Version +* API Type + +Useful Helper functions: +* [`check_valid_key()`](#check_valid_key) +* [`get_valid_models()`](#get_valid_models) + +You can set the API configs using: +* Environment Variables +* litellm variables `litellm.api_key` +* Passing args to `completion()` + +## Environment Variables + +### Setting API Keys + +Set the liteLLM API key or specific provider key: + +```python +import os + +# Set OpenAI API key +os.environ["OPENAI_API_KEY"] = "Your API Key" +os.environ["ANTHROPIC_API_KEY"] = "Your API Key" +os.environ["REPLICATE_API_KEY"] = "Your API Key" +os.environ["TOGETHERAI_API_KEY"] = "Your API Key" +``` + +### Setting API Base, API Version, API Type + +```python +# for azure openai +os.environ['AZURE_API_BASE'] = "https://openai-gpt-4-test2-v-12.openai.azure.com/" +os.environ['AZURE_API_VERSION'] = "2023-05-15" # [OPTIONAL] +os.environ['AZURE_API_TYPE'] = "azure" # [OPTIONAL] + +# for openai +os.environ['OPENAI_API_BASE'] = "https://openai-gpt-4-test2-v-12.openai.azure.com/" +``` + +## litellm variables + +### litellm.api_key +This variable is checked for all providers + +```python +import litellm +# openai call +litellm.api_key = "sk-OpenAIKey" +response = litellm.completion(messages=messages, model="gpt-3.5-turbo") + +# anthropic call +litellm.api_key = "sk-AnthropicKey" +response = litellm.completion(messages=messages, model="claude-2") +``` + +### litellm.provider_key (example litellm.openai_key) + +```python +litellm.openai_key = "sk-OpenAIKey" +response = litellm.completion(messages=messages, model="gpt-3.5-turbo") + +# anthropic call +litellm.anthropic_key = "sk-AnthropicKey" +response = litellm.completion(messages=messages, model="claude-2") +``` + +### litellm.api_base + +```python +import litellm +litellm.api_base = "https://hosted-llm-api.co" +response = litellm.completion(messages=messages, model="gpt-3.5-turbo") +``` + +### litellm.api_version + +```python +import litellm +litellm.api_version = "2023-05-15" +response = litellm.completion(messages=messages, model="gpt-3.5-turbo") +``` + +### litellm.organization +```python +import litellm +litellm.organization = "LiteLlmOrg" +response = litellm.completion(messages=messages, model="gpt-3.5-turbo") +``` + +## Passing Args to completion() + +You can pass the API key within `completion()` call: + +### api_key +```python +from litellm import completion + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +response = completion("command-nightly", messages, api_key="Your-Api-Key") +``` + +### api_base + +```python +from litellm import completion + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +response = completion("command-nightly", messages, api_base="https://hosted-llm-api.co") +``` + +### api_version + +```python +from litellm import completion + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +response = completion("command-nightly", messages, api_version="2023-02-15") +``` + +## Helper Functions + +### `check_valid_key()` + +Check if a user submitted a valid key for the model they're trying to call. + +```python +key = "bad-key" +response = check_valid_key(model="gpt-3.5-turbo", api_key=key) +assert(response == False) +``` + +### `get_valid_models()` + +This helper reads the .env and returns a list of supported llms for user + +```python +old_environ = os.environ +os.environ = {'OPENAI_API_KEY': 'temp'} # mock set only openai key in environ + +valid_models = get_valid_models() +print(valid_models) + +# list of openai supported llms on litellm +expected_models = litellm.open_ai_chat_completion_models + litellm.open_ai_text_completion_models + +assert(valid_models == expected_models) + +# reset replicate env key +os.environ = old_environ +``` + +### `validate_environment(model: str)` + +This helper tells you if you have all the required environment variables for a model, and if not - what's missing. + +```python +from litellm import validate_environment + +print(validate_environment("openai/gpt-3.5-turbo")) +``` \ No newline at end of file diff --git a/docs/my-website/docs/simple_proxy_old_doc.md b/docs/my-website/docs/simple_proxy_old_doc.md new file mode 100644 index 0000000000000000000000000000000000000000..6b65037ffa6e33de53f321ed3f06162cc3ad19b1 --- /dev/null +++ b/docs/my-website/docs/simple_proxy_old_doc.md @@ -0,0 +1,1344 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# 💥 OpenAI Proxy Server + +LiteLLM Server manages: + +* **Unified Interface**: Calling 100+ LLMs [Huggingface/Bedrock/TogetherAI/etc.](#other-supported-models) in the OpenAI `ChatCompletions` & `Completions` format +* **Load Balancing**: between [Multiple Models](#multiple-models---quick-start) + [Deployments of the same model](#multiple-instances-of-1-model) - LiteLLM proxy can handle 1.5k+ requests/second during load tests. +* **Cost tracking**: Authentication & Spend Tracking [Virtual Keys](#managing-auth---virtual-keys) + +[**See LiteLLM Proxy code**](https://github.com/BerriAI/litellm/tree/main/litellm/proxy) + +## Quick Start +View all the supported args for the Proxy CLI [here](https://docs.litellm.ai/docs/simple_proxy#proxy-cli-arguments) + +```shell +$ pip install litellm[proxy] +``` + +```shell +$ litellm --model huggingface/bigcode/starcoder + +#INFO: Proxy running on http://0.0.0.0:8000 +``` + +### Test +In a new shell, run, this will make an `openai.chat.completions` request. Ensure you're using openai v1.0.0+ +```shell +litellm --test +``` + +This will now automatically route any requests for gpt-3.5-turbo to bigcode starcoder, hosted on huggingface inference endpoints. + +### Using LiteLLM Proxy - Curl Request, OpenAI Package + + + + +```shell +curl --location 'http://0.0.0.0:8000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + } +' +``` + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:8000" +) + +# 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) + +``` + + + + +### Server Endpoints +- POST `/chat/completions` - chat completions endpoint to call 100+ LLMs +- POST `/completions` - completions endpoint +- POST `/embeddings` - embedding endpoint for Azure, OpenAI, Huggingface endpoints +- GET `/models` - available models on server +- POST `/key/generate` - generate a key to access the proxy + +### Supported LLMs +All LiteLLM supported LLMs are supported on the Proxy. Seel all [supported llms](https://docs.litellm.ai/docs/providers) + + + +```shell +$ export AWS_ACCESS_KEY_ID= +$ export AWS_REGION_NAME= +$ export AWS_SECRET_ACCESS_KEY= +``` + +```shell +$ litellm --model bedrock/anthropic.claude-v2 +``` + + + +```shell +$ export AZURE_API_KEY=my-api-key +$ export AZURE_API_BASE=my-api-base +``` +``` +$ litellm --model azure/my-deployment-name +``` + + + + +```shell +$ export OPENAI_API_KEY=my-api-key +``` + +```shell +$ litellm --model gpt-3.5-turbo +``` + + + +```shell +$ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL] +``` +```shell +$ litellm --model huggingface/ --api_base https://k58ory32yinf1ly0.us-east-1.aws.endpoints.huggingface.cloud +``` + + + + +```shell +$ litellm --model huggingface/ --api_base http://0.0.0.0:8001 +``` + + + + +```shell +export AWS_ACCESS_KEY_ID= +export AWS_REGION_NAME= +export AWS_SECRET_ACCESS_KEY= +``` + +```shell +$ litellm --model sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b +``` + + + + +```shell +$ export ANTHROPIC_API_KEY=my-api-key +``` +```shell +$ litellm --model claude-instant-1 +``` + + + +Assuming you're running vllm locally + +```shell +$ litellm --model vllm/facebook/opt-125m +``` + + + +```shell +$ export TOGETHERAI_API_KEY=my-api-key +``` +```shell +$ litellm --model together_ai/lmsys/vicuna-13b-v1.5-16k +``` + + + + + +```shell +$ export REPLICATE_API_KEY=my-api-key +``` +```shell +$ litellm \ + --model replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3 +``` + + + + + +```shell +$ litellm --model petals/meta-llama/Llama-2-70b-chat-hf +``` + + + + + +```shell +$ export PALM_API_KEY=my-palm-key +``` +```shell +$ litellm --model palm/chat-bison +``` + + + + + +```shell +$ export AI21_API_KEY=my-api-key +``` + +```shell +$ litellm --model j2-light +``` + + + + + +```shell +$ export COHERE_API_KEY=my-api-key +``` + +```shell +$ litellm --model command-nightly +``` + + + + + + +## Using with OpenAI compatible projects +Set `base_url` to the LiteLLM Proxy server + + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:8000" +) + +# request sent to model set on litellm proxy, `litellm --model` +response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [ + { + "role": "user", + "content": "this is a test request, write a short poem" + } +]) + +print(response) + +``` + + + +#### Start the LiteLLM proxy +```shell +litellm --model gpt-3.5-turbo + +#INFO: Proxy running on http://0.0.0.0:8000 +``` + +#### 1. Clone the repo + +```shell +git clone https://github.com/danny-avila/LibreChat.git +``` + + +#### 2. Modify Librechat's `docker-compose.yml` +LiteLLM Proxy is running on port `8000`, set `8000` as the proxy below +```yaml +OPENAI_REVERSE_PROXY=http://host.docker.internal:8000/v1/chat/completions +``` + +#### 3. Save fake OpenAI key in Librechat's `.env` + +Copy Librechat's `.env.example` to `.env` and overwrite the default OPENAI_API_KEY (by default it requires the user to pass a key). +```env +OPENAI_API_KEY=sk-1234 +``` + +#### 4. Run LibreChat: +```shell +docker compose up +``` + + + + +Continue-Dev brings ChatGPT to VSCode. See how to [install it here](https://continue.dev/docs/quickstart). + +In the [config.py](https://continue.dev/docs/reference/Models/openai) set this as your default model. +```python + default=OpenAI( + api_key="IGNORED", + model="fake-model-name", + context_length=2048, # customize if needed for your model + api_base="http://localhost:8000" # your proxy server url + ), +``` + +Credits [@vividfog](https://github.com/jmorganca/ollama/issues/305#issuecomment-1751848077) for this tutorial. + + + + +```shell +$ pip install aider + +$ aider --openai-api-base http://0.0.0.0:8000 --openai-api-key fake-key +``` + + + +```python +pip install pyautogen +``` + +```python +from autogen import AssistantAgent, UserProxyAgent, oai +config_list=[ + { + "model": "my-fake-model", + "api_base": "http://localhost:8000", #litellm compatible endpoint + "api_type": "open_ai", + "api_key": "NULL", # just a placeholder + } +] + +response = oai.Completion.create(config_list=config_list, prompt="Hi") +print(response) # works fine + +llm_config={ + "config_list": config_list, +} + +assistant = AssistantAgent("assistant", llm_config=llm_config) +user_proxy = UserProxyAgent("user_proxy") +user_proxy.initiate_chat(assistant, message="Plot a chart of META and TESLA stock price change YTD.", config_list=config_list) +``` + +Credits [@victordibia](https://github.com/microsoft/autogen/issues/45#issuecomment-1749921972) for this tutorial. + + + +A guidance language for controlling large language models. +https://github.com/guidance-ai/guidance + +**NOTE:** Guidance sends additional params like `stop_sequences` which can cause some models to fail if they don't support it. + +**Fix**: Start your proxy using the `--drop_params` flag + +```shell +litellm --model ollama/codellama --temperature 0.3 --max_tokens 2048 --drop_params +``` + +```python +import guidance + +# set api_base to your proxy +# set api_key to anything +gpt4 = guidance.llms.OpenAI("gpt-4", api_base="http://0.0.0.0:8000", api_key="anything") + +experts = guidance(''' +{{#system~}} +You are a helpful and terse assistant. +{{~/system}} + +{{#user~}} +I want a response to the following question: +{{query}} +Name 3 world-class experts (past or present) who would be great at answering this? +Don't answer the question yet. +{{~/user}} + +{{#assistant~}} +{{gen 'expert_names' temperature=0 max_tokens=300}} +{{~/assistant}} +''', llm=gpt4) + +result = experts(query='How can I be more productive?') +print(result) +``` + + + +## Proxy Configs +The Config allows you to set the following params + +| Param Name | Description | +|----------------------|---------------------------------------------------------------| +| `model_list` | List of supported models on the server, with model-specific configs | +| `litellm_settings` | litellm Module settings, example `litellm.drop_params=True`, `litellm.set_verbose=True`, `litellm.api_base`, `litellm.cache` | +| `general_settings` | Server settings, example setting `master_key: sk-my_special_key` | +| `environment_variables` | Environment Variables example, `REDIS_HOST`, `REDIS_PORT` | + +#### Example Config +```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: + 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: + rpm: 6 + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/gpt-turbo-large + api_base: https://openai-france-1234.openai.azure.com/ + api_key: + rpm: 1440 + +litellm_settings: + drop_params: True + set_verbose: True + +general_settings: + master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234) + + +environment_variables: + OPENAI_API_KEY: sk-123 + REPLICATE_API_KEY: sk-cohere-is-okay + REDIS_HOST: redis-16337.c322.us-east-1-2.ec2.cloud.redislabs.com + REDIS_PORT: "16337" + REDIS_PASSWORD: +``` + +### Config for Multiple Models - GPT-4, Claude-2 + +Here's how you can use multiple llms with one proxy `config.yaml`. + +#### Step 1: Setup Config +```yaml +model_list: + - model_name: zephyr-alpha # the 1st model is the default on the proxy + litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body + model: huggingface/HuggingFaceH4/zephyr-7b-alpha + api_base: http://0.0.0.0:8001 + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: sk-1233 + - model_name: claude-2 + litellm_params: + model: claude-2 + api_key: sk-claude +``` + +:::info + +The proxy uses the first model in the config as the default model - in this config the default model is `zephyr-alpha` +::: + + +#### Step 2: Start Proxy with config + +```shell +$ litellm --config /path/to/config.yaml +``` + +#### Step 3: Use proxy +Curl Command +```shell +curl --location 'http://0.0.0.0:8000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "zephyr-alpha", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + } +' +``` + +### Load Balancing - Multiple Instances of 1 model +Use this config to load balance between multiple instances of the same model. The proxy will handle routing requests (using LiteLLM's Router). **Set `rpm` in the config if you want maximize throughput** + +#### Example config +requests with `model=gpt-3.5-turbo` will be routed across multiple instances of `azure/gpt-3.5-turbo` +```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: + 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: + rpm: 6 + - model_name: gpt-3.5-turbo + litellm_params: + model: azure/gpt-turbo-large + api_base: https://openai-france-1234.openai.azure.com/ + api_key: + rpm: 1440 +``` + +#### Step 2: Start Proxy with config + +```shell +$ litellm --config /path/to/config.yaml +``` + +#### Step 3: Use proxy +Curl Command +```shell +curl --location 'http://0.0.0.0:8000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + } +' +``` + +### Fallbacks + Cooldowns + Retries + Timeouts + +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 + 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, + "request_timeout": 10 + } +' +``` + +### Config for Embedding Models - xorbitsai/inference + +Here's how you can use multiple llms with one proxy `config.yaml`. +Here is how [LiteLLM calls OpenAI Compatible Embedding models](https://docs.litellm.ai/docs/embedding/supported_embedding#openai-compatible-embedding-models) + +#### Config +```yaml +model_list: + - model_name: custom_embedding_model + litellm_params: + model: openai/custom_embedding # the `openai/` prefix tells litellm it's openai compatible + api_base: http://0.0.0.0:8000/ + - model_name: custom_embedding_model + litellm_params: + model: openai/custom_embedding # the `openai/` prefix tells litellm it's openai compatible + api_base: http://0.0.0.0:8001/ +``` + +Run the proxy using this config +```shell +$ litellm --config /path/to/config.yaml +``` + + +### Managing Auth - Virtual Keys + +Grant other's temporary access to your proxy, with keys that expire after a set duration. + +Requirements: + +- Need to a postgres database (e.g. [Supabase](https://supabase.com/), [Neon](https://neon.tech/), etc) + +You can then generate temporary keys by hitting the `/key/generate` endpoint. + +[**See code**](https://github.com/BerriAI/litellm/blob/7a669a36d2689c7f7890bc9c93e04ff3c2641299/litellm/proxy/proxy_server.py#L672) + +**Step 1: Save postgres db url** + +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: ollama/llama2 + - model_name: gpt-3.5-turbo + litellm_params: + model: ollama/llama2 + +general_settings: + master_key: sk-1234 # [OPTIONAL] if set all calls to proxy will require either this key or a valid generated token + database_url: "postgresql://:@:/" +``` + +**Step 2: Start litellm** + +```shell +litellm --config /path/to/config.yaml +``` + +**Step 3: Generate temporary keys** + +```shell +curl 'http://0.0.0.0:8000/key/generate' \ +--h 'Authorization: Bearer sk-1234' \ +--d '{"models": ["gpt-3.5-turbo", "gpt-4", "claude-2"], "duration": "20m"}' +``` + +- `models`: *list or null (optional)* - Specify the models a token has access too. If null, then token has access to all models on server. + +- `duration`: *str or null (optional)* Specify the length of time the token is valid for. If null, default is set to 1 hour. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d"). + +Expected response: + +```python +{ + "key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token + "expires": "2023-11-19T01:38:25.838000+00:00" # datetime object +} +``` + +### Managing Auth - Upgrade/Downgrade Models + +If a user is expected to use a given model (i.e. gpt3-5), and you want to: + +- try to upgrade the request (i.e. GPT4) +- or downgrade it (i.e. Mistral) +- OR rotate the API KEY (i.e. open AI) +- OR access the same model through different end points (i.e. openAI vs openrouter vs Azure) + +Here's how you can do that: + +**Step 1: Create a model group in config.yaml (save model name, api keys, etc.)** + +```yaml +model_list: + - model_name: my-free-tier + litellm_params: + model: huggingface/HuggingFaceH4/zephyr-7b-beta + api_base: http://0.0.0.0:8001 + - model_name: my-free-tier + litellm_params: + model: huggingface/HuggingFaceH4/zephyr-7b-beta + api_base: http://0.0.0.0:8002 + - model_name: my-free-tier + litellm_params: + model: huggingface/HuggingFaceH4/zephyr-7b-beta + api_base: http://0.0.0.0:8003 + - model_name: my-paid-tier + litellm_params: + model: gpt-4 + api_key: my-api-key +``` + +**Step 2: Generate a user key - enabling them access to specific models, custom model aliases, etc.** + +```bash +curl -X POST "https://0.0.0.0:8000/key/generate" \ +-H "Authorization: Bearer sk-1234" \ +-H "Content-Type: application/json" \ +-d '{ + "models": ["my-free-tier"], + "aliases": {"gpt-3.5-turbo": "my-free-tier"}, + "duration": "30min" +}' +``` + +- **How to upgrade / downgrade request?** Change the alias mapping +- **How are routing between diff keys/api bases done?** litellm handles this by shuffling between different models in the model list with the same model_name. [**See Code**](https://github.com/BerriAI/litellm/blob/main/litellm/router.py) + +### Managing Auth - Tracking Spend + +You can get spend for a key by using the `/key/info` endpoint. + +```bash +curl 'http://0.0.0.0:8000/key/info?key=' \ + -X GET \ + -H 'Authorization: Bearer ' +``` + +This is automatically updated (in USD) when calls are made to /completions, /chat/completions, /embeddings using litellm's completion_cost() function. [**See Code**](https://github.com/BerriAI/litellm/blob/1a6ea20a0bb66491968907c2bfaabb7fe45fc064/litellm/utils.py#L1654). + +**Sample response** + +```python +{ + "key": "sk-tXL0wt5-lOOVK9sfY2UacA", + "info": { + "token": "sk-tXL0wt5-lOOVK9sfY2UacA", + "spend": 0.0001065, + "expires": "2023-11-24T23:19:11.131000Z", + "models": [ + "gpt-3.5-turbo", + "gpt-4", + "claude-2" + ], + "aliases": { + "mistral-7b": "gpt-3.5-turbo" + }, + "config": {} + } +} +``` + +### Save Model-specific params (API Base, API Keys, Temperature, Headers etc.) +You can use the config to save model-specific information like api_base, api_key, temperature, max_tokens, etc. + +**Step 1**: Create a `config.yaml` file +```yaml +model_list: + - model_name: gpt-4-team1 + litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body + model: azure/chatgpt-v-2 + api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ + api_version: "2023-05-15" + azure_ad_token: eyJ0eXAiOiJ + - model_name: gpt-4-team2 + litellm_params: + model: azure/gpt-4 + api_key: sk-123 + api_base: https://openai-gpt-4-test-v-2.openai.azure.com/ + - model_name: mistral-7b + litellm_params: + model: ollama/mistral + api_base: your_ollama_api_base + headers: { + "HTTP-Referer": "litellm.ai", + "X-Title": "LiteLLM Server" + } +``` + +**Step 2**: Start server with config + +```shell +$ litellm --config /path/to/config.yaml +``` + +### Load API Keys from Vault + +If you have secrets saved in Azure Vault, etc. and don't want to expose them in the config.yaml, here's how to load model-specific keys from the environment. + +```python +os.environ["AZURE_NORTH_AMERICA_API_KEY"] = "your-azure-api-key" +``` + +```yaml +model_list: + - model_name: gpt-4-team1 + litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body + model: azure/chatgpt-v-2 + api_base: https://openai-gpt-4-test-v-1.openai.azure.com/ + api_version: "2023-05-15" + api_key: os.environ/AZURE_NORTH_AMERICA_API_KEY +``` + +[**See Code**](https://github.com/BerriAI/litellm/blob/c12d6c3fe80e1b5e704d9846b246c059defadce7/litellm/utils.py#L2366) + +s/o to [@David Manouchehri](https://www.linkedin.com/in/davidmanouchehri/) for helping with this. + +### Config for setting Model Aliases + +Set a model alias for your deployments. + +In the `config.yaml` the model_name parameter is the user-facing name to use for your deployment. + +In the config below requests with `model=gpt-4` will route to `ollama/llama2` + +```yaml +model_list: + - model_name: text-davinci-003 + litellm_params: + model: ollama/zephyr + - model_name: gpt-4 + litellm_params: + model: ollama/llama2 + - model_name: gpt-3.5-turbo + litellm_params: + model: ollama/llama2 +``` +### Caching Responses +Caching can be enabled by adding the `cache` key in 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 + +litellm_settings: + set_verbose: True + cache: # init cache + type: redis # tell litellm to use redis caching +``` + +#### Step 2: Add Redis Credentials to .env +LiteLLM requires the following REDIS credentials in your env to enable caching + + ```shell + REDIS_HOST = "" # REDIS_HOST='redis-18841.c274.us-east-1-3.ec2.cloud.redislabs.com' + REDIS_PORT = "" # REDIS_PORT='18841' + REDIS_PASSWORD = "" # REDIS_PASSWORD='liteLlmIsAmazing' + ``` +#### Step 3: Run proxy with config +```shell +$ litellm --config /path/to/config.yaml +``` + +#### Using Caching +Send the same request twice: +```shell +curl http://0.0.0.0:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "write a poem about litellm!"}], + "temperature": 0.7 + }' + +curl http://0.0.0.0:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "write a poem about litellm!"}], + "temperature": 0.7 + }' +``` + +#### Control caching per completion request +Caching can be switched on/off per `/chat/completions` request +- Caching **on** for completion - pass `caching=True`: + ```shell + curl http://0.0.0.0:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "write a poem about litellm!"}], + "temperature": 0.7, + "caching": true + }' + ``` +- Caching **off** for completion - pass `caching=False`: + ```shell + curl http://0.0.0.0:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "write a poem about litellm!"}], + "temperature": 0.7, + "caching": false + }' + ``` + +### Set Custom Prompt Templates + +LiteLLM by default checks if a model has a [prompt template and applies it](./completion/prompt_formatting.md) (e.g. if a huggingface model has a saved chat template in it's tokenizer_config.json). However, you can also set a custom prompt template on your proxy in the `config.yaml`: + +**Step 1**: Save your prompt template in a `config.yaml` +```yaml +# Model-specific parameters +model_list: + - model_name: mistral-7b # model alias + litellm_params: # actual params for litellm.completion() + model: "huggingface/mistralai/Mistral-7B-Instruct-v0.1" + api_base: "" + api_key: "" # [OPTIONAL] for hf inference endpoints + initial_prompt_value: "\n" + roles: {"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}} + final_prompt_value: "\n" + bos_token: "" + eos_token: "" + max_tokens: 4096 +``` + +**Step 2**: Start server with config + +```shell +$ litellm --config /path/to/config.yaml +``` + +## Debugging Proxy +Run the proxy with `--debug` to easily view debug logs +```shell +litellm --model gpt-3.5-turbo --debug +``` + +When making requests you should see the POST request sent by LiteLLM to the LLM on the Terminal output +```shell +POST Request Sent from LiteLLM: +curl -X POST \ +https://api.openai.com/v1/chat/completions \ +-H 'content-type: application/json' -H 'Authorization: Bearer sk-qnWGUIW9****************************************' \ +-d '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "this is a test request, write a short poem"}]}' +``` + +## Health Check LLMs on Proxy +Use this to health check all LLMs defined in your config.yaml +#### Request +```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/" + } + ] +} +``` + +## Logging Proxy Input/Output - OpenTelemetry + +### Step 1 Start OpenTelemetry Collecter Docker Container +This container sends logs to your selected destination + +#### Install OpenTelemetry Collecter Docker Image +```shell +docker pull otel/opentelemetry-collector:0.90.0 +docker run -p 127.0.0.1:4317:4317 -p 127.0.0.1:55679:55679 otel/opentelemetry-collector:0.90.0 +``` + +#### Set Destination paths on OpenTelemetry Collecter + +Here's the OpenTelemetry yaml config to use with Elastic Search +```yaml +receivers: + otlp: + protocols: + grpc: + endpoint: 0.0.0.0:4317 + +processors: + batch: + timeout: 1s + send_batch_size: 1024 + +exporters: + logging: + loglevel: debug + otlphttp/elastic: + endpoint: "" + headers: + Authorization: "Bearer " + +service: + pipelines: + metrics: + receivers: [otlp] + exporters: [logging, otlphttp/elastic] + traces: + receivers: [otlp] + exporters: [logging, otlphttp/elastic] + logs: + receivers: [otlp] + exporters: [logging,otlphttp/elastic] +``` + +#### Start the OpenTelemetry container with config +Run the following command to start your docker container. We pass `otel_config.yaml` from the previous step + +```shell +docker run -p 4317:4317 \ + -v $(pwd)/otel_config.yaml:/etc/otel-collector-config.yaml \ + otel/opentelemetry-collector:latest \ + --config=/etc/otel-collector-config.yaml +``` + +### Step 2 Configure LiteLLM proxy to log on OpenTelemetry + +#### Pip install opentelemetry +```shell +pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp -U +``` + +#### Set (OpenTelemetry) `otel=True` on the proxy `config.yaml` +**Example config.yaml** + +```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: + rpm: 6 # Rate limit for this deployment: in requests per minute (rpm) + +general_settings: + otel: True # set OpenTelemetry=True, on litellm Proxy + +``` + +#### Set OTEL collector endpoint +LiteLLM will read the `OTEL_ENDPOINT` environment variable to send data to your OTEL collector + +```python +os.environ['OTEL_ENDPOINT'] # defauls to 127.0.0.1:4317 if not provided +``` + +#### Start LiteLLM Proxy +```shell +litellm -config config.yaml +``` + +#### Run a test request to Proxy +```shell +curl --location 'http://0.0.0.0:8000/chat/completions' \ + --header 'Authorization: Bearer sk-1244' \ + --data ' { + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "user", + "content": "request from LiteLLM testing" + } + ] + }' +``` + + +#### Test & View Logs on OpenTelemetry Collecter +On successfull logging you should be able to see this log on your `OpenTelemetry Collecter` Docker Container +```shell +Events: +SpanEvent #0 + -> Name: LiteLLM: Request Input + -> Timestamp: 2023-12-02 05:05:53.71063 +0000 UTC + -> DroppedAttributesCount: 0 + -> Attributes:: + -> type: Str(http) + -> asgi: Str({'version': '3.0', 'spec_version': '2.3'}) + -> http_version: Str(1.1) + -> server: Str(('127.0.0.1', 8000)) + -> client: Str(('127.0.0.1', 62796)) + -> scheme: Str(http) + -> method: Str(POST) + -> root_path: Str() + -> path: Str(/chat/completions) + -> raw_path: Str(b'/chat/completions') + -> query_string: Str(b'') + -> headers: Str([(b'host', b'0.0.0.0:8000'), (b'user-agent', b'curl/7.88.1'), (b'accept', b'*/*'), (b'authorization', b'Bearer sk-1244'), (b'content-length', b'147'), (b'content-type', b'application/x-www-form-urlencoded')]) + -> state: Str({}) + -> app: Str() + -> fastapi_astack: Str() + -> router: Str() + -> endpoint: Str() + -> path_params: Str({}) + -> route: Str(APIRoute(path='/chat/completions', name='chat_completion', methods=['POST'])) +SpanEvent #1 + -> Name: LiteLLM: Request Headers + -> Timestamp: 2023-12-02 05:05:53.710652 +0000 UTC + -> DroppedAttributesCount: 0 + -> Attributes:: + -> host: Str(0.0.0.0:8000) + -> user-agent: Str(curl/7.88.1) + -> accept: Str(*/*) + -> authorization: Str(Bearer sk-1244) + -> content-length: Str(147) + -> content-type: Str(application/x-www-form-urlencoded) +SpanEvent #2 +``` + +### View Log on Elastic Search +Here's the log view on Elastic Search. You can see the request `input`, `output` and `headers` + + + +## Logging Proxy Input/Output - Langfuse +We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successfull LLM calls to langfuse + +**Step 1** Install langfuse + +```shell +pip install langfuse +``` + +**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: ["langfuse"] +``` + +**Step 3**: Start the proxy, make a test request + +Start proxy +```shell +litellm --config config.yaml --debug +``` + +Test Request +``` +litellm --test +``` + +Expected output on Langfuse + + + +## Deploying LiteLLM Proxy + +### Deploy on Render https://render.com/ + + + +## LiteLLM Proxy Performance + +### Throughput - 30% Increase +LiteLLM proxy + Load Balancer gives **30% increase** in throughput compared to Raw OpenAI API + + +### Latency Added - 0.00325 seconds +LiteLLM proxy adds **0.00325 seconds** latency as compared to using the Raw OpenAI API + + + + + +## Proxy CLI Arguments + +#### --host + - **Default:** `'0.0.0.0'` + - The host for the server to listen on. + - **Usage:** + ```shell + litellm --host 127.0.0.1 + ``` + +#### --port + - **Default:** `8000` + - The port to bind the server to. + - **Usage:** + ```shell + litellm --port 8080 + ``` + +#### --num_workers + - **Default:** `1` + - The number of uvicorn workers to spin up. + - **Usage:** + ```shell + litellm --num_workers 4 + ``` + +#### --api_base + - **Default:** `None` + - The API base for the model litellm should call. + - **Usage:** + ```shell + litellm --model huggingface/tinyllama --api_base https://k58ory32yinf1ly0.us-east-1.aws.endpoints.huggingface.cloud + ``` + +#### --api_version + - **Default:** `None` + - For Azure services, specify the API version. + - **Usage:** + ```shell + litellm --model azure/gpt-deployment --api_version 2023-08-01 --api_base https://" + ``` + +#### --model or -m + - **Default:** `None` + - The model name to pass to Litellm. + - **Usage:** + ```shell + litellm --model gpt-3.5-turbo + ``` + +#### --test + - **Type:** `bool` (Flag) + - Proxy chat completions URL to make a test request. + - **Usage:** + ```shell + litellm --test + ``` + +#### --health + - **Type:** `bool` (Flag) + - Runs a health check on all models in config.yaml + - **Usage:** + ```shell + litellm --health + ``` + +#### --alias + - **Default:** `None` + - An alias for the model, for user-friendly reference. + - **Usage:** + ```shell + litellm --alias my-gpt-model + ``` + +#### --debug + - **Default:** `False` + - **Type:** `bool` (Flag) + - Enable debugging mode for the input. + - **Usage:** + ```shell + litellm --debug + ``` + +#### --temperature + - **Default:** `None` + - **Type:** `float` + - Set the temperature for the model. + - **Usage:** + ```shell + litellm --temperature 0.7 + ``` + +#### --max_tokens + - **Default:** `None` + - **Type:** `int` + - Set the maximum number of tokens for the model output. + - **Usage:** + ```shell + litellm --max_tokens 50 + ``` + +#### --request_timeout + - **Default:** `600` + - **Type:** `int` + - Set the timeout in seconds for completion calls. + - **Usage:** + ```shell + litellm --request_timeout 300 + ``` + +#### --drop_params + - **Type:** `bool` (Flag) + - Drop any unmapped params. + - **Usage:** + ```shell + litellm --drop_params + ``` + +#### --add_function_to_prompt + - **Type:** `bool` (Flag) + - If a function passed but unsupported, pass it as a part of the prompt. + - **Usage:** + ```shell + litellm --add_function_to_prompt + ``` + +#### --config + - Configure Litellm by providing a configuration file path. + - **Usage:** + ```shell + litellm --config path/to/config.yaml + ``` + +#### --telemetry + - **Default:** `True` + - **Type:** `bool` + - Help track usage of this feature. + - **Usage:** + ```shell + litellm --telemetry False + ``` + + diff --git a/docs/my-website/docs/troubleshoot.md b/docs/my-website/docs/troubleshoot.md new file mode 100644 index 0000000000000000000000000000000000000000..75a610e0cfe0fc2ecdc4f0961d2aabdd5fc491cb --- /dev/null +++ b/docs/my-website/docs/troubleshoot.md @@ -0,0 +1,20 @@ +# Support & Talk with founders +[Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) + +[Community Discord 💭](https://discord.gg/wuPM9dRgDw) + +Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ + +Our emails ✉️ ishaan@berri.ai / krrish@berri.ai + +[![Chat on WhatsApp](https://img.shields.io/static/v1?label=Chat%20on&message=WhatsApp&color=success&logo=WhatsApp&style=flat-square)](https://wa.link/huol9n) [![Chat on Discord](https://img.shields.io/static/v1?label=Chat%20on&message=Discord&color=blue&logo=Discord&style=flat-square)](https://discord.gg/wuPM9dRgDw) + +## Stable Version + +If you're running into problems with installation / Usage +Use the stable version of litellm + +```shell +pip install litellm==0.1.819 +``` + diff --git a/docs/my-website/docs/tutorials/TogetherAI_liteLLM.md b/docs/my-website/docs/tutorials/TogetherAI_liteLLM.md new file mode 100644 index 0000000000000000000000000000000000000000..08e8d56f0678757b704aafaa9d64cb1cb9b91c68 --- /dev/null +++ b/docs/my-website/docs/tutorials/TogetherAI_liteLLM.md @@ -0,0 +1,137 @@ +# Llama2 Together AI Tutorial +https://together.ai/ + + + +```python +!pip install litellm +``` + + +```python +import os +from litellm import completion +os.environ["TOGETHERAI_API_KEY"] = "" #@param +user_message = "Hello, whats the weather in San Francisco??" +messages = [{ "content": user_message,"role": "user"}] +``` + +## Calling Llama2 on TogetherAI +https://api.together.xyz/playground/chat?model=togethercomputer%2Fllama-2-70b-chat + +```python +model_name = "together_ai/togethercomputer/llama-2-70b-chat" +response = completion(model=model_name, messages=messages) +print(response) +``` + + {'choices': [{'finish_reason': 'stop', 'index': 0, 'message': {'role': 'assistant', 'content': "\n\nI'm not able to provide real-time weather information. However, I can suggest"}}], 'created': 1691629657.9288375, 'model': 'togethercomputer/llama-2-70b-chat', 'usage': {'prompt_tokens': 9, 'completion_tokens': 17, 'total_tokens': 26}} + + +LiteLLM handles the prompt formatting for Together AI's Llama2 models as well, converting your message to the +`[INST] [/INST]` format required. + +[Implementation Code](https://github.com/BerriAI/litellm/blob/64f3d3c56ef02ac5544983efc78293de31c1c201/litellm/llms/prompt_templates/factory.py#L17) + +## With Streaming + + +```python +response = completion(model=model_name, messages=messages, together_ai=True, stream=True) +print(response) +for chunk in response: + print(chunk['choices'][0]['delta']) # same as openai format +``` + + +## Use Llama2 variants with Custom Prompt Templates + +Using a version of Llama2 on TogetherAI that needs custom prompt formatting? + +You can create a custom prompt template. + +Let's make one for `OpenAssistant/llama2-70b-oasst-sft-v10`! + +The accepted template format is: [Reference](https://huggingface.co/OpenAssistant/llama2-70b-oasst-sft-v10) +``` +""" +<|im_start|>system +{system_message}<|im_end|> +<|im_start|>user +{prompt}<|im_end|> +<|im_start|>assistant +""" +``` + +Let's register our custom prompt template: [Implementation Code](https://github.com/BerriAI/litellm/blob/64f3d3c56ef02ac5544983efc78293de31c1c201/litellm/llms/prompt_templates/factory.py#L77) +```python +import litellm + +litellm.register_prompt_template( + model="OpenAssistant/llama2-70b-oasst-sft-v10", + roles={"system":"<|im_start|>system", "assistant":"<|im_start|>assistant", "user":"<|im_start|>user"}, # tell LiteLLM how you want to map the openai messages to this model + pre_message_sep= "\n", + post_message_sep= "\n" +) +``` + +Let's use it! + +```python +from litellm import completion + +# set env variable +os.environ["TOGETHERAI_API_KEY"] = "" + +messages=[{"role":"user", "content": "Write me a poem about the blue sky"}] + +completion(model="together_ai/OpenAssistant/llama2-70b-oasst-sft-v10", messages=messages) +``` + +**Complete Code** + +```python +import litellm +from litellm import completion + +# set env variable +os.environ["TOGETHERAI_API_KEY"] = "" + +litellm.register_prompt_template( + model="OpenAssistant/llama2-70b-oasst-sft-v10", + roles={"system":"<|im_start|>system", "assistant":"<|im_start|>assistant", "user":"<|im_start|>user"}, # tell LiteLLM how you want to map the openai messages to this model + pre_message_sep= "\n", + post_message_sep= "\n" +) + +messages=[{"role":"user", "content": "Write me a poem about the blue sky"}] + +response = completion(model="together_ai/OpenAssistant/llama2-70b-oasst-sft-v10", messages=messages) + +print(response) +``` + +**Output** +```json +{ + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "content": ".\n\nThe sky is a canvas of blue,\nWith clouds that drift and move,", + "role": "assistant", + "logprobs": null + } + } + ], + "created": 1693941410.482018, + "model": "OpenAssistant/llama2-70b-oasst-sft-v10", + "usage": { + "prompt_tokens": 7, + "completion_tokens": 16, + "total_tokens": 23 + }, + "litellm_call_id": "f21315db-afd6-4c1e-b43a-0b5682de4b06" +} +``` diff --git a/docs/my-website/docs/tutorials/ab_test_llms.md b/docs/my-website/docs/tutorials/ab_test_llms.md new file mode 100644 index 0000000000000000000000000000000000000000..3d482c053b02532e39e06e4da1e306a5ecd2c66f --- /dev/null +++ b/docs/my-website/docs/tutorials/ab_test_llms.md @@ -0,0 +1,99 @@ +import Image from '@theme/IdealImage'; + +# Split traffic betwen GPT-4 and Llama2 in Production! +In this tutorial, we'll walk through A/B testing between GPT-4 and Llama2 in production. We'll assume you've deployed Llama2 on Huggingface Inference Endpoints (but any of TogetherAI, Baseten, Ollama, Petals, Openrouter should work as well). + + +# Relevant Resources: + +* 🚀 [Your production dashboard!](https://admin.litellm.ai/) + + +* [Deploying models on Huggingface](https://huggingface.co/docs/inference-endpoints/guides/create_endpoint) +* [All supported providers on LiteLLM](https://docs.litellm.ai/docs/providers) + +# Code Walkthrough + +In production, we don't know if Llama2 is going to provide: +* good results +* quickly + +### 💡 Route 20% traffic to Llama2 +If Llama2 returns poor answers / is extremely slow, we want to roll-back this change, and use GPT-4 instead. + +Instead of routing 100% of our traffic to Llama2, let's **start by routing 20% traffic** to it and see how it does. + +```python +## route 20% of responses to Llama2 +split_per_model = { + "gpt-4": 0.8, + "huggingface/https://my-unique-endpoint.us-east-1.aws.endpoints.huggingface.cloud": 0.2 +} +``` + +## 👨‍💻 Complete Code + +### a) For Local +If we're testing this in a script - this is what our complete code looks like. +```python +from litellm import completion_with_split_tests +import os + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "openai key" +os.environ["HUGGINGFACE_API_KEY"] = "huggingface key" + +## route 20% of responses to Llama2 +split_per_model = { + "gpt-4": 0.8, + "huggingface/https://my-unique-endpoint.us-east-1.aws.endpoints.huggingface.cloud": 0.2 +} + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +completion_with_split_tests( + models=split_per_model, + messages=messages, +) +``` + +### b) For Production + +If we're in production, we don't want to keep going to code to change model/test details (prompt, split%, etc.) for our completion function and redeploying changes. + +LiteLLM exposes a client dashboard to do this in a UI - and instantly updates our completion function in prod. + +#### Relevant Code + +```python +completion_with_split_tests(..., use_client=True, id="my-unique-id") +``` + +#### Complete Code + +```python +from litellm import completion_with_split_tests +import os + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "openai key" +os.environ["HUGGINGFACE_API_KEY"] = "huggingface key" + +## route 20% of responses to Llama2 +split_per_model = { + "gpt-4": 0.8, + "huggingface/https://my-unique-endpoint.us-east-1.aws.endpoints.huggingface.cloud": 0.2 +} + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +completion_with_split_tests( + models=split_per_model, + messages=messages, + use_client=True, + id="my-unique-id" # Auto-create this @ https://admin.litellm.ai/ +) +``` + +### A/B Testing Dashboard after running code - https://admin.litellm.ai/ + diff --git a/docs/my-website/docs/tutorials/azure_openai.md b/docs/my-website/docs/tutorials/azure_openai.md new file mode 100644 index 0000000000000000000000000000000000000000..16436550a0d6c278a663ca45bddd4fd665107a1c --- /dev/null +++ b/docs/my-website/docs/tutorials/azure_openai.md @@ -0,0 +1,147 @@ +# Replacing OpenAI ChatCompletion with Completion() + +* [Supported OpenAI LLMs](https://docs.litellm.ai/docs/providers/openai) +* [Supported Azure OpenAI LLMs](https://docs.litellm.ai/docs/providers/azure) + + + Open In Colab + + +## Completion() - Quick Start +```python +import os +from litellm import completion + +# openai configs +os.environ["OPENAI_API_KEY"] = "" + +# azure openai configs +os.environ["AZURE_API_KEY"] = "" +os.environ["AZURE_API_BASE"] = "https://openai-gpt-4-test-v-1.openai.azure.com/" +os.environ["AZURE_API_VERSION"] = "2023-05-15" + + + +# openai call +response = completion( + model = "gpt-3.5-turbo", + messages = [{ "content": "Hello, how are you?","role": "user"}] +) +print("Openai Response\n") +print(response) + +# azure call +response = completion( + model = "azure/", + messages = [{ "content": "Hello, how are you?","role": "user"}] +) +print("Azure Response\n") +print(response) +``` + +## Completion() with Streaming +```python +import os +from litellm import completion + +# openai configs +os.environ["OPENAI_API_KEY"] = "" + +# azure openai configs +os.environ["AZURE_API_KEY"] = "" +os.environ["AZURE_API_BASE"] = "https://openai-gpt-4-test-v-1.openai.azure.com/" +os.environ["AZURE_API_VERSION"] = "2023-05-15" + + + +# openai call +response = completion( + model = "gpt-3.5-turbo", + messages = [{ "content": "Hello, how are you?","role": "user"}], + stream=True +) +print("OpenAI Streaming response") +for chunk in response: + print(chunk) + +# azure call +response = completion( + model = "azure/", + messages = [{ "content": "Hello, how are you?","role": "user"}], + stream=True +) +print("Azure Streaming response") +for chunk in response: + print(chunk) + +``` + +## Completion() with Streaming + Async +```python +import os +from litellm import acompletion + +# openai configs +os.environ["OPENAI_API_KEY"] = "" + +# azure openai configs +os.environ["AZURE_API_KEY"] = "" +os.environ["AZURE_API_BASE"] = "https://openai-gpt-4-test-v-1.openai.azure.com/" +os.environ["AZURE_API_VERSION"] = "2023-05-15" + + + +# openai call +response = acompletion( + model = "gpt-3.5-turbo", + messages = [{ "content": "Hello, how are you?","role": "user"}], + stream=True +) + +# azure call +response = acompletion( + model = "azure/", + messages = [{ "content": "Hello, how are you?","role": "user"}], + stream=True +) + +``` + +## Completion() multi-threaded + +```python +import os +import threading +from litellm import completion + +# Function to make a completion call +def make_completion(model, messages): + response = completion( + model=model, + messages=messages, + stream=True + ) + + print(f"Response for {model}: {response}") + +# Set your API keys +os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY" +os.environ["AZURE_API_KEY"] = "YOUR_AZURE_API_KEY" + +# Define the messages for the completions +messages = [{"content": "Hello, how are you?", "role": "user"}] + +# Create threads for making the completions +thread1 = threading.Thread(target=make_completion, args=("gpt-3.5-turbo", messages)) +thread2 = threading.Thread(target=make_completion, args=("azure/your-azure-deployment", messages)) + +# Start both threads +thread1.start() +thread2.start() + +# Wait for both threads to finish +thread1.join() +thread2.join() + +print("Both completions are done.") +``` diff --git a/docs/my-website/docs/tutorials/compare_llms.md b/docs/my-website/docs/tutorials/compare_llms.md new file mode 100644 index 0000000000000000000000000000000000000000..a7eda2c85fdcb5d91cc78037edad3048f3827051 --- /dev/null +++ b/docs/my-website/docs/tutorials/compare_llms.md @@ -0,0 +1,370 @@ +import Image from '@theme/IdealImage'; + +# Benchmark LLMs +Easily benchmark LLMs for a given question by viewing +* Responses +* Response Cost +* Response Time + +### Benchmark Output + + +## Setup: +``` +git clone https://github.com/BerriAI/litellm +``` +cd to `litellm/cookbook/benchmark` dir + +Located here: +https://github.com/BerriAI/litellm/tree/main/cookbook/benchmark +``` +cd litellm/cookbook/benchmark +``` + +### Install Dependencies +``` +pip install litellm click tqdm tabulate termcolor +``` + +### Configuration - Set LLM API Keys + LLMs in benchmark.py +In `benchmark/benchmark.py` select your LLMs, LLM API Key and questions + +Supported LLMs: https://docs.litellm.ai/docs/providers + +```python +# Define the list of models to benchmark +models = ['gpt-3.5-turbo', 'claude-2'] + +# Enter LLM API keys +os.environ['OPENAI_API_KEY'] = "" +os.environ['ANTHROPIC_API_KEY'] = "" + +# List of questions to benchmark (replace with your questions) +questions = [ + "When will BerriAI IPO?", + "When will LiteLLM hit $100M ARR?" +] + +``` + +## Run benchmark.py +``` +python3 benchmark.py +``` + +## Expected Output +``` +Running question: When will BerriAI IPO? for model: claude-2: 100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:13<00:00, 4.41s/it] + +Benchmark Results for 'When will BerriAI IPO?': ++-----------------+----------------------------------------------------------------------------------+---------------------------+------------+ +| Model | Response | Response Time (seconds) | Cost ($) | ++=================+==================================================================================+===========================+============+ +| gpt-3.5-turbo | As an AI language model, I cannot provide up-to-date information or predict | 1.55 seconds | $0.000122 | +| | future events. It is best to consult a reliable financial source or contact | | | +| | BerriAI directly for information regarding their IPO plans. | | | ++-----------------+----------------------------------------------------------------------------------+---------------------------+------------+ +| togethercompute | I'm not able to provide information about future IPO plans or dates for BerriAI | 8.52 seconds | $0.000531 | +| r/llama-2-70b-c | or any other company. IPO (Initial Public Offering) plans and timelines are | | | +| hat | typically kept private by companies until they are ready to make a public | | | +| | announcement. It's important to note that IPO plans can change and are subject | | | +| | to various factors, such as market conditions, financial performance, and | | | +| | regulatory approvals. Therefore, it's difficult to predict with certainty when | | | +| | BerriAI or any other company will go public. If you're interested in staying | | | +| | up-to-date with BerriAI's latest news and developments, you may want to follow | | | +| | their official social media accounts, subscribe to their newsletter, or visit | | | +| | their website periodically for updates. | | | ++-----------------+----------------------------------------------------------------------------------+---------------------------+------------+ +| claude-2 | I do not have any information about when or if BerriAI will have an initial | 3.17 seconds | $0.002084 | +| | public offering (IPO). As an AI assistant created by Anthropic to be helpful, | | | +| | harmless, and honest, I do not have insider knowledge about Anthropic's business | | | +| | plans or strategies. | | | ++-----------------+----------------------------------------------------------------------------------+---------------------------+------------+ +``` +## Support +**🤝 Schedule a 1-on-1 Session:** Book a [1-on-1 session](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat) with Krrish and Ishaan, the founders, to discuss any issues, provide feedback, or explore how we can improve LiteLLM for you. + + + diff --git a/docs/my-website/docs/tutorials/compare_llms_2.md b/docs/my-website/docs/tutorials/compare_llms_2.md new file mode 100644 index 0000000000000000000000000000000000000000..20aee68890d29ccba594fb3f411f30ee5ad4dce8 --- /dev/null +++ b/docs/my-website/docs/tutorials/compare_llms_2.md @@ -0,0 +1,123 @@ +import Image from '@theme/IdealImage'; + +# Comparing LLMs on a Test Set using LiteLLM + + +
+ +LiteLLM allows you to use any LLM as a drop in replacement for +`gpt-3.5-turbo` + +This notebook walks through how you can compare GPT-4 vs Claude-2 on a +given test set using litellm + +## Output at the end of this tutorial: + +

+ +
+ +
+ +``` python +!pip install litellm +``` + +
+ +
+ +``` python +from litellm import completion +import litellm + +# init your test set questions +questions = [ + "how do i call completion() using LiteLLM", + "does LiteLLM support VertexAI", + "how do I set my keys on replicate llama2?", +] + + +# set your prompt +prompt = """ +You are a coding assistant helping users using litellm. +litellm is a light package to simplify calling OpenAI, Azure, Cohere, Anthropic, Huggingface API Endpoints. It manages: + +""" +``` + +
+ +
+ +``` python +import os +os.environ['OPENAI_API_KEY'] = "" +os.environ['ANTHROPIC_API_KEY'] = "" +``` + +
+ +
+ +
+ +
+ +## Calling gpt-3.5-turbo and claude-2 on the same questions + +## LiteLLM `completion()` allows you to call all LLMs in the same format + +
+ +
+ +``` python +results = [] # for storing results + +models = ['gpt-3.5-turbo', 'claude-2'] # define what models you're testing, see: https://docs.litellm.ai/docs/providers +for question in questions: + row = [question] + for model in models: + print("Calling:", model, "question:", question) + response = completion( # using litellm.completion + model=model, + messages=[ + {'role': 'system', 'content': prompt}, + {'role': 'user', 'content': question} + ] + ) + answer = response.choices[0].message['content'] + row.append(answer) + print(print("Calling:", model, "answer:", answer)) + + results.append(row) # save results + +``` + +
+ +
+ +## Visualizing Results + +
+ +
+ +``` python +# Create a table to visualize results +import pandas as pd + +columns = ['Question'] + models +df = pd.DataFrame(results, columns=columns) + +df +``` +## Output Table + + +
diff --git a/docs/my-website/docs/tutorials/eval_suites.md b/docs/my-website/docs/tutorials/eval_suites.md new file mode 100644 index 0000000000000000000000000000000000000000..1107fcc17a530334f44a6a8018da1c547dea200f --- /dev/null +++ b/docs/my-website/docs/tutorials/eval_suites.md @@ -0,0 +1,293 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Evaluate LLMs - ML Flow Evals, Auto Eval + +## Using LiteLLM with ML Flow +MLflow provides an API `mlflow.evaluate()` to help evaluate your LLMs https://mlflow.org/docs/latest/llms/llm-evaluate/index.html + +### Pre Requisites +```shell +pip install litellm +``` +```shell +pip install mlflow +``` + + +### Step 1: Start LiteLLM Proxy on the CLI +LiteLLM allows you to create an OpenAI compatible server for all supported LLMs. [More information on litellm proxy here](https://docs.litellm.ai/docs/simple_proxy) + +```shell +$ litellm --model huggingface/bigcode/starcoder + +#INFO: Proxy running on http://0.0.0.0:8000 +``` + +**Here's how you can create the proxy for other supported llms** + + + +```shell +$ export AWS_ACCESS_KEY_ID="" +$ export AWS_REGION_NAME="" # e.g. us-west-2 +$ export AWS_SECRET_ACCESS_KEY="" +``` + +```shell +$ litellm --model bedrock/anthropic.claude-v2 +``` + + + +```shell +$ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL] +``` +```shell +$ litellm --model huggingface/ --api_base https://k58ory32yinf1ly0.us-east-1.aws.endpoints.huggingface.cloud +``` + + + + +```shell +$ export ANTHROPIC_API_KEY=my-api-key +``` +```shell +$ litellm --model claude-instant-1 +``` + + + +Assuming you're running vllm locally + +```shell +$ litellm --model vllm/facebook/opt-125m +``` + + + +```shell +$ litellm --model openai/ --api_base +``` + + + +```shell +$ export TOGETHERAI_API_KEY=my-api-key +``` +```shell +$ litellm --model together_ai/lmsys/vicuna-13b-v1.5-16k +``` + + + + + +```shell +$ export REPLICATE_API_KEY=my-api-key +``` +```shell +$ litellm \ + --model replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3 +``` + + + + + +```shell +$ litellm --model petals/meta-llama/Llama-2-70b-chat-hf +``` + + + + + +```shell +$ export PALM_API_KEY=my-palm-key +``` +```shell +$ litellm --model palm/chat-bison +``` + + + + + +```shell +$ export AZURE_API_KEY=my-api-key +$ export AZURE_API_BASE=my-api-base +``` +``` +$ litellm --model azure/my-deployment-name +``` + + + + + +```shell +$ export AI21_API_KEY=my-api-key +``` + +```shell +$ litellm --model j2-light +``` + + + + + +```shell +$ export COHERE_API_KEY=my-api-key +``` + +```shell +$ litellm --model command-nightly +``` + + + + + + +### Step 2: Run ML Flow +Before running the eval we will set `openai.api_base` to the litellm proxy from Step 1 + +```python +openai.api_base = "http://0.0.0.0:8000" +``` + +```python +import openai +import pandas as pd +openai.api_key = "anything" # this can be anything, we set the key on the proxy +openai.api_base = "http://0.0.0.0:8000" # set api base to the proxy from step 1 + + +import mlflow +eval_data = pd.DataFrame( + { + "inputs": [ + "What is the largest country", + "What is the weather in sf?", + ], + "ground_truth": [ + "India is a large country", + "It's cold in SF today" + ], + } +) + +with mlflow.start_run() as run: + system_prompt = "Answer the following question in two sentences" + logged_model_info = mlflow.openai.log_model( + model="gpt-3.5", + task=openai.ChatCompletion, + artifact_path="model", + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": "{question}"}, + ], + ) + + # Use predefined question-answering metrics to evaluate our model. + results = mlflow.evaluate( + logged_model_info.model_uri, + eval_data, + targets="ground_truth", + model_type="question-answering", + ) + print(f"See aggregated evaluation results below: \n{results.metrics}") + + # Evaluation result for each data record is available in `results.tables`. + eval_table = results.tables["eval_results_table"] + print(f"See evaluation table below: \n{eval_table}") + + +``` + +### ML Flow Output +``` +{'toxicity/v1/mean': 0.00014476531214313582, 'toxicity/v1/variance': 2.5759661361262862e-12, 'toxicity/v1/p90': 0.00014604929747292773, 'toxicity/v1/ratio': 0.0, 'exact_match/v1': 0.0} +Downloading artifacts: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1890.18it/s] +See evaluation table below: + inputs ground_truth outputs token_count toxicity/v1/score +0 What is the largest country India is a large country Russia is the largest country in the world in... 14 0.000146 +1 What is the weather in sf? It's cold in SF today I'm sorry, I cannot provide the current weath... 36 0.000143 +``` + + +## Using LiteLLM with AutoEval +AutoEvals is a tool for quickly and easily evaluating AI model outputs using best practices. +https://github.com/braintrustdata/autoevals + +### Pre Requisites +```shell +pip install litellm +``` +```shell +pip install autoevals +``` + +### Quick Start +In this code sample we use the `Factuality()` evaluator from `autoevals.llm` to test whether an output is factual, compared to an original (expected) value. + +**Autoevals uses gpt-3.5-turbo / gpt-4-turbo by default to evaluate responses** + +See autoevals docs on the [supported evaluators](https://www.braintrustdata.com/docs/autoevals/python#autoevalsllm) - Translation, Summary, Security Evaluators etc + +```python +# auto evals imports +from autoevals.llm import * +################### +import litellm + +# litellm completion call +question = "which country has the highest population" +response = litellm.completion( + model = "gpt-3.5-turbo", + messages = [ + { + "role": "user", + "content": question + } + ], +) +print(response) +# use the auto eval Factuality() evaluator +evaluator = Factuality() +result = evaluator( + output=response.choices[0]["message"]["content"], # response from litellm.completion() + expected="India", # expected output + input=question # question passed to litellm.completion +) + +print(result) +``` + +#### Output of Evaluation - from AutoEvals +```shell +Score( + name='Factuality', + score=0, + metadata= + {'rationale': "The expert answer is 'India'.\nThe submitted answer is 'As of 2021, China has the highest population in the world with an estimated 1.4 billion people.'\nThe submitted answer mentions China as the country with the highest population, while the expert answer mentions India.\nThere is a disagreement between the submitted answer and the expert answer.", + 'choice': 'D' + }, + error=None +) +``` + + + + + + + + + + + diff --git a/docs/my-website/docs/tutorials/fallbacks.md b/docs/my-website/docs/tutorials/fallbacks.md new file mode 100644 index 0000000000000000000000000000000000000000..43494af3ceb6bf0043807366fb22910b86d27177 --- /dev/null +++ b/docs/my-website/docs/tutorials/fallbacks.md @@ -0,0 +1,134 @@ +# Using completion() with Fallbacks for Reliability + +This tutorial demonstrates how to employ the `completion()` function with model fallbacks to ensure reliability. LLM APIs can be unstable, completion() with fallbacks ensures you'll always get a response from your calls + +## Usage +To use fallback models with `completion()`, specify a list of models in the `fallbacks` parameter. + +The `fallbacks` list should include the primary model you want to use, followed by additional models that can be used as backups in case the primary model fails to provide a response. + +```python +response = completion(model="bad-model", fallbacks=["gpt-3.5-turbo" "command-nightly"], messages=messages) +``` + +## How does `completion_with_fallbacks()` work + +The `completion_with_fallbacks()` function attempts a completion call using the primary model specified as `model` in `completion(model=model)`. If the primary model fails or encounters an error, it automatically tries the `fallbacks` models in the specified order. This ensures a response even if the primary model is unavailable. + +### Output from calls +``` +Completion with 'bad-model': got exception Unable to map your input to a model. Check your input - {'model': 'bad-model' + + + +completion call gpt-3.5-turbo +{ + "id": "chatcmpl-7qTmVRuO3m3gIBg4aTmAumV1TmQhB", + "object": "chat.completion", + "created": 1692741891, + "model": "gpt-3.5-turbo-0613", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "I apologize, but as an AI, I do not have the capability to provide real-time weather updates. However, you can easily check the current weather in San Francisco by using a search engine or checking a weather website or app." + }, + "finish_reason": "stop" + } + ], + "usage": { + "prompt_tokens": 16, + "completion_tokens": 46, + "total_tokens": 62 + } +} + +``` + +### Key components of Model Fallbacks implementation: +* Looping through `fallbacks` +* Cool-Downs for rate-limited models + +#### Looping through `fallbacks` +Allow `45seconds` for each request. In the 45s this function tries calling the primary model set as `model`. If model fails it loops through the backup `fallbacks` models and attempts to get a response in the allocated `45s` time set here: +```python +while response == None and time.time() - start_time < 45: + for model in fallbacks: +``` + +#### Cool-Downs for rate-limited models +If a model API call leads to an error - allow it to cooldown for `60s` +```python +except Exception as e: + print(f"got exception {e} for model {model}") + rate_limited_models.add(model) + model_expiration_times[model] = ( + time.time() + 60 + ) # cool down this selected model + pass +``` + +Before making an LLM API call we check if the selected model is in `rate_limited_models`, if so skip making the API call +```python +if ( + model in rate_limited_models +): # check if model is currently cooling down + if ( + model_expiration_times.get(model) + and time.time() >= model_expiration_times[model] + ): + rate_limited_models.remove( + model + ) # check if it's been 60s of cool down and remove model + else: + continue # skip model + +``` + +#### Full code of completion with fallbacks() +```python + + response = None + rate_limited_models = set() + model_expiration_times = {} + start_time = time.time() + fallbacks = [kwargs["model"]] + kwargs["fallbacks"] + del kwargs["fallbacks"] # remove fallbacks so it's not recursive + + while response == None and time.time() - start_time < 45: + for model in fallbacks: + # loop thru all models + try: + if ( + model in rate_limited_models + ): # check if model is currently cooling down + if ( + model_expiration_times.get(model) + and time.time() >= model_expiration_times[model] + ): + rate_limited_models.remove( + model + ) # check if it's been 60s of cool down and remove model + else: + continue # skip model + + # delete model from kwargs if it exists + if kwargs.get("model"): + del kwargs["model"] + + print("making completion call", model) + response = litellm.completion(**kwargs, model=model) + + if response != None: + return response + + except Exception as e: + print(f"got exception {e} for model {model}") + rate_limited_models.add(model) + model_expiration_times[model] = ( + time.time() + 60 + ) # cool down this selected model + pass + return response +``` diff --git a/docs/my-website/docs/tutorials/finetuned_chat_gpt.md b/docs/my-website/docs/tutorials/finetuned_chat_gpt.md new file mode 100644 index 0000000000000000000000000000000000000000..641c45b5f8c45d4be7ae3552f316a8c47fd1b5f4 --- /dev/null +++ b/docs/my-website/docs/tutorials/finetuned_chat_gpt.md @@ -0,0 +1,50 @@ +# Using Fine-Tuned gpt-3.5-turbo +LiteLLM allows you to call `completion` with your fine-tuned gpt-3.5-turbo models +If you're trying to create your custom finetuned gpt-3.5-turbo model following along on this tutorial: https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset + +Once you've created your fine tuned model, you can call it with `litellm.completion()` + +## Usage +```python +import os +from litellm import completion + +# LiteLLM reads from your .env +os.environ["OPENAI_API_KEY"] = "your-api-key" + +response = completion( + model="ft:gpt-3.5-turbo:my-org:custom_suffix:id", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello!"} + ] +) + +print(response.choices[0].message) +``` + +## Usage - Setting OpenAI Organization ID +LiteLLM allows you to specify your OpenAI Organization when calling OpenAI LLMs. More details here: +[setting Organization ID](https://docs.litellm.ai/docs/providers/openai#setting-organization-id-for-completion-calls) +This can be set in one of the following ways: +- Environment Variable `OPENAI_ORGANIZATION` +- Params to `litellm.completion(model=model, organization="your-organization-id")` +- Set as `litellm.organization="your-organization-id"` +```python +import os +from litellm import completion + +# LiteLLM reads from your .env +os.environ["OPENAI_API_KEY"] = "your-api-key" +os.environ["OPENAI_ORGANIZATION"] = "your-org-id" # Optional + +response = completion( + model="ft:gpt-3.5-turbo:my-org:custom_suffix:id", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello!"} + ] +) + +print(response.choices[0].message) +``` \ No newline at end of file diff --git a/docs/my-website/docs/tutorials/first_playground.md b/docs/my-website/docs/tutorials/first_playground.md new file mode 100644 index 0000000000000000000000000000000000000000..bc34e89b6c24c092cd14cd1a409adbb688a4a36e --- /dev/null +++ b/docs/my-website/docs/tutorials/first_playground.md @@ -0,0 +1,187 @@ +# Create your first LLM playground +import Image from '@theme/IdealImage'; + +Create a playground to **evaluate multiple LLM Providers in less than 10 minutes**. If you want to see this in prod, check out our [website](https://litellm.ai/). + +**What will it look like?** +streamlit_playground + +**How will we do this?**: We'll build the server and connect it to our template frontend, ending up with a working playground UI by the end! + +:::info + + Before you start, make sure you have followed the [environment-setup](./installation) guide. Please note, that this tutorial relies on you having API keys from at least 1 model provider (E.g. OpenAI). +::: + +## 1. Quick start + +Let's make sure our keys are working. Run this script in any environment of your choice (e.g. [Google Colab](https://colab.research.google.com/#create=true)). + +🚨 Don't forget to replace the placeholder key values with your keys! + +```python +pip install litellm +``` + +```python +from litellm import completion + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "openai key" ## REPLACE THIS +os.environ["COHERE_API_KEY"] = "cohere key" ## REPLACE THIS +os.environ["AI21_API_KEY"] = "ai21 key" ## REPLACE THIS + + +messages = [{ "content": "Hello, how are you?","role": "user"}] + +# openai call +response = completion(model="gpt-3.5-turbo", messages=messages) + +# cohere call +response = completion("command-nightly", messages) + +# ai21 call +response = completion("j2-mid", messages) +``` + +## 2. Set-up Server + +Let's build a basic Flask app as our backend server. We'll give it a specific route for our completion calls. + +**Notes**: +* 🚨 Don't forget to replace the placeholder key values with your keys! +* `completion_with_retries`: LLM API calls can fail in production. This function wraps the normal litellm completion() call with [tenacity](https://tenacity.readthedocs.io/en/latest/) to retry the call in case it fails. + +LiteLLM specific snippet: + +```python +import os +from litellm import completion_with_retries + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "openai key" ## REPLACE THIS +os.environ["COHERE_API_KEY"] = "cohere key" ## REPLACE THIS +os.environ["AI21_API_KEY"] = "ai21 key" ## REPLACE THIS + + +@app.route('/chat/completions', methods=["POST"]) +def api_completion(): + data = request.json + data["max_tokens"] = 256 # By default let's set max_tokens to 256 + try: + # COMPLETION CALL + response = completion_with_retries(**data) + except Exception as e: + # print the error + print(e) + return response +``` + +The complete code: + +```python +import os +from flask import Flask, jsonify, request +from litellm import completion_with_retries + + +## set ENV variables +os.environ["OPENAI_API_KEY"] = "openai key" ## REPLACE THIS +os.environ["COHERE_API_KEY"] = "cohere key" ## REPLACE THIS +os.environ["AI21_API_KEY"] = "ai21 key" ## REPLACE THIS + +app = Flask(__name__) + +# Example route +@app.route('/', methods=['GET']) +def hello(): + return jsonify(message="Hello, Flask!") + +@app.route('/chat/completions', methods=["POST"]) +def api_completion(): + data = request.json + data["max_tokens"] = 256 # By default let's set max_tokens to 256 + try: + # COMPLETION CALL + response = completion_with_retries(**data) + except Exception as e: + # print the error + print(e) + + return response + +if __name__ == '__main__': + from waitress import serve + serve(app, host="0.0.0.0", port=4000, threads=500) +``` + +### Let's test it +Start the server: +```python +python main.py +``` + +Run this curl command to test it: +```curl +curl -X POST localhost:4000/chat/completions \ +-H 'Content-Type: application/json' \ +-d '{ + "model": "gpt-3.5-turbo", + "messages": [{ + "content": "Hello, how are you?", + "role": "user" + }] +}' +``` + +This is what you should see + +python_code_sample_2 + +## 3. Connect to our frontend template + +### 3.1 Download template + +For our frontend, we'll use [Streamlit](https://streamlit.io/) - this enables us to build a simple python web-app. + +Let's download the playground template we (LiteLLM) have created: + +```zsh +git clone https://github.com/BerriAI/litellm_playground_fe_template.git +``` + +### 3.2 Run it + +Make sure our server from [step 2](#2-set-up-server) is still running at port 4000 + +:::info + + If you used another port, no worries - just make sure you change [this line](https://github.com/BerriAI/litellm_playground_fe_template/blob/411bea2b6a2e0b079eb0efd834886ad783b557ef/app.py#L7) in your playground template's app.py +::: + +Now let's run our app: + +```zsh +cd litellm_playground_fe_template && streamlit run app.py +``` + +If you're missing Streamlit - just pip install it (or check out their [installation guidelines](https://docs.streamlit.io/library/get-started/installation#install-streamlit-on-macoslinux)) + +```zsh +pip install streamlit +``` + +This is what you should see: +streamlit_playground + + +# Congratulations 🚀 + +You've created your first LLM Playground - with the ability to call 50+ LLM APIs. + +Next Steps: +* [Check out the full list of LLM Providers you can now add](https://docs.litellm.ai/docs/providers) \ No newline at end of file diff --git a/docs/my-website/docs/tutorials/gradio_integration.md b/docs/my-website/docs/tutorials/gradio_integration.md new file mode 100644 index 0000000000000000000000000000000000000000..4854fc2ac95268c4a622f052fbbfade035a7ce2f --- /dev/null +++ b/docs/my-website/docs/tutorials/gradio_integration.md @@ -0,0 +1,62 @@ +# Gradio Chatbot + LiteLLM Tutorial +Simple tutorial for integrating LiteLLM completion calls with streaming Gradio chatbot demos + +### Install & Import Dependencies +```python +!pip install gradio litellm +import gradio +import litellm +``` + +### Define Inference Function +Remember to set `model` and `api_base` as expected by the server hosting your LLM. +```python +def inference(message, history): + try: + flattened_history = [item for sublist in history for item in sublist] + full_message = " ".join(flattened_history + [message]) + messages_litellm = [{"role": "user", "content": full_message}] # litellm message format + partial_message = "" + for chunk in litellm.completion(model="huggingface/meta-llama/Llama-2-7b-chat-hf", + api_base="x.x.x.x:xxxx", + messages=messages_litellm, + max_new_tokens=512, + temperature=.7, + top_k=100, + top_p=.9, + repetition_penalty=1.18, + stream=True): + partial_message += chunk['choices'][0]['delta']['content'] # extract text from streamed litellm chunks + yield partial_message + except Exception as e: + print("Exception encountered:", str(e)) + yield f"An Error occured please 'Clear' the error and try your question again" +``` + +### Define Chat Interface +```python +gr.ChatInterface( + inference, + chatbot=gr.Chatbot(height=400), + textbox=gr.Textbox(placeholder="Enter text here...", container=False, scale=5), + description=f""" + CURRENT PROMPT TEMPLATE: {model_name}. + An incorrect prompt template will cause performance to suffer. + Check the API specifications to ensure this format matches the target LLM.""", + title="Simple Chatbot Test Application", + examples=["Define 'deep learning' in once sentence."], + retry_btn="Retry", + undo_btn="Undo", + clear_btn="Clear", + theme=theme, +).queue().launch() +``` +### Launch Gradio App +1. From command line: `python app.py` or `gradio app.py` (latter enables live deployment updates) +2. Visit provided hyperlink in your browser. +3. Enjoy prompt-agnostic interaction with remote LLM server. + +### Recommended Extensions: +* Add command line arguments to define target model & inference endpoints + +Credits to [ZQ](https://x.com/ZQ_Dev), for this tutorial. \ No newline at end of file diff --git a/docs/my-website/docs/tutorials/huggingface_codellama.md b/docs/my-website/docs/tutorials/huggingface_codellama.md new file mode 100644 index 0000000000000000000000000000000000000000..bff301b663b4ebc3e0f2e93bd4cd173851ce28e4 --- /dev/null +++ b/docs/my-website/docs/tutorials/huggingface_codellama.md @@ -0,0 +1,45 @@ +# CodeLlama - Code Infilling + +This tutorial shows how you can call CodeLlama (hosted on Huggingface PRO Inference Endpoints), to fill code. + +This is a specialized task particular to code models. The model is trained to generate the code (including comments) that best matches an existing prefix and suffix. + +This task is available in the base and instruction variants of the **7B** and **13B** CodeLlama models. It is not available for any of the 34B models or the Python versions. + +# usage + +```python +import os +from litellm import longer_context_model_fallback_dict, ContextWindowExceededError, completion + +os.environ["HUGGINGFACE_API_KEY"] = "your-hf-token" # https://huggingface.co/docs/hub/security-tokens + +## CREATE THE PROMPT +prompt_prefix = 'def remove_non_ascii(s: str) -> str:\n """ ' +prompt_suffix = "\n return result" + +### set 
  to indicate the string before and after the part you want codellama to fill 
+prompt = f"
 {prompt_prefix} {prompt_suffix} "
+
+messages = [{"content": prompt, "role": "user"}]
+model = "huggingface/codellama/CodeLlama-34b-Instruct-hf" # specify huggingface as the provider 'huggingface/'
+response = completion(model=model, messages=messages, max_tokens=500)
+```
+
+# output 
+```python
+def remove_non_ascii(s: str) -> str:
+    """ Remove non-ASCII characters from a string.
+
+    Args:
+        s (str): The string to remove non-ASCII characters from.
+
+    Returns:
+        str: The string with non-ASCII characters removed.
+    """
+    result = ""
+    for c in s:
+        if ord(c) < 128:
+            result += c
+    return result
+```
\ No newline at end of file
diff --git a/docs/my-website/docs/tutorials/huggingface_tutorial.md b/docs/my-website/docs/tutorials/huggingface_tutorial.md
new file mode 100644
index 0000000000000000000000000000000000000000..5d569ab8db9ade6665e040c2dae92de621d1a549
--- /dev/null
+++ b/docs/my-website/docs/tutorials/huggingface_tutorial.md
@@ -0,0 +1,118 @@
+# Llama2 - Huggingface Tutorial 
+[Huggingface](https://huggingface.co/) is an open source platform to deploy machine-learnings models. 
+
+## Call Llama2 with Huggingface Inference Endpoints 
+LiteLLM makes it easy to call your public, private or the default huggingface endpoints. 
+
+In this case, let's try and call 3 models:  
+
+| Model                                   | Type of Endpoint |
+| --------------------------------------- | ---------------- |
+| deepset/deberta-v3-large-squad2         | [Default Huggingface Endpoint](#case-1-call-default-huggingface-endpoint) |
+| meta-llama/Llama-2-7b-hf                | [Public Endpoint](#case-2-call-llama2-public-huggingface-endpoint)              |
+| meta-llama/Llama-2-7b-chat-hf           | [Private Endpoint](#case-3-call-llama2-private-huggingface-endpoint)             |
+
+### Case 1: Call default huggingface endpoint
+
+Here's the complete example:
+
+```python
+from litellm import completion 
+
+model = "deepset/deberta-v3-large-squad2"
+messages = [{"role": "user", "content": "Hey, how's it going?"}] # LiteLLM follows the OpenAI format 
+
+### CALLING ENDPOINT
+completion(model=model, messages=messages, custom_llm_provider="huggingface")
+```
+
+What's happening? 
+- model: This is the name of the deployed model on huggingface 
+- messages: This is the input. We accept the OpenAI chat format. For huggingface, by default we iterate through the list and add the message["content"] to the prompt. [Relevant Code](https://github.com/BerriAI/litellm/blob/6aff47083be659b80e00cb81eb783cb24db2e183/litellm/llms/huggingface_restapi.py#L46)
+- custom_llm_provider: Optional param. This is an optional flag, needed only for Azure, Replicate, Huggingface and Together-ai (platforms where you deploy your own models). This enables litellm to route to the right provider, for your model. 
+
+### Case 2: Call Llama2 public Huggingface endpoint
+
+We've deployed `meta-llama/Llama-2-7b-hf` behind a public endpoint - `https://ag3dkq4zui5nu8g3.us-east-1.aws.endpoints.huggingface.cloud`.
+
+Let's try it out: 
+```python
+from litellm import completion 
+
+model = "meta-llama/Llama-2-7b-hf"
+messages = [{"role": "user", "content": "Hey, how's it going?"}] # LiteLLM follows the OpenAI format 
+api_base = "https://ag3dkq4zui5nu8g3.us-east-1.aws.endpoints.huggingface.cloud"
+
+### CALLING ENDPOINT
+completion(model=model, messages=messages, custom_llm_provider="huggingface", api_base=api_base)
+```
+
+What's happening? 
+- api_base: Optional param. Since this uses a deployed endpoint (not the [default huggingface inference endpoint](https://github.com/BerriAI/litellm/blob/6aff47083be659b80e00cb81eb783cb24db2e183/litellm/llms/huggingface_restapi.py#L35)), we pass that to LiteLLM. 
+
+### Case 3: Call Llama2 private Huggingface endpoint
+
+The only difference between this and the public endpoint, is that you need an `api_key` for this. 
+
+On LiteLLM there's 3 ways you can pass in an api_key. 
+
+Either via environment variables, by setting it as a package variable or when calling `completion()`. 
+
+**Setting via environment variables**  
+Here's the 1 line of code you need to add 
+```python
+os.environ["HF_TOKEN"] = "..."
+```
+
+Here's the full code: 
+```python
+from litellm import completion 
+
+os.environ["HF_TOKEN"] = "..."
+
+model = "meta-llama/Llama-2-7b-hf"
+messages = [{"role": "user", "content": "Hey, how's it going?"}] # LiteLLM follows the OpenAI format 
+api_base = "https://ag3dkq4zui5nu8g3.us-east-1.aws.endpoints.huggingface.cloud"
+
+### CALLING ENDPOINT
+completion(model=model, messages=messages, custom_llm_provider="huggingface", api_base=api_base)
+```
+
+**Setting it as package variable**  
+Here's the 1 line of code you need to add 
+```python
+litellm.huggingface_key = "..."
+```
+
+Here's the full code: 
+```python
+import litellm
+from litellm import completion 
+
+litellm.huggingface_key = "..."
+
+model = "meta-llama/Llama-2-7b-hf"
+messages = [{"role": "user", "content": "Hey, how's it going?"}] # LiteLLM follows the OpenAI format 
+api_base = "https://ag3dkq4zui5nu8g3.us-east-1.aws.endpoints.huggingface.cloud"
+
+### CALLING ENDPOINT
+completion(model=model, messages=messages, custom_llm_provider="huggingface", api_base=api_base)
+```
+
+**Passed in during completion call**  
+```python
+completion(..., api_key="...")
+```
+
+Here's the full code: 
+
+```python
+from litellm import completion 
+
+model = "meta-llama/Llama-2-7b-hf"
+messages = [{"role": "user", "content": "Hey, how's it going?"}] # LiteLLM follows the OpenAI format 
+api_base = "https://ag3dkq4zui5nu8g3.us-east-1.aws.endpoints.huggingface.cloud"
+
+### CALLING ENDPOINT
+completion(model=model, messages=messages, custom_llm_provider="huggingface", api_base=api_base, api_key="...")
+```
diff --git a/docs/my-website/docs/tutorials/installation.md b/docs/my-website/docs/tutorials/installation.md
new file mode 100644
index 0000000000000000000000000000000000000000..ecaed0bec9d7bdec3a4203267388f2d7901ca7aa
--- /dev/null
+++ b/docs/my-website/docs/tutorials/installation.md
@@ -0,0 +1,17 @@
+---
+displayed_sidebar: tutorialSidebar
+---
+
+# Set up environment
+
+Let's get the necessary keys to set up our demo environment.
+
+Every LLM provider needs API keys (e.g. `OPENAI_API_KEY`). You can get API keys from OpenAI, Cohere and AI21 **without a waitlist**.
+
+Let's get them for our demo!
+
+**OpenAI**: https://platform.openai.com/account/api-keys  
+**Cohere**: https://dashboard.cohere.com/welcome/login?redirect_uri=%2Fapi-keys  (no credit card required)  
+**AI21**: https://studio.ai21.com/account/api-key (no credit card required)
+
+
diff --git a/docs/my-website/docs/tutorials/litellm_Test_Multiple_Providers.md b/docs/my-website/docs/tutorials/litellm_Test_Multiple_Providers.md
new file mode 100644
index 0000000000000000000000000000000000000000..2503e3cbf6f401cfc4e9fbc0b764fc6ae82b37d3
--- /dev/null
+++ b/docs/my-website/docs/tutorials/litellm_Test_Multiple_Providers.md
@@ -0,0 +1,136 @@
+# Reliability test Multiple LLM Providers with LiteLLM
+
+
+
+*   Quality Testing
+*   Load Testing
+*   Duration Testing
+
+
+
+
+```python
+!pip install litellm python-dotenv
+```
+
+
+```python
+import litellm
+from litellm import load_test_model, testing_batch_completion
+import time
+```
+
+
+```python
+from dotenv import load_dotenv
+load_dotenv()
+```
+
+# Quality Test endpoint
+
+## Test the same prompt across multiple LLM providers
+
+In this example, let's ask some questions about Paul Graham
+
+
+```python
+models = ["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-4", "claude-instant-1", "replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781"]
+context = """Paul Graham (/ɡræm/; born 1964)[3] is an English computer scientist, essayist, entrepreneur, venture capitalist, and author. He is best known for his work on the programming language Lisp, his former startup Viaweb (later renamed Yahoo! Store), cofounding the influential startup accelerator and seed capital firm Y Combinator, his essays, and Hacker News. He is the author of several computer programming books, including: On Lisp,[4] ANSI Common Lisp,[5] and Hackers & Painters.[6] Technology journalist Steven Levy has described Graham as a "hacker philosopher".[7] Graham was born in England, where he and his family maintain permanent residence. However he is also a citizen of the United States, where he was educated, lived, and worked until 2016."""
+prompts = ["Who is Paul Graham?", "What is Paul Graham known for?" , "Is paul graham a writer?" , "Where does Paul Graham live?", "What has Paul Graham done?"]
+messages =  [[{"role": "user", "content": context + "\n" + prompt}] for prompt in prompts] # pass in a list of messages we want to test
+result = testing_batch_completion(models=models, messages=messages)
+```
+
+
+# Load Test endpoint
+
+Run 100+ simultaneous queries across multiple providers to see when they fail + impact on latency
+
+
+```python
+models=["gpt-3.5-turbo", "replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781", "claude-instant-1"]
+context = """Paul Graham (/ɡræm/; born 1964)[3] is an English computer scientist, essayist, entrepreneur, venture capitalist, and author. He is best known for his work on the programming language Lisp, his former startup Viaweb (later renamed Yahoo! Store), cofounding the influential startup accelerator and seed capital firm Y Combinator, his essays, and Hacker News. He is the author of several computer programming books, including: On Lisp,[4] ANSI Common Lisp,[5] and Hackers & Painters.[6] Technology journalist Steven Levy has described Graham as a "hacker philosopher".[7] Graham was born in England, where he and his family maintain permanent residence. However he is also a citizen of the United States, where he was educated, lived, and worked until 2016."""
+prompt = "Where does Paul Graham live?"
+final_prompt = context + prompt
+result = load_test_model(models=models, prompt=final_prompt, num_calls=5)
+```
+
+## Visualize the data
+
+
+```python
+import matplotlib.pyplot as plt
+
+## calculate avg response time
+unique_models = set(result["response"]['model'] for result in result["results"])
+model_dict = {model: {"response_time": []} for model in unique_models}
+for completion_result in result["results"]:
+    model_dict[completion_result["response"]["model"]]["response_time"].append(completion_result["response_time"])
+
+avg_response_time = {}
+for model, data in model_dict.items():
+    avg_response_time[model] = sum(data["response_time"]) / len(data["response_time"])
+
+models = list(avg_response_time.keys())
+response_times = list(avg_response_time.values())
+
+plt.bar(models, response_times)
+plt.xlabel('Model', fontsize=10)
+plt.ylabel('Average Response Time')
+plt.title('Average Response Times for each Model')
+
+plt.xticks(models, [model[:15]+'...' if len(model) > 15 else model for model in models], rotation=45)
+plt.show()
+```
+
+
+    
+![png](litellm_Test_Multiple_Providers_files/litellm_Test_Multiple_Providers_11_0.png)
+    
+
+
+# Duration Test endpoint
+
+Run load testing for 2 mins. Hitting endpoints with 100+ queries every 15 seconds.
+
+
+```python
+models=["gpt-3.5-turbo", "replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781", "claude-instant-1"]
+context = """Paul Graham (/ɡræm/; born 1964)[3] is an English computer scientist, essayist, entrepreneur, venture capitalist, and author. He is best known for his work on the programming language Lisp, his former startup Viaweb (later renamed Yahoo! Store), cofounding the influential startup accelerator and seed capital firm Y Combinator, his essays, and Hacker News. He is the author of several computer programming books, including: On Lisp,[4] ANSI Common Lisp,[5] and Hackers & Painters.[6] Technology journalist Steven Levy has described Graham as a "hacker philosopher".[7] Graham was born in England, where he and his family maintain permanent residence. However he is also a citizen of the United States, where he was educated, lived, and worked until 2016."""
+prompt = "Where does Paul Graham live?"
+final_prompt = context + prompt
+result = load_test_model(models=models, prompt=final_prompt, num_calls=100, interval=15, duration=120)
+```
+
+
+```python
+import matplotlib.pyplot as plt
+
+## calculate avg response time
+unique_models = set(unique_result["response"]['model'] for unique_result in result[0]["results"])
+model_dict = {model: {"response_time": []} for model in unique_models}
+for iteration in result:
+  for completion_result in iteration["results"]:
+    model_dict[completion_result["response"]["model"]]["response_time"].append(completion_result["response_time"])
+
+avg_response_time = {}
+for model, data in model_dict.items():
+    avg_response_time[model] = sum(data["response_time"]) / len(data["response_time"])
+
+models = list(avg_response_time.keys())
+response_times = list(avg_response_time.values())
+
+plt.bar(models, response_times)
+plt.xlabel('Model', fontsize=10)
+plt.ylabel('Average Response Time')
+plt.title('Average Response Times for each Model')
+
+plt.xticks(models, [model[:15]+'...' if len(model) > 15 else model for model in models], rotation=45)
+plt.show()
+```
+
+
+    
+![png](litellm_Test_Multiple_Providers_files/litellm_Test_Multiple_Providers_14_0.png)
+    
+
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diff --git a/docs/my-website/docs/tutorials/lm_evaluation_harness.md b/docs/my-website/docs/tutorials/lm_evaluation_harness.md
new file mode 100644
index 0000000000000000000000000000000000000000..c28f2dac77572219eb2fcd7c6871faf2c777708a
--- /dev/null
+++ b/docs/my-website/docs/tutorials/lm_evaluation_harness.md
@@ -0,0 +1,156 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Benchmark LLMs - LM Harness, FastEval, Flask
+
+## LM Harness Benchmarks
+Evaluate LLMs 20x faster with TGI via litellm proxy's `/completions` endpoint. 
+
+This tutorial assumes you're using the `big-refactor` branch of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor)
+
+NOTE: LM Harness has not updated to using `openai 1.0.0+`, in order to deal with this we will run lm harness in a venv
+
+**Step 1: Start the local proxy**
+see supported models [here](https://docs.litellm.ai/docs/simple_proxy)
+```shell
+$ litellm --model huggingface/bigcode/starcoder
+```
+
+Using a custom api base
+
+```shell
+$ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL]
+$ litellm --model huggingface/tinyllama --api_base https://k58ory32yinf1ly0.us-east-1.aws.endpoints.huggingface.cloud
+```
+OpenAI Compatible Endpoint at http://0.0.0.0:8000
+
+**Step 2: Create a Virtual Env for LM Harness + Use OpenAI 0.28.1**
+We will now run lm harness with a new virtual env with openai==0.28.1
+
+```shell
+python3 -m venv lmharness 
+source lmharness/bin/activate
+```
+
+Pip install openai==0.28.01 in the venv
+```shell
+pip install openai==0.28.01
+```
+
+**Step 3: Set OpenAI API Base & Key**
+```shell
+$ export OPENAI_API_BASE=http://0.0.0.0:8000
+```
+
+LM Harness requires you to set an OpenAI API key `OPENAI_API_SECRET_KEY` for running benchmarks
+```shell
+export OPENAI_API_SECRET_KEY=anything
+```
+
+**Step 4: Run LM-Eval-Harness**
+```shell
+cd lm-evaluation-harness
+```
+
+pip install lm harness dependencies in venv
+```
+python3 -m pip install -e .
+```
+
+```shell
+python3 -m lm_eval \
+  --model openai-completions \
+  --model_args engine=davinci \
+  --task crows_pairs_english_age
+
+```
+## FastEval
+
+**Step 1: Start the local proxy**
+see supported models [here](https://docs.litellm.ai/docs/simple_proxy)
+```shell
+$ litellm --model huggingface/bigcode/starcoder
+```
+
+**Step 2: Set OpenAI API Base & Key**
+```shell
+$ export OPENAI_API_BASE=http://0.0.0.0:8000
+```
+
+Set this to anything since the proxy has the credentials
+```shell
+export OPENAI_API_KEY=anything
+```
+
+**Step 3 Run with FastEval** 
+
+**Clone FastEval**
+```shell
+# Clone this repository, make it the current working directory
+git clone --depth 1 https://github.com/FastEval/FastEval.git
+cd FastEval
+```
+
+**Set API Base on FastEval**
+
+On FastEval make the following **2 line code change** to set `OPENAI_API_BASE`
+
+https://github.com/FastEval/FastEval/pull/90/files
+```python
+try:
+    api_base = os.environ["OPENAI_API_BASE"] #changed: read api base from .env
+    if api_base == None:
+        api_base = "https://api.openai.com/v1"
+    response = await self.reply_two_attempts_with_different_max_new_tokens(
+        conversation=conversation,
+        api_base=api_base, # #changed: pass api_base
+        api_key=os.environ["OPENAI_API_KEY"],
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+```
+
+**Run FastEval**
+Set `-b` to the benchmark you want to run. Possible values are `mt-bench`, `human-eval-plus`, `ds1000`, `cot`, `cot/gsm8k`, `cot/math`, `cot/bbh`, `cot/mmlu` and `custom-test-data`
+
+Since LiteLLM provides an OpenAI compatible proxy `-t` and `-m` don't need to change
+`-t` will remain openai
+`-m` will remain gpt-3.5
+
+```shell
+./fasteval -b human-eval-plus -t openai -m gpt-3.5-turbo
+```
+
+## FLASK - Fine-grained Language Model Evaluation 
+Use litellm to evaluate any LLM on FLASK https://github.com/kaistAI/FLASK 
+
+**Step 1: Start the local proxy**
+```shell
+$ litellm --model huggingface/bigcode/starcoder
+```
+
+**Step 2: Set OpenAI API Base & Key**
+```shell
+$ export OPENAI_API_BASE=http://0.0.0.0:8000
+```
+
+**Step 3 Run with FLASK** 
+
+```shell
+git clone https://github.com/kaistAI/FLASK
+```
+```shell
+cd FLASK/gpt_review
+```
+
+Run the eval 
+```shell
+python gpt4_eval.py -q '../evaluation_set/flask_evaluation.jsonl'
+```
+
+## Debugging 
+
+### Making a test request to your proxy
+This command makes a test Completion, ChatCompletion request to your proxy server
+```shell
+litellm --test
+```
\ No newline at end of file
diff --git a/docs/my-website/docs/tutorials/mock_completion.md b/docs/my-website/docs/tutorials/mock_completion.md
new file mode 100644
index 0000000000000000000000000000000000000000..cadd65e46dca318881b4e941eea3beb197673a51
--- /dev/null
+++ b/docs/my-website/docs/tutorials/mock_completion.md
@@ -0,0 +1,35 @@
+# Mock Completion Responses - Save Testing Costs
+
+Trying to test making LLM Completion calls without calling the LLM APIs ? 
+Pass `mock_response` to `litellm.completion` and litellm will directly return the response without neededing the call the LLM API and spend $$ 
+
+## Using `completion()` with `mock_response`
+
+```python
+from litellm import completion 
+
+model = "gpt-3.5-turbo"
+messages = [{"role":"user", "content":"Why is LiteLLM amazing?"}]
+
+completion(model=model, messages=messages, mock_response="It's simple to use and easy to get started")
+```
+
+## Building a pytest function using `completion`
+
+```python
+from litellm import completion
+import pytest
+
+def test_completion_openai():
+    try:
+        response = completion(
+            model="gpt-3.5-turbo",
+            messages=[{"role":"user", "content":"Why is LiteLLM amazing?"}],
+            mock_response="LiteLLM is awesome"
+        )
+        # Add any assertions here to check the response
+        print(response)
+        print(response['choices'][0]['finish_reason'])
+    except Exception as e:
+        pytest.fail(f"Error occurred: {e}")
+```
diff --git a/docs/my-website/docs/tutorials/model_config_proxy.md b/docs/my-website/docs/tutorials/model_config_proxy.md
new file mode 100644
index 0000000000000000000000000000000000000000..b3ca0be9709f8bf3019c5f0941623805f533f437
--- /dev/null
+++ b/docs/my-website/docs/tutorials/model_config_proxy.md
@@ -0,0 +1,100 @@
+import Image from '@theme/IdealImage';
+
+# Customize Prompt Templates on OpenAI-Compatible server 
+
+**You will learn:** How to set a custom prompt template on our OpenAI compatible server. 
+**How?** We will modify the prompt template for CodeLlama
+
+## Step 1: Start OpenAI Compatible server
+Let's spin up a local OpenAI-compatible server, to call a deployed `codellama/CodeLlama-34b-Instruct-hf` model using Huggingface's [Text-Generation-Inference (TGI)](https://github.com/huggingface/text-generation-inference) format.
+
+```shell
+$ litellm --model huggingface/codellama/CodeLlama-34b-Instruct-hf --api_base https://my-endpoint.com
+
+# OpenAI compatible server running on http://0.0.0.0/8000
+```
+
+In a new shell, run: 
+```shell
+$ litellm --test
+``` 
+This will send a test request to our endpoint. 
+
+Now, let's see what got sent to huggingface. Run: 
+```shell
+$ litellm --logs
+```
+This will return the most recent log (by default logs are stored in a local file called 'api_logs.json').
+
+As we can see, this is the formatting sent to huggingface: 
+
+  
+
+
+This follows [our formatting](https://github.com/BerriAI/litellm/blob/9932371f883c55fd0f3142f91d9c40279e8fe241/litellm/llms/prompt_templates/factory.py#L10) for CodeLlama (based on the [Huggingface's documentation](https://huggingface.co/blog/codellama#conversational-instructions)). 
+
+But this lacks BOS(``) and EOS(``) tokens.
+
+So instead of using the LiteLLM default, let's use our own prompt template to use these in our messages. 
+
+## Step 2: Create Custom Prompt Template
+
+Our litellm server accepts prompt templates as part of a config file. You can save api keys, fallback models, prompt templates etc. in this config. [See a complete config file](../proxy_server.md)
+
+For now, let's just create a simple config file with our prompt template, and tell our server about it. 
+
+Create a file called `litellm_config.toml`:
+
+```shell
+$ touch litellm_config.toml
+```
+We want to add:
+* BOS (``) tokens at the start of every System and Human message
+* EOS (``) tokens at the end of every assistant message. 
+
+Let's open our file in our terminal: 
+```shell
+$ vi litellm_config.toml
+```
+
+paste our prompt template:
+```shell
+[model."huggingface/codellama/CodeLlama-34b-Instruct-hf".prompt_template] 
+MODEL_SYSTEM_MESSAGE_START_TOKEN = "[INST]  <>\n]" 
+MODEL_SYSTEM_MESSAGE_END_TOKEN = "\n<>\n [/INST]\n"
+
+MODEL_USER_MESSAGE_START_TOKEN = "[INST] " 
+MODEL_USER_MESSAGE_END_TOKEN = " [/INST]\n"
+
+MODEL_ASSISTANT_MESSAGE_START_TOKEN = ""
+MODEL_ASSISTANT_MESSAGE_END_TOKEN = ""
+```
+
+save our file (in vim): 
+```shell
+:wq
+```
+
+## Step 3: Run new template
+
+Let's save our custom template to our litellm server by running:
+```shell
+$ litellm --config -f ./litellm_config.toml 
+```
+LiteLLM will save a copy of this file in it's package, so it can persist these settings across restarts.
+
+Re-start our server: 
+```shell
+$ litellm --model huggingface/codellama/CodeLlama-34b-Instruct-hf --api_base https://my-endpoint.com
+```
+
+In a new shell, run: 
+```shell
+$ litellm --test
+``` 
+
+See our new input prompt to Huggingface! 
+
+ 
+
+Congratulations 🎉
\ No newline at end of file
diff --git a/docs/my-website/docs/tutorials/model_fallbacks.md b/docs/my-website/docs/tutorials/model_fallbacks.md
new file mode 100644
index 0000000000000000000000000000000000000000..def76e47329ec24e71247603f4424f47aed6d137
--- /dev/null
+++ b/docs/my-website/docs/tutorials/model_fallbacks.md
@@ -0,0 +1,73 @@
+# Model Fallbacks w/ LiteLLM
+
+Here's how you can implement model fallbacks across 3 LLM providers (OpenAI, Anthropic, Azure) using LiteLLM. 
+
+## 1. Install LiteLLM
+```python 
+!pip install litellm
+```
+
+## 2. Basic Fallbacks Code 
+```python 
+import litellm
+from litellm import embedding, completion
+
+# set ENV variables
+os.environ["OPENAI_API_KEY"] = ""
+os.environ["ANTHROPIC_API_KEY"] = ""
+os.environ["AZURE_API_KEY"] = ""
+os.environ["AZURE_API_BASE"] = ""
+os.environ["AZURE_API_VERSION"] = ""
+
+model_fallback_list = ["claude-instant-1", "gpt-3.5-turbo", "chatgpt-test"]
+
+user_message = "Hello, how are you?"
+messages = [{ "content": user_message,"role": "user"}]
+
+for model in model_fallback_list:
+  try:
+      response = completion(model=model, messages=messages)
+  except Exception as e:
+      print(f"error occurred: {traceback.format_exc()}")
+```
+
+## 3. Context Window Exceptions 
+LiteLLM provides a sub-class of the InvalidRequestError class for Context Window Exceeded errors ([docs](https://docs.litellm.ai/docs/exception_mapping)).
+
+Implement model fallbacks based on context window exceptions. 
+
+LiteLLM also exposes a `get_max_tokens()` function, which you can use to identify the context window limit that's been exceeded. 
+
+```python 
+import litellm
+from litellm import completion, ContextWindowExceededError, get_max_tokens
+
+# set ENV variables
+os.environ["OPENAI_API_KEY"] = ""
+os.environ["COHERE_API_KEY"] = ""
+os.environ["ANTHROPIC_API_KEY"] = ""
+os.environ["AZURE_API_KEY"] = ""
+os.environ["AZURE_API_BASE"] = ""
+os.environ["AZURE_API_VERSION"] = ""
+
+context_window_fallback_list = [{"model":"gpt-3.5-turbo-16k", "max_tokens": 16385}, {"model":"gpt-4-32k", "max_tokens": 32768}, {"model": "claude-instant-1", "max_tokens":100000}]
+
+user_message = "Hello, how are you?"
+messages = [{ "content": user_message,"role": "user"}]
+
+initial_model = "command-nightly"
+try:
+    response = completion(model=initial_model, messages=messages)
+except ContextWindowExceededError as e:
+    model_max_tokens = get_max_tokens(model)
+    for model in context_window_fallback_list:
+        if model_max_tokens < model["max_tokens"]
+        try:
+            response = completion(model=model["model"], messages=messages)
+            return response
+        except ContextWindowExceededError as e:
+            model_max_tokens = get_max_tokens(model["model"])
+            continue
+
+print(response)
+```
\ No newline at end of file
diff --git a/docs/my-website/docs/tutorials/oobabooga.md b/docs/my-website/docs/tutorials/oobabooga.md
new file mode 100644
index 0000000000000000000000000000000000000000..9610143aa3082b028f9e3a75e995076f68683495
--- /dev/null
+++ b/docs/my-website/docs/tutorials/oobabooga.md
@@ -0,0 +1,26 @@
+# Oobabooga Text Web API Tutorial
+
+### Install + Import LiteLLM 
+```python 
+!pip install litellm
+from litellm import completion 
+import os
+```
+
+### Call your oobabooga model
+Remember to set your api_base
+```python
+response = completion(
+  model="oobabooga/WizardCoder-Python-7B-V1.0-GPTQ",
+  messages=[{ "content": "can you write a binary tree traversal preorder","role": "user"}], 
+  api_base="http://localhost:5000",
+  max_tokens=4000
+)
+```
+
+### See your response 
+```python 
+print(response)
+```
+
+Credits to [Shuai Shao](https://www.linkedin.com/in/shuai-sh/), for this tutorial. 
\ No newline at end of file
diff --git a/docs/my-website/docs/tutorials/provider_specific_params.md b/docs/my-website/docs/tutorials/provider_specific_params.md
new file mode 100644
index 0000000000000000000000000000000000000000..9ce5303dfad0824ad77293f741ed16f1d430904b
--- /dev/null
+++ b/docs/my-website/docs/tutorials/provider_specific_params.md
@@ -0,0 +1,34 @@
+### Setting provider-specific Params
+
+Goal: Set max tokens across OpenAI + Cohere
+
+**1. via completion**
+
+LiteLLM will automatically translate max_tokens to the naming convention followed by that specific model provider.
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables 
+os.environ["OPENAI_API_KEY"] = "your-openai-key" 
+os.environ["COHERE_API_KEY"] = "your-cohere-key" 
+
+messages = [{ "content": "Hello, how are you?","role": "user"}]
+
+# openai call
+response = completion(model="gpt-3.5-turbo", messages=messages, max_tokens=100)
+
+# cohere call
+response = completion(model="command-nightly", messages=messages, max_tokens=100)
+print(response)
+```
+
+**2. via provider-specific config**
+
+For every provider on LiteLLM, we've gotten their specific params (following their naming conventions, etc.). You can just set it for that provider by pulling up that provider via `litellm.Config`. 
+
+All provider configs are typed and have docstrings, so you should see them autocompleted for you in VSCode with an explanation of what it means. 
+
+Here's an example of setting max tokens through provider configs. 
+
diff --git a/docs/my-website/docs/tutorials/sagemaker_llms.md b/docs/my-website/docs/tutorials/sagemaker_llms.md
new file mode 100644
index 0000000000000000000000000000000000000000..1fe9594ab57427aad983a6171b1a9e0de533c0b1
--- /dev/null
+++ b/docs/my-website/docs/tutorials/sagemaker_llms.md
@@ -0,0 +1,72 @@
+import Image from '@theme/IdealImage';
+
+# Deploy & Query Llama2-7B on Sagemaker
+
+This tutorial has 2 major components:
+1. Deploy Llama2-7B on Jumpstart
+2. Use LiteLLM to Query Llama2-7B on Sagemaker
+
+## Deploying Llama2-7B on AWS Sagemaker
+### Pre-requisites
+Ensure you have AWS quota for deploying your selected LLM. You can apply for a quota increase here: https://console.aws.amazon.com/servicequotas/home
+* ml.g5.48xlarge
+* ml.g5.2xlarge
+
+### Create an Amazon SageMaker domain to use Studio and Studio Notebooks
+
+- Head to AWS console https://aws.amazon.com/console/ 
+- Navigate to AWS Sagemaker from the console
+- On AWS Sagemaker select 'Create a Sagemaker Domain' 
+
+
+### Deploying Llama2-7B using AWS Sagemaker Jumpstart
+
+- After creating your sagemaker domain, click 'Open Studio', which should take you to AWS sagemaker studio 
+
+- On the left sidebar navigate to SageMaker Jumpstart -> Models, notebooks, solutions
+- Now select the LLM you want to deploy by clicking 'View Model' - (in this case select Llama2-7B)
+
+- Click `Deploy` for the Model you want to deploy
+
+
+- After deploying Llama2, copy your model endpoint 
+
+
+### Use LiteLLM to Query Llama2-7B on Sagemaker
+
+#### Prerequisites
+* `pip install boto3`
+* `pip install litellm`
+* Create your AWS Access Key, get your `AWS_ACCESS_KEY_ID` and  `AWS_SECRET_ACCESS_KEY`. You can create a new aws access key on the aws console under `Security Credentials` under your profile
+
+#### Querying deployed Llama2-7b 
+Set `model` = `sagemaker/` for `completion`. Use the model endpoint you got after deploying llama2-7b on sagemaker. If you used jumpstart your model endpoint will look like this `jumpstart-dft-meta-textgeneration-llama-2-7b`
+
+Code Example:
+```python
+from litellm import completion
+os.environ['AWS_ACCESS_KEY_ID'] = "your-access-key-id"
+os.environ['AWS_SECRET_ACCESS_KEY'] = "your-secret-key"
+
+response = completion(
+            model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", 
+            messages=[{'role': 'user', 'content': 'are you a llama'}],
+            temperature=0.2, # optional params
+            max_tokens=80,
+        )
+
+```
+
+That's it! Happy completion()!
+
+#### Next Steps: 
+- Add Caching: https://docs.litellm.ai/docs/caching/gpt_cache
+- Add Logging and Observability to your deployed LLM: https://docs.litellm.ai/docs/observability/callbacks
+
+
+
+
+
+
+
+
diff --git a/docs/my-website/docs/tutorials/text_completion.md b/docs/my-website/docs/tutorials/text_completion.md
new file mode 100644
index 0000000000000000000000000000000000000000..1d210076e9756665427c4712ba1737d3774e1791
--- /dev/null
+++ b/docs/my-website/docs/tutorials/text_completion.md
@@ -0,0 +1,39 @@
+# Using Text Completion Format - with Completion()
+
+If your prefer interfacing with the OpenAI Text Completion format this tutorial covers how to use LiteLLM in this format
+```python
+response = openai.Completion.create(
+    model="text-davinci-003",
+    prompt='Write a tagline for a traditional bavarian tavern',
+    temperature=0,
+    max_tokens=100)
+```
+
+## Using LiteLLM in the Text Completion format
+### With gpt-3.5-turbo
+```python
+from litellm import text_completion
+response = text_completion(
+    model="gpt-3.5-turbo",
+    prompt='Write a tagline for a traditional bavarian tavern',
+    temperature=0,
+    max_tokens=100)
+```
+
+### With text-davinci-003
+```python
+response = text_completion(
+    model="text-davinci-003",
+    prompt='Write a tagline for a traditional bavarian tavern',
+    temperature=0,
+    max_tokens=100)
+```
+
+### With llama2
+```python
+response = text_completion(
+    model="togethercomputer/llama-2-70b-chat",
+    prompt='Write a tagline for a traditional bavarian tavern',
+    temperature=0,
+    max_tokens=100)
+```
\ No newline at end of file
diff --git a/docs/my-website/docusaurus.config.js b/docs/my-website/docusaurus.config.js
new file mode 100644
index 0000000000000000000000000000000000000000..865bcb446c69e1e8b859c3fe809c2957cfe5a1d6
--- /dev/null
+++ b/docs/my-website/docusaurus.config.js
@@ -0,0 +1,166 @@
+// @ts-check
+// Note: type annotations allow type checking and IDEs autocompletion
+
+const lightCodeTheme = require('prism-react-renderer/themes/github');
+const darkCodeTheme = require('prism-react-renderer/themes/dracula');
+
+/** @type {import('@docusaurus/types').Config} */
+const config = {
+  title: 'liteLLM',
+  tagline: 'Simplify LLM API Calls',
+  favicon: '/img/favicon.ico', 
+
+  // Set the production url of your site here
+  url: 'https://litellm.vercel.app/',
+  // Set the // pathname under which your site is served
+  // For GitHub pages deployment, it is often '//'
+  baseUrl: '/',
+
+  onBrokenLinks: 'warn',
+  onBrokenMarkdownLinks: 'warn',
+
+  // Even if you don't use internalization, you can use this field to set useful
+  // metadata like html lang. For example, if your site is Chinese, you may want
+  // to replace "en" with "zh-Hans".
+  i18n: {
+    defaultLocale: 'en',
+    locales: ['en'],
+  },
+
+  plugins: [
+    [
+      '@docusaurus/plugin-ideal-image',
+      {
+        quality: 100,
+        max: 1920, // max resized image's size.
+        min: 640, // min resized image's size. if original is lower, use that size.
+        steps: 2, // the max number of images generated between min and max (inclusive)
+        disableInDev: false,
+      },
+    ],
+    [ require.resolve('docusaurus-lunr-search'), {
+      languages: ['en'] // language codes
+    }],
+    () => ({
+      name: 'cripchat',
+      injectHtmlTags() {
+        return {
+          headTags: [
+            {
+              tagName: 'script',
+              innerHTML: `window.$crisp=[];window.CRISP_WEBSITE_ID="be07a4d6-dba0-4df7-961d-9302c86b7ebc";(function(){d=document;s=d.createElement("script");s.src="https://client.crisp.chat/l.js";s.async=1;d.getElementsByTagName("head")[0].appendChild(s);})();`,
+            },
+          ],
+        };
+      },
+    }),
+  ],
+
+  presets: [
+    [
+      'classic',
+      /** @type {import('@docusaurus/preset-classic').Options} */
+      ({
+        gtag: {
+          trackingID: 'G-K7K215ZVNC',
+          anonymizeIP: true,
+        },
+        docs: {
+          sidebarPath: require.resolve('./sidebars.js'),
+        },
+        blog: false, // Optional: disable the blog plugin
+        theme: {
+          customCss: require.resolve('./src/css/custom.css'),
+        },
+      }),
+    ],
+  ],
+
+  scripts: [
+    {
+      async: true,
+      src: 'https://www.feedbackrocket.io/sdk/v1.2.js',
+      'data-fr-id': 'GQwepB0f0L-x_ZH63kR_V',
+      'data-fr-theme': 'dynamic',
+    }
+  ],
+
+  themeConfig:
+    /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
+    ({
+      // Replace with your project's social card
+      image: 'img/docusaurus-social-card.png',
+      navbar: {
+        title: '🚅 LiteLLM',
+        items: [
+          {
+            type: 'docSidebar',
+            sidebarId: 'tutorialSidebar',
+            position: 'left',
+            label: 'Docs',
+          },
+          {
+            href: 'https://github.com/BerriAI/litellm',
+            label: 'GitHub',
+            position: 'right',
+          },
+          {
+            href: 'https://discord.com/invite/wuPM9dRgDw',
+            label: 'Discord',
+            position: 'right',
+          },
+          {
+            type: 'html',
+            position: 'right',
+            value:
+              `
+                I'm Confused
+              `
+          },
+        ],
+      },
+      footer: {
+        style: 'dark',
+        links: [
+          {
+            title: 'Docs',
+            items: [
+              {
+                label: 'Tutorial',
+                to: '/docs/index',
+              },
+            ],
+          },
+          {
+            title: 'Community',
+            items: [
+              {
+                label: 'Discord',
+                href: 'https://discord.com/invite/wuPM9dRgDw',
+              },
+              {
+                label: 'Twitter',
+                href: 'https://twitter.com/LiteLLM',
+              },
+            ],
+          },
+          {
+            title: 'More',
+            items: [
+              {
+                label: 'GitHub',
+                href: 'https://github.com/BerriAI/litellm/',
+              },
+            ],
+          },
+        ],
+        copyright: `Copyright © ${new Date().getFullYear()} liteLLM`,
+      },
+      prism: {
+        theme: lightCodeTheme,
+        darkTheme: darkCodeTheme,
+      },
+    }),
+};
+
+module.exports = config;
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+version https://git-lfs.github.com/spec/v1
+oid sha256:0dbc75881ffc5a704ceddd7b2da9a98cf40bffb90f6dc626b806db66167fcef7
+size 2096315
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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6e7153122f539cf62514b7909590fbd6bf3a9ba4c88f08676484e988d31058c
+size 2486017
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diff --git a/docs/my-website/img/traceloop_dash.png b/docs/my-website/img/traceloop_dash.png
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diff --git a/docs/my-website/img/wandb.png b/docs/my-website/img/wandb.png
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diff --git a/docs/my-website/index.md b/docs/my-website/index.md
new file mode 100644
index 0000000000000000000000000000000000000000..7d0698afe0db61e9ca9aa55289c4c54ea8c3106a
--- /dev/null
+++ b/docs/my-website/index.md
@@ -0,0 +1,25 @@
+---
+slug: welcome
+title: Welcome
+authors: [slorber, yangshun]
+tags: [facebook, hello, docusaurus]
+---
+
+[Docusaurus blogging features](https://docusaurus.io/docs/blog) are powered by the [blog plugin](https://docusaurus.io/docs/api/plugins/@docusaurus/plugin-content-blog).
+
+Simply add Markdown files (or folders) to the `blog` directory.
+
+Regular blog authors can be added to `authors.yml`.
+
+The blog post date can be extracted from filenames, such as:
+
+- `2019-05-30-welcome.md`
+- `2019-05-30-welcome/index.md`
+
+A blog post folder can be convenient to co-locate blog post images:
+
+![Docusaurus Plushie](./docusaurus-plushie-banner.jpeg)
+
+The blog supports tags as well!
+
+**And if you don't want a blog**: just delete this directory, and use `blog: false` in your Docusaurus config.
\ No newline at end of file
diff --git a/docs/my-website/package-lock.json b/docs/my-website/package-lock.json
new file mode 100644
index 0000000000000000000000000000000000000000..a605fee7f676a566bf7705492495610d9e48459e
--- /dev/null
+++ b/docs/my-website/package-lock.json
@@ -0,0 +1,13553 @@
+{
+  "name": "my-website",
+  "version": "0.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "my-website",
+      "version": "0.0.0",
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+        "@docusaurus/plugin-google-gtag": "^2.4.1",
+        "@docusaurus/plugin-ideal-image": "^2.4.1",
+        "@docusaurus/preset-classic": "2.4.1",
+        "@mdx-js/react": "^1.6.22",
+        "clsx": "^1.2.1",
+        "docusaurus-lunr-search": "^2.4.1",
+        "prism-react-renderer": "^1.3.5",
+        "react": "^17.0.2",
+        "react-dom": "^17.0.2",
+        "sharp": "^0.32.6",
+        "uuid": "^9.0.1"
+      },
+      "devDependencies": {
+        "@docusaurus/module-type-aliases": "2.4.1"
+      },
+      "engines": {
+        "node": ">=16.14"
+      }
+    },
+    "node_modules/@algolia/autocomplete-core": {
+      "version": "1.9.3",
+      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-core/-/autocomplete-core-1.9.3.tgz",
+      "integrity": "sha512-009HdfugtGCdC4JdXUbVJClA0q0zh24yyePn+KUGk3rP7j8FEe/m5Yo/z65gn6nP/cM39PxpzqKrL7A6fP6PPw==",
+      "dependencies": {
+        "@algolia/autocomplete-plugin-algolia-insights": "1.9.3",
+        "@algolia/autocomplete-shared": "1.9.3"
+      }
+    },
+    "node_modules/@algolia/autocomplete-plugin-algolia-insights": {
+      "version": "1.9.3",
+      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-plugin-algolia-insights/-/autocomplete-plugin-algolia-insights-1.9.3.tgz",
+      "integrity": "sha512-a/yTUkcO/Vyy+JffmAnTWbr4/90cLzw+CC3bRbhnULr/EM0fGNvM13oQQ14f2moLMcVDyAx/leczLlAOovhSZg==",
+      "dependencies": {
+        "@algolia/autocomplete-shared": "1.9.3"
+      },
+      "peerDependencies": {
+        "search-insights": ">= 1 < 3"
+      }
+    },
+    "node_modules/@algolia/autocomplete-preset-algolia": {
+      "version": "1.9.3",
+      "resolved": "https://registry.npmjs.org/@algolia/autocomplete-preset-algolia/-/autocomplete-preset-algolia-1.9.3.tgz",
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+      "dependencies": {
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+    "node_modules/@algolia/cache-in-memory": {
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+      "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g=="
+    },
+    "node_modules/yaml": {
+      "version": "1.10.2",
+      "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.2.tgz",
+      "integrity": "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==",
+      "engines": {
+        "node": ">= 6"
+      }
+    },
+    "node_modules/yocto-queue": {
+      "version": "0.1.0",
+      "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
+      "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
+    "node_modules/zwitch": {
+      "version": "1.0.5",
+      "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-1.0.5.tgz",
+      "integrity": "sha512-V50KMwwzqJV0NpZIZFwfOD5/lyny3WlSzRiXgA0G7VUnRlqttta1L6UQIHzd6EuBY/cHGfwTIck7w1yH6Q5zUw==",
+      "funding": {
+        "type": "github",
+        "url": "https://github.com/sponsors/wooorm"
+      }
+    }
+  }
+}
diff --git a/docs/my-website/package.json b/docs/my-website/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..656f507c79428bf8518822f16181655621232e8a
--- /dev/null
+++ b/docs/my-website/package.json
@@ -0,0 +1,48 @@
+{
+  "name": "my-website",
+  "version": "0.0.0",
+  "private": true,
+  "scripts": {
+    "docusaurus": "docusaurus",
+    "start": "docusaurus start",
+    "build": "docusaurus build",
+    "swizzle": "docusaurus swizzle",
+    "deploy": "docusaurus deploy",
+    "clear": "docusaurus clear",
+    "serve": "docusaurus serve",
+    "write-translations": "docusaurus write-translations",
+    "write-heading-ids": "docusaurus write-heading-ids"
+  },
+  "dependencies": {
+    "@docusaurus/core": "2.4.1",
+    "@docusaurus/plugin-google-gtag": "^2.4.1",
+    "@docusaurus/plugin-ideal-image": "^2.4.1",
+    "@docusaurus/preset-classic": "2.4.1",
+    "@mdx-js/react": "^1.6.22",
+    "clsx": "^1.2.1",
+    "docusaurus-lunr-search": "^2.4.1",
+    "prism-react-renderer": "^1.3.5",
+    "react": "^17.0.2",
+    "react-dom": "^17.0.2",
+    "sharp": "^0.32.6",
+    "uuid": "^9.0.1"
+  },
+  "devDependencies": {
+    "@docusaurus/module-type-aliases": "2.4.1"
+  },
+  "browserslist": {
+    "production": [
+      ">0.5%",
+      "not dead",
+      "not op_mini all"
+    ],
+    "development": [
+      "last 1 chrome version",
+      "last 1 firefox version",
+      "last 1 safari version"
+    ]
+  },
+  "engines": {
+    "node": ">=16.14"
+  }
+}
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
new file mode 100644
index 0000000000000000000000000000000000000000..11f81fa4d1c5300014f56044e155a0b77f2aae2b
--- /dev/null
+++ b/docs/my-website/sidebars.js
@@ -0,0 +1,213 @@
+/**
+ * Creating a sidebar enables you to:
+ - create an ordered group of docs
+ - render a sidebar for each doc of that group
+ - provide next/previous navigation
+
+ The sidebars can be generated from the filesystem, or explicitly defined here.
+
+ Create as many sidebars as you want.
+ */
+
+// @ts-check
+
+/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */
+const sidebars = {
+  // // By default, Docusaurus generates a sidebar from the docs folder structure
+
+  // But you can create a sidebar manually
+  tutorialSidebar: [
+    { type: "doc", id: "index" }, // NEW
+    {
+      type: "category",
+      label: "Completion()",
+      link: {
+        type: 'generated-index',
+        title: 'Completion()',
+        description: 'Details on the completion() function',
+        slug: '/completion',
+      },
+      items: [
+        "completion/input", 
+        "completion/prompt_formatting",
+        "completion/output", 
+        "exception_mapping",
+        "completion/stream", 
+        "completion/message_trimming",
+        "completion/function_call",
+        "completion/model_alias", 
+        "completion/batching",
+        "completion/mock_requests",
+        "completion/reliable_completions",
+      ],
+    },
+    {
+      type: "category",
+      label: "Embedding() & Moderation()",
+      items: [
+        "embedding/supported_embedding", 
+        "embedding/async_embedding",
+        "embedding/moderation",
+      ],
+    },
+    {
+      type: "category",
+      label: "Supported Models & Providers",
+      link: {
+        type: 'generated-index',
+        title: 'Providers',
+        description: 'Learn how to deploy + call models from different providers on LiteLLM',
+        slug: '/providers',
+      },
+      items: [
+        "providers/openai", 
+        "providers/azure", 
+        "providers/huggingface", 
+        "providers/ollama", 
+        "providers/vertex", 
+        "providers/palm", 
+        "providers/anthropic", 
+        "providers/aws_sagemaker",
+        "providers/bedrock", 
+        "providers/anyscale",
+        "providers/perplexity", 
+        "providers/vllm", 
+        "providers/deepinfra",
+        "providers/ai21", 
+        "providers/nlp_cloud",
+        "providers/replicate", 
+        "providers/cohere", 
+        "providers/togetherai", 
+        "providers/aleph_alpha", 
+        "providers/baseten", 
+        "providers/openrouter", 
+        "providers/custom_openai_proxy",
+        "providers/petals",
+      ]
+    },
+    {
+      type: "category",
+      label: "💥 OpenAI Proxy Server",
+      link: {
+        type: 'generated-index',
+        title: '💥 OpenAI Proxy Server',
+        description: `Proxy Server to call 100+ LLMs in a unified interface, load balance deployments, track costs per user`,
+        slug: '/simple_proxy',
+      },
+      items: [
+        "proxy/quick_start", 
+        "proxy/configs", 
+        "proxy/load_balancing", 
+        "proxy/virtual_keys",
+        "proxy/model_management",
+        "proxy/caching",
+        "proxy/logging", 
+        "proxy/cli", 
+        "proxy/deploy", 
+      ]
+    },
+    "routing",
+    "rules",
+    "set_keys",
+    "budget_manager",
+    "secret",
+    "completion/token_usage",
+    {
+      type: 'category',
+      label: 'Tutorials',
+      items: [
+        'tutorials/azure_openai',
+        "tutorials/lm_evaluation_harness",
+        "tutorials/eval_suites",
+        'tutorials/oobabooga',
+        "tutorials/gradio_integration",
+        'tutorials/huggingface_codellama',
+        'tutorials/huggingface_tutorial', 
+        'tutorials/TogetherAI_liteLLM', 
+        'tutorials/finetuned_chat_gpt',
+        'tutorials/sagemaker_llms',
+        'tutorials/text_completion',
+        "tutorials/first_playground",
+        'tutorials/compare_llms',
+        "tutorials/model_fallbacks",
+      ],
+    },
+    {
+      type: "category",
+      label: "Logging & Observability",
+      items: [
+        'debugging/local_debugging',
+        "observability/callbacks",
+        "observability/custom_callback",
+        "observability/langfuse_integration",
+        "observability/sentry",
+        "observability/promptlayer_integration",
+        "observability/wandb_integration",
+        "observability/langsmith_integration",
+        "observability/slack_integration",
+        "observability/traceloop_integration",
+        "observability/llmonitor_integration",
+        "observability/helicone_integration",
+        "observability/supabase_integration",
+        `observability/telemetry`,
+      ],
+    },
+    {
+      type: "category",
+      label: "Caching",
+      link: {
+        type: 'generated-index',
+        title: 'Providers',
+        description: 'Learn how to deploy + call models from different providers on LiteLLM',
+        slug: '/caching',
+      },
+      items: [
+        "caching/local_caching",
+        "caching/redis_cache",
+      ],
+    },
+    {
+      type: "category",
+      label: "LangChain, LlamaIndex Integration",
+      items: [
+        "langchain/langchain"
+      ],
+    },
+    {
+      type: 'category',
+      label: 'Extras',
+      items: [
+        'extras/contributing',
+        "proxy_server",
+        {
+          type: "category",
+          label: "❤️ 🚅 Projects built on LiteLLM",
+          link: {
+            type: 'generated-index',
+            title: 'Projects built on LiteLLM',
+            description: 'Learn how to deploy + call models from different providers on LiteLLM',
+            slug: '/project',
+          },
+          items: [
+            "projects/OpenInterpreter",
+            "projects/FastREPL",
+            "projects/PROMPTMETHEUS",
+            "projects/Codium PR Agent", 
+            "projects/Prompt2Model",
+            "projects/SalesGPT",
+            "projects/Quivr", 
+            "projects/Langstream", 
+            "projects/Otter", 
+            "projects/GPT Migrate", 
+            "projects/YiVal", 
+            "projects/LiteLLM Proxy", 
+          ]
+        },
+      ],
+    },
+    "migration",
+    "troubleshoot",
+  ],
+};
+
+module.exports = sidebars;
diff --git a/docs/my-website/src/components/CrispChat.js b/docs/my-website/src/components/CrispChat.js
new file mode 100644
index 0000000000000000000000000000000000000000..71b543cc7b2e5bd0b6fc01333b96af45b154edd2
--- /dev/null
+++ b/docs/my-website/src/components/CrispChat.js
@@ -0,0 +1,18 @@
+import React, { useEffect } from 'react';
+
+const CrispChat = () => {
+    useEffect(() => {
+        window.$crisp = [];
+        window.CRISP_WEBSITE_ID = "be07a4d6-dba0-4df7-961d-9302c86b7ebc";
+
+        const d = document;
+        const s = d.createElement("script");
+        s.src = "https://client.crisp.chat/l.js";
+        s.async = 1;
+        document.getElementsByTagName("head")[0].appendChild(s);
+    }, [])
+  
+    return null;
+};
+
+export default CrispChat;
\ No newline at end of file
diff --git a/docs/my-website/src/components/HomepageFeatures/index.js b/docs/my-website/src/components/HomepageFeatures/index.js
new file mode 100644
index 0000000000000000000000000000000000000000..78f410ba688844a6d5bee52924ea6650a526158e
--- /dev/null
+++ b/docs/my-website/src/components/HomepageFeatures/index.js
@@ -0,0 +1,64 @@
+import React from 'react';
+import clsx from 'clsx';
+import styles from './styles.module.css';
+
+const FeatureList = [
+  {
+    title: 'Easy to Use',
+    Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default,
+    description: (
+      <>
+        Docusaurus was designed from the ground up to be easily installed and
+        used to get your website up and running quickly.
+      
+    ),
+  },
+  {
+    title: 'Focus on What Matters',
+    Svg: require('@site/static/img/undraw_docusaurus_tree.svg').default,
+    description: (
+      <>
+        Docusaurus lets you focus on your docs, and we'll do the chores. Go
+        ahead and move your docs into the docs directory.
+      
+    ),
+  },
+  {
+    title: 'Powered by React',
+    Svg: require('@site/static/img/undraw_docusaurus_react.svg').default,
+    description: (
+      <>
+        Extend or customize your website layout by reusing React. Docusaurus can
+        be extended while reusing the same header and footer.
+      
+    ),
+  },
+];
+
+function Feature({Svg, title, description}) {
+  return (
+    

+      

+        
+      

+      

+        
{title}

+        
{description}

+      

+    

+  );
+}
+
+export default function HomepageFeatures() {
+  return (
+    

+      

+        

+          {FeatureList.map((props, idx) => (
+            
+          ))}
+        

+      

+    

+  );
+}
diff --git a/docs/my-website/src/components/HomepageFeatures/styles.module.css b/docs/my-website/src/components/HomepageFeatures/styles.module.css
new file mode 100644
index 0000000000000000000000000000000000000000..b248eb2e5dee2c37f58ab867ab87be47ef804386
--- /dev/null
+++ b/docs/my-website/src/components/HomepageFeatures/styles.module.css
@@ -0,0 +1,11 @@
+.features {
+  display: flex;
+  align-items: center;
+  padding: 2rem 0;
+  width: 100%;
+}
+
+.featureSvg {
+  height: 200px;
+  width: 200px;
+}
diff --git a/docs/my-website/src/components/QuickStart.js b/docs/my-website/src/components/QuickStart.js
new file mode 100644
index 0000000000000000000000000000000000000000..bb00cb4182f6503ab070a077423f2d0d72aa4b2e
--- /dev/null
+++ b/docs/my-website/src/components/QuickStart.js
@@ -0,0 +1,63 @@
+import React, { useState, useEffect } from 'react';
+
+const QuickStartCodeBlock = ({ token }) => {
+    return (
+      
+        {`
+        from litellm import completion
+        import os
+  
+        ## set ENV variables
+        os.environ["OPENAI_API_KEY"] = "${token}"
+        os.environ["COHERE_API_KEY"] = "${token}"
+  
+        messages = [{ "content": "Hello, how are you?","role": "user"}]
+  
+        # openai call
+        response = completion(model="gpt-3.5-turbo", messages=messages)
+  
+        # cohere call
+        response = completion("command-nightly", messages)
+        `}
+      

+    );
+  };
+  
+  const QuickStart = () => {
+    const [token, setToken] = useState(null);
+  
+    useEffect(() => {
+      const generateToken = async () => {
+        try {
+          const response = await fetch('https://proxy.litellm.ai/key/new', {
+            method: 'POST',
+            headers: {
+              'Content-Type': 'application/json',
+              'Authorization': 'Bearer sk-liteplayground',
+            },
+            body: JSON.stringify({'total_budget': 100})
+          });
+          
+          if (!response.ok) {
+            throw new Error('Network response was not ok');
+          }
+          
+          const data = await response.json();
+  
+          setToken(`${data.api_key}`);
+      } catch (error) {
+        console.error('Failed to fetch new token: ', error);
+      }
+    };
+  
+    generateToken();
+  }, []);
+  
+  return (
+    

+      
+    

+  );
+  }
+
+  export default QuickStart;
\ No newline at end of file
diff --git a/docs/my-website/src/components/TokenGen.js b/docs/my-website/src/components/TokenGen.js
new file mode 100644
index 0000000000000000000000000000000000000000..5ffa7d48a3eb1d32fab2f13b24043484c4edb019
--- /dev/null
+++ b/docs/my-website/src/components/TokenGen.js
@@ -0,0 +1,50 @@
+import React, { useState, useEffect } from 'react';
+
+const CodeBlock = ({ token }) => {
+  const codeWithToken = `${token}`;
+
+  return (
+    
+      {token ? codeWithToken : ""}
+    

+  );
+};
+
+const TokenGen = () => {
+  const [token, setToken] = useState(null);
+
+  useEffect(() => {
+    const generateToken = async () => {
+      try {
+        const response = await fetch('https://proxy.litellm.ai/key/new', {
+          method: 'POST',
+          headers: {
+            'Content-Type': 'application/json',
+            'Authorization': 'Bearer sk-liteplayground',
+          },
+          body: JSON.stringify({'total_budget': 100})
+        });
+        
+        if (!response.ok) {
+          throw new Error('Network response was not ok');
+        }
+        
+        const data = await response.json();
+
+        setToken(`${data.api_key}`);
+    } catch (error) {
+      console.error('Failed to fetch new token: ', error);
+    }
+  };
+
+  generateToken();
+}, []);
+
+return (
+  

+    
+  

+);
+};
+
+export default TokenGen;
diff --git a/docs/my-website/src/components/queryParamReader.js b/docs/my-website/src/components/queryParamReader.js
new file mode 100644
index 0000000000000000000000000000000000000000..9a5e50502b36e92a15a0dd0bef78e3b3bd24cb84
--- /dev/null
+++ b/docs/my-website/src/components/queryParamReader.js
@@ -0,0 +1,61 @@
+import React, { useState, useEffect } from 'react';
+
+const CodeBlock = ({ token }) => {
+  const codeWithToken = `
+import os
+from litellm import completion
+
+# set ENV variables 
+os.environ["LITELLM_TOKEN"] = '${token}'
+
+messages = [{ "content": "Hello, how are you?","role": "user"}]
+
+# openai call
+response = completion(model="gpt-3.5-turbo", messages=messages)
+
+# cohere call
+response = completion("command-nightly", messages)
+`;
+
+  const codeWithoutToken = `
+from litellm import completion
+
+## set ENV variables
+os.environ["OPENAI_API_KEY"] = "openai key"
+os.environ["COHERE_API_KEY"] = "cohere key"
+
+
+messages = [{ "content": "Hello, how are you?","role": "user"}]
+
+# openai call
+response = completion(model="gpt-3.5-turbo", messages=messages)
+
+# cohere call
+response = completion("command-nightly", messages)
+`;
+  return (
+    
+        {console.log("token: ", token)}
+      {token ? codeWithToken : codeWithoutToken}
+    

+  )
+}
+
+const QueryParamReader = () => {
+  const [token, setToken] = useState(null);
+
+  useEffect(() => {
+    const urlParams = new URLSearchParams(window.location.search);
+    console.log("urlParams: ", urlParams)
+    const token = urlParams.get('token');
+    setToken(token);
+  }, []);
+
+  return (
+    

+      
+    

+  );
+}
+
+export default QueryParamReader;
\ No newline at end of file
diff --git a/docs/my-website/src/components/queryParamToken.js b/docs/my-website/src/components/queryParamToken.js
new file mode 100644
index 0000000000000000000000000000000000000000..8eed74411918bf97ea68471f8ce2f3e13996eb57
--- /dev/null
+++ b/docs/my-website/src/components/queryParamToken.js
@@ -0,0 +1,19 @@
+import React, { useState, useEffect } from 'react';
+
+const QueryParamToken = () => {
+  const [token, setToken] = useState(null);
+
+  useEffect(() => {
+    const urlParams = new URLSearchParams(window.location.search);
+    const token = urlParams.get('token');
+    setToken(token);
+  }, []);
+
+  return (
+    
+      {token ? admin.litellm.ai : ""}
+    
+  );
+}
+
+export default QueryParamToken;
\ No newline at end of file
diff --git a/docs/my-website/src/css/custom.css b/docs/my-website/src/css/custom.css
new file mode 100644
index 0000000000000000000000000000000000000000..2bc6a4cfdef4e06006b9a2501525889aa5e597c9
--- /dev/null
+++ b/docs/my-website/src/css/custom.css
@@ -0,0 +1,30 @@
+/**
+ * Any CSS included here will be global. The classic template
+ * bundles Infima by default. Infima is a CSS framework designed to
+ * work well for content-centric websites.
+ */
+
+/* You can override the default Infima variables here. */
+:root {
+  --ifm-color-primary: #2e8555;
+  --ifm-color-primary-dark: #29784c;
+  --ifm-color-primary-darker: #277148;
+  --ifm-color-primary-darkest: #205d3b;
+  --ifm-color-primary-light: #33925d;
+  --ifm-color-primary-lighter: #359962;
+  --ifm-color-primary-lightest: #3cad6e;
+  --ifm-code-font-size: 95%;
+  --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.1);
+}
+
+/* For readability concerns, you should choose a lighter palette in dark mode. */
+[data-theme='dark'] {
+  --ifm-color-primary: #25c2a0;
+  --ifm-color-primary-dark: #21af90;
+  --ifm-color-primary-darker: #1fa588;
+  --ifm-color-primary-darkest: #1a8870;
+  --ifm-color-primary-light: #29d5b0;
+  --ifm-color-primary-lighter: #32d8b4;
+  --ifm-color-primary-lightest: #4fddbf;
+  --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.3);
+}
diff --git a/docs/my-website/src/pages-fake/index.js b/docs/my-website/src/pages-fake/index.js
new file mode 100644
index 0000000000000000000000000000000000000000..affcd9099c28fbf527b4b68eccf700b17abb2f42
--- /dev/null
+++ b/docs/my-website/src/pages-fake/index.js
@@ -0,0 +1,41 @@
+import React from 'react';
+import clsx from 'clsx';
+import Link from '@docusaurus/Link';
+import useDocusaurusContext from '@docusaurus/useDocusaurusContext';
+import Layout from '@theme/Layout';
+import HomepageFeatures from '@site/src/components/HomepageFeatures';
+
+import styles from './index.module.css';
+
+function HomepageHeader() {
+  const {siteConfig} = useDocusaurusContext();
+  return (
+    

+      

+        
{siteConfig.title}

+        
{siteConfig.tagline}

+        

+          
+            Docusaurus Tutorial - 5min ⏱️
+          
+        

+      

+    

+  );
+}
+
+export default function Home() {
+  const {siteConfig} = useDocusaurusContext();
+  return (
+    
+      
+      

+        
+      

+    
+  );
+}
diff --git a/docs/my-website/src/pages-fake/index.module.css b/docs/my-website/src/pages-fake/index.module.css
new file mode 100644
index 0000000000000000000000000000000000000000..9f71a5da775bd99379fa5c4d5bb73dc816d78bdd
--- /dev/null
+++ b/docs/my-website/src/pages-fake/index.module.css
@@ -0,0 +1,23 @@
+/**
+ * CSS files with the .module.css suffix will be treated as CSS modules
+ * and scoped locally.
+ */
+
+.heroBanner {
+  padding: 4rem 0;
+  text-align: center;
+  position: relative;
+  overflow: hidden;
+}
+
+@media screen and (max-width: 996px) {
+  .heroBanner {
+    padding: 2rem;
+  }
+}
+
+.buttons {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}
diff --git a/docs/my-website/src/pages-fake/markdown-page.md b/docs/my-website/src/pages-fake/markdown-page.md
new file mode 100644
index 0000000000000000000000000000000000000000..9756c5b6685a7fa67f41e11bc2cb6b990c2b12b2
--- /dev/null
+++ b/docs/my-website/src/pages-fake/markdown-page.md
@@ -0,0 +1,7 @@
+---
+title: Markdown page example
+---
+
+# Markdown page example
+
+You don't need React to write simple standalone pages.
diff --git a/docs/my-website/src/pages/completion/input.md b/docs/my-website/src/pages/completion/input.md
new file mode 100644
index 0000000000000000000000000000000000000000..86546bbbaef21f2ea5c0c5e8b9cddacee2d1e4ab
--- /dev/null
+++ b/docs/my-website/src/pages/completion/input.md
@@ -0,0 +1,58 @@
+# Completion Function - completion()
+The Input params are **exactly the same** as the 
+OpenAI Create chat completion, and let you call **Azure OpenAI, Anthropic, Cohere, Replicate, OpenRouter** models in the same format. 
+
+In addition, liteLLM allows you to pass in the following **Optional** liteLLM args:
+`force_timeout`, `azure`, `logger_fn`, `verbose`
+
+## Input - Request Body
+# Request Body
+
+**Required Fields**
+
+- `model`: *string* - ID of the model to use. Refer to the model endpoint compatibility table for details on which models work with the Chat API.
+  
+- `messages`: *array* - A list of messages comprising the conversation so far.
+
+*Note* - Each message in the array contains the following properties:
+
+    - `role`: *string* - The role of the message's author. Roles can be: system, user, assistant, or function.
+    
+    - `content`: *string or null* - The contents of the message. It is required for all messages, but may be null for assistant messages with function calls.
+    
+    - `name`: *string (optional)* - The name of the author of the message. It is required if the role is "function". The name should match the name of the function represented in the content. It can contain characters (a-z, A-Z, 0-9), and underscores, with a maximum length of 64 characters.
+    
+    - `function_call`: *object (optional)* - The name and arguments of a function that should be called, as generated by the model.
+
+
+**Optional Fields**
+
+- `functions`: *array* - A list of functions that the model may use to generate JSON inputs. Each function should have the following properties:
+
+    - `name`: *string* - The name of the function to be called. It should contain a-z, A-Z, 0-9, underscores and dashes, with a maximum length of 64 characters.
+    
+    - `description`: *string (optional)* - A description explaining what the function does. It helps the model to decide when and how to call the function.
+    
+    - `parameters`: *object* - The parameters that the function accepts, described as a JSON Schema object.
+    
+    - `function_call`: *string or object (optional)* - Controls how the model responds to function calls.
+
+- `temperature`: *number or null (optional)* - The sampling temperature to be used, between 0 and 2. Higher values like 0.8 produce more random outputs, while lower values like 0.2 make outputs more focused and deterministic. 
+
+- `top_p`: *number or null (optional)* - An alternative to sampling with temperature. It instructs the model to consider the results of the tokens with top_p probability. For example, 0.1 means only the tokens comprising the top 10% probability mass are considered.
+
+- `n`: *integer or null (optional)* - The number of chat completion choices to generate for each input message.
+
+- `stream`: *boolean or null (optional)* - If set to true, it sends partial message deltas. Tokens will be sent as they become available, with the stream terminated by a [DONE] message.
+
+- `stop`: *string/ array/ null (optional)* - Up to 4 sequences where the API will stop generating further tokens.
+
+- `max_tokens`: *integer (optional)* - The maximum number of tokens to generate in the chat completion.
+
+- `presence_penalty`: *number or null (optional)* - It is used to penalize new tokens based on their existence in the text so far.
+
+- `frequency_penalty`: *number or null (optional)* - It is used to penalize new tokens based on their frequency in the text so far.
+
+- `logit_bias`: *map (optional)* - Used to modify the probability of specific tokens appearing in the completion.
+
+- `user`: *string (optional)* - A unique identifier representing your end-user. This can help OpenAI to monitor and detect abuse.
\ No newline at end of file
diff --git a/docs/my-website/src/pages/completion/output.md b/docs/my-website/src/pages/completion/output.md
new file mode 100644
index 0000000000000000000000000000000000000000..a82023771c2f4ef275a57f3cdf4f10d334092959
--- /dev/null
+++ b/docs/my-website/src/pages/completion/output.md
@@ -0,0 +1,12 @@
+# Completion Function - completion()
+Here's the exact json output you can expect from a litellm `completion` call:
+
+```python 
+{'choices': [{'finish_reason': 'stop',
+   'index': 0,
+   'message': {'role': 'assistant',
+    'content': " I'm doing well, thank you for asking. I am Claude, an AI assistant created by Anthropic."}}],
+ 'created': 1691429984.3852863,
+ 'model': 'claude-instant-1',
+ 'usage': {'prompt_tokens': 18, 'completion_tokens': 23, 'total_tokens': 41}}
+```
\ No newline at end of file
diff --git a/docs/my-website/src/pages/completion/supported.md b/docs/my-website/src/pages/completion/supported.md
new file mode 100644
index 0000000000000000000000000000000000000000..2599353aa3fd06666d524647fbf400b8f9036540
--- /dev/null
+++ b/docs/my-website/src/pages/completion/supported.md
@@ -0,0 +1,73 @@
+# Generation/Completion/Chat Completion Models
+
+### OpenAI Chat Completion Models
+
+| Model Name       | Function Call                          | Required OS Variables                |
+|------------------|----------------------------------------|--------------------------------------|
+| gpt-3.5-turbo    | `completion('gpt-3.5-turbo', messages)` | `os.environ['OPENAI_API_KEY']`       |
+| gpt-3.5-turbo-16k    | `completion('gpt-3.5-turbo-16k', messages)` | `os.environ['OPENAI_API_KEY']`       |
+| gpt-3.5-turbo-16k-0613    | `completion('gpt-3.5-turbo-16k-0613', messages)` | `os.environ['OPENAI_API_KEY']`       |
+| gpt-4            | `completion('gpt-4', messages)`         | `os.environ['OPENAI_API_KEY']`       |
+
+## Azure OpenAI Chat Completion Models
+For Azure calls add the `azure/` prefix to `model`. If your azure deployment name is `gpt-v-2` set `model` = `azure/gpt-v-2`
+
+| Model Name       | Function Call                           | Required OS Variables                     |
+|------------------|-----------------------------------------|-------------------------------------------|
+| gpt-3.5-turbo    | `completion('azure/gpt-3.5-turbo-deployment', messages)` | `os.environ['AZURE_API_KEY']`,`os.environ['AZURE_API_BASE']`,`os.environ['AZURE_API_VERSION']` |
+| gpt-4            | `completion('azure/gpt-4-deployment', messages)`         | `os.environ['AZURE_API_KEY']`,`os.environ['AZURE_API_BASE']`,`os.environ['AZURE_API_VERSION']` |
+
+### OpenAI Text Completion Models
+
+| Model Name       | Function Call                              | Required OS Variables                |
+|------------------|--------------------------------------------|--------------------------------------|
+| text-davinci-003 | `completion('text-davinci-003', messages)` | `os.environ['OPENAI_API_KEY']`       |
+
+### Cohere Models
+
+| Model Name       | Function Call                              | Required OS Variables                |
+|------------------|--------------------------------------------|--------------------------------------|
+| command-nightly  | `completion('command-nightly', messages)` | `os.environ['COHERE_API_KEY']`       |
+
+
+### Anthropic Models
+
+| Model Name       | Function Call                              | Required OS Variables                |
+|------------------|--------------------------------------------|--------------------------------------|
+| claude-instant-1  | `completion('claude-instant-1', messages)` | `os.environ['ANTHROPIC_API_KEY']`       |
+| claude-2  | `completion('claude-2', messages)` | `os.environ['ANTHROPIC_API_KEY']`       |
+
+### Hugging Face Inference API
+
+All [`text2text-generation`](https://huggingface.co/models?library=transformers&pipeline_tag=text2text-generation&sort=downloads) and [`text-generation`](https://huggingface.co/models?library=transformers&pipeline_tag=text-generation&sort=downloads) models are supported by liteLLM. You can use any text model from Hugging Face with the following steps:
+
+* Copy the `model repo` URL from Hugging Face and set it as the `model` parameter in the completion call.
+* Set `hugging_face` parameter to `True`.
+* Make sure to set the hugging face API key
+
+Here are some examples of supported models:
+**Note that the models mentioned in the table are examples, and you can use any text model available on Hugging Face by following the steps above.**
+
+| Model Name       | Function Call                                                                       | Required OS Variables                |
+|------------------|-------------------------------------------------------------------------------------|--------------------------------------|
+| [stabilityai/stablecode-completion-alpha-3b-4k](https://huggingface.co/stabilityai/stablecode-completion-alpha-3b-4k)  | `completion(model="stabilityai/stablecode-completion-alpha-3b-4k", messages=messages, hugging_face=True)` | `os.environ['HF_TOKEN']`       |
+| [bigcode/starcoder](https://huggingface.co/bigcode/starcoder)                           | `completion(model="bigcode/starcoder", messages=messages, hugging_face=True)`          | `os.environ['HF_TOKEN']`       |
+| [google/flan-t5-xxl](https://huggingface.co/google/flan-t5-xxl)                         | `completion(model="google/flan-t5-xxl", messages=messages, hugging_face=True)`         | `os.environ['HF_TOKEN']`       |
+| [google/flan-t5-large](https://huggingface.co/google/flan-t5-large)                     | `completion(model="google/flan-t5-large", messages=messages, hugging_face=True)`       | `os.environ['HF_TOKEN']`       |
+
+### OpenRouter Completion Models
+
+All the text models from [OpenRouter](https://openrouter.ai/docs) are supported by liteLLM.
+
+| Model Name       | Function Call                              | Required OS Variables                |
+|------------------|--------------------------------------------|--------------------------------------|
+| openai/gpt-3.5-turbo | `completion('openai/gpt-3.5-turbo', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OR_API_KEY']`       |
+| openai/gpt-3.5-turbo-16k | `completion('openai/gpt-3.5-turbo-16k', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OR_API_KEY']`       |
+| openai/gpt-4 | `completion('openai/gpt-4', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OR_API_KEY']`       |
+| openai/gpt-4-32k | `completion('openai/gpt-4-32k', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OR_API_KEY']`       |
+| anthropic/claude-2 | `completion('anthropic/claude-2', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OR_API_KEY']`       |
+| anthropic/claude-instant-v1 | `completion('anthropic/claude-instant-v1', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OR_API_KEY']`       |
+| google/palm-2-chat-bison | `completion('google/palm-2-chat-bison', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OR_API_KEY']`       |
+| google/palm-2-codechat-bison | `completion('google/palm-2-codechat-bison', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OR_API_KEY']`       |
+| meta-llama/llama-2-13b-chat | `completion('meta-llama/llama-2-13b-chat', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OR_API_KEY']`       |
+| meta-llama/llama-2-70b-chat | `completion('meta-llama/llama-2-70b-chat', messages)` | `os.environ['OR_SITE_URL']`,`os.environ['OR_APP_NAME']`,`os.environ['OR_API_KEY']`       |
\ No newline at end of file
diff --git a/docs/my-website/src/pages/contact.md b/docs/my-website/src/pages/contact.md
new file mode 100644
index 0000000000000000000000000000000000000000..d5309cd7373e07351b1935de5e8503a9ddb7a90b
--- /dev/null
+++ b/docs/my-website/src/pages/contact.md
@@ -0,0 +1,6 @@
+# Contact Us
+
+[![](https://dcbadge.vercel.app/api/server/wuPM9dRgDw)](https://discord.gg/wuPM9dRgDw)
+
+* [Meet with us 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
+* Contact us at ishaan@berri.ai / krrish@berri.ai
diff --git a/docs/my-website/src/pages/contributing.md b/docs/my-website/src/pages/contributing.md
new file mode 100644
index 0000000000000000000000000000000000000000..6f1e2d01aabcd57565188d3b7a6d09ab82e8b488
--- /dev/null
+++ b/docs/my-website/src/pages/contributing.md
@@ -0,0 +1,35 @@
+# Contributing to Documentation
+
+Clone litellm 
+```
+git clone https://github.com/BerriAI/litellm.git
+```
+
+### Local setup for locally running docs
+
+#### Installation
+```
+pip install mkdocs
+```
+
+#### Locally Serving Docs
+```
+mkdocs serve
+```
+If you see `command not found: mkdocs` try running the following
+```
+python3 -m mkdocs serve
+```
+
+This command builds your Markdown files into HTML and starts a development server to browse your documentation. Open up [http://127.0.0.1:8000/](http://127.0.0.1:8000/) in your web browser to see your documentation. You can make changes to your Markdown files and your docs will automatically rebuild.
+
+[Full tutorial here](https://docs.readthedocs.io/en/stable/intro/getting-started-with-mkdocs.html)
+
+### Making changes to Docs
+- All the docs are placed under the `docs` directory
+- If you are adding a new `.md` file or editing the hierarchy edit `mkdocs.yml` in the root of the project
+- After testing your changes, make a change to the `main` branch of [github.com/BerriAI/litellm](https://github.com/BerriAI/litellm)
+
+
+
+
diff --git a/docs/my-website/src/pages/embedding/supported_embedding.md b/docs/my-website/src/pages/embedding/supported_embedding.md
new file mode 100644
index 0000000000000000000000000000000000000000..534b3a15feaac960b7ba55e6dcd6e865ecb4f465
--- /dev/null
+++ b/docs/my-website/src/pages/embedding/supported_embedding.md
@@ -0,0 +1,5 @@
+# Embedding Models
+
+| Model Name           | Function Call                               | Required OS Variables                |
+|----------------------|---------------------------------------------|--------------------------------------|
+| text-embedding-ada-002 | `embedding('text-embedding-ada-002', input)` | `os.environ['OPENAI_API_KEY']`       |
\ No newline at end of file
diff --git a/docs/my-website/src/pages/index.md b/docs/my-website/src/pages/index.md
new file mode 100644
index 0000000000000000000000000000000000000000..425266219c1be2177ef13881977aae9e5eba540a
--- /dev/null
+++ b/docs/my-website/src/pages/index.md
@@ -0,0 +1,382 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# LiteLLM - Getting Started
+
+https://github.com/BerriAI/litellm
+
+
+## **Call 100+ LLMs using the same Input/Output Format**
+
+## Basic usage 
+
+  Open In Colab
+
+
+```shell
+pip install litellm
+```
+
+
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+response = completion(
+  model="gpt-3.5-turbo", 
+  messages=[{ "content": "Hello, how are you?","role": "user"}]
+)
+```
+
+
+
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables
+os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
+
+response = completion(
+  model="claude-2", 
+  messages=[{ "content": "Hello, how are you?","role": "user"}]
+)
+```
+
+
+
+
+
+```python
+from litellm import completion
+import os
+
+# auth: run 'gcloud auth application-default'
+os.environ["VERTEX_PROJECT"] = "hardy-device-386718"
+os.environ["VERTEX_LOCATION"] = "us-central1"
+
+response = completion(
+  model="chat-bison", 
+  messages=[{ "content": "Hello, how are you?","role": "user"}]
+)
+```
+
+
+
+
+
+```python
+from litellm import completion 
+import os
+
+os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" 
+
+# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints
+response = completion(
+  model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0",
+  messages=[{ "content": "Hello, how are you?","role": "user"}], 
+  api_base="https://my-endpoint.huggingface.cloud"
+)
+
+print(response)
+```
+
+
+
+
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables
+os.environ["AZURE_API_KEY"] = ""
+os.environ["AZURE_API_BASE"] = ""
+os.environ["AZURE_API_VERSION"] = ""
+
+# azure call
+response = completion(
+  "azure/", 
+  messages = [{ "content": "Hello, how are you?","role": "user"}]
+)
+```
+
+
+
+
+
+
+```python
+from litellm import completion
+
+response = completion(
+            model="ollama/llama2", 
+            messages = [{ "content": "Hello, how are you?","role": "user"}], 
+            api_base="http://localhost:11434"
+)
+```
+
+
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables
+os.environ["OPENROUTER_API_KEY"] = "openrouter_api_key" 
+
+response = completion(
+  model="openrouter/google/palm-2-chat-bison", 
+  messages = [{ "content": "Hello, how are you?","role": "user"}],
+)
+```
+
+
+
+
+## Streaming
+Set `stream=True` in the `completion` args. 
+
+
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+response = completion(
+  model="gpt-3.5-turbo", 
+  messages=[{ "content": "Hello, how are you?","role": "user"}],
+  stream=True,
+)
+```
+
+
+
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables
+os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
+
+response = completion(
+  model="claude-2", 
+  messages=[{ "content": "Hello, how are you?","role": "user"}],
+  stream=True,
+)
+```
+
+
+
+
+
+```python
+from litellm import completion
+import os
+
+# auth: run 'gcloud auth application-default'
+os.environ["VERTEX_PROJECT"] = "hardy-device-386718"
+os.environ["VERTEX_LOCATION"] = "us-central1"
+
+response = completion(
+  model="chat-bison", 
+  messages=[{ "content": "Hello, how are you?","role": "user"}],
+  stream=True,
+)
+```
+
+
+
+
+
+```python
+from litellm import completion 
+import os
+
+os.environ["HUGGINGFACE_API_KEY"] = "huggingface_api_key" 
+
+# e.g. Call 'WizardLM/WizardCoder-Python-34B-V1.0' hosted on HF Inference endpoints
+response = completion(
+  model="huggingface/WizardLM/WizardCoder-Python-34B-V1.0",
+  messages=[{ "content": "Hello, how are you?","role": "user"}], 
+  api_base="https://my-endpoint.huggingface.cloud",
+  stream=True,
+)
+
+print(response)
+```
+
+
+
+
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables
+os.environ["AZURE_API_KEY"] = ""
+os.environ["AZURE_API_BASE"] = ""
+os.environ["AZURE_API_VERSION"] = ""
+
+# azure call
+response = completion(
+  "azure/", 
+  messages = [{ "content": "Hello, how are you?","role": "user"}],
+  stream=True,
+)
+```
+
+
+
+
+
+
+```python
+from litellm import completion
+
+response = completion(
+            model="ollama/llama2", 
+            messages = [{ "content": "Hello, how are you?","role": "user"}], 
+            api_base="http://localhost:11434",
+            stream=True,
+)
+```
+
+
+
+```python
+from litellm import completion
+import os
+
+## set ENV variables
+os.environ["OPENROUTER_API_KEY"] = "openrouter_api_key" 
+
+response = completion(
+  model="openrouter/google/palm-2-chat-bison", 
+  messages = [{ "content": "Hello, how are you?","role": "user"}],
+  stream=True,
+)
+```
+
+
+
+
+## Exception handling 
+
+LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM. 
+
+```python 
+from openai.error import OpenAIError
+from litellm import completion
+
+os.environ["ANTHROPIC_API_KEY"] = "bad-key"
+try: 
+    # some code 
+    completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}])
+except OpenAIError as e:
+    print(e)
+```
+
+## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
+LiteLLM exposes pre defined callbacks to send data to Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack
+```python
+from litellm import completion
+
+## set env variables for logging tools
+os.environ["LANGFUSE_PUBLIC_KEY"] = ""
+os.environ["LANGFUSE_SECRET_KEY"] = ""
+os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"
+
+os.environ["OPENAI_API_KEY"]
+
+# set callbacks
+litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langfuse, llmonitor, supabase
+
+#openai call
+response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
+```
+
+## Calculate Costs, Usage, Latency
+
+Pass the completion response to `litellm.completion_cost(completion_response=response)` and get the cost
+
+```python
+from litellm import completion, completion_cost
+import os
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+response = completion(
+  model="gpt-3.5-turbo", 
+  messages=[{ "content": "Hello, how are you?","role": "user"}]
+)
+
+cost = completion_cost(completion_response=response)
+print("Cost for completion call with gpt-3.5-turbo: ", f"${float(cost):.10f}")
+```
+
+**Output**
+```shell
+Cost for completion call with gpt-3.5-turbo:  $0.0000775000
+```
+
+### Track Costs, Usage, Latency for streaming
+Use a callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback
+
+```python
+import litellm
+
+# track_cost_callback 
+def track_cost_callback(
+    kwargs,                 # kwargs to completion
+    completion_response,    # response from completion
+    start_time, end_time    # start/end time
+):
+    try:
+        # check if it has collected an entire stream response
+        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
+            )
+            print("streaming response_cost", response_cost)
+    except:
+        pass
+# set callback 
+litellm.success_callback = [track_cost_callback] # set custom callback function
+
+# litellm.completion() call
+response = completion(
+    model="gpt-3.5-turbo",
+    messages=[
+        {
+            "role": "user",
+            "content": "Hi 👋 - i'm openai"
+        }
+    ],
+    stream=True
+)
+```
+
+
+Need a dedicated key? Email us @ krrish@berri.ai
+
+
+## More details
+* [exception mapping](./exception_mapping.md)
+* [retries + model fallbacks for completion()](./completion/reliable_completions.md)
+* [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md)
\ No newline at end of file
diff --git a/docs/my-website/src/pages/intro.md b/docs/my-website/src/pages/intro.md
new file mode 100644
index 0000000000000000000000000000000000000000..8a2e69d95f9f7de70b8fb31dbf98fbb27deb7012
--- /dev/null
+++ b/docs/my-website/src/pages/intro.md
@@ -0,0 +1,47 @@
+---
+sidebar_position: 1
+---
+
+# Tutorial Intro
+
+Let's discover **Docusaurus in less than 5 minutes**.
+
+## Getting Started
+
+Get started by **creating a new site**.
+
+Or **try Docusaurus immediately** with **[docusaurus.new](https://docusaurus.new)**.
+
+### What you'll need
+
+- [Node.js](https://nodejs.org/en/download/) version 16.14 or above:
+  - When installing Node.js, you are recommended to check all checkboxes related to dependencies.
+
+## Generate a new site
+
+Generate a new Docusaurus site using the **classic template**.
+
+The classic template will automatically be added to your project after you run the command:
+
+```bash
+npm init docusaurus@latest my-website classic
+```
+
+You can type this command into Command Prompt, Powershell, Terminal, or any other integrated terminal of your code editor.
+
+The command also installs all necessary dependencies you need to run Docusaurus.
+
+## Start your site
+
+Run the development server:
+
+```bash
+cd my-website
+npm run start
+```
+
+The `cd` command changes the directory you're working with. In order to work with your newly created Docusaurus site, you'll need to navigate the terminal there.
+
+The `npm run start` command builds your website locally and serves it through a development server, ready for you to view at http://localhost:3000/.
+
+Open `docs/intro.md` (this page) and edit some lines: the site **reloads automatically** and displays your changes.
diff --git a/docs/my-website/src/pages/observability/callbacks.md b/docs/my-website/src/pages/observability/callbacks.md
new file mode 100644
index 0000000000000000000000000000000000000000..be27d76da277d405b210de3bca3179628468a989
--- /dev/null
+++ b/docs/my-website/src/pages/observability/callbacks.md
@@ -0,0 +1,30 @@
+# Callbacks
+
+## Use Callbacks to send Output Data to Posthog, Sentry etc
+
+liteLLM provides `success_callbacks` and `failure_callbacks`, making it easy for you to send data to a particular provider depending on the status of your responses.
+
+liteLLM supports:
+
+- [LLMonitor](https://llmonitor.com/docs)
+- [Helicone](https://docs.helicone.ai/introduction)
+- [Sentry](https://docs.sentry.io/platforms/python/)
+- [PostHog](https://posthog.com/docs/libraries/python)
+- [Slack](https://slack.dev/bolt-python/concepts)
+
+### Quick Start
+
+```python
+from litellm import completion
+
+# set callbacks
+litellm.success_callback=["posthog", "helicone", "llmonitor"]
+litellm.failure_callback=["sentry", "llmonitor"]
+
+## set env variables
+os.environ['SENTRY_DSN'], os.environ['SENTRY_API_TRACE_RATE']= ""
+os.environ['POSTHOG_API_KEY'], os.environ['POSTHOG_API_URL'] = "api-key", "api-url"
+os.environ["HELICONE_API_KEY"] = ""
+
+response = completion(model="gpt-3.5-turbo", messages=messages)
+```
diff --git a/docs/my-website/src/pages/observability/helicone_integration.md b/docs/my-website/src/pages/observability/helicone_integration.md
new file mode 100644
index 0000000000000000000000000000000000000000..273d22d4feb667669faeb98c73b7eff3bab46eef
--- /dev/null
+++ b/docs/my-website/src/pages/observability/helicone_integration.md
@@ -0,0 +1,55 @@
+# Helicone Tutorial 
+[Helicone](https://helicone.ai/) is an open source observability platform that proxies your OpenAI traffic and provides you key insights into your spend, latency and usage.
+
+## Use Helicone to log requests across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM)
+liteLLM provides `success_callbacks` and `failure_callbacks`, making it easy for you to send data to a particular provider depending on the status of your responses. 
+
+In this case, we want to log requests to Helicone when a request succeeds. 
+
+### Approach 1: Use Callbacks 
+Use just 1 line of code, to instantly log your responses **across all providers** with helicone: 
+```
+litellm.success_callback=["helicone"]
+```
+
+Complete code
+```python
+from litellm import completion
+
+## set env variables
+os.environ["HELICONE_API_KEY"] = "your-helicone-key" 
+os.environ["OPENAI_API_KEY"], os.environ["COHERE_API_KEY"] = "", ""
+
+# set callbacks
+litellm.success_callback=["helicone"]
+
+#openai call
+response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) 
+
+#cohere call
+response = completion(model="command-nightly", messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}]) 
+```
+
+### Approach 2: [OpenAI + Azure only] Use Helicone as a proxy
+Helicone provides advanced functionality like caching, etc. Helicone currently supports this for Azure and OpenAI.
+
+If you want to use Helicone to proxy your OpenAI/Azure requests, then you can - 
+
+- Set helicone as your base url via: `litellm.api_url` 
+- Pass in helicone request headers via: `litellm.headers` 
+
+Complete Code
+```
+import litellm
+from litellm import completion
+
+litellm.api_base = "https://oai.hconeai.com/v1"
+litellm.headers = {"Helicone-Auth": f"Bearer {os.getenv('HELICONE_API_KEY')}"}
+
+response = litellm.completion(
+    model="gpt-3.5-turbo",
+    messages=[{"role": "user", "content": "how does a court case get to the Supreme Court?"}]
+)
+
+print(response)
+```
diff --git a/docs/my-website/src/pages/observability/supabase_integration.md b/docs/my-website/src/pages/observability/supabase_integration.md
new file mode 100644
index 0000000000000000000000000000000000000000..6ae4f65daee5d4960f139ade586928301919f492
--- /dev/null
+++ b/docs/my-website/src/pages/observability/supabase_integration.md
@@ -0,0 +1,80 @@
+# Supabase Tutorial 
+[Supabase](https://supabase.com/) is an open source Firebase alternative.
+Start your project with a Postgres database, Authentication, instant APIs, Edge Functions, Realtime subscriptions, Storage, and Vector embeddings.
+
+## Use Supabase to log requests and see total spend across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM)
+liteLLM provides `success_callbacks` and `failure_callbacks`, making it easy for you to send data to a particular provider depending on the status of your responses. 
+
+In this case, we want to log requests to Supabase in both scenarios - when it succeeds and fails. 
+
+### Create a supabase table 
+
+Go to your Supabase project > go to the [Supabase SQL Editor](https://supabase.com/dashboard/projects) and create a new table with this configuration.
+
+Note: You can change the table name. Just don't change the column names. 
+
+```sql
+create table
+  public.request_logs (
+    id bigint generated by default as identity,
+    created_at timestamp with time zone null default now(),
+    model text null default ''::text,
+    messages json null default '{}'::json,
+    response json null default '{}'::json,
+    end_user text null default ''::text,
+    error json null default '{}'::json,
+    response_time real null default '0'::real,
+    total_cost real null,
+    additional_details json null default '{}'::json,
+    constraint request_logs_pkey primary key (id)
+  ) tablespace pg_default;
+```
+
+### Use Callbacks 
+Use just 2 lines of code, to instantly see costs and log your responses **across all providers** with Supabase: 
+
+```
+litellm.success_callback=["supabase"]
+litellm.failure_callback=["supabase"]
+```
+
+Complete code
+```python
+from litellm import completion
+
+## set env variables
+### SUPABASE
+os.environ["SUPABASE_URL"] = "your-supabase-url" 
+os.environ["SUPABASE_KEY"] = "your-supabase-key" 
+
+## LLM API KEY
+os.environ["OPENAI_API_KEY"] = ""
+
+# set callbacks
+litellm.success_callback=["supabase"]
+litellm.failure_callback=["supabase"]
+
+#openai call
+response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) 
+
+#bad call
+response = completion(model="chatgpt-test", messages=[{"role": "user", "content": "Hi 👋 - i'm a bad call to test error logging"}]) 
+```
+
+### Additional Controls 
+
+**Different Table name**
+
+If you modified your table name, here's how to pass the new name.
+
+```python 
+litellm.modify_integration("supabase",{"table_name": "litellm_logs"})
+```
+
+**Identify end-user**
+
+Here's how to map your llm call to an end-user 
+
+```python
+litellm.identify({"end_user": "krrish@berri.ai"})
+```
\ No newline at end of file
diff --git a/docs/my-website/src/pages/secret.md b/docs/my-website/src/pages/secret.md
new file mode 100644
index 0000000000000000000000000000000000000000..74878cbe96dcaac7f93323462ae26c0e09494f77
--- /dev/null
+++ b/docs/my-website/src/pages/secret.md
@@ -0,0 +1,33 @@
+# Secret Managers
+liteLLM reads secrets from yoour secret manager, .env file 
+
+- [Infisical Secret Manager](#infisical-secret-manager)
+- [.env Files](#env-files)
+
+For expected format of secrets see [supported LLM models](https://litellm.readthedocs.io/en/latest/supported)
+
+## Infisical Secret Manager
+Integrates with [Infisical's Secret Manager](https://infisical.com/) for secure storage and retrieval of API keys and sensitive data.
+
+### Usage
+liteLLM manages reading in your LLM API secrets/env variables from Infisical for you
+
+```
+import litellm
+from infisical import InfisicalClient
+
+litellm.secret_manager = InfisicalClient(token="your-token")
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "What's the weather like today?"},
+]
+
+response = litellm.completion(model="gpt-3.5-turbo", messages=messages)
+
+print(response)
+```
+
+
+## .env Files
+If no secret manager client is specified, Litellm automatically uses the `.env` file to manage sensitive data.
diff --git a/docs/my-website/src/pages/stream.md b/docs/my-website/src/pages/stream.md
new file mode 100644
index 0000000000000000000000000000000000000000..5e8cc32ca2e801cb5ad47d3d0a9c6033d86651b4
--- /dev/null
+++ b/docs/my-website/src/pages/stream.md
@@ -0,0 +1,33 @@
+# Streaming Responses & Async Completion
+
+- [Streaming Responses](#streaming-responses)
+- [Async Completion](#async-completion)
+
+## Streaming Responses
+LiteLLM supports streaming the model response back by passing `stream=True` as an argument to the completion function
+### Usage
+```python
+response = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
+for chunk in response:
+    print(chunk['choices'][0]['delta'])
+
+```
+
+## Async Completion
+Asynchronous Completion with LiteLLM
+LiteLLM provides an asynchronous version of the completion function called `acompletion`
+### Usage
+```
+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)
+
+```
\ No newline at end of file
diff --git a/docs/my-website/src/pages/token_usage.md b/docs/my-website/src/pages/token_usage.md
new file mode 100644
index 0000000000000000000000000000000000000000..b7fc0b083d9ac95c423a848941d09e16c962dcee
--- /dev/null
+++ b/docs/my-website/src/pages/token_usage.md
@@ -0,0 +1,45 @@
+# Token Usage
+By default LiteLLM returns token usage in all completion requests ([See here](https://litellm.readthedocs.io/en/latest/output/))
+
+However, we also expose 3 public helper functions to calculate token usage across providers:
+
+- `token_counter`: This returns the number of tokens for a given input - it uses the tokenizer based on the model, and defaults to tiktoken if no model-specific tokenizer is available. 
+
+- `cost_per_token`: This returns the cost (in USD) for prompt (input) and completion (output) tokens. It utilizes our model_cost map which can be found in `__init__.py` and also as a [community resource](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json).
+
+- `completion_cost`: This returns the overall cost (in USD) for a given LLM API Call. It combines `token_counter` and `cost_per_token` to return the cost for that query (counting both cost of input and output). 
+
+## Example Usage 
+
+1. `token_counter`
+
+```python
+from litellm import token_counter
+
+messages = [{"user": "role", "content": "Hey, how's it going"}]
+print(token_counter(model="gpt-3.5-turbo", messages=messages))
+```
+
+2. `cost_per_token`
+
+```python
+from litellm import cost_per_token
+
+prompt_tokens =  5
+completion_tokens = 10
+prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = cost_per_token(model="gpt-3.5-turbo", prompt_tokens=prompt_tokens, completion_tokens=completion_tokens))
+
+print(prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar)
+```
+
+3. `completion_cost`
+
+```python
+from litellm import completion_cost
+
+prompt = "Hey, how's it going"
+completion = "Hi, I'm gpt - I am doing well"
+cost_of_query = completion_cost(model="gpt-3.5-turbo", prompt=prompt, completion=completion))
+
+print(cost_of_query)
+```
diff --git a/docs/my-website/src/pages/troubleshoot.md b/docs/my-website/src/pages/troubleshoot.md
new file mode 100644
index 0000000000000000000000000000000000000000..05dbf56caae2b97d803163793b58a6286eab7111
--- /dev/null
+++ b/docs/my-website/src/pages/troubleshoot.md
@@ -0,0 +1,11 @@
+# Troubleshooting 
+
+## Stable Version
+
+If you're running into problems with installation / Usage 
+Use the stable version of litellm 
+
+```
+pip install litellm==0.1.345
+```
+
diff --git a/docs/my-website/src/pages/tutorial-basics/_category_.json b/docs/my-website/src/pages/tutorial-basics/_category_.json
new file mode 100644
index 0000000000000000000000000000000000000000..2e6db55b1eb61147f3aef1965dbdd79fdd505406
--- /dev/null
+++ b/docs/my-website/src/pages/tutorial-basics/_category_.json
@@ -0,0 +1,8 @@
+{
+  "label": "Tutorial - Basics",
+  "position": 2,
+  "link": {
+    "type": "generated-index",
+    "description": "5 minutes to learn the most important Docusaurus concepts."
+  }
+}
diff --git a/docs/my-website/src/pages/tutorial-basics/congratulations.md b/docs/my-website/src/pages/tutorial-basics/congratulations.md
new file mode 100644
index 0000000000000000000000000000000000000000..04771a00b72f80ee4d829890a8229085da77db4d
--- /dev/null
+++ b/docs/my-website/src/pages/tutorial-basics/congratulations.md
@@ -0,0 +1,23 @@
+---
+sidebar_position: 6
+---
+
+# Congratulations!
+
+You have just learned the **basics of Docusaurus** and made some changes to the **initial template**.
+
+Docusaurus has **much more to offer**!
+
+Have **5 more minutes**? Take a look at **[versioning](../tutorial-extras/manage-docs-versions.md)** and **[i18n](../tutorial-extras/translate-your-site.md)**.
+
+Anything **unclear** or **buggy** in this tutorial? [Please report it!](https://github.com/facebook/docusaurus/discussions/4610)
+
+## What's next?
+
+- Read the [official documentation](https://docusaurus.io/)
+- Modify your site configuration with [`docusaurus.config.js`](https://docusaurus.io/docs/api/docusaurus-config)
+- Add navbar and footer items with [`themeConfig`](https://docusaurus.io/docs/api/themes/configuration)
+- Add a custom [Design and Layout](https://docusaurus.io/docs/styling-layout)
+- Add a [search bar](https://docusaurus.io/docs/search)
+- Find inspirations in the [Docusaurus showcase](https://docusaurus.io/showcase)
+- Get involved in the [Docusaurus Community](https://docusaurus.io/community/support)
diff --git a/docs/my-website/src/pages/tutorial-basics/create-a-blog-post.md b/docs/my-website/src/pages/tutorial-basics/create-a-blog-post.md
new file mode 100644
index 0000000000000000000000000000000000000000..ea472bbaf8713ca7c51b135eb3524346232a6adf
--- /dev/null
+++ b/docs/my-website/src/pages/tutorial-basics/create-a-blog-post.md
@@ -0,0 +1,34 @@
+---
+sidebar_position: 3
+---
+
+# Create a Blog Post
+
+Docusaurus creates a **page for each blog post**, but also a **blog index page**, a **tag system**, an **RSS** feed...
+
+## Create your first Post
+
+Create a file at `blog/2021-02-28-greetings.md`:
+
+```md title="blog/2021-02-28-greetings.md"
+---
+slug: greetings
+title: Greetings!
+authors:
+  - name: Joel Marcey
+    title: Co-creator of Docusaurus 1
+    url: https://github.com/JoelMarcey
+    image_url: https://github.com/JoelMarcey.png
+  - name: Sébastien Lorber
+    title: Docusaurus maintainer
+    url: https://sebastienlorber.com
+    image_url: https://github.com/slorber.png
+tags: [greetings]
+---
+
+Congratulations, you have made your first post!
+
+Feel free to play around and edit this post as much you like.
+```
+
+A new blog post is now available at [http://localhost:3000/blog/greetings](http://localhost:3000/blog/greetings).
diff --git a/docs/my-website/src/pages/tutorial-basics/create-a-document.md b/docs/my-website/src/pages/tutorial-basics/create-a-document.md
new file mode 100644
index 0000000000000000000000000000000000000000..ffddfa8eb8aca79bb1b384b6270f86a695c9f43f
--- /dev/null
+++ b/docs/my-website/src/pages/tutorial-basics/create-a-document.md
@@ -0,0 +1,57 @@
+---
+sidebar_position: 2
+---
+
+# Create a Document
+
+Documents are **groups of pages** connected through:
+
+- a **sidebar**
+- **previous/next navigation**
+- **versioning**
+
+## Create your first Doc
+
+Create a Markdown file at `docs/hello.md`:
+
+```md title="docs/hello.md"
+# Hello
+
+This is my **first Docusaurus document**!
+```
+
+A new document is now available at [http://localhost:3000/docs/hello](http://localhost:3000/docs/hello).
+
+## Configure the Sidebar
+
+Docusaurus automatically **creates a sidebar** from the `docs` folder.
+
+Add metadata to customize the sidebar label and position:
+
+```md title="docs/hello.md" {1-4}
+---
+sidebar_label: 'Hi!'
+sidebar_position: 3
+---
+
+# Hello
+
+This is my **first Docusaurus document**!
+```
+
+It is also possible to create your sidebar explicitly in `sidebars.js`:
+
+```js title="sidebars.js"
+module.exports = {
+  tutorialSidebar: [
+    'intro',
+    // highlight-next-line
+    'hello',
+    {
+      type: 'category',
+      label: 'Tutorial',
+      items: ['tutorial-basics/create-a-document'],
+    },
+  ],
+};
+```
diff --git a/docs/my-website/src/pages/tutorial-basics/create-a-page.md b/docs/my-website/src/pages/tutorial-basics/create-a-page.md
new file mode 100644
index 0000000000000000000000000000000000000000..20e2ac300556e9baedcaf67474244f7059a53c40
--- /dev/null
+++ b/docs/my-website/src/pages/tutorial-basics/create-a-page.md
@@ -0,0 +1,43 @@
+---
+sidebar_position: 1
+---
+
+# Create a Page
+
+Add **Markdown or React** files to `src/pages` to create a **standalone page**:
+
+- `src/pages/index.js` → `localhost:3000/`
+- `src/pages/foo.md` → `localhost:3000/foo`
+- `src/pages/foo/bar.js` → `localhost:3000/foo/bar`
+
+## Create your first React Page
+
+Create a file at `src/pages/my-react-page.js`:
+
+```jsx title="src/pages/my-react-page.js"
+import React from 'react';
+import Layout from '@theme/Layout';
+
+export default function MyReactPage() {
+  return (
+    
+      
My React page

+      
This is a React page

+    
+  );
+}
+```
+
+A new page is now available at [http://localhost:3000/my-react-page](http://localhost:3000/my-react-page).
+
+## Create your first Markdown Page
+
+Create a file at `src/pages/my-markdown-page.md`:
+
+```mdx title="src/pages/my-markdown-page.md"
+# My Markdown page
+
+This is a Markdown page
+```
+
+A new page is now available at [http://localhost:3000/my-markdown-page](http://localhost:3000/my-markdown-page).
diff --git a/docs/my-website/src/pages/tutorial-basics/deploy-your-site.md b/docs/my-website/src/pages/tutorial-basics/deploy-your-site.md
new file mode 100644
index 0000000000000000000000000000000000000000..1c50ee063ef416333b994a910d10d05f984bf589
--- /dev/null
+++ b/docs/my-website/src/pages/tutorial-basics/deploy-your-site.md
@@ -0,0 +1,31 @@
+---
+sidebar_position: 5
+---
+
+# Deploy your site
+
+Docusaurus is a **static-site-generator** (also called **[Jamstack](https://jamstack.org/)**).
+
+It builds your site as simple **static HTML, JavaScript and CSS files**.
+
+## Build your site
+
+Build your site **for production**:
+
+```bash
+npm run build
+```
+
+The static files are generated in the `build` folder.
+
+## Deploy your site
+
+Test your production build locally:
+
+```bash
+npm run serve
+```
+
+The `build` folder is now served at [http://localhost:3000/](http://localhost:3000/).
+
+You can now deploy the `build` folder **almost anywhere** easily, **for free** or very small cost (read the **[Deployment Guide](https://docusaurus.io/docs/deployment)**).
diff --git a/docs/my-website/src/pages/tutorial-basics/markdown-features.mdx b/docs/my-website/src/pages/tutorial-basics/markdown-features.mdx
new file mode 100644
index 0000000000000000000000000000000000000000..0337f34d6a548ad421da85891201570cc4c644df
--- /dev/null
+++ b/docs/my-website/src/pages/tutorial-basics/markdown-features.mdx
@@ -0,0 +1,150 @@
+---
+sidebar_position: 4
+---
+
+# Markdown Features
+
+Docusaurus supports **[Markdown](https://daringfireball.net/projects/markdown/syntax)** and a few **additional features**.
+
+## Front Matter
+
+Markdown documents have metadata at the top called [Front Matter](https://jekyllrb.com/docs/front-matter/):
+
+```text title="my-doc.md"
+// highlight-start
+---
+id: my-doc-id
+title: My document title
+description: My document description
+slug: /my-custom-url
+---
+// highlight-end
+
+## Markdown heading
+
+Markdown text with [links](./hello.md)
+```
+
+## Links
+
+Regular Markdown links are supported, using url paths or relative file paths.
+
+```md
+Let's see how to [Create a page](/create-a-page).
+```
+
+```md
+Let's see how to [Create a page](./create-a-page.md).
+```
+
+**Result:** Let's see how to [Create a page](./create-a-page.md).
+
+## Images
+
+Regular Markdown images are supported.
+
+You can use absolute paths to reference images in the static directory (`static/img/docusaurus.png`):
+
+```md
+![Docusaurus logo](/img/docusaurus.png)
+```
+
+![Docusaurus logo](/img/docusaurus.png)
+
+You can reference images relative to the current file as well. This is particularly useful to colocate images close to the Markdown files using them:
+
+```md
+![Docusaurus logo](./img/docusaurus.png)
+```
+
+## Code Blocks
+
+Markdown code blocks are supported with Syntax highlighting.
+
+    ```jsx title="src/components/HelloDocusaurus.js"
+    function HelloDocusaurus() {
+        return (
+            
Hello, Docusaurus!

+        )
+    }
+    ```
+
+```jsx title="src/components/HelloDocusaurus.js"
+function HelloDocusaurus() {
+  return 
Hello, Docusaurus!
;
+}
+```
+
+## Admonitions
+
+Docusaurus has a special syntax to create admonitions and callouts:
+
+    :::tip My tip
+
+    Use this awesome feature option
+
+    :::
+
+    :::danger Take care
+
+    This action is dangerous
+
+    :::
+
+:::tip My tip
+
+Use this awesome feature option
+
+:::
+
+:::danger Take care
+
+This action is dangerous
+
+:::
+
+## MDX and React Components
+
+[MDX](https://mdxjs.com/) can make your documentation more **interactive** and allows using any **React components inside Markdown**:
+
+```jsx
+export const Highlight = ({children, color}) => (
+   {
+      alert(`You clicked the color ${color} with label ${children}`)
+    }}>
+    {children}
+  
+);
+
+This is Docusaurus green !
+
+This is Facebook blue !
+```
+
+export const Highlight = ({children, color}) => (
+   {
+      alert(`You clicked the color ${color} with label ${children}`);
+    }}>
+    {children}
+  
+);
+
+This is Docusaurus green !
+
+This is Facebook blue !
diff --git a/docs/my-website/src/pages/tutorial-extras/_category_.json b/docs/my-website/src/pages/tutorial-extras/_category_.json
new file mode 100644
index 0000000000000000000000000000000000000000..a8ffcc19300ec5973db4c87090a2bc9e5cf73aa0
--- /dev/null
+++ b/docs/my-website/src/pages/tutorial-extras/_category_.json
@@ -0,0 +1,7 @@
+{
+  "label": "Tutorial - Extras",
+  "position": 3,
+  "link": {
+    "type": "generated-index"
+  }
+}
diff --git a/docs/my-website/src/pages/tutorial-extras/img/docsVersionDropdown.png b/docs/my-website/src/pages/tutorial-extras/img/docsVersionDropdown.png
new file mode 100644
index 0000000000000000000000000000000000000000..97e4164618b5f8beda34cfa699720aba0ad2e342
Binary files /dev/null and b/docs/my-website/src/pages/tutorial-extras/img/docsVersionDropdown.png differ
diff --git a/docs/my-website/src/pages/tutorial-extras/img/localeDropdown.png b/docs/my-website/src/pages/tutorial-extras/img/localeDropdown.png
new file mode 100644
index 0000000000000000000000000000000000000000..e257edc1f932985396bf59584c7ccfaddf955779
Binary files /dev/null and b/docs/my-website/src/pages/tutorial-extras/img/localeDropdown.png differ
diff --git a/docs/my-website/src/pages/tutorial-extras/manage-docs-versions.md b/docs/my-website/src/pages/tutorial-extras/manage-docs-versions.md
new file mode 100644
index 0000000000000000000000000000000000000000..e12c3f3444fa77632269373d7b420e16b649022f
--- /dev/null
+++ b/docs/my-website/src/pages/tutorial-extras/manage-docs-versions.md
@@ -0,0 +1,55 @@
+---
+sidebar_position: 1
+---
+
+# Manage Docs Versions
+
+Docusaurus can manage multiple versions of your docs.
+
+## Create a docs version
+
+Release a version 1.0 of your project:
+
+```bash
+npm run docusaurus docs:version 1.0
+```
+
+The `docs` folder is copied into `versioned_docs/version-1.0` and `versions.json` is created.
+
+Your docs now have 2 versions:
+
+- `1.0` at `http://localhost:3000/docs/` for the version 1.0 docs
+- `current` at `http://localhost:3000/docs/next/` for the **upcoming, unreleased docs**
+
+## Add a Version Dropdown
+
+To navigate seamlessly across versions, add a version dropdown.
+
+Modify the `docusaurus.config.js` file:
+
+```js title="docusaurus.config.js"
+module.exports = {
+  themeConfig: {
+    navbar: {
+      items: [
+        // highlight-start
+        {
+          type: 'docsVersionDropdown',
+        },
+        // highlight-end
+      ],
+    },
+  },
+};
+```
+
+The docs version dropdown appears in your navbar:
+
+![Docs Version Dropdown](./img/docsVersionDropdown.png)
+
+## Update an existing version
+
+It is possible to edit versioned docs in their respective folder:
+
+- `versioned_docs/version-1.0/hello.md` updates `http://localhost:3000/docs/hello`
+- `docs/hello.md` updates `http://localhost:3000/docs/next/hello`
diff --git a/docs/my-website/src/pages/tutorial-extras/translate-your-site.md b/docs/my-website/src/pages/tutorial-extras/translate-your-site.md
new file mode 100644
index 0000000000000000000000000000000000000000..caeaffb05548b1e9d0cee001dd8dc401480935a1
--- /dev/null
+++ b/docs/my-website/src/pages/tutorial-extras/translate-your-site.md
@@ -0,0 +1,88 @@
+---
+sidebar_position: 2
+---
+
+# Translate your site
+
+Let's translate `docs/intro.md` to French.
+
+## Configure i18n
+
+Modify `docusaurus.config.js` to add support for the `fr` locale:
+
+```js title="docusaurus.config.js"
+module.exports = {
+  i18n: {
+    defaultLocale: 'en',
+    locales: ['en', 'fr'],
+  },
+};
+```
+
+## Translate a doc
+
+Copy the `docs/intro.md` file to the `i18n/fr` folder:
+
+```bash
+mkdir -p i18n/fr/docusaurus-plugin-content-docs/current/
+
+cp docs/intro.md i18n/fr/docusaurus-plugin-content-docs/current/intro.md
+```
+
+Translate `i18n/fr/docusaurus-plugin-content-docs/current/intro.md` in French.
+
+## Start your localized site
+
+Start your site on the French locale:
+
+```bash
+npm run start -- --locale fr
+```
+
+Your localized site is accessible at [http://localhost:3000/fr/](http://localhost:3000/fr/) and the `Getting Started` page is translated.
+
+:::caution
+
+In development, you can only use one locale at a same time.
+
+:::
+
+## Add a Locale Dropdown
+
+To navigate seamlessly across languages, add a locale dropdown.
+
+Modify the `docusaurus.config.js` file:
+
+```js title="docusaurus.config.js"
+module.exports = {
+  themeConfig: {
+    navbar: {
+      items: [
+        // highlight-start
+        {
+          type: 'localeDropdown',
+        },
+        // highlight-end
+      ],
+    },
+  },
+};
+```
+
+The locale dropdown now appears in your navbar:
+
+![Locale Dropdown](./img/localeDropdown.png)
+
+## Build your localized site
+
+Build your site for a specific locale:
+
+```bash
+npm run build -- --locale fr
+```
+
+Or build your site to include all the locales at once:
+
+```bash
+npm run build
+```
diff --git a/docs/my-website/static/.nojekyll b/docs/my-website/static/.nojekyll
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/docs/my-website/static/img/docusaurus-social-card.png b/docs/my-website/static/img/docusaurus-social-card.png
new file mode 100644
index 0000000000000000000000000000000000000000..62aabbdca0ac55e9003abc7614e83a0818b01877
Binary files /dev/null and b/docs/my-website/static/img/docusaurus-social-card.png differ
diff --git a/docs/my-website/static/img/docusaurus.png b/docs/my-website/static/img/docusaurus.png
new file mode 100644
index 0000000000000000000000000000000000000000..f458149e3c8f53335f28fbc162ae67f55575c881
Binary files /dev/null and b/docs/my-website/static/img/docusaurus.png differ
diff --git a/docs/my-website/static/img/favicon.ico b/docs/my-website/static/img/favicon.ico
new file mode 100644
index 0000000000000000000000000000000000000000..88caa2b8315708c3577006ecc656ccc7f3c97a12
Binary files /dev/null and b/docs/my-website/static/img/favicon.ico differ
diff --git a/docs/my-website/static/img/logo.svg b/docs/my-website/static/img/logo.svg
new file mode 100644
index 0000000000000000000000000000000000000000..9db6d0d066e3df092350a71e4c4ec5a90190b7c3
--- /dev/null
+++ b/docs/my-website/static/img/logo.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/docs/my-website/static/img/undraw_docusaurus_mountain.svg b/docs/my-website/static/img/undraw_docusaurus_mountain.svg
new file mode 100644
index 0000000000000000000000000000000000000000..af961c49a8888259853fe621b30052e8ae1c8128
--- /dev/null
+++ b/docs/my-website/static/img/undraw_docusaurus_mountain.svg
@@ -0,0 +1,171 @@
+
+  Easy to Use
+  
+    
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+    
+    
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+        
+        
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+        
+        
+          
+          
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+          
+          
+        
+        
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+      
+      
+      
+      
+      
+    
+  
+
diff --git a/docs/my-website/static/img/undraw_docusaurus_react.svg b/docs/my-website/static/img/undraw_docusaurus_react.svg
new file mode 100644
index 0000000000000000000000000000000000000000..94b5cf08f88f2202ce8c7abe820c4f6c5336b836
--- /dev/null
+++ b/docs/my-website/static/img/undraw_docusaurus_react.svg
@@ -0,0 +1,170 @@
+
+  Powered by React
+  
+    
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+    
+    
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+      
+        
+        
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+        
+        
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+        
+        
+          
+          
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+          
+          
+        
+        
+          
+          
+          
+          
+          
+          
+          
+          
+          
+          
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+      
+      
+      
+      
+      
+    
+    
+      
+      
+      
+      
+    
+  
+
diff --git a/docs/my-website/static/img/undraw_docusaurus_tree.svg b/docs/my-website/static/img/undraw_docusaurus_tree.svg
new file mode 100644
index 0000000000000000000000000000000000000000..d9161d33920c9708a2f809cdb8b7c410917ce302
--- /dev/null
+++ b/docs/my-website/static/img/undraw_docusaurus_tree.svg
@@ -0,0 +1,40 @@
+
+  Focus on What Matters
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+  
+
diff --git a/docs/my-website/yarn.lock b/docs/my-website/yarn.lock
new file mode 100644
index 0000000000000000000000000000000000000000..16af6457ee843c75f09107c2360492e6606817d3
--- /dev/null
+++ b/docs/my-website/yarn.lock
@@ -0,0 +1,8229 @@
+# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
+# yarn lockfile v1
+
+
+"@algolia/autocomplete-core@1.9.3":
+  version "1.9.3"
+  resolved "https://registry.npmjs.org/@algolia/autocomplete-core/-/autocomplete-core-1.9.3.tgz"
+  integrity sha512-009HdfugtGCdC4JdXUbVJClA0q0zh24yyePn+KUGk3rP7j8FEe/m5Yo/z65gn6nP/cM39PxpzqKrL7A6fP6PPw==
+  dependencies:
+    "@algolia/autocomplete-plugin-algolia-insights" "1.9.3"
+    "@algolia/autocomplete-shared" "1.9.3"
+
+"@algolia/autocomplete-plugin-algolia-insights@1.9.3":
+  version "1.9.3"
+  resolved "https://registry.npmjs.org/@algolia/autocomplete-plugin-algolia-insights/-/autocomplete-plugin-algolia-insights-1.9.3.tgz"
+  integrity sha512-a/yTUkcO/Vyy+JffmAnTWbr4/90cLzw+CC3bRbhnULr/EM0fGNvM13oQQ14f2moLMcVDyAx/leczLlAOovhSZg==
+  dependencies:
+    "@algolia/autocomplete-shared" "1.9.3"
+
+"@algolia/autocomplete-preset-algolia@1.9.3":
+  version "1.9.3"
+  resolved "https://registry.npmjs.org/@algolia/autocomplete-preset-algolia/-/autocomplete-preset-algolia-1.9.3.tgz"
+  integrity sha512-d4qlt6YmrLMYy95n5TB52wtNDr6EgAIPH81dvvvW8UmuWRgxEtY0NJiPwl/h95JtG2vmRM804M0DSwMCNZlzRA==
+  dependencies:
+    "@algolia/autocomplete-shared" "1.9.3"
+
+"@algolia/autocomplete-shared@1.9.3":
+  version "1.9.3"
+  resolved "https://registry.npmjs.org/@algolia/autocomplete-shared/-/autocomplete-shared-1.9.3.tgz"
+  integrity sha512-Wnm9E4Ye6Rl6sTTqjoymD+l8DjSTHsHboVRYrKgEt8Q7UHm9nYbqhN/i0fhUYA3OAEH7WA8x3jfpnmJm3rKvaQ==
+
+"@algolia/cache-browser-local-storage@4.19.1":
+  version "4.19.1"
+  resolved "https://registry.npmjs.org/@algolia/cache-browser-local-storage/-/cache-browser-local-storage-4.19.1.tgz"
+  integrity sha512-FYAZWcGsFTTaSAwj9Std8UML3Bu8dyWDncM7Ls8g+58UOe4XYdlgzXWbrIgjaguP63pCCbMoExKr61B+ztK3tw==
+  dependencies:
+    "@algolia/cache-common" "4.19.1"
+
+"@algolia/cache-common@4.19.1":
+  version "4.19.1"
+  resolved "https://registry.npmjs.org/@algolia/cache-common/-/cache-common-4.19.1.tgz"
+  integrity sha512-XGghi3l0qA38HiqdoUY+wvGyBsGvKZ6U3vTiMBT4hArhP3fOGLXpIINgMiiGjTe4FVlTa5a/7Zf2bwlIHfRqqg==
+
+"@algolia/cache-in-memory@4.19.1":
+  version "4.19.1"
+  resolved "https://registry.npmjs.org/@algolia/cache-in-memory/-/cache-in-memory-4.19.1.tgz"
+  integrity sha512-+PDWL+XALGvIginigzu8oU6eWw+o76Z8zHbBovWYcrtWOEtinbl7a7UTt3x3lthv+wNuFr/YD1Gf+B+A9V8n5w==
+  dependencies:
+    "@algolia/cache-common" "4.19.1"
+
+"@algolia/client-account@4.19.1":
+  version "4.19.1"
+  resolved "https://registry.npmjs.org/@algolia/client-account/-/client-account-4.19.1.tgz"
+  integrity sha512-Oy0ritA2k7AMxQ2JwNpfaEcgXEDgeyKu0V7E7xt/ZJRdXfEpZcwp9TOg4TJHC7Ia62gIeT2Y/ynzsxccPw92GA==
+  dependencies:
+    "@algolia/client-common" "4.19.1"
+    "@algolia/client-search" "4.19.1"
+    "@algolia/transporter" "4.19.1"
+
+"@algolia/client-analytics@4.19.1":
+  version "4.19.1"
+  resolved "https://registry.npmjs.org/@algolia/client-analytics/-/client-analytics-4.19.1.tgz"
+  integrity sha512-5QCq2zmgdZLIQhHqwl55ZvKVpLM3DNWjFI4T+bHr3rGu23ew2bLO4YtyxaZeChmDb85jUdPDouDlCumGfk6wOg==
+  dependencies:
+    "@algolia/client-common" "4.19.1"
+    "@algolia/client-search" "4.19.1"
+    "@algolia/requester-common" "4.19.1"
+    "@algolia/transporter" "4.19.1"
+
+"@algolia/client-common@4.19.1":
+  version "4.19.1"
+  resolved "https://registry.npmjs.org/@algolia/client-common/-/client-common-4.19.1.tgz"
+  integrity sha512-3kAIVqTcPrjfS389KQvKzliC559x+BDRxtWamVJt8IVp7LGnjq+aVAXg4Xogkur1MUrScTZ59/AaUd5EdpyXgA==
+  dependencies:
+    "@algolia/requester-common" "4.19.1"
+    "@algolia/transporter" "4.19.1"
+
+"@algolia/client-personalization@4.19.1":
+  version "4.19.1"
+  resolved "https://registry.npmjs.org/@algolia/client-personalization/-/client-personalization-4.19.1.tgz"
+  integrity sha512-8CWz4/H5FA+krm9HMw2HUQenizC/DxUtsI5oYC0Jxxyce1vsr8cb1aEiSJArQT6IzMynrERif1RVWLac1m36xw==
+  dependencies:
+    "@algolia/client-common" "4.19.1"
+    "@algolia/requester-common" "4.19.1"
+    "@algolia/transporter" "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==
+  dependencies:
+    "@algolia/client-common" "4.19.1"
+    "@algolia/requester-common" "4.19.1"
+    "@algolia/transporter" "4.19.1"
+
+"@algolia/events@^4.0.1":
+  version "4.0.1"
+  resolved "https://registry.npmjs.org/@algolia/events/-/events-4.0.1.tgz"
+  integrity sha512-FQzvOCgoFXAbf5Y6mYozw2aj5KCJoA3m4heImceldzPSMbdyS4atVjJzXKMsfX3wnZTFYwkkt8/z8UesLHlSBQ==
+
+"@algolia/logger-common@4.19.1":
+  version "4.19.1"
+  resolved "https://registry.npmjs.org/@algolia/logger-common/-/logger-common-4.19.1.tgz"
+  integrity sha512-i6pLPZW/+/YXKis8gpmSiNk1lOmYCmRI6+x6d2Qk1OdfvX051nRVdalRbEcVTpSQX6FQAoyeaui0cUfLYW5Elw==
+
+"@algolia/logger-console@4.19.1":
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