# liteLLM Proxy Server: 50+ LLM Models, Error Handling, Caching ### Azure, Llama2, OpenAI, Claude, Hugging Face, Replicate Models [![PyPI Version](https://img.shields.io/pypi/v/litellm.svg)](https://pypi.org/project/litellm/) [![PyPI Version](https://img.shields.io/badge/stable%20version-v0.1.345-blue?color=green&link=https://pypi.org/project/litellm/0.1.1/)](https://pypi.org/project/litellm/0.1.1/) ![Downloads](https://img.shields.io/pypi/dm/litellm) [![litellm](https://img.shields.io/badge/%20%F0%9F%9A%85%20liteLLM-OpenAI%7CAzure%7CAnthropic%7CPalm%7CCohere%7CReplicate%7CHugging%20Face-blue?color=green)](https://github.com/BerriAI/litellm) [![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/DYqQAW?referralCode=t3ukrU) ![4BC6491E-86D0-4833-B061-9F54524B2579](https://github.com/BerriAI/litellm/assets/17561003/f5dd237b-db5e-42e1-b1ac-f05683b1d724) ## What does liteLLM proxy do - Make `/chat/completions` requests for 50+ LLM models **Azure, OpenAI, Replicate, Anthropic, Hugging Face** Example: for `model` use `claude-2`, `gpt-3.5`, `gpt-4`, `command-nightly`, `stabilityai/stablecode-completion-alpha-3b-4k` ```json { "model": "replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1", "messages": [ { "content": "Hello, whats the weather in San Francisco??", "role": "user" } ] } ``` - **Consistent Input/Output** Format - Call all models using the OpenAI format - `completion(model, messages)` - Text responses will always be available at `['choices'][0]['message']['content']` - **Error Handling** Using Model Fallbacks (if `GPT-4` fails, try `llama2`) - **Logging** - Log Requests, Responses and Errors to `Supabase`, `Posthog`, `Mixpanel`, `Sentry`, `Lunary`,`Athina`, `Helicone` (Any of the supported providers here: https://litellm.readthedocs.io/en/latest/advanced/ **Example: Logs sent to Supabase** Screenshot 2023-08-11 at 4 02 46 PM - **Token Usage & Spend** - Track Input + Completion tokens used + Spend/model - **Caching** - Implementation of Semantic Caching - **Streaming & Async Support** - Return generators to stream text responses ## API Endpoints ### `/chat/completions` (POST) This endpoint is used to generate chat completions for 50+ support LLM API Models. Use llama2, GPT-4, Claude2 etc #### Input This API endpoint accepts all inputs in raw JSON and expects the following inputs - `model` (string, required): ID of the model to use for chat completions. See all supported models [here]: (https://litellm.readthedocs.io/en/latest/supported/): eg `gpt-3.5-turbo`, `gpt-4`, `claude-2`, `command-nightly`, `stabilityai/stablecode-completion-alpha-3b-4k` - `messages` (array, required): A list of messages representing the conversation context. Each message should have a `role` (system, user, assistant, or function), `content` (message text), and `name` (for function role). - Additional Optional parameters: `temperature`, `functions`, `function_call`, `top_p`, `n`, `stream`. See the full list of supported inputs here: https://litellm.readthedocs.io/en/latest/input/ #### Example JSON body For claude-2 ```json { "model": "claude-2", "messages": [ { "content": "Hello, whats the weather in San Francisco??", "role": "user" } ] } ``` ### Making an API request to the Proxy Server ```python import requests import json # TODO: use your URL url = "http://localhost:5000/chat/completions" payload = json.dumps({ "model": "gpt-3.5-turbo", "messages": [ { "content": "Hello, whats the weather in San Francisco??", "role": "user" } ] }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) ``` ### Output [Response Format] Responses from the server are given in the following format. All responses from the server are returned in the following format (for all LLM models). More info on output here: https://litellm.readthedocs.io/en/latest/output/ ```json { "choices": [ { "finish_reason": "stop", "index": 0, "message": { "content": "I'm sorry, but I don't have the capability to provide real-time weather information. However, you can easily check the weather in San Francisco by searching online or using a weather app on your phone.", "role": "assistant" } } ], "created": 1691790381, "id": "chatcmpl-7mUFZlOEgdohHRDx2UpYPRTejirzb", "model": "gpt-3.5-turbo-0613", "object": "chat.completion", "usage": { "completion_tokens": 41, "prompt_tokens": 16, "total_tokens": 57 } } ``` ## Installation & Usage ### Running Locally 1. Clone liteLLM repository to your local machine: ``` git clone https://github.com/BerriAI/liteLLM-proxy ``` 2. Install the required dependencies using pip ``` pip install -r requirements.txt ``` 3. Set your LLM API keys ``` os.environ['OPENAI_API_KEY]` = "YOUR_API_KEY" or set OPENAI_API_KEY in your .env file ``` 4. Run the server: ``` python main.py ``` ## Deploying 1. Quick Start: Deploy on Railway [![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/DYqQAW?referralCode=t3ukrU) 2. `GCP`, `AWS`, `Azure` This project includes a `Dockerfile` allowing you to build and deploy a Docker Project on your providers # Support / Talk with founders - [Our calendar 👋](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 ## Roadmap - [ ] Support hosted db (e.g. Supabase) - [ ] Easily send data to places like posthog and sentry. - [ ] Add a hot-cache for project spend logs - enables fast checks for user + project limitings - [ ] Implement user-based rate-limiting - [ ] Spending controls per project - expose key creation endpoint - [ ] Need to store a keys db -> mapping created keys to their alias (i.e. project name) - [ ] Easily add new models as backups / as the entry-point (add this to the available model list)