View Code? Open in Web Editor NEW

This project forked from berriai/litellm

lightweight package to simplify LLM API calls - Azure, OpenAI, Cohere, Anthropic, Replicate. Manages input/output translation

License: MIT License

Python 60.59% Jupyter Notebook 39.14% Dockerfile 0.27%

litellm's Introduction

🚅 litellm

a light package to simplify calling OpenAI, Azure, Cohere, Anthropic, Huggingface API Endpoints. It manages:

translating inputs to the provider's completion and embedding endpoints
guarantees consistent output, text responses will always be available at ['choices'][0]['message']['content']
exception mapping - common exceptions across providers are mapped to the OpenAI exception types

usage

Demo - https://litellm.ai/
Read the docs - https://litellm.readthedocs.io/en/latest/

quick start

pip install litellm

from litellm import completion

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)

# azure openai call
response = completion("chatgpt-test", messages, azure=True)

# hugging face call
response = completion(model="stabilityai/stablecode-completion-alpha-3b-4k", messages=messages, hugging_face=True)

# openrouter call
response = completion("google/palm-2-codechat-bison", messages)

Code Sample: Getting Started Notebook

Stable version

pip install litellm==0.1.345

Streaming Queries

liteLLM supports streaming the model response back, pass stream=True to get a streaming iterator in response. Streaming is supported for OpenAI, Azure, Anthropic models

response = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
for chunk in response:
    print(chunk['choices'][0]['delta'])

# claude 2
result = completion('claude-2', messages, stream=True)
for chunk in result:
  print(chunk['choices'][0]['delta'])