🚅 LiteLLM

Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]

100+ Supported Models | Docs | Demo Website

📣1-click deploy your own LLM proxy server. Grab time, if you're interested!

LiteLLM 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

pip install litellm

from litellm import completion
import os
## 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(model="command-nightly", messages=messages)

Stable version

pip install litellm==0.1.424

Streaming

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

Support / talk with founders

• Schedule Demo 👋
• Community Discord 💭
• Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
• Our emails ✉️ ishaan@berri.ai / krrish@berri.ai

Why did we build this

• Need for simplicity: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI, Cohere

Contributors