cookbook/litellm_router/test_questions/question1.txt

Given this context, what is litellm? LiteLLM about: About
Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs). 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.
10/05/2023: LiteLLM is adopting Semantic Versioning for all commits. Learn more
10/16/2023: Self-hosted OpenAI-proxy server Learn more

Usage (Docs)
Important
LiteLLM v1.0.0 is being launched to require openai>=1.0.0. Track this here

Open In Colab
pip install litellm
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)

# cohere call
response = completion(model="command-nightly", messages=messages)
print(response)
Streaming (Docs)
liteLLM supports streaming the model response back, pass stream=True to get a streaming iterator in response.
Streaming is supported for all models (Bedrock, Huggingface, TogetherAI, Azure, OpenAI, etc.)

from litellm import completion
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'])