Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]
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
🤝 Schedule a 1-on-1 Session: Book a 1-on-1 session with Krrish and Ishaan, the founders, to discuss any issues, provide feedback, or explore how we can improve LiteLLM for you.
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"
os.environ["ANTHROPIC_API_KEY"] = "anthropic 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)
# anthropic
response = completion(model="claude-2", messages=messages)Stable version
pip install litellm==0.1.424
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'])- Schedule Demo 👋
- Community Discord 💭
- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
- Our emails ✉️ [email protected] / [email protected]
- Need for simplicity: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI, Cohere