Skip to content

coffee-cup-app/llmtest2

Repository files navigation

🚅 LiteLLM

Call all LLM APIs using the OpenAI format [Bedrock, 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.

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)

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.)

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'])

Supported Provider (Docs)

Provider Completion Streaming Async Completion Async Streaming
openai
cohere
anthropic
replicate
huggingface
together_ai
openrouter
vertex_ai
palm
ai21
baseten
azure
sagemaker
bedrock
vllm
nlp_cloud
aleph alpha
petals
ollama
deepinfra

Read the Docs

Logging Observability - Log LLM Input/Output (Docs)

LiteLLM exposes pre defined callbacks to send data to LLMonitor, Langfuse, Helicone, Promptlayer, Traceloop, Slack

from litellm import completion

## set env variables for logging tools
os.environ["PROMPTLAYER_API_KEY"] = "your-promptlayer-key"
os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"

os.environ["OPENAI_API_KEY"]

# set callbacks
litellm.success_callback = ["promptlayer", "llmonitor"] # log input/output to promptlayer, llmonitor, supabase

#openai call
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])

Contributing

To contribute: Clone the repo locally -> Make a change -> Submit a PR with the change.

Here's how to modify the repo locally: Step 1: Clone the repo

git clone https://github.com/BerriAI/litellm.git

Step 2: Navigate into the project, and install dependencies:

cd litellm
poetry install

Step 3: Test your change:

cd litellm/tests # pwd: Documents/litellm/litellm/tests
pytest .

Step 4: Submit a PR with your changes! 🚀

  • push your fork to your GitHub repo
  • submit a PR from there

Support / talk with founders

Why did we build this

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

Contributors

.

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages