Skip to content

Latest commit

 

History

History
375 lines (280 loc) · 30 KB

README.md

File metadata and controls

375 lines (280 loc) · 30 KB
Raw

🚅 LiteLLM

Deploy to Render Deploy on Railway

Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]

LiteLLM Proxy Server (LLM Gateway) | Hosted Proxy (Preview) | Enterprise Tier

PyPI Version CircleCI Y Combinator W23 Whatsapp Discord

LiteLLM manages:

  • Translate inputs to provider's completion, embedding, and image_generation endpoints
  • Consistent output, text responses will always be available at ['choices'][0]['message']['content']
  • Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - Router
  • Set Budgets & Rate limits per project, api key, model LiteLLM Proxy Server (LLM Gateway)

Jump to LiteLLM Proxy (LLM Gateway) Docs
Jump to Supported LLM Providers

🚨 Stable Release: Use docker images with the -stable tag. These have undergone 12 hour load tests, before being published.

Support for more providers. Missing a provider or LLM Platform, raise a feature request.

Usage (Docs)

Important

LiteLLM v1.0.0 now requires openai>=1.0.0. Migration guide here
LiteLLM v1.40.14+ now requires pydantic>=2.0.0. No changes required.

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)

Call any model supported by a provider, with model=<provider_name>/<model_name>. There might be provider-specific details here, so refer to provider docs for more information

Async (Docs)

from litellm import acompletion
import asyncio

async def test_get_response():
    user_message = "Hello, how are you?"
    messages = [{"content": user_message, "role": "user"}]
    response = await acompletion(model="gpt-3.5-turbo", messages=messages)
    return response

response = asyncio.run(test_get_response())
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 part in response:
    print(part.choices[0].delta.content or "")

# claude 2
response = completion('claude-2', messages, stream=True)
for part in response:
    print(part.choices[0].delta.content or "")

Logging Observability (Docs)

LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, DynamoDB, s3 Buckets, Helicone, Promptlayer, Traceloop, Athina, Slack, MLflow

from litellm import completion

## set env variables for logging tools
os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
os.environ["HELICONE_API_KEY"] = "your-helicone-auth-key"
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["ATHINA_API_KEY"] = "your-athina-api-key"

os.environ["OPENAI_API_KEY"]

# set callbacks
litellm.success_callback = ["lunary", "langfuse", "athina", "helicone"] # log input/output to lunary, langfuse, supabase, athina, helicone etc

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

LiteLLM Proxy Server (LLM Gateway) - (Docs)

Track spend + Load Balance across multiple projects

Hosted Proxy (Preview)

The proxy provides:

  1. Hooks for auth
  2. Hooks for logging
  3. Cost tracking
  4. Rate Limiting

📖 Proxy Endpoints - Swagger Docs

Quick Start Proxy - CLI

pip install 'litellm[proxy]'

Step 1: Start litellm proxy

$ litellm --model huggingface/bigcode/starcoder

#INFO: Proxy running on http://0.0.0.0:4000

Step 2: Make ChatCompletions Request to Proxy

Important

💡 Use LiteLLM Proxy with Langchain (Python, JS), OpenAI SDK (Python, JS) Anthropic SDK, Mistral SDK, LlamaIndex, Instructor, Curl

import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000") # set proxy to base_url
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)

Proxy Key Management (Docs)

Connect the proxy with a Postgres DB to create proxy keys

# Get the code
git clone https://github.com/BerriAI/litellm

# Go to folder
cd litellm

# Add the master key - you can change this after setup
echo 'LITELLM_MASTER_KEY="sk-1234"' > .env

# Add the litellm salt key - you cannot change this after adding a model
# It is used to encrypt / decrypt your LLM API Key credentials
# We recommned - https://1password.com/password-generator/ 
# password generator to get a random hash for litellm salt key
echo 'LITELLM_SALT_KEY="sk-1234"' > .env

source .env

# Start
docker-compose up

UI on /ui on your proxy server ui_3

Set budgets and rate limits across multiple projects POST /key/generate

Request

curl 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data-raw '{"models": ["gpt-3.5-turbo", "gpt-4", "claude-2"], "duration": "20m","metadata": {"user": "[email protected]", "team": "core-infra"}}'

Expected Response

{
    "key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token
    "expires": "2023-11-19T01:38:25.838000+00:00" # datetime object
}

Supported Providers (Docs)

Provider Completion Streaming Async Completion Async Streaming Async Embedding Async Image Generation
openai ✅ ✅ ✅ ✅ ✅ ✅
azure ✅ ✅ ✅ ✅ ✅ ✅
aws - sagemaker ✅ ✅ ✅ ✅ ✅
aws - bedrock ✅ ✅ ✅ ✅ ✅
google - vertex_ai ✅ ✅ ✅ ✅ ✅ ✅
google - palm ✅ ✅ ✅ ✅
google AI Studio - gemini ✅ ✅ ✅ ✅
mistral ai api ✅ ✅ ✅ ✅ ✅
cloudflare AI Workers ✅ ✅ ✅ ✅
cohere ✅ ✅ ✅ ✅ ✅
anthropic ✅ ✅ ✅ ✅
empower ✅ ✅ ✅ ✅
huggingface ✅ ✅ ✅ ✅ ✅
replicate ✅ ✅ ✅ ✅
together_ai ✅ ✅ ✅ ✅
openrouter ✅ ✅ ✅ ✅
ai21 ✅ ✅ ✅ ✅
baseten ✅ ✅ ✅ ✅
vllm ✅ ✅ ✅ ✅
nlp_cloud ✅ ✅ ✅ ✅
aleph alpha ✅ ✅ ✅ ✅
petals ✅ ✅ ✅ ✅
ollama ✅ ✅ ✅ ✅ ✅
deepinfra ✅ ✅ ✅ ✅
perplexity-ai ✅ ✅ ✅ ✅
Groq AI ✅ ✅ ✅ ✅
Deepseek ✅ ✅ ✅ ✅
anyscale ✅ ✅ ✅ ✅
IBM - watsonx.ai ✅ ✅ ✅ ✅ ✅
voyage ai ✅
xinference [Xorbits Inference] ✅
FriendliAI ✅ ✅ ✅ ✅

Read the Docs

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 -E extra_proxy -E proxy

Step 3: Test your change:

cd litellm/tests # pwd: Documents/litellm/litellm/tests
poetry run flake8
poetry run pytest .

Step 4: Submit a PR with your changes! 🚀

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

Building LiteLLM Docker Image

Follow these instructions if you want to build / run the LiteLLM Docker Image yourself.

Step 1: Clone the repo

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

Step 2: Build the Docker Image

Build using Dockerfile.non_root

docker build -f docker/Dockerfile.non_root -t litellm_test_image .

Step 3: Run the Docker Image

Make sure config.yaml is present in the root directory. This is your litellm proxy config file.

docker run \
    -v $(pwd)/proxy_config.yaml:/app/config.yaml \
    -e DATABASE_URL="postgresql://xxxxxxxx" \
    -e LITELLM_MASTER_KEY="sk-1234" \
    -p 4000:4000 \
    litellm_test_image \
    --config /app/config.yaml --detailed_debug

Enterprise

For companies that need better security, user management and professional support

Talk to founders

This covers:

  • ✅ Features under the LiteLLM Commercial License:
  • ✅ Feature Prioritization
  • ✅ Custom Integrations
  • ✅ Professional Support - Dedicated discord + slack
  • ✅ Custom SLAs
  • ✅ Secure access with Single Sign-On

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