Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]
LiteLLM manages:
- Translate inputs to provider's
completion
andembedding
endpoints - Consistent output, text responses will always be available at
['choices'][0]['message']['content']
- Load-balance multiple deployments (e.g. Azure/OpenAI) -
Router
1k+ requests/second
Usage (Docs)
Important
LiteLLM v1.0.0 now requires openai>=1.0.0
. Migration guide here
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)
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 Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack
from litellm import completion
## set env variables for logging tools
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"
os.environ["OPENAI_API_KEY"]
# set callbacks
litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langfuse, llmonitor, supabase
#openai call
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi π - i'm openai"}])
OpenAI Proxy - (Docs)
Track spend across multiple projects/people.
$ litellm --model huggingface/bigcode/starcoder
#INFO: Proxy running on http:https://0.0.0.0:8000
import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="http:https://0.0.0.0:8000") # 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)
A simple UI to add new models and let your users create keys.
Live here: https://litellm-jyqbcbn44mcxqq6ufopuha.streamlit.app/
Code: https://github.com/BerriAI/litellm/tree/main/ui
![Screenshot 2023-12-26 at 8 33 53 AM](https://private-user-images.githubusercontent.com/17561003/292796420-274254d8-c5fe-4645-9123-100045a7fb21.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.kwWS_3bHPd0D24uvDxP7ekY4_UvoRDUA1voTX0Jq6do)
Supported Provider (Docs)
Provider | Completion | Streaming | Async Completion | Async Streaming |
---|---|---|---|---|
openai | β | β | β | β |
azure | β | β | β | β |
aws - sagemaker | β | β | β | β |
aws - bedrock | β | β | β | β |
cohere | β | β | β | β |
anthropic | β | β | β | β |
huggingface | β | β | β | β |
replicate | β | β | β | β |
together_ai | β | β | β | β |
openrouter | β | β | β | β |
google - vertex_ai | β | β | β | β |
google - palm | β | β | β | β |
mistral ai api | β | β | β | β |
ai21 | β | β | β | β |
baseten | β | β | β | β |
vllm | β | β | β | β |
nlp_cloud | β | β | β | β |
aleph alpha | β | β | β | β |
petals | β | β | β | β |
ollama | β | β | β | β |
deepinfra | β | β | β | β |
perplexity-ai | β | β | β | β |
anyscale | β | β | β | β |
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
poetry run flake8
poetry run pytest .
Step 4: Submit a PR with your changes! π
- push your fork to your GitHub repo
- submit a PR from there
- Schedule Demo π
- Community Discord π
- Our numbers π +1 (770) 8783-106 / β+1 (412) 618-6238β¬
- Our emails βοΈ [email protected] / [email protected]
- Need for simplicity: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI and Cohere.