Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]
LiteLLM manages
- Translating inputs to the provider's
completion
andembedding
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 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)
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'])
Logging Observability (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"}])
OpenAI Proxy - (Docs)
If you don't want to make code changes to add the litellm package to your code base, you can use litellm proxy. Create a server to call 100+ LLMs (Huggingface/Bedrock/TogetherAI/etc) in the OpenAI ChatCompletions & Completions format
$ litellm --model huggingface/bigcode/starcoder
#INFO: Proxy running on http:https://0.0.0.0:8000
import openai
client = openai.OpenAI(api_key="anything", base_url="http:https://0.0.0.0:8000")
print(openai.chat.completions.create(model="test", messages=[{"role":"user", "content":"Hey!"}]))
Supported Provider (Docs)
Provider | Completion | Streaming | Async Completion | Async Streaming |
---|---|---|---|---|
openai | ✅ | ✅ | ✅ | ✅ |
azure | ✅ | ✅ | ✅ | ✅ |
aws - sagemaker | ✅ | ✅ | ✅ | ✅ |
aws - bedrock | ✅ | ✅ | ✅ | ✅ |
cohere | ✅ | ✅ | ✅ | ✅ |
anthropic | ✅ | ✅ | ✅ | ✅ |
huggingface | ✅ | ✅ | ✅ | ✅ |
replicate | ✅ | ✅ | ✅ | ✅ |
together_ai | ✅ | ✅ | ✅ | ✅ |
openrouter | ✅ | ✅ | ✅ | ✅ |
google - vertex_ai | ✅ | ✅ | ✅ | ✅ |
google - palm | ✅ | ✅ | ✅ | ✅ |
ai21 | ✅ | ✅ | ✅ | ✅ |
baseten | ✅ | ✅ | ✅ | ✅ |
vllm | ✅ | ✅ | ✅ | ✅ |
nlp_cloud | ✅ | ✅ | ✅ | ✅ |
aleph alpha | ✅ | ✅ | ✅ | ✅ |
petals | ✅ | ✅ | ✅ | ✅ |
ollama | ✅ | ✅ | ✅ | ✅ |
deepinfra | ✅ | ✅ | ✅ | ✅ |
perplexity-ai | ✅ | ✅ | ✅ | ✅ |
anyscale | ✅ | ✅ | ✅ | ✅ |
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
- 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 and Cohere.