deepinfra/litellm

deepinfra/litellm

Description: Call all LLM APIs using the OpenAI format. Use Azure, OpenAI, Cohere, Anthropic, Ollama, VLLM, Sagemaker, HuggingFace, Replicate (100+ LLMs)

License: MIT

Stars: 0

Forks: 0

Open issues: 0

Created: 2023-10-18T16:41:28Z

Pushed: 2024-01-08T16:54:36Z

Default branch: main

Fork: yes

Parent repository: BerriAI/litellm

Archived: no

README:

🚅 LiteLLM

Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]

Schedule Demo · Feature Request

Docs 100+ Supported Models Demo Video

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

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 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'])

OpenAI Proxy Server (Docs)

Create an OpenAI API compatible server to call any non-openai model (e.g. Huggingface, TogetherAI, Ollama, etc.)

This works for async + streaming as well.

litellm --model

#INFO: litellm proxy running on http://0.0.0.0:8000

Running your model locally or on a custom endpoint ? Set the --api-base parameter see how

Self-host server (Docs)

1. Clone the repo

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

2. Modify template_secrets.toml

[keys]
OPENAI_API_KEY="sk-..."

[general]
default_model = "gpt-3.5-turbo"

3. Deploy

docker build -t litellm . && docker run -p 8000:8000 litellm

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

• Schedule Demo 👋
• [Community Discord…