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OpenAI-Compatible Endpoints

To call models hosted behind an openai proxy, make 2 changes:

  1. Put openai/ in front of your model name, so litellm knows you're trying to call an openai-compatible endpoint.

  2. Do NOT add anything additional to the base url e.g. /v1/embedding. LiteLLM uses the openai-client to make these calls, and that automatically adds the relevant endpoints.

Usage - completion​

import litellm
import os

response = litellm.completion(
    model="openai/mistral,               # add `openai/` prefix to model so litellm knows to route to OpenAI
    api_key="sk-1234",                  # api key to your openai compatible endpoint
    api_base="http://0.0.0.0:4000",     # set API Base of your Custom OpenAI Endpoint
    messages=[
                {
                    "role": "user",
                    "content": "Hey, how's it going?",
                }
    ],
)
print(response)

Usage - embedding​

import litellm
import os

response = litellm.embedding(
    model="openai/GPT-J",               # add `openai/` prefix to model so litellm knows to route to OpenAI
    api_key="sk-1234",                  # api key to your openai compatible endpoint
    api_base="http://0.0.0.0:4000",     # set API Base of your Custom OpenAI Endpoint
    input=["good morning from litellm"]
)
print(response)

Usage with LiteLLM Proxy Server​

Here's how to call an OpenAI-Compatible Endpoint with the LiteLLM Proxy Server

  1. Modify the config.yaml 

    model_list:
      - model_name: my-model
        litellm_params:
          model: openai/<your-model-name>  # add openai/ prefix to route as OpenAI provider
          api_base: <model-api-base>       # add api base for OpenAI compatible provider
          api_key: api-key                 # api key to send your model

  2. Start the proxy 

    $ litellm --config /path/to/config.yaml

  3. Send Request to LiteLLM Proxy Server

    import openai
    client = openai.OpenAI(
        api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
        base_url="http://0.0.0.0:4000" # litellm-proxy-base url
    )

    response = client.chat.completions.create(
        model="my-model",
        messages = [
            {
                "role": "user",
                "content": "what llm are you"
            }
        ],
    )

    print(response)