litellm-mirror/docs/my-website/docs/providers/openai_compatible.md

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OpenAI-Compatible Endpoints

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

1. For /chat/completions: Put openai/ in front of your model name, so litellm knows you're trying to call an openai /chat/completions endpoint.

2. For /completions: Put text-completion-openai/ in front of your model name, so litellm knows you're trying to call an openai /completions endpoint. [NOT REQUIRED for openai/ endpoints called via /v1/completions route].

3. 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

:::info

If you see Not Found Error when testing make sure your api_base has the /v1 postfix

Example: http://vllm-endpoint.xyz/v1

:::

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)

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "my-model",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ],
}'

Advanced - Disable System Messages

Some VLLM models (e.g. gemma) don't support system messages. To map those requests to 'user' messages, use the supports_system_message flag.

model_list:
- model_name: my-custom-model
   litellm_params:
      model: openai/google/gemma
      api_base: http://my-custom-base
      api_key: "" 
      supports_system_message: False # 👈 KEY CHANGE