OpenAI-Compatible Endpoints
To call models hosted behind an openai proxy, make 2 changes:
For
/chat/completions
: Putopenai/
in front of your model name, so litellm knows you're trying to call an openai/chat/completions
endpoint.For
/completions
: Puttext-completion-openai/
in front of your model name, so litellm knows you're trying to call an openai/completions
endpoint.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
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 modelStart the proxy
$ litellm --config /path/to/config.yaml
Send Request to LiteLLM Proxy Server
- OpenAI Python v1.0.0+
- curl
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"
}
],
}'