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Health Checks

Use this to health check all LLMs defined in your config.yaml

Summary

The proxy exposes:

  • a /health endpoint which returns the health of the LLM APIs
  • a /health/readiness endpoint for returning if the proxy is ready to accept requests
  • a /health/liveliness endpoint for returning if the proxy is alive

/health

Request

Make a GET Request to /health on the proxy

info

This endpoint makes an LLM API call to each model to check if it is healthy.

curl --location 'http://0.0.0.0:4000/health' -H "Authorization: Bearer sk-1234"

You can also run litellm -health it makes a get request to http://0.0.0.0:4000/health for you

litellm --health

Response

{
"healthy_endpoints": [
{
"model": "azure/gpt-35-turbo",
"api_base": "https://my-endpoint-canada-berri992.openai.azure.com/"
},
{
"model": "azure/gpt-35-turbo",
"api_base": "https://my-endpoint-europe-berri-992.openai.azure.com/"
}
],
"unhealthy_endpoints": [
{
"model": "azure/gpt-35-turbo",
"api_base": "https://openai-france-1234.openai.azure.com/"
}
]
}

Embedding Models

We need some way to know if the model is an embedding model when running checks, if you have this in your config, specifying mode it makes an embedding health check

model_list:
- model_name: azure-embedding-model
litellm_params:
model: azure/azure-embedding-model
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-07-01-preview"
model_info:
mode: embedding # 👈 ADD THIS

Image Generation Models

We need some way to know if the model is an image generation model when running checks, if you have this in your config, specifying mode it makes an image generation health check

model_list:
- model_name: dall-e-3
litellm_params:
model: azure/dall-e-3
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-07-01-preview"
model_info:
mode: image_generation # 👈 ADD THIS

Text Completion Models

We need some way to know if the model is a text completion model when running checks, if you have this in your config, specifying mode it makes an embedding health check

model_list:
- model_name: azure-text-completion
litellm_params:
model: azure/text-davinci-003
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-07-01-preview"
model_info:
mode: completion # 👈 ADD THIS

Speech to Text Models

model_list:
- model_name: whisper
litellm_params:
model: whisper-1
api_key: os.environ/OPENAI_API_KEY
model_info:
mode: audio_transcription

Text to Speech Models

# OpenAI Text to Speech Models
- model_name: tts
litellm_params:
model: openai/tts-1
api_key: "os.environ/OPENAI_API_KEY"
model_info:
mode: audio_speech

Batch Models (Azure Only)

For Azure models deployed as 'batch' models, set mode: batch.

model_list:
- model_name: "batch-gpt-4o-mini"
litellm_params:
model: "azure/batch-gpt-4o-mini"
api_key: os.environ/AZURE_API_KEY
api_base: os.environ/AZURE_API_BASE
model_info:
mode: batch

Expected Response

{
"healthy_endpoints": [
{
"api_base": "https://...",
"model": "azure/gpt-4o-mini",
"x-ms-region": "East US"
}
],
"unhealthy_endpoints": [],
"healthy_count": 1,
"unhealthy_count": 0
}

Background Health Checks

You can enable model health checks being run in the background, to prevent each model from being queried too frequently via /health.

info

This makes an LLM API call to each model to check if it is healthy.

Here's how to use it:

  1. in the config.yaml add:
general_settings: 
background_health_checks: True # enable background health checks
health_check_interval: 300 # frequency of background health checks
  1. Start server
$ litellm /path/to/config.yaml
  1. Query health endpoint:
curl --location 'http://0.0.0.0:4000/health'

Hide details

The health check response contains details like endpoint URLs, error messages, and other LiteLLM params. While this is useful for debugging, it can be problematic when exposing the proxy server to a broad audience.

You can hide these details by setting the health_check_details setting to False.

general_settings: 
health_check_details: False

/health/readiness

Unprotected endpoint for checking if proxy is ready to accept requests

Example Request:

curl http://0.0.0.0:4000/health/readiness

Example Response:

{
"status": "connected",
"db": "connected",
"cache": null,
"litellm_version": "1.40.21",
"success_callbacks": [
"langfuse",
"_PROXY_track_cost_callback",
"response_taking_too_long_callback",
"_PROXY_MaxParallelRequestsHandler",
"_PROXY_MaxBudgetLimiter",
"_PROXY_CacheControlCheck",
"ServiceLogging"
],
"last_updated": "2024-07-10T18:59:10.616968"
}

If the proxy is not connected to a database, then the "db" field will be "Not connected" instead of "connected" and the "last_updated" field will not be present.

/health/liveliness

Unprotected endpoint for checking if proxy is alive

Example Request:

curl -X 'GET' \
'http://0.0.0.0:4000/health/liveliness' \
-H 'accept: application/json'

Example Response:

"I'm alive!"

Advanced - Call specific models

To check health of specific models, here's how to call them:

1. Get model id via /model/info

curl -X GET 'http://0.0.0.0:4000/v1/model/info' \
--header 'Authorization: Bearer sk-1234' \

Expected Response

{
"model_name": "bedrock-anthropic-claude-3",
"litellm_params": {
"model": "anthropic.claude-3-sonnet-20240229-v1:0"
},
"model_info": {
"id": "634b87c444..", # 👈 UNIQUE MODEL ID
}

2. Call specific model via /chat/completions

curl -X POST 'http://localhost:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-D '{
"model": "634b87c444.." # 👈 UNIQUE MODEL ID
"messages": [
{
"role": "user",
"content": "ping"
}
],
}
'