Skip to main content

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

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/"
        }
    ]
}

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.

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'

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

/health/readiness​

Unprotected endpoint for checking if proxy is ready to accept requests

Example Request: 

curl --location 'http://0.0.0.0:4000/health/readiness'

Example Response:

If proxy connected to a database 

{
    "status": "healthy",
    "db": "connected",
    "litellm_version":"1.19.2",
}

If proxy not connected to a database 

{
    "status": "healthy",
    "db": "Not connected",
    "litellm_version":"1.19.2",
}

/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!"