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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"
    }
  ],
}
'