litellm/docs/my-website/docs/proxy/reliability.md

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Fallbacks, Retries, Timeouts, Cooldowns

If a call fails after num_retries, fall back to another model group.

If the error is a context window exceeded error, fall back to a larger model group (if given).

See Code

Set via config

model_list:
  - model_name: zephyr-beta
    litellm_params:
        model: huggingface/HuggingFaceH4/zephyr-7b-beta
        api_base: http://0.0.0.0:8001
  - model_name: zephyr-beta
    litellm_params:
        model: huggingface/HuggingFaceH4/zephyr-7b-beta
        api_base: http://0.0.0.0:8002
  - model_name: zephyr-beta
    litellm_params:
        model: huggingface/HuggingFaceH4/zephyr-7b-beta
        api_base: http://0.0.0.0:8003
  - model_name: gpt-3.5-turbo
    litellm_params:
        model: gpt-3.5-turbo
        api_key: <my-openai-key>
  - model_name: gpt-3.5-turbo-16k
    litellm_params:
        model: gpt-3.5-turbo-16k
        api_key: <my-openai-key>

litellm_settings:
  num_retries: 3 # retry call 3 times on each model_name (e.g. zephyr-beta)
  request_timeout: 10 # raise Timeout error if call takes longer than 10s. Sets litellm.request_timeout 
  fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo"]}] # fallback to gpt-3.5-turbo if call fails num_retries 
  context_window_fallbacks: [{"zephyr-beta": ["gpt-3.5-turbo-16k"]}, {"gpt-3.5-turbo": ["gpt-3.5-turbo-16k"]}] # fallback to gpt-3.5-turbo-16k if context window error
  allowed_fails: 3 # cooldown model if it fails > 1 call in a minute. 

Set dynamically

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "zephyr-beta",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ],
      "fallbacks": [{"zephyr-beta": ["gpt-3.5-turbo"]}],
      "context_window_fallbacks": [{"zephyr-beta": ["gpt-3.5-turbo"]}],
      "num_retries": 2,
      "timeout": 10
    }
'

Custom Timeouts, Stream Timeouts - Per Model

For each model you can set timeout & stream_timeout under litellm_params

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/gpt-turbo-small-eu
      api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
      api_key: <your-key>
      timeout: 0.1                      # timeout in (seconds)
      stream_timeout: 0.01              # timeout for stream requests (seconds)
      max_retries: 5
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/gpt-turbo-small-ca
      api_base: https://my-endpoint-canada-berri992.openai.azure.com/
      api_key: 
      timeout: 0.1                      # timeout in (seconds)
      stream_timeout: 0.01              # timeout for stream requests (seconds)
      max_retries: 5

Start Proxy

$ litellm --config /path/to/config.yaml

Setting Dynamic Timeouts - Per Request

LiteLLM Proxy supports setting a timeout per request

Example Usage

curl --location 'http://0.0.0.0:4000/chat/completions' \
     --header 'Content-Type: application/json' \
     --data-raw '{
        "model": "gpt-3.5-turbo",
        "messages": [
            {"role": "user", "content": "what color is red"}
        ],
        "logit_bias": {12481: 100},
        "timeout": 1
     }'

import openai


client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "user", "content": "what color is red"}
    ],
    logit_bias={12481: 100},
    timeout=1
)

print(response)