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🔥 Load Balancing, Fallbacks, Retries, Timeouts

Quick Start - Load Balancing

Step 1 - Set deployments on config

Example config below. Here requests with model=gpt-3.5-turbo will be routed across multiple instances of azure/gpt-3.5-turbo

model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/<your-deployment-name>
api_base: <your-azure-endpoint>
api_key: <your-azure-api-key>
rpm: 6 # Rate limit for this deployment: in requests per minute (rpm)
- 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: <your-azure-api-key>
rpm: 6
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/gpt-turbo-large
api_base: https://openai-france-1234.openai.azure.com/
api_key: <your-azure-api-key>
rpm: 1440

Step 2: Start Proxy with config

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

Test - Load Balancing

Here requests with model=gpt-3.5-turbo will be routed across multiple instances of azure/gpt-3.5-turbo

👉 Key Change: model="gpt-3.5-turbo"

Check the model_id in Response Headers to make sure the requests are being load balanced

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": "this is a test request, write a short poem"
}
]
)

print(response)

Test - Client Side Fallbacks

In this request the following will occur:

  1. The request to model="zephyr-beta" will fail
  2. litellm proxy will loop through all the model_groups specified in fallbacks=["gpt-3.5-turbo"]
  3. The request to model="gpt-3.5-turbo" will succeed and the client making the request will get a response from gpt-3.5-turbo

👉 Key Change: "fallbacks": ["gpt-3.5-turbo"]

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

response = client.chat.completions.create(
model="zephyr-beta",
messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
],
extra_body={
"fallbacks": ["gpt-3.5-turbo"]
}
)

print(response)

Advanced

Fallbacks + Retries + Timeouts + Cooldowns

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.

Context Window Fallbacks (Pre-Call Checks + Fallbacks)

Before call is made check if a call is within model context window with enable_pre_call_checks: true.

See Code

1. Setup config

For azure deployments, set the base model. Pick the base model from this list, all the azure models start with azure/.

Filter older instances of a model (e.g. gpt-3.5-turbo) with smaller context windows

router_settings:
enable_pre_call_checks: true # 1. Enable pre-call checks

model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/chatgpt-v-2
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-07-01-preview"
model_info:
base_model: azure/gpt-4-1106-preview # 2. 👈 (azure-only) SET BASE MODEL

- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo-1106
api_key: os.environ/OPENAI_API_KEY

2. Start proxy

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

# RUNNING on http://0.0.0.0:4000

3. Test it!

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

text = "What is the meaning of 42?" * 5000

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages = [
{"role": "system", "content": text},
{"role": "user", "content": "Who was Alexander?"},
],
)

print(response)

Content Policy Fallbacks

Fallback across providers (e.g. from Azure OpenAI to Anthropic) if you hit content policy violation errors.

model_list:
- model_name: gpt-3.5-turbo-small
litellm_params:
model: azure/chatgpt-v-2
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-07-01-preview"

- model_name: claude-opus
litellm_params:
model: claude-3-opus-20240229
api_key: os.environ/ANTHROPIC_API_KEY

litellm_settings:
content_policy_fallbacks: [{"gpt-3.5-turbo-small": ["claude-opus"]}]

EU-Region Filtering (Pre-Call Checks)

Before call is made check if a call is within model context window with enable_pre_call_checks: true.

Set 'region_name' of deployment.

Note: LiteLLM can automatically infer region_name for Vertex AI, Bedrock, and IBM WatsonxAI based on your litellm params. For Azure, set litellm.enable_preview = True.

1. Set Config

router_settings:
enable_pre_call_checks: true # 1. Enable pre-call checks

model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/chatgpt-v-2
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-07-01-preview"
region_name: "eu" # 👈 SET EU-REGION

- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo-1106
api_key: os.environ/OPENAI_API_KEY

- model_name: gemini-pro
litellm_params:
model: vertex_ai/gemini-pro-1.5
vertex_project: adroit-crow-1234
vertex_location: us-east1 # 👈 AUTOMATICALLY INFERS 'region_name'

2. Start proxy

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

# RUNNING on http://0.0.0.0:4000

3. Test it!

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

# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.with_raw_response.create(
model="gpt-3.5-turbo",
messages = [{"role": "user", "content": "Who was Alexander?"}]
)

print(response)

print(f"response.headers.get('x-litellm-model-api-base')")

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
}'