Reliability - Fallbacks, Azure Deployments, etc.

Prevent failed calls and slow response times with multiple deployments for API calls (E.g. multiple azure-openai deployments).

Manage Multiple Deployments

Use this if you're trying to load-balance across multiple deployments (e.g. Azure/OpenAI).

Router prevents failed requests, by picking the deployment which is below rate-limit and has the least amount of tokens used.

In production, Router connects to a Redis Cache to track usage across multiple deployments.

Quick Start

from litellm import Router

model_list = [{ # list of model deployments 
    "model_name": "gpt-3.5-turbo", # openai model name 
    "litellm_params": { # params for litellm completion/embedding call 
        "model": "azure/chatgpt-v-2", 
        "api_key": os.getenv("AZURE_API_KEY"),
        "api_version": os.getenv("AZURE_API_VERSION"),
        "api_base": os.getenv("AZURE_API_BASE")
    }
}, {
    "model_name": "gpt-3.5-turbo", # openai model name 
    "litellm_params": { # params for litellm completion/embedding call 
        "model": "azure/chatgpt-functioncalling", 
        "api_key": os.getenv("AZURE_API_KEY"),
        "api_version": os.getenv("AZURE_API_VERSION"),
        "api_base": os.getenv("AZURE_API_BASE")
    }
}, {
    "model_name": "gpt-3.5-turbo", # openai model name 
    "litellm_params": { # params for litellm completion/embedding call 
        "model": "gpt-3.5-turbo", 
        "api_key": os.getenv("OPENAI_API_KEY"),
    }
}]

router = Router(model_list=model_list)

# openai.ChatCompletion.create replacement
response = router.completion(model="gpt-3.5-turbo", 
                messages=[{"role": "user", "content": "Hey, how's it going?"}]

print(response)

Redis Queue

In production, we use Redis to track usage across multiple Azure deployments.

router = Router(model_list=model_list, 
                redis_host=os.getenv("REDIS_HOST"), 
                redis_password=os.getenv("REDIS_PASSWORD"), 
                redis_port=os.getenv("REDIS_PORT"))

print(response)

Deploy Router

1. Clone repo

 git clone https://github.com/BerriAI/litellm

2. Create + Modify router_config.yaml (save your azure/openai/etc. deployment info)

cp ./router_config_template.yaml ./router_config.yaml

3. Build + Run docker image

docker build -t litellm-proxy . --build-arg CONFIG_FILE=./router_config.yaml 

docker run --name litellm-proxy -e PORT=8000 -p 8000:8000 litellm-proxy

Test

curl 'http://0.0.0.0:8000/router/completions' \
--header 'Content-Type: application/json' \
--data '{
    "model": "gpt-3.5-turbo",
    "messages": [{"role": "user", "content": "Hey"}]
}'

Retry failed requests

Call it in completion like this completion(..num_retries=2).

Here's a quick look at how you can use it:

from litellm import completion

user_message = "Hello, whats the weather in San Francisco??"
messages = [{"content": user_message, "role": "user"}]

# normal call 
response = completion(
            model="gpt-3.5-turbo",
            messages=messages,
            num_retries=2
        )

Fallbacks

Helper utils

LiteLLM supports the following functions for reliability:

• litellm.longer_context_model_fallback_dict: Dictionary which has a mapping for those models which have larger equivalents
• num_retries: use tenacity retries
• completion() with fallbacks: switch between models/keys/api bases in case of errors.

Context Window Fallbacks

from litellm import completion

fallback_dict = {"gpt-3.5-turbo": "gpt-3.5-turbo-16k"}
messages = [{"content": "how does a court case get to the Supreme Court?" * 500, "role": "user"}]

completion(model="gpt-3.5-turbo", messages=messages, context_window_fallback_dict=ctx_window_fallback_dict)

Fallbacks - Switch Models/API Keys/API Bases

LLM APIs can be unstable, completion() with fallbacks ensures you'll always get a response from your calls

Usage

To use fallback models with completion(), specify a list of models in the fallbacks parameter.

The fallbacks list should include the primary model you want to use, followed by additional models that can be used as backups in case the primary model fails to provide a response.

switch models

response = completion(model="bad-model", messages=messages, 
    fallbacks=["gpt-3.5-turbo" "command-nightly"])

switch api keys/bases (E.g. azure deployment)

Switch between different keys for the same azure deployment, or use another deployment as well.

api_key="bad-key"
response = completion(model="azure/gpt-4", messages=messages, api_key=api_key,
    fallbacks=[{"api_key": "good-key-1"}, {"api_key": "good-key-2", "api_base": "good-api-base-2"}])

Check out this section for implementation details

Implementation Details

Fallbacks

Output from calls

Completion with 'bad-model': got exception Unable to map your input to a model. Check your input - {'model': 'bad-model'



completion call gpt-3.5-turbo
{
  "id": "chatcmpl-7qTmVRuO3m3gIBg4aTmAumV1TmQhB",
  "object": "chat.completion",
  "created": 1692741891,
  "model": "gpt-3.5-turbo-0613",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "I apologize, but as an AI, I do not have the capability to provide real-time weather updates. However, you can easily check the current weather in San Francisco by using a search engine or checking a weather website or app."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 16,
    "completion_tokens": 46,
    "total_tokens": 62
  }
}

How does fallbacks work

When you pass fallbacks to completion, it makes the first completion call using the primary model specified as model in completion(model=model). If the primary model fails or encounters an error, it automatically tries the fallbacks models in the specified order. This ensures a response even if the primary model is unavailable.

Key components of Model Fallbacks implementation:

• Looping through fallbacks
• Cool-Downs for rate-limited models

Looping through fallbacks

Allow 45seconds for each request. In the 45s this function tries calling the primary model set as model. If model fails it loops through the backup fallbacks models and attempts to get a response in the allocated 45s time set here:

while response == None and time.time() - start_time < 45:
        for model in fallbacks:

Cool-Downs for rate-limited models

If a model API call leads to an error - allow it to cooldown for 60s

except Exception as e:
  print(f"got exception {e} for model {model}")
  rate_limited_models.add(model)
  model_expiration_times[model] = (
      time.time() + 60
  )  # cool down this selected model
  pass

Before making an LLM API call we check if the selected model is in rate_limited_models, if so skip making the API call

if (
  model in rate_limited_models
):  # check if model is currently cooling down
  if (
      model_expiration_times.get(model)
      and time.time() >= model_expiration_times[model]
  ):
      rate_limited_models.remove(
          model
      )  # check if it's been 60s of cool down and remove model
  else:
      continue  # skip model

Full code of completion with fallbacks()

    response = None
    rate_limited_models = set()
    model_expiration_times = {}
    start_time = time.time()
    fallbacks = [kwargs["model"]] + kwargs["fallbacks"]
    del kwargs["fallbacks"]  # remove fallbacks so it's not recursive

    while response == None and time.time() - start_time < 45:
        for model in fallbacks:
            # loop thru all models
            try:
                if (
                    model in rate_limited_models
                ):  # check if model is currently cooling down
                    if (
                        model_expiration_times.get(model)
                        and time.time() >= model_expiration_times[model]
                    ):
                        rate_limited_models.remove(
                            model
                        )  # check if it's been 60s of cool down and remove model
                    else:
                        continue  # skip model

                # delete model from kwargs if it exists
                if kwargs.get("model"):
                    del kwargs["model"]

                print("making completion call", model)
                response = litellm.completion(**kwargs, model=model)

                if response != None:
                    return response

            except Exception as e:
                print(f"got exception {e} for model {model}")
                rate_limited_models.add(model)
                model_expiration_times[model] = (
                    time.time() + 60
                )  # cool down this selected model
                pass
    return response