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Modify / Reject Incoming Requests

  • Modify data before making llm api calls on proxy
  • Reject data before making llm api calls / before returning the response
  • Enforce 'user' param for all openai endpoint calls

See a complete example with our parallel request rate limiter

Quick Start​

  1. In your Custom Handler add a new async_pre_call_hook function

This function is called just before a litellm completion call is made, and allows you to modify the data going into the litellm call See Code

from litellm.integrations.custom_logger import CustomLogger
import litellm
from litellm.proxy.proxy_server import UserAPIKeyAuth, DualCache
from typing import Optional, Literal

# This file includes the custom callbacks for LiteLLM Proxy
# Once defined, these can be passed in proxy_config.yaml
class MyCustomHandler(CustomLogger): # https://docs.litellm.ai/docs/observability/custom_callback#callback-class
    # Class variables or attributes
    def __init__(self):
        pass

    #### CALL HOOKS - proxy only #### 

    async def async_pre_call_hook(self, user_api_key_dict: UserAPIKeyAuth, cache: DualCache, data: dict, call_type: Literal[
            "completion",
            "text_completion",
            "embeddings",
            "image_generation",
            "moderation",
            "audio_transcription",
        ]): 
        data["model"] = "my-new-model"
        return data 

    async def async_post_call_failure_hook(
        self, original_exception: Exception, user_api_key_dict: UserAPIKeyAuth
    ):
        pass

    async def async_post_call_success_hook(
        self,
        user_api_key_dict: UserAPIKeyAuth,
        response,
    ):
        pass

    async def async_moderation_hook( # call made in parallel to llm api call
        self,
        data: dict,
        user_api_key_dict: UserAPIKeyAuth,
        call_type: Literal["completion", "embeddings", "image_generation"],
    ):
        pass

    async def async_post_call_streaming_hook(
        self,
        user_api_key_dict: UserAPIKeyAuth,
        response: str,
    ):
        pass
proxy_handler_instance = MyCustomHandler()
  1. Add this file to your proxy config
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo

litellm_settings:
  callbacks: custom_callbacks.proxy_handler_instance # sets litellm.callbacks = [proxy_handler_instance]
  1. Start the server + test the request
$ litellm /path/to/config.yaml
curl --location 'http://0.0.0.0:4000/chat/completions' \
    --data ' {
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "good morning good sir"
        }
    ],
    "user": "ishaan-app",
    "temperature": 0.2
    }'

[BETA] NEW async_moderation_hook​

Run a moderation check in parallel to the actual LLM API call.

In your Custom Handler add a new async_moderation_hook function

  • This is currently only supported for /chat/completion calls.
  • This function runs in parallel to the actual LLM API call.
  • If your async_moderation_hook raises an Exception, we will return that to the user.
info

We might need to update the function schema in the future, to support multiple endpoints (e.g. accept a call_type). Please keep that in mind, while trying this feature

See a complete example with our Llama Guard content moderation hook

from litellm.integrations.custom_logger import CustomLogger
import litellm
from fastapi import HTTPException

# This file includes the custom callbacks for LiteLLM Proxy
# Once defined, these can be passed in proxy_config.yaml
class MyCustomHandler(CustomLogger): # https://docs.litellm.ai/docs/observability/custom_callback#callback-class
    # Class variables or attributes
    def __init__(self):
        pass

    #### ASYNC #### 
    
    async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time):
        pass

    async def async_log_pre_api_call(self, model, messages, kwargs):
        pass

    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
        pass

    async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
        pass

    #### CALL HOOKS - proxy only #### 

    async def async_pre_call_hook(self, user_api_key_dict: UserAPIKeyAuth, cache: DualCache, data: dict, call_type: Literal["completion", "embeddings"]):
        data["model"] = "my-new-model"
        return data 
    
    async def async_moderation_hook( ### 👈 KEY CHANGE ###
        self,
        data: dict,
    ):
        messages = data["messages"]
        print(messages)
        if messages[0]["content"] == "hello world": 
            raise HTTPException(
                    status_code=400, detail={"error": "Violated content safety policy"}
                )

proxy_handler_instance = MyCustomHandler()
  1. Add this file to your proxy config
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo

litellm_settings:
  callbacks: custom_callbacks.proxy_handler_instance # sets litellm.callbacks = [proxy_handler_instance]
  1. Start the server + test the request
$ litellm /path/to/config.yaml
curl --location 'http://0.0.0.0:4000/chat/completions' \
    --data ' {
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "Hello world"
        }
    ],
    }'

Advanced - Enforce 'user' param​

Set enforce_user_param to true, to require all calls to the openai endpoints to have the 'user' param.

See Code

general_settings:
  enforce_user_param: True

Result

Advanced - Return rejected message as response​

For chat completions and text completion calls, you can return a rejected message as a user response.

Do this by returning a string. LiteLLM takes care of returning the response in the correct format depending on the endpoint and if it's streaming/non-streaming.

For non-chat/text completion endpoints, this response is returned as a 400 status code exception.

1. Create Custom Handler​

from litellm.integrations.custom_logger import CustomLogger
import litellm
from litellm.utils import get_formatted_prompt

# This file includes the custom callbacks for LiteLLM Proxy
# Once defined, these can be passed in proxy_config.yaml
class MyCustomHandler(CustomLogger):
    def __init__(self):
        pass

    #### CALL HOOKS - proxy only #### 

    async def async_pre_call_hook(self, user_api_key_dict: UserAPIKeyAuth, cache: DualCache, data: dict, call_type: Literal[
            "completion",
            "text_completion",
            "embeddings",
            "image_generation",
            "moderation",
            "audio_transcription",
        ]) -> Optional[dict, str, Exception]: 
        formatted_prompt = get_formatted_prompt(data=data, call_type=call_type)

        if "Hello world" in formatted_prompt:
            return "This is an invalid response"

        return data 

proxy_handler_instance = MyCustomHandler()

2. Update config.yaml​

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo

litellm_settings:
  callbacks: custom_callbacks.proxy_handler_instance # sets litellm.callbacks = [proxy_handler_instance]

3. Test it!​

$ litellm /path/to/config.yaml
curl --location 'http://0.0.0.0:4000/chat/completions' \
    --data ' {
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "Hello world"
        }
    ],
    }'

Expected Response

{
    "id": "chatcmpl-d00bbede-2d90-4618-bf7b-11a1c23cf360",
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "message": {
                "content": "This is an invalid response.", # 👈 REJECTED RESPONSE
                "role": "assistant"
            }
        }
    ],
    "created": 1716234198,
    "model": null,
    "object": "chat.completion",
    "system_fingerprint": null,
    "usage": {}
}