Scrub Logged Data

Redact messages / mask PII before sending data to logging integrations (langfuse/etc.).

See our Presidio PII Masking for reference.

1. Setup a custom callback

from litellm.integrations.custom_logger import CustomLogger

class MyCustomHandler(CustomLogger):
    async def async_logging_hook(
        self, kwargs: dict, result: Any, call_type: str
    ) -> Tuple[dict, Any]:
        """
        For masking logged request/response. Return a modified version of the request/result. 
        
        Called before `async_log_success_event`.
        """
        if (
            call_type == "completion" or call_type == "acompletion"
        ):  # /chat/completions requests
            messages: Optional[List] = kwargs.get("messages", None)

            kwargs["messages"] = [{"role": "user", "content": "MASK_THIS_ASYNC_VALUE"}]

        return kwargs, responses

    def logging_hook(
        self, kwargs: dict, result: Any, call_type: str
    ) -> Tuple[dict, Any]:
        """
        For masking logged request/response. Return a modified version of the request/result.

        Called before `log_success_event`.
        """
        if (
            call_type == "completion" or call_type == "acompletion"
        ):  # /chat/completions requests
            messages: Optional[List] = kwargs.get("messages", None)

            kwargs["messages"] = [{"role": "user", "content": "MASK_THIS_SYNC_VALUE"}]

        return kwargs, responses


customHandler = MyCustomHandler()

2. Connect custom handler to LiteLLM

import litellm

litellm.callbacks = [customHandler]

3. Test it!

# pip install langfuse 

import os
import litellm
from litellm import completion 

os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
# Optional, defaults to https://cloud.langfuse.com
os.environ["LANGFUSE_HOST"] # optional
# LLM API Keys
os.environ['OPENAI_API_KEY']=""

litellm.callbacks = [customHandler]
litellm.success_callback = ["langfuse"]



## sync 
response = completion(model="gpt-3.5-turbo", messages=[{ "role": "user", "content": "Hi 👋 - i'm openai"}],
                              stream=True)
for chunk in response: 
    continue


## async
import asyncio 

def async completion():
    response = await acompletion(model="gpt-3.5-turbo", messages=[{ "role": "user", "content": "Hi 👋 - i'm openai"}],
                              stream=True)
    async for chunk in response: 
        continue
asyncio.run(completion())