Quest LLM Judge

A Python package for handling question-answering functionality with your RAG (Retrieval-Augmented Generation) capabilities.

Installation

Install the package using pip:

pip install quest_llm_judge

Quick Start

Here's a simple example of how to use the package:

from quest_llm_judge.handler import LlmJudge
from flask import Flask

# Initialize FastAPI app
app = Flask()

# Initialize the handler
handler = LlmJudge()

# Define your query response function
def get_query_response(query: str) -> str:
    # Add your query processing logic here
    # This function should return the answer as a string
    return "Your processed answer here"

# Create the endpoint
handler.create_rag_response_endpoint(
    app=app,
    get_query_response=get_query_response
)

Usage

1. First, import the necessary components:

   from quest_llm_quest.handler import LlmJudge

2. Initialize the handler instance:

   handler = LlmJudge()

3. Define your query response function:

   def get_query_response(query: str) -> str:
       # Your implementation here
       return answer

4. Create the endpoint using the handler:

   handler.create_rag_response_endpoint(
       app=app,
       get_query_response=get_query_response
   )

API Reference

LlmJudge

create_rag_response_endpoint(app, get_query_response)

Creates an endpoint for handling RAG-based question-answering.

Parameters:

• app: Flask application instance
• get_query_response: Function that processes the query and returns an answer

The function should have the following signature:

def get_query_response(query: str) -> str

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.