Evaluate Industrial RAG Answer Correctness and Citation Quality with Ragas and LangChain

"""
Production implementation for evaluating industrial RAG answer correctness and citation quality using LangChain.
Provides secure, scalable operations for analyzing results and citations.
"""

from typing import Dict, Any, List
import os
import logging
import httpx
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, ValidationError
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, Session

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

Base = declarative_base()

class Config:
    DATABASE_URL: str = os.getenv('DATABASE_URL', 'sqlite:///./test.db')
    RETRY_LIMIT: int = 3

class Result(Base):
    __tablename__ = 'results'
    id = Column(Integer, primary_key=True, index=True)
    answer = Column(String)
    citation_quality = Column(String)

engine = create_engine(Config.DATABASE_URL)
Base.metadata.create_all(bind=engine)
DBSession = sessionmaker(autocommit=False, autoflush=False, bind=engine)

app = FastAPI()

class InputData(BaseModel):
    answer: str
    citation: str

def validate_input(data: InputData) -> bool:
    """Validate request data.
    
    Args:
        data: InputData object to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if not data.answer or not data.citation:
        raise ValueError('Answer and citation must not be empty')
    return True

async def fetch_data(url: str) -> Dict[str, Any]:
    """Fetch data from an external API.
    
    Args:
        url: API endpoint to fetch data from
    Returns:
        Parsed JSON response
    Raises:
        HTTPException: If the request fails
    """
    async with httpx.AsyncClient() as client:
        response = await client.get(url)
        response.raise_for_status()
        return response.json()

async def save_to_db(session: Session, result: Result) -> None:
    """Save the evaluation result to the database.
    
    Args:
        session: Database session
        result: Result object to save
    """
    session.add(result)
    session.commit()
    session.refresh(result)

async def evaluate_answer(data: InputData) -> str:
    """Evaluate the provided answer and citation.
    
    Args:
        data: InputData object containing answer and citation
    Returns:
        Evaluation result
    """
    try:
        validate_input(data)
        # Simulated evaluation logic
        if "good" in data.citation:
            quality = "high"
        else:
            quality = "low"
        result = Result(answer=data.answer, citation_quality=quality)
        return quality
    except ValueError as e:
        logger.error(f'Validation error: {str(e)}')
        raise HTTPException(status_code=400, detail=str(e))

@app.post('/evaluate/', response_model=str)
async def evaluate(data: InputData) -> str:
    """API endpoint to evaluate an answer.
    
    Args:
        data: InputData object containing answer and citation
    Returns:
        Evaluation result as string
    Raises:
        HTTPException: If errors occur
    """
    session = DBSession()
    try:
        quality = await evaluate_answer(data)
        result = Result(answer=data.answer, citation_quality=quality)
        await save_to_db(session, result)
        return quality
    except HTTPException as e:
        logger.warning(f'HTTP error: {e.detail}')
        raise e
    except Exception as e:
        logger.error(f'Unexpected error: {str(e)}')
        raise HTTPException(status_code=500, detail='Internal Server Error')
    finally:
        session.close()

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)