Retrieve Equipment Documentation with LangChain RAG and 4-Bit Quantized Models

                      
                     
"""
Production implementation for retrieving equipment documentation using LangChain RAG with 4-bit quantized models.
Provides secure, scalable operations for documentation retrieval.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import requests
import time
from langchain import LangChain
from langchain.document_loaders import DocumentLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

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

class Config:
    """Configuration class for environment variables."""
    database_url: str = os.getenv('DATABASE_URL')
    openai_api_key: str = os.getenv('OPENAI_API_KEY')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.

    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'equipment_id' not in data:
        raise ValueError('Missing equipment_id in input data')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection.

    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}

def fetch_data(equipment_id: str) -> Optional[Dict[str, Any]]:
    """Fetch equipment data from external API.

    Args:
        equipment_id: ID of equipment to fetch
    Returns:
        Equipment data if found, else None
    Raises:
        Exception: If the API call fails
    """
    try:
        url = f'https://api.example.com/equipment/{equipment_id}'
        response = requests.get(url)
        response.raise_for_status()
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        return None

def transform_records(records: List[Dict[str, Any]]) -> List[str]:
    """Transform records for embedding processing.

    Args:
        records: List of raw records
    Returns:
        List of transformed documents
    """
    return [f"{record['name']}: {record['description']}" for record in records]

def create_embeddings(documents: List[str]) -> Any:
    """Generate embeddings using LangChain.

    Args:
        documents: List of documents to embed
    Returns:
        FAISS vector store with embeddings
    """
    embeddings = OpenAIEmbeddings(api_key=Config.openai_api_key)
    vector_store = FAISS.from_texts(documents, embeddings)
    return vector_store

def query_documents(vector_store: Any, query: str) -> List[str]:
    """Query the vector store for relevant documents.

    Args:
        vector_store: FAISS vector store
        query: Search query string
    Returns:
        List of relevant document snippets
    """
    return vector_store.similarity_search(query)

def save_to_db(data: Dict[str, Any]) -> None:
    """Save the processed data to the database.

    Args:
        data: Processed data to save
    Raises:
        Exception: If database operation fails
    """
    # Placeholder for database save logic
    logger.info('Data saved to database.')

class EquipmentDocumentationRetriever:
    """Main class to orchestrate equipment documentation retrieval."""
    def __init__(self, config: Config):
        self.config = config

    def retrieve_documentation(self, equipment_id: str) -> List[str]:
        """Main workflow to retrieve and process documentation.

        Args:
            equipment_id: ID of equipment to retrieve documentation for
        Returns:
            List of relevant documentation snippets
        """
        # Validate input
        try:
            validate_input({'equipment_id': equipment_id})
            sanitized_data = sanitize_fields({'equipment_id': equipment_id})
            logger.info('Input validated and sanitized.')

            # Fetch data
            raw_data = fetch_data(sanitized_data['equipment_id'])
            if not raw_data:
                logger.warning('No data found for the given equipment_id.')
                return []

            # Transform records
            transformed_docs = transform_records(raw_data)
            logger.info(f'Transformed {len(transformed_docs)} documents.')

            # Create embeddings
            vector_store = create_embeddings(transformed_docs)
            logger.info('Embeddings created successfully.')

            # Query documents
            results = query_documents(vector_store, 'Retrieve documentation')
            logger.info(f'Retrieved {len(results)} results.')

            # Save to DB
            save_to_db({'equipment_id': equipment_id, 'results': results})

            return results

        except ValueError as ve:
            logger.error(f'Validation error: {ve}')
            return []
        except Exception as e:
            logger.error(f'An unexpected error occurred: {e}')
            return []

if __name__ == '__main__':
    config = Config()
    retriever = EquipmentDocumentationRetriever(config)
    results = retriever.retrieve_documentation(equipment_id='12345')
    print(results)