src/search.py

import os
from dataclasses import dataclass
from typing import List, Optional

from dotenv import load_dotenv
from langchain_groq import ChatGroq
from langchain_core.messages import HumanMessage, SystemMessage

from src.vectorstore import FaissVectorStore

load_dotenv()

@dataclass
class RetrievalResult:
    index: int
    distance: float
    text: Optional[str]

class RAGSearch:
    def __init__(
        self,
        persist_dir: str = "faiss_store",
        embedding_model: str = "all-MiniLM-L6-v2",
        llm_model: str = "llama-3.1-8b-instant",
    ):
        # Vector store setup
        self.vectorstore = FaissVectorStore(persist_dir=persist_dir, embedding_model=embedding_model)

        faiss_path = os.path.join(persist_dir, "faiss.index")
        meta_path = os.path.join(persist_dir, "metadata.pkl")

        if not (os.path.exists(faiss_path) and os.path.exists(meta_path)):
            # Build from local 'Research/data' directory if index doesn't exist  
            from src.data_loader import load_all_documents
            # Try multiple possible data directories
            data_dirs = ["Research/data", "data", "Data"]
            docs = []
            
            for data_dir in data_dirs:
                if os.path.exists(data_dir):
                    print(f"[INFO] Checking for documents in: {data_dir}")
                    docs = load_all_documents(data_dir)
                    if docs:
                        print(f"[INFO] Found {len(docs)} documents in {data_dir}")
                        break
                        
            if not docs:
                print("[WARNING] No documents found in any data directory. Vector store will be empty.")
                print("[INFO] Please add documents to 'Research/data/', 'data/', or 'Data/' directory.")
                # Create empty index for now
                self.vectorstore.index = None
                self.vectorstore.metadata = []
            else:
                self.vectorstore.build_from_documents(docs)
        else:
            self.vectorstore.load()

        # LLM setup
        groq_api_key = os.getenv("GROQ_API_KEY")
        if not groq_api_key:
            raise ValueError("GROQ_API_KEY missing in environment")
        self.llm_model = llm_model
        self.embedding_model = embedding_model
        self.llm = ChatGroq(api_key=groq_api_key, model=llm_model, temperature=0.1)
        print(f"[INFO] Groq LLM initialized: {llm_model}")

    def retrieve(self, query: str, top_k: int = 5) -> List[RetrievalResult]:
        # Check if vector store is empty
        if self.vectorstore.index is None or len(self.vectorstore.metadata) == 0:
            print("[WARNING] Vector store is empty. No documents to search.")
            return []
            
        results = self.vectorstore.query(query_text=query, top_k=top_k)
        out: List[RetrievalResult] = []
        for r in results:
            text = r["metadata"]["texts"] if r.get("metadata") and r["metadata"].get("texts") else None
            out.append(RetrievalResult(index=int(r["index"]), distance=float(r["distance"]), text=text))
        return out

    def summarize(self, query: str, retrieved: List[RetrievalResult]) -> str:
        if not retrieved:
            return "No documents are available in the vector store. Please add some documents to the data directory and restart the application."
            
        texts = [r.text for r in retrieved if r.text]
        context = "\n\n".join(texts)
        if not context:
            return "No relevant documents found for your query."
        
        # Using proper message formatting for better LLM interaction
        system_message = SystemMessage(content="You are a helpful assistant that summarizes documents based on queries. Provide clear, concise summaries with relevant quotes when appropriate.")
        human_message = HumanMessage(content=f"""
Based on the following context, answer the query: '{query}'

Context:
{context}

Please provide a comprehensive answer based solely on the provided context. If you quote specific information, indicate it clearly.
""")
        
        try:
            response = self.llm.invoke([system_message, human_message])
            return response.content
        except Exception as e:
            return f"Error generating response: {str(e)}. Please check your GROQ_API_KEY is set correctly."

    def search_and_summarize(self, query: str, top_k: int = 5) -> str:
        retrieved = self.retrieve(query, top_k=top_k)
        return self.summarize(query, retrieved)

if __name__ == "__main__":
    rag_search = RAGSearch()
    query = "What is Database Management System?"
    summary = rag_search.search_and_summarize(query, top_k=3)
    print("Summary:", summary)