# faiss_manager.py
import faiss
import numpy as np

class FAISSLiteManager:
    def __init__(self, db_path: str):
        self.db_path = db_path
        self.dimension = 384  # all-MiniLM-L6-v2 dimension
        self.index = self._initialize_index()
        
    def _initialize_index(self):
        """Initialize FAISS index with SQLite backend"""
        try:
            return faiss.read_index(f"{self.db_path}.faiss")
        except:
            # Create new index
            index = faiss.IndexFlatIP(self.dimension)
            faiss.write_index(index, f"{self.db_path}.faiss")
            return index
            
    async def store_embedding(self, session_id: str, text: str, embedding: list):
        """Store embedding with session context"""
        # Convert to numpy array
        vector = np.array([embedding], dtype=np.float32)
        
        # Add to index
        self.index.add(vector)
        
        # Store metadata in SQLite
        await self._store_metadata(session_id, text, len(self.index.ntotal) - 1)
    
    async def search_similar(self, query_embedding: list, k: int = 5) -> list:
        """
        Search for similar embeddings
        """
        vector = np.array([query_embedding], dtype=np.float32)
        distances, indices = self.index.search(vector, k)
        
        # Retrieve metadata for results
        results = await self._retrieve_metadata(indices[0])
        return results
    
    async def _store_metadata(self, session_id: str, text: str, index_position: int):
        """
        Store metadata in SQLite database
        """
        # TODO: Implement SQLite storage
        pass
    
    async def _retrieve_metadata(self, indices: list) -> list:
        """
        Retrieve metadata for given indices
        """
        # TODO: Implement SQLite retrieval
        return []
    
    def save_index(self):
        """
        Save the FAISS index to disk
        """
        faiss.write_index(self.index, f"{self.db_path}.faiss")
    
    def get_index_size(self) -> int:
        """
        Get the number of vectors in the index
        """
        return self.index.ntotal