import faiss
import json
import numpy as np
from pathlib import Path
from src.utils.config import VECTOR_DB_PATH, EMBEDDING_DIM

from typing import List


class VectorStore:
    """
    Wrapper for FAISS vector storage, with ID-to-text mapping.
    """
    def __init__(self, index_path: Path = VECTOR_DB_PATH):
        self.index_path = index_path.with_suffix(".index")
        self.meta_path = index_path.with_suffix(".json")

        self.index = faiss.IndexFlatL2(EMBEDDING_DIM)
        self.metadata = []  # list of dicts: {"id": str, "text": str}

        # Try loading if exists
        if self.index_path.exists() and self.meta_path.exists():
            try:
                self.load()

            except Exception as e:
                print(f"[WARN] Failed to load vector store: {e}")
                
                # Reinitialize clean if corrupted
                self.index = faiss.IndexFlatL2(EMBEDDING_DIM)
                self.metadata = []



    def add(self, embeddings: list[list[float]], metadata: List[dict]):
        """
        Add new embeddings and their metadata (e.g., {"id": "doc1_chunk0", "text": "..."})
        """
        self.index.add(np.array(embeddings).astype("float32"))
        self.metadata.extend(metadata)
        self.save()


    def search(self, query_embedding: list[float], top_k: int = 5) -> List[dict]:
        """
        Perform vector search and return metadata of top_k results.
        """
        D, I = self.index.search(np.array([query_embedding]).astype("float32"), top_k)

        return [self.metadata[i] for i in I[0]]


    def save(self) -> None:
        """
        Save data to an external file.
        """
        self.index_path.parent.mkdir(parents = True, exist_ok = True)

        faiss.write_index(self.index, str(self.index_path))

        with open(self.meta_path, 'w', encoding = "utf-8") as f:
            json.dump(self.metadata, f, ensure_ascii = False, indent = 2)


    def load(self) -> None:
        """
        Load data from an external file.
        """
        self.index = faiss.read_index(str(self.index_path))

        with open(self.meta_path, 'r', encoding = "utf-8") as f:
            self.metadata = json.load(f)