# vector/store.py — NumPy cosine index (no FAISS)
from typing import List, Tuple
from pathlib import Path
import json, numpy as np

class FaissStore:
    def __init__(self, dim: int, index_dir: str):
        self.dim = dim
        self.index_dir = Path(index_dir)
        self.index_dir.mkdir(parents=True, exist_ok=True)
        self.vec_path = self.index_dir / "vecs.npy"
        self.meta_path = self.index_dir / "meta.json"
        self.vecs = np.load(self.vec_path) if self.vec_path.exists() else np.zeros((0, dim), dtype="float32")
        self.meta = json.loads(self.meta_path.read_text()) if self.meta_path.exists() else []

    def add(self, embeddings: List[List[float]], metadatas: List[dict]):
        arr = np.asarray(embeddings, dtype="float32")
        self.vecs = np.vstack([self.vecs, arr]) if self.vecs.size else arr
        self.meta.extend(metadatas)

    def save(self):
        np.save(self.vec_path, self.vecs)
        self.meta_path.write_text(json.dumps(self.meta))

    def search(self, query_emb: List[float], k: int = 6) -> List[Tuple[float, dict]]:
        if len(self.meta) == 0:
            return []
        q = np.asarray(query_emb, dtype="float32")
        q /= (np.linalg.norm(q) + 1e-9)
        X = self.vecs
        X = X / (np.linalg.norm(X, axis=1, keepdims=True) + 1e-9)
        sims = X @ q
        idx = np.argsort(-sims)[:k]
        return [(float(sims[i]), self.meta[i]) for i in idx]