"""
Simple pluggable VectorStore with a FAISS adapter and a numpy brute-force fallback.

This file provides:
- EmbeddingAdapter: deterministic text->vector adapter for development.
- VectorStore: in-memory store that uses FAISS when available.
- get_global_vector_store(): convenience singleton for the app to reuse.

Designed to be lightweight and safe to import when FAISS is not installed.
"""
from typing import Optional, Dict, Any, List, Tuple
import hashlib
import numpy as np
try:
    import faiss
except Exception:
    faiss = None


class EmbeddingAdapter:
    """Deterministic embedding adapter for development.

    It hashes the input text and produces a fixed-size float vector. Not
    a production-quality embedder but useful for development and tests.
    """
    def __init__(self, dim: int = 128):
        self.dim = dim

    def embed(self, text: str) -> np.ndarray:
        h = hashlib.sha256(text.encode("utf-8")).digest()
        # Expand digest material to required dim by repeating digest
        needed = self.dim
        data = (h * ((needed * 32) // len(h) + 1))[:needed]
        arr = np.frombuffer(data, dtype=np.uint8).astype(np.float32)
        # normalize to unit vector
        if arr.sum() == 0:
            return np.zeros(self.dim, dtype=np.float32)
        vec = arr / np.linalg.norm(arr)
        return vec


class VectorStore:
    def __init__(self, dim: int = 128):
        self.dim = dim
        self._emb = EmbeddingAdapter(dim=dim)
        self._meta: Dict[str, Dict[str, Any]] = {}
        self._vectors: Dict[str, np.ndarray] = {}
        self._faiss_index = None
        self._use_faiss = False
        self._build_index()

    def _build_index(self):
        if faiss is None:
            self._use_faiss = False
            self._faiss_index = None
            return
        try:
            index = faiss.IndexFlatL2(self.dim)
            self._faiss_index = index
            self._use_faiss = True
        except Exception:
            self._use_faiss = False
            self._faiss_index = None

    def add_vector(self, id: str, vector: np.ndarray, metadata: Optional[Dict[str, Any]] = None):
        v = np.asarray(vector, dtype=np.float32)
        if v.shape != (self.dim,):
            raise ValueError(f"vector must have shape ({self.dim},), got {v.shape}")
        self._vectors[id] = v
        self._meta[id] = metadata or {}
        if self._use_faiss and self._faiss_index is not None:
            try:
                # faiss expects a 2D array
                self._faiss_index.add(np.expand_dims(v, axis=0))
            except Exception:
                # fallback: rebuild index
                self._rebuild_faiss_index()

    def add_text(self, id: str, text: str, metadata: Optional[Dict[str, Any]] = None):
        vec = self._emb.embed(text)
        self.add_vector(id, vec, metadata)

    def _rebuild_faiss_index(self):
        if faiss is None:
            return
        try:
            index = faiss.IndexFlatL2(self.dim)
            if len(self._vectors) > 0:
                mats = np.stack(list(self._vectors.values(), axis=0).astype(np.float32))
                index.add(mats)
            self._faiss_index = index
            self._use_faiss = True
        except Exception:
            self._faiss_index = None
            self._use_faiss = False

    def get(self, id: str) -> Optional[Dict[str, Any]]:
        if id not in self._vectors:
            return None
        return {"id": id, "vector": self._vectors[id], "metadata": self._meta.get(id, {})}

    def query_vector(self, vector: np.ndarray, k: int = 5) -> List[Tuple[str, float, Dict[str, Any]]]:
        v = np.asarray(vector, dtype=np.float32)
        if self._use_faiss and self._faiss_index is not None:
            D, I = self._faiss_index.search(np.expand_dims(v, axis=0), k)
            # faiss returns distances and indices
            results: List[Tuple[str, float, Dict[str, Any]]] = []
            ids = list(self._vectors.keys())
            for dist, idx in zip(D[0], I[0]):
                if idx < 0 or idx >= len(ids):
                    continue
                rid = ids[idx]
                results.append((rid, float(dist), self._meta.get(rid, {})))
            return results
        # fallback brute-force
        results = []
        for rid, rv in self._vectors.items():
            dist = float(np.linalg.norm(rv - v))
            results.append((rid, dist, self._meta.get(rid, {})))
        results.sort(key=lambda x: x[1])
        return results[:k]

    def query_text(self, text: str, k: int = 5) -> List[Tuple[str, float, Dict[str, Any]]]:
        vec = self._emb.embed(text)
        return self.query_vector(vec, k=k)


# simple global store for the app
_GLOBAL_STORE: Optional[VectorStore] = None


def get_global_vector_store() -> VectorStore:
    global _GLOBAL_STORE
    if _GLOBAL_STORE is None:
        _GLOBAL_STORE = VectorStore()
    return _GLOBAL_STORE
"""Simple pluggable vector store with FAISS backend and numpy fallback.

This file provides a minimal VectorStore interface used by the REST API.
It intentionally keeps dependencies optional: if `faiss` isn't installed the
implementation falls back to an in-memory numpy-based nearest-neighbour search
for development and testing.
"""
from typing import List, Optional, Dict, Any
try:
    import faiss
    FAISS_AVAILABLE = True
except Exception:
    faiss = None  # type: ignore
    FAISS_AVAILABLE = False

import numpy as np


class VectorStore:
    """A tiny vector store abstraction.

    - `add(ids, vectors, metas)` stores vectors and optional metadata.
    - `search(query_vector, top_k)` returns nearest neighbours with scores.
    """

    def __init__(self, dim: int = 128):
        self.dim = dim
        if FAISS_AVAILABLE:
            # Use IndexFlatL2 for simplicity (no IDs support so we map manually)
            self.index = faiss.IndexFlatL2(dim)
            self._id_map: Dict[int, Any] = {}
            self._next_index = 0
        else:
            self.vectors = np.zeros((0, dim), dtype=np.float32)
            self.ids: List[str] = []
            self.metas: Dict[str, Any] = {}

    def add(self, ids: List[str], vectors: np.ndarray, metas: Optional[List[Any]] = None) -> int:
        """Add vectors to the store.

        Args:
            ids: list of string IDs (one per vector).
            vectors: numpy array of shape (N, dim).
            metas: optional list of metadata objects parallel to ids.

        Returns:
            number of indexed vectors after insertion.
        """
        vecs = np.asarray(vectors, dtype=np.float32)
        if vecs.ndim != 2 or vecs.shape[1] != self.dim:
            raise ValueError(f"vectors must be shape (N, {self.dim})")

        if FAISS_AVAILABLE:
            self.index.add(vecs)
            for i, id_ in enumerate(ids):
                self._id_map[self._next_index] = {"id": id_, "meta": metas[i] if metas else None}
                self._next_index += 1
            return int(self.index.ntotal)
        else:
            if self.vectors.size == 0:
                self.vectors = vecs
            else:
                self.vectors = np.vstack([self.vectors, vecs])
            self.ids.extend(ids)
            if metas:
                for i, id_ in enumerate(ids):
                    self.metas[id_] = metas[i]
            return len(self.ids)

    def search(self, query_vector: np.ndarray, top_k: int = 5) -> List[Dict[str, Any]]:
        """Return nearest neighbours as a list of {id, score, meta}.

        Score is L2 distance (lower is better).
        """
        q = np.asarray(query_vector, dtype=np.float32)
        if q.ndim == 1:
            q = q.reshape(1, -1)
        if q.shape[1] != self.dim:
            raise ValueError(f"query_vector must have dimension {self.dim}")

        if FAISS_AVAILABLE:
            D, I = self.index.search(q, top_k)
            results = []
            for dist, idx in zip(D[0], I[0]):
                if idx < 0:
                    continue
                info = self._id_map.get(int(idx), {"id": str(idx), "meta": None})
                results.append({"id": info["id"], "score": float(dist), "meta": info.get("meta")})
            return results
        else:
            if self.vectors.shape[0] == 0:
                return []
            # compute L2 distances
            diffs = self.vectors - q
            dists = np.linalg.norm(diffs, axis=1)
            idxs = np.argsort(dists)[:top_k]
            out = []
            for i in idxs:
                out.append({"id": self.ids[int(i)], "score": float(dists[int(i)]), "meta": self.metas.get(self.ids[int(i)])})
            return out


_default_store: Optional[VectorStore] = None


def get_default_store(dim: int = 128) -> VectorStore:
    global _default_store
    if _default_store is None:
        _default_store = VectorStore(dim=dim)
    return _default_store
"""Simple pluggable vector store with FAISS backend (optional) and numpy brute-force fallback.

This module provides a lightweight interface used by the API for indexing and nearest-neighbor
search. FAISS is optional; if it's not installed the implementation falls back to an in-memory
brute-force search using NumPy (if available) or pure Python.
"""
from typing import List, Dict, Optional, Tuple

try:
    import faiss
    _has_faiss = True
except Exception:
    faiss = None  # type: ignore
    _has_faiss = False

try:
    import numpy as np
    _has_numpy = True
except Exception:
    np = None  # type: ignore
    _has_numpy = False


class VectorStore:
    """In-memory vector store with optional FAISS acceleration.

    Usage:
        store = VectorStore(dim=128)
        store.add('id1', vector, metadata={...})
        results = store.search(query_vector, k=5)
    """

    def __init__(self, dim: int = 128, use_faiss: bool = True):
        self.dim = dim
        self.ids: List[str] = []
        self.vectors: List = []  # numpy arrays if available, else lists
        self.metadatas: List[Dict] = []
        self._index = None

        self._use_faiss = use_faiss and _has_faiss
        if self._use_faiss:
            # Use inner product (cosine if vectors normalized externally)
            self._index = faiss.IndexFlatIP(dim)

    def _ensure_numpy(self, vec):
        if _has_numpy:
            return np.asarray(vec, dtype=np.float32)
        return vec

    def add(self, id: str, vector, metadata: Optional[Dict] = None):
        metadata = metadata or {}
        vec = self._ensure_numpy(vector)
        self.ids.append(id)
        self.vectors.append(vec)
        self.metadatas.append(metadata)
        if self._use_faiss:
            # faiss needs contiguous float32 arrays
            arr = np.asarray(vec, dtype=np.float32).reshape(1, -1)
            self._index.add(arr)

    def search(self, query_vector, k: int = 5) -> List[Tuple[str, float, Dict]]:
        """Return list of (id, score, metadata) ordered by descending score.

        Score semantics: if FAISS IndexFlatIP is used, it's inner product. The
        fallback uses cosine similarity when numpy is available.
        """
        if len(self.ids) == 0:
            return []

        q = self._ensure_numpy(query_vector)

        if self._use_faiss:
            q_arr = np.asarray(q, dtype=np.float32).reshape(1, -1)
            D, I = self._index.search(q_arr, min(k, len(self.ids)))
            results = []
            for score, idx in zip(D[0].tolist(), I[0].tolist()):
                if idx < 0:
                    continue
                results.append((self.ids[idx], float(score), self.metadatas[idx]))
            return results

        # numpy brute-force fallback
        if _has_numpy:
            mats = np.vstack([np.asarray(v, dtype=np.float32).reshape(1, -1) for v in self.vectors])
            qv = np.asarray(q, dtype=np.float32).reshape(-1)
            # cosine similarity
            norms = np.linalg.norm(mats, axis=1) * (np.linalg.norm(qv) + 1e-12)
            sims = (mats.dot(qv)) / (norms + 1e-12)
            idxs = sims.argsort()[::-1][:k]
            return [(self.ids[i], float(sims[i]), self.metadatas[i]) for i in idxs]

        # pure-python fallback: dot product over lists
        def dot(a, b):
            return sum(x * y for x, y in zip(a, b))

        scores = [dot(v, q) for v in self.vectors]
        ordered = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:k]
        return [(self.ids[i], float(scores[i]), self.metadatas[i]) for i in ordered]


# provide a module-level default store for simple usage
default_store = VectorStore(dim=128, use_faiss=True)

__all__ = ["VectorStore", "default_store"]