"""SQLite + numpy vector store.

Zero external dependencies (SQLite is built into Python). For our scale
(< 10K chunks total per user), a brute-force cosine-sim scan in numpy is
~5-20ms — there's no need for an ANN index. If we ever need it, we can
swap in FAISS or Chroma without touching the API.
"""

from __future__ import annotations

import sqlite3
import uuid
from contextlib import contextmanager
from datetime import datetime
from pathlib import Path

import numpy as np

DEFAULT_DB = Path("data/knowledge.sqlite")


class KnowledgeStore:
    def __init__(self, db_path: str | Path = DEFAULT_DB) -> None:
        self.db_path = Path(db_path)
        self.db_path.parent.mkdir(parents=True, exist_ok=True)
        self._init_schema()

    @contextmanager
    def _conn(self):
        c = sqlite3.connect(self.db_path)
        c.row_factory = sqlite3.Row
        c.execute("PRAGMA foreign_keys = ON")
        try:
            yield c
            c.commit()
        finally:
            c.close()

    def _init_schema(self) -> None:
        with self._conn() as c:
            c.executescript(
                """
                CREATE TABLE IF NOT EXISTS documents (
                    id           TEXT PRIMARY KEY,
                    name         TEXT NOT NULL,
                    format       TEXT NOT NULL,
                    size_bytes   INTEGER DEFAULT 0,
                    chunk_count  INTEGER DEFAULT 0,
                    uploaded_at  TEXT NOT NULL
                );
                CREATE TABLE IF NOT EXISTS chunks (
                    id           INTEGER PRIMARY KEY AUTOINCREMENT,
                    document_id  TEXT NOT NULL,
                    chunk_index  INTEGER NOT NULL,
                    text         TEXT NOT NULL,
                    embedding    BLOB NOT NULL,
                    FOREIGN KEY (document_id) REFERENCES documents(id) ON DELETE CASCADE
                );
                CREATE INDEX IF NOT EXISTS idx_chunks_doc ON chunks(document_id);
                """
            )

    # ── writes ──────────────────────────────────────────────────────────
    def add_document(
        self,
        name: str,
        fmt: str,
        chunks: list[str],
        embeddings: np.ndarray,
        size_bytes: int = 0,
    ) -> str:
        if len(chunks) != len(embeddings):
            raise ValueError(
                f"chunks ({len(chunks)}) and embeddings ({len(embeddings)}) must match"
            )
        doc_id = uuid.uuid4().hex[:12]
        with self._conn() as c:
            c.execute(
                "INSERT INTO documents (id, name, format, size_bytes, chunk_count, uploaded_at) "
                "VALUES (?, ?, ?, ?, ?, ?)",
                (doc_id, name, fmt, size_bytes, len(chunks), datetime.utcnow().isoformat()),
            )
            for i, (chunk, emb) in enumerate(zip(chunks, embeddings)):
                c.execute(
                    "INSERT INTO chunks (document_id, chunk_index, text, embedding) "
                    "VALUES (?, ?, ?, ?)",
                    (doc_id, i, chunk, emb.astype(np.float32).tobytes()),
                )
        return doc_id

    def delete_document(self, document_id: str) -> bool:
        with self._conn() as c:
            c.execute("DELETE FROM chunks WHERE document_id = ?", (document_id,))
            cur = c.execute("DELETE FROM documents WHERE id = ?", (document_id,))
            return cur.rowcount > 0

    def clear_all(self) -> None:
        with self._conn() as c:
            c.execute("DELETE FROM chunks")
            c.execute("DELETE FROM documents")

    # ── reads ───────────────────────────────────────────────────────────
    def list_documents(self) -> list[dict]:
        with self._conn() as c:
            rows = c.execute(
                "SELECT id, name, format, size_bytes, chunk_count, uploaded_at "
                "FROM documents ORDER BY uploaded_at DESC"
            ).fetchall()
        return [dict(r) for r in rows]

    def stats(self) -> dict:
        with self._conn() as c:
            doc_count = c.execute("SELECT COUNT(*) FROM documents").fetchone()[0]
            chunk_count = c.execute("SELECT COUNT(*) FROM chunks").fetchone()[0]
            total_size = c.execute(
                "SELECT COALESCE(SUM(size_bytes), 0) FROM documents"
            ).fetchone()[0]
        return {
            "document_count": doc_count,
            "chunk_count": chunk_count,
            "total_bytes": total_size,
        }

    def search(self, query_embedding: np.ndarray, top_k: int = 3) -> list[dict]:
        """Cosine-similarity search across all chunks.

        Returns the top-k most relevant chunks with full provenance metadata.
        Embeddings are assumed L2-normalised so dot product == cosine sim.
        """
        with self._conn() as c:
            rows = c.execute(
                """
                SELECT chunks.id, chunks.document_id, chunks.chunk_index, chunks.text,
                       chunks.embedding, documents.name AS doc_name,
                       documents.format AS doc_format
                FROM chunks
                JOIN documents ON chunks.document_id = documents.id
                """
            ).fetchall()
        if not rows:
            return []

        emb_matrix = np.stack(
            [np.frombuffer(r["embedding"], dtype=np.float32) for r in rows]
        )
        q = query_embedding.astype(np.float32)
        q_norm = np.linalg.norm(q)
        if q_norm > 0:
            q = q / q_norm
        scores = emb_matrix @ q
        top = np.argsort(scores)[::-1][:top_k]

        return [
            {
                "chunk_id": int(rows[i]["id"]),
                "document_id": rows[i]["document_id"],
                "document_name": rows[i]["doc_name"],
                "document_format": rows[i]["doc_format"],
                "chunk_index": int(rows[i]["chunk_index"]),
                "text": rows[i]["text"],
                "score": float(scores[i]),
            }
            for i in top
        ]