import os
import pickle
from typing import List

import faiss
import numpy as np

from app.services.neon_index_store import NeonIndexStore


class LocalVectorStore:
    def __init__(
        self,
        base_dir: str,
        rag_index_db_url: str = "",
        neon_max_retries: int = 5,
        neon_retry_backoff_sec: float = 1.0,
        neon_connect_timeout_sec: int = 10,
    ) -> None:
        self.base_dir = base_dir
        self.cloud_store = (
            NeonIndexStore(
                rag_index_db_url,
                max_retries=neon_max_retries,
                base_backoff_sec=neon_retry_backoff_sec,
                connect_timeout_sec=neon_connect_timeout_sec,
            )
            if rag_index_db_url
            else None
        )
        self._hydration_error_logged_semesters: set[int] = set()
        self._setup_error_logged = False
        os.makedirs(self.base_dir, exist_ok=True)

        if self.cloud_store:
            try:
                self.cloud_store.ensure_table()
            except Exception as exc:
                if not self._setup_error_logged:
                    print(f"Neon index table setup skipped: {exc}")
                    self._setup_error_logged = True

    def upsert_documents(
        self,
        semester: int,
        course_code: str,
        chunks: List[str],
        embeddings: List[List[float]],
    ) -> None:
        self._ensure_local_semester_data(semester)

        records_path = self._semester_records_path(semester)
        records = self._load_records(records_path)

        records = [r for r in records if r.get("course_code") != course_code]
        for chunk, vector in zip(chunks, embeddings):
            records.append(
                {
                    "course_code": course_code,
                    "chunk": chunk,
                    "embedding": vector,
                }
            )

        self._save_records(records_path, records)
        self._rebuild_faiss_index(semester, records)
        self._sync_semester_to_cloud(semester)

    def search(self, semester: int, query_embedding: List[float], top_k: int = 6) -> List[dict]:
        self._ensure_local_semester_data(semester)

        records_path = self._semester_records_path(semester)
        index_path = self._semester_index_path(semester)
        records = self._load_records(records_path)
        if not records:
            return []

        if not os.path.exists(index_path):
            self._rebuild_faiss_index(semester, records)

        if not os.path.exists(index_path):
            return []

        index = faiss.read_index(index_path)

        q = np.array(query_embedding, dtype=np.float32).reshape(1, -1)
        faiss.normalize_L2(q)

        k = min(top_k, len(records))
        _, indices = index.search(q, k)

        hits = []
        for idx in indices[0].tolist():
            if idx == -1:
                continue
            if 0 <= idx < len(records):
                record = records[idx]
                hits.append(
                    {
                        "course_code": record.get("course_code", ""),
                        "chunk": record.get("chunk", ""),
                    }
                )

        return hits

    def _semester_records_path(self, semester: int) -> str:
        return os.path.join(self.base_dir, f"semester_{semester}.pkl")

    def _semester_index_path(self, semester: int) -> str:
        return os.path.join(self.base_dir, f"semester_{semester}.faiss")

    def _rebuild_faiss_index(self, semester: int, records: List[dict]) -> None:
        index_path = self._semester_index_path(semester)

        if not records:
            if os.path.exists(index_path):
                os.remove(index_path)
            return

        vectors = np.array([r["embedding"] for r in records], dtype=np.float32)
        if vectors.ndim != 2 or vectors.shape[0] == 0:
            return

        faiss.normalize_L2(vectors)
        dim = vectors.shape[1]
        index = faiss.IndexFlatIP(dim)
        index.add(vectors)
        faiss.write_index(index, index_path)

    def _ensure_local_semester_data(self, semester: int) -> None:
        records_path = self._semester_records_path(semester)
        index_path = self._semester_index_path(semester)

        if os.path.exists(records_path) and os.path.exists(index_path):
            return

        if not self.cloud_store:
            return

        try:
            payload = self.cloud_store.load_semester_files(semester)
            if not payload:
                return

            faiss_bytes, records_bytes = payload
            with open(index_path, "wb") as f:
                f.write(faiss_bytes)
            with open(records_path, "wb") as f:
                f.write(records_bytes)
            self._hydration_error_logged_semesters.discard(semester)
        except Exception as exc:
            if semester not in self._hydration_error_logged_semesters:
                print(f"Neon index hydration failed for semester {semester}: {exc}")
                self._hydration_error_logged_semesters.add(semester)

    def _sync_semester_to_cloud(self, semester: int) -> None:
        if not self.cloud_store:
            return

        records_path = self._semester_records_path(semester)
        index_path = self._semester_index_path(semester)

        if not (os.path.exists(records_path) and os.path.exists(index_path)):
            return

        try:
            with open(index_path, "rb") as f:
                faiss_bytes = f.read()
            with open(records_path, "rb") as f:
                records_bytes = f.read()

            self.cloud_store.save_semester_files(semester, faiss_bytes, records_bytes)
        except Exception as exc:
            print(f"Neon index sync failed for semester {semester}: {exc}")

    @staticmethod
    def _load_records(path: str) -> List[dict]:
        if not os.path.exists(path):
            return []
        with open(path, "rb") as f:
            return pickle.load(f)

    @staticmethod
    def _save_records(path: str, data: List[dict]) -> None:
        with open(path, "wb") as f:
            pickle.dump(data, f)