"""BM25 sparse retriever backed by the existing Milvus collection.

Falls back to no-op if rank_bm25 or jieba is not installed.
The index is built lazily on first query and can be refreshed after
new documents are ingested by calling refresh().
"""

from __future__ import annotations

import logging
import threading
from typing import TYPE_CHECKING

from app.domain.retrieval import RetrievedChunk

if TYPE_CHECKING:
    from app.infrastructure.vectorstore.milvus_vector_index import MilvusVectorIndex

logger = logging.getLogger(__name__)

try:
    from rank_bm25 import BM25Okapi
    import jieba

    jieba.setLogLevel(logging.WARNING)
    _BM25_AVAILABLE = True
except ImportError:
    _BM25_AVAILABLE = False
    logger.warning("rank_bm25 or jieba not installed – BM25 retrieval disabled. Run: pip install rank-bm25 jieba")


def _tokenize(text: str) -> list[str]:
    """Tokenize Chinese/mixed text with jieba."""
    return list(jieba.cut(text))


class BM25Retriever:
    """Sparse BM25 retriever that indexes chunks stored in Milvus.

    Thread-safe: the index is protected by a lock so concurrent requests
    during the initial build do not race.
    """

    def __init__(self, vector_index: "MilvusVectorIndex") -> None:
        self._vector_index = vector_index
        self._lock = threading.Lock()
        self._index: "BM25Okapi | None" = None
        self._chunks: list[RetrievedChunk] = []

    @property
    def available(self) -> bool:
        return _BM25_AVAILABLE

    def _load_all_chunks(self) -> list[RetrievedChunk]:
        """Fetch all chunk records from Milvus (no vectors needed)."""
        try:
            rows = self._vector_index.collection.query(
                expr='doc_id != ""',
                output_fields=["id", "doc_id", "doc_name", "content", "section_title", "page_number"],
                limit=16384,
            )
        except Exception:
            logger.exception("BM25Retriever: failed to fetch chunks from Milvus")
            return []
        return [
            RetrievedChunk(
                chunk_id=str(row.get("id", "")),
                doc_id=str(row.get("doc_id", "")),
                doc_name=str(row.get("doc_name", "")),
                content=str(row.get("content", "")),
                score=0.0,
                section_title=str(row.get("section_title", "")),
                page_number=int(row.get("page_number") or 0),
                metadata={},
            )
            for row in rows
            if row.get("content")
        ]

    def _ensure_built(self) -> None:
        if self._index is not None:
            return
        with self._lock:
            if self._index is not None:
                return
            self._build()

    def _build(self) -> None:
        logger.info("BM25Retriever: building index …")
        chunks = self._load_all_chunks()
        if not chunks:
            logger.warning("BM25Retriever: no chunks found, index is empty")
            self._chunks = []
            self._index = BM25Okapi([[]])
            return
        tokenized = [_tokenize(c.content) for c in chunks]
        self._chunks = chunks
        self._index = BM25Okapi(tokenized)
        logger.info("BM25Retriever: index built with %d chunks", len(chunks))

    def refresh(self) -> None:
        """Rebuild the index (call after new documents are ingested)."""
        with self._lock:
            self._index = None
            self._chunks = []
            self._build()

    def retrieve(self, query: str, top_k: int, filters: str | None = None) -> list[RetrievedChunk]:
        """Return top_k chunks ranked by BM25 score."""
        if not _BM25_AVAILABLE:
            return []
        self._ensure_built()
        if not self._chunks:
            return []

        tokens = _tokenize(query)
        scores = self._index.get_scores(tokens)  # type: ignore[union-attr]

        # Pair and sort descending by score
        ranked = sorted(
            ((float(scores[i]), self._chunks[i]) for i in range(len(self._chunks))),
            key=lambda x: x[0],
            reverse=True,
        )

        results: list[RetrievedChunk] = []
        for score, chunk in ranked[: top_k * 2]:
            if score <= 0:
                break
            # Apply simple regulation_type filter if provided
            if filters and chunk.metadata.get("regulation_type"):
                types = [t.strip() for t in filters.split(",")]
                if chunk.metadata.get("regulation_type") not in types:
                    continue
            results.append(
                RetrievedChunk(
                    chunk_id=chunk.chunk_id,
                    doc_id=chunk.doc_id,
                    doc_name=chunk.doc_name,
                    content=chunk.content,
                    score=score,
                    section_title=chunk.section_title,
                    page_number=chunk.page_number,
                    metadata=chunk.metadata,
                )
            )
            if len(results) >= top_k:
                break
        return results