"""BM25 hybrid search algorithm using Qdrant native RRF fusion."""

import logging
from typing import Any

from qdrant_client import models
from qdrant_client.models import FieldCondition, Filter, MatchValue

from nextcloud_mcp_server.config import get_settings
from nextcloud_mcp_server.embedding import get_bm25_service, get_embedding_service
from nextcloud_mcp_server.observability.metrics import record_qdrant_operation
from nextcloud_mcp_server.observability.tracing import trace_operation
from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
from nextcloud_mcp_server.vector.placeholder import get_placeholder_filter
from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client

logger = logging.getLogger(__name__)


class BM25HybridSearchAlgorithm(SearchAlgorithm):
    """
    Hybrid search combining dense semantic vectors with BM25 sparse vectors.

    Uses Qdrant's native Reciprocal Rank Fusion (RRF) to automatically merge
    results from both dense (semantic) and sparse (BM25 keyword) searches.
    This provides the best of both worlds: semantic understanding for conceptual
    queries and precise keyword matching for specific terms, acronyms, and codes.

    The fusion happens efficiently in the database using the prefetch mechanism,
    eliminating the need for application-layer result merging.
    """

    def __init__(self, score_threshold: float = 0.0, fusion: str = "rrf"):
        """
        Initialize BM25 hybrid search algorithm.

        Args:
            score_threshold: Minimum fusion score (0-1, default: 0.0 to allow fusion scoring)
                           Note: Both RRF and DBSF produce normalized scores
            fusion: Fusion algorithm to use: "rrf" (Reciprocal Rank Fusion, default)
                   or "dbsf" (Distribution-Based Score Fusion)

        Raises:
            ValueError: If fusion is not "rrf" or "dbsf"
        """
        if fusion not in ("rrf", "dbsf"):
            raise ValueError(
                f"Invalid fusion algorithm '{fusion}'. Must be 'rrf' or 'dbsf'"
            )

        self.score_threshold = score_threshold
        self.fusion = models.Fusion.RRF if fusion == "rrf" else models.Fusion.DBSF
        self.fusion_name = fusion

    @property
    def name(self) -> str:
        return "bm25_hybrid"

    @property
    def requires_vector_db(self) -> bool:
        return True

    async def search(
        self,
        query: str,
        user_id: str,
        limit: int = 10,
        doc_type: str | None = None,
        **kwargs: Any,
    ) -> list[SearchResult]:
        """
        Execute hybrid search using dense + sparse vectors with native RRF fusion.

        Returns unverified results from Qdrant. Access verification should be
        performed separately at the final output stage using verify_search_results().

        Deduplicates by (doc_id, doc_type, chunk_start_offset, chunk_end_offset)
        to show multiple chunks from the same document while avoiding duplicate chunks.

        Args:
            query: Natural language or keyword search query
            user_id: User ID for filtering
            limit: Maximum results to return
            doc_type: Optional document type filter
            **kwargs: Additional parameters (score_threshold override)

        Returns:
            List of unverified SearchResult objects ranked by RRF fusion score

        Raises:
            McpError: If vector sync is not enabled or search fails
        """
        settings = get_settings()
        score_threshold = kwargs.get("score_threshold", self.score_threshold)

        logger.info(
            f"BM25 hybrid search: query='{query}', user={user_id}, "
            f"limit={limit}, score_threshold={score_threshold}, doc_type={doc_type}, "
            f"fusion={self.fusion_name}"
        )

        # Generate dense embedding for semantic search
        with trace_operation("search.get_embedding_service"):
            embedding_service = get_embedding_service()
        with trace_operation("search.dense_embedding"):
            dense_embedding = await embedding_service.embed(query)
        # Store for reuse by callers (e.g., viz_routes PCA visualization)
        self.query_embedding = dense_embedding
        logger.debug(f"Generated dense embedding (dimension={len(dense_embedding)})")

        # Generate sparse embedding for BM25 keyword search
        with trace_operation("search.get_bm25_service"):
            bm25_service = get_bm25_service()
        with trace_operation("search.sparse_embedding_bm25"):
            sparse_embedding = await bm25_service.encode_async(query)
        logger.debug(
            f"Generated sparse embedding "
            f"({len(sparse_embedding['indices'])} non-zero terms)"
        )

        # Build Qdrant filter
        filter_conditions = [
            get_placeholder_filter(),  # Always exclude placeholders from user-facing queries
            FieldCondition(
                key="user_id",
                match=MatchValue(value=user_id),
            ),
        ]

        # Add doc_type filter if specified
        if doc_type:
            filter_conditions.append(
                FieldCondition(
                    key="doc_type",
                    match=MatchValue(value=doc_type),
                )
            )

        query_filter = Filter(must=filter_conditions)

        # Execute hybrid search with Qdrant native RRF fusion
        with trace_operation("search.get_qdrant_client"):
            qdrant_client = await get_qdrant_client()

        try:
            # Use prefetch to run both dense and sparse searches
            # Qdrant will automatically merge results using RRF
            with trace_operation(
                "search.qdrant_query",
                attributes={"query.limit": limit * 2, "query.fusion": self.fusion_name},
            ):
                search_response = await qdrant_client.query_points(
                    collection_name=settings.get_collection_name(),
                    prefetch=[
                        # Dense semantic search
                        models.Prefetch(
                            query=dense_embedding,
                            using="dense",
                            limit=limit * 2,  # Get extra for deduplication
                            filter=query_filter,
                        ),
                        # Sparse BM25 search
                        models.Prefetch(
                            query=models.SparseVector(
                                indices=sparse_embedding["indices"],
                                values=sparse_embedding["values"],
                            ),
                            using="sparse",
                            limit=limit * 2,  # Get extra for deduplication
                            filter=query_filter,
                        ),
                    ],
                    # Fusion query (RRF or DBSF based on initialization)
                    query=models.FusionQuery(fusion=self.fusion),
                    limit=limit * 2,  # Get extra for deduplication
                    score_threshold=score_threshold,
                    with_payload=True,
                    with_vectors=False,  # Don't return vectors to save bandwidth
                )
            record_qdrant_operation("search", "success")
        except Exception:
            record_qdrant_operation("search", "error")
            raise

        logger.info(
            f"Qdrant {self.fusion_name.upper()} fusion returned {len(search_response.points)} results "
            f"(before deduplication)"
        )

        if search_response.points:
            # Log top 3 fusion scores to help with threshold tuning
            top_scores = [p.score for p in search_response.points[:3]]
            logger.debug(
                f"Top 3 {self.fusion_name.upper()} fusion scores: {top_scores}"
            )

        # Deduplicate by (doc_id, doc_type, chunk_start, chunk_end)
        # This allows multiple chunks from same doc, but removes duplicate chunks
        with trace_operation(
            "search.deduplicate",
            attributes={"dedupe.num_points": len(search_response.points)},
        ):
            seen_chunks = set()
            results = []

            for result in search_response.points:
                # doc_id can be int (notes) or str (files - file paths)
                doc_id = result.payload["doc_id"]
                doc_type = result.payload.get("doc_type", "note")
                chunk_start = result.payload.get("chunk_start_offset")
                chunk_end = result.payload.get("chunk_end_offset")
                chunk_key = (doc_id, doc_type, chunk_start, chunk_end)

                # Skip if we've already seen this exact chunk
                if chunk_key in seen_chunks:
                    continue

                seen_chunks.add(chunk_key)

                # Return unverified results (verification happens at output stage)
                results.append(
                    SearchResult(
                        id=doc_id,
                        doc_type=doc_type,
                        title=result.payload.get("title", "Untitled"),
                        excerpt=result.payload.get("excerpt", ""),
                        score=result.score,  # Fusion score (RRF or DBSF)
                        metadata={
                            "chunk_index": result.payload.get("chunk_index"),
                            "total_chunks": result.payload.get("total_chunks"),
                            "search_method": f"bm25_hybrid_{self.fusion_name}",
                        },
                        chunk_start_offset=result.payload.get("chunk_start_offset"),
                        chunk_end_offset=result.payload.get("chunk_end_offset"),
                        page_number=result.payload.get("page_number"),
                        chunk_index=result.payload.get("chunk_index", 0),
                        total_chunks=result.payload.get("total_chunks", 1),
                        point_id=str(result.id),  # Qdrant point ID for batch retrieval
                    )
                )

                if len(results) >= limit:
                    break

        logger.info(f"Returning {len(results)} unverified results after deduplication")
        if results:
            result_details = [
                f"{r.doc_type}_{r.id} (score={r.score:.3f}, title='{r.title}')"
                for r in results[:5]  # Show top 5
            ]
            logger.debug(f"Top results: {', '.join(result_details)}")

        return results
