mcp-nextcloud/nextcloud_mcp_server/search/semantic.py

"""Semantic search algorithm using vector similarity (Qdrant)."""

import logging
from typing import Any

from qdrant_client.models import FieldCondition, Filter, MatchValue

from nextcloud_mcp_server.config import get_settings
from nextcloud_mcp_server.embedding import get_embedding_service
from nextcloud_mcp_server.observability.metrics import record_qdrant_operation
from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client

logger = logging.getLogger(__name__)


class SemanticSearchAlgorithm(SearchAlgorithm):
    """Semantic search using vector similarity in Qdrant.

    Searches documents by meaning rather than exact keywords using
    768-dimensional embeddings and cosine distance.
    """

    def __init__(self, score_threshold: float = 0.7):
        """Initialize semantic search algorithm.

        Args:
            score_threshold: Minimum similarity score (0-1, default: 0.7)
        """
        self.score_threshold = score_threshold

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

    @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 semantic search using vector similarity.

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

        Args:
            query: Natural language 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 similarity 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"Semantic search: query='{query}', user={user_id}, "
            f"limit={limit}, score_threshold={score_threshold}, doc_type={doc_type}"
        )

        # Generate embedding for query
        embedding_service = get_embedding_service()
        query_embedding = await embedding_service.embed(query)
        logger.debug(
            f"Generated embedding for query (dimension={len(query_embedding)})"
        )

        # Build Qdrant filter
        filter_conditions = [
            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),
                )
            )

        # Search Qdrant
        qdrant_client = await get_qdrant_client()
        try:
            search_response = await qdrant_client.query_points(
                collection_name=settings.get_collection_name(),
                query=query_embedding,
                using="dense",  # Use named dense vector (BM25 hybrid collections)
                query_filter=Filter(must=filter_conditions),
                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 returned {len(search_response.points)} results "
            f"(before deduplication)"
        )

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

        # Deduplicate by (doc_id, doc_type) - multiple chunks per document
        seen_docs = 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")
            doc_key = (doc_id, doc_type)

            # Skip if we've already seen this document
            if doc_key in seen_docs:
                continue

            seen_docs.add(doc_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,
                    metadata={
                        "chunk_index": result.payload.get("chunk_index"),
                        "total_chunks": result.payload.get("total_chunks"),
                    },
                    chunk_start_offset=result.payload.get("chunk_start_offset"),
                    chunk_end_offset=result.payload.get("chunk_end_offset"),
                )
            )

            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