Chris CoutinhoandClaude Opus 4.7
6aba589a6e
Addresses reviewer feedback on PR #773: - Backfill set_payload now uses wait=True to avoid a race where _ensure_keyword_payload_indexes builds the KEYWORD index before fire-and-forget writes have committed, leaving int payloads invisible to filters. - Batch points sharing the same int doc_id into a single set_payload call (one document → many chunks → one round-trip instead of N). - Drop _has_int_doc_id_sample short-circuit. The sample's false-negative window (clean first 256 results, ints further in) is gone; full scroll is the dominant cost on first run anyway. - Simplify _ensure_keyword_payload_indexes: the "already exists" 400 branch was dead code (Qdrant returns 200 on identical re-create); any 400 now logs a warning and continues. - search/context.py: comment the broadened file-type guard. Add explicit not doc_id.isdigit() checks at the top of note/news_item/deck_card branches in _fetch_document_text so malformed payloads surface as warnings instead of being swallowed by the broad except. Also extracts build_search_result_from_point into search/algorithms.py to deduplicate the 71-line payload-extraction loop shared by SemanticSearchAlgorithm and BM25HybridSearchAlgorithm. This fixes SonarQube's quality-gate failure (4.0% new-code duplication, max 3%). Test coverage: - 7 new unit tests for build_search_result_from_point covering missing payload, note/file/deck_card metadata, int doc_id coercion, and metadata_extras merging. - Replace _has_int_doc_id_sample tests with clean-collection no-op and per-batch grouping tests. - Update set_payload assertions from wait=False to wait=True. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
245 lines
9.4 KiB
Python
245 lines
9.4 KiB
Python
"""BM25 hybrid search algorithm using Qdrant native RRF fusion."""
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import logging
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from typing import Any
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from qdrant_client import models
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from qdrant_client.models import FieldCondition, Filter, MatchValue
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from nextcloud_mcp_server.config import get_settings
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from nextcloud_mcp_server.embedding import get_bm25_service, get_embedding_service
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from nextcloud_mcp_server.observability.metrics import record_qdrant_operation
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from nextcloud_mcp_server.observability.tracing import trace_operation
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from nextcloud_mcp_server.search.algorithms import (
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SearchAlgorithm,
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SearchResult,
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build_search_result_from_point,
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)
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from nextcloud_mcp_server.vector.placeholder import get_placeholder_filter
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from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
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logger = logging.getLogger(__name__)
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class BM25HybridSearchAlgorithm(SearchAlgorithm):
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"""
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Hybrid search combining dense semantic vectors with BM25 sparse vectors.
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Uses Qdrant's native Reciprocal Rank Fusion (RRF) to automatically merge
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results from both dense (semantic) and sparse (BM25 keyword) searches.
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This provides the best of both worlds: semantic understanding for conceptual
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queries and precise keyword matching for specific terms, acronyms, and codes.
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The fusion happens efficiently in the database using the prefetch mechanism,
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eliminating the need for application-layer result merging.
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"""
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def __init__(self, score_threshold: float = 0.0, fusion: str = "rrf"):
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"""
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Initialize BM25 hybrid search algorithm.
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Args:
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score_threshold: Minimum fusion score (0-1, default: 0.0 to allow fusion scoring)
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Note: Both RRF and DBSF produce normalized scores
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fusion: Fusion algorithm to use: "rrf" (Reciprocal Rank Fusion, default)
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or "dbsf" (Distribution-Based Score Fusion)
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Raises:
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ValueError: If fusion is not "rrf" or "dbsf"
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"""
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if fusion not in ("rrf", "dbsf"):
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raise ValueError(
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f"Invalid fusion algorithm '{fusion}'. Must be 'rrf' or 'dbsf'"
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)
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self.score_threshold = score_threshold
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self.fusion = models.Fusion.RRF if fusion == "rrf" else models.Fusion.DBSF
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self.fusion_name = fusion
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@property
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def name(self) -> str:
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return "bm25_hybrid"
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@property
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def requires_vector_db(self) -> bool:
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return True
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async def search(
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self,
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query: str,
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user_id: str,
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limit: int = 10,
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doc_type: str | None = None,
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**kwargs: Any,
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) -> list[SearchResult]:
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"""
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Execute hybrid search using dense + sparse vectors with native RRF fusion.
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Returns unverified results from Qdrant. Access verification is
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performed separately at the server tool layer via
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``nextcloud_mcp_server.search.verification.verify_search_results``
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(see ADR-019).
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Deduplicates by (doc_id, doc_type, chunk_start_offset, chunk_end_offset)
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to show multiple chunks from the same document while avoiding duplicate chunks.
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Args:
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query: Natural language or keyword search query
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user_id: User ID for filtering
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limit: Maximum results to return
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doc_type: Optional document type filter
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**kwargs: Additional parameters (score_threshold override)
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Returns:
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List of unverified SearchResult objects ranked by RRF fusion score
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Raises:
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McpError: If vector sync is not enabled or search fails
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"""
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settings = get_settings()
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score_threshold = kwargs.get("score_threshold", self.score_threshold)
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logger.info(
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f"BM25 hybrid search: query='{query}', user={user_id}, "
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f"limit={limit}, score_threshold={score_threshold}, doc_type={doc_type}, "
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f"fusion={self.fusion_name}"
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)
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# Generate dense embedding for semantic search
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with trace_operation("search.get_embedding_service"):
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embedding_service = get_embedding_service()
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with trace_operation("search.dense_embedding"):
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dense_embedding = await embedding_service.embed(query)
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# Store for reuse by callers (e.g., viz_routes PCA visualization)
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self.query_embedding = dense_embedding
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logger.debug(f"Generated dense embedding (dimension={len(dense_embedding)})")
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# Generate sparse embedding for BM25 keyword search
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with trace_operation("search.get_bm25_service"):
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bm25_service = get_bm25_service()
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with trace_operation("search.sparse_embedding_bm25"):
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sparse_embedding = await bm25_service.encode_async(query)
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logger.debug(
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f"Generated sparse embedding "
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f"({len(sparse_embedding['indices'])} non-zero terms)"
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)
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# Build Qdrant filter
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filter_conditions = [
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get_placeholder_filter(), # Always exclude placeholders from user-facing queries
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FieldCondition(
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key="user_id",
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match=MatchValue(value=user_id),
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),
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]
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# Add doc_type filter if specified
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if doc_type:
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filter_conditions.append(
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FieldCondition(
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key="doc_type",
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match=MatchValue(value=doc_type),
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)
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)
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query_filter = Filter(must=filter_conditions)
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# Execute hybrid search with Qdrant native RRF fusion
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with trace_operation("search.get_qdrant_client"):
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qdrant_client = await get_qdrant_client()
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try:
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# Use prefetch to run both dense and sparse searches
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# Qdrant will automatically merge results using RRF
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with trace_operation(
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"search.qdrant_query",
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attributes={"query.limit": limit * 2, "query.fusion": self.fusion_name},
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):
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search_response = await qdrant_client.query_points(
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collection_name=settings.get_collection_name(),
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prefetch=[
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# Dense semantic search
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models.Prefetch(
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query=dense_embedding,
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using="dense",
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limit=limit * 2, # Get extra for deduplication
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filter=query_filter,
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),
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# Sparse BM25 search
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models.Prefetch(
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query=models.SparseVector(
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indices=sparse_embedding["indices"],
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values=sparse_embedding["values"],
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),
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using="sparse",
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limit=limit * 2, # Get extra for deduplication
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filter=query_filter,
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),
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],
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# Fusion query (RRF or DBSF based on initialization)
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query=models.FusionQuery(fusion=self.fusion),
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limit=limit * 2, # Get extra for deduplication
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score_threshold=score_threshold,
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with_payload=True,
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with_vectors=False, # Don't return vectors to save bandwidth
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)
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record_qdrant_operation("search", "success")
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except Exception:
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record_qdrant_operation("search", "error")
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raise
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logger.info(
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f"Qdrant {self.fusion_name.upper()} fusion returned {len(search_response.points)} results "
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f"(before deduplication)"
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)
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if search_response.points:
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# Log top 3 fusion scores to help with threshold tuning
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top_scores = [p.score for p in search_response.points[:3]]
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logger.debug(
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f"Top 3 {self.fusion_name.upper()} fusion scores: {top_scores}"
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)
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# Deduplicate by (doc_id, doc_type, chunk_start, chunk_end)
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# This allows multiple chunks from same doc, but removes duplicate chunks
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with trace_operation(
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"search.deduplicate",
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attributes={"dedupe.num_points": len(search_response.points)},
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):
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seen_chunks: set[tuple[str, str, Any, Any]] = set()
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results: list[SearchResult] = []
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metadata_extras = {
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"search_method": f"bm25_hybrid_{self.fusion_name}",
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}
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for point in search_response.points:
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sr = build_search_result_from_point(
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point, metadata_extras=metadata_extras
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)
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if sr is None:
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continue
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chunk_key = (
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sr.id,
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sr.doc_type,
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sr.chunk_start_offset,
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sr.chunk_end_offset,
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)
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if chunk_key in seen_chunks:
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continue
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seen_chunks.add(chunk_key)
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results.append(sr)
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if len(results) >= limit:
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break
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logger.info(f"Returning {len(results)} unverified results after deduplication")
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if results:
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result_details = [
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f"{r.doc_type}_{r.id} (score={r.score:.3f}, title='{r.title}')"
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for r in results[:5] # Show top 5
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]
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logger.debug(f"Top results: {', '.join(result_details)}")
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return results
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