Chris CoutinhoandClaude Opus 4.7
37db82613d
The vector index has always been strictly per-user: every Qdrant payload
carries a `user_id` and the search filter is `user_id == querying_user`.
A file Alice indexed cannot be discovered by Bob even if she has shared
it with him — Bob would have to re-index it under his own user_id to
make it searchable, which means duplicate index entries for every share
recipient.
Switch to ownership-with-ACL-expansion:
- New `nextcloud_mcp_server.search.access_filter` module:
- `list_accessible_owners(sharing_client, user_id)` calls the OCS
Sharing API (`shared_with_me=true`) and returns
`{user_id} ∪ {uid_owner of each share}`. Fails open to `[user_id]`
so a misbehaving Sharing API doesn't black-hole search.
- `build_ownership_filter(user_id, accessible_owners)` returns a
Qdrant `Filter` whose `should` branch matches either the new
`owner_id IN accessible_owners` field or the legacy `user_id` field.
The legacy branch keeps points indexed before this change reachable
without a migration backfill.
- Indexer payload (`vector/processor.py`) now writes `owner_id` alongside
`user_id`. `DocumentTask` gains an optional `owner_id` field; today the
scanner always runs as the owner so the processor falls back to
`user_id`, but the field is plumbed so a future shared-with-me crawler
can set the true owner without reshaping the payload contract.
- `SemanticSearchAlgorithm.search` and `BM25HybridSearchAlgorithm.search`
accept `accessible_owners` via kwargs and use the new ownership filter.
Default behaviour with no kwarg is unchanged (self-only).
- Both user-facing callers — the MCP tool path (`server/semantic.py`) and
the visualization Starlette route (`auth/viz_routes.py`) — compute
`accessible_owners` from the authenticated Nextcloud client before
invoking the search algorithm. Eviction, scanner deletion, placeholder,
and chunk-context paths intentionally keep the legacy `user_id`
semantics (those are "operations on a specific user's records", not
cross-user reads).
- 10 new unit tests in `tests/unit/search/test_access_filter.py` cover
self-only default, owner expansion, dedup, fallback fields, OCS
failure, and the legacy `should`-branch shape.
Pairs with cbcoutinho/astrolabe#89 — together they let an Astrolabe user
find content owners have shared with them without going through any
re-authorization flow or re-indexing.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
249 lines
9.5 KiB
Python
249 lines
9.5 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.access_filter import build_ownership_filter
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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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accessible_owners: list[str] | None = kwargs.get("accessible_owners")
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logger.info(
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"BM25 hybrid search: query='%s', user=%s, limit=%s, score_threshold=%s, doc_type=%s, fusion=%s",
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query,
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user_id,
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limit,
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score_threshold,
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doc_type,
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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("Generated dense embedding (dimension=%s)", 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 = await 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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"Generated sparse embedding (%s non-zero terms)",
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len(sparse_embedding["indices"]),
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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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build_ownership_filter(user_id, accessible_owners),
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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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"Qdrant %s fusion returned %s results (before deduplication)",
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self.fusion_name.upper(),
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len(search_response.points),
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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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"Top 3 %s fusion scores: %s", self.fusion_name.upper(), 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("Returning %s unverified results after deduplication", len(results))
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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("Top results: %s", ", ".join(result_details))
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return results
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