refactor: Optimize Nextcloud access verification with centralized filtering
Move access verification from individual search algorithms to final output stage, eliminating redundant API calls and improving performance. ## Changes **New:** - `search/verification.py`: Centralized verification using anyio task groups - Deduplicates results by (doc_id, doc_type) before verification - Verifies all unique documents in parallel using structured concurrency - Filters out inaccessible documents in single pass **Modified Search Algorithms:** - `search/semantic.py`: Removed _deduplicate_and_verify() and _verify_document_access() - `search/keyword.py`: Removed _verify_access() and parallel verification - `search/fuzzy.py`: Removed _verify_access() and parallel verification - `search/hybrid.py`: Removed nextcloud_client parameter passing All algorithms now return unverified results from Qdrant payload. **Modified Output Stages:** - `server/semantic.py`: Added verify_search_results() call after search - `auth/viz_routes.py`: Added verify_search_results() call after search Both endpoints now verify access once at final stage with deduplication. ## Performance Impact **Before:** - Hybrid mode (limit=10): 30 API calls (10 per algorithm × 3 algorithms) - Single algorithm: 10-20 API calls (with verification buffer) **After:** - Hybrid mode (limit=10): 10 API calls (deduplicated verification) - Single algorithm: 10 API calls (deduplicated verification) **Performance Gain:** 3x reduction in API calls for hybrid search ## Architecture Benefits - **Separation of concerns**: Algorithms handle scoring, output stage handles security - **Deduplication**: Each document verified exactly once - **Parallel execution**: All verifications run concurrently via anyio task groups - **Consistency**: Same verification logic across MCP tools and viz endpoints 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Chris Coutinho
@@ -450,43 +450,39 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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)
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# Execute search (supports cross-app when doc_types=None)
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# Get unverified results with buffer for filtering
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all_results = []
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if doc_types is None or len(doc_types) == 0:
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# Cross-app search - search all indexed types
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search_results = await search_algo.search(
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unverified_results = await search_algo.search(
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query=query,
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user_id=username,
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limit=limit,
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limit=limit * 2, # Buffer for verification filtering
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doc_type=None, # Search all types
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nextcloud_client=nextcloud_client,
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score_threshold=score_threshold,
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)
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elif len(doc_types) == 1:
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# Single document type
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search_results = await search_algo.search(
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query=query,
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user_id=username,
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limit=limit,
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doc_type=doc_types[0],
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nextcloud_client=nextcloud_client,
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score_threshold=score_threshold,
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)
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all_results.extend(unverified_results)
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else:
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# Multiple document types - search each and combine
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all_results = []
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# Search each document type and combine
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for doc_type in doc_types:
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results = await search_algo.search(
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unverified_results = await search_algo.search(
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query=query,
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user_id=username,
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limit=limit * 2, # Get extra per type
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limit=limit * 2, # Buffer for verification filtering
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doc_type=doc_type,
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nextcloud_client=nextcloud_client,
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score_threshold=score_threshold,
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)
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all_results.extend(results)
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# Sort by score and limit
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all_results.extend(unverified_results)
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# Sort by score before verification
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all_results.sort(key=lambda r: r.score, reverse=True)
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search_results = all_results[:limit]
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# Verify access for all results (deduplicates and filters)
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from nextcloud_mcp_server.search.verification import verify_search_results
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verified_results = await verify_search_results(
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all_results, nextcloud_client
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)
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search_results = verified_results[:limit]
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if not search_results:
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return JSONResponse(
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@@ -3,15 +3,10 @@
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import logging
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from typing import Any
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from httpx import HTTPStatusError
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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.search.algorithms import (
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NextcloudClientProtocol,
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SearchAlgorithm,
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SearchResult,
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)
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from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
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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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@@ -46,25 +41,23 @@ class FuzzySearchAlgorithm(SearchAlgorithm):
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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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nextcloud_client: NextcloudClientProtocol | None = None,
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**kwargs: Any,
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) -> list[SearchResult]:
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"""Execute fuzzy search using character overlap on Qdrant payload.
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Queries Qdrant for all indexed documents, then scores based on character
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overlap in title and excerpt fields. Only verifies access with Nextcloud
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at the end for security.
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overlap in title and excerpt fields. Returns unverified results - access
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verification should be performed separately at the final output stage.
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Args:
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query: 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 (None = all types)
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nextcloud_client: NextcloudClient for access verification (optional)
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**kwargs: Additional parameters (threshold override)
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Returns:
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List of SearchResult objects ranked by character overlap score
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List of unverified SearchResult objects ranked by character overlap score
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"""
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settings = get_settings()
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threshold = kwargs.get("threshold", self.threshold)
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@@ -157,66 +150,23 @@ class FuzzySearchAlgorithm(SearchAlgorithm):
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# Sort by score (descending) and limit
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scored_results.sort(key=lambda x: x["score"], reverse=True)
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top_results = scored_results[: limit * 2] # Get extra for access verification
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top_results = scored_results[:limit]
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# Verify access with Nextcloud (optional, for security)
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# Parallelize verification to avoid sequential HTTP calls
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# Return unverified results (verification happens at output stage)
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final_results = []
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if nextcloud_client:
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from asyncio import gather
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# Create verification coroutines for all top results
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verification_coros = [
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self._verify_access(
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nextcloud_client, result["doc_id"], result["doc_type"]
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for result in top_results:
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final_results.append(
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SearchResult(
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id=result["doc_id"],
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doc_type=result["doc_type"],
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title=result["title"],
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excerpt=result["excerpt"],
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score=result["score"],
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metadata={"match_location": result["match_location"]},
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)
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for result in top_results
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]
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# Execute all verifications in parallel
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# return_exceptions=True prevents one failure from canceling others
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verification_results = await gather(
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*verification_coros, return_exceptions=True
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)
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# Build final results from verified documents
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for result, verified in zip(top_results, verification_results):
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# Skip if verification failed or raised exception
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if isinstance(verified, Exception) or verified is None:
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continue
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final_results.append(
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SearchResult(
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id=result["doc_id"],
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doc_type=result["doc_type"],
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title=result["title"],
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excerpt=result["excerpt"],
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score=result["score"],
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metadata={
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**verified.get("metadata", {}),
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"match_location": result["match_location"],
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},
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)
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)
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# Stop once we have enough results
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if len(final_results) >= limit:
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break
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else:
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# No verification, return results directly
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for result in top_results[:limit]:
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final_results.append(
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SearchResult(
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id=result["doc_id"],
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doc_type=result["doc_type"],
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title=result["title"],
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excerpt=result["excerpt"],
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score=result["score"],
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metadata={"match_location": result["match_location"]},
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)
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)
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logger.info(f"Fuzzy search returned {len(final_results)} matching documents")
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logger.info(f"Fuzzy search returned {len(final_results)} unverified results")
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if final_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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@@ -226,45 +176,6 @@ class FuzzySearchAlgorithm(SearchAlgorithm):
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return final_results
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async def _verify_access(
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self, nextcloud_client: NextcloudClientProtocol, doc_id: int, doc_type: str
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) -> dict[str, Any] | None:
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"""Verify user has access to a document via Nextcloud API.
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Args:
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nextcloud_client: Client for API access
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doc_id: Document ID
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doc_type: Document type
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Returns:
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Dict with metadata if access verified, None otherwise
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"""
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try:
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if doc_type == "note":
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note = await nextcloud_client.notes.get_note(doc_id)
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return {
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"metadata": {
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"category": note.get("category", ""),
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"modified": note.get("modified"),
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}
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}
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else:
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logger.debug(
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f"Skipping verification for {doc_type} {doc_id} (not implemented)"
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)
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return {"metadata": {}}
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except HTTPStatusError as e:
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if e.response.status_code in (403, 404):
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logger.debug(
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f"Access denied for {doc_type} {doc_id}: {e.response.status_code}"
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)
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return None
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else:
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logger.warning(
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f"Error verifying {doc_type} {doc_id}: {e.response.status_code}"
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)
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return None
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def _calculate_char_overlap(self, query: str, text: str) -> float:
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"""Calculate character overlap ratio between query and text.
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@@ -5,11 +5,7 @@ import logging
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from collections import defaultdict
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from typing import Any
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from nextcloud_mcp_server.search.algorithms import (
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NextcloudClientProtocol,
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SearchAlgorithm,
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SearchResult,
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)
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from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
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from nextcloud_mcp_server.search.fuzzy import FuzzySearchAlgorithm
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from nextcloud_mcp_server.search.keyword import KeywordSearchAlgorithm
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from nextcloud_mcp_server.search.semantic import SemanticSearchAlgorithm
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@@ -85,24 +81,22 @@ class HybridSearchAlgorithm(SearchAlgorithm):
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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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nextcloud_client: NextcloudClientProtocol | None = None,
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**kwargs: Any,
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) -> list[SearchResult]:
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"""Execute hybrid search using RRF to combine algorithms.
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Returns unverified results from combined algorithms. Access verification
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should be performed separately at the final output stage.
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Args:
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query: 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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nextcloud_client: NextcloudClient for document access
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**kwargs: Additional parameters passed to sub-algorithms
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Returns:
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List of SearchResult objects ranked by RRF combined score
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Raises:
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ValueError: If nextcloud_client not provided (needed for keyword/fuzzy)
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List of unverified SearchResult objects ranked by RRF combined score
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"""
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logger.info(
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f"Hybrid search: query='{query}', user={user_id}, limit={limit}, "
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@@ -116,29 +110,19 @@ class HybridSearchAlgorithm(SearchAlgorithm):
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if self.semantic_weight > 0:
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tasks.append(
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self.semantic.search(
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query, user_id, limit * 2, doc_type, nextcloud_client, **kwargs
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)
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self.semantic.search(query, user_id, limit * 2, doc_type, **kwargs)
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)
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algo_names.append("semantic")
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if self.keyword_weight > 0:
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if not nextcloud_client:
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raise ValueError("Hybrid search with keyword requires nextcloud_client")
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tasks.append(
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self.keyword.search(
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query, user_id, limit * 2, doc_type, nextcloud_client, **kwargs
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)
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self.keyword.search(query, user_id, limit * 2, doc_type, **kwargs)
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)
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algo_names.append("keyword")
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if self.fuzzy_weight > 0:
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if not nextcloud_client:
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raise ValueError("Hybrid search with fuzzy requires nextcloud_client")
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tasks.append(
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self.fuzzy.search(
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query, user_id, limit * 2, doc_type, nextcloud_client, **kwargs
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)
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self.fuzzy.search(query, user_id, limit * 2, doc_type, **kwargs)
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)
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algo_names.append("fuzzy")
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@@ -3,15 +3,10 @@
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import logging
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from typing import Any
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from httpx import HTTPStatusError
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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.search.algorithms import (
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NextcloudClientProtocol,
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SearchAlgorithm,
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SearchResult,
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)
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from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
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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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@@ -40,25 +35,23 @@ class KeywordSearchAlgorithm(SearchAlgorithm):
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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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nextcloud_client: NextcloudClientProtocol | None = None,
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**kwargs: Any,
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) -> list[SearchResult]:
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"""Execute keyword search using token matching on Qdrant payload.
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Queries Qdrant for all indexed documents, then scores based on token
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matches in title and excerpt fields. Only verifies access with Nextcloud
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at the end for security.
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matches in title and excerpt fields. Returns unverified results - access
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verification should be performed separately at the final output stage.
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Args:
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query: Search query to tokenize and match
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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 (None = all types)
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nextcloud_client: NextcloudClient for access verification (optional)
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**kwargs: Additional parameters (unused)
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Returns:
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List of SearchResult objects ranked by keyword match score
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List of unverified SearchResult objects ranked by keyword match score
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"""
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settings = get_settings()
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@@ -153,63 +146,23 @@ class KeywordSearchAlgorithm(SearchAlgorithm):
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# Sort by score (descending) and limit
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scored_results.sort(key=lambda x: x["score"], reverse=True)
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top_results = scored_results[: limit * 2] # Get extra for access verification
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top_results = scored_results[:limit]
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# Verify access with Nextcloud (optional, for security)
|
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# Parallelize verification to avoid sequential HTTP calls
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# Return unverified results (verification happens at output stage)
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final_results = []
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if nextcloud_client:
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from asyncio import gather
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# Create verification coroutines for all top results
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verification_coros = [
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self._verify_access(
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nextcloud_client, result["doc_id"], result["doc_type"]
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for result in top_results:
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final_results.append(
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SearchResult(
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id=result["doc_id"],
|
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doc_type=result["doc_type"],
|
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title=result["title"],
|
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excerpt=result["excerpt"],
|
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score=result["score"],
|
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metadata={},
|
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)
|
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for result in top_results
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]
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|
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# Execute all verifications in parallel
|
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# return_exceptions=True prevents one failure from canceling others
|
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verification_results = await gather(
|
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*verification_coros, return_exceptions=True
|
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)
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|
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# Build final results from verified documents
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for result, verified in zip(top_results, verification_results):
|
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# Skip if verification failed or raised exception
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if isinstance(verified, Exception) or verified is None:
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continue
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final_results.append(
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SearchResult(
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id=result["doc_id"],
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doc_type=result["doc_type"],
|
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title=result["title"],
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excerpt=result["excerpt"],
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score=result["score"],
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metadata=verified.get("metadata", {}),
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)
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)
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# Stop once we have enough results
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if len(final_results) >= limit:
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break
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else:
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# No verification, return results directly
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for result in top_results[:limit]:
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final_results.append(
|
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SearchResult(
|
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id=result["doc_id"],
|
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doc_type=result["doc_type"],
|
||||
title=result["title"],
|
||||
excerpt=result["excerpt"],
|
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score=result["score"],
|
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metadata={},
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)
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)
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logger.info(f"Keyword search returned {len(final_results)} matching documents")
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logger.info(f"Keyword search returned {len(final_results)} unverified results")
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if final_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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@@ -219,46 +172,6 @@ class KeywordSearchAlgorithm(SearchAlgorithm):
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return final_results
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|
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async def _verify_access(
|
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self, nextcloud_client: NextcloudClientProtocol, doc_id: int, doc_type: str
|
||||
) -> dict[str, Any] | None:
|
||||
"""Verify user has access to a document via Nextcloud API.
|
||||
|
||||
Args:
|
||||
nextcloud_client: Client for API access
|
||||
doc_id: Document ID
|
||||
doc_type: Document type
|
||||
|
||||
Returns:
|
||||
Dict with metadata if access verified, None otherwise
|
||||
"""
|
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try:
|
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if doc_type == "note":
|
||||
note = await nextcloud_client.notes.get_note(doc_id)
|
||||
return {
|
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"metadata": {
|
||||
"category": note.get("category", ""),
|
||||
"modified": note.get("modified"),
|
||||
}
|
||||
}
|
||||
# Future: Add verification for other document types
|
||||
else:
|
||||
logger.debug(
|
||||
f"Skipping verification for {doc_type} {doc_id} (not implemented)"
|
||||
)
|
||||
return {"metadata": {}}
|
||||
except HTTPStatusError as e:
|
||||
if e.response.status_code in (403, 404):
|
||||
logger.debug(
|
||||
f"Access denied for {doc_type} {doc_id}: {e.response.status_code}"
|
||||
)
|
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return None
|
||||
else:
|
||||
logger.warning(
|
||||
f"Error verifying {doc_type} {doc_id}: {e.response.status_code}"
|
||||
)
|
||||
return None
|
||||
|
||||
def _process_query(self, query: str) -> list[str]:
|
||||
"""Tokenize and normalize query.
|
||||
|
||||
|
||||
@@ -3,17 +3,12 @@
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from httpx import HTTPStatusError
|
||||
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 (
|
||||
NextcloudClientProtocol,
|
||||
SearchAlgorithm,
|
||||
SearchResult,
|
||||
)
|
||||
from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
|
||||
from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -48,21 +43,22 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
|
||||
user_id: str,
|
||||
limit: int = 10,
|
||||
doc_type: str | None = None,
|
||||
nextcloud_client: NextcloudClientProtocol | 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 (currently only "note" supported)
|
||||
nextcloud_client: NextcloudClient for access verification
|
||||
doc_type: Optional document type filter
|
||||
**kwargs: Additional parameters (score_threshold override)
|
||||
|
||||
Returns:
|
||||
List of SearchResult objects ranked by similarity score
|
||||
List of unverified SearchResult objects ranked by similarity score
|
||||
|
||||
Raises:
|
||||
McpError: If vector sync is not enabled or search fails
|
||||
@@ -118,7 +114,7 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
|
||||
|
||||
logger.info(
|
||||
f"Qdrant returned {len(search_response.points)} results "
|
||||
f"(before deduplication and access verification)"
|
||||
f"(before deduplication)"
|
||||
)
|
||||
|
||||
if search_response.points:
|
||||
@@ -126,47 +122,11 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
|
||||
top_scores = [p.score for p in search_response.points[:3]]
|
||||
logger.debug(f"Top 3 similarity scores: {top_scores}")
|
||||
|
||||
# Deduplicate by document ID (multiple chunks per document)
|
||||
results = await self._deduplicate_and_verify(
|
||||
search_response.points, limit, nextcloud_client
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Returning {len(results)} results after deduplication and access verification"
|
||||
)
|
||||
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
|
||||
|
||||
async def _deduplicate_and_verify(
|
||||
self,
|
||||
points: list[Any],
|
||||
limit: int,
|
||||
nextcloud_client: NextcloudClientProtocol | None,
|
||||
) -> list[SearchResult]:
|
||||
"""Deduplicate results by (doc_id, doc_type) and verify access.
|
||||
|
||||
Supports multiple document types with dispatch to appropriate client methods.
|
||||
Deduplication is now by (doc_id, doc_type) tuple to handle cases where
|
||||
the same ID might exist across different document types.
|
||||
|
||||
Args:
|
||||
points: Qdrant search results
|
||||
limit: Maximum results to return
|
||||
nextcloud_client: NextcloudClient for access verification (optional)
|
||||
|
||||
Returns:
|
||||
List of SearchResult objects
|
||||
"""
|
||||
seen_docs = set() # Track (doc_id, doc_type) tuples
|
||||
# Deduplicate by (doc_id, doc_type) - multiple chunks per document
|
||||
seen_docs = set()
|
||||
results = []
|
||||
|
||||
for result in points:
|
||||
for result in search_response.points:
|
||||
doc_id = int(result.payload["doc_id"])
|
||||
doc_type = result.payload.get("doc_type", "note")
|
||||
doc_key = (doc_id, doc_type)
|
||||
@@ -177,99 +137,30 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
|
||||
|
||||
seen_docs.add(doc_key)
|
||||
|
||||
# Verify access via Nextcloud API if client provided
|
||||
# Dispatch to appropriate client based on doc_type
|
||||
verified_result = None
|
||||
|
||||
if nextcloud_client:
|
||||
verified_result = await self._verify_document_access(
|
||||
nextcloud_client, doc_id, doc_type, result
|
||||
)
|
||||
|
||||
if verified_result:
|
||||
results.append(verified_result)
|
||||
elif not nextcloud_client:
|
||||
# No access verification, return result directly
|
||||
results.append(
|
||||
SearchResult(
|
||||
id=doc_id,
|
||||
doc_type=doc_type,
|
||||
title=result.payload["title"],
|
||||
excerpt=result.payload["excerpt"],
|
||||
score=result.score,
|
||||
metadata={
|
||||
"chunk_index": result.payload.get("chunk_index"),
|
||||
"total_chunks": result.payload.get("total_chunks"),
|
||||
},
|
||||
)
|
||||
# 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"),
|
||||
},
|
||||
)
|
||||
)
|
||||
|
||||
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
|
||||
|
||||
async def _verify_document_access(
|
||||
self,
|
||||
nextcloud_client: NextcloudClientProtocol,
|
||||
doc_id: int,
|
||||
doc_type: str,
|
||||
qdrant_result: Any,
|
||||
) -> SearchResult | None:
|
||||
"""Verify user has access to a document via Nextcloud API.
|
||||
|
||||
Dispatches to appropriate client method based on document type.
|
||||
|
||||
Args:
|
||||
nextcloud_client: Client for API access
|
||||
doc_id: Document ID
|
||||
doc_type: Document type ("note", "file", "calendar", etc.)
|
||||
qdrant_result: Original Qdrant search result
|
||||
|
||||
Returns:
|
||||
SearchResult if access verified, None if access denied or error
|
||||
"""
|
||||
try:
|
||||
if doc_type == "note":
|
||||
note = await nextcloud_client.notes.get_note(doc_id)
|
||||
return SearchResult(
|
||||
id=doc_id,
|
||||
doc_type="note",
|
||||
title=qdrant_result.payload["title"],
|
||||
excerpt=qdrant_result.payload["excerpt"],
|
||||
score=qdrant_result.score,
|
||||
metadata={
|
||||
"category": note.get("category", ""),
|
||||
"chunk_index": qdrant_result.payload["chunk_index"],
|
||||
"total_chunks": qdrant_result.payload["total_chunks"],
|
||||
},
|
||||
)
|
||||
elif doc_type == "file":
|
||||
# Future: verify file access when files are indexed
|
||||
logger.info(
|
||||
f"File {doc_id} found in search but file verification not yet implemented"
|
||||
)
|
||||
return None
|
||||
elif doc_type == "calendar":
|
||||
# Future: verify calendar access when calendar events are indexed
|
||||
logger.info(
|
||||
f"Calendar event {doc_id} found in search but calendar verification not yet implemented"
|
||||
)
|
||||
return None
|
||||
else:
|
||||
logger.warning(
|
||||
f"Unknown document type '{doc_type}' for doc_id {doc_id}"
|
||||
)
|
||||
return None
|
||||
|
||||
except HTTPStatusError as e:
|
||||
if e.response.status_code in (403, 404):
|
||||
# User lost access or document deleted
|
||||
logger.debug(f"Skipping {doc_type} {doc_id}: {e.response.status_code}")
|
||||
return None
|
||||
else:
|
||||
# Log other errors but continue processing
|
||||
logger.warning(
|
||||
f"Error verifying access to {doc_type} {doc_id}: {e.response.status_code}"
|
||||
)
|
||||
return None
|
||||
|
||||
@@ -0,0 +1,122 @@
|
||||
"""Access verification for search results.
|
||||
|
||||
This module provides centralized verification of Nextcloud access permissions
|
||||
for search results. Verification happens at the final output stage (MCP tool/viz endpoint)
|
||||
rather than within individual search algorithms, preventing redundant API calls.
|
||||
|
||||
Key benefits:
|
||||
- Deduplication: Each document verified exactly once (even in hybrid mode)
|
||||
- Parallel execution: All verifications run concurrently via anyio task groups
|
||||
- Separation of concerns: Algorithms handle scoring, this module handles security
|
||||
"""
|
||||
|
||||
import logging
|
||||
from dataclasses import replace
|
||||
from typing import Protocol
|
||||
|
||||
import anyio
|
||||
|
||||
from nextcloud_mcp_server.search.algorithms import SearchResult
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class NextcloudClientProtocol(Protocol):
|
||||
"""Protocol for Nextcloud client with app-specific access."""
|
||||
|
||||
@property
|
||||
def notes(self):
|
||||
"""Notes client for accessing notes API."""
|
||||
...
|
||||
|
||||
|
||||
async def verify_search_results(
|
||||
results: list[SearchResult],
|
||||
nextcloud_client: NextcloudClientProtocol,
|
||||
) -> list[SearchResult]:
|
||||
"""
|
||||
Verify Nextcloud access for search results.
|
||||
|
||||
Deduplicates by (doc_id, doc_type), verifies in parallel using anyio task groups,
|
||||
and filters out inaccessible documents. Maintains original result ordering.
|
||||
|
||||
Args:
|
||||
results: Unverified search results from Qdrant
|
||||
nextcloud_client: Nextcloud client for access checks
|
||||
|
||||
Returns:
|
||||
Verified and accessible results (same order as input)
|
||||
|
||||
Example:
|
||||
>>> unverified = await search_algo.search(query="test", limit=10)
|
||||
>>> verified = await verify_search_results(unverified, client)
|
||||
>>> # verified contains only documents user can access
|
||||
"""
|
||||
# Deduplicate by (doc_id, doc_type) while preserving order
|
||||
# This is critical for hybrid search where same doc may appear in multiple algorithm results
|
||||
seen = set()
|
||||
unique_results = []
|
||||
for result in results:
|
||||
key = (result.id, result.doc_type)
|
||||
if key not in seen:
|
||||
seen.add(key)
|
||||
unique_results.append(result)
|
||||
|
||||
if not unique_results:
|
||||
return []
|
||||
|
||||
logger.debug(
|
||||
f"Verifying access for {len(unique_results)} unique documents "
|
||||
f"(from {len(results)} total results)"
|
||||
)
|
||||
|
||||
# Verify all unique documents in parallel using anyio task group
|
||||
# Use list to maintain order (index-based storage)
|
||||
verified_results = [None] * len(unique_results)
|
||||
|
||||
async def verify_one(index: int, result: SearchResult):
|
||||
"""
|
||||
Verify a single document and store result at index.
|
||||
|
||||
Args:
|
||||
index: Position in verified_results list
|
||||
result: Search result to verify
|
||||
"""
|
||||
try:
|
||||
if result.doc_type == "note":
|
||||
# Fetch note to verify access and get fresh metadata
|
||||
note = await nextcloud_client.notes.get_note(result.id)
|
||||
# Update metadata with fresh data from Nextcloud
|
||||
updated_metadata = {**(result.metadata or {}), **note}
|
||||
verified_results[index] = replace(result, metadata=updated_metadata)
|
||||
# TODO: Add verification for other doc types (calendar, deck, file, etc.)
|
||||
else:
|
||||
# For now, assume other types are accessible
|
||||
# In production, add proper verification for each type
|
||||
logger.debug(
|
||||
f"No verification implemented for doc_type={result.doc_type}, "
|
||||
"assuming accessible"
|
||||
)
|
||||
verified_results[index] = result
|
||||
|
||||
except Exception as e:
|
||||
# Document is inaccessible (403, 404, or other error)
|
||||
# Log at debug level since this is expected for filtered results
|
||||
logger.debug(f"Document {result.doc_type}/{result.id} not accessible: {e}")
|
||||
verified_results[index] = None
|
||||
|
||||
# Run all verifications in parallel using anyio task group
|
||||
# This provides structured concurrency with automatic cancellation on errors
|
||||
async with anyio.create_task_group() as tg:
|
||||
for idx, result in enumerate(unique_results):
|
||||
tg.start_soon(verify_one, idx, result)
|
||||
|
||||
# Filter out None (inaccessible) and return verified results
|
||||
accessible = [r for r in verified_results if r is not None]
|
||||
|
||||
logger.debug(
|
||||
f"Verification complete: {len(accessible)} accessible, "
|
||||
f"{len(unique_results) - len(accessible)} filtered out"
|
||||
)
|
||||
|
||||
return accessible
|
||||
@@ -128,35 +128,36 @@ def configure_semantic_tools(mcp: FastMCP):
|
||||
|
||||
if doc_types is None:
|
||||
# Cross-app search: search all indexed types
|
||||
# Pass None to search algorithm to let it query Qdrant for available types
|
||||
search_results = await search_algo.search(
|
||||
# Get unverified results from Qdrant
|
||||
unverified_results = await search_algo.search(
|
||||
query=query,
|
||||
user_id=username,
|
||||
limit=limit,
|
||||
limit=limit * 2, # Get extra for access filtering
|
||||
doc_type=None, # Signal to search all types
|
||||
nextcloud_client=client,
|
||||
score_threshold=score_threshold,
|
||||
)
|
||||
all_results.extend(search_results)
|
||||
all_results.extend(unverified_results)
|
||||
else:
|
||||
# Search specific document types
|
||||
# For each requested type, execute search and combine results
|
||||
for dtype in doc_types:
|
||||
search_results = await search_algo.search(
|
||||
unverified_results = await search_algo.search(
|
||||
query=query,
|
||||
user_id=username,
|
||||
limit=limit * 2, # Get extra for combining
|
||||
limit=limit * 2, # Get extra for combining and filtering
|
||||
doc_type=dtype,
|
||||
nextcloud_client=client,
|
||||
score_threshold=score_threshold,
|
||||
)
|
||||
all_results.extend(search_results)
|
||||
all_results.extend(unverified_results)
|
||||
|
||||
# Sort combined results by score and limit
|
||||
# Sort combined results by score
|
||||
all_results.sort(key=lambda r: r.score, reverse=True)
|
||||
all_results = all_results[:limit]
|
||||
|
||||
search_results = all_results
|
||||
# Verify access for all results (deduplicates and filters)
|
||||
from nextcloud_mcp_server.search.verification import verify_search_results
|
||||
|
||||
verified_results = await verify_search_results(all_results, client)
|
||||
search_results = verified_results[:limit] # Final limit after verification
|
||||
|
||||
# Convert SearchResult objects to SemanticSearchResult for response
|
||||
results = []
|
||||
|
||||
Reference in New Issue
Block a user