refactor: Move background tasks to server lifespan and deprecate SSE transport
- Move scanner/processor tasks from FastMCP session lifespan to Starlette server lifespan (correct architecture: background tasks run once at server level, not per-session) - Change default CLI transport from SSE to streamable-http - Remove SSE transport option from CLI (SSE is deprecated) - Remove SSE client session factory from test fixtures - Add tracing instrumentation to BM25 hybrid search operations for better observability 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Chris Coutinho
@@ -9,6 +9,7 @@ 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 SearchAlgorithm, SearchResult
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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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@@ -99,15 +100,19 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
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)
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# Generate dense embedding for semantic search
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embedding_service = get_embedding_service()
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dense_embedding = await embedding_service.embed(query)
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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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bm25_service = get_bm25_service()
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sparse_embedding = await bm25_service.encode_async(query)
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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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@@ -134,38 +139,44 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
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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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qdrant_client = await get_qdrant_client()
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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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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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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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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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# 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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@@ -185,47 +196,51 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
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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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seen_chunks = set()
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results = []
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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()
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results = []
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for result in search_response.points:
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# doc_id can be int (notes) or str (files - file paths)
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doc_id = result.payload["doc_id"]
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doc_type = result.payload.get("doc_type", "note")
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chunk_start = result.payload.get("chunk_start_offset")
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chunk_end = result.payload.get("chunk_end_offset")
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chunk_key = (doc_id, doc_type, chunk_start, chunk_end)
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for result in search_response.points:
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# doc_id can be int (notes) or str (files - file paths)
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doc_id = result.payload["doc_id"]
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doc_type = result.payload.get("doc_type", "note")
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chunk_start = result.payload.get("chunk_start_offset")
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chunk_end = result.payload.get("chunk_end_offset")
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chunk_key = (doc_id, doc_type, chunk_start, chunk_end)
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# Skip if we've already seen this exact chunk
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if chunk_key in seen_chunks:
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continue
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# Skip if we've already seen this exact chunk
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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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seen_chunks.add(chunk_key)
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# Return unverified results (verification happens at output stage)
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results.append(
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SearchResult(
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id=doc_id,
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doc_type=doc_type,
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title=result.payload.get("title", "Untitled"),
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excerpt=result.payload.get("excerpt", ""),
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score=result.score, # Fusion score (RRF or DBSF)
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metadata={
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"chunk_index": result.payload.get("chunk_index"),
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"total_chunks": result.payload.get("total_chunks"),
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"search_method": f"bm25_hybrid_{self.fusion_name}",
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},
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chunk_start_offset=result.payload.get("chunk_start_offset"),
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chunk_end_offset=result.payload.get("chunk_end_offset"),
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page_number=result.payload.get("page_number"),
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chunk_index=result.payload.get("chunk_index", 0),
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total_chunks=result.payload.get("total_chunks", 1),
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point_id=str(result.id), # Qdrant point ID for batch retrieval
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# Return unverified results (verification happens at output stage)
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results.append(
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SearchResult(
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id=doc_id,
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doc_type=doc_type,
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title=result.payload.get("title", "Untitled"),
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excerpt=result.payload.get("excerpt", ""),
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score=result.score, # Fusion score (RRF or DBSF)
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metadata={
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"chunk_index": result.payload.get("chunk_index"),
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"total_chunks": result.payload.get("total_chunks"),
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"search_method": f"bm25_hybrid_{self.fusion_name}",
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},
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chunk_start_offset=result.payload.get("chunk_start_offset"),
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chunk_end_offset=result.payload.get("chunk_end_offset"),
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page_number=result.payload.get("page_number"),
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chunk_index=result.payload.get("chunk_index", 0),
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total_chunks=result.payload.get("total_chunks", 1),
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point_id=str(result.id), # Qdrant point ID for batch retrieval
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)
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)
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)
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if len(results) >= limit:
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break
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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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