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
@@ -22,6 +22,7 @@ from starlette.requests import Request
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from starlette.responses import HTMLResponse, JSONResponse
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from nextcloud_mcp_server.config import get_settings
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from nextcloud_mcp_server.observability.tracing import trace_operation
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from nextcloud_mcp_server.search import (
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BM25HybridSearchAlgorithm,
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SemanticSearchAlgorithm,
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@@ -139,7 +140,10 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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_get_authenticated_client_for_userinfo,
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)
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async with await _get_authenticated_client_for_userinfo(request) as nc_client: # noqa: F841
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with trace_operation("vector_viz.get_auth_client"):
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auth_client_ctx = await _get_authenticated_client_for_userinfo(request)
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async with auth_client_ctx as nc_client: # noqa: F841
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# Create search algorithm (no client needed - verification removed)
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if algorithm == "semantic":
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search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold)
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@@ -159,24 +163,40 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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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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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, # Buffer for verification filtering
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doc_type=None, # Search all types
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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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# Search each document type and combine
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for doc_type in doc_types:
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with trace_operation(
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"vector_viz.search_execute",
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attributes={
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"search.algorithm": algorithm,
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"search.limit": limit * 2,
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"search.doc_type": "all",
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},
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):
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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, # Buffer for verification filtering
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doc_type=doc_type,
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doc_type=None, # Search all types
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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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# Search each document type and combine
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for doc_type in doc_types:
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with trace_operation(
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"vector_viz.search_execute",
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attributes={
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"search.algorithm": algorithm,
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"search.limit": limit * 2,
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"search.doc_type": doc_type,
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},
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):
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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, # Buffer for verification filtering
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doc_type=doc_type,
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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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# Sort by score before verification
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all_results.sort(key=lambda r: r.score, reverse=True)
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@@ -190,22 +210,26 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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# Store original scores and normalize for visualization
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# (best result = 1.0, worst result = 0.0 within THIS result set)
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# This makes visual encoding meaningful regardless of RRF normalization
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if search_results:
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scores = [r.score for r in search_results]
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min_score, max_score = min(scores), max(scores)
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score_range = max_score - min_score if max_score > min_score else 1.0
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with trace_operation(
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"vector_viz.score_normalize",
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attributes={"normalize.num_results": len(search_results)},
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):
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if search_results:
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scores = [r.score for r in search_results]
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min_score, max_score = min(scores), max(scores)
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score_range = max_score - min_score if max_score > min_score else 1.0
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logger.info(
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f"Normalizing scores for viz: original range [{min_score:.3f}, {max_score:.3f}] "
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f"→ [0.0, 1.0]"
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)
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logger.info(
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f"Normalizing scores for viz: original range [{min_score:.3f}, {max_score:.3f}] "
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f"→ [0.0, 1.0]"
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)
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# Store original score and rescale to 0-1 for visualization
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for r in search_results:
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# Store original score before normalization
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r.original_score = r.score
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# Rescale for visual encoding
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r.score = (r.score - min_score) / score_range
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# Store original score and rescale to 0-1 for visualization
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for r in search_results:
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# Store original score before normalization
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r.original_score = r.score
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# Rescale for visual encoding
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r.score = (r.score - min_score) / score_range
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if not search_results:
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return JSONResponse(
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@@ -220,7 +244,9 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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# Fetch vectors for specific matching chunks from Qdrant using batch retrieve
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vector_fetch_start = time.perf_counter()
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qdrant_client = await get_qdrant_client()
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with trace_operation("vector_viz.get_qdrant_client"):
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qdrant_client = await get_qdrant_client()
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chunk_vectors_map = {} # Map (doc_id, chunk_start, chunk_end) -> vector
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@@ -231,12 +257,16 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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if point_ids:
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# Single batch retrieve call instead of N sequential scroll calls
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# This is ~50x faster for 50 results (1 HTTP request vs 50)
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points_response = await qdrant_client.retrieve(
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collection_name=settings.get_collection_name(),
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ids=point_ids,
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with_vectors=["dense"],
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with_payload=["doc_id", "chunk_start_offset", "chunk_end_offset"],
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)
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with trace_operation(
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"vector_viz.vector_retrieve",
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attributes={"retrieve.num_points": len(point_ids)},
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):
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points_response = await qdrant_client.retrieve(
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collection_name=settings.get_collection_name(),
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ids=point_ids,
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with_vectors=["dense"],
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with_payload=["doc_id", "chunk_start_offset", "chunk_end_offset"],
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)
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# Build chunk_vectors_map from batch response
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for point in points_response:
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@@ -367,9 +397,16 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
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import anyio
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coords_3d, pca = await anyio.to_thread.run_sync( # type: ignore[attr-defined]
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lambda: _compute_pca(all_vectors_normalized)
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)
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with trace_operation(
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"vector_viz.pca_compute",
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attributes={
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"pca.num_vectors": len(all_vectors_normalized),
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"pca.embedding_dim": embedding_dim,
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},
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):
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coords_3d, pca = await anyio.to_thread.run_sync( # type: ignore[attr-defined]
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lambda: _compute_pca(all_vectors_normalized)
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)
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pca_duration = time.perf_counter() - pca_start
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# After fit, these attributes are guaranteed to be set
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