Commit Graph
13 Commits
Author SHA1 Message Date
Chris CoutinhoandClaude Opus 4.7 1ed8362f78 refactor(search): address PR #750 round 11 review feedback
Three minor fixes from the round-11 review on PR #750:

- bm25_hybrid.py:209 — Comment said `doc_id` is `int (notes) or str (files)`,
  which is backwards. Notes, news_items, and deck_cards are stored as `str`
  (scanner.py:241, 666, 867); files are stored as `int` (scanner.py:425).
  Updated to point readers at scanner.py as the source of truth.

- verification.py:338 — Lowered the News-API 403/404 log line from `info`
  to `debug`. The News app being uninstalled or disabled is a predictable
  operational state (matching the other verifiers' debug-on-not-found
  paths), so this should not generate operator-dashboard noise. Transient
  errors immediately below stay at `warning` because they're unexpected.

- semantic.py:809 — `nc_get_vector_sync_status` was reading
  `document_receive_stream` via `getattr(..., None)`, but the attribute is
  guaranteed-defined on both `AppContext` and `OAuthAppContext` (as a
  field with `None` default). The defensive `getattr` masked typos that
  the eviction_task_group access at semantic.py:197-199 deliberately
  surfaces. Switched to direct access; the `if … is None:` value-check
  below is preserved (the attribute can legitimately be None before sync
  starts).

Items deliberately deferred (with rationale in the plan file):
- News verifier semaphore-hold during get_items (reviewer: "not required
  here, just worth tracking"; ADR already lists follow-ups).
- Hardcoded 2× over-fetch / VERIFICATION_OVERFETCH (TODO already in code).
- Integration test for the real Qdrant eviction filter (reviewer marked
  low-priority; type-preservation chain is unit-tested).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-01 23:00:42 +02:00
Chris CoutinhoandClaude Opus 4.7 7784ec02d7 refactor(search): address PR #750 review feedback
- Cap all_results to limit*2 after sort in the per-doc_types branch of
  nc_semantic_search to bound over-verification (was unbounded N-types).
- Switch BatchVerifier from (client, doc_ids, user_id) to (client, results,
  semaphore). Verifiers now read file paths and deck board/stack ids from
  SearchResult.metadata instead of doing fresh Qdrant scrolls — eliminates
  one duplicate round-trip per file/deck-card verification.
- Bound per-id verification concurrency with a shared anyio.Semaphore
  (default 20, matching server/semantic.py context-expansion convention).
  Prevents httpx pool exhaustion / rate limiting on large search pages.
- Propagate stack_id from Qdrant payload to SearchResult.metadata in both
  bm25_hybrid.py and semantic.py (board_id was already propagated).
- Drop now-unused _resolve_file_path / _resolve_deck_metadata helpers.
- Drop redundant int(d) in requested predicate from _verify_news_items.
- Rewrite eviction comment to be honest about inline (not background)
  execution and the resulting latency coupling.
- ADR-019 status: Proposed -> Accepted.
- Add news property to NextcloudClientProtocol.
- Widen SearchResult.id and SemanticSearchResult.id to int | str to match
  BatchVerifier signature and document support for future string-id types.
- Flip openWorldHint to True on nc_semantic_search_answer (it calls into
  Nextcloud via nc_semantic_search).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-01 08:22:28 +02:00
Chris CoutinhoandClaude Opus 4.7 d90e793d19 feat(search): verify-on-read for semantic search results (ADR-019)
The vector index lags Nextcloud (5-min webhook cron + scanner interval),
producing ghost records for deleted/unshared documents until the next
reconciliation. Verify each unique document against Nextcloud at query
time, drop inaccessible results, and lazily evict the corresponding
Qdrant points.

Per-doc_type batch verifiers: notes/files/deck cards run concurrently
per id; news items use a single fetch + intersect to avoid the per-item
fetch-all amplification. Transient errors fail open (keep result, log
warning) — only definitive 4xx drops. Multiple chunks of the same doc
collapse to one verification call.

Wired into nc_semantic_search before the limit trim and before context
expansion. nc_semantic_search_answer's per-note re-fetch retained as a
sub-second race guard since verification now happens upstream.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-01 07:45:01 +02:00
Chris CoutinhoandClaude Sonnet 4.5 85db90a2df feat(search): add file_path metadata and chunk offsets to search results
Changes:
- Add file_path to metadata in semantic and BM25 hybrid search algorithms
  for PDF viewer integration (search/semantic.py:161-163, search/bm25_hybrid.py:230-232)
- Include chunk_start_offset, chunk_end_offset, page_number, and page_count
  in search results for rich chunk display (api/management.py:981-1004)
- Add point_id field to SearchResult for batch retrieval (models/semantic.py)
- Fix type narrowing for chunk context API parameters (api/management.py:1102-1111)
- Fix None-safety in doc_types discovery (search/algorithms.py:114)

This enables the Astroglobe UI to display PDF pages at the correct
location for matched chunks.

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Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2025-12-18 00:01:29 +01:00
Chris CoutinhoandClaude Sonnet 4.5 20404cf3f2 feat(vector): add Deck card vector search with visualization support
Adds comprehensive vector search support for Nextcloud Deck cards,
including semantic search indexing, chunk preview in the vector viz UI,
and proper deep linking to cards.

**Vector Search Indexing**
- Add deck_card scanning in scanner.py (scan_deck_cards function)
- Index cards from non-archived, non-deleted boards
- Store metadata: board_id, board_title, stack_id, stack_title, card_type, duedate, owner
- Content structure: title + "\n\n" + description (matches indexing format)
- Incremental sync based on lastModified timestamp
- Deletion tracking with grace period

**Vector Visualization Support**
- Add deck_card handler in context.py for chunk preview expansion
- Include board_id in search result metadata (bm25_hybrid.py, semantic.py)
- Expose metadata in viz_routes.py JSON responses
- Update vector-viz.js to construct proper Deck URLs: /apps/deck/board/{board_id}/card/{card_id}
- Update vector_viz.html filter label from "Deck" to "Deck Cards"

**Bug Fixes**
- Skip soft-deleted boards (deletedAt > 0) to prevent 403 Forbidden errors
- Applies to scanner, processor, and context expansion code paths
- Deck API returns deleted boards but rejects stack access with 403

**Testing**
- Add integration tests in test_deck_vector_search.py:
  - test_deck_card_semantic_search: Filtered search with doc_type="deck_card"
  - test_deck_card_appears_in_cross_app_search: Cross-app search includes deck cards
  - test_deck_card_chunk_context: Chunk context fetching for viz preview

**Documentation**
- Update README.md: Add Deck cards to semantic search feature list
- Update semantic-search-architecture.md: Document deck_card support
- Update nc_semantic_search tool documentation

**Type Safety**
- Fix type narrowing for page_boundaries (could be None) using cast()
- Fix scanner.py payload None check for type safety

Resolves vector search for Deck cards across indexing, search, and visualization.

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Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2025-12-13 23:51:18 +01:00
Chris CoutinhoandClaude Sonnet 4.5 3f06e2ee77 fix: resolve all type checking errors (8 errors fixed)
Fixed 8 type checker errors across the codebase:

- vector/scanner.py: Handle None scroll results with null-safe iteration
- search/{bm25_hybrid,semantic}.py: Add None checks for result.payload
- auth/{unified_verifier,webhook_routes}.py: Assert non-None auth credentials
- client/webdav.py: Add None checks before int() conversions
- providers/openai.py: Assert embedding_model is not None
- search/algorithms.py: Explicitly type doc_types set and cast values
- observability/logging_config.py: Match parent class signature (log_data)

Also fixed test_create_tag_creates_system_tag to match WebDAV implementation
(was testing OCS API endpoint, now tests correct WebDAV endpoint with
Content-Location header).

Type checker: 0 errors (down from 8), 20 warnings (ignored)
Tests: All 192 unit tests passing

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Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2025-12-08 01:09:02 +01:00
Chris CoutinhoandClaude fafeaf3d83 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

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-23 04:02:30 +01:00
Chris CoutinhoandClaude b0612cfa0f perf: Optimize vector viz search performance
- Replace sequential Qdrant scroll calls with batch retrieve
  (50 HTTP requests → 1 request, ~50x faster vector fetch)

- Add point_id to SearchResult to enable batch retrieval by Qdrant point ID

- Reuse query embedding from search algorithm in viz_routes
  (eliminates redundant embedding call, saves ~30ms)

- Make BM25 encode() async with thread pool to avoid blocking event loop
  (~4.4s was blocking, now properly async)

- Run PCA computation in thread pool to avoid blocking event loop
  (~1.2s was blocking, now properly async)

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-22 19:47:43 +01:00
Chris CoutinhoandClaude 13b2d0048c feat: Implement Qdrant placeholder state management
Introduces a placeholder-based state tracking system to prevent duplicate
document processing during the gap between scanner queuing and processor
completion.

**Key Changes:**

1. **Placeholder Helper Functions** (`vector/placeholder.py`):
   - `write_placeholder_point()` - Creates zero-vector placeholder when queuing
   - `query_document_metadata()` - Queries for existing entry (placeholder or real)
   - `delete_placeholder_point()` - Removes placeholder before writing real vectors
   - `get_placeholder_filter()` - Filters placeholders from user-facing queries

2. **Scanner Updates** (`vector/scanner.py`):
   - Replace `indexed_at` comparison with `modified_at` comparison
   - Write placeholder before queuing each document
   - Query per-document metadata instead of bulk-querying indexed_at
   - Fixes bug where files were resubmitted every scan cycle

3. **Processor Updates** (`vector/processor.py`):
   - Delete placeholder before upserting real vectors
   - Ensures no duplicate points in Qdrant

4. **Query Filters** (all search files):
   - Add `get_placeholder_filter()` to all user-facing queries
   - Ensures placeholders never appear in search results or visualizations
   - Applied to: bm25_hybrid.py, semantic.py, viz_routes.py, algorithms.py

**Architecture:**
- Placeholders use zero vectors with dimension from embedding service
- Payload includes `is_placeholder: True` flag for filtering
- Status field tracks: "pending", "processing", "completed", "failed"
- Deterministic UUIDs using uuid5 for consistent point IDs

**Impact:**
- Eliminates duplicate processing of same documents
- Fixes race condition where long-running documents get queued multiple times
- Prevents scanner from resubmitting files every scan cycle
- Maintains clean separation between in-flight and indexed documents

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-20 15:04:00 +01:00
Chris CoutinhoandClaude 327d843f64 feat: Implement per-chunk vector visualization with context expansion
Major improvements to vector visualization page:
- Refactor PCA to display individual chunks instead of averaged documents
- Add context expansion module for fetching surrounding text from notes and PDFs
- Update deduplication to use (doc_id, doc_type, chunk_start, chunk_end) keys
- Fix Alpine.js rendering with chunk-specific keys including offsets
- Refactor authentication helper to return NextcloudClient for better reuse
- Add async context manager support to NextcloudClient

Technical details:
- viz_routes.py: Fetch specific chunk vectors instead of averaging per document
- context.py: New module supporting both notes and PDF text extraction via PyMuPDF
- search algorithms: Extract page_number, chunk_index, total_chunks from Qdrant
- vector-viz.js/html: Use chunk positions in expansion tracking keys

This enables users to see which specific chunks match their query
and view them with surrounding context in the PCA visualization.

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-20 11:22:20 +01:00
Chris CoutinhoandClaude b8010270c1 fix: Add async/await, PDF metadata, and type safety fixes
This commit addresses multiple issues with async operations, PDF metadata
extraction, and type safety in document processing and search.

## Async/Await Fixes
- processor.py:259 - Added await for chunker.chunk_text(content)
- processor.py:270 - Added await for bm25_service.encode_batch(chunk_texts)
- tests/unit/test_document_chunker.py - Converted all 12 test methods to async

## PDF Metadata Enhancement
- pymupdf.py:143 - Added file_size metadata extraction
- pymupdf.py:145-206 - Refactored to extract text page-by-page
  - Manually loop through pages instead of using page_chunks=True
  - Generate page_boundaries metadata for precise page tracking
  - Works around pymupdf.layout.activate() breaking page_chunks=True
- processor.py:32-66 - Added assign_page_numbers() helper function
  - Assigns page numbers to chunks based on overlap with page boundaries
  - Handles chunks spanning multiple pages
- processor.py:298-300 - Call assign_page_numbers() for PDF files

## Type Safety Fixes
- bm25_hybrid.py:184 - Removed int() conversion of doc_id
- semantic.py:131 - Removed int() conversion of doc_id
- viz_routes.py:275 - Removed int() conversion of doc_id
- Added comments documenting that doc_id can be int (notes) or str (file paths)

## Testing
- All 18 tests passing (12 unit + 6 integration)
- No type errors in modified files
- Container logs show successful processing
- Vector viz searches working correctly

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-20 02:37:07 +01:00
Chris CoutinhoandClaude 3464b21845 fix: Relax SearchResult validation to support DBSF fusion scores > 1.0
Fix false-positive validation error where DBSF (Distribution-Based Score
Fusion) correctly produces scores > 1.0 but SearchResult validation
incorrectly rejected them.

**Root Cause**: SearchResult.__post_init__() enforced scores in [0.0, 1.0]
range, but DBSF sums normalized scores from multiple retrieval systems
(dense semantic + sparse BM25), resulting in scores like 1.55 when both
systems strongly agree a document is relevant.

**Changes**:
- Relaxed validation to allow any score ≥ 0.0 (algorithms.py:147-157)
- Updated SearchResult and SemanticSearchResult documentation to explain
  score ranges for RRF ([0.0, 1.0]) vs DBSF (unbounded)
- Added comprehensive test coverage for both fusion methods
- Added DBSF fusion option to vector visualization UI
- Updated viz routes and vizApp() to support fusion parameter selection

**Testing**: All 157 unit tests pass, type checking passes, ruff passes

Fixes error: "Configuration error: Score must be between 0.0 and 1.0, got 1.1528953"

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-17 06:32:30 +01:00
Chris CoutinhoandClaude 6fe5596c13 feat: Implement BM25 hybrid search with native Qdrant RRF fusion
Replace custom keyword/fuzzy search algorithms with industry-standard BM25
sparse vectors, combined with dense semantic vectors using Qdrant's native
Reciprocal Rank Fusion (RRF). This consolidates search architecture and
improves relevance for both semantic and keyword queries.

Key changes:
- Add fastembed dependency for BM25 sparse vector generation
- Update Qdrant collection schema to support named vectors (dense + sparse)
- Create BM25SparseEmbeddingProvider using FastEmbed's Qdrant/bm25 model
- Implement BM25HybridSearchAlgorithm with native Qdrant RRF prefetch
- Update document processor to generate both dense and sparse embeddings
- Simplify nc_semantic_search() tool to use BM25 hybrid only
- Remove legacy keyword.py, fuzzy.py, and custom hybrid.py (736 lines)
- Update ADR-014 with implementation notes and test results

Benefits:
- Consolidated architecture (single Qdrant database)
- Native database-level RRF fusion (more efficient)
- Industry-standard BM25 (replaces brittle custom keyword search)
- Better relevance across semantic and keyword queries
- Simplified codebase (-285 net lines)

Tests: All 125 tests passing (118 unit, 7 integration)

Implements ADR-014: Replace Custom Keyword Search with BM25 Hybrid Search

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-16 06:59:44 +01:00