Reorganize README to promote Smithery as the fastest way to get started:
- Quick Start now features Smithery one-click deployment
- Docker instructions moved to separate "Docker (Self-Hosted)" section
- Added note about Smithery's stateless mode limitations
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Co-Authored-By: Claude <noreply@anthropic.com>
ADR-016: For container runtime deployment, Smithery does not auto-generate
the .well-known/mcp-config endpoint like it does for Python CLI runtime.
Changes:
- Remove [tool.smithery] from pyproject.toml (not used in container mode)
- Remove smithery_server.py (Python CLI runtime specific)
- Add .well-known/mcp-config endpoint to return JSON Schema config
- Add SmitheryConfigMiddleware to extract config from URL query params
- Use ContextVar to pass session config to tool handlers
The container runtime passes config as URL query parameters to /mcp:
GET /mcp?nextcloud_url=...&username=...&app_password=...
Tested:
- All 164 unit tests passing
- Docker container builds successfully
- .well-known/mcp-config returns valid JSON Schema
- Health endpoints working
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Co-Authored-By: Claude <noreply@anthropic.com>
Adds support for Smithery hosted deployment with stateless operation:
- Add DeploymentMode enum with SELF_HOSTED and SMITHERY_STATELESS modes
- Add get_deployment_mode() to detect mode from SMITHERY_DEPLOYMENT env var
- Update get_client() to create per-request clients from session config
- Add conditional tool registration (skip semantic search in Smithery mode)
- Add conditional /app admin UI mounting (skip in Smithery mode)
- Create smithery.yaml with configSchema for user credentials
- Create Dockerfile.smithery for minimal stateless container
- Create smithery_main.py entrypoint for Smithery deployment
In Smithery mode:
- Users provide nextcloud_url, username, app_password via session config
- Each request creates a fresh NextcloudClient (no state between requests)
- Semantic search tools are disabled (no vector database)
- Admin UI (/app) is disabled (no webhooks, vector viz)
All existing self-hosted functionality remains unchanged.
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Co-Authored-By: Claude <noreply@anthropic.com>
Add architecture decision record for supporting Smithery-hosted MCP
server in a stateless mode for multi-user public Nextcloud instances.
Key decisions:
- New SMITHERY_STATELESS deployment mode alongside SELF_HOSTED
- Session-based configuration (nextcloud_url, username, app_password)
- Feature subset excluding semantic search and background sync
- Admin UI (/app) excluded in Smithery mode
- Per-request client creation from session config
This enables users to try the MCP server without self-hosting
infrastructure while supporting multiple Nextcloud instances.
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Co-Authored-By: Claude <noreply@anthropic.com>
Drawing directly with ImageDraw on RGBA mode doesn't blend alpha
properly. Use Image.alpha_composite() with a transparent overlay
to achieve correct semi-transparent highlight fills.
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Co-Authored-By: Claude <noreply@anthropic.com>
Replace parallel per-page extraction with single to_markdown(page_chunks=True)
call. This is more efficient as pymupdf4llm can optimize internally for
full-document processing instead of making N separate calls for N pages.
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Co-Authored-By: Claude <noreply@anthropic.com>
Phase 1 - PDF Highlighting Optimization:
- Render each page ONCE instead of once per chunk (N chunks = 1 render, not N)
- Use PIL to draw bounding boxes on copied base images (fast) instead of
re-rendering page via pymupdf (slow)
- Add _find_chunk_bbox() to extract bbox without modifying page
Phase 2 - Parallel Page Extraction:
- Use anyio task group with run_sync() for parallel page extraction
- Each page extracted in separate thread via anyio.to_thread.run_sync()
- Event loop stays responsive during extraction
- Remove obsolete _process_sync() method
Expected improvement: 30-50% reduction in total PDF processing time.
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Co-Authored-By: Claude <noreply@anthropic.com>
Previously, pymupdf4llm.to_markdown() was called twice - once in
PyMuPDFProcessor during indexing and again in PDFHighlighter during
visualization. Different image path lengths caused different character
offsets, leading to highlighted pages not matching their chunks.
Also fixed issue where all chunks on the same page showed all highlights
instead of just their own highlight. Now restores original page contents
between chunks using xref stream caching.
Changes:
- Add PDFHighlighter class requiring pre-computed page_boundaries and
full_text from document processor (no fallback extraction)
- Pass pre-computed data from processor to highlighter
- Extract page-relative portion of chunk text for cross-page chunks
- Add bounding box highlighting using text anchor search
- Run highlight generation in parallel with embedding/BM25
- Cache and restore page contents to isolate highlights per chunk
Results: Highlighting success rate improved from 51% to 95% (121/128).
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Co-Authored-By: Claude <noreply@anthropic.com>
Implements optional context expansion for semantic search results that
fetches adjacent chunks (N-1 and N+1) from Qdrant to provide before/after
context. Removes configurable chunk overlap (default 200 chars) to avoid
duplicate text appearing in both context and excerpt.
Key changes:
- Add include_context and context_chars parameters to nc_semantic_search
and nc_semantic_search_answer tools
- Implement Qdrant cache fast path for chunk retrieval (avoids re-fetching
and re-parsing documents, especially important for PDFs)
- Add _get_chunk_by_index_from_qdrant() to fetch adjacent chunks
- Remove chunk overlap from before_context (last N chars) and after_context
(first N chars) to prevent duplicate text
- Fetch context in parallel with anyio.Semaphore (max 20 concurrent)
- Pass through page_number from SearchResult to SemanticSearchResult
- Remove document-level deduplication (keep chunk-level dedup from algorithm)
Context expansion is opt-in via include_context=true parameter. When enabled:
- Populates has_context_expansion, marked_text, before_context, after_context
- Adds truncation flags when context exceeds context_chars limit
- Falls back to document fetch for legacy data with truncated excerpts
Related: nextcloud_mcp_server/search/context.py:87-382,
nextcloud_mcp_server/server/semantic.py:161-255
The processor was not setting is_placeholder field when writing real
document chunks to Qdrant. This caused the placeholder filter to exclude
all documents (since None != False), resulting in 0 search results.
Now explicitly sets is_placeholder: False in payload when writing real
indexed chunks, allowing search filters to correctly distinguish between
placeholders and real documents.
- Switch from sequential loop to /api/embed batch endpoint
- Use 'input' array parameter instead of individual 'prompt' requests
- Process in chunks of 32 to avoid quality degradation (issue #6262)
- Reduces HTTP overhead: 128 texts = 4 requests instead of 128
- Maintains backward compatibility with embed() for single embeddings
Ref: ollama/ollama#6262
- Changed from 2x (120s) to 5x (300s) scan interval
- Large PDFs take 3-4 minutes to process, need longer threshold
- Prevents premature requeuing of in-flight documents
- Only requeue documents if placeholder is older than 2x scan interval (120s default)
- Prevents scanner from immediately requeuing in-flight documents
- Fixes issue where PDFs were being reprocessed every 60 seconds
- Staleness check applied to both notes and files scanning logic
Qdrant validation rejects None for sparse vectors in named vector dicts.
Use models.SparseVector(indices=[], values=[]) instead to create valid
empty sparse vectors for placeholder points.
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Co-Authored-By: Claude <noreply@anthropic.com>
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>
When vector visualization search returns zero results, the code was returning
query_coords: null, which caused JavaScript error "can't access property 0,
queryCoords is null" when the frontend tried to access the array.
Changed to return empty array [] to match expected type and prevent crash.
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Co-Authored-By: Claude <noreply@anthropic.com>
This commit fixes two critical issues with PDF processing:
1. **Text extraction mismatch (context expansion bug)**:
- Indexing used pymupdf4llm.to_markdown() producing markdown text
- Context expansion used page.get_text() producing plain text
- Different text formats caused character offset misalignment
- Search would find correct chunk, but expansion showed wrong section
- Fixed by making context.py use pymupdf4llm.to_markdown() consistently
2. **Diagnostic logging for page number assignment**:
- Added logging to verify page_boundaries exist in metadata
- Added logging to verify assign_page_numbers() assigns values
- Helps diagnose why page numbers show as null in search results
3. **mime_type storage bug**:
- Fixed incorrect field reference in processor.py:405
- Was using file_metadata.get("content_type", "")
- Should use content_type from WebDAV response
Changes:
- nextcloud_mcp_server/search/context.py: Use pymupdf4llm.to_markdown()
for PDF text extraction to match indexing method
- nextcloud_mcp_server/vector/processor.py: Add diagnostic logging for
page boundaries and assignment, fix mime_type storage
- tests/unit/client/test_webdav.py: Fix import sorting
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Co-Authored-By: Claude <noreply@anthropic.com>
- algorithms.py: Revert SearchResult.id to int (all docs use int IDs now)
- semantic.py: Revert SemanticSearchResult.id to int, remove Union import
- viz_routes.py: Remove str() conversion when querying doc_id from Qdrant
- viz_routes.py: Convert doc_id from query param to int in chunk context
Fixes vector visualization which was collapsing all chunks to a single
point because Qdrant queries were failing to match doc_id (string vs int).
- scanner.py: Use file_info['id'] as doc_id instead of file_path
- scanner.py: Pass file_path in DocumentTask for content retrieval
- processor.py: Store file_path in Qdrant payload for later lookup
- context.py: Add _get_file_path_from_qdrant() to resolve file_id → file_path
- context.py: Update get_chunk_with_context() to handle file ID resolution
This makes the system resilient to file renames since file IDs are stable
identifiers in Nextcloud, while file paths can change.
Notes are indexed as "{title}\n\n{content}" in processor.py but were
being retrieved as just content during chunk expansion, causing
chunk_start_offset and chunk_end_offset to be misaligned.
This fix reconstructs the full content structure when fetching notes
for chunk expansion, ensuring the displayed chunks match the excerpts
shown in search results.
Fixes chunk/excerpt mismatch reported in vector visualization.
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Co-Authored-By: Claude <noreply@anthropic.com>
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>
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>
smithery-ai[bot]