Implements Alembic for managing token storage database schema versions.
Migrations run automatically on startup with full backward compatibility.
**Changes:**
- Add Alembic dependency (1.14.0+) and SQLAlchemy (auto-installed)
- Create migration infrastructure in alembic/ directory
- Add initial migration (001) capturing current schema
- Modify RefreshTokenStorage.initialize() to run migrations via anyio
- Add CLI commands: db upgrade, current, history, downgrade, migrate
- Add comprehensive migration documentation
**Backward Compatibility:**
- Pre-Alembic databases automatically stamped with revision 001
- No schema changes for existing databases
- Automatic upgrade on first startup after update
**Migration Strategy:**
Three scenarios handled:
1. New database → Run migrations from scratch
2. Pre-Alembic database → Stamp with 001 (no changes)
3. Alembic-managed → Upgrade to latest
**Architecture:**
- Uses anyio.to_thread.run_sync() for structured concurrency
- Alembic env.py runs with anyio.run() in worker thread
- SQLite-friendly migration patterns documented
- No ThreadPoolExecutor needed (anyio handles it)
**CLI Usage:**
```bash
nextcloud-mcp-server db upgrade # Upgrade to latest
nextcloud-mcp-server db current # Show version
nextcloud-mcp-server db history # View changelog
nextcloud-mcp-server db downgrade # Rollback (with confirmation)
nextcloud-mcp-server db migrate "description" # Create migration
```
**Testing:**
- All 13 webhook storage tests pass
- New/pre-Alembic database scenarios validated
- anyio integration tested
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Add full integration for the Nextcloud News (RSS/Atom reader) app:
- Add NewsClient with complete CRUD operations for folders, feeds, and items
- Add 8 read-only MCP tools for listing/getting folders, feeds, items
- Add Pydantic models for News entities with camelCase alias support
- Add vector sync support for starred + unread items
- Add HTML to Markdown converter using markdownify for better embeddings
- Add Docker post-install hook to enable News app
- Add 25 unit tests for NewsClient API methods
Vector sync indexes starred and unread items, providing a balanced approach
that captures important (starred) and current (unread) content without
indexing the entire article history.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Adds OpenAI provider to the unified provider architecture (ADR-015),
supporting:
- OpenAI API (api.openai.com)
- GitHub Models API (models.github.ai/inference)
- OpenAI-compatible endpoints (Fireworks, Together, etc.)
Features:
- Embedding support with text-embedding-3-small/large models
- Text generation via chat completions API
- Automatic retry with exponential backoff for rate limits
- Provider auto-detection in registry (priority after Bedrock)
Environment variables:
- OPENAI_API_KEY: API key (required)
- OPENAI_BASE_URL: Base URL override (optional)
- OPENAI_EMBEDDING_MODEL: Embedding model (default: text-embedding-3-small)
- OPENAI_GENERATION_MODEL: Generation model (default: gpt-4o-mini)
Also adds:
- Integration tests for RAG pipeline with MCP sampling
- MCP client sampling support for integration tests
- Ground truth Q&A pairs for Nextcloud User Manual
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Migrates from custom word-based chunking to LangChain's MarkdownTextSplitter
for better semantic search quality. This implements the chunking portion of
ADR-011.
Changes:
- Replace custom regex word chunker with MarkdownTextSplitter
- Optimized for Markdown content (headers, code blocks, lists)
- Convert from word-based (512 words) to character-based (2048 chars) chunking
- Maintain backward-compatible ChunkWithPosition interface
- Update configuration defaults and validation
- Update all unit tests (12/12 passing)
Benefits:
- Respects markdown structure boundaries
- Never breaks code blocks or headers mid-chunk
- Preserves semantic coherence within chunks
- Expected 20-30% improvement in recall quality
- Industry-standard approach (used by production RAG systems)
Note: Full reindex required to apply new chunking to existing documents.
Current vector database still contains old word-based chunks.
Related: ADR-011 (Improving Semantic Search Quality)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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