Add complete CI/CD pipeline for automated Astrolabe app releases:
- GitHub Actions workflow for build, sign, and publish
- Makefile for app store package creation
- Version synchronization between info.xml and package.json
- CHANGELOG.md with v0.1.0 release notes
feat: configure commitizen monorepo with independent versioning
Enable independent versioning for three components using scope-based commits:
- MCP Server (feat(mcp) or unscoped): v* tags, updates pyproject.toml + Chart.yaml:appVersion
- Helm Chart (feat(helm)): nextcloud-mcp-server-* tags, updates Chart.yaml:version
- Astrolabe App (feat(astrolabe)): astrolabe-v* tags, updates info.xml + package.json
Changes:
- Add .cz.toml configs for Astrolabe and Helm chart
- Update root pyproject.toml with scope filtering and tag ignores
- Create bump helper scripts (bump-mcp.sh, bump-helm.sh, bump-astrolabe.sh)
- Add CONTRIBUTING.md with version management documentation
- Create component-specific changelogs
- Configure appVersion/version separation for Helm chart
This allows each component to release independently while maintaining
proper version tracking and changelog generation.
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>
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
🤖 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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