Adds a manually-triggered GitHub Actions workflow for RAG evaluation:
- Builds Nextcloud User Manual PDF from documentation source
- Uploads PDF to Nextcloud via WebDAV
- Tags file with 'vector-index' for vector sync indexing
- Waits for vector sync to complete
- Runs RAG integration tests with OpenAI/GitHub Models API
Inputs:
- embedding_model: OpenAI embedding model (default: openai/text-embedding-3-small)
- generation_model: OpenAI generation model (default: openai/gpt-4o-mini)
🤖 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>
The Smithery scanner was reporting "0 tools" despite the server returning
valid tool definitions. Root cause: the server was returning SSE-formatted
responses (event: message\ndata: {...}) which the scanner couldn't parse.
Changes:
- Add json_response=True to FastMCP for Smithery stateless mode
- Clean up verbose docstring examples in semantic.py and webdav.py
The MCP spec allows both SSE and plain JSON responses for HTTP transport.
Setting json_response=True returns Content-Type: application/json with
plain JSON-RPC instead of text/event-stream with SSE format.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- 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)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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
🤖 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>
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.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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
smithery-ai[bot]