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>
Extracted vector visualization HTML template to separate file to resolve
syntax conflicts between Jinja2, Alpine.js, and CSS. Added chunk context
endpoint for fetching matched chunks with surrounding text.
Changes:
- Moved vector_viz.html to templates/ directory (separates Jinja2/Alpine.js/CSS)
- Added /app/chunk-context endpoint for retrieving chunk text with context
- Updated .dockerignore to include HTML files in Docker builds
- Moved anthropic and boto3 to main dependencies (needed for production features)
- Added jinja2 dependency for template rendering
Fixes Jinja2 TemplateSyntaxError caused by CSS colons being parsed as
Jinja2 syntax when template was inline in Python code.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Refactored LLM provider infrastructure to support sustainable additions of new providers with both embedding and text generation capabilities.
## Major Changes
### Unified Provider Architecture (ADR-015)
- Created `nextcloud_mcp_server/providers/` with unified Provider ABC
- Providers now support optional capabilities (embeddings and/or generation)
- Auto-detection registry with priority: Bedrock → Ollama → Simple
- Backward compatible - existing code continues to work
### New Providers
- **BedrockProvider**: Full Amazon Bedrock integration
- Embeddings: Titan Embed, Cohere Embed models
- Generation: Claude, Llama, Titan Text, Mistral models
- Model-specific request/response handling
- AWS credential chain integration
- **OllamaProvider**: Migrated with both capabilities support
- **AnthropicProvider**: Moved from test code to production providers
- **SimpleProvider**: Migrated in-memory fallback provider
### Breaking Changes
None - full backward compatibility maintained:
- `embedding.get_embedding_service()` still works
- RAG evaluation tests updated to use unified providers
- All existing tests pass (127 unit tests)
### Testing
- Added 9 comprehensive Bedrock unit tests with mocked boto3
- All existing unit tests pass
- Type checking (ty) and linting (ruff) pass
- Verified backward compatibility
### Documentation
- `docs/ADR-015-unified-provider-architecture.md`: Comprehensive ADR
- `docs/bedrock-setup.md`: AWS setup guide with IAM permissions
- `CLAUDE.md`: Updated with provider architecture section
### Dependencies
- Added `boto3>=1.35.0` to dev dependencies (optional)
## Environment Variables
### Bedrock
- `AWS_REGION`: AWS region (e.g., "us-east-1")
- `BEDROCK_EMBEDDING_MODEL`: Model ID for embeddings
- `BEDROCK_GENERATION_MODEL`: Model ID for generation
- `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`: Optional credentials
### Ollama
- `OLLAMA_BASE_URL`: API URL
- `OLLAMA_EMBEDDING_MODEL`: Embedding model (default: "nomic-embed-text")
- `OLLAMA_GENERATION_MODEL`: Generation model
## AWS Bedrock Permissions Required
Minimal IAM policy:
```json
{
"Effect": "Allow",
"Action": ["bedrock:InvokeModel"],
"Resource": ["arn:aws:bedrock:*::foundation-model/*"]
}
```
See `docs/bedrock-setup.md` for detailed setup instructions.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add ADR-013 documenting RAG evaluation architecture
- Implement two-part evaluation: Context Recall (retrieval) + Answer Correctness (generation)
- Create Click CLI for ground truth generation and corpus upload
- Add pytest fixtures and tests for retrieval/generation quality
- Use BeIR/nfcorpus dataset with 5 selected test queries
- Support Ollama and Anthropic LLM providers
- Generate synthetic ground truth answers offline
- Add comprehensive documentation in tests/rag_evaluation/README.md
The framework separates one-time setup (generate/upload) from test execution,
making tests much faster (~6-12 min vs ~15-25 min per run).
Tests are manual only (not in CI) and require external LLM access.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Simplifies the OpenTelemetry tracing setup by removing the redundant
OTEL_ENABLED flag and using the presence of OTEL_EXPORTER_OTLP_ENDPOINT
to determine if tracing should be enabled. This follows the standard
OpenTelemetry environment variable conventions more closely.
Changes:
- Remove OTEL_ENABLED/tracing_enabled flag in favor of checking if
OTEL_EXPORTER_OTLP_ENDPOINT is set
- Add OTEL_EXPORTER_VERIFY_SSL configuration option for OTLP endpoints
with self-signed certificates (defaults to false for development)
- Move HTTPXClientInstrumentor initialization to module level to ensure
httpx calls are traced across all Nextcloud API requests
- Add tracing spans to vector sync operations (scan_user_documents)
- Fix authorization header logging to only warn about missing headers
in OAuth mode (BasicAuth mode doesn't use Authorization headers)
- Update observability documentation to reflect simplified configuration
- Refactor Dockerfile to use --no-editable flag for uv sync
Breaking changes:
- OTEL_ENABLED environment variable is removed
- Tracing is now automatically enabled when OTEL_EXPORTER_OTLP_ENDPOINT
is set
Migration guide:
- Remove OTEL_ENABLED=true from environment configuration
- Tracing will be enabled automatically if OTEL_EXPORTER_OTLP_ENDPOINT
is configured
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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