Graph Connectivity Awareness:
- Add db_degree property to all KG implementations (NetworkX, Postgres, Neo4j, Mongo, Memgraph)
- Show database degree vs visual degree in node panel with amber badge
- Add visual indicator (amber border) for nodes with hidden connections
- Add "Load X hidden connection(s)" button to expand hidden neighbors
- Add configurable "Expand Depth" setting (1-5) in graph settings
- Use global maxNodes setting for node expansion consistency
Orphan Connection UI:
- Add OrphanConnectionDialog component for manual orphan entity connection
- Add OrphanConnectionControl button in graph sidebar
- Expose /graph/orphans/connect API endpoint for frontend use
Backend Improvements:
- Add get_orphan_entities() and connect_orphan_entities() to base storage
- Add orphan connection configuration parameters
- Improve entity extraction with relationship density requirements
Frontend:
- Add graphExpandDepth and graphIncludeOrphans to settings store
- Add min_degree and include_orphans graph filtering parameters
- Update translations (en.json, zh.json)
Implement automatic orphan entity connection system that identifies entities with
no relationships and creates meaningful connections via vector similarity + LLM
validation. This improves knowledge graph connectivity and retrieval quality.
Changes:
- Add orphan connection configuration parameters (thresholds, cross-connect settings)
- Implement aconnect_orphan_entities() method with 4-step validation pipeline
- Add SQL templates for efficient orphan and candidate entity queries
- Create POST /graph/orphans/connect API endpoint with configurable parameters
- Add orphan connection validation prompt for LLM-based relationship verification
- Include relationship density requirement in extraction prompts to prevent orphans
- Update docker-compose.test.yml with optimized extraction parameters
- Add quality validation test suite (run_quality_tests.py) for retrieval evaluation
- Add unit test framework (test_orphan_connection_quality.py) with test cases
- Enable auto-run of orphan connection after document processing
- **API:** The `graph/entity/edit` endpoint now returns a detailed `operation_summary` for better client-side handling of update, rename, and merge outcomes.
- **Web UI:** Added an "auto-merge on rename" option. The UI now gracefully handles merge success, partial failures (update OK, merge fail), and other errors with specific user feedback.
- Changed create_entity to use rag.acreate_entity() instead of direct graph manipulation
- Changed create_relation to use rag.acreate_relation() instead of direct graph manipulation
- This ensures vector embeddings are created and entities/relations are searchable
- Adds proper concurrency locks and metadata population
Added three new REST API endpoints for direct knowledge graph manipulation:
- POST /graph/entity/create: Create new entities in the knowledge graph
- POST /graph/relation/create: Create relationships between entities
- POST /graph/entities/merge: Merge duplicate/misspelled entities while preserving relationships
The merge endpoint is particularly useful for consolidating entities discovered after document processing, fixing spelling errors, and cleaning up the knowledge graph. All relationships from source entities are transferred to the target entity, with intelligent handling of duplicate relationships.
Updated API documentation in lightrag/api/README.md with usage examples for all three endpoints.
- Introduced min_degree parameter in graph query
- Updated UI to include minimum degree setting
- Modified API to handle min_degree parameter
- Updated graph query logic in LightRAG
- Simplify graph search parameters in API
- Add inclusive search and min degree options
- Update Vite config for local development
- Enhance graph query endpoint in web UI
- Add new Vite config file for local setup
