- 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.
Add a new `/documents/reprocess_failed` API endpoint and corresponding
UI button to retry processing of failed and pending documents. This
addresses a common recovery scenario when document processing fails due
to server crashes, network errors, or LLM service outages.
Backend changes:
- Add ReprocessResponse model with status, message, and track_id fields
- Add POST /documents/reprocess_failed endpoint that triggers background
reprocessing of FAILED, PENDING, and interrupted PROCESSING documents
- Reuses existing apipeline_process_enqueue_documents for consistency
- Includes comprehensive docstring and logging for observability
Frontend changes:
- Add TypeScript types and API function for the new endpoint
- Add retry handler with intelligent polling (fast refresh → normal)
- Add "Retry Failed" button in Documents page toolbar
- Button disabled when pipeline is busy to prevent duplicate operations
- Complete i18n support (English and Chinese translations)
This feature provides a convenient way to recover from processing
failures without requiring a full filesystem rescan.
- Add new aquery_llm/query_llm methods providing structured responses
- Consolidate /query and /query/stream endpoints to use unified aquery_llm
- Optimize cache handling by moving cache checks before LLM calls
• Add include_references param to QueryRequest
• Extend QueryResponse with references field
• Create unified QueryResult data structures
• Refactor kg_query and naive_query functions
• Update streaming to send references first
