Add citation tracking and display system across backend and frontend components.
Backend changes include citation.py for document attribution, enhanced query routes
with citation metadata, improved prompt templates, and PostgreSQL schema updates.
Frontend includes CitationMarker component, HoverCard UI, QuerySettings refinements,
and ChatMessage enhancements for displaying document sources. Update dependencies
and docker-compose test configuration for improved development workflow.
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
• Track if we acquired the pipeline lock
• Auto-acquire pipeline when idle
• Only release if we acquired it
• Prevent concurrent deletion conflicts
• Improve deletion job validation
* Capture max_token_size before decorator
* Apply wrapper after capturing attribute
* Prevent decorator from stripping dataclass
* Ensure token limit is properly set
- Add EMBEDDING_TOKEN_LIMIT env var
- Set max_token_size on embedding func
- Add token limit property to LightRAG
- Validate summary length vs limit
- Log warning when limit exceeded
- Add Awaitable and Union type imports
- Update chunking_func type annotation
- Handle coroutine results with await
- Add return type validation
- Update docstring for async support
Fixes two compatibility issues in workspace isolation:
1. Problem: lightrag_server.py calls initialize_pipeline_status()
without workspace parameter, causing pipeline to initialize in
global namespace instead of rag's workspace.
Solution: Add set_default_workspace() mechanism in shared_storage.
LightRAG.initialize_storages() now sets default workspace, which
initialize_pipeline_status() uses when called without parameters.
2. Problem: /health endpoint hardcoded to use "pipeline_status",
cannot return workspace-specific status or support frontend
workspace selection.
Solution: Add LIGHTRAG-WORKSPACE header support. Endpoint now
extracts workspace from header or falls back to server default,
returning correct workspace-specific pipeline status.
Changes:
- lightrag/kg/shared_storage.py: Add set/get_default_workspace()
- lightrag/lightrag.py: Call set_default_workspace() in initialize_storages()
- lightrag/api/lightrag_server.py: Add get_workspace_from_request() helper,
update /health endpoint to support LIGHTRAG-WORKSPACE header
Testing:
- Backward compatibility: Old code works without modification
- Multi-instance safety: Explicit workspace passing preserved
- /health endpoint: Supports both default and header-specified workspaces
Related: #2353
Problem:
In multi-tenant scenarios, different workspaces share a single global
pipeline_status namespace, causing pipelines from different tenants to
block each other, severely impacting concurrent processing performance.
Solution:
- Extended get_namespace_data() to recognize workspace-specific pipeline
namespaces with pattern "{workspace}:pipeline" (following GraphDB pattern)
- Added workspace parameter to initialize_pipeline_status() for per-tenant
isolated pipeline namespaces
- Updated all 7 call sites to use workspace-aware locks:
* lightrag.py: process_document_queue(), aremove_document()
* document_routes.py: background_delete_documents(), clear_documents(),
cancel_pipeline(), get_pipeline_status(), delete_documents()
Impact:
- Different workspaces can process documents concurrently without blocking
- Backward compatible: empty workspace defaults to "pipeline_status"
- Maintains fail-fast: uninitialized pipeline raises clear error
- Expected N× performance improvement for N concurrent tenants
Bug fixes:
- Fixed AttributeError by using self.workspace instead of self.global_config
- Fixed pipeline status endpoint to show workspace-specific status
- Fixed delete endpoint to check workspace-specific busy flag
Code changes: 4 files, 141 insertions(+), 28 deletions(-)
Testing: All syntax checks passed, comprehensive workspace isolation tests completed
- **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.
- Pass chunk storages to merge function
- Merge relation chunk tracking data
- Merge entity chunk tracking data
- Delete old chunk tracking records
- Persist chunk storage updates
• Add delete_llm_cache parameter to API
• Collect cache IDs from text chunks
• Delete cache after graph operations
• Update UI with new checkbox option
• Add i18n translations for cache option
- Add entity_chunks & relation_chunks storage
- Implement KEEP/FIFO limit strategies
- Update env.example with new settings
- Add migration for chunk tracking data
- Support all KV storage
• Add DocStatus.PREPROCESSED enum value
• Update API routes and response models
• Add preprocessed filter in web UI
• Update localization files
• Handle preprocessed status in deletion
- Store file_path in full_docs storage
- Update PostgreSQL implementation by map file_path to doc_name
- Other storage implementation automatically handles the new field
