Add extensive test suites for API routes and utilities:
- Implement test_search_routes.py (406 lines) for search endpoint validation
- Implement test_upload_routes.py (724 lines) for document upload workflows
- Implement test_s3_client.py (618 lines) for S3 storage operations
- Implement test_citation_utils.py (352 lines) for citation extraction
- Implement test_chunking.py (216 lines) for text chunking validation
Add S3 storage client implementation:
- Create lightrag/storage/s3_client.py with S3 operations
- Add storage module initialization with exports
- Integrate S3 client with document upload handling
Enhance API routes and core functionality:
- Add search_routes.py with full-text and graph search endpoints
- Add upload_routes.py with multipart document upload support
- Update operate.py with bulk operations and health checks
- Enhance postgres_impl.py with bulk upsert and parameterized queries
- Update lightrag_server.py to register new API routes
- Improve utils.py with citation and formatting utilities
Update dependencies and configuration:
- Add S3 and test dependencies to pyproject.toml
- Update docker-compose.test.yml for testing environment
- Sync uv.lock with new dependencies
Apply code quality improvements across all modified files:
- Add type hints to function signatures
- Update imports and router initialization
- Fix logging and error handling
Format entire codebase with ruff and add type hints across all modules:
- Apply ruff formatting to all Python files (121 files, 17K insertions)
- Add type hints to function signatures throughout lightrag core and API
- Update test suite with improved type annotations and docstrings
- Add pyrightconfig.json for static type checking configuration
- Create prompt_optimized.py and test_extraction_prompt_ab.py test files
- Update ruff.toml and .gitignore for improved linting configuration
- Standardize code style across examples, reproduce scripts, and utilities
- Implement bulk upsert_nodes/edges via UNWIND reducing round trips
- Add health_check for graph connectivity and AGE catalog status
- Switch to parameterized queries preventing Cypher injection
- Fix node ID sanitization: strip control chars, escape quotes
- Add KaTeX extensions (mhchem for chemistry, copy-tex for copying)
- Add CASCADE to AGE extension for PostgreSQL
- Remove future dependency, replace passlib with bcrypt
- Fix Jina embedding configuration and provider defaults
- Update gunicorn help text and bump API version to 0258
- Documentation and README updates
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
Previously, configure_vchordrq would fail silently when probes was empty
(the default), preventing epsilon from being configured. Now each parameter
is handled independently with conditional execution, and configuration
errors fail-fast instead of being swallowed.
This fixes the documented epsilon setting being impossible to use in the
default configuration.
• Remove premature ID normalization
• Add lookup mapping for node resolution
• Filter results by requested nodes only
• Improve error logging with workspace
- Batch index existence checks into single query (16+ queries -> 1 query)
- Batch timestamp column checks into single query (8 queries -> 1 query)
- Batch field length checks into single query (5 queries -> 1 query)
Performance improvement: ~70-80% faster initialization (35s -> 5-10s)
Key optimizations:
1. check_tables(): Use ANY($1) to check all indexes at once
2. _migrate_timestamp_columns(): Batch all column type checks
3. _migrate_field_lengths(): Batch all field definition checks
All changes are backward compatible with no schema or API changes.
Reduces database round-trips by batching information_schema queries.
- 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
Prepared statement caching is disabled by setting
`statement_cache_size=0` in the `asyncpg` connection pool parameters.
This is necessary to prevent
`asyncpg.exceptions.InvalidSQLStatementNameError` when using
transaction-level connection poolers like Supabase Supavisor or
pgbouncer, which do not support prepared statements.
- 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
- Add get_doc_by_file_path to all storages
- Skip processed files in scan operation
- Check duplicates in upload endpoints
- Check duplicates in text insert APIs
- Return status info in duplicate responses
- Add get_popular_labels() method
- Add search_labels() with fuzzy matching
- Use native SQL for better performance
- Include proper scoring and ranking
