- 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
(cherry picked from commit dc62c78f98)
* Unify empty workspace behavior by changing workspace from "_" to ""
* Fixed incorrect empty workspace detection in get_all_update_flags_status()
(cherry picked from commit d54d0d55d9)
- Fix result ordering in vector stores
- Update KV storage get_by_ids methods
- Maintain order in doc status storage
- Return None for missing IDs
(cherry picked from commit 9be22dd666)
* feat: Implement multi-tenant architecture with tenant and knowledge base models
- Added data models for tenants, knowledge bases, and related configurations.
- Introduced role and permission management for users in the multi-tenant system.
- Created a service layer for managing tenants and knowledge bases, including CRUD operations.
- Developed a tenant-aware instance manager for LightRAG with caching and isolation features.
- Added a migration script to transition existing workspace-based deployments to the new multi-tenant architecture.
* chore: ignore lightrag/api/webui/assets/ directory
* chore: stop tracking lightrag/api/webui/assets (ignore in .gitignore)
* feat: Initialize LightRAG Multi-Tenant Stack with PostgreSQL
- Added README.md for project overview, setup instructions, and architecture details.
- Created docker-compose.yml to define services: PostgreSQL, Redis, LightRAG API, and Web UI.
- Introduced env.example for environment variable configuration.
- Implemented init-postgres.sql for PostgreSQL schema initialization with multi-tenant support.
- Added reproduce_issue.py for testing default tenant access via API.
* feat: Enhance TenantSelector and update related components for improved multi-tenant support
* feat: Enhance testing capabilities and update documentation
- Updated Makefile to include new test commands for various modes (compatibility, isolation, multi-tenant, security, coverage, and dry-run).
- Modified API health check endpoint in Makefile to reflect new port configuration.
- Updated QUICK_START.md and README.md to reflect changes in service URLs and ports.
- Added environment variables for testing modes in env.example.
- Introduced run_all_tests.sh script to automate testing across different modes.
- Created conftest.py for pytest configuration, including database fixtures and mock services.
- Implemented database helper functions for streamlined database operations in tests.
- Added test collection hooks to skip tests based on the current MULTITENANT_MODE.
* feat: Implement multi-tenant support with demo mode enabled by default
- Added multi-tenant configuration to the environment and Docker setup.
- Created pre-configured demo tenants (acme-corp and techstart) for testing.
- Updated API endpoints to support tenant-specific data access.
- Enhanced Makefile commands for better service management and database operations.
- Introduced user-tenant membership system with role-based access control.
- Added comprehensive documentation for multi-tenant setup and usage.
- Fixed issues with document visibility in multi-tenant environments.
- Implemented necessary database migrations for user memberships and legacy support.
* feat(audit): Add final audit report for multi-tenant implementation
- Documented overall assessment, architecture overview, test results, security findings, and recommendations.
- Included detailed findings on critical security issues and architectural concerns.
fix(security): Implement security fixes based on audit findings
- Removed global RAG fallback and enforced strict tenant context.
- Configured super-admin access and required user authentication for tenant access.
- Cleared localStorage on logout and improved error handling in WebUI.
chore(logs): Create task logs for audit and security fixes implementation
- Documented actions, decisions, and next steps for both audit and security fixes.
- Summarized test results and remaining recommendations.
chore(scripts): Enhance development stack management scripts
- Added scripts for cleaning, starting, and stopping the development stack.
- Improved output messages and ensured graceful shutdown of services.
feat(starter): Initialize PostgreSQL with AGE extension support
- Created initialization scripts for PostgreSQL extensions including uuid-ossp, vector, and AGE.
- Ensured successful installation and verification of extensions.
* feat: Implement auto-select for first tenant and KB on initial load in WebUI
- Removed WEBUI_INITIAL_STATE_FIX.md as the issue is resolved.
- Added useTenantInitialization hook to automatically select the first available tenant and KB on app load.
- Integrated the new hook into the Root component of the WebUI.
- Updated RetrievalTesting component to ensure a KB is selected before allowing user interaction.
- Created end-to-end tests for multi-tenant isolation and real service interactions.
- Added scripts for starting, stopping, and cleaning the development stack.
- Enhanced API and tenant routes to support tenant-specific pipeline status initialization.
- Updated constants for backend URL to reflect the correct port.
- Improved error handling and logging in various components.
* feat: Add multi-tenant support with enhanced E2E testing scripts and client functionality
* update client
* Add integration and unit tests for multi-tenant API, models, security, and storage
- Implement integration tests for tenant and knowledge base management endpoints in `test_tenant_api_routes.py`.
- Create unit tests for tenant isolation, model validation, and role permissions in `test_tenant_models.py`.
- Add security tests to enforce role-based permissions and context validation in `test_tenant_security.py`.
- Develop tests for tenant-aware storage operations and context isolation in `test_tenant_storage_phase3.py`.
* feat(e2e): Implement OpenAI model support and database reset functionality
* Add comprehensive test suite for gpt-5-nano compatibility
- Introduced tests for parameter normalization, embeddings, and entity extraction.
- Implemented direct API testing for gpt-5-nano.
- Validated .env configuration loading and OpenAI API connectivity.
- Analyzed reasoning token overhead with various token limits.
- Documented test procedures and expected outcomes in README files.
- Ensured all tests pass for production readiness.
* kg(postgres_impl): ensure AGE extension is loaded in session and configure graph initialization
* dev: add hybrid dev helper scripts, Makefile, docker-compose.dev-db and local development docs
* feat(dev): add dev helper scripts and local development documentation for hybrid setup
* feat(multi-tenant): add detailed specifications and logs for multi-tenant improvements, including UX, backend handling, and ingestion pipeline
* feat(migration): add generated tenant/kb columns, indexes, triggers; drop unused tables; update schema and docs
* test(backward-compat): adapt tests to new StorageNameSpace/TenantService APIs (use concrete dummy storages)
* chore: multi-tenant and UX updates — docs, webui, storage, tenant service adjustments
* tests: stabilize integration tests + skip external services; fix multi-tenant API behavior and idempotency
- gpt5_nano_compatibility: add pytest-asyncio markers, skip when OPENAI key missing, prevent module-level asyncio.run collection, add conftest
- Ollama tests: add server availability check and skip markers; avoid pytest collection warnings by renaming helper classes
- Graph storage tests: rename interactive test functions to avoid pytest collection
- Document & Tenant routes: support external_ids for idempotency; ensure HTTPExceptions are re-raised
- LightRAG core: support external_ids in apipeline_enqueue_documents and idempotent logic
- Tests updated to match API changes (tenant routes & document routes)
- Add logs and scripts for inspection and audit
- 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
• Replace pyuca with centralized utils function
• Add pinyin sort keys for file paths
• Update MongoDB indexes with zh collation
• Migrate existing indexes for compatibility
• Support Chinese chars in Redis/JSON storage
• Keep PostgreSQL sorting order controled by Database Collate order
- Add file_path sorting support to all database backends (JSON, Redis, PostgreSQL, MongoDB)
- Implement smart column header switching between "ID" and "File Name" based on display mode
- Add automatic sort field switching when toggling between ID and file name display
- Create composite indexes for workspace+file_path in PostgreSQL and MongoDB for better query performance
- Update frontend to maintain sort state when switching display modes
- Add internationalization support for "fileName" in English and Chinese locales
This enhancement improves user experience by providing intuitive file-based sorting
while maintaining performance through optimized database indexes.
- Add pagination support to BaseDocStatusStorage interface and all implementations (PostgreSQL, MongoDB, Redis, JSON)
- Implement RESTful API endpoints for paginated document queries and status counts
- Create reusable pagination UI components with internationalization support
- Optimize performance with database-level pagination and efficient in-memory processing
- Maintain backward compatibility while adding configurable page sizes (10-200 items)
- Add metadata field to DocProcessingStatus with start_time and end_time tracking
- Record processing timestamps using Unix time format (seconds precision)
- Update all storage backends (JSON, MongoDB, Redis, PostgreSQL) for new field support
- Maintain backward compatibility with default values for existing data
- Add error_msg field for better error tracking during document processing
- The previous implementation of the shared Redis connection pool had a critical issue where any Redis storage instance would disconnect the global shared pool upon closing. This caused `ConnectionError` exceptions for other instances still using the pool.
- This commit resolves the issue by introducing a reference counting mechanism in `RedisConnectionManager`.
Refactored the LLM cache to a flat Key-Value (KV) structure, replacing the previous nested format. The old structure used the 'mode' as a key and stored specific cache content as JSON nested under it. This change significantly enhances cache recall efficiency.
Enhance RedisKVStorage: Implement connection pooling and error handling. Refactor async methods to use context managers for Redis operations, improving resource management and error logging. Batch processing added for key operations to optimize performance.
