Added comprehensive documentation for the new include_chunk_content parameter
that enables retrieval of actual chunk text content in API responses.
Documentation Updates:
- Added "Include Chunk Content in References" section to API README
- Explained use cases: RAG evaluation, debugging, citations, transparency
- Provided JSON request/response examples
- Clarified parameter interaction with include_references
OpenAPI/Swagger Examples:
- Added "Response with chunk content" example to /query endpoint
- Shows complete reference structure with content field
- Demonstrates realistic chunk text content
This makes the feature discoverable through:
1. API documentation (README.md)
2. Interactive Swagger UI (http://localhost:9621/docs)
3. Code examples for developers
(cherry picked from commit 963ad4c637)
* 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 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
The top_k parameter already has a default value set in the QueryParam class (base.py), making these checks unnecessary. This change simplifies the code while maintaining the same functionality.
Changes:
Remove top_k check in query_text function
Remove top_k check in query_text_stream function
