Enable a single LightRAG server instance to serve multiple isolated workspaces
via HTTP header-based routing. This allows multi-tenant SaaS deployments where
each tenant's data is completely isolated.
Key features:
- Header-based workspace routing (LIGHTRAG-WORKSPACE, X-Workspace-ID fallback)
- Process-local pool of LightRAG instances with LRU eviction
- FastAPI dependency (get_rag) for workspace resolution per request
- Full backward compatibility - existing deployments work unchanged
- Strict multi-tenant mode option (LIGHTRAG_ALLOW_DEFAULT_WORKSPACE=false)
- Configurable pool size (LIGHTRAG_MAX_WORKSPACES_IN_POOL)
- Graceful shutdown with workspace finalization
Configuration:
- LIGHTRAG_DEFAULT_WORKSPACE: Default workspace (falls back to WORKSPACE)
- LIGHTRAG_ALLOW_DEFAULT_WORKSPACE: Require explicit header when false
- LIGHTRAG_MAX_WORKSPACES_IN_POOL: Max concurrent workspace instances (default: 50)
Files:
- New: lightrag/api/workspace_manager.py (core multi-workspace module)
- New: tests/test_multi_workspace_server.py (17 unit tests)
- New: render.yaml (Render deployment blueprint)
- Modified: All route files to use get_rag dependency
- Updated: README.md, env.example with documentation
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
BREAKING CHANGE: content field is now List[str] instead of str
- Add ReferenceItem Pydantic model for type safety
- Update /query and /query/stream to return content as list
- Update OpenAPI schema and examples
- Add migration guide to API README
- Fix RAGAS evaluation to handle list format
Addresses PR #2297 feedback. Tested with RAGAS: 97.37% score.
BREAKING CHANGE: The `content` field in query response references is now
an array of strings instead of a concatenated string. This preserves
individual chunk boundaries when a single file has multiple chunks.
Changes:
- Update QueryResponse Pydantic model to accept List[str] for content
- Modify query_text endpoint to return content as list (query_routes.py:425)
- Modify query_text_stream endpoint to support chunk content enrichment
- Update OpenAPI schema and examples to reflect array structure
- Update API README with breaking change notice and migration guide
- Fix RAGAS evaluation to flatten chunk content lists
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
- 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
