• Add _sanitize_json_data helper function
• Recursively clean strings in data
• Sanitize before JSON serialization
• Prevent encoding-related crashes
• Use existing sanitize_text_for_encoding
- Update import from PyPDF2 to pypdf
- Change dependency to pypdf>=6.1.0
- Update all requirements files
- Remove PyPDF2 from lock file
- Use modern pypdf library
- 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
The stream and timeout parameters were moved from **kwargs to explicit
parameters in a previous commit, but were not being passed to the OpenAI
API, causing streaming responses to fail and fall back to non-streaming
mode.Fixes the issue where stream=True was being silently ignored, resulting
in unexpected non-streaming behavior.
- Snapshot JSON data before yielding batches
- Release lock during batch processing
- Exclude source type from target selection
- Add detailed docstring for lock behavior
- Filter available storage types properly
- Add streaming migration for memory efficiency
- Implement graceful exit with Enter/0
- Add progress indicators for counting
- Optimize batch processing by storage type
- Update docs with new progress displays
• Remove premature ID normalization
• Add lookup mapping for node resolution
• Filter results by requested nodes only
• Improve error logging with workspace
