Why this change is needed:
PostgreSQL vector storage needs model isolation to prevent dimension
conflicts when different workspaces use different embedding models.
Without this, the first workspace locks the vector dimension for all
subsequent workspaces, causing failures.
How it solves it:
- Implements dynamic table naming with model suffix: {table}_{model}_{dim}d
- Adds setup_table() method mirroring Qdrant's approach for consistency
- Implements 4-branch migration logic: both exist -> warn, only new -> use,
neither -> create, only legacy -> migrate
- Batch migration: 500 records/batch (same as Qdrant)
- No automatic rollback to support idempotent re-runs
Impact:
- PostgreSQL tables now isolated by embedding model and dimension
- Automatic data migration from legacy tables on startup
- Backward compatible: model_name=None defaults to "unknown"
- All SQL operations use dynamic table names
Testing:
- 6 new tests for PostgreSQL migration (100% pass)
- Tests cover: naming, migration trigger, scenarios 1-3
- 3 additional scenario tests added for Qdrant completeness
Co-Authored-By: Claude <noreply@anthropic.com>
Why this change is needed:
To implement vector storage model isolation for Qdrant, allowing different workspaces to use different embedding models without conflict, and automatically migrating existing data.
How it solves it:
- Modified QdrantVectorDBStorage to use model-specific collection suffixes
- Implemented automated migration logic from legacy collections to new schema
- Fixed Shared-Data lock re-entrancy issue in multiprocess mode
- Added comprehensive tests for collection naming and migration triggers
Impact:
- Existing users will have data automatically migrated on next startup
- New workspaces will use isolated collections based on embedding model
- Fixes potential lock-related bugs in shared storage
Testing:
- Added tests/test_qdrant_migration.py passing
- Verified migration logic covers all 4 states (New/Legacy existence combinations)
Why this change is needed:
To enforce consistent naming and migration strategy across all vector storages.
How it solves it:
- Added _generate_collection_suffix() helper
- Added _get_legacy_collection_name() and _get_new_collection_name() interfaces
Impact:
Prepares storage implementations for multi-model support.
Testing:
Added tests/test_base_storage_integrity.py passing.
Why this change is needed:
To support vector storage model isolation, we need to track which model is used for embeddings and generate unique identifiers for collections/tables.
How it solves it:
- Added model_name field to EmbeddingFunc
- Added get_model_identifier() method to generate sanitized suffix
- Added unit tests to verify behavior
Impact:
Enables subsequent changes in storage backends to isolate data by model.
Testing:
Added tests/test_embedding_func.py passing.
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.
• Track if we acquired the pipeline lock
• Auto-acquire pipeline when idle
• Only release if we acquired it
• Prevent concurrent deletion conflicts
• Improve deletion job validation
- Add GitHub Actions workflow for CI
- Mark integration tests requiring services
- Add offline test markers for isolated tests
- Skip integration tests by default
- Configure pytest markers and collection
• Remove unused tempfile import
• Use consistent project temp/ structure
• Clean up existing directories first
• Create directories with os.makedirs
• Use descriptive test directory names
Add specific content assertions to detect cross-contamination between workspaces.
Previously only checked that workspaces had different data, now verifies:
- Each workspace contains only its own text content
- Each workspace does NOT contain the other workspace's content
- Cross-contamination would be immediately detected
This ensures the test can find problems, not just pass.
Changes:
- Add assertions for "Artificial Intelligence" and "Machine Learning" in project_a
- Add assertions for "Deep Learning" and "Neural Networks" in project_b
- Add negative assertions to verify data leakage doesn't occur
- Add detailed output messages showing what was verified
Testing:
- pytest tests/test_workspace_isolation.py::test_lightrag_end_to_end_workspace_isolation
- Test passes with proper content isolation verified
Why this change is needed:
The mock LLM function was returning JSON format, which is incorrect
for LightRAG's entity extraction. This caused "Complete delimiter
can not be found" warnings and resulted in 0 entities/relations
being extracted during tests.
How it solves it:
- Updated mock_llm_func to return correct tuple-delimited format
- Format: entity<|#|>name<|#|>type<|#|>description
- Format: relation<|#|>source<|#|>target<|#|>keywords<|#|>description
- Added proper completion delimiter: <|COMPLETE|>
- Now correctly extracts 2 entities and 1 relation
Impact:
- E2E test now properly validates entity/relation extraction
- No more "Complete delimiter" warnings
- Tests can now detect extraction-related bugs
- Graph files contain actual data (2 nodes, 1 edge) instead of empty graphs
Testing:
All 11 tests pass in 2.42s with proper entity extraction:
- Chunk 1 of 1 extracted 2 Ent + 1 Rel (previously 0 Ent + 0 Rel)
- Graph files now 2564 bytes (previously 310 bytes)
Why this change is needed:
The test file was using a custom TestResults class for tracking test
execution and results, which is not standard practice for pytest-based
test suites. This makes the tests harder to integrate with CI/CD pipelines
and reduces compatibility with pytest plugins and tooling.
How it solves it:
- Removed custom TestResults class and manual result tracking
- Added @pytest.mark.asyncio decorator to all async test functions
- Converted all results.add() calls to standard pytest assert statements
- Added pytest fixture (setup_shared_data) for common test setup
- Removed custom main() runner (pytest handles test discovery/execution)
- Kept all test logic, assertions, and debugging print statements intact
Impact:
- All 11 test functions maintain identical behavior and coverage
- Tests now follow pytest conventions and integrate with pytest ecosystem
- Test output is cleaner and more informative with pytest's reporting
- Easier to run selective tests using pytest's filtering options
Testing:
Verified by running: uv run pytest tests/test_workspace_isolation.py -v
Result: All 11 tests passed in 2.41s
• Add workspace param to get_namespace_data
• Update docstring with proper usage example
• Simplify demo to show correct workflow
• Remove confusing before/after comparison
• Clarify tool should run after init
- Add _default_workspace to global vars
- Set _default_workspace to None on cleanup
- Ensure complete resource cleanup
- Fix missing workspace finalization
