40 lines
1.8 KiB
Markdown
40 lines
1.8 KiB
Markdown
# Task Log: Multi-Tenant vs Workspace Audit
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**Date:** 2024-12-05 01:30 UTC
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**Mode:** Beastmode
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**Task:** Audit Multi-Tenant vs Workspace redundancy
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## Actions
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1. Fetched upstream LightRAG README to understand workspace feature
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2. Analyzed core `lightrag/lightrag.py` workspace parameter usage
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3. Analyzed `lightrag/kg/postgres_impl.py` for table schemas with workspace
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4. Analyzed `lightrag/services/tenant_service.py` for tenant management
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5. Analyzed `lightrag/tenant_rag_manager.py` for RAG instance management
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6. Analyzed `lightrag/models/tenant.py` for data models
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7. Analyzed `lightrag/security.py` for workspace/tenant validation
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8. Analyzed `starter/init-postgres.sql` for database schema
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9. Used sequential thinking to compare architectures
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10. Created comprehensive audit report: `docs/0009-multi-tenant-vs-workspace-audit.md`
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11. Implemented cascade delete for `delete_knowledge_base()` method
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12. Implemented cascade delete for `delete_tenant()` method
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13. Fixed linting issues with ruff
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## Decisions
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- **Tenant feature is NOT redundant** - it's a proper application layer on top of workspace
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- Workspace provides storage-level isolation (HOW data is separated)
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- Tenant provides application-level multi-tenancy (WHO can access WHAT)
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- Composite workspace format `{tenant_id}:{kb_id}` bridges the two layers
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## Next Steps
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1. Add unit tests for cascade delete functionality
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2. Consider adding DB triggers for referential integrity
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3. Update user-facing documentation with workspace naming convention
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## Lessons/Insights
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- LightRAG uses a layered architecture that correctly separates storage isolation from access control
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- Generated columns in PostgreSQL allow querying by tenant/KB without schema changes
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- The `TenantRAGManager` acts as a bridge, creating composite workspace identifiers
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