* 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
330 lines
14 KiB
Python
330 lines
14 KiB
Python
"""
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PostgreSQL Multi-Tenant Support Module
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This module provides utilities and helpers for implementing multi-tenant isolation
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in PostgreSQL storage implementations. It includes:
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1. Table DDL updates with tenant_id and kb_id columns
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2. SQL template builders that automatically add tenant filtering
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3. Composite key generation
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4. Data migration utilities
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All storage implementations (KV, Vector, Graph, DocStatus) use these utilities
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to ensure proper tenant isolation at the database level.
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"""
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import os
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from typing import Any, Dict, List, Optional, Tuple
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from datetime import datetime
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# ============================================================================
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# TABLE DDL TEMPLATES WITH TENANT SUPPORT
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# ============================================================================
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def get_updated_tables_dict() -> Dict[str, Dict[str, str]]:
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# Returns the updated TABLES dictionary with tenant_id and kb_id columns.
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# This function generates table DDLs that include:
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# - tenant_id VARCHAR(255) NOT NULL
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# - kb_id VARCHAR(255) NOT NULL
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# - Composite PRIMARY KEY on (tenant_id, kb_id, workspace, id)
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# - Composite indexes for common query patterns
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embedding_dim = int(os.environ.get("EMBEDDING_DIM", 1024))
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return {
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"LIGHTRAG_DOC_FULL": {
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"ddl": """CREATE TABLE LIGHTRAG_DOC_FULL (
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tenant_id VARCHAR(255) NOT NULL,
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kb_id VARCHAR(255) NOT NULL,
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id VARCHAR(255) NOT NULL,
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workspace VARCHAR(255),
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doc_name VARCHAR(1024),
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content TEXT,
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meta JSONB,
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create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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CONSTRAINT LIGHTRAG_DOC_FULL_PK PRIMARY KEY (tenant_id, kb_id, id)
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)"""
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},
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"LIGHTRAG_DOC_CHUNKS": {
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"ddl": """CREATE TABLE LIGHTRAG_DOC_CHUNKS (
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tenant_id VARCHAR(255) NOT NULL,
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kb_id VARCHAR(255) NOT NULL,
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id VARCHAR(255) NOT NULL,
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workspace VARCHAR(255),
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full_doc_id VARCHAR(256),
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chunk_order_index INTEGER,
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tokens INTEGER,
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content TEXT,
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file_path TEXT NULL,
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llm_cache_list JSONB NULL DEFAULT '[]'::jsonb,
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create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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CONSTRAINT LIGHTRAG_DOC_CHUNKS_PK PRIMARY KEY (tenant_id, kb_id, id)
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)"""
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},
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"LIGHTRAG_VDB_CHUNKS": {
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"ddl": f"""CREATE TABLE LIGHTRAG_VDB_CHUNKS (
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tenant_id VARCHAR(255) NOT NULL,
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kb_id VARCHAR(255) NOT NULL,
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id VARCHAR(255) NOT NULL,
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workspace VARCHAR(255),
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full_doc_id VARCHAR(256),
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chunk_order_index INTEGER,
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tokens INTEGER,
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content TEXT,
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content_vector VECTOR({embedding_dim}),
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file_path TEXT NULL,
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create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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CONSTRAINT LIGHTRAG_VDB_CHUNKS_PK PRIMARY KEY (tenant_id, kb_id, id)
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)"""
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},
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"LIGHTRAG_VDB_ENTITY": {
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"ddl": f"""CREATE TABLE LIGHTRAG_VDB_ENTITY (
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tenant_id VARCHAR(255) NOT NULL,
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kb_id VARCHAR(255) NOT NULL,
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id VARCHAR(255) NOT NULL,
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workspace VARCHAR(255),
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entity_name VARCHAR(512),
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content TEXT,
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content_vector VECTOR({embedding_dim}),
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create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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chunk_ids VARCHAR(255)[] NULL,
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file_path TEXT NULL,
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CONSTRAINT LIGHTRAG_VDB_ENTITY_PK PRIMARY KEY (tenant_id, kb_id, id)
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)"""
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},
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"LIGHTRAG_VDB_RELATION": {
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"ddl": f"""CREATE TABLE LIGHTRAG_VDB_RELATION (
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tenant_id VARCHAR(255) NOT NULL,
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kb_id VARCHAR(255) NOT NULL,
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id VARCHAR(255) NOT NULL,
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workspace VARCHAR(255),
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source_id VARCHAR(512),
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target_id VARCHAR(512),
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content TEXT,
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content_vector VECTOR({embedding_dim}),
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create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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chunk_ids VARCHAR(255)[] NULL,
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file_path TEXT NULL,
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CONSTRAINT LIGHTRAG_VDB_RELATION_PK PRIMARY KEY (tenant_id, kb_id, id)
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)"""
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},
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"LIGHTRAG_LLM_CACHE": {
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"ddl": """CREATE TABLE LIGHTRAG_LLM_CACHE (
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tenant_id VARCHAR(255) NOT NULL,
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kb_id VARCHAR(255) NOT NULL,
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workspace VARCHAR(255),
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id VARCHAR(255) NOT NULL,
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original_prompt TEXT,
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return_value TEXT,
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chunk_id VARCHAR(255) NULL,
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cache_type VARCHAR(32),
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queryparam JSONB NULL,
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create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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update_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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CONSTRAINT LIGHTRAG_LLM_CACHE_PK PRIMARY KEY (tenant_id, kb_id, id)
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)"""
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},
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"LIGHTRAG_DOC_STATUS": {
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"ddl": """CREATE TABLE LIGHTRAG_DOC_STATUS (
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tenant_id VARCHAR(255) NOT NULL,
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kb_id VARCHAR(255) NOT NULL,
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workspace VARCHAR(255),
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id VARCHAR(255) NOT NULL,
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content_summary VARCHAR(255) NULL,
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content_length INT4 NULL,
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chunks_count INT4 NULL,
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status VARCHAR(64) NULL,
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file_path TEXT NULL,
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chunks_list JSONB NULL DEFAULT '[]'::jsonb,
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track_id VARCHAR(255) NULL,
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metadata JSONB NULL DEFAULT '{}'::jsonb,
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error_msg TEXT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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CONSTRAINT LIGHTRAG_DOC_STATUS_PK PRIMARY KEY (tenant_id, kb_id, id)
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)"""
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},
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"LIGHTRAG_FULL_ENTITIES": {
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"ddl": """CREATE TABLE LIGHTRAG_FULL_ENTITIES (
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tenant_id VARCHAR(255) NOT NULL,
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kb_id VARCHAR(255) NOT NULL,
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id VARCHAR(255) NOT NULL,
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workspace VARCHAR(255),
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entity_names JSONB,
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count INTEGER,
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create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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CONSTRAINT LIGHTRAG_FULL_ENTITIES_PK PRIMARY KEY (tenant_id, kb_id, id)
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)"""
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},
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"LIGHTRAG_FULL_RELATIONS": {
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"ddl": """CREATE TABLE LIGHTRAG_FULL_RELATIONS (
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tenant_id VARCHAR(255) NOT NULL,
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kb_id VARCHAR(255) NOT NULL,
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id VARCHAR(255) NOT NULL,
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workspace VARCHAR(255),
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relation_pairs JSONB,
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count INTEGER,
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create_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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update_time TIMESTAMP(0) DEFAULT CURRENT_TIMESTAMP,
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CONSTRAINT LIGHTRAG_FULL_RELATIONS_PK PRIMARY KEY (tenant_id, kb_id, id)
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)"""
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},
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}
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# ============================================================================
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# SQL TEMPLATE BUILDERS WITH TENANT FILTERING
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# ============================================================================
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class TenantSQLBuilder:
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# Helper class for building tenant-aware SQL queries
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@staticmethod
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def add_tenant_filter(sql: str, table_alias: str = "", param_index: int = 1) -> Tuple[str, int]:
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# Add tenant_id and kb_id filters to a WHERE clause.
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# Args:
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# sql: Original SQL query
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# table_alias: Optional table alias (e.g., "t." for table t)
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# param_index: Starting parameter index ($1, $2, etc.)
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# Returns:
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# Tuple of (modified_sql, next_param_index)
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prefix = f"{table_alias}." if table_alias else ""
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tenant_filter = f"{prefix}tenant_id=${param_index} AND {prefix}kb_id=${param_index + 1}"
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if "WHERE" in sql:
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sql = sql.replace("WHERE", f"WHERE {tenant_filter} AND", 1)
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else:
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sql += f" WHERE {tenant_filter}"
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return sql, param_index + 2
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@staticmethod
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def build_filtered_query(
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base_query: str,
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tenant_id: str,
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kb_id: str,
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additional_params: List[Any] = None
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) -> Tuple[str, List[Any]]:
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# Build a complete query with tenant filtering.
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# Args:
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# base_query: Base SQL query with placeholders
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# tenant_id: Tenant ID
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# kb_id: Knowledge base ID
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# additional_params: Additional query parameters
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# Returns:
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# Tuple of (query, params)
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params = [tenant_id, kb_id]
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if additional_params:
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params.extend(additional_params)
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return base_query, params
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# ============================================================================
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# MIGRATION UTILITIES
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# ============================================================================
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async def add_tenant_columns_migration(db, table_name: str, tenant_id: str = "default", kb_id: str = "default"):
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# Migrate existing table to add tenant_id and kb_id columns.
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# This migration:
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# 1. Adds tenant_id and kb_id columns (if not exist)
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# 2. Populates them with default values for existing data
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# 3. Updates primary key constraints
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# 4. Creates new composite indexes
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# Args:
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# db: PostgreSQL database connection
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# table_name: Name of table to migrate
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# tenant_id: Default tenant ID for existing data
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# kb_id: Default KB ID for existing data
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try:
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# Check if tenant_id column exists
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check_sql = f"""
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SELECT column_name
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FROM information_schema.columns
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WHERE table_name = '{table_name.lower()}' AND column_name = 'tenant_id'
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"""
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exists = await db.query(check_sql)
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if not exists:
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# Add tenant_id and kb_id columns
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alter_sql = f"""
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ALTER TABLE {table_name}
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ADD COLUMN tenant_id VARCHAR(255) NOT NULL DEFAULT '{tenant_id}',
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ADD COLUMN kb_id VARCHAR(255) NOT NULL DEFAULT '{kb_id}'
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"""
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await db.execute(alter_sql)
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# Update existing primary key and constraints
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# This is table-specific, handled in migration methods
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except Exception as e:
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from lightrag.utils import logger
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logger.warning(f"Failed to migrate {table_name}: {e}")
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def get_composite_key(tenant_id: str, kb_id: str, *args) -> str:
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# Generate a composite key for multi-tenant data isolation.
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# Args:
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# tenant_id: Tenant ID
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# kb_id: Knowledge base ID
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# *args: Additional key components
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# Returns:
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# Composite key string
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parts = [tenant_id, kb_id] + list(args)
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return ":".join(str(p) for p in parts if p)
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# ============================================================================
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# INDEX CREATION HELPERS
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# ============================================================================
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def get_tenant_indexes(table_name: str) -> List[Dict[str, str]]:
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# Get recommended composite indexes for multi-tenant tables.
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# Args:
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# table_name: Name of the table
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# Returns:
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# List of index creation statements
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base_name = table_name.lower()
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indexes = [
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{
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"name": f"idx_{base_name}_tenant_kb",
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"sql": f"CREATE INDEX IF NOT EXISTS idx_{base_name}_tenant_kb ON {table_name}(tenant_id, kb_id)"
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},
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{
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"name": f"idx_{base_name}_tenant_kb_id",
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"sql": f"CREATE INDEX IF NOT EXISTS idx_{base_name}_tenant_kb_id ON {table_name}(tenant_id, kb_id, id)"
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},
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{
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"name": f"idx_{base_name}_tenant_kb_workspace",
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"sql": f"CREATE INDEX IF NOT EXISTS idx_{base_name}_tenant_kb_workspace ON {table_name}(tenant_id, kb_id, workspace)"
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},
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]
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return indexes
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# ============================================================================
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# HELPER METHODS FOR STORAGE CLASSES
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# ============================================================================
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def ensure_tenant_context(
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tenant_id: Optional[str] = None,
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kb_id: Optional[str] = None,
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default_tenant: str = "default",
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default_kb: str = "default"
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) -> Tuple[str, str]:
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# Ensure tenant and KB IDs are set, using defaults if necessary.
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# Args:
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# tenant_id: Tenant ID (may be None)
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# kb_id: Knowledge base ID (may be None)
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# default_tenant: Default tenant ID if not provided
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# default_kb: Default KB ID if not provided
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# Returns:
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# Tuple of (tenant_id, kb_id)
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return (
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tenant_id or default_tenant,
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kb_id or default_kb
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)
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