The Visual Schema Relationship Mapper is a User Interface feature enabling users to visualize and customize schema relationships. The Schema Relationship Explorer Feature provides the User Interface components for schema visualizations and relationship management.

User Interface Components implement and use the User Schema Relationship Mapper to visualize table joins and relations for users. Visual Schema Relationship Mapper is part of the User Interface Components enabling visualization and relationship management.

User navigates analysis interface, uses DiscoveryFlow to discover schema consolidations in Danish, reviews suggestions, then executes SQL queries on unified schema for better success rate (up to 95%). UI interactions include entering questions, visual join paths, and approving consolidation.

The Multilingual Support (Danish) epic includes the User Language Preference capability. The User Language Preference capability requires the Language column in users table. The User Language Preference capability uses the PATCH /api/v1/auth/me API endpoint to update the user's language setting. The User Language Preference capability uses the User model where the language column is added. The User Language Preference capability updates the RegisterRequest schema to include the language field. The User Language Preference capability uses the API auth endpoint for language preference to update and retrieve user language settings.

The User Language Preference capability uses the User model where the language column is added.

Represents a user's question or request for data analysis.

Includes user registration and JWT-based login for secure access. DataLens Development includes user registration as part of its API endpoints.

User Interface Components implement and use the User Schema Relationship Mapper to visualize table joins and relations for users. Schema Relationship Explorer is an instance or feature of the User Schema Relationship Mapper used for data visualization.

Vanna 2.0 implements user-aware identity propagation through system prompts, tool execution, and SQL filtering for permissions. Vanna 2.0 uses user-aware identity propagation through system prompts, tool execution, and SQL filtering. Vanna 2.0 architecture is governed by user-aware identity flow at every layer

For enterprise multi-tenant scaling, DataLens requires adding a UserResolver component to extract identity from authentication. DataLens requires adding UserResolver for multi-tenant user permissions. DataLens needs UserResolver to add multi-tenant permissions like Vanna

Organizations have users tables which store information about users belonging to the organization.

Entity with no existing summary and no relevant message content; no update provided.

API endpoint is likely affected by uvicorn module cache causing KeyError:0 bug FastAPI depends on Uvicorn for serving the application.

DataLens consults the UX Expert when needing validation or feedback on user experience concerns.

The Discovery Service applies the Value overlap join strategy to identify joins by data overlap with 75% confidence.

Vanna is a lightweight AI platform used for autonomous data extraction, planning, verification, and iterative refinement, all running locally with Ollama models like qwen3-coder-next. It supports fast, zero-cost inference on shared GPU hardware. Vanna depends on qdrant-client for vector database integration.

Vanna 2.0 is a comparable AI data platform to DataLens, with differences including multi-LLM and multi-DB support, whereas DataLens currently has single LLM and single DB focus. The architecture comparison document validates design decisions related to Vanna 2.0. Vanna 2.0 uses user-aware identity propagation through system prompts, tool execution, and SQL filtering. Vanna 2.0 uses a tool registry to extend tools with access group permissions. Vanna 2.0 provides streaming UI components to stream structured response objects like tables and charts. Vanna 2.0 utilizes lifecycle hooks for quota checking, logging, and content filtering at request lifecycle points. Vanna 2.0 provides a pre-built web component for embedding the chat interface in various frontend frameworks. Vanna 2.0 enforces row-level security by filtering queries per user permissions. Vanna 2.0 tracks every query per user for compliance through audit logs. Vanna 2.0 architecture is governed by user-aware identity flow at every layer Vanna 2.0 architecture uses a tool registry for extensible tools with permissions Vanna 2.0 architecture supports streaming UI components with progress updates and structured data Vanna 2.0 architecture uses lifecycle hooks for quota checking, logging, and content filtering Vanna 2.0 enforces row-level security filtering queries per user permissions Vanna 2.0 records audit logs tracking queries per user for compliance

Text-to-SQL integrates with Vanna.AI. Vanna 2.0 implements user-aware identity propagation through system prompts, tool execution, and SQL filtering for permissions. Vanna 2.0 implements a Tool Registry that supports extensible tools with access control via access_groups. Vanna 2.0 provides streaming server-sent events responses with progress updates and structured UI components. Vanna 2.0 implements lifecycle hooks for quota checking, logging, and content filtering at request lifecycle points. Vanna 2.0 enforces row-level security by filtering queries based on user permissions. Vanna 2.0 maintains audit logs tracking every query per user for compliance purposes. SQLAgent integrates with Vanna.AI for SQL generation from natural language questions. The architecture comparison with Vanna 2.0 and WrenAI validates architectural design decisions related to Vanna 2.0. DataLens provides a native Text-to-SQL endpoint that covers functionality similar to Vanna AI in the SVGV Budget Analysis Project ecosystem. The Text-to-SQL capability integrates with the external system Vanna.AI for best practice implementations and query optimization. The Text-to-SQL Agent integrates with Vanna.AI to generate SQL queries from natural language. Vanna AI and DataLens are functionally equivalent in providing Text-to-SQL capabilities, but DataLens includes a built-in Text-to-SQL API. Vanna.AI integrates schema and documents embedding into ChromaDB.

Requirement to monitor vector embedding progress; recently fixed for accurate reporting.

Vectorize Progress Tracking uses the RQ job queue to track status and progress of asynchronous embedding jobs. Vectorize Progress Tracking obtains chunk counts from DuckDB service to compute embedding progress accurately. Vectorize Progress Tracking queries the RQ job queue and chunk counts to provide accurate vectorization progress percentages. Direct communicator Jesper prefers the longer fix implementing vectorize progress tracking rather than just quick AI summary fixes.

Background workers include the vectorize worker as a component. The batch processor orchestrator depends on the vectorize worker to generate embeddings for processed chunks.