The DataLens Master Implementation Plan includes the Testing, Benchmarks, Polish epic. Testing, Benchmarks, Polish phase uses the DABStep dataset for query accuracy measurement. Testing, Benchmarks, Polish references DABStep for data analysis benchmarks. Testing, Benchmarks, Polish uses KramaBench for data analysis benchmarking.

Implements a Transform-Extract-Transform pipeline for standardized data processing across CSV, Excel, and PDF files. DataLens Development implements the TET Pipeline pattern for data extraction standardization as a core business process.

The DataLens Master Implementation Plan includes the Text-to-SQL epic. Text-to-SQL integrates with Vanna.AI. Text-to-SQL integrates with Ollama. Text-to-SQL uses DuckDB for schema auto-training. The file backend/app/services/text_to_sql.py is part of the TEXT-TO-SQL Service. The QuestionRouter class uses the TEXT-TO-SQL Service for SQL query generation. The Backend implements the Text-to-SQL capability. The DataLens Platform uses the Text-to-SQL capability. Text-to-SQL is integrated within the DataLens platform backend. The Text-to-SQL feature uses Ollama hosted on elin accessible via the Ollama URL. The Text-to-SQL capability integrates with the external system Vanna.AI for best practice implementations and query optimization. Text-to-SQL capability integrates with Ollama external system for AI model usage within schema auto-training and natural language to SQL pipelines. The Text-to-SQL capability uses DuckDB as a data platform for schema auto-training and SQL query operations. The DataLens platform backend includes the Text-to-SQL feature. QuestionRouter uses Text-to-SQL capability for structured query execution through TextToSQLService. The Frontend uses the Text-to-SQL analysis capability. The DataLens Platform incorporates Text-to-SQL functionality for natural language queries. SQLAgent realizes the Text-to-SQL capability by converting natural language to SQL executions. The DataLens platform backend uses Text-to-SQL capabilities.

AI Core capability requires the Text-to-SQL with Ollama capability. DS-STAR AI cataloging system uses Text-to-SQL with Ollama for natural language query translation.

Timeout issues with long-running queries are mitigated by streaming responses via the /ask-stream endpoint to prevent HTTP client timeouts. Timeout issues during query classification are mitigated by switching to keyword classification which takes under 10ms compared to prior 120s LLM classification.

Recent code changes to improve system loading have caused responses to now return empty in the OpenClaw integration. Reverting to last known good commit is planned to restore proper budget data extraction.

Vanna 2.0 implements a Tool Registry that supports extensible tools with access control via access_groups. Vanna 2.0 uses a tool registry to extend tools with access group permissions. Vanna 2.0 architecture uses a tool registry for extensible tools with permissions

DataLens requires a Tool Registry pattern to support extensible custom actions beyond SQL, such as email and notifications, with permissions. DataLens requires implementing the tool registry pattern to support extensible custom tools. DataLens requires a Tool Registry pattern to add extensible custom actions beyond SQL

DataLens Development implements the Transform-Extract-Transform Pipeline to standardize data extraction workflows.

Process of creating temporary unified views from related tables to improve Arctic query execution.

The UI Flow allows the User to enter Danish questions and interact with the Data Discovery feature. The UI Flow is implemented using Frontend components such as DiscoveryFlow, DiscoveryLoading, ConsolidationReview, and ConsolidationCard. The Frontend DiscoveryFlow component is part of the UI Flow for the Data Discovery feature.

The unified ask capability is realized in Phase C, which provides a unified question interface combining SQL and semantic routing. The batch upload pipeline is integrated with the unified ask interface to provide query capabilities. DataLens provides the unified ask interface for natural language question routing over structured and semantic data.

The DataLens Platform supports a file upload feature for CSV, Excel, and PDF files. The File upload feature in the DataLens Platform uses python-multipart to handle multipart form data uploads. aiofiles is used in the File upload feature of the DataLens Platform to handle asynchronous file operations.

Represents a user of the system, external stakeholder with variable influence. The UI Flow allows the User to enter Danish questions and interact with the Data Discovery feature. The User is expected to provide the Anthropic API key User uses the Agent Chat interface to interact with the SVGV budget analysis system User asks Danish language budget queries to the DataLens SVGV Budget analysis system Organization physical table includes multiple User entities representing users belonging to an organization. The queries table includes a user_id column that associates each query with a user. The file_uploads table includes an uploaded_by field that links each file to the user who uploaded it. Each agent session is associated with a user via the user_id column in agent_sessions. User interacts with Project data via the API, querying and managing project-specific information. User interacts with IronClaw Gateway as part of the platform to ask Danish budget questions and receive clarified and executed results. User accesses the theo Backend as main API and service endpoint to interact with the platform for budget analysis. Frontend serves the User interface for querying and displaying budget analysis results in Danish. User interacts with OpenClaw as the agent orchestration platform to ask Danish budget questions. OpenClaw HTTP streaming serves real-time token streaming to the User interface for interactive experience. Playwright is used for end-to-end testing to validate User interactions and the platform's functionality. Organization physical table contains User physical table as members belonging to the organization. PostgreSQL database stores user information for authentication and project tracking. Query data entity references the User entity by user_id.

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.

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.

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