Enables queries across multiple files using standard schemas, with ongoing enhancements to schema management and mapping.

CSV extractor component implements the CSV Extraction requirement.

Operational process for ingesting CSV files into DuckDB, now stable and part of the batch extraction workflow.

DataLens Project includes a fully tested CSV extractor as part of its data ingestion. CSV Extractor loads validated and cleaned CSV files into DuckDB.

Data source to be validated, cleaned, and loaded into DuckDB for analysis. DS-STAR Orchestrator processes CSV files during autonomous extraction DS-STAR Orchestrator processes Excel files during autonomous extraction DS-STAR Orchestrator processes CSV files during autonomous extraction DS-STAR Orchestrator processes Excel files during autonomous extraction Extractor Agents validate and clean CSV files for loading into the database.

Docling extraction is constrained by the CUDA 12.8 technology on elin GPU server.

Extensible tools for additional functionalities like email or notifications, integrated into the agent framework.

Commit implementing backend support for language preference, prompts to respond in Danish or English.

Testing, Benchmarks, Polish references DABStep for data analysis benchmarks. The DataLens Master Implementation Plan uses DABStep as a dataset to benchmark query accuracy during testing. Data analysis benchmarks include DABStep as a benchmark component.

Testing, Benchmarks, Polish phase uses the DABStep dataset for query accuracy measurement. The Data analysis benchmarks use the DABStep dataset for measuring query accuracy.

490+ Danish budget tables with complex Danish names and descriptions. Handle schema comprehension, translation, and relevance filtering using multi-stage semantic and re-ranking processes. Focused on high-quality, fast SQL generation while managing large schemas. Multi-Stage Text-to-SQL Architecture handles 490+ Danish budget tables as the input schema. The Backend Service schema_graph.py uses the Danish Budget Tables to perform join key analysis and table clustering for consolidation.

The SVGV Budget Analysis Project is owned by the Danish Government - Styrelsen for Grøn Arealomlægning og Vandmiljø. The Danish Government's agency Styrelsen for Grøn Arealomlægning og Vandmiljø is the client for the SVGV Budget Analysis Project.

In use for Danish-language question understanding and keyword-based routing, improving NLP processing in the platform.

Contains 473 extracted budget tables (SVGV dataset) with ~351,842 rows, structured for Danish municipal budget analysis, queryable via PostgreSQL.

The Discovery Service processes Danish questions for entity extraction, table ranking, and join detection. User asks Danish language budget queries to the DataLens SVGV Budget analysis system Agent Chat interface handles Danish language budget queries from users

Full Danish language support implemented across LLM summaries, analysis, and UI translations. User preferences stored in database, with Danish UI and Danish summaries now active for admin and general users.

Danish table names, such as 'udgifter_til_sociale_ydelser_2023', are translated into English, e.g., 'expenses social benefits 2023', aiding semantic understanding in data processing.

TableCatalog uses a Danish to English translation dictionary to translate Danish table names into English keywords.

The project dashboard UI is validated by the dashboard.spec.ts frontend test case.

Data entities include structured tables and summaries, with ongoing validation of integrity.

DataLens Master Implementation Plan incorporates Data analysis benchmarks like DABStep and KramaBench. Data analysis benchmarks include DABStep as a benchmark component. Data analysis benchmarks include KramaBench as one of the benchmarking tools. The DataLens Master Implementation Plan includes research and testing with Data analysis benchmarks like DABStep dataset and KramaBench. The Data analysis benchmarks use the DABStep dataset for measuring query accuracy.

PostgreSQL is used as the primary data storage for extracted data in the new platform, supporting concurrent read/write access, schema per project, and full-text search, replacing DuckDB for better performance and scalability.

The Frontend catalog dashboard is planned to be implemented as a SvelteKit frontend page for user interaction. FileAnalyzer produces data catalogs used in the extraction planning process. The data catalog generation process depends on the upload directory where user files are stored.