npm dependency: svelte-check@^4.3.6, used in frontend, version 4.3.6, license type and approval status unspecified.

The Frontend uses the SvelteKit UI component framework. The Frontend uses SvelteKit framework for UI components and routing. The frontend /ask-stream switch is implemented in the SvelteKit Framework to provide streaming UI experience and progress states. The Frontend UI Translation use case uses SvelteKit i18n plugin to provide UI translations. The Frontend uses SvelteKit as a third-party component. DataLens Agent Mode frontend is built using SvelteKit framework, supporting real-time streaming UI. Home page is implemented as a SvelteKit page. login page is implemented as a SvelteKit page. projects page is implemented as a SvelteKit page. project details page is implemented as a SvelteKit page. project agent page is implemented as a SvelteKit page. project analyze page is implemented as a SvelteKit page. project workflow page is implemented as a SvelteKit page. new project page is implemented as a SvelteKit page. projects page is built using the SvelteKit framework. projects > :id page is built using the SvelteKit framework. projects > :id > agent page is built using the SvelteKit framework. projects > :id > analyze page is built using the SvelteKit framework. projects > :id > workflow page is built using the SvelteKit framework. projects > new page is built using the SvelteKit framework. The projects > :id > analyze page is implemented as a SvelteKit page.

The Frontend catalog dashboard is planned to be implemented as a SvelteKit frontend page for user interaction. DataLens Platform plans to use the SvelteKit frontend framework for user interface development, though it is not yet built. The SvelteKit frontend communicates with the FastAPI backend via API endpoints. The SvelteKit frontend provides its own production-ready Dockerfile for deployment. The Analysis View page is part of the SvelteKit frontend. DataLens Project includes SvelteKit frontend that implements the complete user interface and UX flows.

SVGV project involves processing 214 files (Excel, PDFs, CSVs, Word, PPT, MSG) for budget analysis; recent batch extraction of 8 files has been completed, with ongoing validation and metadata storage, aiming for comprehensive financial insights. SQLCoder-7B is planned to be used for full SVGV analysis after deployment is complete.

SVGV Budget 2026 project has uploaded over 200 files for detailed financial and policy analysis, with recent successful extraction, validation, and deployment of GPU-accelerated document processing, moving toward comprehensive insights. Project 14 contains the SVGV Budget 2026 data used in testing the Data Discovery feature. The E2E Test Suite uses real SVGV budget data for end-to-end validation of the Data Discovery feature. Project 14 contains the SVGV Budget 2026 data used in testing the Data Discovery feature. The E2E Test Suite uses real SVGV budget data for end-to-end validation of the Data Discovery feature. The E2E Test Suite uses real SVGV Budget 2026 data to validate the end-to-end discovery and analysis workflow.

The SVGV Budget Analysis Project is owned by the Danish Government - Styrelsen for Grøn Arealomlægning og Vandmiljø. Bridge Consulting worked as a consultant on the SVGV Budget Analysis Project. HBS Economics worked as a consultant on the SVGV Budget Analysis Project. Ajeto worked as a consultant on the SVGV Budget Analysis Project. The SVGV Budget Analysis Project uses Docling for LLM-driven normalization and Excel extraction processing. The SVGV Budget Analysis Phase 2 epic contains the requirement for Phase 2 MVP with 33 out of 35 questions answered. The analytical results documented in ANALYTICAL_RESULTS.md correspond to the SVGV Budget Analysis Phase 2 project. The Danish Government's agency Styrelsen for Grøn Arealomlægning og Vandmiljø is the client for the SVGV Budget Analysis Project. HBS Economics is one of the consulting firms involved in the SVGV Budget Analysis Project. Ajeto is a consulting partner contributing to the SVGV Budget Analysis Project. Docling is used to extract tabular data from Excel files of the SVGV Budget Analysis Project. SVGV Budget Analysis is the Project 4 deployed on the platform for batch extraction and analysis.

The SVGV bulk extraction process processes the 132 SVGV files to extract data into DuckDB tables The Backend container runs the SVGV bulk extraction process to parse files and store data

The Data Discovery feature supports the SVGV dataset containing 132 files, 473 tables, and 351K rows.

A large dataset related to Danish government budget analysis, comprising 242 files with complex Excel structures, being processed through Docling for extraction and analysis. DataLens uses the SVGV dataset for analysis and processing. The SVGV data extraction and query readiness is validated by the E2E test suite. The SVGV data extraction and query readiness is validated by the E2E test suite. SVGV extraction produced the SVGV dataset with 132 extracted files and 473 created tables. SVGV extraction produced the SVGV dataset with 132 extracted files and 473 created tables. RQ Worker processed the SVGV extraction jobs and is currently idle after completion. The Data Discovery system requires the SVGV dataset for providing intelligent consolidation and discovery features. SVGV extraction produced the SVGV dataset with 132 extracted files and 473 created tables. SVGV extraction produced the SVGV dataset with 132 extracted files and 473 created tables. RQ Worker processed the SVGV extraction jobs and is currently idle after completion. The Data Discovery system requires the SVGV dataset for providing intelligent consolidation and discovery features. SVGV extraction produced the SVGV dataset with 132 extracted files and 473 created tables. SVGV extraction produced the SVGV dataset with 132 extracted files and 473 created tables. RQ Worker processed the SVGV extraction jobs and is currently idle after completion. The Data Discovery system requires the SVGV dataset for providing intelligent consolidation and discovery features. The Data Discovery feature supports the SVGV dataset consisting of 132 files, 473 tables, and 351K rows.

DuckDB uses data extracted from the SVGV files to create queryable tables PostgreSQL catalogs metadata for SVGV files but does not contain extracted budget tables after system restart. Extraction API processes SVGV files to extract data tables and write them to DuckDB.

SVGV project focuses on analyzing the complex financial structure using document extraction, LLM-assisted summarization, and semantic search, aiming to answer key budgetary questions efficiently.

The SVGV Full Reset process depends on RQ Worker extraction processing to handle extraction jobs after resetting files and schema. The SVGV Full Reset includes dropping and recreating the PostgreSQL project_14 schema as part of the reset. The full SVGV dataset reset and re-extraction process involves dropping and recreating the project_14 schema to an empty state. The full SVGV dataset reset and re-extraction process queues 132 extraction jobs for processing. The E2E Test Suite validates the fresh SVGV Full Reset data by running tests against the reset and extracted dataset.

Set of 8 real files used for validation of extraction, with summaries pending generation.

Test project using SVGV files and batch processing setup, with successful extraction and planning for scaling.

Document summarizing validation results with 8 files, confirming successful extraction, API responses, and successful platform deployment.

System Orchestration Tests validate prompt correctness, GDPR guardrails, session lifecycle, and logging in DataLens Agent Mode.

The DataLens OpenClaw Integration has been negatively affected by the System returning empty responses condition.

The backend runs as a systemd service with GPU/DS-STAR access on elin. The backend runs as a systemd service on elin with GPU and DS-STAR access.

DiscoveryService uses Table to represent database tables with metadata in consolidation recommendations.

The DOCX extractor handles tables by embedding them as JSON within text chunks instead of separate DuckDB tables.

The Backend discovery service requires Table ranking to prioritize relevant tables.

TableCatalog obtains table schemas and metadata from PostgreSQL to build comprehensive project table catalogs. The Backend provides catalog functionality TableCatalog uses a Danish to English translation dictionary to translate Danish table names into English keywords.

TableEmbeddingIndex uses the nomic-embed-text model to compute semantic embeddings for table names and schemas. TableEmbeddingIndex stores and queries table embeddings in a Qdrant collection for semantic search. Multi-Stage Text-to-SQL Architecture realizes the TableEmbeddingIndex use case for semantic candidate table search. TableEmbeddingIndex uses nomic-embed-text for embedding table names and descriptions.