Session 56 with 3 messages (text, thinking, thinking) corresponds to no entries in the agent_findings table for project 14, indicating no findings created yet. Session 56, which includes messages, is related to the absence of data in the agent_findings table, indicating query execution paths stopping before findings are created.

DataLens Development has added Standard Models as logical data entities for normalized cross-file queries and AI mapping.

DataLens Development implements Standard Schemas to enable cross-file data analysis via AI-powered schema mapping. The Standard Schemas capability requires the SchemaMapper Service for AI-powered column mapping.

StandardBudgetRecord data entity is used for modeling government budget and spending data in projects. StandardBudgetRecord data entity relates to StandardFinancialTransaction data entity as both involve financial data management.

StandardFinancialTransaction data entity represents financial transactions relevant to projects. StandardSalaryRecord and StandardFinancialTransaction data entities are related as they both model financial compensation and transactions data. StandardBudgetRecord data entity relates to StandardFinancialTransaction data entity as both involve financial data management.

StandardGeographicData data entity models geographic data used in project contexts. StandardHealthRecord data entity uses StandardGeographicData data entity fields for location-related health data analysis.

StandardHealthRecord data entity represents health-related data used in context of projects. StandardHealthRecord data entity uses StandardGeographicData data entity fields for location-related health data analysis.

StandardSalaryRecord entity is a data model for salary and compensation data likely used in context of projects. StandardSalaryRecord and StandardFinancialTransaction data entities are related as they both model financial compensation and transactions data.

Backend API requires mounting persistent storage volumes for uploads and DuckDB files.

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.

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

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

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.

TableReranker uses the Qwen3 model to re-rank candidate tables and select the most relevant ones for query answering. Multi-Stage Text-to-SQL Architecture realizes the TableReranker use case for LLM-based candidate table re-ranking.

Extractor Agents extract tables from PDFs.

The DOCX extractor embeds extracted tables as JSON within text chunks instead of storing them as separate DuckDB tables. The Docling extraction system uses a business rule that tables extracted from documents are embedded as JSON within semantic chunks.

The Data Consolidation capability creates TEMP VIEWs to unify related tables into a single schema for Arctic SQL generation.

test_data_анкеты table is stored in DuckDB (analytics.db)

test_data_шаблон table is stored in DuckDB (analytics.db)

The Text chunks table in DuckDB is a physical table supporting DuckDB SQL queries. The text chunks table in DuckDB with full-text search support is mapped to the DuckDB SQL data store.

Entity representing text chunks used in extraction and semantic search processes.

A temporary unified view created to enhance Arctic query handling over split tables, based on schema relationships, to improve answer accuracy.

Transient views created during consolidation are used as input schema by Arctic for SQL generation.

The consolidated view contains the unified schema for querying purposes.

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

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