Phase 2 file types include PPTX files Phase 2 Strategy Research & Decision Point considers 3 PPTX files for processing in Phase 2 as a low priority task. Opus 4.6 recommends processing all 3 PPTX files due to low cost and consistent summary value.

A React/Vue component enabling chat interface, embedding the system's question-answering features. Vanna 2.0 provides a pre-built web component for embedding the chat interface in various frontend frameworks.

DataLens Agent Mode implements the prepare-data skill for cleaning and transforming datasets via SQL or Python operations.

PrepareDataSkill produces SkillResult during data cleaning and transformation executions. ExtractionCoordinator uses PrepareDataSkill to process data after extraction across CPU and GPU services.

The /api/v1/discovery endpoints include the preview endpoint.

Background workers include the prioritize worker as a component. The batch processor orchestrator uses the prioritize worker to assign processing tiers.

DataLens Agent Mode supports a Private Model Backend using Ollama self-hosted LLMs for GDPR-compliant inference.

The process_message function is expected to call the _run_query function to execute queries, but current data flow problem stops execution before _run_query is reached. In agent_skills.py, the process_message() function calls _run_query asynchronously to generate query results.

Stores processed data files, with no additional details specified.

The project dashboard includes a processing pipeline panel as part of its interface.

ProcessingJob physical table records background jobs executed for projects. The processing_jobs table contains a project_id column relating jobs to projects. FileUpload physical table depends on ProcessingJob physical table representing background processing jobs of uploaded files. Projects include processing_jobs tables that track background jobs related to files or data processing for the project.

The Project dashboard displays the processing pipeline panel utilizing ProcessingJob model data for status and progress.

Production-Ready Infrastructure relies on DuckDB for analytics data storage with read-only connections and timeouts. Production-Ready Infrastructure integrates with Qdrant for vector storage. Production-Ready Infrastructure uses Ollama for GPU-accelerated LLM inference and embeddings.

Corrected progress tracking by removing faulty ORM import, enabling accurate real-time display of catalog, extraction, and vectorization statuses.

Acceptance document with unconditional delivery, contents unspecified in messages; no update provided.

Represents a data analysis project involving internal stakeholders with high influence. Data models such as StandardSalaryRecord, StandardHealthRecord, StandardFinancialTransaction, StandardGeographicData, and StandardBudgetRecord are used within projects to structure relevant data. The project entity is linked to physical tables like FileUpload, Query, Insight, and ProcessingJob, which manage project files, executed queries, insights, and background tasks respectively. Each Project's data is stored in a dedicated DuckDB file (e.g., project_4.duckdb) managed by DuckDBService. Each Project's semantic data is stored in a dedicated Qdrant collection used by QdrantService. User interacts with Project data via the API, querying and managing project-specific information. Project physical table contains multiple FileUpload physical tables representing uploaded files associated with the project. PostgreSQL database stores project metadata like org_id and created_by user. Query data entity references the Project entity by project_id.

Project 13 uses the FastAPI backend for file extraction and DuckDB storage as part of its data platform.

Project 14 involves processing 131 SVGV files for budget analysis; current progress includes uploading, extraction, and initial query testing with plans to fully validate and utilize generated SQL and summaries. Project 14 initially used DuckDB to store extracted data tables during extraction pipeline operations. Arctic-Text2SQL-R1-7B queries Project 14 data through DuckDB schemas for SQL generation. Project 14 data was migrated from DuckDB to PostgreSQL to enable concurrent reads and writes. Arne Hauge is a verified user with access to Project 14. Project 14 contains the SVGV Budget 2026 data used in testing the Data Discovery feature. Project 4 data is stored in a dedicated DuckDB project database file at /app/storage/project_4.duckdb. Project 14 will transition to using its own PostgreSQL schema for extracted data storage. The DataLens project is approved and accessed by the user admin@exerun.com. DuckDB hosts the data tables extracted for Project 14 from SVGV files for analytical queries.

Full extraction of 132 SVGV files completed successfully, data cataloged, summaries generated in Danish, and system verified operational, ready for analysis.

SVGV Budget Analysis is the Project 4 deployed on the platform for batch extraction and analysis.

Comprehensive plan to build a multi-tenant data platform for municipal finance data, leveraging AI extraction and structured schemas.

project agent page is implemented as a SvelteKit page.

Project context in summary generation is required by the DSStarService file cataloging workflow to produce contextually relevant AI summaries.

Project Creation Form uses the scope field renamed as "Project Goal" in the UI for project creation. The page projects > new is part of the projects page. The Project Creation UI includes a textarea for the scope (project goal) with word count validation and generation support.

The Project dashboard displays the processing pipeline panel utilizing ProcessingJob model data for status and progress. The Project dashboard uses file prioritization features to show tier badges and organize files. The Project Dashboard displays the project's scope as the Project Goal for users. The project dashboard includes a processing pipeline panel as part of its interface. The project dashboard contains a folder-grouped file inventory with tier badges and filtering capabilities. The project dashboard UI is validated by the dashboard.spec.ts frontend test case.

The Project Dashboard UI displays the project goal to the user. Dashboard file.ai_summary display renders data stored in the FileUpload record ai_summary column for users to see file summaries. The Frontend provides user interfaces including the Dashboard for displaying file info, AI summaries, and progress status.