Defines the data structure for creating new projects in the platform, facilitating API validation and input handling in project setup.

Models the structure for requests related to analysis recommendations, aiding API validation for insights generation.

The Discovery Service uses a 4-factor relevance score to rank tables by matching criteria.

The main agent relies on @MoltBot as the infrastructure coordinator for requests and change approvals. DataLens agent depends on MoltBot as infrastructure coordinator for changes and port coordination.

Internal function to classify questions as structured, textual, or hybrid, using LLM or keywords.

Admin User was configured with language set to Danish to receive all summaries and UI in Danish. The Admin User belongs to the organization Exerun. Danish Language Support was implemented so that Admin User interacts with the system in Danish language.

Playwright E2E tests use the test user admin@exerun.com for authentication and functional validation. The DataLens project is approved and accessed by the user admin@exerun.com.

DataLens requires adding AfterQuery hooks to intercept requests for cost tracking and filtering.

User uses the Agent Chat interface to interact with the SVGV budget analysis system Agent Chat integrates with OpenClaw Gateway for processing user queries with Claude Agent Chat interface handles Danish language budget queries from users

User testing to verify agent chat system's responsiveness and stability, including streaming responses, Danish language support, and no timeout errors, conducted at datalens.exerun.com.

The 003_agent_tables.sql migration file creates the agent session database tables required for the IronClaw agent. Jesper DevOps is responsible for executing the agent migration file 003_agent_tables.sql to enable the IronClaw agent database functionality. The SQL migration execution script is used to apply the agent migration file 003_agent_tables.sql to the PostgreSQL database on theo. Agent database migration corresponds to the migration script backend/migrations/003_agent_tables.sql which was applied.

The IronClaw agent feature includes the Agent orchestration system as a core capability.

DataLens uses an Agent Selector UI that allows users to select or auto-route queries among different agents like Budget Analyzer and Direct SQL. DataLens provides a user-facing agent selector to pick or auto-route among agents. Agent Selector Dropdown UI component is part of the Backend frontend interface to select AI agents for query answering. Direct SQL is an option in the Agent Selector Dropdown for performing raw structured queries. Agent Selector directs queries to the Budget Analyzer agent Agent Selector directs queries to the Policy Researcher agent Agent Selector directs queries to the Efficiency Analyzer agent Agent Selector directs queries to Direct SQL agent

Agent Skills Integration modifies _run_query to generate findings from query results and stream them as insights. Agent Skills Integration renders each streamed finding as a separate IronClawMessage. _run_query method is modified as part of Agent Skills Integration to generate findings from query results The process_message() method in agent_skills.py calls the _run_query() function asynchronously to execute queries. agent_skills.py process_message() yields IronClawMessage instances for communicating agent responses and errors. Opus analysis investigates the behavior of agent_skills.py process_message() to diagnose async exception issues. The Logging framework instruments agent_skills.py process_message() to capture errors and execution flow. The process_message() method in agent_skills.py calls the _run_query() function asynchronously to execute queries. agent_skills.py process_message() yields IronClawMessage instances for communicating agent responses and errors. Opus analysis investigates the behavior of agent_skills.py process_message() to diagnose async exception issues. The Logging framework instruments agent_skills.py process_message() to capture errors and execution flow.

AgentWarmingService assembles warm context that is used by SkillExecutor in agent sessions.

Batch upload is part of the enhanced AI cataloging process in DataLens. The FastAPI backend uses AI cataloging to automatically discover table structures on file upload. The Backend implements AI cataloging capabilities. The DataLens Platform uses AI cataloging via integration with the DS-STAR FileAnalyzer, which runs automatically upon file upload.

AI Core capability requires the DS-STAR autonomous extraction capability. AI Core capability requires the Text-to-SQL with Ollama capability. AI Core capability requires the Document RAG capability. The DataLens Platform uses the AI Core capabilities to perform autonomous extraction, text-to-SQL, and document retrieval augmented generation. The AI Core includes DS-STAR autonomous extraction components such as PlannerAgent, VerifierAgent, RouterAgent, and Orchestrator. The AI Core includes Document RAG technology built using nomic-embed-text and Qdrant for vector search.

AI Generated Goals are derived using the Hybrid (manual + AI-Assist) Approach combining user brief input with AI expansion via Claude.

Verifiable requirement with successful Phase 2 implementation; summaries are generated asynchronously.

Existing requirement to generate AI summaries asynchronously; now operational with background tasks.

AI Summary Generation stores summaries in the FileUpload record ai_summary column after extraction completes in the cataloging workflow. The Files API supports AI Summary Generation by exposing necessary endpoints and data fields for storage and retrieval of AI summaries. The Extraction coordinator is part of the workflow that triggers AI Summary Generation after file extraction completes. AI Summary Generation is implemented asynchronously using the RQ job queue to avoid blocking HTTP responses during file list retrieval. Direct communicator Jesper approved progressing with AI Summary Generation and vectorize progress tracking fixes in the same session for accuracy. The AI Summary Generation feature uses the RQ job queue for asynchronous summary generation after extraction completes. AI Summary Generation stores the generated summaries in the ai_summary column of the FileUpload records.

Phase 2 requirement: moved AI summary generation to async background queue, resolving performance and timeout issues.

Claude is used to power the AI-assisted project goal generation feature. The AI-assisted goal generation capability is realized through the generate-goal API endpoint.

Supports AI-driven schema detection and mapping to facilitate cross-file data analysis, though full persistence and application are still pending.

Ajeto worked as a consultant on the SVGV Budget Analysis Project. Ajeto is a consulting partner contributing to the SVGV Budget Analysis Project.

Opus 4.6 recommends against full 153-file load due to complexity and risk, suggesting a phased selective ingestion instead.