The reset-and-reextract.py script processes the 132 files for full reset and re-extraction of the SVGV dataset. The monitor-extraction.sh script monitors the extraction progress of the 132 files including queue size and extracted/pending counts. The RQ worker processes extraction jobs for the 132 SVGV dataset files. The project_14 schema in PostgreSQL stores extracted data from the 132 SVGV dataset files. The file_uploads data entity tracks the processing status of the 132 files in the project_14 schema. The 132 SVGV files map to DuckDB tables, which are used to store extracted budget data PostgreSQL owns metadata for the 132 SVGV files in the project catalog The SVGV bulk extraction process processes the 132 SVGV files to extract data into DuckDB tables The full SVGV dataset reset and re-extraction process queues 132 extraction jobs for processing. The 132 extraction jobs are processed by the RQ worker.

Defined 5 standard schemas for common data types, supporting standardized analysis and cross-file joins, with implementation progressing.

Creates prompt for LLM to generate SQL, including schema context and examples.

The function _find_numeric_columns was fixed to support decimal types by importing Decimal and updating type detection, deployed in commit 507c94b.

Checks if the project database contains tables, indicating structured data.

An entity that routes and manages data analysis queries, supporting multiple paths like structured, textual, or hybrid.

Agent Memory Store is implemented via PostgreSQL tables which store cross-session memory for the agent.

The `agent_findings` table persistently stores findings linked to agent sessions. Logging is necessary when AgentFinding is created to verify that findings are saved and streamed to the frontend properly. 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. AgentFinding data entities belong to projects representing findings generated in agent analysis. Agent findings are associated with sessions via the session_id column in agent_findings. Agent findings also relate to projects through the project_id column. 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. AgentSession data entity contains AgentFinding data entity as part of its structure. AgentFinding data entity uses AgentSkillLog data entity to log skills-related events. Insight physical table is derived from AgentFinding data entity representing findings extracted by the agent. Agent sessions contain agent_findings tables which store findings related to each session.

The `agent_messages` table contains conversation history linked to agent sessions for cross-session memory. AgentMessage data entities are part of AgentSession data entities representing messages within an agent session. Messages in agent_messages are linked to a particular session via the session_id column. AgentSession data entity includes AgentMessage data entity as a related component. Agent sessions include agent_messages tables which hold messages associated with each session.

The `agent_sessions` table stores records of agent analysis sessions, linked to projects by `project_id`, and logs associated with each session such as GDPR flags, messages, findings, and skill logs. It is stored in the `003_agent_tables.sql` migration file, and the entity is a physical table in PostgreSQL with `id` as primary key.

The `agent_skill_log` table stores skill execution logs associated with records in the `agent_sessions` table. AgentSkillLog data entity records logs for AgentSession entities related to skill execution. AgentSession data entity incorporates AgentSkillLog data entity as part of its information model. AgentFinding data entity uses AgentSkillLog data entity to log skills-related events.

AnalysisWorkflow physical table defines stepwise analysis workflows for Projects. WorkflowStep physical table entries are steps that constitute an AnalysisWorkflow. Each workflow step belongs to an analysis workflow identified by workflow_id. AnalysisWorkflow physical table contains an ordered set of WorkflowStep physical tables as steps in the analysis process. Analysis workflows contain ordered workflow_steps representing individual steps in an analysis process.

The analytical results documented in ANALYTICAL_RESULTS.md correspond to the SVGV Budget Analysis Phase 2 project.

DataLens needs to add an audit_logs table and related query tracking middleware to provide compliance and track which user ran each query. DataLens requires adding an audit_logs table and query tracking middleware to support audit logging.

The Backend Service question_router.py depends on the Backend Service query_enhancer.py to extract entities and enhance queries for consolidated SQL generation. The Backend Service query_enhancer.py uses Danish language keywords and entity recognition to extract columns and values from Danish questions.

The Arctic-Text2SQL-R1-7B model is used via the Backend Service question_router.py to generate SQL. The Qwen3 model is used within the Backend Service question_router.py for schema selection and question ranking tasks. The Backend Service question_router.py depends on the Backend Service schema_graph.py for schema graph construction and join discovery. The Backend Service question_router.py depends on the Backend Service query_enhancer.py to extract entities and enhance queries for consolidated SQL generation. The Backend Service question_router.py uses the Backend Service consolidation.py to manage transient consolidated views as part of query execution.

The Backend Service question_router.py depends on the Backend Service schema_graph.py for schema graph construction and join discovery. The Backend Service schema_graph.py uses the Danish Budget Tables to perform join key analysis and table clustering for consolidation.

Existing service for establishing table indexes; no detailed updates provided.

Backend models initialized, no additional details provided.

The agent models are part of the IronClaw-powered Agent Mode subsystem.

The file backend/app/services/question_router.py is part of the QUESTION ROUTER. The QuestionRouter class is defined within backend/app/services/question_router.py. question_router logging is a specific logging to be added after SQL query execution to provide diagnostics. question_router logging is to be implemented in backend/app/services/question_router.py. question_router logging is to be implemented in backend/app/services/question_router.py. question_router logging is a specific logging to be added after SQL query execution to provide diagnostics. question_router logging is to be implemented in backend/app/services/question_router.py. question_router logging is to be implemented in backend/app/services/question_router.py. backend/app/services/question_router.py implements core logic for table schema limiting, embedding search integration, and Qwen3 re-ranking in the Multi-Stage Text-to-SQL Architecture. Integration requires modifying backend/app/services/question_router.py to incorporate consolidated views in query routing. QuestionRouter is defined in backend/app/services/question_router.py.

The Data Discovery feature contains the TableIndex entity. The discovery.py service uses the TableIndex for semantic table matching. Intelligent consolidation uses TableIndex for semantic table matching. The Data Discovery feature contains the TableIndex entity. The discovery.py service uses the TableIndex for semantic table matching. Intelligent consolidation uses TableIndex for semantic table matching. backend/app/services/table_index.py implements the TableEmbeddingIndex use case as part of the Multi-Stage Text-to-SQL Architecture.

Test fixture CSV for agent personnel data, no details provided.

Test fixture CSV for sales data, no details provided.

Test fixture CSV for timeseries data, no details provided.

Existing entity, relevant to ongoing budget analysis projects.

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