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

aiofiles is used in the File upload feature of the DataLens Platform to handle asynchronous file operations.

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

Alembic can be used to manage SQL migration files for database schema evolution in DataLens. Alembic depends on SQLAlchemy for database migrations.

Not currently set up; migrations are applied manually via SQL files. A plan to implement automated auto-migration on startup is recommended to prevent deployment issues, especially for agent-related tables.

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

The Backend exposes an analysis API The Backend implements the Analysis API requirement supporting Text-to-SQL and query history.

API endpoint facilitating SQL query execution against extracted data, integral for structured data analysis. The file backend/app/api/analysis.py contains the /analysis/query endpoint.

Analysis Recommendations generation uses the project scope and file catalog to suggest specific analysis questions. The insights table is the data source that Analysis Recommendations build upon to generate actionable cards using the project's goal context.

Provides suggested actions or insights based on analysis, aiding strategic decision-making, with full API deployment in progress.

The Phase 2 MVP includes the Analysis Report deliverable documented in ANALYTICAL_RESULTS.md file.

Contains Danish summaries and consolidated analysis results, updated after data extraction and review. Used for displaying project insights.

The Analysis View page is part of the SvelteKit frontend.

The unified analysis page is validated by the analysis.spec.ts frontend test case.

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.

InsightService uses AnalysisSuggestion to generate smart analysis recommendations. InsightService generates AnalysisSuggestion using schema analysis via Ollama.

Dependencies injected into each tool call within backend/app/services/pydantic_agent.py, for data analysis.

Structured SQL queries generated via the DataLens platform's natural language interface, validated during extensive testing to ensure correctness and efficiency in extracting insights from the processed datasets.

This requirement was not met; the formal verification of queries in the system has not yet achieved compliance.

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

Initially unspecified; planned to generate user answers based on improved schema and query understanding.

AnswerSynthesizer leverages Qwen3 to synthesize human-readable answers in Danish or English from SQL query results. Multi-Stage Text-to-SQL Architecture realizes the AnswerSynthesizer use case for generating human-readable answers with source attribution.

Anthropic is a third-party AI service provider mentioned alongside others like OpenAI and Google, used for hosting or API access for AI models in DataLens. Pydantic-ai-slim integrates with anthropic for AI model support.

OpenClaw Gateway integrates with the Anthropic API for language model processing Agent Gateway integrates with Anthropic API to provide cloud LLM model backend services.

IronClaw Service uses Anthropic Claude as the cloud LLM provider backend option. Claude is used to power the AI-assisted project goal generation feature. IronClaw Gateway integration backend on elin calls Anthropic Claude model for processing queries and generating results. IronClaw Gateway uses Anthropic Claude as the large language model backend to process user queries with improved speed and privacy. IronClaw service on elin uses Anthropic Claude model for large language processing and SQL generation. Claude in the architecture is the Anthropic Claude large language model used remotely.