Lightweight Implementation includes the DS-STAR Intelligence Layer with custom agents in about 1500 lines of code. Lightweight Implementation uses SQLAgent for text-to-SQL functionality. Lightweight Implementation uses RAG Agent for document question answering without heavy dependencies.

The Multilingual Support (Danish) epic includes the LLM Prompt Injection use case. The LLM Prompt Injection use case modifies prompts in catalog.py to include language parameter for file summaries. The LLM Prompt Injection use case modifies prompts in question_router.py to include language parameter for answer synthesis.

Logging is needed for findings generation to trace data flow and identify failure points during findings creation. Logging is necessary when AgentFinding is created to verify that findings are saved and streamed to the frontend properly. Logging is associated with SQL extraction regex fix as it will be added to trace extraction and execution results. question_router logging is a specific logging to be added after SQL query execution to provide diagnostics. findings_generator logging is a specific logging to be added at the start of findings generation for diagnostics. agent.py logging is a specific logging to be added when creating AgentFinding records to confirm saving of findings. Skill Logging records are stored in the agent_skill_log database table capturing skill execution details. Logging will be added to question_router to trace execution after SQL query to help diagnose findings generation issues. Logging will be added to findings_generator at the start of generate_findings to monitor numeric columns detection and diagnose failure points. Logging will be added in agent.py when AgentFinding records are created to verify findings saving to the database. The Logging framework instruments agent_skills.py process_message() to capture errors and execution flow.

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

Monitoring Cluster requires the Consolidation Mechanism to unify monitoring-related datasets for accurate analysis.

The architecture employs Arctic-Text2SQL-R1-7B for simple queries and Qwen3 for complex query handling, including schema selection, join discovery, and answer synthesis. It classifies query complexity to route models effectively, facilitating scaling to 490+ tables with optimized stages like schema compression, reranking, and error recovery. The Multi-Stage Text2SQL Architecture uses the Arctic Model as the specialized SQL generation stage with limited context window.

Planner produces multi-step extraction plans for data processing.

The DataLens Platform implements Multi-Tenant Architecture capability.

The DataLens Project employs a multi-tenant data model for organization isolation. The DataLens platform backend uses a multi-org data model. The Backend implements the multi-tenant data model.

Provides organization-level data isolation supporting multiple tenants, essential for scalable deployment.

The DataLens platform backend uses a multi-org data model.

The Multilingual Support (Danish) epic includes the User Language Preference capability. The Multilingual Support (Danish) epic includes the LLM Prompt Injection use case. The Multilingual Support (Danish) epic includes the Frontend UI Translation use case. The Multilingual Support (Danish) epic involves modifying Backend files to implement language preference and LLM prompt injection. The Multilingual Support (Danish) epic uses the API auth endpoint for language preference to enable user language selection. Danish Language Support was implemented so that Admin User interacts with the system in Danish language. The Backend Service query_enhancer.py uses Danish language keywords and entity recognition to extract columns and values from Danish questions.

The Real-World Validation System uses datasets sourced from Municipal Finance Data Research for testing.

PHASE2_SCOPE_DECISION.md recommends starting with the MVP option for 5-7 days, intending to expand to Ambitious if policy questions arise. The SVGV Budget Analysis Phase 2 epic contains the requirement for Phase 2 MVP with 33 out of 35 questions answered. The Phase 2 MVP includes the requirement for Batch Upload of all 150 SVGV budget files. The Phase 2 MVP includes Partial Extraction of 44 files (Excel and PDF) into DuckDB. The Phase 2 MVP includes the Analysis Report deliverable documented in ANALYTICAL_RESULTS.md file. Start MVP plan is recommended to be started before expanding to address Policy Questions if needed. Opus 4.6 recommends the MVP approach as a minimum viable product completing 25 of 35 analytical questions in 5 to 7 days with selective ingestion.

PHASE2_SCOPE_DECISION.md contains a comparison between MVP and Ambitious options. Expansion to Ambitious plan involves adding support for Policy Questions.

SQLAgent realizes the Natural Language Querying capability converting questions into SQL queries. RAG Agent realizes the Natural Language Querying capability by semantic search and answer generation over documents.

The Platform Backend implements a natural language query endpoint to generate and execute SQL queries from user questions.

Provides users a way to ask questions in natural language and receive structured answers. Processes queries to generate SQL or semantic search, returning results with confidence scores and source attribution, aiming to improve data system accessibility and interaction.

Post-deployment activities include creating a user, uploading data, verifying system functionality, and initial testing to ensure successful system operation and readiness.

DataLens uses notebooks as part of its data analysis work. DataLens agent uses notebooks as part of its workflow for data analysis.

Data from NOVANA files integrated into the SVGV project; on-demand extraction and schema standardization implemented.

NSQL-6B identified as a potential alternative for SQL generation to improve inference speed and SQL quality in future upgrades, complementing current SQLCoder-7B deployment.

The ollama user operates the Ollama inference service hosting the Qwen3-coder-next model used as a fallback LLM for query generation.

DataLens Development implements OpenAPI Client Generation to create auto-generated API clients for frontend and Python users.