ProjectCreate validation for scope applies to the ProjectCreate pydantic model to enforce a minimum word count. ProjectCreate validation for scope requires word count validation to enforce text length. ProjectCreate validation for scope validates that the scope field meets minimum word count requirements.

The environment variable is not specified or set. It is required for frontend backend communication but currently not met. DataLens Platform requires setting PUBLIC_API_URL environment variable to the public backend API URL for frontend connectivity. Frontend requires backend API URL configured as PUBLIC_API_URL environment variable to connect to backend. DataLens Platform requires setting PUBLIC_API_URL environment variable to the public backend API URL for frontend connectivity. Frontend requires backend API URL configured as PUBLIC_API_URL environment variable to connect to backend.

GPU Infrastructure requires Python environment setup with all dependencies.

EmbeddingService produces embeddings used by QdrantService for semantic search and vector collections. QdrantService supports TableIndexService by providing vector collections for semantic table search indexes. DataLensAgentMemory is backed by QdrantService to provide vector-based agent memory. QuestionRouter uses the Qdrant Service for semantic vector search. Qdrant Service is defined in backend/app/services/qdrant_service.py. Qdrant Service uses Ollama Embedding Service to create vector embeddings. The search method is part of the Qdrant Service. QdrantService initialization was changed to lazy loading in the QuestionRouter to prevent startup timeouts

Requirement category with unmet compliance; involves iterative process for improving query accuracy. The plan includes query refinement to reuse the Verifier Agent for regenerating SQL queries upon failure.

DataLens Agent Mode implements the query-data skill to route natural language and SQL queries for data analysis.

QueryDataSkill produces SkillResult when executing natural language queries via Text-to-SQL. DuckDBService provides database connections and query execution needed by QueryDataSkill. QuestionRouter routes classification results to QueryDataSkill for SQL query execution. TextToSQLService enables QueryDataSkill to convert natural language queries to SQL queries.

Refers to Retrieve-Augment-Generate technique used in backend for document retrieval and question answering. Document RAG provides a RAG query interface to perform semantic searches over documents.

The Real-World Validation System uses datasets sourced from Municipal Finance Data Research for testing. DataLens Development includes an automated validation framework that runs real-world dataset tests.

Endpoint for generating project recommendations, not yet implemented.

A formal requirement model for API requests related to recommendations, with compliance issues noted.

Covers system recovery strategies post-crash, including fallback mechanisms for Qdrant unavailability and lazy-loading benefits.

Backend API requires Redis connection string configured via REDIS_URL environment variable. Backend API requires Redis connection string configured via REDIS_URL environment variable.

The User Language Preference capability updates the RegisterRequest schema to include the language field.

DataLens displays results including natural language answers, semantic matches, generated SQL, confidence scores, and source attribution.

Rich metadata including hierarchy, confidence, and provenance is enforced by the Docling extraction system as a business rule for DS-STAR reasoning. DS-STAR reasoning uses rich metadata such as hierarchy and provenance produced by the Docling extraction system for advanced AI cataloging and analysis.

RingfencedSkills uses ElinSkillClient to execute constrained skill operations for DataLens agent. RingfencedSkills replace raw SQL skills with constrained operations used by SkillExecutor when executing agent skills. ElinSkillClient integrates with RingfencedSkills to perform ringfenced skill executions on elin.

Safety net cleanup enhances SQL extraction regex fix by stripping explanation text markers after extraction to ensure pure SQL before execution. Safety net cleanup was deployed on theo.

Implementing AI-based schema detection is a 'must-have' feature. Current development is ongoing, with integration of Ollama for detecting schema types and suggesting column mappings, aiming for full functionality.

Developed to improve table relevancy and joinability detection, enabling more reliable user queries through schema relationship discovery.