The plan includes a Verifier agent to check data quality after extraction.

DS-STAR Intelligence includes the VerifierAgent component. Verifier Agent is part of the plan, serving as an LLM-judge to check data completeness, accuracy, and consistency. VerifierAgent is included in the DS-STAR pipeline. DS-STAR Orchestrator includes VerifierAgent as part of its iterative refinement loop DSStarOrchestrator incorporates the verifier agent for quality scoring of extraction DS-STAR Orchestrator includes VerifierAgent as part of its iterative refinement loop DSStarOrchestrator incorporates the verifier agent for quality scoring of extraction DS-STAR Intelligence capability includes the VerifierAgent component responsible for quality assessment and LLM-judge functionality. The DS-STAR pipeline includes the VerifierAgent component. The Verifier Agent operates within the plan to check data quality and extraction completeness. Router Agent uses outputs from Verifier Agent to decide extraction plan adjustments.

Visual Schema Relationship Mapper is part of the User Interface Components enabling visualization and relationship management. The Visual Schema Relationship Mapper is a User Interface feature enabling users to visualize and customize schema relationships.

Validation report confirming correctness and quality of visualization outputs.

VisualizationService uses VisualizeSkill to generate automatic data visualizations using Lux.

DataLens Agent Mode includes the visualize skill for generating interactive charts using Plotly.

VisualizeSkill uses SkillResult when generating Plotly chart specifications. VisualizationService uses VisualizeSkill to generate automatic data visualizations using Lux.

npm dependency: vite@^7.3.1, used in frontend, version 7.3.1, license type unspecified, no approval or risk assessment noted.

GPU Infrastructure requires deployment of vLLM with Qwen2.5-Coder-14B-AWQ model. The implementation plan includes the vLLM component for GPU infrastructure. GPU Infrastructure uses vLLM for large language model execution on elin. The vLLM component uses the Qwen2.5-Coder-14B-AWQ model version. The plan uses vLLM for large language model inference on elin GPU.

The Docker deployment includes volumes configurations for persistent storage. The Docker deployment configures volumes for data persistence

IronClaw incorporates WASM sandboxing as a technical constraint for tool isolation.

DataLens Agent Mode uses WebSocket for real-time communication with IronClaw.

Word count validation constrains the scope field to have a hard minimum of 20 words. ProjectCreate validation for scope requires word count validation to enforce text length.

Docker service with custom image, ports not exposed, used for batch processing. The worker (Docker) service is defined in docker-compose.yml. The worker (Docker) service is defined in docker-compose.coolify.yml.

The worker container runs the RQ worker instance responsible for processing extraction jobs. The worker container is idle, waiting for extraction jobs in the RQ worker queue after the reset. The Worker container hosts the RQ worker process for asynchronous job processing.

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.

WorkflowService uses BatchProcessor to orchestrate analysis workflows and data processing pipelines. WorkflowService orchestrates pipelines that may be started and monitored by BatchProcessor.

WrenAI implements a Semantic Layer using MDL Models to define table schemas, metrics, joins, and governance rules. WrenAI integrates with multiple LLM providers including OpenAI, Anthropic, Ollama, and others to support text-to-SQL generation. WrenAI generates AI summaries and charts as part of its generated insights. The architecture comparison with Vanna 2.0 and WrenAI validates architectural design decisions related to WrenAI. The architecture comparison document validates design decisions related to WrenAI. WrenAI employs a semantic layer with YAML definitions encoding schema, metrics, joins, and governance rules. WrenAI supports multiple LLM providers including OpenAI, Anthropic, Ollama, and Bedrock. WrenAI primarily offers a CLI tool with an optional web UI for querying. WrenAI uses a semantic layer with YAML model definitions for schema, metrics, and governance

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