The RQ job queue is utilized and managed by backend app api files.py for async AI summary generation processing.

File for backend data models, no updates needed. The models directory contains backend/app/models/models.py and backend/app/models/database.py files for ORM and database connections.

Performs semantic search via Qdrant and synthesizes answers for unstructured data questions.

Agent Chat interface uses OpenClawHttpClient component for communication with agent backend FindingsPanelNew replaces FindingsBoard in the agent page user interface The Analysis chat interface is a planned feature to provide conversational query capability in the DataLens Platform.

Agent config files (SOUL.md, AGENTS.md, MEMORY.md) are part of the DataLens platform backend. The DataLens platform backend includes agent configuration files such as SOUL.md, AGENTS.md, and MEMORY.md. Agent configuration includes the SOUL.md file. Agent configuration includes the AGENTS.md file. Agent configuration includes the MEMORY.md file. The DataLens platform backend includes an Agent config.

The ChatPane component is part of the frontend agent interface for IronClaw-powered Agent Mode.

User can pick or automatically select data analysis agents, see which was used, and view confidence scores, supporting transparent agent strategy in DataLens.

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.

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.

Updated documentation includes new endpoints for discovery, agent, and migration processes. Reflects recent API registration and functionality, ensuring developers can reference current API structure.

The Backend Service question_router.py uses the Backend Service consolidation.py to manage transient consolidated views as part of query execution.

Uses Docling GPU on elin for DOCX extraction with semantic chunking, heading hierarchy, and table embedding as JSON. Falls back to python-docx for simple files; refactored for Phase 2 GPU-first processing. The DocxExtractor is part of the Docling extraction system for DOCX documents using GPU extraction. The docx_extractor.py extractor interfaces with Docling on elin GPU via SSH for DOCX extraction with semantic chunking and rich metadata. GPU-first document extraction includes extracting DOCX files using backend/app/extractors/docx_extractor.py that calls Docling on elin GPU. The DOCX extractor uses Docling for extraction on the elin GPU as a mandatory tool to perform semantic chunking with embedded JSON tables and rich metadata. The RQ worker calls the DOCX extractor for extraction using Docling and fails hard if extraction fails, enforcing the no fallback policy. The DOCX extractor produces semantic chunks with rich metadata including hierarchy and provenance to support DS-STAR queries for document reasoning.

The PPTX extractor uses Docling on elin GPU via SSH to extract PPTX files preserving slide semantics and metadata, without fallback. It is based on python-pptx for slide and text extraction, with enhancements for semantic chunking and slide layout detection (title, bullets, blank). It extracts image metadata such as counts and types for added context, integrated into GPU-first extraction workflow.

Embedding service uses Ollama running on elin GPU for batch embedding of document chunks. Embedding service uses the nomic-embed-text 768-dimensional model via Ollama for GPU accelerated vector embedding. EmbeddingService is used by the Docling extraction system to produce GPU-accelerated embeddings for semantic chunk vectors. batch_vectorize_job depends on EmbeddingService to perform batch vectorization of extracted document chunks. BatchProcessor uses EmbeddingService to vectorize extracted data in the processing pipeline. EmbeddingService integrates with OpenClawHttpClient to use Ollama on elin GPU for embedding computations. The embedding_service.py provides centralized embedding generation by communicating with Ollama running on elin GPU. GPU-first document extraction uses the embedding service in backend/app/services/embedding_service.py which communicates with Ollama on the GPU for embeddings. The embedding service performs batch embeddings using Ollama's nomic-embed-text model on GPU, supporting GPU utilization monitoring and automatic retries. The extraction worker chains extraction results to the embedding service for batch vectorization on GPU after successful DOCX/PPTX extraction.

The extraction worker uses the DOCX extractor for GPU-first document extraction of DOCX files. The extraction worker uses the PPTX extractor for GPU-first extraction of PPTX files. The extraction worker chains to the batch vectorize job to process GPU embeddings after extraction. The extraction worker uses Docling as mandatory extractor for DOCX/PPTX files and fails extraction if Docling fails. The extract.py worker is modified to write extracted data using pg_data_service.py instead of DuckDBService. The extract.py worker invokes Docling-based extractors for DOCX and PPTX files and enforces a no-fallback failure policy if Docling fails. The extract.py worker runs as part of the RQ workers to process extraction jobs asynchronously. The RQ worker calls the DOCX extractor for extraction using Docling and fails hard if extraction fails, enforcing the no fallback policy. The RQ worker calls the PPTX extractor for extraction using Docling, failing hard on extraction errors without fallback. The extraction worker chains extraction results to the embedding service for batch vectorization on GPU after successful DOCX/PPTX extraction.

Test suite verifying Docling installation and extraction quality on elin GPU, ensuring no fallback errors and GPU readiness. The test_docling_extractors.py test suite verifies Docling installation and extraction quality on elin, including DOCX and PPTX extraction and the no fallback behavior.

The Frontend uses Badge.svelte UI component.

The DataLens platform backend uses brainstorming as part of its skills. The DataLens platform backend includes the brainstorming skill. The DataLens platform backend integrates the brainstorming skill.

The BulkUpload component is part of the DataLens frontend interface supporting batch file uploads. The BulkUpload component uses Svelte 5 runes for its frontend implementation. The frontend layer owns the BulkUpload UI component and related user experience features. The BulkUpload component implements the user interface for the batch upload pipeline capability.

The Frontend uses Button.svelte UI component.

The Frontend uses Card.svelte UI component.