DataLens utilizes the shared GPU box named elin for data analysis workloads, running Docling on an RTX 4000 Ada 20GB GPU for document extraction, with SSH and network setup for Ollama and Qdrant APIs. The platform depends on elin for GPU inference, document extraction, and DS-STAR agents, supporting large document processing with limited tested capabilities for large PDFs. elin hosts the Ollama LLM server including SQLCoder-7B and Arctic-Text2SQL-R1-7B models accessible by theo. DataLens operates using the GPU box (elin) for agentic data analysis workloads with GPU acceleration. The hybrid deployment involves backend running on elin. Hybrid deployment architecture runs all Python and AI workloads on elin. The hybrid deployment involves backend running on elin. Hybrid deployment architecture runs all Python and AI workloads on elin.

The Extraction Pipeline (GPU-First) utilizes the elin GPU processing server which hosts the RTX 4000 GPU, runs Docling for extraction, Ollama for embeddings, and CUDA 12.8. The DataLens Platform uses the GPU box (elin) which hosts Ollama, Qdrant, and DS-STAR agents for AI capabilities. The theo backend integrates with the elin GPU server via SSH to orchestrate Docling extraction and embedding jobs on the GPU hardware running on elin. Docling extraction for DOCX and PPTX files requires the GPU hardware on the elin server to accelerate the extraction and embedding processes. The Backend API integrates with the Ollama GPU service to run LLM models qwen3-coder-next and nomic-embed-text for query classification and embeddings generation.

The Docker deployment environment contains the Frontend, Backend, PostgreSQL, and Redis services.

The Frontend UI Translation use case involves modifying Frontend files to implement i18n.

GPU server NVIDIA RTX 4000 SFF Ada 20GB deployed for Docling extraction and embedding tasks, supporting GPU-first data processing.

The GPU Box refers to the GPU hardware infrastructure on elin, used for high-performance inference tasks including Ollama models and vector embeddings. It supports GPU-accelerated extraction, embedding, and document search, enabling efficient AI computations for the DataLens platform.

Infrastructure includes GPU usage as a monitored specification. GPU usage monitoring depends on the elin server. The DataLens DS-STAR Implementation Plan includes the GPU Infrastructure as a requirement. GPU Infrastructure requires deployment of vLLM with Qwen2.5-Coder-14B-AWQ model. GPU Infrastructure requires deployment of Qdrant vector database. GPU Infrastructure requires installation of DuckDB database system. GPU Infrastructure requires Python environment setup with all dependencies. GPU Infrastructure uses vLLM for large language model execution on elin. GPU Infrastructure includes the use of Qdrant vector database for semantic search capabilities. GPU Infrastructure uses vLLM for large language model execution on elin. GPU Infrastructure includes the use of Qdrant vector database for semantic search capabilities. GPU Infrastructure uses vLLM for large language model execution on elin. GPU Infrastructure includes the use of Qdrant vector database for semantic search capabilities. The plan considers GPU usage on elin especially for Ollama calls and embedding models.

Access to GPU and DS-STAR components is configured on elin for GPU-intensive extraction and AI tasks, enabling full pipeline operation without fallback. The backend has access to GPU and DS-STAR resources on elin. The backend runs as a systemd service on elin with GPU and DS-STAR access.

Typescript setup for i18n utilities, managing language switching and translations.

Infrastructure includes GPU usage as a monitored specification. Infrastructure includes Disk space as a monitored specification. Infrastructure includes Network as a monitored specification. The DataLens Master Implementation Plan depends on the Infrastructure specification to manage computational resources and environment. DataLens Master Implementation Plan depends on the operational Infrastructure for deployment and execution. The implementation plan builds upon the existing infrastructure.

The Install_docling_elin.sh script installs Docling 2.75.0 and dependencies on the elin GPU server for mandatory extraction.

The ironclaw (Docker) service is defined in docker-compose.yml.

The DataLens Platform uses a multi-tenant PostgreSQL schema for data management. DataLens Platform uses a multi-tenant PostgreSQL schema for organizations, users, projects, files, and catalog data. The Backend uses a PostgreSQL schema Project 14 will transition to using its own PostgreSQL schema for extracted data storage. Each project schema such as Project 14's schema is a distinct namespace within PostgreSQL. The Platform Backend uses a multi-tenant PostgreSQL schema to support data modeling and authentication. The DataLens platform backend uses a multi-org data model.

SQLCoder-7B deployed on elin GPU, running at 2-3 seconds inference speed, enabling 3-5x faster DataLens queries than previous models. It generates valid DuckDB SQL and is integrated with the backend for improved query performance.

Infrastructure includes Network as a monitored specification. Network between theo and elin relies on SSH tunnel managed by theo stakeholder. Network between theo and elin relies on SSH tunnel managed by the elin server. The Docker deployment includes network configurations for service communication. The Docker deployment configures networking for container communication Network connection stability, such as SSH tunnel between theo and elin and OpenClaw node status, is part of resource management.

The Frontend is implemented using Node.js runtime environment.

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

The Qdrant vector search service uses Ollama embeddings for generating vector representations of data. RAG Agent uses Ollama embeddings via nomic-embed-text for document retrieval The Docling extraction system utilizes Ollama embeddings (nomic-embed-text) to generate vector embeddings for semantic search and reasoning. The nomic-embed-text component is part of Ollama embeddings used for GPU batch embedding processing of document chunks. Qdrant vectors store the vector embeddings generated by Ollama embeddings from Docling extracted chunks for semantic search. Ollama embeddings run on the RTX 4000 SFF Ada 20GB GPU for batch processing of text chunks.

The backend container is configured to integrate with Ollama via the configured Ollama URL at elin. The Text-to-SQL feature uses Ollama hosted on elin accessible via the Ollama URL.

OpenClaw agent operates within the OpenClaw agent workspace located on the agent server, hosting agent personas and configuration files.

OpenClaw Gateway health monitors the status of the OpenClaw Gateway service

OpenClaw HTTP API is a core component of the OpenClaw infrastructure providing agent communication and streaming services.

OpenClaw node is a component or server related to OpenClaw system. The DataLens Project uses OpenClaw node pairing for communication between theo and elin servers. The lib/elin.py wrapper in the DataLens Project uses OpenClaw node pairing for remote invocation. The DataLens Master Implementation Plan leverages the OpenClaw node deployed on the agent server for agent orchestration and skill execution. The implementation plan depends on a stable OpenClaw node connection which should be reconnected if needed.