Chat with Your Own Documents (RAG Flow)

Unlock Generative AI for Corporate Data with Zero Risk

Many organizations want to leverage Generative AI to query their internal corporate data. However, a critical concern remains: How do you do this without exposing sensitive documents to third-party governments or public cloud providers like OpenAI, Google, or Microsoft?

The GLBNXT platform solves this by allowing you to build Retrieval Augmented Generation (RAG) pipelines that are fully local, modular, and EU-compliant. Your data is processed, stored, and retrieved without ever leaving your sovereign infrastructure.

The Sovereign Tech Stack

In this tutorial, we build a modular agent using open components available on the GLBNXT platform:

• Storage: MinIO for storing raw documents (PDFs, images, text).

• Vector Database: PostgreSQL with pgvector to store searchable data embeddings.

• LLM: Local GPU-accelerated models (e.g., Mistral, Gemma, or Llama) to handle reasoning and OCR.

• Orchestration: n8n to handle the ingestion, pre-processing, and retrieval logic.

Step-by-Step Workflow

1. The Ingestion Pipeline

Before chatting, data must be processed. The n8n workflow handles this automatically:

• Loop & Download: The system retrieves files uploaded to your private MinIO bucket.

• Decision Logic: It checks the file type. If it is an image or scanned document, it uses the Mistral LLM for OCR (Optical Character Recognition) to extract text.

• Vectorization: The extracted content is converted into vectors and stored in the PostgreSQL database.

2. The Retrieval (Chat) Interface

Once the data is vectorized, you can interact with it via a chat interface. The AI agent is configured with specific tool calls that determine when and how to fetch information.

3. Context-Aware Responses

The agent is capable of "switching context" within a single conversation.

• Example: You can ask for a summary of a technical manual (e.g., ISPMA software product management).

• Follow-up: In the same chat, you can ask about a specific person's education from a completely different file (e.g., a resume for "Leyon Vandervort").

• Result: The agent intelligently retrieves the correct document for each specific question.

4. Full Traceability

Unlike "black box" public AI tools, GLBNXT provides full transparency. You can view the execution logs in real-time to see exactly:

• Which tool the agent called.

• Which documents were retrieved from the vector storage.

• How the answer was constructed.

Why Build This on GLBNXT?

• 100% Data Sovereignty: Your corporate knowledge base never leaves the EU.

• Model Agnostic: Swap models (Mistral, Llama, etc.) based on your specific requirements.

• Secure Orchestration: Manage complex logic and permissions locally without external API dependencies.