RAG-LLM Architecture with Ollama and RAGFlow on Kubuntu Linux

Local AI Infrastructure, Domain-Specific LLMs, Generic LLMs, and Engineering Applications

Abstract

Retrieval-Augmented Generation (RAG) combined with Large Language Models (LLMs) is transforming enterprise computing, engineering analysis, research automation, and intelligent knowledge management. Organizations increasingly seek privacy-preserving local AI infrastructures capable of running on desktops, laptops, workstations, and VPS/cloud environments without dependency on external APIs.

This paper presents a comprehensive guide to installing and configuring a local RAG-LLM environment using Ollama and RAGFlow on Kubuntu Linux systems. The paper also explains the theoretical foundations of RAG systems, vector databases, embeddings, semantic search, and AI workflows.

A major focus is placed on understanding the difference between:

  • Generic LLMs
  • Domain-specific LLMs
  • Fine-tuned models
  • Retrieval systems
  • Hybrid enterprise AI architectures

The paper further explores practical applications in:

  • Electrical engineering
  • Computer engineering
  • Industrial IoT
  • HVDC systems
  • Power systems
  • Consulting engineering
  • Research organizations
  • SME digital transformation

1. Introduction

Artificial Intelligence is moving from cloud-centric systems toward localized enterprise AI infrastructure. Organizations increasingly require:

  • Data privacy
  • Local inference
  • Reduced API costs
  • Faster response times
  • Domain specialization
  • Secure knowledge management

Traditional cloud LLM systems suffer from several limitations:

Limitation

Problem

Hallucination

Incorrect information

Limited context

Cannot access company documents

Privacy concerns

Sensitive data leaves organization

API cost

Expensive at scale

Internet dependency

Cloud reliance

Generic knowledge

Poor domain expertise

RAG architectures solve many of these problems.

2. Understanding Large Language Models

What is an LLM?

A Large Language Model is a neural network trained on enormous amounts of text data to predict the next token in a sequence.

Examples include:

Model

Organization

Llama 3

Meta

Mistral

Mistral AI

Gemma

Google

Qwen

Alibaba Cloud

DeepSeek

DeepSeek

3. Generic LLM vs Domain-Specific LLM

This distinction is critically important in enterprise AI.

3.1 Generic LLM

A generic LLM is trained on broad internet-scale data.

Examples:

  • Llama 3
  • GPT models
  • Mistral
  • Gemma
  • Qwen

These models understand:

  • General language
  • Coding
  • Mathematics
  • Conversation
  • Basic science
  • Writing tasks

However, they may lack deep knowledge in:

  • HVDC engineering
  • IEC standards
  • Protection systems
  • Transient stability
  • SCADA protocols
  • Industrial systems

Characteristics of Generic LLMs

Feature

Generic LLM

Broad knowledge

Excellent

Domain expertise

Moderate

Flexibility

High

Hallucination risk

Medium to high

Enterprise customization

Limited

Cost efficiency

Good

3.2 Domain-Specific LLM

A domain-specific LLM is specialized for a particular industry or knowledge area.

Examples:

Domain

Specialized Model

Medicine

Medical LLM

Legal

Legal AI

Finance

Financial LLM

Engineering

Technical engineering LLM

Cybersecurity

Security-focused LLM

Domain-Specific LLM Training Sources

Technical Manuals Research Papers Standards Industrial Logs SCADA Data Engineering Reports Protection Studies Equipment Documentation

Characteristics of Domain-Specific LLMs

Feature

Domain-Specific LLM

Specialized expertise

Excellent

General reasoning

Moderate

Hallucination

Lower in domain

Training cost

High

Fine tuning complexity

High

Industry accuracy

Very high

3.3 Why Domain-Specific LLMs Matter

Example:

Question:

“Explain commutation failure in LCC-HVDC systems.”

A generic LLM may provide:

  • Basic textbook explanation
  • Limited practical engineering insight

A domain-specific engineering model may provide:

  • Fault current analysis
  • Thyristor behavior
  • Reactive power dynamics
  • Protection logic
  • PSCAD/EMTDC interpretation
  • Mitigation strategies

This dramatically improves engineering productivity.

4. RAG vs Fine-Tuning

Many organizations misunderstand this distinction.

Fine-Tuning

Fine-tuning changes model weights.

Advantages:

  • Deep specialization
  • Consistent responses
  • Better domain adaptation

Disadvantages:

  • Expensive
  • GPU intensive
  • Requires ML expertise
  • Hard to update

RAG (Retrieval-Augmented Generation)

RAG keeps the base model unchanged but adds external knowledge retrieval.

Advantages:

  • Easier updates
  • Lower cost
  • Enterprise friendly
  • Better document grounding
  • No retraining needed

Disadvantages:

  • Retrieval quality matters
  • More infrastructure components

Enterprise Preference

Most organizations prefer:

Generic LLM + RAG Knowledge System + Domain Documents

Instead of expensive fine tuning.

5. How RAG-LLM Systems Work

Core Pipeline

User Question │ ▼ Embedding Generation │ ▼ Vector Database Search │ ▼ Relevant Document Retrieval │ ▼ Prompt Construction │ ▼ Large Language Model │ ▼ Generated Response

6. Understanding Embeddings

Embeddings convert text into numerical vectors.

Example:

"HVDC converter transformer" ↓ [0.293, -0.442, 0.983, ...]

Similar concepts have similar vector positions.

Semantic Similarity Example

7. Components of a RAG System

Component

Function

LLM

Generates responses

Embedding model

Creates vectors

Vector database

Stores embeddings

Retriever

Finds relevant context

Prompt builder

Constructs prompts

Document parser

Processes files

UI

User interaction

8. What is Ollama?

Ollama Official Website

Ollama is a local LLM runtime that enables running modern AI models on:

  • Linux
  • Kubuntu
  • Ubuntu
  • macOS
  • Windows

Features:

  • Offline AI
  • GPU acceleration
  • Local inference
  • REST API
  • Easy model management

9. What is RAGFlow?

RAGFlow Official Website

RAGFlow GitHub Repository

RAGFlow is a comprehensive enterprise RAG platform featuring:

  • OCR
  • Knowledge graphs
  • Agent workflows
  • Hybrid retrieval
  • Multi-user environment
  • Document ingestion
  • Semantic retrieval
  • Vector indexing

10. Complete RAGFlow + Ollama Architecture

┌────────────────────┐ │ User Web Browser │ └─────────┬──────────┘ │ ▼ ┌────────────────────┐ │ RAGFlow Frontend │ └─────────┬──────────┘ │ ▼ ┌────────────────────┐ │ RAGFlow Backend │ └─────────┬──────────┘ │ ┌───────────────────┼───────────────────┐ ▼ ▼ ▼ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ Elasticsearch│ │ Redis Cache │ │ MinIO Object │ │ Vector Store │ │ Session Mgmt │ │ Storage │ └──────┬───────┘ └──────────────┘ └──────────────┘ │ ▼ ┌────────────────────┐ │ Embedding Models │ └─────────┬──────────┘ │ ▼ ┌────────────────────┐ │ Ollama Runtime │ │ Llama / Qwen │ └────────────────────┘

11. Kubuntu Installation Requirements

Recommended Kubuntu Version

Version

Recommendation

Kubuntu 22.04 LTS

Stable

Kubuntu 24.04 LTS

Recommended

12. Hardware Requirements

CPU-Only Deployment

Resource

Minimum

CPU

4 cores

RAM

16 GB

Storage

50 GB SSD

GPU Deployment

Resource

Recommended

GPU

NVIDIA RTX 3060+

VRAM

12 GB+

RAM

32 GB

CPU

Ryzen 7 / Intel i7

13. Installing Docker

Docker Documentation

sudo apt update sudo apt upgrade -y sudo apt install docker.io docker-compose-v2 git curl -y

Enable Docker:

sudo systemctl enable docker sudo systemctl start docker

Add user:

sudo usermod -aG docker $USER

14. Installing Ollama

curl -fsSL https://ollama.com/install.sh | sh

Verify:

ollama --version

15. Installing Models

General Purpose Models

ollama pull llama3:8b ollama pull mistral:7b ollama pull qwen3

Domain-Specific Engineering Models

Potential approaches:

Method

Description

Fine-tuning

Train engineering model

RAG documents

Add engineering PDFs

Hybrid

Generic LLM + engineering retrieval

16. Installing Embedding Models

ollama pull nomic-embed-text

Other options:

Embedding Model

Purpose

bge-large

Accurate retrieval

mxbai-embed-large

Enterprise retrieval

e5-large

Multilingual

17. Installing RAGFlow

git clone https://github.com/infiniflow/ragflow.git cd ragflow

18. Configure Ollama Integration

Edit configuration:

nano docker/.env

Add:

OLLAMA_BASE_URL=http://host.docker.internal:11434

Linux alternative:

OLLAMA_BASE_URL=http://172.17.0.1:11434

19. Start RAGFlow

cd docker docker compose up -d

20. Access RAGFlow

http://localhost>

VPS:

http://SERVER_IP>

21. Engineering Use Cases

Electrical Engineering

Applications include:

HVDC Engineering RAG Example

Example query:

“Explain transient stability during pole blocking in VSC-HVDC.”

The system retrieves:

Then generates grounded engineering explanations.

22. Industrial IoT Applications

RAG systems can integrate with:

Industrial AI Workflow

Industrial Sensors │ ▼ SCADA / Historian │ ▼ Document + Data Storage │ ▼ RAG Retrieval Layer │ ▼ Local LLM │ ▼ Industrial AI Assistant

23. Why Local AI Matters

Advantages:

Feature

Benefit

Offline operation

Works without internet

Privacy

Internal data protection

Lower cost

No API billing

Faster inference

Local execution

Customization

Domain specialization

24. Generic LLM + RAG vs Domain-Specific LLM

25. Recommended Enterprise Architecture

Most enterprises should use:

Generic LLM + Enterprise RAG + Domain Documents + Vector Database + Workflow Automation

This provides:

  • High flexibility
  • Lower cost
  • Easier updates
  • Strong domain grounding

26. Vector Databases

Popular choices:

Database

Use

Elasticsearch

Enterprise scale

Qdrant

Lightweight

Weaviate

AI-native

Milvus

Large scale

ChromaDB

Small projects

27. Security Architecture

Important Security Components

Component

Purpose

Firewall

Access control

Reverse proxy

Secure routing

HTTPS

Encryption

Authentication

User control

VPN

Secure remote access

28. VPS Deployment

Recommended providers:

Provider

Strength

Hetzner

Affordable servers

DigitalOcean

Easy deployment

Linode

Small enterprise

Vultr

GPU VPS

29. Kubernetes and Scalability

Enterprise deployments may use:

  • Kubernetes
  • Docker Swarm
  • GPU clusters
  • Multi-node vector databases

30. Future of RAG Systems

Future enterprise AI trends include:

  • Multi-agent AI
  • Autonomous workflows
  • Multimodal RAG
  • Vision-language models
  • Real-time industrial AI
  • Edge AI
  • TinyML integration

31. Conclusion

RAG-LLM systems represent a major evolution in enterprise artificial intelligence. By combining retrieval systems with local language models, organizations can build secure, scalable, and domain-aware AI infrastructures.

The combination of:

  • Ollama
  • RAGFlow
  • Kubuntu Linux
  • Vector databases
  • Domain knowledge repositories

creates a powerful foundation for:

  • Engineering AI assistants
  • Research automation
  • Industrial intelligence
  • SME digital transformation
  • Knowledge management
  • Scientific computing

For electrical and computer engineering organizations, RAG architectures enable the creation of intelligent assistants capable of understanding:

  • HVDC systems
  • Power electronics
  • SCADA environments
  • System studies
  • Protection coordination
  • Industrial IoT
  • Research publications

The future of enterprise AI will likely rely heavily on hybrid architectures where:

Generic LLMs + Domain Knowledge + RAG Retrieval + Workflow Automation + Local Infrastructure

become the standard model for intelligent organizations.

References and Resources  

 

 

 

 

 

 

 

 

Conceptual Semantic Clustering in Embedding Space

Engineering concepts with similar meaning cluster closely in vector space used by RAG systems.

concept

x

y

HVDC converter

1.2

1.5

LCC HVDC

1.4

1.7

FACTS device

2.1

2.4

Transformer protection

4.5

4.9

Differential relay

4.8

5.1

SCADA telemetry

7.2

7.4

Generic LLM plus RAG vs Domain-Specific LLM

Comparison of practical enterprise AI deployment characteristics.

category

genericRag

domainLlm

Deployment simplicity

9

5

Domain expertise

7

10

Update flexibility

10

4

Training cost efficiency

9

3

Infrastructure complexity

7

4