veritasgraph 0.3.0

Enterprise-Grade Graph RAG Framework - Don't Chunk. Graph. Vision-Native Document Analysis with Hierarchical Tree Support

VeritasGraph

Enterprise-Grade Graph RAG for Secure, On-Premise AI with Verifiable Attribution

VeritasGraph is a production-ready, end-to-end framework for building advanced question-answering and summarization systems that operate entirely within your private infrastructure.

It is architected to overcome the fundamental limitations of traditional vector-search-based Retrieval-Augmented Generation (RAG) by leveraging a knowledge graph to perform complex, multi-hop reasoning.

Baseline RAG systems excel at finding direct answers but falter when faced with questions that require connecting disparate information or understanding a topic holistically. VeritasGraph addresses this challenge directly, providing not just answers, but transparent, auditable reasoning paths with full source attribution for every generated claim, establishing a new standard for trust and reliability in enterprise AI.

📦 Installation

# Install from PyPI
pip install veritasgraph

# With optional dependencies
pip install veritasgraph[web]      # Gradio UI + visualization
pip install veritasgraph[graphrag] # Microsoft GraphRAG integration  
pip install veritasgraph[ingest]   # YouTube & web article ingestion
pip install veritasgraph[all]      # Everything

Quick Start

from veritasgraph import VisionRAGPipeline, VisionRAGConfig

# Configure for local Ollama models
config = VisionRAGConfig(vision_model="llama3.2-vision:11b")
pipeline = VisionRAGPipeline(config)

# Ingest a PDF document (automatically extracts hierarchical structure)
doc = pipeline.ingest_pdf("document.pdf")

# Query with full visual context
result = pipeline.query("What are the key findings in the tables?")

🌳 NEW: Hierarchical Tree Support

The Power of PageIndex's Tree + The Flexibility of a Graph

VeritasGraph now combines two powerful retrieval paradigms:

• Tree-based navigation - Human-like retrieval through Table of Contents structure
• Graph-based search - Semantic similarity across the entire document

from veritasgraph import VisionRAGPipeline

pipeline = VisionRAGPipeline()
doc = pipeline.ingest_pdf("report.pdf")

# View the document's hierarchical structure (like a Table of Contents)
print(pipeline.get_document_tree())
# Output:
# Document Root
# ├── [1] Introduction (pp. 1-5)
# │   ├── [1.1] Background (pp. 1-2)
# │   └── [1.2] Objectives (pp. 3-5)
# ├── [2] Methodology (pp. 6-15)
# │   ├── [2.1] Data Collection (pp. 6-10)
# │   └── [2.2] Analysis Framework (pp. 11-15)
# └── [3] Results (pp. 16-30)

# Navigate to a specific section (tree-based retrieval)
section = pipeline.navigate_to_section("Methodology")
print(section['breadcrumb'])  # ['Document Root', 'Methodology']
print(section['children'])    # [Data Collection, Analysis Framework]

# Or use graph-based semantic search
result = pipeline.query("What methodology was used?")
# Returns answer with section context: "📍 Location: Document > Methodology > Analysis Framework"

Why Hierarchical Trees Matter

Traditional RAG	VeritasGraph with Trees
Chunks documents randomly	Preserves document structure
Loses section context	Maintains parent-child relationships
Can't navigate by structure	Supports TOC-style navigation
No hierarchy awareness	Full tree traversal (ancestors, siblings, children)

CLI Usage

veritasgraph --version                                    # Show version
veritasgraph info                                         # Check dependencies  
veritasgraph init my_project                              # Initialize new project
veritasgraph ingest document.pdf --ingest-mode=document-centric  # Don't Chunk. Graph.

📄 NEW: "Don't Chunk. Graph." - Document-Centric Ingestion

Stop Chunking. Start Graphing.

Traditional RAG systems split documents into arbitrary 500-token chunks, destroying context and structure. VeritasGraph's document-centric mode treats whole pages or sections as single retrievable nodes:

from veritasgraph import VisionRAGPipeline, VisionRAGConfig, IngestMode

# "Don't Chunk. Graph." - Document-centric mode (default)
config = VisionRAGConfig(
    vision_model="llama3.2-vision:11b",
    ingest_mode="document-centric"  # whole pages/sections as nodes
)
pipeline = VisionRAGPipeline(config)
doc = pipeline.ingest_pdf("annual_report.pdf")

# Each page is a single node with full content preserved
# Tables stay intact. Charts stay with their context.

Ingestion Modes

Mode	Description	Best For
document-centric	Whole pages/sections as nodes (default)	Most documents
page	Each page = one node	Slide decks, reports
section	Each section = one node	Structured documents
chunk	Traditional 500-token chunks	Legacy compatibility
auto	Automatically choose	Mixed content

Why Document-Centric Wins

Traditional Chunking	Document-Centric
Splits tables mid-row	Tables stay complete
Loses chart context	Charts with captions
Arbitrary boundaries	Natural structure
500-token fragments	Full page/section

➡️⚡️ Live documentation

📖 Article

📄 Research Paper

🎮 Try Live Demo - Stable URL - always redirects to current server

---

Why VeritasGraph?

✅ Fully On-Premise & Secure

Maintain 100% control over your data and AI models, ensuring maximum security and privacy.

✅ Verifiable Attribution

Every generated claim is traced back to its source document, guaranteeing transparency and accountability.

✅ Advanced Graph Reasoning

Answer complex, multi-hop questions that go beyond the capabilities of traditional vector search engines.

✅ Hierarchical Tree + Graph (NEW!)

Combines PageIndex-style TOC navigation with graph flexibility. Navigate documents like humans do (through sections and subsections) while also leveraging semantic search across the entire graph.

✅ Interactive Graph Visualization

Explore your knowledge graph with an interactive 2D graph explorer powered by PyVis, showing entities, relationships, and reasoning paths in real-time.

✅ Open-Source & Sovereign

Build a sovereign knowledge asset, free from vendor lock-in, with full ownership and customization.

✅ MCP (Model Context Protocol) Integration

Connect AI assistants to Power BI and enterprise tools through the cutting-edge Model Context Protocol standard.

---

🔌 NEW: Power BI MCP Server - AI Meets Business Intelligence

VeritasGraph now includes an enterprise-grade Model Context Protocol (MCP) server for Power BI! Enable AI assistants like Claude, ChatGPT, or any MCP-compatible client to interact with your Power BI data through natural language.

🎯 What is MCP?

The Model Context Protocol (MCP) is an open standard that enables AI models to securely interact with external tools and data sources. Think of it as a universal API for AI assistants - allowing them to query databases, call APIs, and perform actions while maintaining security and auditability.

⚡ Key Features

Feature	Description
🔄 Dual Connectivity	Connect to both Power BI Desktop (local) and Power BI Service (cloud)
💬 Natural Language DAX	Execute DAX queries through conversational AI
📊 34+ Tools	Comprehensive toolkit for data exploration and model management
🔒 Enterprise Security	PII detection, audit logging, and configurable access policies
🛡️ RLS Testing	Test Row-Level Security roles during development
✏️ Safe Refactoring	PBIP-based editing preserves report visual integrity

🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│              AI Assistant (Claude/ChatGPT)              │
└────────────────────────┬────────────────────────────────┘
                         │ MCP Protocol
                         ▼
┌─────────────────────────────────────────────────────────┐
│              Power BI MCP Server (34 Tools)             │
├─────────────────────────────────────────────────────────┤
│  Security Layer │ Audit Logger │ Access Policies        │
├─────────────────────────────────────────────────────────┤
│  Desktop Connector │ XMLA Connector │ PBIP Connector    │
└────────┬───────────────────┬────────────────┬───────────┘
         ▼                   ▼                ▼
   Power BI Desktop    Power BI Service    PBIP Files

🛠️ Available Tools

Desktop Operations:

• desktop_discover_instances - Auto-discover running Power BI Desktop
• desktop_execute_dax - Run DAX queries with security processing
• desktop_list_tables/columns/measures - Explore your data model

Cloud Operations:

• list_workspaces - Access Power BI Service workspaces
• list_datasets - Browse cloud-hosted datasets
• execute_dax - Query cloud data with full security

PBIP Operations (Safe Refactoring):

• pbip_rename_table/column/measure - Rename without breaking visuals
• scan_table_dependencies - Preview rename impact before changes

🚀 Quick Start

# Navigate to the MCP server
cd mcp/powerbi-mcp

# Install dependencies
pip install -r requirements.txt

# Start the backend API
cd backend
# Install dependencies
pip install -r requirements.txt
uvicorn app:app --host 0.0.0.0 --port 8002

# Or run the MCP server directly
python src/server.py

🎥 MCP Server Demo (YouTube)

Full walk-through of the Power BI MCP server: starting the backend, calling tools via the Model Context Protocol, and chatting with datasets through VeritasGraph.

💡 Example: Natural Language to DAX

User: "Show me total sales by region for Q4 2025"

AI Assistant (via MCP):

EVALUATE
SUMMARIZE(
    FILTER(Sales, Sales[Date] >= DATE(2025,10,1)),
    Geography[Region],
    "Total Sales", SUM(Sales[Amount])
)

📖 Documentation

• Power BI MCP Server README - Full setup guide and tool reference
• Backend API Documentation - REST API endpoints
• MCP Client Setup - Next.js chat interface

---

🚀 Demo

Video Walkthrough

A brief video demonstrating the core functionality of VeritasGraph, from data ingestion to multi-hop querying with full source attribution.

📺 YouTube Tutorial

🎬 Watch on YouTube: VeritasGraph - Enterprise Graph RAG Demo

Windows

Linux

---

⚡ NEW: Instant Knowledge Ingest

Instantly add YouTube videos and web articles to your knowledge graph!

Features:

• 📺 YouTube Videos - Automatically extracts transcripts (including auto-generated captions)
• 📰 Web Articles - Extracts main content from blog posts, news articles, documentation
• 📝 Paste Text - Copy-paste content directly from files, documents, PDFs
• 🔄 One-Click Indexing - Build or update your knowledge graph instantly

How to Use:

1. Go to the ⚡ Instant Knowledge tab
2. Paste a YouTube URL or web article URL
3. Click Ingest URL to extract content
4. Click Update Index to add to your knowledge graph
5. Switch to Chat tab and start querying!

---

System Architecture Screenshot

The following diagram illustrates the end-to-end pipeline of the VeritasGraph system:

 graph TD
    subgraph "Indexing Pipeline (One-Time Process)"
        A --> B{Document Chunking};
        B --> C{"LLM-Powered Extraction<br/>(Entities & Relationships)"};
        C --> D[Vector Index];
        C --> E[Knowledge Graph];
    end

    subgraph "Query Pipeline (Real-Time)"
        F[User Query] --> G{Hybrid Retrieval Engine};
        G -- "1. Vector Search for Entry Points" --> D;
        G -- "2. Multi-Hop Graph Traversal" --> E;
        G --> H{Pruning & Re-ranking};
        H -- "Rich Reasoning Context" --> I{LoRA-Tuned LLM Core};
        I -- "Generated Answer + Provenance" --> J{Attribution & Provenance Layer};
        J --> K[Attributed Answer];
    end

    style A fill:#f2f2f2,stroke:#333,stroke-width:2px
    style F fill:#e6f7ff,stroke:#333,stroke-width:2px
    style K fill:#e6ffe6,stroke:#333,stroke-width:2px

---

Five-Minute Magic Onboarding (Docker)

Clone the repo and run a full VeritasGraph stack (Ollama + Neo4j + Gradio app) with one command:

1. Update docker/five-minute-magic-onboarding/.env with your Neo4j password (defaults for the rest).
2. From the same folder run:

   cd docker/five-minute-magic-onboarding
   docker compose up --build
   

3. Services exposed:
   • Gradio UI: http://127.0.0.1:7860/
   • Neo4j Browser: http://localhost:7474/
   • Ollama API: http://localhost:11434/

See docker/five-minute-magic-onboarding/README.md for deeper details.

---

🌐 Free Cloud Deployment (Share with Developers)

Share VeritasGraph with your team using these free deployment options:

Option 1: Gradio Share Link (Easiest - 72 hours)

Run with the --share flag to get a public URL instantly:

cd graphrag-ollama-config
python app.py --share

This creates a temporary public URL like https://xxxxx.gradio.live that works for 72 hours. Perfect for quick demos!

Option 2: Ngrok (Persistent Local Tunnel)

Keep Ollama running locally while exposing the UI to the internet:

1. Install ngrok: https://ngrok.com/download (free account required)

2. Start your app locally:

   cd graphrag-ollama-config
   python app.py --host 0.0.0.0 --port 7860
   

3. In another terminal, create the tunnel:

   ngrok http 7860
   

4. Share the ngrok URL (e.g., https://abc123.ngrok.io) with developers.

Option 3: Cloudflare Tunnel (Free, No Account Required)

# Install cloudflared
# Windows: winget install cloudflare.cloudflared
# Mac: brew install cloudflared
# Linux: https://developers.cloudflare.com/cloudflare-one/connections/connect-apps/install-and-setup/

# Start the tunnel
cloudflared tunnel --url http://localhost:7860

Option 4: Hugging Face Spaces (Permanent Free Hosting)

For a permanent demo (without local Ollama), deploy to Hugging Face Spaces:

1. Create a new Space at https://huggingface.co/spaces
2. Choose "Gradio" as the SDK
3. Upload your graphrag-ollama-config folder
4. Set environment variables in Space settings (use OpenAI/Groq API instead of Ollama)

Comparison Table

Method	Duration	Local Ollama	Setup Time	Best For
--share	72 hours	✅ Yes	1 min	Quick demos
Ngrok	Unlimited*	✅ Yes	5 min	Team evaluation
Cloudflare	Unlimited*	✅ Yes	5 min	Team evaluation
HF Spaces	Permanent	❌ No (use cloud LLM)	15 min	Public showcase

*Free tier has some limitations

---

OpenAI-Compatible API Support

VeritasGraph supports any OpenAI-compatible API, making it easy to use with various LLM providers:

Provider	Type	Notes
OpenAI	Cloud	Native API support
Azure OpenAI	Cloud	Full Azure integration
Groq	Cloud	Ultra-fast inference
Together AI	Cloud	Open-source models
OpenRouter	Cloud	Multi-provider routing
Anyscale	Cloud	Scalable endpoints
LM Studio	Local	Easy local deployment
LocalAI	Local	Docker-friendly
vLLM	Local/Server	High-performance serving
Ollama	Local	Default setup

Quick Setup

1. Copy the configuration files:

   cd graphrag-ollama-config
   cp settings_openai.yaml settings.yaml
   cp .env.openai.example .env
   

2. Edit .env with your provider settings:

   # Example: OpenAI
   GRAPHRAG_API_KEY=sk-your-openai-api-key
   GRAPHRAG_LLM_MODEL=gpt-4-turbo-preview
   GRAPHRAG_LLM_API_BASE=https://api.openai.com/v1
   GRAPHRAG_EMBEDDING_MODEL=text-embedding-3-small
   GRAPHRAG_EMBEDDING_API_BASE=https://api.openai.com/v1
   

3. Run GraphRAG:

   python -m graphrag.index --root . --config settings_openai.yaml
   python app.py
   

Hybrid Configurations

Mix different providers for LLM and embeddings (e.g., Groq for fast LLM + local Ollama for embeddings):

GRAPHRAG_API_KEY=gsk_your-groq-key
GRAPHRAG_LLM_MODEL=llama-3.1-70b-versatile
GRAPHRAG_LLM_API_BASE=https://api.groq.com/openai/v1
GRAPHRAG_EMBEDDING_API_KEY=ollama
GRAPHRAG_EMBEDDING_MODEL=nomic-embed-text
GRAPHRAG_EMBEDDING_API_BASE=http://localhost:11434/v1

📖 Full documentation: See OPENAI_COMPATIBLE_API.md for detailed provider configurations, environment variables reference, and troubleshooting.

---

Switching Between Ollama and OpenAI-Compatible APIs

You can easily switch between different LLM providers by editing your .env file. Here are the most common configurations:

Option 1: Full Ollama (100% Local/Private)

# LLM - Ollama
GRAPHRAG_API_KEY=ollama
GRAPHRAG_LLM_MODEL=llama3.1-12k
GRAPHRAG_LLM_API_BASE=http://localhost:11434/v1

# Embeddings - Ollama
GRAPHRAG_EMBEDDING_MODEL=nomic-embed-text
GRAPHRAG_EMBEDDING_API_BASE=http://localhost:11434/v1
GRAPHRAG_EMBEDDING_API_KEY=ollama

Option 2: Full OpenAI (Cloud)

# LLM - OpenAI
GRAPHRAG_API_KEY=sk-proj-your-key
GRAPHRAG_LLM_MODEL=gpt-4-turbo-preview
GRAPHRAG_LLM_API_BASE=https://api.openai.com/v1

# Embeddings - OpenAI
GRAPHRAG_EMBEDDING_MODEL=text-embedding-3-small
GRAPHRAG_EMBEDDING_API_BASE=https://api.openai.com/v1
GRAPHRAG_EMBEDDING_API_KEY=sk-proj-your-key

Option 3: Hybrid (OpenAI LLM + Ollama Embeddings)

Best of both worlds - powerful cloud LLM with local embeddings for privacy:

# LLM - OpenAI
GRAPHRAG_API_KEY=sk-proj-your-key
GRAPHRAG_LLM_MODEL=gpt-4-turbo-preview
GRAPHRAG_LLM_API_BASE=https://api.openai.com/v1

# Embeddings - Ollama (local)
GRAPHRAG_EMBEDDING_MODEL=nomic-embed-text
GRAPHRAG_EMBEDDING_API_BASE=http://localhost:11434/v1
GRAPHRAG_EMBEDDING_API_KEY=ollama

Quick Reference

Provider	API Base	API Key	Example Model
Ollama	http://localhost:11434/v1	ollama	llama3.1-12k
OpenAI	https://api.openai.com/v1	sk-proj-...	gpt-4-turbo-preview
Groq	https://api.groq.com/openai/v1	gsk_...	llama-3.1-70b-versatile
Together AI	https://api.together.xyz/v1	your-key	meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo
LM Studio	http://localhost:1234/v1	lm-studio	(model loaded in LM Studio)

⚠️ Important: Embeddings must match your index! If you indexed with nomic-embed-text (768 dimensions), you must query with the same model. Switching embedding models requires re-indexing your documents.

---

Guide to build graphrag with local LLM

Environment

I'm using Ollama ( llama3.1) on Windows / Linux and Ollama (nomic-text-embed) for text embeddings

Please don't use WSL if you use LM studio for embeddings because it will have issues connecting to the services on Windows (LM studio)

IMPORTANT! Fix your model context length in Ollama

Ollama's default context length is 2048, which might truncate the input and output when indexing

I'm using 12k context here (10*1024=12288), I tried using 10k before, but the results still gets truncated

Input / Output truncated might get you a completely out of context report in local search!!

Note that if you change the model in setttings.yaml and try to reindex, it will restart the whole indexing!

First, pull the models we need to use

ollama serve
# in another terminal
ollama pull llama3.1
ollama pull nomic-embed-text

Then build the model with the Modelfile in this repo

ollama create llama3.1-12k -f ./Modelfile

Steps for GraphRAG Indexing

First, activate the conda enviroment

conda create -n rag python=<any version below 3.12>
conda activate rag

Clone this project then cd the directory

cd graphrag-ollama-config

Then pull the code of graphrag (I'm using a local fix for graphrag here) and install the package

cd graphrag-ollama
pip install -e ./
 

You can skip this step if you used this repo, but this is for initializing the graphrag folder

pip install sympy
pip install future
pip install ollama
python -m graphrag.index --init --root .

Create your .env file

cp .env.example .env

Move your input text to ./input/

Double check the parameters in .env and settings.yaml, make sure in setting.yaml, it should be "community_reports" instead of "community_report"

Then finetune the prompts (this is important, this will generate a much better result)

You can find more about how to tune prompts here

python -m graphrag.prompt_tune --root . --domain "Christmas" --method random --limit 20 --language English --max-tokens 2048 --chunk-size 256  --no-entity-types --output ./prompts

Then you can start the indexing

python -m graphrag.index --root .

You can check the logs in ./output/<timestamp>/reports/indexing-engine.log for errors

Test a global query

python -m graphrag.query \
--root . \
--method global \
"What are the top themes in this story?"

Using the UI

First, make sure requirements are installed

pip install -r requirements.txt

Then run the app using

gradio app.py

To use the app, visit http://127.0.0.1:7860/

---

🔗 Interactive Graph Visualization

VeritasGraph includes an interactive 2D knowledge graph explorer that visualizes entities and relationships in real-time!

Graph Explorer Tab

Interactive knowledge graph showing entities, communities, and relationships

Chat with Graph Context

Query responses with full source attribution and graph visualization

Features

Feature	Description
Query-aware subgraph	Shows only entities related to your query
Community coloring	Nodes grouped by community membership
Red highlight	Query-related entities shown in red
Node sizing	Bigger nodes = more connections
Interactive	Drag, zoom, hover for entity details
Full graph explorer	View entire knowledge graph

How It Works

1. After each query, the system extracts the relevant subgraph (nodes/edges) used for reasoning
2. PyVis generates an interactive HTML visualization
3. Switch to the 🔗 Graph Explorer tab to see the visualization
4. Click "Explore Full Graph" to view the entire knowledge graph

Toggle Visualization

Use the checkbox "🔗 Show Graph Visualization" in the left panel to enable/disable automatic graph updates after each query.

---

🔌 Power BI MCP Integration

VeritasGraph includes a Model Context Protocol (MCP) server that integrates the GraphRAG knowledge graph with Power BI, enabling intelligent dataset selection and DAX query generation.

Architecture

┌─────────────────────┐     ┌──────────────────────┐     ┌─────────────────────┐
│   Next.js Client    │────▶│  Power BI MCP Server │────▶│  GraphRAG API       │
│   (Chat Interface)  │     │  (Python + MCP)      │     │  (FastAPI)          │
└─────────────────────┘     └──────────────────────┘     └─────────────────────┘
         │                           │                            │
         │                           ▼                            ▼
         │                  ┌──────────────────┐         ┌─────────────────────┐
         │                  │  Power BI API    │         │  Knowledge Graph    │
         │                  │  (REST/XMLA)     │         │  (Parquet Files)    │
         └──────────────────┴──────────────────┴─────────┴─────────────────────┘

Components

Component	Location	Port	Description
GraphRAG API	graphrag-ollama-config/api.py	7860	REST API exposing GraphRAG search functions
Power BI MCP Server	mcp/powerbi-mcp/src/server.py	-	MCP server with Power BI tools
Next.js Client	mcp/Client/	3000	Chat interface for interacting with Power BI

GraphRAG API Endpoints

Endpoint	Method	Description
/health	GET	Health check and index status
/query	POST	Execute GraphRAG queries (local/global search)
/ingest	POST	Ingest text content into knowledge graph
/index	POST	Trigger GraphRAG indexing
/status	GET	Get current indexing status
/files	GET	List input files

Quick Start

1. Start GraphRAG API Server:

   cd graphrag-ollama-config
   python api.py --host 127.0.0.1 --port 7860
   

   Or use the startup script:

   .\start-graphrag-api.ps1
   

2. Start Ollama (LLM):

   ollama serve
   

3. Start the MCP Client:

   cd mcp/Client
   npm run dev
   

4. Access the Chat Interface:

   • Open http://localhost:3000
   • Chat with your Power BI data using natural language

Available MCP Tools

The Power BI MCP server provides these tools:

Tool	Description
list_workspaces	List all Power BI workspaces
list_datasets	List datasets in a workspace
get_tables	Get tables from a dataset
get_columns	Get columns from a table
execute_dax	Execute DAX queries
suggest_dataset_for_query	Use knowledge graph to suggest best dataset
generate_dax_with_context	Generate DAX with schema-aware context
index_schema_to_knowledge	Index Power BI schema to GraphRAG

Configuration

Set these environment variables in mcp/Client/.env:

PYTHON_PATH=C:/Projects/graphrag/VeritasGraph/.venv/Scripts/python.exe
MCP_SERVER_SCRIPT=c:/Projects/graphrag/VeritasGraph/mcp/powerbi-mcp/src/server.py
LLM_API_URL=http://127.0.0.1:11434/v1/chat/completions
LLM_MODEL=llama3.1:latest
GRAPHRAG_API_URL=http://127.0.0.1:7860
POWERBI_ACCESS_TOKEN=<your-power-bi-token>

Example Usage

Query the chat interface with natural language:

"Show me the top 10 customers by revenue from last month"

The system will:

1. Query the GraphRAG knowledge graph for relevant schema context
2. Identify the appropriate Power BI dataset
3. Generate a valid DAX query
4. Execute it against Power BI
5. Return formatted results with source attribution

---

📑 Table of Contents

• Core Capabilities
• The Architectural Blueprint
• Beyond Semantic Search
• Secure On-Premise Deployment Guide
• API Usage & Examples
• Project Philosophy & Future Roadmap
• Acknowledgments & Citations

---

1. Core Capabilities

VeritasGraph integrates four critical components into a cohesive, powerful, and secure system:

• Multi-Hop Graph Reasoning – Move beyond semantic similarity to traverse complex relationships within your data.
• Efficient LoRA-Tuned LLM – Fine-tuned using Low-Rank Adaptation for efficient, powerful on-premise deployment.
• End-to-End Source Attribution – Every statement is linked back to specific source documents and reasoning paths.
• Secure & Private On-Premise Architecture – Fully deployable within your infrastructure, ensuring data sovereignty.

---

2. The Architectural Blueprint: From Unstructured Data to Attributed Insights

The VeritasGraph pipeline transforms unstructured documents into a structured knowledge graph for attributable reasoning.

Stage 1: Automated Knowledge Graph Construction

• Document Chunking – Segment input docs into granular TextUnits.
• Entity & Relationship Extraction – LLM extracts structured triplets (head, relation, tail).
• Graph Assembly – Nodes + edges stored in a graph database (e.g., Neo4j).

Stage 2: The Hybrid Retrieval Engine

• Query Analysis & Entry-Point Identification – Vector search finds relevant entry nodes.
• Contextual Expansion via Multi-Hop Traversal – Graph traversal uncovers hidden relationships.
• Pruning & Re-Ranking – Removes noise, keeps most relevant facts for reasoning.

Stage 3: The LoRA-Tuned Reasoning Core

• Augmented Prompting – Context formatted with query, sources, and instructions.
• LLM Generation – Locally hosted, LoRA-tuned open-source model generates attributed answers.
• LoRA Fine-Tuning – Specialization for reasoning + attribution with efficiency.

Stage 4: The Attribution & Provenance Layer

• Metadata Propagation – Track source IDs, chunks, and graph nodes.
• Traceable Generation – Model explicitly cites sources.
• Structured Attribution Output – JSON object with provenance + reasoning trail.

---

3. Beyond Semantic Search: Solving the Multi-Hop Challenge

Traditional RAG fails at complex reasoning (e.g., linking an engineer across projects and patents).
VeritasGraph succeeds by combining:

• Semantic search → finds entry points.
• Graph traversal → connects the dots.
• LLM reasoning → synthesizes final answer with citations.

---

4. Secure On-Premise Deployment Guide

Prerequisites

Hardware

• CPU: 16+ cores
• RAM: 64GB+ (128GB recommended)
• GPU: NVIDIA GPU with 24GB+ VRAM (A100, H100, RTX 4090)

Software

• Docker & Docker Compose
• Python 3.10+
• NVIDIA Container Toolkit

Configuration

• Copy .env.example → .env
• Populate with environment-specific values

6. Project Philosophy & Future Roadmap

Philosophy

VeritasGraph is founded on the principle that the most powerful AI systems should also be the most transparent, secure, and controllable.

The project's philosophy is a commitment to democratizing enterprise-grade AI, providing organizations with the tools to build their own sovereign knowledge assets.

This stands in contrast to reliance on opaque, proprietary, cloud-based APIs, empowering organizations to maintain full control over their data and reasoning processes.

Roadmap

Planned future enhancements include:

• Expanded Database Support – Integration with more graph databases and vector stores.

• Advanced Graph Analytics – Community detection and summarization for holistic dataset insights (inspired by Microsoft’s GraphRAG).

• Agentic Framework – Multi-step reasoning tasks, breaking down complex queries into sub-queries.

• Visualization UI – A web interface for graph exploration and attribution path inspection.

🏆 Awards & Recognition

VeritasGraph has been recognized for its contribution to the field of AI and Knowledge Graphs.

ICASF 2025

Presented at the International Conference on Applied Science and Future Technology (ICASF 2025).

📄 View Appreciation Certificate

📚 Citation

If you use VeritasGraph in your research, please cite our paper:

VeritasGraph: A Sovereign GraphRAG Framework for Enterprise-Grade AI with Verifiable Attribution

@article{VeritasGraph2025,
  title={VeritasGraph: A Sovereign GraphRAG Framework for Enterprise-Grade AI with Verifiable Attribution},
  author={Bibin Prathap},
  journal={International Conference on Applied Science and Future Technology (ICASF)},
  year={2025}
}

7. Acknowledgments & Citations

This project builds upon the foundational research and open-source contributions of the AI community.

We acknowledge the influence of the following works:

• HopRAG – pioneering research on graph-structured RAG and multi-hop reasoning.

• Microsoft GraphRAG – comprehensive approach to knowledge graph extraction and community-based reasoning.

• LangChain & LlamaIndex – robust ecosystems that accelerate modular RAG system development.

• Neo4j – foundational graph database technology enabling scalable Graph RAG implementations.

Star History