VeritasGraph: The All-in-One GraphRAG Framework

Stop chunking blindly. Combine the structure of Tree-Search with the reasoning of Knowledge Graphs. Runs locally or in the cloud.

🎯 Traditional RAG guesses based on similarity. VeritasGraph reasons based on structure.
Don't just find the document—understand the connection.

🌳 + 🔗 Graph + Tree: The Ultimate Retrieval

Document Root
├── [1] Introduction
│   ├── [1.1] Background ←── Tree Navigation
│   └── [1.2] Objectives
├── [2] Methodology ←───────── Graph Links
│   └── relates_to ──────────→ [3.1] Findings
└── [3] Results

📊 Feature Comparison

VeritasGraph is a production-ready framework that solves the fundamental problem with vector-search RAG: context blindness. While traditional RAG chunks your documents into isolated fragments and hopes cosine similarity finds the right one, VeritasGraph builds a knowledge graph that actually understands how your information connects.

The result? Multi-hop reasoning that answers complex questions, transparent attribution for every claim, and a hierarchical tree structure that navigates documents like a human would—all running on your own infrastructure.

🚀 Get Started in 2 Lines

No GPU? No problem. Try VeritasGraph instantly:

pip install veritasgraph
veritasgraph demo --mode=lite

That's it. This launches an interactive demo using cloud APIs (OpenAI/Anthropic)—no local models required.

🎬 See It In Action

▶️ Watch VeritasGraph build reasoning paths in real-time.

▶️ Convert Charts & Tables to Knowledge Graphs in Minutes | Vision RAG Tutorial

💡 What you're seeing: A query triggers multi-hop reasoning across the knowledge graph. Nodes light up as connections are discovered, showing exactly how the answer was found—not just what was found.

Choose Your Path

# Lite mode (cloud APIs, zero setup)
export OPENAI_API_KEY="sk-..."
veritasgraph demo --mode=lite

# Local mode (100% offline with Ollama)
veritasgraph demo --mode=local --model=llama3.2

# Full mode (complete GraphRAG pipeline)
veritasgraph start --mode=full

📦 Installation

# Basic install (includes lite mode)
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 (Python API)

Once you're ready to integrate VeritasGraph into your code:

from veritasgraph import VisionRAGPipeline

# Simplest usage - auto-detects available models
pipeline = VisionRAGPipeline()
doc = pipeline.ingest_pdf("document.pdf")
result = pipeline.query("What are the key findings?")
print(result.answer)

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?")

🌳 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

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.

� Ingestion Capabilities

VeritasGraph offers multiple ways to ingest content into your knowledge graph:

"Don't Chunk. Graph." - Document-Centric Mode

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

from veritasgraph import VisionRAGPipeline, VisionRAGConfig

config = VisionRAGConfig(ingest_mode="document-centric")  # Tables stay intact!
pipeline = VisionRAGPipeline(config)
doc = pipeline.ingest_pdf("annual_report.pdf")

⚡ Instant Knowledge Ingest

Add content to your knowledge graph with one click:

# CLI ingestion
veritasgraph ingest https://youtube.com/watch?v=xxx
veritasgraph ingest https://example.com/article
veritasgraph ingest document.pdf --mode=document-centric

Ingestion Modes

➡️⚡️ 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.

🚀 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

Linux

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:

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

*Free tier has some limitations

OpenAI-Compatible API Support

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

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

⚠️ 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

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

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

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.

📑 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.

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

🏆 Awards & Citation

@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}
}