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🚀 Sentinel Quick Start Guide

Get up and running with Sentinel in less than 5 minutes!

What is Sentinel?

Sentinel is a Self-Healing Temporal Knowledge Graph that automatically:

  • 📥 Scrapes and monitors web content
  • 🧠 Extracts knowledge using AI
  • 📊 Stores it in a temporal graph database
  • 🔧 Automatically updates when content changes
  • ⏰ Tracks how knowledge evolves over time

Prerequisites

Before you begin, make sure you have:

  • Python 3.11+ installed
  • Docker installed and running (for Neo4j)
  • Ollama installed (for local LLM) OR an OpenAI API key

Installation

Step 1: Install Sentinel

pip install sentinel-core[all]

Or for development:

git clone https://github.com/Om7035/Sentinel-The-Self-Healing-Knowledge-Graph
cd Sentinel-The-Self-Healing-Knowledge-Graph
pip install -e ".[all]"

Step 2: Start Neo4j

docker run -d \
  --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/password \
  neo4j:latest

Wait ~30 seconds for Neo4j to start, then verify at http://localhost:7474

Step 3: Start Ollama (Optional - for local LLM)

If you want to use a local LLM instead of OpenAI:

# Install Ollama from https://ollama.ai
ollama pull llama3
ollama serve

Step 4: Run Setup Wizard

sentinel init

This interactive wizard will:

  • ✅ Check your Docker installation
  • ✅ Collect your API keys
  • ✅ Generate a .env file
  • ✅ Verify all connections

Follow the prompts and you're done!

Your First Knowledge Graph

1. Process a URL

sentinel watch https://stripe.com/pricing

This will:

  1. Scrape the Stripe pricing page
  2. Extract entities and relationships using AI
  3. Store them in your Neo4j graph

2. Ask Questions

sentinel ask "How much does Stripe cost?"

Sentinel will query the knowledge graph and give you an answer based on what it learned.

3. Check Status

sentinel status

See your graph statistics, database connection, and scraper status.

Using Sentinel in Your Code

import asyncio
from sentinel_core import Sentinel, GraphManager, GraphExtractor
from sentinel_core.scraper import get_scraper

async def main():
    # Initialize components
    graph = GraphManager()
    scraper = get_scraper()  # Auto-detects best available scraper
    extractor = GraphExtractor(model_name="ollama/llama3")
    
    # Create Sentinel instance
    sentinel = Sentinel(graph, scraper, extractor)
    
    # Process a URL
    result = await sentinel.process_url("https://example.com")
    print(f"Extracted {result['extracted_nodes']} nodes!")
    
    # Query the graph
    snapshot = graph.get_graph_snapshot()
    print(f"Total nodes: {snapshot['metadata']['node_count']}")
    
    # Clean up
    graph.close()

if __name__ == "__main__":
    asyncio.run(main())

Next Steps

Explore the CLI

# Get help
sentinel --help

# Run a healing cycle (update stale knowledge)
sentinel heal --days 7

# See what would be healed without doing it
sentinel heal --dry-run

Explore the Web UI

Start the API server:

cd sentinel_platform/api
uvicorn main:app --reload

Start the frontend:

cd sentinel_platform/ui
npm install
npm run dev

Visit http://localhost:3000 to see your knowledge graph in 3D!

Advanced Configuration

Edit your .env file to customize:

# Use OpenAI instead of Ollama
OLLAMA_MODEL=gpt-4
OPENAI_API_KEY=sk-...

# Use Firecrawl for premium scraping
FIRECRAWL_API_KEY=fc-...

# Customize Neo4j
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your-password

Troubleshooting

"Could not connect to Neo4j"

Make sure Docker is running and Neo4j is started:

docker ps  # Should show neo4j container
docker logs neo4j  # Check for errors

"Ollama connection failed"

Make sure Ollama is running:

ollama serve
# In another terminal:
ollama list  # Should show your models

"No scraper available"

The local scraper should always work. If you see this error:

pip install beautifulsoup4 markdownify requests

Getting Help

What's Next?

  • ⏰ Time Travel: Query your graph at any point in the past
  • 🔧 Self-Healing: Automatic updates when content changes
  • 🌐 Multi-Source: Combine knowledge from multiple websites
  • 🤖 RAG Pipelines: Use Sentinel as a knowledge base for your AI apps

Happy graphing! 🎉