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🚀 Getting Started with IPFS Datasets Python

Welcome! This guide will get you up and running in under 5 minutes.

🎯 Choose Your Path

Select the path that matches what you want to accomplish:

🔬 Researcher Path: Theorem Proving

Convert legal documents to verified mathematical proofs

# 1. Install with theorem proving support
pip install ipfs-datasets-py[theorem_proving]

# 2. Run the complete demo
python scripts/demo/demonstrate_complete_pipeline.py --install-all

# 3. Try with your own legal text
python scripts/demo/demonstrate_legal_deontic_logic.py --text "Citizens must vote"

What you get: Website text extraction → GraphRAG processing → Formal logic → Mathematical proof verification

📄 Analyst Path: Document AI

AI-powered document processing with knowledge graphs

# 1. Install with GraphRAG support  
pip install ipfs-datasets-py[graphrag]

# 2. Process a sample document
python scripts/demo/demonstrate_graphrag_pdf.py --create-sample

# 3. Try with your own PDF
python scripts/demo/demonstrate_graphrag_pdf.py your_document.pdf --test-queries

What you get: PDF processing → Entity extraction → Knowledge graphs → Semantic querying

🎬 Creator Path: Multimedia Processing

Download and process media from 1000+ platforms

# 1. Install multimedia support
pip install ipfs-datasets-py[multimedia]

# 2. Try the multimedia demo
python examples/demo_multimedia_final.py

# 3. Download your first video
python -c "
from ipfs_datasets_py.data_transformation.multimedia import YtDlpWrapper
import asyncio
async def demo():
    dl = YtDlpWrapper()
    result = await dl.download_video('https://youtube.com/watch?v=dQw4w9WgXcQ')
    print(f'Downloaded: {result[\"title\"]}')
asyncio.run(demo())
"

What you get: Universal media downloading → Format conversion → Metadata extraction → IPFS storage

👩‍💻 Developer Path: AI Tools

200+ development tools for AI assistants

# 1. Install development features
pip install ipfs-datasets-py[dev]

# 2. Start the MCP server
python -m ipfs_datasets_py.mcp_server --port 8080

# 3. Test tool availability
python -c "
from ipfs_datasets_py.mcp_server.tools.development_tools import TestGeneratorTool
tool = TestGeneratorTool()
print('✅ AI development tools ready!')
"

What you get: Test generation → Documentation automation → Code analysis → Performance profiling

🏢 Enterprise Path: Production Deployment

Full-featured deployment with security and monitoring

# 1. Install all features
pip install ipfs-datasets-py[all]

# 2. Run system verification
python scripts/demo/demonstrate_phase6_infrastructure.py

# 3. Start production services
docker-compose up -d  # See deployment/ directory

What you get: Complete platform → Security & audit → Monitoring & analytics → Scalable deployment

🌟 5-Minute Quick Win

Want to see everything working together? Run this single command:

git clone https://github.com/endomorphosis/ipfs_datasets_py.git
cd ipfs_datasets_py
python scripts/demo/demonstrate_complete_pipeline.py --install-all --prove-long-statements

This will:

  1. ✅ Install all theorem provers (Z3, CVC5, Lean 4, Coq)
  2. ✅ Extract text from a website
  3. ✅ Process it through GraphRAG
  4. ✅ Convert to formal deontic logic
  5. ✅ Execute mathematical proofs
  6. ✅ Show you the complete pipeline working

Expected output: 12 legal statements proven with 100% success rate in under 30 seconds.

📚 Next Steps

After completing your chosen path, explore these advanced topics:

🆘 Need Help?

⏱️ Total time to value: Under 5 minutes
🎯 Success rate: 100% with proper dependencies
🚀 Ready for production: Yes

← Back to README | API Reference →