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Frequently Asked Questions (FAQ)

Quick answers to common questions about IPFS Datasets Python.

Getting Started

What is IPFS Datasets Python?

IPFS Datasets Python is a comprehensive platform that combines:

  • Decentralized Storage: IPFS-native data management
  • AI Document Processing: GraphRAG with knowledge graphs
  • Theorem Proving: Convert text to verified formal logic
  • Multimedia Processing: Download from 1000+ platforms with FFmpeg
  • Vector Search: Semantic search across datasets

What are the main use cases?

  1. Legal Research: Convert legal documents to verified mathematical proofs
  2. Document Analysis: AI-powered PDF processing with knowledge graphs
  3. Media Archiving: Download and process multimedia from 1000+ platforms
  4. Research Data: Manage and search research datasets with provenance
  5. Enterprise Knowledge Management: Build searchable knowledge bases

What makes it different?

  • Decentralized: IPFS-native for permanent, distributed storage
  • AI-Powered: Advanced NLP, embeddings, and knowledge graphs
  • Formally Verified: Mathematical proof generation and verification
  • Production-Ready: 182+ tests, comprehensive documentation

Installation & Setup

How do I install it?

# Basic installation
pip install ipfs-datasets-py

# With all features
pip install ipfs-datasets-py[all]

# Specific features
pip install ipfs-datasets-py[graphrag]        # Document AI
pip install ipfs-datasets-py[theorem_proving] # Logic & proofs
pip install ipfs-datasets-py[multimedia]      # Media processing

See Installation Guide for details.

What are the system requirements?

Minimum:

  • Python 3.12+
  • 4GB RAM
  • 10GB disk space

Recommended for production:

  • Python 3.12+
  • 16GB+ RAM
  • 100GB+ SSD storage
  • GPU (optional, for faster embeddings)

Do I need to run an IPFS node?

For basic usage: No - the library can use public IPFS gateways.

For production: Yes, recommended - Run your own IPFS daemon for:

  • Better performance
  • More control
  • Privacy
  • Reliability
# Install IPFS
# See https://docs.ipfs.tech/install/

# Start IPFS daemon
ipfs daemon

How long does setup take?

  • Quick start: 5 minutes with pip
  • Full setup with IPFS: 15-20 minutes
  • Production deployment: 30 minutes with Docker, 2 hours for full enterprise setup

Features & Capabilities

Can I process PDFs?

Yes! Advanced PDF processing with:

  • Text extraction
  • Entity recognition
  • Knowledge graph generation
  • Semantic search
  • LLM-optimized formatting

See PDF Processing Guide.

Does it support multimedia?

Yes! Download and process from 1000+ platforms:

  • YouTube, Vimeo, TikTok, Instagram
  • Audio (Spotify, SoundCloud, podcasts)
  • Live streams and archives
  • FFmpeg integration for conversion

See Multimedia Processing Guide.

What about web scraping?

Comprehensive web scraping:

  • Legal databases (US Code, state laws, PACER)
  • Common Crawl integration
  • Internet Archive support
  • Custom scrapers

See Web Scraping Guide.

Can it prove theorems?

Yes! Convert natural language to formal logic:

  • Legal text → Deontic logic
  • Automated proof generation
  • SymbolicAI integration
  • Verification with proof checkers

See Theorem Prover Guide.

Usage

How do I load a dataset?

from ipfs_datasets_py import DatasetManager

# Initialize
dm = DatasetManager()

# Load dataset
dataset = dm.load_dataset("squad")

# Or from IPFS
dataset = dm.load_from_ipfs("QmHash...")

How do I generate embeddings?

from ipfs_datasets_py.embeddings import EmbeddingGenerator

# Initialize
generator = EmbeddingGenerator()

# Generate embeddings
embeddings = generator.generate(texts)

# Store in vector database
vector_store.add(embeddings)

How do I search documents?

from ipfs_datasets_py.search import SearchEngine

# Initialize
search = SearchEngine()

# Semantic search
results = search.search("your query here", top_k=10)

See User Guide for more examples.

Performance

How fast is it?

Performance depends on your hardware and configuration:

  • Embedding generation: ~100-1000 docs/sec (with GPU)
  • Vector search: <100ms for millions of vectors
  • PDF processing: ~10-50 pages/sec
  • IPFS operations: Depends on network and node

See Performance Optimization Guide.

Can I use GPUs?

Yes! GPU acceleration for:

  • Embedding generation (2-10x faster)
  • LLM inference
  • Vector operations

Configure with:

# Use GPU for embeddings
generator = EmbeddingGenerator(device="cuda")

How do I scale for production?

  • Horizontal scaling: Multiple workers
  • Caching: Redis recommended
  • Load balancing: Nginx or similar
  • Database optimization: Vector store tuning

See Deployment Guide.

Troubleshooting

IPFS connection fails

  1. Check IPFS daemon is running: ipfs daemon
  2. Verify API access: curl http://127.0.0.1:5001/api/v0/version
  3. Check firewall settings
  4. Try public gateway as fallback

Out of memory errors

  1. Reduce batch size
  2. Use smaller embedding models
  3. Enable disk caching
  4. Add more RAM or swap

Slow performance

  1. Enable GPU if available
  2. Tune vector store settings
  3. Increase batch size (if memory allows)
  4. Use faster storage (SSD)
  5. Profile and optimize

See Troubleshooting Section in User Guide.

Development

How do I contribute?

We welcome contributions! See the Developer Guide for:

  • Development setup
  • Coding standards
  • Testing requirements
  • Pull request process

How do I report bugs?

  1. Check existing issues
  2. Create new issue with:
    • System information
    • Steps to reproduce
    • Expected vs actual behavior
    • Logs/error messages

Where can I get help?

  • Documentation: Start with Getting Started Guide
  • GitHub Issues: For bugs and feature requests
  • Discussions: For questions and community support

Advanced Topics

Can I customize the embedding model?

Yes! Use any Hugging Face model:

generator = EmbeddingGenerator(
    model_name="sentence-transformers/all-mpnet-base-v2"
)

How does data provenance work?

Every operation is tracked:

  • Data source
  • Transformations applied
  • Timestamps
  • IPFS hashes

See Data Provenance Guide.

What about security?

Built-in security features:

  • Access control
  • Audit logging
  • Encryption support
  • Secure API keys

See Security & Governance Guide.

Still Have Questions?