docs

IPFS Datasets Python Documentation

Welcome to the comprehensive documentation for IPFS Datasets Python - a production-ready decentralized AI data platform.

🚀 Quick Navigation

For New Users

• Getting Started - Introduction and basic concepts
• Installation Guide - Install and setup instructions
• User Guide - Comprehensive usage guide
• Quick Start Examples - Code examples to get started

For Developers

• Developer Guide - Development guidelines and patterns
• API Reference - Complete API documentation
• Contributing - How to contribute to the project

Latest Features (February 2026)

🗄️ IPLD Vector Database - Production-ready distributed vector search

• Vector Store Guides - Implementation guides
• 18 MCP tools for vector operations
• 95% test coverage, 150+ tests

📚 Knowledge Graphs Enhanced - Modular extraction and query system

• Knowledge Graph Guides - Complete documentation
• 110+ new tests with comprehensive coverage
• Unified query engine with hybrid search

📖 Documentation Reorganized - Clean, structured hierarchy

• Archive - Historical reports and planning docs
• Guides - 45 organized feature guides
• Reduced clutter: 85% fewer files in docs root

Documentation Structure

Core Documentation (Root Level)

Essential guides for all users:

• index.md - Main documentation portal
• getting_started.md - Introduction to basic concepts
• installation.md - Installation instructions
• user_guide.md - Comprehensive user guide
• developer_guide.md - Guide for developers
• unified_dashboard.md - Dashboard documentation
• FEATURES.md - Complete feature list
• CHANGELOG.md - Version history

Organized Documentation Directories

guides/ - Feature Guides and How-To Documentation

Organized by feature and component:

• guides/knowledge_graphs/ - Knowledge graph documentation (16 guides)

  • Implementation guides, migration paths, quick references
  • Entity extraction, relationship mapping, query engine

• guides/processors/ - Processor subsystem documentation (29 guides)

  • Architecture, migration guides, quick references
  • File conversion, multimedia processing, data transformation

• guides/deployment/ - Deployment and runner setup guides

• guides/tools/ - Tool-specific documentation (MCP, scrapers, web search)

• guides/infrastructure/ - Infrastructure and CI/CD guides

• guides/security/ - Security, audit logging, and governance

• guides/reference/ - API reference and technical documentation

tutorials/ - Step-by-Step Tutorials

Hands-on tutorials for specific features and use cases.

examples/ - Usage Examples

Code samples and practical examples for common scenarios.

architecture/ - System Architecture

Technical design documents and architecture diagrams.

reports/ - Project Reports

Historical completion reports and project summaries (44+ files).

archive/ - Archived Documentation

Historical documentation and deprecated content organized into:

• archive/completion_reports/ - Phase, session, and task completion reports (44 files)
• archive/knowledge_graphs/ - Historical KG planning and reports (29 files)
• archive/processors/ - Historical processor planning and reports (27 files)
• archive/root_status_reports/ - Root directory status reports (25 files)
• archive/reorganization/ - Documentation reorganization history
• archive/deprecated/ - Deprecated and obsolete documents

Component Documentation

Direct links to module documentation:

• Vector Stores - IPLD vector database, FAISS, Qdrant, Elasticsearch
• Embeddings - Embedding generation and management
• Search - Advanced search including RAG and GraphRAG
• Knowledge Graphs - Extraction, query, and storage
• PDF Processing - PDF analysis and processing
• Multimedia - Media processing capabilities
• LLM - Language model integration
• MCP Tools - 200+ tools for AI assistants
• IPLD - InterPlanetary Linked Data
• Audit - Security and audit logging

🔍 Finding Documentation

By Topic

• Vector Search & Embeddings: See guides/knowledge_graphs/ and ../ipfs_datasets_py/vector_stores/
• Knowledge Graphs: See guides/knowledge_graphs/ for all KG documentation
• File Processing: See guides/processors/ for file conversion and multimedia
• MCP Integration: See guides/tools/ for MCP server and tool documentation
• Deployment: See guides/deployment/ for production deployment guides
• Security: See guides/security/ for audit logging and governance

By Use Case

• Getting Started: Start with getting_started.md and installation.md
• Building AI Applications: See user_guide.md and MCP documentation
• Contributing Code: See developer_guide.md and architecture docs
• Production Deployment: See guides/deployment/ and guides/infrastructure/

Documentation Maintenance

Guidelines

1. Centralization: All documentation lives in the docs/ directory
2. Organization: Follow the established structure for different doc types
3. Cross-Referencing: Use relative links between documentation files
4. Archive Old Docs: Move completed session/phase reports to archive/
5. Update Guides: Keep permanent guides in guides/ up to date
6. Index Files: Each subdirectory should include a README.md or index file

Build and publish docs site

This repository now includes a root-level mkdocs.yml configured to publish docs from docs/, including generated API pages in docs/api/.

• Build site locally: mkdocs build
• Serve docs locally: mkdocs serve

If MkDocs is not installed in your environment, install it with pip install mkdocs.

Recent Reorganization (February 2026)

The documentation was comprehensively reorganized to improve navigation:

• Archived 100+ files: Session reports, phase completions, and planning docs moved to archive/
• Created guides structure: 45 permanent guides organized by feature in guides/
• Reduced clutter: 85% reduction in docs root files (177 → 27 core files)

For details, see DOCS_REORGANIZATION_2026_02_16.md.

Need Help?

• General Questions: See FAQ or User Guide
• Bug Reports: Open an issue on GitHub
• Feature Requests: Check existing issues or open a new one
• Contributing: See CONTRIBUTING.md for guidelines