Skip to content

Core Features

Distributed Knowledge offers a range of powerful features that enable collaborative intelligence across a network of nodes. This document outlines the core features that make up the system.

Federated Architecture

At the heart of Distributed Knowledge is its federated architecture:

  • Decentralized Design: No central authority or control point
  • Peer-to-Peer Interactions: Direct communication between network nodes
  • Network Resilience: The system remains functional even when nodes drop out
  • Horizontal Scaling: Capacity grows as new nodes join the network
  • Emergent Intelligence: Collective capabilities exceed those of individual nodes

Knowledge Sharing

Distributed Knowledge enables efficient sharing of information:

  • Query Broadcasting: Send questions to multiple nodes simultaneously
  • Direct Messaging: Target specific nodes with relevant expertise
  • Knowledge Aggregation: Combine insights from multiple sources
  • Answer Summarization: Consolidate multiple responses into coherent answers
  • Citation Tracking: Maintain references to information sources

Privacy-Preserving Intelligence

The system balances collaboration with privacy:

  • Hybrid Privacy Model: Share public knowledge while protecting private information
  • Selective Sharing: Control what information is accessible to other nodes
  • Approval System: Review and approve incoming queries
  • Anonymized Responses (Soon): Option to provide answers without revealing identity
  • Private Knowledge Bases: Maintain local information that isn't shared

Real-time Synchronization

Information flows through the network in real-time:

  • Persistent WebSocket Connections: Maintain live connections between nodes
  • Immediate Updates: Receive responses as soon as they're available
  • Concurrent Processing: Handle multiple queries and responses simultaneously
  • Live User Status: Track which nodes are currently active
  • Event-Driven Architecture: React to network changes immediately

Multi-Provider LLM Support

The system works with various LLM providers:

  • Anthropic Integration: Connect to Claude models
  • OpenAI Support: Use GPT models for generation
  • Ollama Compatibility: Run local open-source models
  • Provider Abstraction: Switch between providers without changing application code
  • Model Configuration: Customize parameters for each provider

Retrieval Augmented Generation (RAG)

Enhanced responses through contextual retrieval:

  • Vector Database: Store and retrieve semantic embeddings
  • Document Processing: Convert various formats into useful knowledge chunks
  • Semantic Search: Find information based on meaning rather than keywords
  • Context Windows: Provide LLMs with the most relevant information
  • Source Tracking: Maintain provenance for all retrieved information

MCP Server Integration

The system integrates with the Model Context Protocol:

  • Tool Exposure: Access network capabilities through standardized tools
  • Query Management: Create, track, and manage questions
  • Answer Processing: Review, edit, and summarize responses
  • User Management: Interact with network participants
  • Knowledge Base Management: Update and maintain information sources

Secure Communication

All interactions are protected through comprehensive security measures:

  • End-to-End Encryption: Messages are encrypted between sender and receiver
  • Identity Verification: Cryptographic confirmation of node identities
  • Message Signing: Digital signatures ensure content integrity
  • Access Control: Permissions restrict actions to authorized users
  • Secure Connections: TLS/SSL encryption for all network traffic

Autonomous Operation

The system is designed for minimal human intervention:

  • Automatic Approval: Rules determine which queries are automatically accepted
  • Self-Organization: The network adapts to changing conditions
  • Independent Nodes: Each node operates according to its own configuration
  • Resilient Connections: Automatic reconnection after network disruptions
  • Persistent State: Maintains operation across restarts

Open Ecosystem

Distributed Knowledge embraces openness and interoperability:

  • Open Source: Core components available under permissive licenses
  • Standardized Protocols: Well-defined interfaces for integration
  • Extensible Design: Framework for adding new capabilities
  • Community Governance: Transparent development and decision-making
  • Multi-Platform Support: Works across different operating systems