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Architecture Overview

Distributed Knowledge employs a federated architecture designed to foster collective intelligence through secure, real-time communication among decentralized nodes. This document outlines the system's architecture and details the interactions between its core components.

Core Architecture

The Distributed Knowledge architecture consists of the following key components:

1. WebSocket Communication Layer

The foundation of Distributed Knowledge is its real-time communication system:

  • Secure WebSocket Protocol: Provides encrypted, bidirectional communication
  • Authentication System: Verifies identities through public/private key pairs
  • Message Routing: Handles direct and broadcast message delivery

2. Knowledge Management Layer

The system's ability to manage and retrieve information:

  • Vector Database: Stores and retrieves embeddings for RAG functionality
  • Document Processing: Converts raw documents into useful knowledge chunks
  • Knowledge Synchronization: Maintains consistency across the network
  • Privacy Controls: Ensures data is shared according to user preferences

3. LLM Integration Layer

The intelligence layer that processes queries and generates responses:

  • Multi-Provider Support: Works with Anthropic, OpenAI, and Ollama
  • Context Management: Prepares relevant context for LLM prompts
  • Response Generation: Produces answers based on available knowledge
  • Answer Validation: Ensures responses meet quality and accuracy standards

4. MCP (Model Context Protocol) Server

The interface that allows other systems to interact with Distributed Knowledge:

  • Tool Integration: Exposes DK capabilities as tools
  • Query/Response Flow: Manages the lifecycle of questions and answers
  • User Management: Handles user interactions and permissions
  • Automatic Approval System: Filters responses based on defined criteria

Data Flow

  1. Query Submission:
  2. A user submits a question via MCP tool or direct message

  3. The query is routed to appropriate nodes based on addressing

  4. Knowledge Retrieval:

  5. The system searches the vector database for relevant documents

  6. Matching information is retrieved and prepared as context

  7. Response Generation:

  8. The query and retrieved context are sent to the configured LLM

  9. The LLM generates a response based on the provided information

  10. Approval Process:

  11. Generated responses are checked against automatic approval criteria

  12. Responses either proceed directly or await manual approval

  13. Answer Delivery:

  14. Approved answers are delivered to the requesting user
  15. Responses are stored for future reference and evaluation

Security Model

Distributed Knowledge employs several security measures:

  • End-to-End Encryption: All messages are encrypted in transit
  • Identity Verification: Public key cryptography confirms node identities
  • Permission System: Controls who can query specific nodes
  • Privacy Controls: Allows users to define what information is shared
  • Cryptographic Signatures: Ensures message integrity and authenticity

Federated Architecture Benefits

The federated nature of Distributed Knowledge offers several advantages:

  • No Single Point of Failure: The network remains operational even if some nodes go offline
  • Distributed Processing: Workload is spread across multiple nodes
  • Knowledge Specialization: Nodes can focus on specific domains of expertise
  • Progressive Enhancement: The network becomes more capable as nodes join
  • Resilience: The system can adapt to changing conditions and requirements

Next Steps