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System Architecture

🚧 Documentation In Progress

This documentation is being actively developed. More details will be added soon.

Overview

This document provides a comprehensive overview of the MCP Codebase Insight system architecture. For detailed workflow information, please see the Workflows Documentation.

Architecture Components

Core Systems

  • Vector Store System
  • Knowledge Base
  • Task Management
  • Health Monitoring
  • Error Handling
  • Metrics Collection
  • Cache Management

Documentation

  • ADR Management
  • Documentation Tools
  • API Documentation

Testing

  • Test Framework
  • SSE Testing
  • Integration Testing

Detailed Documentation

System Overview

This document provides a comprehensive overview of the MCP Codebase Insight system architecture, focusing on system interactions, dependencies, and design considerations.

Core Systems

1. Vector Store System (src/mcp_codebase_insight/core/vector_store.py)

  • Purpose: Manages code embeddings and semantic search capabilities
  • Key Components:
    • Qdrant integration for vector storage
    • Embedding generation and management
    • Search optimization and caching
  • Integration Points:
    • Knowledge Base for semantic understanding
    • Cache Management for performance optimization
    • Health Monitoring for system status

2. Knowledge Base (src/mcp_codebase_insight/core/knowledge.py)

  • Purpose: Central repository for code insights and relationships
  • Key Components:
    • Pattern detection and storage
    • Relationship mapping
    • Semantic analysis
  • Feedback Loops:
    • Updates vector store with new patterns
    • Receives feedback from code analysis
    • Improves pattern detection over time

3. Task Management (src/mcp_codebase_insight/core/tasks.py)

  • Purpose: Handles async operations and job scheduling
  • Key Components:
    • Task scheduling and prioritization
    • Progress tracking
    • Resource management
  • Bottleneck Mitigation:
    • Task queuing strategies
    • Resource allocation
    • Error recovery

4. Health Monitoring (src/mcp_codebase_insight/core/health.py)

  • Purpose: System health and performance monitoring
  • Key Components:
    • Component status tracking
    • Performance metrics
    • Alert system
  • Feedback Mechanisms:
    • Real-time status updates
    • Performance optimization triggers
    • System recovery procedures

5. Error Handling (src/mcp_codebase_insight/core/errors.py)

  • Purpose: Centralized error management
  • Key Components:
    • Error classification
    • Recovery strategies
    • Logging and reporting
  • Resilience Features:
    • Graceful degradation
    • Circuit breakers
    • Error propagation control

System Interactions

Critical Paths

  1. Code Analysis Flow:

    sequenceDiagram
    participant CA as Code Analysis
    participant KB as Knowledge Base
    participant VS as Vector Store
    participant CM as Cache
    
    CA->>VS: Request embeddings
    VS->>CM: Check cache
    CM-->>VS: Return cached/null
    VS->>KB: Get patterns
    KB-->>VS: Return patterns
    VS-->>CA: Return analysis
    Loading

  2. Health Monitoring Flow:

    sequenceDiagram
    participant HM as Health Monitor
    participant CS as Component State
    participant TM as Task Manager
    participant EH as Error Handler
    
    HM->>CS: Check states
    CS->>TM: Verify tasks
    TM-->>CS: Task status
    CS-->>HM: System status
    HM->>EH: Report issues
    Loading

Performance Considerations

Caching Strategy

  • Multi-level caching (memory and disk)
  • Cache invalidation triggers
  • Cache size management

Scalability Points

  1. Vector Store:

    • Horizontal scaling capabilities
    • Batch processing optimization
    • Search performance tuning

  2. Task Management:

    • Worker pool management
    • Task prioritization
    • Resource allocation

Error Recovery

Failure Scenarios

  1. Vector Store Unavailable:

    • Fallback to cached results
    • Graceful degradation of search
    • Automatic reconnection

  2. Task Overload:

    • Dynamic task throttling
    • Priority-based scheduling
    • Resource reallocation

System Evolution

Extension Points

  1. Knowledge Base:

    • Plugin system for new patterns
    • Custom analyzers
    • External integrations

  2. Monitoring:

    • Custom metrics
    • Alert integrations
    • Performance profiling

Next Steps

  1. Documentation Needs:

    • Detailed component interaction guides
    • Performance tuning documentation
    • Deployment architecture guides

  2. System Improvements:

    • Enhanced caching strategies
    • More robust error recovery
    • Better performance monitoring