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LLM-MCP Implementation Progress

Overview​

This document tracks the implementation progress of AI streaming, vector operations, and function calling capabilities in the llm-mcp npm project.

Completed Tasks​

  1. Architecture Analysis

    • Analyzed existing LLM Platform components for integration points
    • Designed standalone streaming and vector operations architecture
    • Determined implementation approach for production-ready features

  2. Implemented Core Streaming Components

    • Created TypeScript interfaces for AI streaming components
    • Implemented StreamingState class for operation state management
    • Implemented StreamingMetrics class for metrics tracking
    • Created AiStreamingService implementation

  3. Implemented Provider Framework

    • Created AiProvider interface
    • Implemented AbstractAiProvider base class
    • Set up provider registration and capability management

  4. Created Documentation

    • Detailed integration guide in AI_STREAMING_INTEGRATION.md
    • Usage examples for streaming service
    • Instructions for Drupal integration

  5. Implemented Vector Operations

    • Created interface for vector operations
    • Implemented VectorOperationsService for vector storage and retrieval
    • Added semantic search and RAG workflows
    • Created tests for vector operations

  6. Implemented Function Calling System

    • Created interfaces for function definitions and execution
    • Implemented FunctionManagerImpl for function registration and execution
    • Added security controls and validation
    • Created tests for function calling system

Next Steps​

  1. Integration Testing

    • Create end-to-end tests for all implemented components
    • Test bidirectional communication with Drupal
    • Verify metrics collection and threshold monitoring

  2. Advanced Documentation Enhancement

    • Add more complex usage examples
    • Create migration guide for existing implementations
    • Document security considerations

  3. Provider Implementations

    • Create specific provider implementations for OpenAI, Anthropic, etc.
    • Implement provider selection strategies
    • Add provider-specific optimizations

  4. Performance Tuning

    • Optimize vector operations for large datasets
    • Implement caching for frequently used functions
    • Add connection pooling for external services

Benefits of Integration​

  1. Reduced Duplication

    • Centralized streaming implementation
    • Unified vector operations
    • Shared provider management
    • Common function calling system

  2. Improved Architecture

    • Cleaner separation of concerns
    • Standardized interfaces
    • Better testability
    • Enhanced type safety

  3. Enhanced Features

    • More robust streaming with error handling
    • Advanced metrics tracking
    • Unified vector database operations
    • Comprehensive function calling system
    • Seamless bidirectional integration with Drupal