Athlete Performance Analytics Platform - Technical Showcase

A production application demonstrating advanced LLM integration, RAG systems, and modern full-stack architecture patterns.

🎯 Project Overview

This repository showcases the technical architecture and implementation patterns from a production application that integrates:

• LLM Integration (Anthropic Claude) with structured tool calling
• RAG System (Retrieval-Augmented Generation) with vector embeddings
• MCP-like Architecture for clean separation of concerns
• Modern Full-Stack development with Next.js, TypeScript, and PostgreSQL

Note: This repository contains sanitized code examples and architecture documentation for showcaase purposes. The full production application remains private to protect intellectual property.

🏗️ Architecture Highlights

MCP-Like Tool Integration Pattern

A custom architecture pattern that mimics Model Context Protocol (MCP) to provide clean separation between LLM communication logic and data functions.

Key Benefits:

• Single gateway for all LLM tool calls
• Easy migration path to real MCP server
• Extensible tool registry pattern
• Comprehensive error handling

View Architecture Details →

RAG Implementation

A production-ready RAG system using vector embeddings and semantic search.

Key Features:

• Google Document AI for advanced text extraction (Form Parser, Layout Parser)
• Automatic PDF splitting for large files (>15 pages or >4MB)
• OpenAI embeddings (text-embedding-3-small)
• pgvector for PostgreSQL vector similarity search
• Vercel Blob Storage for document persistence
• Individual document re-processing to avoid timeout issues
• Fallback mechanisms (unpdf) for reliability
• Automatic knowledge base integration
• Top-K retrieval with similarity scoring

Architecture Note:

• RAG is used for general knowledge, research articles, and contextual explanations
• Precise numeric/reference data is stored in PostgreSQL (not RAG) for deterministic queries

View RAG Implementation →

Full-Stack Design

Modern web application architecture with:

• Frontend: Next.js 14, React 18, TypeScript, Tailwind CSS
• Backend: Next.js API Routes, Server Actions
• Database: PostgreSQL (Neon) with Prisma ORM, pgvector extension
• AI/ML: Anthropic Claude API, OpenAI Embeddings
• Document Processing: Google Document AI, pdf-lib for PDF splitting
• Storage: Vercel Blob Storage for document persistence
• Deployment: Vercel

View Tech Stack Details →

📚 Documentation

• Architecture Overview - System design and architecture patterns
• MCP Architecture - Tool registry pattern and implementation
• RAG Implementation - Vector embeddings and semantic search
• LLM Integration - Tool calling and structured outputs
• Tech Stack - Technology choices and rationale

💻 Code Examples

Sanitized code examples demonstrating key patterns:

• MCP Router Pattern - Tool registry and routing
• RAG Implementation - Vector embeddings and search
• LLM Integration - Tool calling examples

🛠️ Key Technical Features

1. Custom Tool Registry Pattern

Clean separation between LLM communication and data functions:

// Extensible tool registry
const TOOL_REGISTRY = {
  'analyze_data': {
    description: 'Comprehensive data analysis tool',
    handler: async (params) => { /* ... */ }
  }
}

2. RAG System with Vector Search

Production-ready retrieval-augmented generation:

• Vector embedding generation
• Semantic similarity search
• Knowledge base integration
• Context retrieval for LLM

3. Structured LLM Outputs

Type-safe JSON generation with validation:

• Schema validation
• Error handling
• Data integrity measures
• Reliable structured outputs

4. Production-Ready Patterns

• Comprehensive error handling
• Logging and monitoring
• Type safety with TypeScript
• Scalable architecture

💡 Technical Insights

Why MCP-Like Architecture?

• Provides clean separation of concerns
• Makes LLM integration maintainable
• Allows easy addition of new tools
• Facilitates migration to real MCP server

RAG Implementation Decisions

• Chose pgvector over external vector DB for simplicity
• OpenAI embeddings for reliability
• Top-K retrieval for performance
• Integration with existing PostgreSQL infrastructure

LLM Integration Challenges

• Structured output reliability
• Data integrity enforcement
• Error handling and fallbacks
• Token optimization

🚀 Key Achievements

• ✅ Implemented custom MCP-like architecture for LLM tool integration
• ✅ Built production RAG system with vector embeddings
• ✅ Designed scalable data models for multi-modal analytics
• ✅ Created evidence-based analysis engine with citation requirements
• ✅ Deployed production application with optimized performance

📄 License

Technical showcase - Code examples are for demonstration and educational purposes.

Note: This repository contains sanitized code examples and architecture documentation. The full production application remains private to protect intellectual property.

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Connect

• LinkedIn: http://linkedin.com/in/david-elliott-6304555a
• Email: ellttdave218@gmail.com