https://github.com/ellttdave/athlete-performance-platform-showcase
Technical showcase production application demonstrating LLM integration, MCP pattern, RAG systems, and modern architecture.
https://github.com/ellttdave/athlete-performance-platform-showcase
anthropic-claude embeddings healthcare-technology llm-integration-tool-calling mcp nextjs nodejs openai-text-embeddings performance-analytics pgvector postgresql prisma-orm rag-retrieval-augmented-generation react sports-science tailwindcss tool-calling typescript vector-search vercel
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Technical showcase production application demonstrating LLM integration, MCP pattern, RAG systems, and modern architecture.
- Host: GitHub
- URL: https://github.com/ellttdave/athlete-performance-platform-showcase
- Owner: ellttdave
- License: mit
- Created: 2025-10-29T18:29:08.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-11-07T01:04:11.000Z (7 months ago)
- Last Synced: 2025-11-07T02:32:55.164Z (7 months ago)
- Topics: anthropic-claude, embeddings, healthcare-technology, llm-integration-tool-calling, mcp, nextjs, nodejs, openai-text-embeddings, performance-analytics, pgvector, postgresql, prisma-orm, rag-retrieval-augmented-generation, react, sports-science, tailwindcss, tool-calling, typescript, vector-search, vercel
- Language: TypeScript
- Homepage:
- Size: 36.1 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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# 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 →](./docs/MCP_ARCHITECTURE.md)
### 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 →](./docs/RAG_IMPLEMENTATION.md)
### 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 →](./docs/TECH_STACK.md)
## 📚 Documentation
- **[Architecture Overview](./docs/ARCHITECTURE.md)** - System design and architecture patterns
- **[MCP Architecture](./docs/MCP_ARCHITECTURE.md)** - Tool registry pattern and implementation
- **[RAG Implementation](./docs/RAG_IMPLEMENTATION.md)** - Vector embeddings and semantic search
- **[LLM Integration](./docs/LLM_INTEGRATION.md)** - Tool calling and structured outputs
- **[Tech Stack](./docs/TECH_STACK.md)** - Technology choices and rationale
## 💻 Code Examples
Sanitized code examples demonstrating key patterns:
- **[MCP Router Pattern](./showcase/mcp-router/)** - Tool registry and routing
- **[RAG Implementation](./showcase/rag/)** - Vector embeddings and search
- **[LLM Integration](./showcase/llm-integration/)** - Tool calling examples
## 🛠️ Key Technical Features
### 1. Custom Tool Registry Pattern
Clean separation between LLM communication and data functions:
```typescript
// 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