Enterprise RAG System
Building a production-ready RAG system from scratch without frameworks to understand core ML fundamentals.
Type
AI System
My Roles
ML Engineer / System Architect
Duration
3 months
Status
Production
Team
Solo project
Stack
python
TypeScript
Context
First AI system developed at the company to serve as internal knowledge base. Built entirely from scratch without using frameworks to deeply understand RAG fundamentals and establish solid ML foundations.
Technical objectives
1
Build RAG system without existing frameworks
2
Create scalable vector database architecture
3
Implement efficient retrieval and ranking
4
Deploy production-ready system
Architecture Overview
The system follows a classic RAG pipeline: document ingestion, chunking, embedding generation, vector storage, query processing, retrieval, and response generation. Each component was built from scratch to understand the underlying mechanics rather than relying on pre-built solutions.
Data Ingestion & Processing
Built custom document processors for various formats (PDF, DOCX, TXT, HTML). Implemented intelligent chunking strategies with overlap to maintain context. Created metadata extraction system for enhanced filtering and retrieval accuracy.
Vector Database Implementation
Designed and implemented a custom vector database using efficient similarity search algorithms. Built indexing system for fast retrieval and implemented persistence layer for data durability. Added real-time updates capability for dynamic knowledge base.
Retrieval & Ranking System
Implemented hybrid retrieval combining semantic and keyword search. Built custom reranking algorithm to improve relevance. Added context window management and query expansion for better results quality.
Production Deployment
Created Docker containerization for consistent deployment. Implemented monitoring and logging system for performance tracking. Built API endpoints with authentication and rate limiting. Added caching layer for frequently accessed queries.
Technical Challenges & Solutions
1
Framework-free Implementation
Building without frameworks meant implementing core algorithms from scratch. This deep dive into vector similarity, embedding techniques, and retrieval methods provided invaluable understanding of RAG fundamentals.
2
Performance Optimization
Balancing retrieval speed with accuracy required optimizing indexing strategies and implementing efficient similarity search algorithms. Custom caching and pre-computation improved response times significantly.
3
Context Preservation
Handling various document formats and maintaining context across chunks required sophisticated preprocessing. Developed adaptive chunking strategies based on document type and content structure.
4
Production Readiness
Moving from prototype to production involved implementing robust error handling, monitoring, and scalability considerations. Built comprehensive testing suite and monitoring dashboard.
Impact & Results
The RAG system now serves as the company's primary internal knowledge base, handling hundreds of queries daily with high accuracy. Response time averages 1.2 seconds for complex queries. The system has reduced internal documentation search time by 75% and improved knowledge sharing across teams. Most importantly, building it from scratch provided deep ML understanding that informed all subsequent AI projects. The architecture and learnings from this project became the foundation for client RAG systems, demonstrating the value of understanding fundamentals rather than just using black-box solutions.