Production Enterprise RAG System Tutorial — build a complete AI Knowledge Base Chatbot using Claude Code, NestJS, ChromaDB, and Gemini API. This is Phase 5 (Eval-Driven RAG Systems) of the AI Engineer 360 Course.

In this hands-on project, we build a real Enterprise Retrieval-Augmented Generation (RAG) application from scratch. Upload PDF documents, generate Gemini embeddings, store vectors in ChromaDB, retrieve the most relevant context, and answer questions with cited sources through a professional AI chat interface.

What You'll Build
Enterprise AI Knowledge Base
PDF Upload & Parsing
Recursive Chunking (800 / 150 Overlap)
Gemini Embeddings
ChromaDB Vector Storage
Top-5 Semantic Retrieval
Secure Anti-Hallucination Prompt
AI Chat with Source References
Tailwind CSS Professional Interface

Tech Stack
* Claude Code
* NestJS
* ChromaDB
* Gemini API
* Tailwind CSS
* Vanilla JavaScript
* TypeScript

This modular architecture is built for future enterprise features including Ragas Evaluation, Redis Memory, Hybrid Search, Metadata Filtering, Streaming Responses, Authentication, Multi-User Support, Caching, and Observability—without major refactoring.

Subscribe to The Techzeen and follow the complete AI Engineer 360 journey from beginner to Production AI Engineer.

CONNECT & LEARN WITH US
Visit Our Website:
https://www.thetechzeen.com/

Source Code:
https://github.com/farzeen-ali/RAG-using-Claude-Code-Nest-JS-ChromaDB-Gemini-API

Full Playlist (AI ENGINEER 360°):
https://www.youtube.com/playlist?list=PL5OhSdfH4uDtyUmg0r-F5UeQ9sineyE9t

Get Lecture Notes (GitHub):
https://github.com/farzeen-ali/AI-Engineer-360

Join Techzeen AI WhatsApp Community:
https://whatsapp.com/channel/0029VbD3dLD5vKA2UgUCpX19

#RAG #AIEngineer360 #TheTechzeen #EnterpriseRAG #NestJS #ChromaDB #GeminiAPI #ClaudeCode #VectorDatabase #AIEngineering