Hybrid Search + Re-ranking Implementation in a Production RAG System using NestJS, ChromaDB, Gemini 2.5 Flash, BM25, and Claude Code.

In this video, we upgrade our existing Enterprise RAG application by combining Vector Search + BM25 Keyword Search, implementing a modular Re-ranking layer, improving prompt engineering, adding confidence scoring, retrieval logging, embedding caching, and a production-ready retrieval pipeline.

You'll Learn
Hybrid Search (BM25 + Vector Search)
Merge & Deduplicate Results
Production Re-ranking Architecture
Future-ready Cohere Re-rank Integration
Secure Prompt Engineering
Retrieval Pipeline Logging
Better Frontend UX & Confidence Indicators
Clean SOLID NestJS Architecture

Tech Stack: NestJS • ChromaDB • Gemini 2.5 Flash • Claude Code • TypeScript • Tailwind CSS

This is Phase 5 (Eval-Driven RAG Systems) of the AI Engineer 360 series, where we build real enterprise AI applications from scratch.

Subscribe to The Techzeen for production-ready AI Engineering tutorials.

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

#HybridSearch #ProductionRAG #TheTechzeen #AIEngineer360 #BM25 #ChromaDB #NestJS #Reranking #GeminiAPI #ClaudeCode #EnterpriseAI #EvalDrivenRAG