Building AI Agent with Qdrant | Node.js + OpenAI + VectorDB | VectorDB Backend– Tutorial #7

Building AI Agent with Qdrant | Node.js + OpenAI + VectorDB | VectorDB Backend– Tutorial #7

In this 7th episode of our full-stack *RAG (Retrieval-Augmented Generation) Multi-Agent* series, we’ll build a specialized *AI Agent that only answers health-related queries* by storing and retrieving knowledge from **Qdrant Vector Database**. We’ll create a dedicated `healthinfo` collection with `id`, `title`, `content`, and `vector` fields—powering semantic search to provide accurate responses from trained data.

📺 Full Playlist:    • Vector DB + RAG Tutorial Series  

💻 GitHub Repo: https://github.com/Jitugopale/VectorD...

What You'll Learn

Creating and managing collections in Qdrant (`healthinfo`)
Generating embeddings with OpenAI and storing them in Qdrant
Performing *semantic search* on vector data for health queries
Restricting your AI Agent to *answer only health-related questions*
Integrating Qdrant search with an Express.js backend

Topics Covered

✅ Qdrant setup & collection creation
✅ Embedding generation with OpenAI API
✅ Inserting & querying vector data
✅ Semantic search for context-aware answers
✅ AI Agent restricted to health domain

Tech Stack

Node.js & Express.js
Qdrant VectorDB for semantic search
OpenAI Embeddings API for vector representation
GPT Models for intelligent responses
REST API integration with backend services

This tutorial is perfect for developers who want to train domain-specific AI agents and build practical semantic search pipelines using **Qdrant + OpenAI**—a core building block for production-ready **RAG systems**.

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