MongoDB Atlas Vector Search — Local Development with Docker
In this video, we build a VECTOR DATABASE using MongoDB on macOS — completely locally using Docker.
You’ll learn how to run MongoDB Atlas Vector Search on your Mac without using MongoDB Atlas Cloud. We use MongoDB Atlas Local (Docker image) to enable vector embeddings, vector indexes, and semantic search — perfect for AI, RAG, and LLM-based applications.
This is a full step-by-step tutorial, ideal for beginners and backend/AI engineers.
⏱️ TIMESTAMPS
00:00 – What is Vector Search?
02:30 – Why MongoDB for Vector Databases?
05:00 – Atlas Vector Search vs Regular MongoDB
07:00 – Installing Docker on macOS
09:00 – Running MongoDB Atlas Local (Docker)
12:00 – Connecting without mongosh
15:00 – Creating sample embedding data
18:00 – Creating a Vector Search Index
22:00 – Running $vectorSearch queries
🧠 WHAT YOU’LL LEARN
What a vector database is (simple explanation)
How MongoDB Vector Search works
How to run MongoDB Atlas locally using Docker
How to create vector indexes
How to query embeddings using $vectorSearch
How to work without mongosh installed
Best practices for local AI development
🛠️ TECH USED
MongoDB Atlas Local
Docker Desktop (macOS)
MongoDB Vector Search
Embeddings (conceptual overview)
🚀 WHO THIS IS FOR
AI & LLM developers
Backend engineers
Developers building RAG pipelines
Anyone exploring vector databases
📌 USE CASES
Semantic search
Recommendation systems
Chatbots with memory
AI document search (RAG)
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#MongoDB #VectorDatabase #Docker #AI #LLM #RAG #SemanticSearch #macOS