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Setting Up and Managing Milvus with Attu: Deploying a Vector Database for Large-Scale RAG

By IT From Zero October 4, 2026

Step-by-step guide to deploying Milvus Vector Database and the Attu GUI with Docker Compose for large-scale RAG. Learn schema design, HNSW indexing, and RAM sizing.

Using ChromaDB with Python: Build a Lightweight Local Vector Database for AI Applications

By IT From Zero October 2, 2026

Suffering from OOM killer memory crashes due to in-memory NumPy vectors? Here is how to integrate ChromaDB PersistentClient in Python for a lightweight, robust local vector database.

Database

Deploying Weaviate with Docker: Building Optimized Hybrid Search for RAG Systems

By IT From Zero September 13, 2026

A detailed guide on deploying Weaviate with Docker and optimizing Hybrid Search. Includes real-world experience on RAM management and search weighting for RAG systems.

LanceDB: The ‘SQLite’ of Vector Databases – A Lightweight Storage Solution for AI Applications

By IT From Zero July 29, 2026

LanceDB is a lightweight embedded vector database that functions like SQLite but for AI. Learn how to install and optimize Vector Search for your RAG applications.

MySQL

MySQL 9.0 and VECTOR: Embedding Storage Solution for AI Apps Without a Secondary DB

By IT From Zero July 22, 2026

MySQL 9.0 officially supports the VECTOR data type. Learn how to store AI embeddings, calculate Cosine distance, and build efficient semantic search systems directly in MySQL.

Database

Setting Up Milvus Vector Database with Docker: Building a Knowledge Store for RAG and AI

By IT From Zero June 18, 2026

A guide to installing Milvus Vector Database with Docker Compose, creating collections, storing vector embeddings, and building a complete RAG pipeline. Includes tips on choosing index types, chunking strategies, and production optimization from real-world experience.

Database

Getting Started with ChromaDB: The Open-Source Vector Database for Building AI Agent Knowledge Systems

By IT From Zero June 13, 2026

ChromaDB is an open-source vector database for semantic storage and search — lightweight enough to run locally, powerful enough to serve as the foundation for RAG systems and AI Agents. This guide walks through installation, document indexing, semantic search queries, and Docker deployment with practical, step-by-step examples.

Database

pgvector Guide: Storing and Searching Vector Embeddings in PostgreSQL for AI and RAG Applications

By IT From Zero April 22, 2026

pgvector is a PostgreSQL extension that lets you store and search vector embeddings directly in your existing database — no need to deploy a separate system. This guide covers installation, HNSW index creation, semantic search, and integrating pgvector into a Python RAG pipeline, with practical monitoring tips from real-world experience.

Qdrant Installation and Usage Guide: A Powerful Vector Database for AI and RAG Applications

By IT From Zero March 10, 2026

This article provides a detailed guide on how to install and use Qdrant, a powerful vector database, to build effective AI and RAG applications. I share practical experience after more than 6 months of deploying Qdrant in a production environment, analyzing its pros and cons, and providing sample Python code to help you get started immediately.

Building a RAG Application with LangChain and Python: From Failure to Production

By IT From Zero February 28, 2026

Six months of hands-on experience building a RAG (Retrieval-Augmented Generation) system with LangChain and Python for an internal document lookup chatbot. From hallucination issues and context window limitations to a solution that actually works in production, with complete code examples.