Vector Database Archives

Mastering LanceDB on Linux: Fast, Embedded Vector Search Without the Infrastructure Headache

August 23, 2026

Ditch the heavy infrastructure. Learn how to deploy LanceDB on Linux for high-performance, embedded vector search in your AI applications.

Database

Deploying Milvus on Docker: A Production Guide for Enterprise AI and RAG Applications

August 19, 2026

A hands-on guide to deploying Milvus vector database on Docker, based on six months of production experience running a 50-million-vector RAG pipeline. Covers Standalone setup, configuration tuning for performance, index selection, and monitoring with Attu and Prometheus.

Database

Setting Up ChromaDB: A Practical Guide to Local Vector Storage for RAG

July 31, 2026

Tired of slow or inaccurate searches in your AI apps? This guide explores how to implement ChromaDB, an open-source vector database designed for local RAG development.

Build a Personal AI Knowledge Base with Obsidian, Ollama and ChromaDB: Offline Semantic Search for Your Notes

July 20, 2026

Keyword search fails when you need it most — 2 AM, 600 notes, and the answer is in there somewhere with a title you don't remember. This guide builds a fully offline personal AI knowledge base using Obsidian, Ollama, and ChromaDB so you can search your notes by meaning, not by exact wording, with no cloud dependency.

Advanced RAG: Solving the ‘Broken Table’ Problem with Multi-vector Retrieval

June 7, 2026

Standard RAG fails when tables are split across chunks. This guide shows how to use Multi-vector Retrieval to link searchable summaries to full-context tables and images, boosting accuracy for complex documents.

Fixing RAG Retrieval Failures: A Practical Guide to BM25 and Hybrid Search

May 1, 2026

Vector search often misses exact technical terms and specific IDs. This guide explains how to combine BM25 and Vector Search using Hybrid Search and RRF to increase RAG accuracy by up to 20%.

Stop Tagging Images: Build a Semantic Search Engine with CLIP and Qdrant

April 14, 2026

Forget brittle tags and manual labeling. This guide shows you how to build a modern, semantic image search engine using CLIP embeddings and the high-performance Qdrant vector database.

Extract and Process PDF Tables with Docling for RAG Systems

April 8, 2026

After 6 months of using Docling in production for PDF-heavy RAG systems, I share how to extract tables properly, chunk them intelligently, and index them for accurate retrieval. Covers quick start, pipeline setup, and hard-won practical tips.

RAG Explained: How to Keep Your LLMs Honest in Production

March 17, 2026

Is your LLM hallucinating or providing outdated info in production? Retrieval-Augmented Generation (RAG) is your solution. This tutorial details RAG's architecture and offers a Python example for building reliable, fact-based AI systems.

Vector Database Explained: Pinecone, Weaviate, ChromaDB for AI Engineers

March 17, 2026

Vector databases like Pinecone, Weaviate, and ChromaDB are crucial for today's AI applications and semantic search. This tutorial explains the core problem they address, compares their features, and offers practical code examples to guide your project's tool selection.