Vector Database Archives

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Is your LLM hallucinating or providing outdated info in production?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.