Vector Database Archives

Building Large-Scale Semantic Search with Milvus and Python: Data Partitioning and Query Optimization for Millions of Vectors

October 7, 2026

Scaling semantic search to millions of vectors requires structured partitioning and tuned indexing. This guide demonstrates how to deploy Milvus with Python, implement Partition Keys, and configure HNSW graphs for low-latency queries.

Database

Deploying Qdrant on Docker: A 6-Month Production Review for RAG and Semantic Search

October 5, 2026

A hands-on production review of running Qdrant on Docker for 20M+ embeddings. Includes Docker Compose templates, scalar quantization benchmarks, gRPC tuning, and Prometheus monitoring configs.

Database

PostgreSQL pgvector Tutorial: Storing Embeddings and Building Fast Semantic Search

October 2, 2026

Eliminate sync lag and extra infrastructure by moving semantic search into PostgreSQL. Learn how to store embeddings with pgvector, configure HNSW indexes for sub-10ms response times, and implement production-ready hybrid search.

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.

Building Large-Scale Semantic Search with Milvus and Python: Data Partitioning and Query Optimization for Millions of Vectors

Deploying Qdrant on Docker: A 6-Month Production Review for RAG and Semantic Search

PostgreSQL pgvector Tutorial: Storing Embeddings and Building Fast Semantic Search

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

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

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

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

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

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

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