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Home » Semantic Search
AI tutorial - IT technology blog
Posted inAI

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.
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Database tutorial - IT technology blog
Posted inDatabase

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.
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Database tutorial - IT technology blog
Posted inDatabase

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.
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AI tutorial - IT technology blog
Posted inAI

Stop Searching, Start Finding: Self-Host a Private AI Search with Khoj and Docker

September 12, 2026
Keyword search is outdated. Learn how to use Khoj and Docker to build a private, semantic search engine that understands the meaning behind your notes and documents.
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AI tutorial - IT technology blog
Posted inAI

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.
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AI tutorial - IT technology blog
Posted inAI

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.
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