Tag: #vector-search
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 9 posts
Next-Gen Search Engines 2026 — Meilisearch vs Typesense vs Elasticsearch vs OpenSearch vs Quickwit vs Vespa Deep Dive (The Landscape After Elastic)
The 2026 search-engine landscape started at Elasticsearch and scattered everywhere. Meilisearch and Typesense have taken in-app search to a minimalism aesthetic, AWS made an escape route via OpenSearch, Quickwit arrived
2026-05-14 · 21 min read #search-engine#meilisearch#typesense#elasticsearch#opensearchElasticsearch, OpenSearch, and Lucene Internals — Inverted Index, BM25, Sharding, Vector Search, Hybrid RAG (2025)
Why is search hard? Lucene Segments and Merge, the math of Inverted Index, how BM25 beat TF-IDF, primary/replica shards and routing, Ingest pipelines, the Query DSL maze, kNN and HNSW, the 2021 Elastic vs AWS license war
2026-04-15 · 11 min read #elasticsearch#opensearch#lucene#search#vector-searchElasticsearch Complete Guide 2025: From Search Engine to Log Analytics & Vector Search
Everything about Elasticsearch! Inverted index internals, mapping/analyzers, Query DSL, aggregations, ELK stack, vector search (kNN/HNSW), cluster operations, performance optimization, and interview questions.
2026-03-24 · 21 min read #elasticsearch#search#elk-stack#kibana#logstashEmbedding Model Selection Guide 2025: From OpenAI to Open-Source Options
A practical 2025 comparison of the major embedding models — OpenAI, Cohere, BGE-M3, E5-mistral, nomic-embed — with MTEB benchmark analysis, language-specific recommendations, and a decision framework for choosing the rig
2026-03-18 · 9 min read #embedding#embedding-model#rag#ai-development#openaiHybrid Search Guide: Combining BM25 and Vector Search for Better RAG
Pure vector search has a blind spot: exact keyword matching. This guide explains how to combine BM25 and vector search using RRF (Reciprocal Rank Fusion) to get the best of both worlds — with real code, benchmark numbers
2026-03-18 · 7 min read #hybrid-search#bm25#vector-search#rag#rrfComplete Guide to Embedding Models: Vector Search, RAG, and Sentence Transformers in Practice
A comprehensive guide covering embedding fundamentals, key model comparisons (OpenAI, Cohere, BGE, E5, GTE, Jina), Sentence Transformers usage, vector databases (Pinecone, Weaviate, Milvus, Chroma, FAISS) and indexing st
2026-03-13 · 22 min read #llm#embedding#vector-search#rag#sentence-transformersRAG Quality Evaluation and Failure Pattern Analysis: Diagnosing and Improving Retrieval-Augmented Generation
A systematic guide to evaluating RAG (Retrieval-Augmented Generation) system quality and analyzing common failure patterns. Covers evaluation metrics for Retriever, Reranker, and Generator components, framework compariso
2026-03-07 · 15 min read #rag#llm#evaluation#ragas#deepevalSlack Bot + LangChain RAG Chatbot Practical Guide — Building an Internal Document Search Bot
Build a Slack chatbot that searches internal documents using LangChain and RAG. Covers document embedding, vector DB, prompt engineering, and Slack Bolt integration with complete code.
2026-03-03 · 28 min read #chatbot#langchain#rag#slack-bot#vector-searchPostgreSQL + pgvector Vector Search Practical Guide: From RAG to Hybrid Search
Implement vector similarity search with the PostgreSQL pgvector extension. Covers installation through HNSW/IVFFlat indexes, hybrid search (vector + full-text search), and RAG pipeline integration with practical code.
2026-03-02 · 9 min read #postgresql#pgvector#vector-search#embedding#rag