How AI Understands Your Queries: Core Techniques of Semantic Vector Search

The article explains why traditional keyword search often fails when user questions differ from knowledge‑base wording, introduces semantic search that matches queries and documents via vector similarity, details query understanding and rewriting techniques, lists common pitfalls, provides a full Python implementation, and shares best‑practice recommendations.

AIPythonRAG

0 likes · 16 min read

How AI Understands Your Queries: Core Techniques of Semantic Vector Search

AI Tech Publishing

Feb 19, 2026 · Artificial Intelligence

Add Long-Term Memory to Your Agent with Lightweight RAG (Lesson 5)

This tutorial shows how to equip an AI agent with long‑term memory using Retrieval‑Augmented Generation (RAG), covering the concepts of vector embeddings, FAISS indexing, building and querying a knowledge base, and providing complete Python code examples.

AgentEmbeddingFAISS

0 likes · 13 min read

Add Long-Term Memory to Your Agent with Lightweight RAG (Lesson 5)

Mingyi World Elasticsearch

Dec 20, 2025 · Artificial Intelligence

How to Build an Enterprise‑Grade Intelligent Document QA System with Everything plus RAG

This article walks through the need for fast, accurate answers from massive document collections, compares plain keyword search and pure LLM chat, and presents a hybrid Retrieval‑Augmented Generation solution built with open‑source components, detailing architecture, hybrid retrieval, prompt engineering, deployment, performance tuning, and common pitfalls.

ElasticsearchHybrid RetrievalPython

0 likes · 12 min read

How to Build an Enterprise‑Grade Intelligent Document QA System with Everything plus RAG

Data Party THU

Sep 25, 2025 · Artificial Intelligence

Mastering Triplet Loss in Sentence‑Transformers: A Step‑by‑Step Guide

This article explains the concept of triplet loss, its mathematical formulation, the different batch‑wise implementations in the sentence_transformers library, their advantages and drawbacks, and provides a complete Python example for training a text‑embedding model with Triplet Loss.

EmbeddingPyTorchPython

0 likes · 12 min read

Mastering Triplet Loss in Sentence‑Transformers: A Step‑by‑Step Guide

AI Algorithm Path

Jun 15, 2025 · Artificial Intelligence

Fine‑Tuning Text Embeddings for Domain‑Specific Search: A Complete Walkthrough

This article explains why generic text‑embedding models often fail in specialized retrieval tasks, then demonstrates how to fine‑tune such models using contrastive learning, curated job‑listing data, and the Sentence‑Transformers library, achieving near‑perfect accuracy on a job‑matching benchmark.

Sentence-Transformerscontrastive learningfine-tuning

0 likes · 11 min read

How AI Understands Your Queries: Core Techniques of Semantic Vector Search

Add Long-Term Memory to Your Agent with Lightweight RAG (Lesson 5)

How to Build an Enterprise‑Grade Intelligent Document QA System with Everything plus RAG

Mastering Triplet Loss in Sentence‑Transformers: A Step‑by‑Step Guide

Fine‑Tuning Text Embeddings for Domain‑Specific Search: A Complete Walkthrough

How to Build an Enterprise‑Grade Intelligent Document QA System with Everything plus RAG