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Vector Indexing

10 articles · Page 1 of 1
dbaplus Community
dbaplus Community
May 17, 2026 · Artificial Intelligence

Why Grep Is Replacing Vector Indexes: RAG Isn’t Dead, It’s Evolving

The article dissects Claude Code’s LLM‑driven Grep search, showing how multi‑round tool calls replace static vector‑based RAG, presents ripgrep performance benchmarks, compares Claude Code with Cursor and Codex, and argues that zero‑index search is optimal for local code bases while larger projects still need indexing.

Claude CodeCode searchLLM Agents
0 likes · 36 min read
Why Grep Is Replacing Vector Indexes: RAG Isn’t Dead, It’s Evolving
AI Architect Hub
AI Architect Hub
May 10, 2026 · Artificial Intelligence

RAG Series Recap: From Chunking to Prompt – A Complete Technical Roadmap

This article systematically reviews the nine‑stage RAG pipeline—from data cleaning and text chunking through embedding, vector indexing, retrieval, reranking, and finally prompt assembly—highlighting core concepts, practical code snippets, common pitfalls, and optimization tips for building production‑grade systems.

AIEmbeddingLLM
0 likes · 22 min read
RAG Series Recap: From Chunking to Prompt – A Complete Technical Roadmap
James' Growth Diary
James' Growth Diary
Apr 14, 2026 · Artificial Intelligence

How Does Cursor Work? Inside the Architecture of an AI Coding Assistant

The article dissects Cursor's four‑layer architecture, explains how it builds context from the current file, vector retrieval and @‑references, compares Cmd+K inline edits with Chat mode, and shares practical tips to avoid common pitfalls when using the AI‑powered IDE tool.

AI coding assistantCursorVector Indexing
0 likes · 14 min read
How Does Cursor Work? Inside the Architecture of an AI Coding Assistant
AndroidPub
AndroidPub
Apr 2, 2026 · Artificial Intelligence

How to Build Offline, Privacy‑First AI with On‑Device Retrieval‑Augmented Generation

This article explains how to implement on‑device Retrieval‑Augmented Generation (RAG) for large language models, covering embedding, vector indexing, model selection, quantization, data chunking, incremental updates, hybrid search, and agentic RAG to deliver fast, private, and personalized AI experiences on mobile devices.

EmbeddingLLMRAG
0 likes · 18 min read
How to Build Offline, Privacy‑First AI with On‑Device Retrieval‑Augmented Generation
Past Memory Big Data
Past Memory Big Data
Mar 27, 2026 · Big Data

Why AI Workloads Require Rebuilding Parquet: A Deep Dive into Lance

The article explains how traditional Parquet‑based lakehouse architectures, optimized for large‑scale scans, struggle with AI workloads that need ultra‑low‑latency random access, and how Lance redesigns the storage format, indexing and write path to provide O(1) addressing, native vector support, and seamless integration with native execution engines.

AI workloadsData LakeLance
0 likes · 12 min read
Why AI Workloads Require Rebuilding Parquet: A Deep Dive into Lance
AI Tech Publishing
AI Tech Publishing
Feb 1, 2026 · Artificial Intelligence

How Clawdbot Implements a Persistent, Search‑Driven Memory System

Clawdbot, an open‑source AI assistant, uses local Markdown files and a SQLite‑based vector index to provide a transparent, searchable, and long‑term memory that separates temporary context from durable storage, enabling autonomous task handling across sessions.

AI assistantClawdbotSQLite
0 likes · 10 min read
How Clawdbot Implements a Persistent, Search‑Driven Memory System
Big Data Technology & Architecture
Big Data Technology & Architecture
Apr 22, 2025 · Artificial Intelligence

Introduction to Retrieval‑Augmented Generation (RAG) and Vector Indexing with StarRocks and DeepSeek

This article explains the fundamentals of Retrieval‑Augmented Generation, demonstrates how to create and query vector indexes using StarRocks, shows how DeepSeek provides embeddings and answer generation, and walks through a complete end‑to‑end RAG pipeline with code examples and a web UI.

AIDeepSeekEmbedding
0 likes · 20 min read
Introduction to Retrieval‑Augmented Generation (RAG) and Vector Indexing with StarRocks and DeepSeek
Baidu Geek Talk
Baidu Geek Talk
Mar 23, 2023 · Artificial Intelligence

Advanced Image Search in Baidu Netdisk: Semantic Vector Retrieval and Multi-Modal Fusion

Baidu Netdisk’s new image search combines ERNIE‑ViL‑based semantic vectors, cross‑modal matching and metadata such as timestamps, GPS and facial tags, using LSH‑optimized indexing to let users find specific photos among billions with natural‑language queries, delivering faster, more accurate results without manual tagging.

ERNIE-ViLLSH hashingMultimodal AI
0 likes · 11 min read
Advanced Image Search in Baidu Netdisk: Semantic Vector Retrieval and Multi-Modal Fusion