#vector-search

Vector Search From First Principles: Navigable Small World Graphs

From greedy graph search to the small-world structure behind HNSW

Vector Search From First Principles: What Does “Nearest” Actually Mean?

Vector search has become a core building block of modern search and AI systems, powering semantic search, recommendations, multimodal…

Building a Grounded RAG Agent on Azure AI Search and Azure AI Foundry

Introduction

Building Agentic RAG for Medical Reports: When the AI Reasons, Retrieves, and Still Stays Blind to…

How I designed MediFlow Assistant: an AI assistant that understands clinical reports, answers follow-up questions, and keeps patient…

Build AI Vector Search Apps in Kotlin with Micronaut, Exposed, Oracle JDBC/UCP, and the Oracle AI…

by Juarez Junior

AWS S3 Vectors Returning Empty or Fewer Results Than top_k? Here’s the Fix

I was building a visual semantic search pipeline where we use AWS S3 Vectors to store image embeddings. Everything worked fine until one…

Multi-Model Data Types: The Foundation for AI-Driven Analytics

How to eliminate AI data silos — unifying vector, text, and relational queries in one engine

Graph-Compressed Embeddings: Why Your RAG Pipeline Doesn’t Need to Store Every Vector in Full…

If you’ve built a RAG (Retrieval-Augmented Generation) system at any real scale, you already know the quiet, unglamorous cost center…

How Vector Databases Measure ‘Closeness’: Dot Product, Cosine, and Euclidean

In my previous two posts I covered ‘Vector Databases Explained for Java Developers’ and ‘How do we “see” a vector with 384 dimensions’…

You Don’t Need a Vector Database. You Need a WHERE Clause.

A 2026 sequel to Semantic Search in Snowflake: A Journey from SQL to Vectors

What is Tensor along with RAG?

Hi Folks, Lets start with understanding about what is "Tensor" 🤔 ? that is some DeepShift😝.

Text embeddings aren’t enough for similarity joins

Article 2 of 3 in the Similarity Joins series

L1 vs L2 Normalization Explained Like You’re Five (But Accurate Enough for ML Engineers)

“I know the formulas. But I still don’t know what L1 and L2 normalization actually mean.”

The Real-Time Brain: Why Redis Iris is the Foundation for Production AI Agents

In the world of Generative AI, the intelligence of your model is only half the battle. The other half is context. As we move from simple…

When Search Can See: Building Native Semantic Image Search in ODC

Most of the RAG stack I have been building inside ODC starts with the same assumption. The content is text. Chunk it, embed it, store it…

Your RAG Is Failing. The Issue May Not Be the LLM.

Building a baseline vector search retriever in Python before adding prompts, agents, or fine-tuning

Evaluating an Oracle Database Schema for AI-Ready Data with the oracle-ai-ready-data Skill

Checking whether an Oracle Database schema is ready for Select AI, NL2SQL, RAG, Vector Search, and AI Agents

Implement an Oracle-Backed Semantic Cache for Spring Applications

Not All Search Is the Same. Here Are the 4 Types You Need to Know

Most developers pick one search method and stick with it. But the right choice depends entirely on what you are building. Here is a…

brinicle does keyword search and vector search, not just hybrid

Brinicle blends keyword and vector search into one query. It also does each one purely, on its own fast path. Here is the single dial that…

I shrank a vector index ~20× on my laptop. Search quality barely noticed.

When Google’s TurboQuant made the rounds “extreme vector compression with near-optimal distortion,” a memory-chip-stock wobble, an academic…

Introducing brinicle: disk-first search for vectors, hybrid retrieval, and autocomplete

A brinicle is the spike of ice that forms under sea ice and grows downward, freezing a path toward the seafloor. We liked the image enough…

brinicle: Retrieval Without the Memory Tax

Vector search is no longer an isolated infrastructure problem. Modern products increasingly need several retrieval modes at once: semantic…

Vector Databases: 20 Scenario-Based Questions & Solutions (Part 2 of 2)

AI Engineer Interview Preparation

106 Vespa | Attribute Store vs Document Store

When people first learn Vespa, one of the most confusing concepts is why the same document is stored in multiple places:

102 Vespa | Architecture

What this module covers

101 Vespa | Search Fundamentals

How Vespa Search Engine Writing it's own Story

I Deployed TurboVec Inside Snowflake SPCS: 8x Compression, 63x Faster, 96% Recall

How data-oblivious quantization achieves 8x compression with 96% recall — tested on 100K real vectors from a public dataset.

The GraphRAG Pivot: Why Vector Search Failed My Enterprise AI and How Knowledge Graphs Fixed It

I spent three months optimising my vector database. Tweaking chunk sizes. Adjusting overlap parameters. Experimenting with different…

Understanding HNSW: The Engine Behind Fast Vector Search

To understand HNSW, imagine walking into a library with millions of books, looking for a specific book based on a brief description of the…

MilvusLite Kit: Configuration over Code for Vector Search and RAG Workflows

I’ve been using Milvus Lite in a few local RAG and semantic search projects recently. After the third or fourth project, I noticed I was…

The Data Infrastructure Problem Behind Autonomous Driving

We had an autonomous-driving data review where the model team did not ask for more GPUs first. They asked for better data retrieval.

pgvector Semantic Search in Rails: Catch What Keyword Misses

How keyword search drops cancel my plan when your docs say terminate subscription, and the embedding plus ranking fix that closes the gap

How Vector Search Actually Works: pgvector, LSH, HNSW, and Semantic Caching Under the Hood

From brute force failure to sub-100ms retrieval across 10 million vectors, a ground-up explanation of every layer.

What is Milvus? A Developer’s Introduction to Vector Databases

If you’re building AI applications like RAG systems, semantic search, recommendations, or anything that uses embeddings. You’ll eventually…

A Complete End-to-End Guide to Implementing a GraphRAG Workflow Using FalkorDB, Hugging Face, Groq…

Combine graph queries, vector search, and LLM reasoning into one intelligent workflow with working code that you can try yourself and build…

AI-Native Database Architecture: How the Engine Is Built

Have you ever heard the term I work for claps! If you like this article, it would mean a lot if you clapped or subscribed!

Vektör Aramanın SQLite’ı: MonaVec’i Neden Yazdık?

MonaVec, vektör aramanın SQLite’ı olmak için yazılmış gömülü bir çekirdek — tek bir dosya, tek bir kütüphane çağrısı. Sunucu yok, eğitim…

why do you need hybrid search in rag?

vector search and keyword search each have a blind spot. here’s how to combine them without the scores falling apart.

I Built High-Performance Vector Search Systems on AWS and Learned Why Most RAG Pipelines Fail at…

How I engineered ultra-fast semantic retrieval architectures using OpenSearch, serverless embeddings, vector indexing, ANN search, and…

pick the right vector index for your project.

if you are working with vector search, at some point you have to pick an index. the three you will run into most often are IVFFlat, HNSW…

What is a Vector Database ?

Vector Databases: The Essential Guide for Modern AI Applications

Easiest way to understand Vector Embeddings and Vector Search

Why Searching 1 Million Arrays of 1536 Numbers Does NOT Melt Your CPU

Retrieval in RAG — A Practical Single Page Cheat Sheet for Search Techniques

Most people think Retrieval-Augmented Generation (RAG) is just “vector search.” In reality, modern retrieval systems combine multiple…

Building a Basic RAG Pipeline: A Practical Guide to Retrieval-Augmented Generation

Large language models are impressive, but they have a quirk: they often sound confident even when they lack the right information. If you…

Building Search in Oracle APEX: Standard Search and Vector Search, Step by Step

Part 1 covered what Search Configurations are, what the five search types do, what you need before using each one, and what each column…

Beyond Vector Search: What Your Database Can Do with Embeddings

The similarity join: an SQL operator for predictions, analytics, and joining tables that have no foreign keys

Approximate Nearest Neighbor Search in Production: From Brute-Force to IVF+PQ

Exact similarity search over a billion items is correct, thorough, and hopelessly slow. Here’s how giving up 3% accuracy bought a 12x…

Building a Full RAG System with turbovec: The Memory-Efficient Vector Index That Needs No Training

How to build a fast, private, production-ready Retrieval-Augmented Generation pipeline in Python using turbovec — the Rust-powered vector…

FREE FREE FREE — Building a High-Performance Local AI Stack: Spring Boot 4, Spring AI, Ollama, and…

Java is dead, or so people keep saying on LinkedIn.