#embeddings-search

BM25 vs. Dense Vector Search: A Practical Guide for Engineers Building Search and RAG Systems

1. Introduction

Embedding interoperability is becoming its own category

For the last few years, most of the attention in AI infrastructure has gone toward the models themselves: bigger models, cheaper models…

Dynamic Embeddings and Blue-Green Vector Rollouts

How enterprise RAG systems stay semantically fresh without downtime

Building a Semantic Image Search Engine with FastAPI, Celery, and Supabase

A tiny example of a distributed system

Vector Store for Graph RAG: Can We Use the Existing Analytical DB?

Can your analytical warehouse double as the vector layer for enterprise AI memory? I tested it.

The Great Vector Migration: Migrate Your Vector Database with Zero Downtime

Google recently deprecated its legacy text embedding models (text-embedding-004, text-embedding-005) in favour of gemini-embedding-001. If…

Semantic Font Search at Cricut

The Problem

AI for Full-Stack Engineers (Part 4): Embeddings & Vector Databases — The New “Index”

You already know how to index data, optimize queries, and design for scale. Embeddings aren’t a new concept — they’re a new way to index…

Bridging Vision and Language: My GSoC 2025 Journey Building the Multimodal Embedding Explorer…

How I spent my summer visualizing intelligence, detecting noisy captions, and learning to see AI differently.

How to Choose the Perfect Embedding Model for Your RAG System

Why Model Choice Can Break Your System

Exploring hybrid search and custom models in OpenSearch on TrueFoundry (Part II)

Exploring hybrid search and custom models in OpenSearch on TrueFoundry (Part I)