Vector Database

Machine Learning

Artificial Intelligence

Semantic Search

Explore recommended reads, summarized by AI

Building Semantic Search Systems

Technical approaches to designing, implementing, and optimizing semantic search with embeddings and vector databases.

Embeddings and Knowledge Integration

Production Implementation

Vector Databases and Infrastructure

Popular this week

In

EmTech at The Met

by

Derek Au

Tech Notes for Semantic Search at The Met

Dr. Theophano Mitsa

From TF-IDF to Transformers: How Machines Learned to Search by Meaning

Harsh Raj

Agentic AI in Practice: A Case Study in Multi-Agent System Design

In

Towards AI

by

Srikanth Dongala

EmbeddingGemma 2: Multimodal Search for the Cost of a Text Model

Sunil Veeramachaneni

Day 2 of 100: From Tokens to Meaning — Understanding Embeddings and Cosine Similarity

Selected by Medium

Dr. Theophano Mitsa

From TF-IDF to Transformers: How Machines Learned to Search by Meaning

Mark Burgess

From A.I. to Comprehension, with an SST extended Knowledge Graph

Harish K Rao

From Experimental Notebooks to Production: A Data Engineer’s perspective of Scaling Data Science…

Latest stories

Atharv Bhatnagar

Building a Semantic Search Engine: From Text to Meaning

Vijay joshi

Traditional Full-Text Search V/S. Vector Search

Andrei Fateev

Part 2a: Why Static System Prompts Fail at Scale — and How Knowledge Catalog Grounds BigQuery…

Ram

We Searched 14 Same-Meaning Query Pairs to See How Semantic Search Works

Rishu Kumar

Vector Databases: Understanding the Fundamentals and Internal Architecture

See more stories

Who to follow

Popular this week

In

EmTech at The Met

by

Derek Au

Tech Notes for Semantic Search at The Met

Dr. Theophano Mitsa

From TF-IDF to Transformers: How Machines Learned to Search by Meaning

Harsh Raj

Agentic AI in Practice: A Case Study in Multi-Agent System Design

In

Towards AI

by

Srikanth Dongala

EmbeddingGemma 2: Multimodal Search for the Cost of a Text Model

Sunil Veeramachaneni

Day 2 of 100: From Tokens to Meaning — Understanding Embeddings and Cosine Similarity

Selected by Medium

Dr. Theophano Mitsa

From TF-IDF to Transformers: How Machines Learned to Search by Meaning

Mark Burgess

From A.I. to Comprehension, with an SST extended Knowledge Graph

Harish K Rao

From Experimental Notebooks to Production: A Data Engineer’s perspective of Scaling Data Science…

Latest stories

Atharv Bhatnagar

Building a Semantic Search Engine: From Text to Meaning

Vijay joshi

Traditional Full-Text Search V/S. Vector Search

Andrei Fateev

Part 2a: Why Static System Prompts Fail at Scale — and How Knowledge Catalog Grounds BigQuery…

Ram

We Searched 14 Same-Meaning Query Pairs to See How Semantic Search Works

Rishu Kumar

Vector Databases: Understanding the Fundamentals and Internal Architecture

See more stories

Who to follow