Vector Search - aitoearn

BigQuery ML

Using BigQuery ML to Solve Zeotap’s Lookalike Audience Problem

# Building Lookalike Audiences in BigQuery with Jaccard Similarity and AI-Powered Workflows **Editor’s Note:** This post is part of a series showcasing how organizations use **Google Cloud’s** unique data science capabilities compared with other cloud platforms. Google Cloud offers **end-to-end vector embedding generation and search**, customizable with task-optimized models

BigQuery

BigQuery: Efficient Vector Search Integration in Columnar Storage

# Integrating Vector Search into BigQuery’s Distributed Architecture In today’s AI-driven world, **vector search** has become a standard requirement for content retrieval, recommendation systems, and semantic matching. When Google announced **general availability of vector search in BigQuery** in early 2024, it joined the growing ranks of mature databases offering

BigQuery

BigQuery Vector Search Technology Explained

Introduction: Embeddings and Vector Search Embeddings are essential data structures at the crossroads of data and AI. They capture the underlying semantic meaning of the data they represent. This becomes most apparent when embeddings are compared — revealing relationships through distance measurements in a shared vector space. Vector search is the

HNSW

Scalability Study of HNSW

Scaling HNSWs in Redis Original article: Scaling HNSWs (via Hacker News) Salvatore Sanfilippo has spent much of this year working on vector sets for Redis, first released in Redis 8 (May 2024). --- Background A central part of this effort involved implementing HNSW — Hierarchical Navigable Small World, an indexing technique

pgvector

Reasons Against pgvector: Technical Challenges at Scale

The Case Against pgvector — Key Takeaways Read the full article: The Case Against pgvector (Hacker News discussion) While the title might sound provocative, Alex Jacobs’ write‑up provides excellent technical insights. It explores challenges encountered when operating pgvector — a PostgreSQL vector indexing extension — at scale, especially: * Maintaining large indexes with

RAG

Building a RAG Application with Spring Boot, Spring AI, MongoDB Atlas Vector Search, and OpenAI

Key Takeaways * RAG overcomes LLM limitations — Retrieval‑Augmented Generation blends text generation with live information retrieval from enterprise databases, producing accurate, explainable, up‑to‑date responses. * Spring Boot + Spring AI — Provide a robust enterprise integration layer for AI, enabling multi‑provider support without disruptive codebase changes. * MongoDB Atlas vector search

RAG systems

Deploying a Multimodal RAG System with vLLM

# Transcript: Multimodal RAG Systems, Vector Search, and AI Publishing ## Introduction **Stephen Batifol**: Today we’re going to talk about **multimodal RAG systems** using **vLLM** and **Pixtral** from Mistral. We’ll also cover: - **Vector search & vector databases** - **Index types and trade-offs** - **Embedding models** - A live **demo*