google Vector Vector Vector Vector Product

GitHub Repo https://github.com/GoogleCloudPlatform/psearch

GoogleCloudPlatform/psearch

AI-powered product search platform built on Google Cloud Platform (GCP). Leverages Spanner's hybrid search capabilities (vector similarity & text search), Vertex AI (Gemini & embedding APIs), and a microservices architecture (Cloud Run). Features AI-driven filter suggestions, content enrichment, and a rules engine. See README for details.

GitHub Repo https://github.com/CoreyVidal/Google-products-vector-logos

CoreyVidal/Google-products-vector-logos

A collection of only the highest quality and most accurate icons for various Google products.

GitHub Repo https://github.com/google/vectorio

google/vectorio

Library for Go (golang) to implement writev system call (not an official Google product)

GitHub Repo https://github.com/123NeNaD/semantic-image-search-with-mongodb

123NeNaD/semantic-image-search-with-mongodb

A demo application that implements semantic image search using MongoDB's Vector Search and Google's Vertex AI multi-modal model. The application allows users to search through an e-commerce dataset of 44,000 fashion products using either natural language descriptions or uploading an image of a similar product.

GitHub Repo https://github.com/Omerbea/vector-catalog

Omerbea/vector-catalog

Semantic product catalog — Google Gemini embeddings generated by a PostgreSQL trigger, not your application

GitHub Repo https://github.com/SthembisoMfusi/The-product-compass

SthembisoMfusi/The-product-compass

A Python and Node.js-based e-commerce application that leverages Google's BigQuery to provide intelligent, semantic product recommendations. This project moves beyond simple category-based suggestions by using vector embeddings and vector search to find functionally and stylistically similar substitute products when an item is out of stock.

GitHub Repo https://github.com/faris771/Production-Ready-RAG

faris771/Production-Ready-RAG

A production-grade Retrieval Augmented Generation (RAG) system built with FastAPI, Inngest, Qdrant, and Google Gemini. This system allows you to ingest PDF documents, store their embeddings in a vector database, and query them using natural language with AI-powered responses.

GitHub Repo https://github.com/ritwickbhargav80/CoRA

ritwickbhargav80/CoRA

CoRA is an intelligent retail assistant built with Google’s ADK, enhancing online shopping through semantic product discovery, smart recommendations, and natural customer interactions. It uses vector embeddings, parallel agents, and MongoDB vector search to deliver highly contextual product suggestions.

GitHub Repo https://github.com/mohammedanasa/rag-production-app

mohammedanasa/rag-production-app

A minimal RAG stack that ingests PDFs, chunks them, embeds with Google GenAI, stores vectors in Qdrant, and answers questions using an Inngest workflow.

GitHub Repo https://github.com/keshavgarg139/Google-News-Vector-Approach-for-Product-Similarity-

keshavgarg139/Google-News-Vector-Approach-for-Product-Similarity-

Using the Vector Space Model based on Google News to find the similarity between the text and thus further combining that similarity with various parameters for product similarity.