Query Vector Vector Vector Vector Product Vector

GitHub Repo https://github.com/digitaldreams/VectorCart

digitaldreams/VectorCart

An experimental e-commerce platform built on Symfony AI components, using PostgreSQL pgvector for semantic product search and AI agents to handle recommendations, queries, and store interactions.

GitHub Repo https://github.com/ellipsion/semantic-vector-search

ellipsion/semantic-vector-search

A hybrid product search in Next.js using semantic vector querying.

GitHub Repo https://github.com/kevin-gatimu/products-api-cosmosdb-typescript-vector-search

kevin-gatimu/products-api-cosmosdb-typescript-vector-search

In this guide, we will build a Products API using Express and TypeScript, and integrate Azure Cosmos DB (with vector search capabilities) for managing product data. We'll also implement vector search, enabling us to query items based on vector similarity.

GitHub Repo https://github.com/MurtazaAbidi/Vector-Space-Model

MurtazaAbidi/Vector-Space-Model

The query processing of VSM is quite tricky, you need of optimize every aspect of computation. The high-dimensional vector product and similarity values of query (q) and documents (d) need to optimized.

GitHub Repo https://github.com/playwithllm/store

playwithllm/store

A RAG-driven image product search that showcases MERN, Milvus for vector indexing, Transformers, and a local Ollama Gemma LLM. Explore integrated embeddings, store vectors in Milvus, and manipulate queries with advanced language understanding.

GitHub Repo https://github.com/chandann23/ProductFiletrs

chandann23/ProductFiletrs

Project to learn tanstack query , zod , vector database

GitHub Repo https://github.com/AbhishekGY/vector_database

AbhishekGY/vector_database

Building a vector database from scratch in pure Python that supports: inserting vectors, quantizing them with Product Quantization, persisting the index to disk, and querying for approximate nearest neighbours via a simple API. No FAISS. No Pinecone.

GitHub Repo https://github.com/felixmaximilian/mips

felixmaximilian/mips

Scala implementation of a Branch and Bound Balltree algorithm to find points in vector space that have largest inner product to a given query (maximum inner product search).

GitHub Repo https://github.com/belayethossain7/django-vector-search-pinecone

belayethossain7/django-vector-search-pinecone

A Django web app that integrates Pinecone vector database for semantic search. It uses product embeddings to enable intelligent search functionality based on user queries, powered by Sentence-Transformers.

GitHub Repo https://github.com/unais5/Vector-Space-Model

unais5/Vector-Space-Model

The query processing of VSM is quite tricky, you need of optimize every aspect of computation. The high-dimensional vector product and similarity values of query (q) and documents (d) need to optimized. Basic Assumption for Vector Space Model (VSM) Retrieval Model 1.Simple model based on linear algebra. Terms are considered as features using a weighting scheme. 2.Allows partial matching of documents with the queries. Hence, able to produce good institutive scoring. Continuous scoring between queries and documents. 3.Ranking of documents are possible using relevance score between document and query.