A cloud-native vector database, storage for next generation AI applications

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

Vector Hub - Library for easy discovery, and consumption of State-of-the-art models to turn data into vectors. (text2vec, image2vec, video2vec, graph2vec, bert, inception, etc)

Vector AI — A platform for building vector based applications. Encode, query and analyse data using vectors.

Python, Java implementation of TS-SS called from "A Hybrid Geometric Approach for Measuring Similarity Level Among Documents and Document Clustering"

This repository contains various ways to calculate sentence vector similarity using NLP models

Vector Storage is a vector database that enables semantic similarity searches on text documents in the browser's local storage. It uses OpenAI embeddings to convert documents into vectors and allows searching for similar documents based on cosine similarity.

cuVS - a library for vector search and clustering on the GPU

Timescale Vector Cookbook. A collection of recipes to build applications with LLMs using PostgreSQL and Timescale Vector.

VQLite - Simple and Lightweight Vector Search Engine based on Google ScaNN

Resume Matcher Website

vasco: MIC & MINE statistics for Postgres

V3CTRON | Vector Embeddings Data Retrieval | ChatGPT Plugin

Vector Embedding Server in under 100 lines of code

AI Github assistant for your repo. Your proactive GitHub bot that auto-detects duplicates using OpenAI embeddings and Supabase magic!

Shotit is a screenshot-to-video search engine tailored for TV & Film, blazing-fast and compute-efficient.

Text/Image search for similar products

Fun with Game of Thrones word embeddings

This code example shows how to make a chatbot for semantic search over documents using Streamlit, LangChain, and various vector databases. The chatbot lets users ask questions and get answers from a document collection. The code is in Python and can be customized for different scenarios and data.

Scripts for reading, extracting, and organizing data from either HTML or PDF documents and prepare them to be converted into embeddings for use in context-augmented LLM queries.