vector-similarity

Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search

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.

Embeddable, in-memory, document-oriented database with a high-level Query builder interface.

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)

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

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"

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.

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

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

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

Maximal Information Coefficient (MIC) Extension for Postgres

OLD Resume Matcher Website (Not used anymore)

Vector Embedding Server in under 100 lines of code

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

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.

🧠 leverage advanced AI embeddings to perform multilingual zero-shot text classification. Whether you're dealing with unlabelled data or seeking to classify text against dynamic and user-defined labels, this library provides a seamless and efficient solution.

Fun with Game of Thrones word embeddings