A fast, efficient universal vector embedding utility package.

Neum AI is a best-in-class framework to manage the creation and synchronization of vector embeddings at large scale.

VectorFlow is a high volume vector embedding pipeline that ingests raw data, transforms it into vectors and writes it to a vector DB of your choice.

NucliaDB, The AI Search database for RAG

Fast word vectors with little memory usage in Python

Radient turns many data types (not just text) into vectors for similarity search, RAG, regression analysis, and more.

Postgres/pgvector Python Client

The RAG Experiment Accelerator is a versatile tool designed to expedite and facilitate the process of conducting experiments and evaluations using Azure Cognitive Search and RAG pattern.

Vector-geometry toolbelt for 3D points and vectors

(distributed) vector database

a modular multimodal framework for ai applications

A demo app showcasing Vector Search using Azure AI Search, Azure OpenAI for text embeddings, and Azure AI Vision for image embeddings.

Source code for our AAAI 2020 paper P-SIF: Document Embeddings using Partition Averaging

A python back-end web app to visualize a two dimensional vector field, and to calculate divergence and curl at any point.

Simple Icons Blender add-on

Hyperdimensional Computing Library for building Vector Symbolic Architectures in Python 3

Methods of numerical modelling with undergraduate level computational physics solutions with non-linear equations, gaussian probabilities, and Euler RK4 integration

Improving Document Classification with Multi-Sense Embeddings Source Code (ECAI 2020)

Examples of vector DB indexing and query with various vector databases.

A simple interface where users can search for contextually relevant text passages in documents. It employs DistilBERT model for semantic embeddings and FAISS for efficient similarity search. When users enter a query, the system returns the most contextually relevant text passage from the corpus and highlights the matched keywords.