A fast, efficient universal vector embedding utility package.

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.

NucliaDB, The AI Search database for RAG

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.

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

a modular multimodal framework for ai applications

Postgres/pgvector Python Client

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

Fast word vectors with little memory usage in Python

Vector-geometry toolbelt for 3D points and vectors

(distributed) vector database

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

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

RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

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

Interactive Q&A App: This GitHub repository showcases the implementation of an interactive question-answering application using Langchain, Pinecone, and Streamlit. Experience the synergy of language models and efficient search with retrieval augmented generation.

Coordinates in JAX

misc scripts

QGIS plugin to configure automatic vector field updates when creating or modifying features.