Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

KektorDB is an in-memory vector database built from scratch in Go. It provides an HNSW-based engine for approximate nearest neighbor search, metadata filtering, and a JSON-based REST API.

A curated list of awesome works related to high dimensional structure/vector search & database

⚡ World’s Fastest Vector Database for AI & RAG

vector db built by someone with no idea how to build a vector db

Multilingual toolkit for evaluating LLMs using embeddings

Epsilla is a high performance Vector Database Management System

Lightweight Semantic Chunking Library. Plug any embedding provider/API. Batch embeddings for efficiency and handling API rate limits.

High level library for batched embeddings generation, blazingly-fast web-based RAG and quantized indexes processing ⚡

Analysis of embeddings and age biases in image generation models using CLIP, DINO, ResNet and Stable Diffusion XL

An open-source project for crawling RSS feeds and websites, extracting news content, and storing it with vector embeddings for semantic search, clustering and visualization..

VectorLite is a Rust-native, in-process vector store that brings sub-millisecond search and local embeddings to your AI agents and edge systems.

Learning project: modular RAG pipeline for legal document search & Q&A using SBERT, Pinecone, and FastAPI.

RoleRadar turns free-form requests like “Data Analyst roles in New York with SQL experience.” into structured filters and semantic-vector queries, delivering spot-on matches in seconds.

RAG Mini Project — Retrieval‑Augmented Generation chatbot with FastAPI backend (Docker on Hugging Face Spaces) and Streamlit frontend (Render), featuring document ingestion, vector search, and LLM‑powered answers

A command-line tool to index and perform hybrid semantic & lexical search over text files

Demonstrating RAG with streamlit.

The Virtual Feature Store. Turn your existing data infrastructure into a feature store.

Experimenting with Pinecone as vector data continues to take center stage in AI-native systems. The purpose of this project is to explore the core capabilities, benchmark performance across different embedding models, and better understand what is possible with vector search in production environments.

A Python dictionary that uses semantic similarity for key matching instead of exact matches. This library allows you to retrieve values using keys that are semantically similar to the ones stored, making it ideal for natural language interfaces, etc.