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

Near-optimal vector quantization from Google's ICLR 2026 paper — 95% recall, 5x compression, zero preprocessing, pure Python FAISS replacement

Multi-modal embeded database for edge devices

Code and results for "Revisiting RaBitQ and TurboQuant: a symmetric comparison of methods, theory, and experiments".

A (not very) frequently updated list of ANN vector search papers on declarative recall through early termination, published in top data management venues.

Accelerate vector similarity search and embedding management for efficient AI applications with zvec.

Production-ready multimodal retrieval system built with OpenCLIP, Qdrant, FastAPI and Streamlit. Includes full evaluation pipeline (Recall@K, mAP, nDCG) and Docker-based deployment.

A framework for the implementation of candidate generation/retrieval algorithms for recommender systems.

Approximate shortest path between the nodes in a (knowledge or any other) graph

RAG pipeline prototype MVP project

A basic RAG pipeline which uses gpt-oss-20b model to answer the user query with the external knowledge stored in a vector database.

A WebGPU-powered vector database for local semantic search, exact similarity queries, and benchmarked embedding workflows.

Building a Custom Vector Search Engine with Weaviate : The project discusses the architecture of Weaviate, an open-source vector database and provides a tutorial implementation of a custom vector search engine using Weaviate Cloud Service(WCS).

Transform-domain representation enabling 3–4× storage reduction with direct ANN search and novel multi-resolution signals. UK patent application under accelerated examination (Green Channel).

App Store Search example using ReactiveSearch pipelines and aNN