This post explores how to solve challenges in vector search using NVIDIA cuVS with the Meta Faiss library. It covers the benefits of integration, performance improvements, benchmarks, and code examples.
This article details the process of building a fast vector search system for a large legal dataset (Australian High Court decisions). It covers choosing embedding providers, performance benchmarks, using USearch and Isaacus embeddings, and the importance of API terms of service. It focuses on achieving speed and scalability while maintaining reasonable accuracy.
Turso is the small database for your biggest ideas. The most efficient way to build for apps, AI, agents, and everything in between. It's an embedded database engine that goes anywhere, offering features like vector search, async design, and SQLite compatibility.
An article detailing the reasons for creating Turso, a Rust-based rewrite of SQLite, addressing limitations in performance, modern features, and contribution model.
This article explains the internal workings of vector databases, highlighting that they don't perform a brute-force search as commonly described. It details algorithms like HNSW, IVF, and PQ, the tradeoffs between recall, speed, and memory, and how different RAG patterns impact vector database usage. It also discusses production challenges like filtering, updates, and sharding.
sqlite-vec is an extremely small, 'fast enough' vector search SQLite extension designed to run anywhere. It allows storing and querying of float, int8, and binary vectors using virtual tables, written in pure C with no dependencies. It supports storing non-vector data in metadata, auxiliary, or partition key columns. It is a Mozilla Builders project with additional sponsorship from companies like Fly.io, Turso, SQLite Cloud, and Shinkai.
A simple project demonstrating Retrieval Augmented Generation (RAG) using SQLite, sqlite-vec, and OpenAI. It embeds text files, stores them in a SQLite database, and retrieves relevant documents using vector search. The project features lightweight single-file SQLite databases, vector search capabilities, and OpenAI integration for embeddings and chat responses.
The article explores the concept of Retrieval-Augmented Generation (RAG) using SQLite, specifically with the sqlite-vec extension and the OpenAI API. It outlines a simplified approach to RAG, moving away from complex frameworks and cloud vector databases, using SQLite's virtual tables for vector search and semantic understanding.
Introducing sqlite-vec, a new SQLite extension for vector search written entirely in C. It's a stable release and can be installed in multiple ways. It runs on various platforms, is fast, and supports quantization techniques for efficient storage and search.
