klotz: sqlite* + vector search*

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  1. A from-scratch reimplementation of Stanford's XTR-Warp semantic search engine written in safe Rust. It is designed for client-side deployment, utilizing a single-file SQLite database for storage without the need for external API keys, vector databases, or complex chunking strategies. The engine offers high performance with extremely low end-to-end search latency and supports hybrid search by combining semantic results with standard BM25 functionality.
    Key features and components:
    - High-speed semantic search capable of running on local devices.
    - SQLite backend for easy data persistence and portability.
    - Support for various backends including T5 quantized weights via candle and OpenVINO.
    - Pickbrain CLI example for indexing AI coding session transcripts (Claude Code/OpenAI Codex).
    - Hardware acceleration support for Apple Silicon (Metal) and x86 (fbgemm).
    - Available as a Node.js native module.
  2. 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.
    2025-10-07 Tags: , , , , , by klotz
  3. An article detailing the reasons for creating Turso, a Rust-based rewrite of SQLite, addressing limitations in performance, modern features, and contribution model.
    2025-10-07 Tags: , , , , , , by klotz
  4. 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.
  5. 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.
  6. 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.

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