vector search

Training GitHub Repository Embeddings with Stars

GitHub Stars, often used as bookmarks, provide valuable insights into which repositories are semantically similar. By processing approximately 1TB of raw data from GitHub Archive, an interest matrix for 4 million developers was created, leading to the training of embeddings for over 300,000 repositories using Metric Learning techniques. A client-only demo was developed that conducts vector searches directly in the browser via WebAssembly, eliminating the need for a backend. This system not only identifies non-obvious library alternatives but also facilitates semantic comparisons of developer profiles, offering a powerful tool for developers to explore and utilize GitHub repositories more effectively. This matters because it enhances the ability to discover and compare software projects and developer interests, potentially leading to more innovative and collaborative projects.

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Posted on

Jan 6, 2026

by

TechWithoutHype

in

Deep Dives, Learning

Topics: machine learning, vector search, WebAssembly

GraphQLite: Embedded Graph Database with SQLite

GraphQLite is an SQLite extension designed for those building GraphRAG systems who prefer not to use Neo4j for storing knowledge graphs. It introduces Cypher query support, allowing users to store entities and relationships in a graph structure and utilize Cypher for context expansion during data retrieval. By integrating with sqlite-vec for vector search, GraphQLite provides a comprehensive embedded RAG stack within a single database file. It also includes graph algorithms like PageRank and community detection, which help identify key entities and cluster related concepts. This extension is particularly useful for developers looking for a streamlined solution to manage graph data efficiently. This matters because it offers a lightweight, integrated alternative for handling complex graph data without the overhead of additional database systems.

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Posted on

Dec 31, 2025

by

TechWithoutHype

in

Deep Dives, How-Tos

Topics: data retrieval, vector search, embedded systems

EdgeVec v0.7.0: Browser-Based Vector Search

EdgeVec v0.7.0 is a browser-based vector database designed to provide local AI applications with cloud-like vector search capabilities without network dependency. It introduces significant updates such as binary quantization for a 32x memory reduction, SIMD acceleration for up to 8.75x faster processing, and IndexedDB persistence for data retention across sessions. These features enable efficient local document search, offline retrieval-augmented generation (RAG), and privacy-preserving AI assistants by allowing data to remain entirely on the user's device. This matters because it empowers users to perform advanced searches and AI tasks locally, maintaining privacy and reducing reliance on cloud services.