{"repo":"jonathanglasmeyer/knowledge-vector-search","free":true,"listed":false,"github":"https://github.com/jonathanglasmeyer/knowledge-vector-search","clone":"git clone https://github.com/jonathanglasmeyer/knowledge-vector-search.git","description":"Find relevant information in your documents using natural language queries - no more keyword matching or manual browsing through hundreds of files. Built for speed and efficiency, this lightweight system searches through 500+ documents in under 300ms while using only 100MB of memory.","language":"Python","stars":14,"topics":["knowledge-management","obsidian","python","sqlite-vec","vector-search"],"license":"MIT","category":"productivity","readme_excerpt":"Knowledge Vector Search Find relevant information in your documents using natural language queries - no more keyword matching or manual browsing through hundreds of files. Built for speed and efficiency, this lightweight system searches through 500+ documents in under 300ms while using only 100MB of memory. Perfect for knowledge bases, research collections, and personal document libraries. Features - ONNX-optimized embeddings - FastEmbed with 384-dimensional vectors (23MB vs 6.8GB PyTorch models, CPU-optimized) - Fast semantic search - 300ms queries across 500+ documents using sqlite-vec cosine similarity - Incremental updates - SHA256-based change detection, only reprocess modified files - Smart search wrapper - automatic index updates when documents change - SQLite-based storage - zero external dependencies, portable, 6KB per document - Metadata extraction - YAML frontmatter and document properties (ideal for Obsidian and other Markdown knowledge bases) - Simple Python API and command-line tools Processing Flow: Markdown → text extraction + frontmatter parsing → FastEmbed embedding → sqlite-vec storage → cosine similarity search Try It Yourself Want to see semantic search in action? We've included both tech docs and recipes to show how it finds the right content type based on context: Notice: Even with mixed content types, it finds semantically relevant results without cross-contamination! Quick Start Installation Requirements : Python 3.12+ Recommended: Using uv (fast, rel","default_branch":null,"files":null,"tree":[],"storefront":"/r/jonathanglasmeyer","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/jonathanglasmeyer/knowledge-vector-search/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}