{"repo":"likeslines-maker/VectorRAG.Net","free":true,"listed":false,"github":"https://github.com/likeslines-maker/VectorRAG.Net","clone":"git clone https://github.com/likeslines-maker/VectorRAG.Net.git","description":"VectorRAG.Net is a .NET-native high-performance vector database library for semantic search and RAG (Retrieval-Augmented Generation). Core search is based on Random Hyperplane LSH candidate generation with exact rerank by dot/cosine.","language":"C#","stars":162,"topics":["bm25","embeddings","llm","rag","semantic-search","vector-database","vector-search","ann-search","approximate-nearest-neighbor","artificial-intelligence"],"license":null,"category":"ai-agents","readme_excerpt":"VectorRAG.Net High-performance embedded vector database for .NET 8. VectorRAG.Net is an in-process vector database designed for Retrieval-Augmented Generation (RAG), semantic search, local AI assistants and low-latency applications. Unlike external vector databases, VectorRAG.Net runs directly inside your application process. No HTTP. No Docker. No external services. No network latency. Features SIMD-optimized vector search LSH-based ANN indexing Hybrid Search (Vector + BM25) Automatic document chunking Metadata filtering Snapshot save/load Runtime metrics Fully embedded architecture .NET 8 native implementation --- Installation --- Quick Start --- Add Documents --- Vector Search --- Hybrid Search --- Metadata Filtering --- Save Snapshot --- Load Snapshot --- Typical Use Cases RAG Systems Store embeddings locally and retrieve context without external vector services. Internal Knowledge Bases Low-latency semantic search for documentation and support portals. Desktop AI Applications Run completely offline without Docker, cloud services or internet access. Edge Computing Vector search on embedded devices, industrial controllers and local servers. Corporate Offline Systems Suitable for environments where external network access is prohibited. --- Performance Example benchmark: Operation Average Time ---------------------- ------------ Vector Search (TopK=5) 15.15 μs Hybrid Search 116.73 μs Actual performance depends on embedding dimensions, dataset size and hardware. --- Licensin","default_branch":null,"files":null,"tree":[],"storefront":"/r/likeslines-maker","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/likeslines-maker/VectorRAG.Net/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."}