{"repo":"KevKibe/memvectordb","free":true,"listed":false,"github":"https://github.com/KevKibe/memvectordb","clone":"git clone https://github.com/KevKibe/memvectordb.git","description":"⚡️Lightning fast in-memory VectorDB written in rust🦀","language":"Rust","stars":31,"topics":["in-memory-caching","in-memory-database","vector","vector-database","vectordatabase"],"license":null,"category":"databases-storage","readme_excerpt":"MemVectorDB An fast in-memory VectorDB in rust. ⚡️ Features - Fast: MemVectorDB stores vectors in-memory, ensuring fast insertion and retrieval operations. - Vertical Scalability: With vectors stored in-memory, MemVectorDB can scale vertically based on available system resources. - Metadata Support: Supports metadata storage, beneficial for RAG (Retrieval Augmented Generation) applications and pipelines. - Option for Persistence: Supports full restoration of data from logs. - Open Source: MIT Licensed, free forever. 🚀 Usage 1. Clone the repository: 2. Build dependencies: 3. Start the DB. - DB runs on http://localhost:8000 4. Restore the DB from logs. - DB runs on http://localhost:8000 🐳 Using Docker 1. Pull the Docker image: - On x86 64 (Intel/AMD) systems: - On ARM-based systems (e.g., M1, M2, M3): 2. Run the Docker container: - On x86 64 (Intel/AMD) systems: - On ARM-based systems (e.g., M1, M2, M3): 3. Run the Docker container with DB restoration: - On x86 64 (Intel/AMD) systems: - On ARM-based systems (e.g., M1, M2, M3): 4. DB runs on http://localhost:8000 MemVectorDB Python client: Docs Load Tests - All tests done with 100000 requests on a Macbook Air M1. POST GET DELETE PUT","default_branch":null,"files":null,"tree":[],"storefront":"/r/KevKibe","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/KevKibe/memvectordb/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."}