{"repo":"fzliu/radient","free":true,"listed":false,"github":"https://github.com/fzliu/radient","clone":"git clone https://github.com/fzliu/radient.git","description":"Radient turns many data types (not just text) into vectors for similarity search, RAG, regression analysis, and more.","language":"Python","stars":281,"topics":["audio","embeddings","fraud-detection","graphs","images","molecular-search","molecules","recommender-system","text","vectors"],"license":"BSD-2-Clause","category":"media-processing","readme_excerpt":"Radient Radient is a developer-friendly, lightweight library for generating and working with embeddings. Radient supports simple vectorization (i.e. turning data into vectors) as well as building retrieval systems (i.e. complex end-to-end workflows for embedding-based search). If you find this project helpful or interesting, please consider giving it a star. :star: Getting started Basic vectorization can be performed as follows: The above snippet vectorizes the string \"Hello, world!\" using a default model, namely bge-small-en-v1.5 from sentence-transformers . If your Python environment does not contain the sentence-transformers library, Radient will prompt you for it: You can type \"Y\" to have Radient install it for you automatically. Each vectorizer can take a method parameter along with optional keyword arguments which get passed directly to the underlying vectorization library. For example, we can pick Mixbread AI's mxbai-embed-large-v1 model using the sentence-transformers library via: More than just text With Radient, you're not limited to text. Audio, graphs, images, and molecules can be vectorized as well: For production use cases with large quantities of data, performance is key. Radient also provides an accelerate function to optimize some vectorizers on-the-fly: On a 2.3 GHz Quad-Core Intel Core i7, the original vectorizer returns in 32ms, while the accelerated vectorizer returns in 17ms. Building unstructured data ETL Aside from running experiments, pure vectorizati","default_branch":null,"files":null,"tree":[],"storefront":"/r/fzliu","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/fzliu/radient/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."}