galsl/embedding_vectors repository overview

A vector-embedding is a numeric representation, typically a lengthy list of numbers known as dimensions, used to represent various forms of data such as text or images. One notable application is in Retrieval Augmented Generative (RAG) systems, which allow users to employ generative AI models based on their private data or knowledge base. These systems leverage vector-embedding to ensure that the AI model incorporates the specific content the user wants it to consider. For example, if a company has extensive documentation and private troubleshooting chats regarding its products, a standard AI model might not be aware of this information. However, by applying vector-embedding to this private data, the AI model can provide more relevant answers tailored to the company's specific context and needs.

the image contains applications that enable the creation of embedding-vectors, building an index, and using similarity algorithms for searching TOP-K vectors.