What is vector embedding in AI?

Vector embedding in AI is a way of converting data (like text, images, or audio) into numbers so that a computer can understand and work with it.

For example, when we type a word or sentence, the AI cannot understand the text directly. Therefore, it converts the text into a list of numbers (known as a vector) that represents the meaning of that text. Words or sentences with similar meanings will have vectors that are close to each other.

This helps AI in tasks like:

• Finding similar documents or images
• Understanding the context of a sentence
• Powering chatbots and search engines

Vector embedding is like giving meaning to data in a language that AI can understand.

All LLMs are pre-trained and they are typically general purpose models. when you want to add your custom domain knowledge to the LLM there are 2 options. you either take the foundation model and spend efforts to finetune it or you can use RAG . RAG stands for Retrieval-Augmented Generation , when you get a user query specific to your data you can do search on your data and filter the closest results and then take the top results to pass to LLM and generate the response. 

 

the Search that you do on your data , instead of traditional text based serach we use something called vector serach. Vector search is an information retrieval method that represents given data as multi-dimensional numerical vectors, known as embeddings, within a vector space. Instead of matching keywords, it finds items that are semantically similar by calculating the proximity of their vectors in the vector space using distance metrics. This is where Vector embeddings is used in AI. 

 

Hope this helps ! Happy learning !