> For the complete documentation index, see [llms.txt](https://docs.dat-hub.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.dat-hub.com/overview/core-concepts/embeddings.md). # Embeddings Embeddings transform complex data (e.g., words, images, sounds) into a simpler numerical form understandable by computers. They serve as a bridge, translating human language and visual information into numbers. ## Why Use Embeddings? * **Similarity:** Words or images with similar meanings or appearances have vectors that are close together. This aids in tasks like finding synonyms or similar images. Therefore, vector embeddings for ‘dog’ and ‘cat’ will be close, while the embedding for a ‘sparrow' will be much farther away. * **Context:** Embeddings capture the context of words or images. For instance, the word "play" will have different embeddings in the context of chess, a stereo system, or a theatre. ## Use Cases Thus, embeddings have useful applications for businesses such as: 1. When a customer views a dress on an online fashion retailer's site, the "You may also like" section could suggest complementary shoes, bags, and accessories. 2. A video/music streaming platform provides personalized content recommendations for users based on their viewing/listening history, ratings, and interactions with the platform. 3. An ecommerce platform can analyze sudden drops in purchase frequency or negative interactions with customer support to identify potential customer churn, allowing for timely remedial actions. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.dat-hub.com/overview/core-concepts/embeddings.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.