# Encoding Vector AI has deployed a few encoders that follow a very standard encoding scheme. This is to assist users with encoding as easily as possible. These encoders are summarised in the table below. | Encoder Name | Description | | :----------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | text | Encode English text data into a vector. | | text- multi | Encode multi-lingual text data into a vector. However, if you are looking to get good English vectors, we recommend the text encoder as it has better performance. | | image | Encode an image. | | image-text | Encode an image to make it searchable with text. However, if you are looking for good image similarity, we recommend the image encoder for better performance. | | text-image | Encode text to make it searchable with images. However, If you are looking for text similarity, we recommend the text encoder for better performance. | --- # Agent Instructions: 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: ``` GET https://learn.getvectorai.com/vector-search/encoding.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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.