> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bytez.com/llms.txt
> Use this file to discover all available pages before exploring further.

# feature-extraction

> Convert text into vectors (embeddings) that capture semantic meaning

<AccordionGroup>
  <Accordion defaultOpen="true" title="Basic usage">
    Each feature extraction model is different, so check readme.md for the model manual. Below is an example of text embedding model

    <CodeGroup>
      ```javascript javascript theme={null}
      import Bytez from 'bytez.js';

      // insert your key
      const sdk = new Bytez('BYTEZ_KEY');

      // choose your model
      const model = sdk.model('nomic-ai/nomic-embed-text-v1.5');

      // provide the model with input
      const input = "search_document: Turn this text into a vector";

      // send to the model
      const { error, output } = await model.run(input);

      // observe the output
      console.log({ error, output });

      ```

      ```python python theme={null}
      from bytez import Bytez

      # insert your key
      sdk = Bytez("BYTEZ_KEY")

      # choose your model
      model = sdk.model("nomic-ai/nomic-embed-text-v1.5")

      # provide the model with input
      input = "search_document: Turn this text into a vector"

      # send to the model
      result = model.run(input)

      # observe the output
      print({"error": result.error, "output": result.output})

      ```

      ```bash http theme={null}
      curl -X POST 'https://api.bytez.com/models/v2/nomic-ai/nomic-embed-text-v1.5' \
      -H 'Authorization: BYTEZ_KEY' \
      -H 'Content-Type: application/json' \
      --data '{
        "text": "search_document: Turn this text into a vector"
      }'
      ```
    </CodeGroup>
  </Accordion>
</AccordionGroup>