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

# Embeddings

> Generate embeddings for semantic search and similarity.

Embeddings turn text into high‑dimensional vectors. Use them for semantic search, clustering, or similarity scoring across your own data.

## Prerequisites

* An API token. See [Backoffice Developer Console](/en/developer-manual/get-started/developer-console).
* Your Zylon hostname (replace `{BASE_URL}` in the examples).

## Before you start

* **Order matters**: `data[index]` aligns with your input order.
* **Store the vectors** in your database or vector index to power search and similarity.
* **Use consistent preprocessing** (same casing and formatting) for better similarity results.

## Create embeddings

Generate embeddings with `POST /embeddings`. The `input` can be a single string or an array.

| `input` shape    | When to use                         |
| ---------------- | ----------------------------------- |
| Single string    | One text at a time.                 |
| Array of strings | Batch inputs and keep output order. |

<Tabs>
  <Tab title="Single input">
    ```bash theme={null}
    curl -X POST "https://{BASE_URL}/api/gpt/v1/embeddings" \
      -H "Authorization: Bearer {API_TOKEN}" \
      -H "Content-Type: application/json" \
      -d '{
        "input": "Summarize Q1 support trends in one vector."
      }'
    ```

    <Accordion title="Example response">
      ```json theme={null}
      {
        "object": "list",
        "model": "private-gpt",
        "data": [
          {
            "index": 0,
            "object": "embedding",
            "embedding": [
              0.697265625,
              0.5078125,
              0.01129150390625,
              0.244873046875,
              -0.285888671875,
              0.058135986328125,
              0.01922607421875,
              -0.11431884765625
            ]
          }
        ]
      }
      ```
    </Accordion>
  </Tab>

  <Tab title="Batch input">
    ```bash theme={null}
    curl -X POST "https://{BASE_URL}/api/gpt/v1/embeddings" \
      -H "Authorization: Bearer {API_TOKEN}" \
      -H "Content-Type: application/json" \
      -d '{
        "input": [
          "Billing issues drove 38% of cases.",
          "Priority bugs were resolved within 48 hours."
        ]
      }'
    ```

    <Accordion title="Example response">
      ```json theme={null}
      {
        "object": "list",
        "model": "private-gpt",
        "data": [
          {
            "index": 0,
            "object": "embedding",
            "embedding": [
              -0.435546875,
              -0.49169921875,
              -0.48876953125,
              1.41015625,
              0.11114501953125,
              -1.2626953125
            ]
          },
          {
            "index": 1,
            "object": "embedding",
            "embedding": [
              -0.6748046875,
              0.242431640625,
              -0.31396484375,
              0.53955078125,
              -0.84716796875,
              -0.006961822509765625
            ]
          }
        ]
      }
      ```
    </Accordion>
  </Tab>
</Tabs>

## Errors and edge cases

* **401/403**: token missing or invalid.
* **413**: input too large.
* **400**: invalid JSON or input type.