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

# Embeddings

> Generate vector embeddings for text using the embeddings endpoint.

## Generate embeddings

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    from meshapi import MeshAPI, EmbeddingsParams

    client = MeshAPI(base_url="https://api.meshapi.ai", token="rsk_...")

    result = client.embeddings.create(
        EmbeddingsParams(
            model="openai/text-embedding-3-small",
            input="MeshAPI embeddings smoke test",
        )
    )

    print(result.model)               # model used
    print(len(result.data))           # number of embedding items
    print(len(result.data[0].embedding))  # vector dimension
    ```
  </Tab>

  <Tab title="Node.js">
    ```javascript theme={null}
    const result = await client.embeddings.create({
      model: "openai/text-embedding-3-small",
      input: "MeshAPI embeddings smoke test",
    });

    console.log(result.model);
    console.log(result.data[0].embedding.length); // vector dimension
    ```
  </Tab>

  <Tab title="Go">
    ```go theme={null}
    model := "openai/text-embedding-3-small"
    resp, err := client.Embeddings.Create(ctx, meshapi.EmbeddingsParams{
        Model: &model,
        Input: "MeshAPI embeddings smoke test",
    })
    if err != nil {
        log.Fatal(err)
    }
    fmt.Printf("model=%s items=%d dims=%d\n", resp.Model, len(resp.Data), len(resp.Data[0].Embedding.Floats()))
    ```
  </Tab>
</Tabs>

## Batch embeddings

Pass a slice/array of strings to embed multiple inputs in a single request.

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    result = client.embeddings.create(
        EmbeddingsParams(
            model="openai/text-embedding-3-small",
            input=["First document", "Second document", "Third document"],
        )
    )
    for item in result.data:
        print(f"index={item.index} dims={len(item.embedding)}")
    ```
  </Tab>

  <Tab title="Node.js">
    ```javascript theme={null}
    const result = await client.embeddings.create({
      model: "openai/text-embedding-3-small",
      input: ["First document", "Second document", "Third document"],
    });
    result.data.forEach(item => {
      console.log(`index=${item.index} dims=${item.embedding.length}`);
    });
    ```
  </Tab>

  <Tab title="Go">
    ```go theme={null}
    model := "openai/text-embedding-3-small"
    resp, err := client.Embeddings.Create(ctx, meshapi.EmbeddingsParams{
        Model: &model,
        Input: []string{"First document", "Second document", "Third document"},
    })
    for _, item := range resp.Data {
        fmt.Printf("index=%d dims=%d\n", item.Index, len(item.Embedding.Floats()))
    }
    ```
  </Tab>
</Tabs>

## Response fields

| Field                      | Description                 |
| -------------------------- | --------------------------- |
| `result.data`              | Array of embedding objects  |
| `result.data[i].embedding` | Float vector                |
| `result.data[i].index`     | Position in the input array |
| `result.model`             | Model used                  |
| `result.usage`             | Token counts                |