---
title: Embeddings
description: Generate text embeddings for semantic search, retrieval, and RAG.
---

Embeddings turn text into numeric vectors you can store in a vector database, search with cosine similarity, or use in RAG pipelines. The vector length depends on the model (typically 384–1024 dimensions).

## Recommended models

- [embeddinggemma](https://ollama.com/library/embeddinggemma)
- [qwen3-embedding](https://ollama.com/library/qwen3-embedding)
- [all-minilm](https://ollama.com/library/all-minilm)

## Generate embeddings

<Tabs>
  <Tab title="CLI">
    Generate embeddings directly from the command line:

    ```shell
    ollama run embeddinggemma "Hello world"
    ```

    You can also pipe text to generate embeddings:

    ```shell
    echo "Hello world" | ollama run embeddinggemma
    ```

    Output is a JSON array.

  </Tab>
  <Tab title="cURL">
    ```shell
    curl -X POST http://localhost:11434/api/embed \
      -H "Content-Type: application/json" \
      -d '{
        "model": "embeddinggemma",
        "input": "The quick brown fox jumps over the lazy dog."
      }'
    ```
  </Tab>
  <Tab title="Python">
    ```python
    import ollama

    single = ollama.embed(
      model='embeddinggemma',
      input='The quick brown fox jumps over the lazy dog.'
    )
    print(len(single['embeddings'][0]))  # vector length
    ```
  </Tab>
  <Tab title="JavaScript">
    ```javascript
    import ollama from 'ollama'

    const single = await ollama.embed({
      model: 'embeddinggemma',
      input: 'The quick brown fox jumps over the lazy dog.',
    })
    console.log(single.embeddings[0].length) // vector length
    ```
  </Tab>
</Tabs>

<Note>
  The `/api/embed` endpoint returns L2‑normalized (unit‑length) vectors.
</Note>

## Generate a batch of embeddings

Pass an array of strings to `input`.

<Tabs>
  <Tab title="cURL">
    ```shell
    curl -X POST http://localhost:11434/api/embed \
      -H "Content-Type: application/json" \
      -d '{
        "model": "embeddinggemma",
        "input": [
          "First sentence",
          "Second sentence",
          "Third sentence"
        ]
      }'
    ```
  </Tab>
  <Tab title="Python">
    ```python
    import ollama

    batch = ollama.embed(
      model='embeddinggemma',
      input=[
        'The quick brown fox jumps over the lazy dog.',
        'The five boxing wizards jump quickly.',
        'Jackdaws love my big sphinx of quartz.',
      ]
    )
    print(len(batch['embeddings']))  # number of vectors
    ```
  </Tab>
  <Tab title="JavaScript">
    ```javascript
    import ollama from 'ollama'

    const batch = await ollama.embed({
      model: 'embeddinggemma',
      input: [
        'The quick brown fox jumps over the lazy dog.',
        'The five boxing wizards jump quickly.',
        'Jackdaws love my big sphinx of quartz.',
      ],
    })
    console.log(batch.embeddings.length) // number of vectors
    ```
  </Tab>
</Tabs>

## Tips

- Use cosine similarity for most semantic search use cases.
- Use the same embedding model for both indexing and querying.


