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
• qwen3-embedding
• all-minilm

Generate embeddings

CLI

Generate embeddings directly from the command line:

    ollama run embeddinggemma "Hello world"

You can also pipe text to generate embeddings:

    echo "Hello world" | ollama run embeddinggemma

Output is a JSON array.

cURL

    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."
      }'

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

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

Note

The /api/embed endpoint returns L2‑normalized (unit‑length) vectors.

Generate a batch of embeddings

Pass an array of strings to input.

cURL

    curl -X POST http://localhost:11434/api/embed \
      -H "Content-Type: application/json" \
      -d '{
        "model": "embeddinggemma",
        "input": [
          "First sentence",
          "Second sentence",
          "Third sentence"
        ]
      }'

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

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

Tips

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