embeddings — agent.viscover.site

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

Generate embeddings

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.

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."
  }'
```

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
```

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
```

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

Generate a batch of embeddings

Pass an array of strings to input.

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"
    ]
  }'
```

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
```

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
```

Tips

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