Embeddings | Ollama English

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).

Generate embeddings

CLI

cURL

Python

JavaScript

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 -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."
  }'

import ollama

single = ollama.embed(
  model='embeddinggemma',
  input='The quick brown fox jumps over the lazy dog.'
)
print(len(single['embeddings'][0]))  # vector length

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

CLIcURLPythonJavaScript

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.

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

Python

JavaScript

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

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

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

cURLPythonJavaScript

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

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

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.