Generate Embeddings — Ollama Toolkit 0.1.9 documentation

# Generate Embeddings

Generate embeddings from a model. Embeddings are vector representations of text that can be used for semantic search, clustering, and other NLP tasks.

## Endpoint

## Parameters

Parameter | Type | Description | Required
--- | --- | --- | ---
model | string | Name of model to generate embeddings from | Yes
input | string/array | Text or list of text to generate embeddings for | Yes

### Advanced Parameters (Optional)

Parameter | Type | Description
--- | --- | ---
truncate | boolean | Truncates the end of each input to fit within context length. Returns error if false and context length is exceeded. Defaults to true
options | object | Additional model parameters listed in the documentation for the Modelfile
keep_alive | string | Controls how long the model will stay loaded into memory following the request (default: 5m)

## Response

Field | Type | Description
--- | --- | ---
model | string | The model name
embeddings | array | Array of embedding vectors (array of floats)
total_duration | number | Time spent generating the embeddings (nanoseconds)
load_duration | number | Time spent loading the model (nanoseconds)
prompt_eval_count | number | Number of tokens processed

## Examples

### Single Input Request

    curl http://localhost:11434/api/embed -d '{
      "model": "all-minilm",
      "input": "Why is the sky blue?"
    }'

#### Response

    {
      "model": "all-minilm",
      "embeddings": [[
        0.010071029, -0.0017594862, 0.05007221, 0.04692972, 0.054916814,
        0.008599704, 0.105441414, -0.025878139, 0.12958129, 0.031952348
      ]],
      "total_duration": 14143917,
      "load_duration": 1019500,
      "prompt_eval_count": 8
    }

### Multiple Input Request

    curl http://localhost:11434/api/embed -d '{
      "model": "all-minilm",
      "input": ["Why is the sky blue?", "Why is the grass green?"]
    }'

#### Response

    {
      "model": "all-minilm",
      "embeddings": [[
        0.010071029, -0.0017594862, 0.05007221, 0.04692972, 0.054916814,
        0.008599704, 0.105441414, -0.025878139, 0.12958129, 0.031952348
      ],[
        -0.0098027075, 0.06042469, 0.025257962, -0.006364387, 0.07272725,
        0.017194884, 0.09032035, -0.051705178, 0.09951512, 0.09072481
      ]]
    }

In Python, try:

    python -m ollama_toolkit.examples.embedding_example --text "Your text here"