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"