Embeddings

`client.embeddings.create()`

Generate embeddings for one or more input strings.

result = client.embeddings.create(
    model="minnow-em-v1",
    input="query: what is DPDP?",
)

Parameters

model

str

required

Embedding model ID. Example: "minnow-em-v1".

input

str | List[str]

required

A single string or a list of strings to embed.

dimensions

int

Request a specific MRL dimension. Supported values: 896, 512, 256, 128, 64. Defaults to the model’s native dimension (896 for minnow-em-v1).

encoding_format

str

"float" (default) or "base64".

extra

dict

Any additional parameters passed through to the API.

Returns: `EmbeddingResponse`

Field	Type	Description
`model`	`str`	Model used
`data`	`List[Embedding]`	One item per input string
`usage`	`Usage` or None	Token counts

Each Embedding:

Field	Type	Description
`index`	`int`	Position in the input list
`embedding`	`List[float]`	The dense embedding vector
`object`	`str`	Always `"embedding"`

Example — batch with MRL

result = client.embeddings.create(
    model="minnow-em-v1",
    input=[
        "query: insurance claim process",
        "passage: Claims must be filed within 30 days...",
    ],
    dimensions=256,
)

for item in result.data:
    print(f"[{item.index}] dim={len(item.embedding)}")

Getting Started

Guides

API Reference

`client.embeddings.create()`

Parameters

Returns: `EmbeddingResponse`

Example — batch with MRL

​client.embeddings.create()

​Parameters

​Returns: EmbeddingResponse

​Example — batch with MRL

`client.embeddings.create()`

Parameters

Returns: `EmbeddingResponse`

Example — batch with MRL