langchain_nomic.embeddings.NomicEmbeddings

class langchain_nomic.embeddings.NomicEmbeddings(*, model: str, nomic_api_key: Optional[str] = ..., dimensionality: Optional[int] = ..., inference_mode: Literal['remote'] = ...)

class langchain_nomic.embeddings.NomicEmbeddings(*, model: str, nomic_api_key: Optional[str] = ..., dimensionality: Optional[int] = ..., inference_mode: Literal['local', 'dynamic'], device: Optional[str] = ...)

class langchain_nomic.embeddings.NomicEmbeddings(*, model: str, nomic_api_key: Optional[str] = ..., dimensionality: Optional[int] = ..., inference_mode: str, device: Optional[str] = ...)

NomicEmbeddings embedding model.

Example

from langchain_nomic import NomicEmbeddings

model = NomicEmbeddings()

Initialize NomicEmbeddings model.

Parameters

• model (str) – model name

• nomic_api_key (Optional[str]) – optionally, set the Nomic API key. Uses the NOMIC_API_KEY environment variable by default.

• dimensionality (Optional[int]) – The embedding dimension, for use with Matryoshka-capable models. Defaults to full-size.

• inference_mode (str) – How to generate embeddings. One of remote, local (Embed4All), or dynamic (automatic). Defaults to remote.

• device (Optional[str]) – The device to use for local embeddings. Choices include cpu, gpu, nvidia, amd, or a specific device name. See the docstring for GPT4All.__init__ for more info. Typically defaults to CPU. Do not use on macOS.

• vision_model (Optional[str]) –

Methods

__init__()	Initialize NomicEmbeddings model.
aembed_documents(texts)	Asynchronous Embed search docs.
aembed_query(text)	Asynchronous Embed query text.
embed(texts, *, task_type)	Embed texts.
embed_documents(texts)	Embed search docs.
embed_image(uris)
embed_query(text)	Embed query text.

__init__(*, model: str, nomic_api_key: Optional[str] = None, dimensionality: Optional[int] = None, inference_mode: Literal['remote'] = 'remote')

__init__(*, model: str, nomic_api_key: Optional[str] = None, dimensionality: Optional[int] = None, inference_mode: Literal['local', 'dynamic'], device: Optional[str] = None)

__init__(*, model: str, nomic_api_key: Optional[str] = None, dimensionality: Optional[int] = None, inference_mode: str, device: Optional[str] = None)

Initialize NomicEmbeddings model.

Parameters

• model – model name

• nomic_api_key – optionally, set the Nomic API key. Uses the NOMIC_API_KEY environment variable by default.

• dimensionality – The embedding dimension, for use with Matryoshka-capable models. Defaults to full-size.

• inference_mode – How to generate embeddings. One of remote, local (Embed4All), or dynamic (automatic). Defaults to remote.

• device – The device to use for local embeddings. Choices include cpu, gpu, nvidia, amd, or a specific device name. See the docstring for GPT4All.__init__ for more info. Typically defaults to CPU. Do not use on macOS.

async aembed_documents(texts: List[str]) → List[List[float]]

Asynchronous Embed search docs.

Parameters

texts (List[str]) –

Return type

List[List[float]]

async aembed_query(text: str) → List[float]

Asynchronous Embed query text.

Parameters

text (str) –

Return type

List[float]

embed(texts: List[str], *, task_type: str) → List[List[float]]

Embed texts.

Parameters

• texts (List[str]) – list of texts to embed

• task_type (str) – the task type to use when embedding. One of search_query, search_document, classification, clustering

Return type

List[List[float]]

embed_documents(texts: List[str]) → List[List[float]]

Embed search docs.

Parameters

texts (List[str]) – list of texts to embed as documents

Return type

List[List[float]]

embed_image(uris: List[str]) → List[List[float]]

Parameters

uris (List[str]) –

Return type

List[List[float]]

embed_query(text: str) → List[float]

Embed query text.

Parameters

text (str) – query text

Return type

List[float]