langchain-nomic

Nomic partner integration for LangChain.

Modules:

Name	Description
embeddings	Nomic partner integration for LangChain.

Classes:

Name	Description
NomicEmbeddings	NomicEmbeddings embedding model.

NomicEmbeddings

Bases: Embeddings

NomicEmbeddings embedding model.

Example:

.. code-block:: python

    from langchain_nomic import NomicEmbeddings

    model = NomicEmbeddings()

Methods:

Name	Description
aembed_documents	Asynchronous Embed search docs.
aembed_query	Asynchronous Embed query text.
__init__	Initialize NomicEmbeddings model.
embed	Embed texts.
embed_documents	Embed search docs.
embed_query	Embed query text.
embed_image	Embed images.

aembed_documents async

aembed_documents(texts: list[str]) -> list[list[float]]

Asynchronous Embed search docs.

Parameters:

Name	Type	Description	Default
texts	list[str]	List of text to embed.	required

Returns:

Type	Description
list[list[float]]	List of embeddings.

aembed_query async

aembed_query(text: str) -> list[float]

Asynchronous Embed query text.

Parameters:

Name	Type	Description	Default
text	str	Text to embed.	required

Returns:

Type	Description
list[float]	Embedding.

__init__

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

Initialize NomicEmbeddings model.

Parameters:

Name	Type	Description	Default
model	str	model name	required
nomic_api_key	Optional[str]	optionally, set the Nomic API key. Uses the NOMIC_API_KEY environment variable by default.	None
dimensionality	Optional[int]	The embedding dimension, for use with Matryoshka-capable models. Defaults to full-size.	None
inference_mode	str	How to generate embeddings. One of 'remote', 'local' (Embed4All), or 'dynamic' (automatic). Defaults to 'remote'.	'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.	None
vision_model	Optional[str]	The vision model to use for image embeddings.	None

embed

embed(
    texts: list[str], *, task_type: str
) -> list[list[float]]

Embed texts.

Parameters:

Name	Type	Description	Default
texts	list[str]	list of texts to embed	required
task_type	str	the task type to use when embedding. One of 'search_query', 'search_document', 'classification', 'clustering'	required

embed_documents

embed_documents(texts: list[str]) -> list[list[float]]

Embed search docs.

Parameters:

Name	Type	Description	Default
texts	list[str]	list of texts to embed as documents	required

embed_query

embed_query(text: str) -> list[float]

Embed query text.

Parameters:

Name	Type	Description	Default
text	str	query text	required

embed_image

embed_image(uris: list[str]) -> list[list[float]]

Embed images.

Parameters:

Name	Type	Description	Default
uris	list[str]	list of image URIs to embed	required