`langchain-nomic`¶

Reference docs

This page contains reference documentation for Nomic. See the docs for conceptual guides, tutorials, and examples on using Nomic modules.

langchain_nomic ¶

Nomic partner integration for LangChain.

NomicEmbeddings ¶

Bases: Embeddings

NomicEmbeddings embedding model.

Example

from langchain_nomic import NomicEmbeddings

model = NomicEmbeddings()

METHOD	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.

PARAMETER	DESCRIPTION
`texts`	List of text to embed. TYPE: `list[str]`

RETURNS	DESCRIPTION
`list[list[float]]`	List of embeddings.

aembed_query `async` ¶

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

Asynchronous Embed query text.

PARAMETER	DESCRIPTION
`text`	Text to embed. TYPE: `str`

RETURNS	DESCRIPTION
`list[float]`	Embedding.

init ¶

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

Initialize NomicEmbeddings model.

PARAMETER	DESCRIPTION
`model`	Model name TYPE: `str`
`nomic_api_key`	Optionally, set the Nomic API key. Uses the `NOMIC_API_KEY` environment variable by default. TYPE: `str \| None` DEFAULT: `None`
`dimensionality`	The embedding dimension, for use with Matryoshka-capable models. Defaults to full-size. TYPE: `int \| None` DEFAULT: `None`
`inference_mode`	How to generate embeddings. One of `'remote'`, `'local'` (Embed4All), or `'dynamic'` (automatic). TYPE: `str` DEFAULT: `'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'`. Warning Do not use on macOS. TYPE: `str \| None` DEFAULT: `None`
`vision_model`	The vision model to use for image embeddings. TYPE: `str \| None` DEFAULT: `None`

embed ¶

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

Embed texts.

PARAMETER	DESCRIPTION
`texts`	List of texts to embed TYPE: `list[str]`
`task_type`	The task type to use when embedding. One of `'search_query'`, `'search_document'`, `'classification'`, `'clustering'` TYPE: `str`

embed_documents ¶

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

Embed search docs.

PARAMETER	DESCRIPTION
`texts`	List of texts to embed as documents TYPE: `list[str]`

embed_query ¶

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

Embed query text.

PARAMETER	DESCRIPTION
`text`	Query text TYPE: `str`

embed_image ¶

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

Embed images.

PARAMETER	DESCRIPTION
`uris`	List of image URIs to embed TYPE: `list[str]`

langchain-nomic¶