mirror of
https://github.com/hwchase17/langchain.git
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145 lines
4.0 KiB
Python
145 lines
4.0 KiB
Python
"""Nomic partner integration for LangChain."""
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from __future__ import annotations
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import os
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from typing import Literal, overload
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import nomic # type: ignore[import]
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from langchain_core.embeddings import Embeddings
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from nomic import embed
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class NomicEmbeddings(Embeddings):
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"""NomicEmbeddings embedding model.
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Example:
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```python
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from langchain_nomic import NomicEmbeddings
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model = NomicEmbeddings()
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```
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"""
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@overload
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def __init__(
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self,
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*,
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model: str,
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nomic_api_key: str | None = ...,
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dimensionality: int | None = ...,
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inference_mode: Literal["remote"] = ...,
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) -> None: ...
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@overload
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def __init__(
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self,
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*,
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model: str,
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nomic_api_key: str | None = ...,
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dimensionality: int | None = ...,
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inference_mode: Literal["local", "dynamic"],
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device: str | None = ...,
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) -> None: ...
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@overload
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def __init__(
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self,
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*,
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model: str,
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nomic_api_key: str | None = ...,
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dimensionality: int | None = ...,
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inference_mode: str,
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device: str | None = ...,
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) -> None: ...
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def __init__(
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self,
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*,
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model: str,
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nomic_api_key: str | None = None,
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dimensionality: int | None = None,
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inference_mode: str = "remote",
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device: str | None = None,
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vision_model: str | None = None,
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):
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"""Initialize NomicEmbeddings model.
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Args:
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model: model name
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nomic_api_key: optionally, set the Nomic API key. Uses the `NOMIC_API_KEY`
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environment variable by default.
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dimensionality: The embedding dimension, for use with Matryoshka-capable
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models. Defaults to full-size.
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inference_mode: How to generate embeddings. One of `'remote'`, `'local'`
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(Embed4All), or `'dynamic'` (automatic). Defaults to `'remote'`.
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device: The device to use for local embeddings. Choices include
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`'cpu'`, `'gpu'`, `'nvidia'`, `'amd'`, or a specific device
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name. See the docstring for `GPT4All.__init__` for more info.
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Typically defaults to `'cpu'`. Do not use on macOS.
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vision_model: The vision model to use for image embeddings.
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"""
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_api_key = nomic_api_key or os.environ.get("NOMIC_API_KEY")
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if _api_key:
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nomic.login(_api_key)
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self.model = model
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self.dimensionality = dimensionality
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self.inference_mode = inference_mode
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self.device = device
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self.vision_model = vision_model
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def embed(self, texts: list[str], *, task_type: str) -> list[list[float]]:
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"""Embed texts.
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Args:
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texts: list of texts to embed
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task_type: the task type to use when embedding. One of `'search_query'`,
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`'search_document'`, `'classification'`, `'clustering'`
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"""
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output = embed.text(
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texts=texts,
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model=self.model,
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task_type=task_type,
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dimensionality=self.dimensionality,
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inference_mode=self.inference_mode,
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device=self.device,
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)
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return output["embeddings"]
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def embed_documents(self, texts: list[str]) -> list[list[float]]:
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"""Embed search docs.
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Args:
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texts: list of texts to embed as documents
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"""
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return self.embed(
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texts=texts,
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task_type="search_document",
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)
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def embed_query(self, text: str) -> list[float]:
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"""Embed query text.
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Args:
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text: query text
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"""
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return self.embed(
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texts=[text],
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task_type="search_query",
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)[0]
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def embed_image(self, uris: list[str]) -> list[list[float]]:
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"""Embed images.
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Args:
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uris: list of image URIs to embed
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"""
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return embed.image(
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images=uris,
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model=self.vision_model,
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)["embeddings"]
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