Toggle Menu
Prev Up Next

# Source code for langchain_nomic.embeddings

    import os
    from typing import List, Optional

    import nomic  # type: ignore
    from langchain_core.embeddings import Embeddings
    from nomic import embed  # type: ignore

    class NomicEmbeddings(Embeddings):
        """NomicEmbeddings embedding model.

        Example:
            .. code-block:: python

                from langchain_nomic import NomicEmbeddings

                model = NomicEmbeddings()
        """

        def __init__(
            self,
            *,
            model: str,
            nomic_api_key: Optional[str] = None,
            dimensionality: Optional[int] = None,
        ):
            """Initialize NomicEmbeddings model.

            Args:
                model: model name
                nomic_api_key: optionally, set the Nomic API key. Uses the NOMIC_API_KEY
                    environment variable by default.
            """
            _api_key = nomic_api_key or os.environ.get("NOMIC_API_KEY")
            if _api_key:
                nomic.login(_api_key)
            self.model = model
            self.dimensionality = dimensionality

        def embed(self, texts: List[str], *, task_type: str) -> List[List[float]]:
            """Embed texts.

            Args:
                texts: list of texts to embed
                task_type: the task type to use when embedding. One of `search_query`,
                    `search_document`, `classification`, `clustering`
            """

            output = embed.text(
                texts=texts,
                model=self.model,
                task_type=task_type,
                dimensionality=self.dimensionality,
            )
            return output["embeddings"]

        def embed_documents(self, texts: List[str]) -> List[List[float]]:
            """Embed search docs.

            Args:
                texts: list of texts to embed as documents
            """
            return self.embed(
                texts=texts,
                task_type="search_document",
            )

        def embed_query(self, text: str) -> List[float]:
            """Embed query text.

            Args:
                text: query text
            """
            return self.embed(
                texts=[text],
                task_type="search_query",
            )[0]