Source code for langchain.vectorstores.docarray.in_memory
"""Wrapper around in-memory storage."""
from __future__ import annotations
from typing import Any, Dict, List, Literal, Optional
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.docarray.base import (
    DocArrayIndex,
    _check_docarray_import,
)
[docs]class DocArrayInMemorySearch(DocArrayIndex):
    """In-memory `DocArray` storage for exact search.
    To use it, you should have the ``docarray`` package with version >=0.32.0 installed.
    You can install it with `pip install "langchain[docarray]"`.
    """
[docs]    @classmethod
    def from_params(
        cls,
        embedding: Embeddings,
        metric: Literal[
            "cosine_sim", "euclidian_dist", "sgeuclidean_dist"
        ] = "cosine_sim",
        **kwargs: Any,
    ) -> DocArrayInMemorySearch:
        """Initialize DocArrayInMemorySearch store.
        Args:
            embedding (Embeddings): Embedding function.
            metric (str): metric for exact nearest-neighbor search.
                Can be one of: "cosine_sim", "euclidean_dist" and "sqeuclidean_dist".
                Defaults to "cosine_sim".
            **kwargs: Other keyword arguments to be passed to the get_doc_cls method.
        """
        _check_docarray_import()
        from docarray.index import InMemoryExactNNIndex
        doc_cls = cls._get_doc_cls(space=metric, **kwargs)
        doc_index = InMemoryExactNNIndex[doc_cls]()  # type: ignore
        return cls(doc_index, embedding)
[docs]    @classmethod
    def from_texts(