space_type: "l2", "l1", "cosinesimil", "linf", "innerproduct"; default: "l2"
            ef_search: Size of the dynamic list used during k-NN searches. Higher values
            lead to more accurate but slower searches; default: 512
            ef_construction: Size of the dynamic list used during k-NN graph creation.
            Higher values lead to more accurate graph but slower indexing speed;
            default: 512
            m: Number of bidirectional links created for each new element. Large impact
            on memory consumption. Between 2 and 100; default: 16
        Keyword Args for Script Scoring or Painless Scripting:
            is_appx_search: False
        """
        embeddings = embedding.embed_documents(texts)
        return cls.from_embeddings(
            embeddings,
            texts,
            embedding,
            metadatas=metadatas,
            bulk_size=bulk_size,
            ids=ids,
            **kwargs,
        )
[docs]    @classmethod
    def from_embeddings(
        cls,
        embeddings: List[List[float]],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        bulk_size: int = 500,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> OpenSearchVectorSearch:
        """Construct OpenSearchVectorSearch wrapper from pre-vectorized embeddings.
        Example:
            .. code-block:: python
                from langchain_community.vectorstores import OpenSearchVectorSearch
                from langchain_community.embeddings import OpenAIEmbeddings
                embedder = OpenAIEmbeddings()
                embeddings = embedder.embed_documents(["foo", "bar"])