ndims: int = ADA_TOKEN_COUNT,
        table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
        ids: Optional[List[str]] = None,
        pre_delete_table: bool = False,
        **kwargs: Any,
    ) -> Hologres:
        """Construct Hologres wrapper from raw documents and pre-
        generated embeddings.
        Return VectorStore initialized from documents and embeddings.
        Postgres connection string is required
        "Either pass it as a parameter
        or set the HOLOGRES_CONNECTION_STRING environment variable.
        Example:
            .. code-block:: python
                from langchain.vectorstores import Hologres
                from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                text_embeddings = embeddings.embed_documents(texts)
                text_embedding_pairs = list(zip(texts, text_embeddings))
                faiss = Hologres.from_embeddings(text_embedding_pairs, embeddings)
        """
        texts = [t[0] for t in text_embeddings]
        embeddings = [t[1] for t in text_embeddings]
        return cls.__from(
            texts,
            embeddings,
            embedding,
            metadatas=metadatas,
            ids=ids,
            ndims=ndims,
            table_name=table_name,
            pre_delete_table=pre_delete_table,
            **kwargs,
        )
[docs]    @classmethod
    def from_existing_index(
        cls: Type[Hologres],
        embedding: Embeddings,
        ndims: int = ADA_TOKEN_COUNT,
        table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
        pre_delete_table: bool = False,
        **kwargs: Any,
    ) -> Hologres:
        """