This is a user friendly interface that:
            1. Embeds documents.
            2. Creates an in memory docstore
            3. Initializes the USearch database
        This is intended to be a quick way to get started.
        Example:
            .. code-block:: python
                from langchain.vectorstores import USearch
                from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                usearch = USearch.from_texts(texts, embeddings)
        """
        embeddings = embedding.embed_documents(texts)
        documents: List[Document] = []
        if ids is None:
            ids = np.array([str(id) for id, _ in enumerate(texts)])
        for i, text in enumerate(texts):
            metadata = metadatas[i] if metadatas else {}
            documents.append(Document(page_content=text, metadata=metadata))
        docstore = InMemoryDocstore(dict(zip(ids, documents)))
        usearch = dependable_usearch_import()
        index = usearch.Index(ndim=len(embeddings[0]), metric=metric)
        index.add(np.array(ids), np.array(embeddings))
        return cls(embedding, index, docstore, ids.tolist())