# Get id and docs from target FAISS object
        full_info = []
        for i, target_id in target.index_to_docstore_id.items():
            doc = target.docstore.search(target_id)
            if not isinstance(doc, Document):
                raise ValueError("Document should be returned")
            full_info.append((starting_len + i, target_id, doc))
        # Add information to docstore and index_to_docstore_id.
        self.docstore.add({_id: doc for _, _id, doc in full_info})
        index_to_id = {index: _id for index, _id, _ in full_info}
        self.index_to_docstore_id.update(index_to_id)
    @classmethod
    def __from(
        cls,
        texts: List[str],
        embeddings: List[List[float]],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        normalize_L2: bool = False,
        **kwargs: Any,
    ) -> FAISS:
        faiss = dependable_faiss_import()
        index = faiss.IndexFlatL2(len(embeddings[0]))
        vector = np.array(embeddings, dtype=np.float32)
        if normalize_L2:
            faiss.normalize_L2(vector)
        index.add(vector)
        documents = []
        if ids is None:
            ids = [str(uuid.uuid4()) for _ in texts]
        for i, text in enumerate(texts):
            metadata = metadatas[i] if metadatas else {}
            documents.append(Document(page_content=text, metadata=metadata))
        index_to_id = dict(enumerate(ids))
        docstore = InMemoryDocstore(dict(zip(index_to_id.values(), documents)))
        return cls(
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/vectorstores/faiss.html