from __future__ import annotations

import uuid
import warnings
from concurrent.futures import ThreadPoolExecutor
from typing import (
    Any,
    Awaitable,
    Callable,
    Dict,
    Iterable,
    List,
    Optional,
    Set,
    Tuple,
    Type,
    TypeVar,
    Union,
)

import numpy as np
from astrapy.db import (
    AstraDB as AstraDBClient,
)
from astrapy.db import (
    AsyncAstraDB as AsyncAstraDBClient,
)
from astrapy.info import CollectionVectorServiceOptions
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.runnables.utils import gather_with_concurrency
from langchain_core.utils.iter import batch_iterate
from langchain_core.vectorstores import VectorStore

from langchain_astradb.utils.astradb import (
    SetupMode,
    _AstraDBCollectionEnvironment,
)
from langchain_astradb.utils.mmr import maximal_marginal_relevance

T = TypeVar("T")
U = TypeVar("U")
DocDict = Dict[str, Any]  # dicts expressing entries to insert

# Batch/concurrency default values (if parameters not provided):
# Size of batches for bulk insertions:
#   (20 is the max batch size for the HTTP API at the time of writing)
DEFAULT_BATCH_SIZE = 20
# Number of threads to insert batches concurrently:
DEFAULT_BULK_INSERT_BATCH_CONCURRENCY = 16
# Number of threads in a batch to insert pre-existing entries:
DEFAULT_BULK_INSERT_OVERWRITE_CONCURRENCY = 10
# Number of threads (for deleting multiple rows concurrently):
DEFAULT_BULK_DELETE_CONCURRENCY = 20

# indexing options when creating a collection
DEFAULT_INDEXING_OPTIONS = {"allow": ["metadata"]}


def _unique_list(lst: List[T], key: Callable[[T], U]) -> List[T]:
    visited_keys: Set[U] = set()
    new_lst = []
    for item in lst:
        item_key = key(item)
        if item_key not in visited_keys:
            visited_keys.add(item_key)
            new_lst.append(item)
    return new_lst


class AstraDBVectorStore(VectorStore):
    @staticmethod
    def _filter_to_metadata(filter_dict: Optional[Dict[str, Any]]) -> Dict[str, Any]:
        if filter_dict is None:
            return {}
        else:
            metadata_filter = {}
            for k, v in filter_dict.items():
                if k and k[0] == "$":
                    if isinstance(v, list):
                        metadata_filter[k] = [
                            AstraDBVectorStore._filter_to_metadata(f) for f in v
                        ]
                    else:
                        # assume each list item can be fed back to this function
                        metadata_filter[k] = AstraDBVectorStore._filter_to_metadata(v)  # type: ignore[assignment]
                else:
                    metadata_filter[f"metadata.{k}"] = v

            return metadata_filter

    @staticmethod
    def _normalize_metadata_indexing_policy(
        metadata_indexing_include: Optional[Iterable[str]],
        metadata_indexing_exclude: Optional[Iterable[str]],
        collection_indexing_policy: Optional[Dict[str, Any]],
    ) -> Dict[str, Any]:
        """
        Validate the constructor indexing parameters and normalize them
        into a ready-to-use dict for the 'options' when creating a collection.
        """
        none_count = sum(
            [
                1 if var is None else 0
                for var in [
                    metadata_indexing_include,
                    metadata_indexing_exclude,
                    collection_indexing_policy,
                ]
            ]
        )
        if none_count >= 2:
            if metadata_indexing_include is not None:
                return {
                    "allow": [
                        f"metadata.{md_field}" for md_field in metadata_indexing_include
                    ]
                }
            elif metadata_indexing_exclude is not None:
                return {
                    "deny": [
                        f"metadata.{md_field}" for md_field in metadata_indexing_exclude
                    ]
                }
            elif collection_indexing_policy is not None:
                return collection_indexing_policy
            else:
                return DEFAULT_INDEXING_OPTIONS
        else:
            raise ValueError(
                "At most one of the parameters `metadata_indexing_include`,"
                " `metadata_indexing_exclude` and `collection_indexing_policy`"
                " can be specified as non null."
            )

    def __init__(
        self,
        *,
        collection_name: str,
        embedding: Optional[Embeddings] = None,
        token: Optional[str] = None,
        api_endpoint: Optional[str] = None,
        astra_db_client: Optional[AstraDBClient] = None,
        async_astra_db_client: Optional[AsyncAstraDBClient] = None,
        namespace: Optional[str] = None,
        metric: Optional[str] = None,
        batch_size: Optional[int] = None,
        bulk_insert_batch_concurrency: Optional[int] = None,
        bulk_insert_overwrite_concurrency: Optional[int] = None,
        bulk_delete_concurrency: Optional[int] = None,
        setup_mode: SetupMode = SetupMode.SYNC,
        pre_delete_collection: bool = False,
        metadata_indexing_include: Optional[Iterable[str]] = None,
        metadata_indexing_exclude: Optional[Iterable[str]] = None,
        collection_indexing_policy: Optional[Dict[str, Any]] = None,
        collection_vector_service_options: Optional[
            CollectionVectorServiceOptions
        ] = None,
    ) -> None:
        """Wrapper around DataStax Astra DB for vector-store workloads.

        For quickstart and details, visit
        https://docs.datastax.com/en/astra/astra-db-vector/

        Example:
            .. code-block:: python

                from langchain_astradb.vectorstores import AstraDBVectorStore
                from langchain_openai.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                vectorstore = AstraDBVectorStore(
                    embedding=embeddings,
                    collection_name="my_store",
                    token="AstraCS:...",
                    api_endpoint="https://<DB-ID>-<REGION>.apps.astra.datastax.com"
                )

                vectorstore.add_texts(["Giraffes", "All good here"])
                results = vectorstore.similarity_search("Everything's ok", k=1)

        Args:
            embedding: the embeddings function or service to use.
                This enables client-side embedding functions or calls to external
                embedding providers. Only one of `embedding` or
                `collection_vector_service_options` can be provided.
            collection_name: name of the Astra DB collection to create/use.
            token: API token for Astra DB usage.
            api_endpoint: full URL to the API endpoint, such as
                `https://<DB-ID>-us-east1.apps.astra.datastax.com`.
            astra_db_client: *alternative to token+api_endpoint*,
                you can pass an already-created 'astrapy.db.AstraDB' instance.
            async_astra_db_client: *alternative to token+api_endpoint*,
                you can pass an already-created 'astrapy.db.AsyncAstraDB' instance.
            namespace: namespace (aka keyspace) where the collection is created.
                Defaults to the database's "default namespace".
            metric: similarity function to use out of those available in Astra DB.
                If left out, it will use Astra DB API's defaults (i.e. "cosine" - but,
                for performance reasons, "dot_product" is suggested if embeddings are
                normalized to one).
            batch_size: Size of batches for bulk insertions.
            bulk_insert_batch_concurrency: Number of threads or coroutines to insert
                batches concurrently.
            bulk_insert_overwrite_concurrency: Number of threads  or coroutines in a
                batch to insert pre-existing entries.
            bulk_delete_concurrency: Number of threads (for deleting multiple rows
                concurrently).
            pre_delete_collection: whether to delete the collection before creating it.
                If False and the collection already exists, the collection will be used
                as is.
            metadata_indexing_include: an allowlist of the specific metadata subfields
                that should be indexed for later filtering in searches.
            metadata_indexing_exclude: a denylist of the specific metadata subfields
                that should not be indexed for later filtering in searches.
            collection_indexing_policy: a full "indexing" specification for
                what fields should be indexed for later filtering in searches.
                This dict must conform to to the API specifications
                (see docs.datastax.com/en/astra/astra-db-vector/api-reference/
                data-api-commands.html#advanced-feature-indexing-clause-on-createcollection)
            collection_vector_service_options: specifies the use of server-side
                embeddings within Astra DB. Only one of `embedding` or
                `collection_vector_service_options` can be provided.
                NOTE: This feature is under current development.


        Note:
            For concurrency in synchronous :meth:`~add_texts`:, as a rule of thumb, on a
            typical client machine it is suggested to keep the quantity
            bulk_insert_batch_concurrency * bulk_insert_overwrite_concurrency
            much below 1000 to avoid exhausting the client multithreading/networking
            resources. The hardcoded defaults are somewhat conservative to meet
            most machines' specs, but a sensible choice to test may be:

            - bulk_insert_batch_concurrency = 80
            - bulk_insert_overwrite_concurrency = 10

            A bit of experimentation is required to nail the best results here,
            depending on both the machine/network specs and the expected workload
            (specifically, how often a write is an update of an existing id).
            Remember you can pass concurrency settings to individual calls to
            :meth:`~add_texts` and :meth:`~add_documents` as well.
        """
        # Embedding and collection_vector_service_options are mutually exclusive,
        # as both specify how to produce embeddings
        if embedding is None and collection_vector_service_options is None:
            raise ValueError(
                "Either an `embedding` or a `collection_vector_service_options`\
                    must be provided."
            )

        if embedding is not None and collection_vector_service_options is not None:
            raise ValueError(
                "Only one of `embedding` or `collection_vector_service_options`\
                    can be provided."
            )

        self.embedding_dimension: Optional[int] = None
        self.embedding = embedding
        self.collection_name = collection_name
        self.token = token
        self.api_endpoint = api_endpoint
        self.namespace = namespace
        self.collection_vector_service_options = collection_vector_service_options
        # Concurrency settings
        self.batch_size: int = batch_size or DEFAULT_BATCH_SIZE
        self.bulk_insert_batch_concurrency: int = (
            bulk_insert_batch_concurrency or DEFAULT_BULK_INSERT_BATCH_CONCURRENCY
        )
        self.bulk_insert_overwrite_concurrency: int = (
            bulk_insert_overwrite_concurrency
            or DEFAULT_BULK_INSERT_OVERWRITE_CONCURRENCY
        )
        self.bulk_delete_concurrency: int = (
            bulk_delete_concurrency or DEFAULT_BULK_DELETE_CONCURRENCY
        )
        # "vector-related" settings
        self.metric = metric
        embedding_dimension: Union[int, Awaitable[int], None] = None
        if self.embedding is not None:
            if setup_mode == SetupMode.ASYNC:
                embedding_dimension = self._aget_embedding_dimension()
            elif setup_mode == SetupMode.SYNC or setup_mode == SetupMode.OFF:
                embedding_dimension = self._get_embedding_dimension()

        # indexing policy setting
        self.indexing_policy: Dict[str, Any] = self._normalize_metadata_indexing_policy(
            metadata_indexing_include=metadata_indexing_include,
            metadata_indexing_exclude=metadata_indexing_exclude,
            collection_indexing_policy=collection_indexing_policy,
        )

        self.astra_env = _AstraDBCollectionEnvironment(
            collection_name=collection_name,
            token=token,
            api_endpoint=api_endpoint,
            astra_db_client=astra_db_client,
            async_astra_db_client=async_astra_db_client,
            namespace=namespace,
            setup_mode=setup_mode,
            pre_delete_collection=pre_delete_collection,
            embedding_dimension=embedding_dimension,
            metric=metric,
            requested_indexing_policy=self.indexing_policy,
            default_indexing_policy=DEFAULT_INDEXING_OPTIONS,
            collection_vector_service_options=collection_vector_service_options,
        )
        self.astra_db = self.astra_env.astra_db
        self.async_astra_db = self.astra_env.async_astra_db
        self.collection = self.astra_env.collection
        self.async_collection = self.astra_env.async_collection

    def _get_embedding_dimension(self) -> int:
        assert self.embedding is not None

        if self.embedding_dimension is None:
            self.embedding_dimension = len(
                self.embedding.embed_query(text="This is a sample sentence.")
            )
        return self.embedding_dimension

    async def _aget_embedding_dimension(self) -> int:
        assert self.embedding is not None

        if self.embedding_dimension is None:
            self.embedding_dimension = len(
                await self.embedding.aembed_query(text="This is a sample sentence.")
            )
        return self.embedding_dimension

    @property
    def embeddings(self) -> Optional[Embeddings]:
        """
        Accesses the supplied embeddings object. If using server-side embeddings,
        this will return None.
        """
        return self.embedding

    def _using_vectorize(self) -> bool:
        """Indicates whether server-side embeddings are being used."""
        return self.collection_vector_service_options is not None

    def _select_relevance_score_fn(self) -> Callable[[float], float]:
        """
        The underlying API calls already returns a "score proper",
        i.e. one in [0, 1] where higher means more *similar*,
        so here the final score transformation is not reversing the interval:
        """
        return lambda score: score

    def clear(self) -> None:
        """Empty the collection of all its stored entries."""
        self.astra_env.ensure_db_setup()
        self.collection.delete_many({})

    async def aclear(self) -> None:
        """Empty the collection of all its stored entries."""
        await self.astra_env.aensure_db_setup()
        await self.async_collection.delete_many({})

    def delete_by_document_id(self, document_id: str) -> bool:
        """
        Remove a single document from the store, given its document ID.

        Args:
            document_id: The document ID

        Returns
            True if a document has indeed been deleted, False if ID not found.
        """
        self.astra_env.ensure_db_setup()
        # self.collection is not None (by _ensure_astra_db_client)
        deletion_response = self.collection.delete_one(document_id)
        return ((deletion_response or {}).get("status") or {}).get(
            "deletedCount", 0
        ) == 1

    async def adelete_by_document_id(self, document_id: str) -> bool:
        """
        Remove a single document from the store, given its document ID.

        Args:
            document_id: The document ID

        Returns
            True if a document has indeed been deleted, False if ID not found.
        """
        await self.astra_env.aensure_db_setup()
        deletion_response = await self.async_collection.delete_one(document_id)
        return ((deletion_response or {}).get("status") or {}).get(
            "deletedCount", 0
        ) == 1

    def delete(
        self,
        ids: Optional[List[str]] = None,
        concurrency: Optional[int] = None,
        **kwargs: Any,
    ) -> Optional[bool]:
        """Delete by vector ids.

        Args:
            ids: List of ids to delete.
            concurrency: max number of threads issuing single-doc delete requests.
                Defaults to instance-level setting.

        Returns:
            True if deletion is successful, False otherwise.
        """

        if kwargs:
            warnings.warn(
                "Method 'delete' of AstraDBVectorStore vector store invoked with "
                f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), "
                "which will be ignored."
            )

        if ids is None:
            raise ValueError("No ids provided to delete.")

        _max_workers = concurrency or self.bulk_delete_concurrency
        with ThreadPoolExecutor(max_workers=_max_workers) as tpe:
            _ = list(
                tpe.map(
                    self.delete_by_document_id,
                    ids,
                )
            )
        return True

    async def adelete(
        self,
        ids: Optional[List[str]] = None,
        concurrency: Optional[int] = None,
        **kwargs: Any,
    ) -> Optional[bool]:
        """Delete by vector ids.

        Args:
            ids: List of ids to delete.
            concurrency: max concurrency of single-doc delete requests.
                Defaults to instance-level setting.

        Returns:
            True if deletion is successful, False otherwise.
        """
        if kwargs:
            warnings.warn(
                "Method 'adelete' of AstraDBVectorStore invoked with "
                f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), "
                "which will be ignored."
            )

        if ids is None:
            raise ValueError("No ids provided to delete.")

        return all(
            await gather_with_concurrency(
                concurrency, *[self.adelete_by_document_id(doc_id) for doc_id in ids]
            )
        )

    def delete_collection(self) -> None:
        """
        Completely delete the collection from the database (as opposed
        to :meth:`~clear`, which empties it only).
        Stored data is lost and unrecoverable, resources are freed.
        Use with caution.
        """
        self.astra_env.ensure_db_setup()
        self.astra_db.delete_collection(
            collection_name=self.collection_name,
        )

    async def adelete_collection(self) -> None:
        """
        Completely delete the collection from the database (as opposed
        to :meth:`~aclear`, which empties it only).
        Stored data is lost and unrecoverable, resources are freed.
        Use with caution.
        """
        await self.astra_env.aensure_db_setup()
        await self.async_astra_db.delete_collection(
            collection_name=self.collection_name,
        )

    @staticmethod
    def _get_documents_to_insert(
        texts: Iterable[str],
        embedding_vectors: List[List[float]],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
    ) -> List[DocDict]:
        if ids is None:
            ids = [uuid.uuid4().hex for _ in texts]
        if metadatas is None:
            metadatas = [{} for _ in texts]
        #
        documents_to_insert = [
            {
                "content": b_txt,
                "_id": b_id,
                "$vector": b_emb,
                "metadata": b_md,
            }
            for b_txt, b_emb, b_id, b_md in zip(
                texts,
                embedding_vectors,
                ids,
                metadatas,
            )
        ]
        # make unique by id, keeping the last
        uniqued_documents_to_insert = _unique_list(
            documents_to_insert[::-1],
            lambda document: document["_id"],
        )[::-1]
        return uniqued_documents_to_insert

    @staticmethod
    def _get_vectorize_documents_to_insert(
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
    ) -> List[DocDict]:
        if ids is None:
            ids = [uuid.uuid4().hex for _ in texts]
        if metadatas is None:
            metadatas = [{} for _ in texts]
        #
        documents_to_insert = [
            {
                "_id": b_id,
                "$vectorize": b_txt,
                "metadata": b_md,
            }
            for b_txt, b_id, b_md in zip(
                texts,
                ids,
                metadatas,
            )
        ]
        # make unique by id, keeping the last
        uniqued_documents_to_insert = _unique_list(
            documents_to_insert[::-1],
            lambda document: document["_id"],
        )[::-1]
        return uniqued_documents_to_insert

    @staticmethod
    def _get_missing_from_batch(
        document_batch: List[DocDict], insert_result: Dict[str, Any]
    ) -> Tuple[List[str], List[DocDict]]:
        if "status" not in insert_result:
            raise ValueError(
                f"API Exception while running bulk insertion: {str(insert_result)}"
            )
        batch_inserted = insert_result["status"]["insertedIds"]
        # estimation of the preexisting documents that failed
        missed_inserted_ids = {document["_id"] for document in document_batch} - set(
            batch_inserted
        )
        errors = insert_result.get("errors", [])
        # careful for other sources of error other than "doc already exists"
        num_errors = len(errors)
        unexpected_errors = any(
            error.get("errorCode") != "DOCUMENT_ALREADY_EXISTS" for error in errors
        )
        if num_errors != len(missed_inserted_ids) or unexpected_errors:
            raise ValueError(
                f"API Exception while running bulk insertion: {str(errors)}"
            )
        # deal with the missing insertions as upserts
        missing_from_batch = [
            document
            for document in document_batch
            if document["_id"] in missed_inserted_ids
        ]
        return batch_inserted, missing_from_batch

    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        *,
        batch_size: Optional[int] = None,
        batch_concurrency: Optional[int] = None,
        overwrite_concurrency: Optional[int] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run texts through the embeddings and add them to the vectorstore.

        If passing explicit ids, those entries whose id is in the store already
        will be replaced.

        Args:
            texts: Texts to add to the vectorstore.
            metadatas: Optional list of metadatas.
            ids: Optional list of ids.
            batch_size: Number of documents in each API call.
                Check the underlying Astra DB HTTP API specs for the max value
                (20 at the time of writing this). If not provided, defaults
                to the instance-level setting.
            batch_concurrency: number of threads to process
                insertion batches concurrently. Defaults to instance-level
                setting if not provided.
            overwrite_concurrency:  number of threads to process
                pre-existing documents in each batch (which require individual
                API calls). Defaults to instance-level setting if not provided.

        Note:
            There are constraints on the allowed field names
            in the metadata dictionaries, coming from the underlying Astra DB API.
            For instance, the `$` (dollar sign) cannot be used in the dict keys.
            See this document for details:
            https://docs.datastax.com/en/astra/astra-db-vector/api-reference/data-api.html

        Returns:
            The list of ids of the added texts.
        """

        if kwargs:
            warnings.warn(
                "Method 'add_texts' of AstraDBVectorStore vector store invoked with "
                f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), "
                "which will be ignored."
            )
        self.astra_env.ensure_db_setup()

        if self._using_vectorize():
            documents_to_insert = self._get_vectorize_documents_to_insert(
                texts, metadatas, ids
            )
        else:
            assert self.embedding is not None
            embedding_vectors = self.embedding.embed_documents(list(texts))
            documents_to_insert = self._get_documents_to_insert(
                texts, embedding_vectors, metadatas, ids
            )

        def _handle_batch(document_batch: List[DocDict]) -> List[str]:
            # self.collection is not None (by _ensure_astra_db_client)
            im_result = self.collection.insert_many(  # type: ignore[union-attr]
                documents=document_batch,
                options={"ordered": False},
                partial_failures_allowed=True,
            )
            batch_inserted, missing_from_batch = self._get_missing_from_batch(
                document_batch, im_result
            )

            def _handle_missing_document(missing_document: DocDict) -> str:
                # self.collection is not None (by _ensure_astra_db_client)
                replacement_result = self.collection.find_one_and_replace(  # type: ignore[union-attr]
                    filter={"_id": missing_document["_id"]},
                    replacement=missing_document,
                )
                return replacement_result["data"]["document"]["_id"]

            _u_max_workers = (
                overwrite_concurrency or self.bulk_insert_overwrite_concurrency
            )
            with ThreadPoolExecutor(max_workers=_u_max_workers) as tpe2:
                batch_replaced = list(
                    tpe2.map(
                        _handle_missing_document,
                        missing_from_batch,
                    )
                )
            return batch_inserted + batch_replaced

        _b_max_workers = batch_concurrency or self.bulk_insert_batch_concurrency
        with ThreadPoolExecutor(max_workers=_b_max_workers) as tpe:
            all_ids_nested = tpe.map(
                _handle_batch,
                batch_iterate(
                    batch_size or self.batch_size,
                    documents_to_insert,
                ),
            )
        return [iid for id_list in all_ids_nested for iid in id_list]

    async def aadd_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        *,
        batch_size: Optional[int] = None,
        batch_concurrency: Optional[int] = None,
        overwrite_concurrency: Optional[int] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run texts through the embeddings and add them to the vectorstore.

        If passing explicit ids, those entries whose id is in the store already
        will be replaced.

        Args:
            texts: Texts to add to the vectorstore.
            metadatas: Optional list of metadatas.
            ids: Optional list of ids.
            batch_size: Number of documents in each API call.
                Check the underlying Astra DB HTTP API specs for the max value
                (20 at the time of writing this). If not provided, defaults
                to the instance-level setting.
            batch_concurrency: number of threads to process
                insertion batches concurrently. Defaults to instance-level
                setting if not provided.
            overwrite_concurrency:  number of threads to process
                pre-existing documents in each batch (which require individual
                API calls). Defaults to instance-level setting if not provided.

        Note:
            There are constraints on the allowed field names
            in the metadata dictionaries, coming from the underlying Astra DB API.
            For instance, the `$` (dollar sign) cannot be used in the dict keys.
            See this document for details:
            https://docs.datastax.com/en/astra/astra-db-vector/api-reference/data-api.html

        Returns:
            The list of ids of the added texts.
        """
        if kwargs:
            warnings.warn(
                "Method 'aadd_texts' of AstraDBVectorStore invoked with "
                f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), "
                "which will be ignored."
            )
        await self.astra_env.aensure_db_setup()

        if self._using_vectorize():
            # using server-side embeddings
            documents_to_insert = self._get_vectorize_documents_to_insert(
                texts, metadatas, ids
            )
        else:
            assert self.embedding is not None
            embedding_vectors = await self.embedding.aembed_documents(list(texts))
            documents_to_insert = self._get_documents_to_insert(
                texts, embedding_vectors, metadatas, ids
            )

        async def _handle_batch(document_batch: List[DocDict]) -> List[str]:
            # self.async_collection is not None here for sure
            im_result = await self.async_collection.insert_many(
                documents=document_batch,
                options={"ordered": False},
                partial_failures_allowed=True,
            )
            batch_inserted, missing_from_batch = self._get_missing_from_batch(
                document_batch, im_result
            )

            async def _handle_missing_document(missing_document: DocDict) -> str:
                # self.async_collection is not None here for sure
                replacement_result = await self.async_collection.find_one_and_replace(
                    filter={"_id": missing_document["_id"]},
                    replacement=missing_document,
                )
                return replacement_result["data"]["document"]["_id"]

            _u_max_workers = (
                overwrite_concurrency or self.bulk_insert_overwrite_concurrency
            )
            batch_replaced = await gather_with_concurrency(
                _u_max_workers,
                *[_handle_missing_document(doc) for doc in missing_from_batch],
            )
            return batch_inserted + batch_replaced

        _b_max_workers = batch_concurrency or self.bulk_insert_batch_concurrency
        all_ids_nested = await gather_with_concurrency(
            _b_max_workers,
            *[
                _handle_batch(batch)
                for batch in batch_iterate(
                    batch_size or self.batch_size,
                    documents_to_insert,
                )
            ],
        )

        return [iid for id_list in all_ids_nested for iid in id_list]

    def similarity_search_with_score_id_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
    ) -> List[Tuple[Document, float, str]]:
        """Return docs most similar to embedding vector with score and id.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of (Document, score, id), the most similar to the query vector.
        """
        self.astra_env.ensure_db_setup()
        metadata_parameter = self._filter_to_metadata(filter)
        #
        hits = list(
            # self.collection is not None (by _ensure_astra_db_client)
            self.collection.paginated_find(  # type: ignore[union-attr]
                filter=metadata_parameter,
                sort={"$vector": embedding},
                options={"limit": k, "includeSimilarity": True},
                projection={
                    "_id": 1,
                    "content": 1,
                    "metadata": 1,
                },
            )
        )
        #
        return [
            (
                Document(
                    page_content=hit["content"],
                    metadata=hit["metadata"],
                ),
                hit["$similarity"],
                hit["_id"],
            )
            for hit in hits
        ]

    async def asimilarity_search_with_score_id_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
    ) -> List[Tuple[Document, float, str]]:
        """Return docs most similar to embedding vector with score and id.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of (Document, score, id), the most similar to the query vector.
        """
        await self.astra_env.aensure_db_setup()
        metadata_parameter = self._filter_to_metadata(filter)
        #
        return [
            (
                Document(
                    page_content=hit["content"],
                    metadata=hit["metadata"],
                ),
                hit["$similarity"],
                hit["_id"],
            )
            async for hit in self.async_collection.paginated_find(
                filter=metadata_parameter,
                sort={"$vector": embedding},
                options={"limit": k, "includeSimilarity": True},
                projection={
                    "_id": 1,
                    "content": 1,
                    "metadata": 1,
                },
            )
        ]

    def _similarity_search_with_score_id_with_vectorize(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
    ) -> List[Tuple[Document, float, str]]:
        """Return docs most similar to the query with score and id using $vectorize.

        This is only available when using server-side embeddings.
        """
        self.astra_env.ensure_db_setup()
        metadata_parameter = self._filter_to_metadata(filter)
        #
        hits = list(
            # self.collection is not None (by _ensure_astra_db_client)
            self.collection.paginated_find(  # type: ignore[union-attr]
                filter=metadata_parameter,
                sort={"$vectorize": query},
                options={"limit": k, "includeSimilarity": True},
                projection={
                    "_id": 1,
                    "$vectorize": 1,
                    "metadata": 1,
                },
            )
        )
        #
        return [
            (
                Document(
                    # text content is stored in $vectorize instead of content
                    page_content=hit["$vectorize"],
                    metadata=hit["metadata"],
                ),
                hit["$similarity"],
                hit["_id"],
            )
            for hit in hits
        ]

    async def _asimilarity_search_with_score_id_with_vectorize(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
    ) -> List[Tuple[Document, float, str]]:
        """Return docs most similar to the query with score and id using $vectorize.

        This is only available when using server-side embeddings.
        """
        await self.astra_env.aensure_db_setup()
        metadata_parameter = self._filter_to_metadata(filter)
        #
        return [
            (
                Document(
                    # text content is stored in $vectorize instead of content
                    page_content=hit["$vectorize"],
                    metadata=hit["metadata"],
                ),
                hit["$similarity"],
                hit["_id"],
            )
            async for hit in self.async_collection.paginated_find(
                filter=metadata_parameter,
                sort={"$vectorize": query},
                options={"limit": k, "includeSimilarity": True},
                projection={
                    "_id": 1,
                    "$vectorize": 1,
                    "metadata": 1,
                },
            )
        ]

    def similarity_search_with_score_id(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
    ) -> List[Tuple[Document, float, str]]:
        """Return docs most similar to the query with score and id.

        Args:
            query: Query to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of (Document, score, id), the most similar to the query.
        """

        if self._using_vectorize():
            return self._similarity_search_with_score_id_with_vectorize(
                query=query,
                k=k,
                filter=filter,
            )
        else:
            assert self.embedding is not None
            embedding_vector = self.embedding.embed_query(query)
            return self.similarity_search_with_score_id_by_vector(
                embedding=embedding_vector,
                k=k,
                filter=filter,
            )

    async def asimilarity_search_with_score_id(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
    ) -> List[Tuple[Document, float, str]]:
        """Return docs most similar to the query with score and id.

        Args:
            query: Query to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of (Document, score, id), the most similar to the query.
        """
        if self._using_vectorize():
            return await self._asimilarity_search_with_score_id_with_vectorize(
                query=query,
                k=k,
                filter=filter,
            )
        else:
            assert self.embedding is not None
            embedding_vector = await self.embedding.aembed_query(query)
            return await self.asimilarity_search_with_score_id_by_vector(
                embedding=embedding_vector,
                k=k,
                filter=filter,
            )

    def similarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to embedding vector with score.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of (Document, score), the most similar to the query vector.
        """
        return [
            (doc, score)
            for (doc, score, doc_id) in self.similarity_search_with_score_id_by_vector(
                embedding=embedding,
                k=k,
                filter=filter,
            )
        ]

    async def asimilarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to embedding vector with score.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of (Document, score), the most similar to the query vector.
        """
        return [
            (doc, score)
            for (
                doc,
                score,
                doc_id,
            ) in await self.asimilarity_search_with_score_id_by_vector(
                embedding=embedding,
                k=k,
                filter=filter,
            )
        ]

    def similarity_search(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to query.

        Args:
            query: Query to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of Documents most similar to the query.
        """
        if self._using_vectorize():
            return [
                doc
                for (doc, _, _) in self._similarity_search_with_score_id_with_vectorize(
                    query,
                    k,
                    filter=filter,
                )
            ]
        else:
            assert self.embedding is not None
            embedding_vector = self.embedding.embed_query(query)
            return self.similarity_search_by_vector(
                embedding_vector,
                k,
                filter=filter,
            )

    async def asimilarity_search(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to query.

        Args:
            query: Query to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of Documents most similar to the query.
        """
        if self._using_vectorize():
            return [
                doc
                for (
                    doc,
                    _,
                    _,
                ) in await self._asimilarity_search_with_score_id_with_vectorize(
                    query,
                    k,
                    filter=filter,
                )
            ]
        else:
            assert self.embedding is not None
            embedding_vector = await self.embedding.aembed_query(query)
            return await self.asimilarity_search_by_vector(
                embedding_vector,
                k,
                filter=filter,
            )

    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of Documents most similar to the query vector.
        """
        return [
            doc
            for doc, _ in self.similarity_search_with_score_by_vector(
                embedding,
                k,
                filter=filter,
            )
        ]

    async def asimilarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of Documents most similar to the query vector.
        """
        return [
            doc
            for doc, _ in await self.asimilarity_search_with_score_by_vector(
                embedding,
                k,
                filter=filter,
            )
        ]

    def similarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query with score.

        Args:
            query: Query to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of (Document, score), the most similar to the query vector.
        """
        if self._using_vectorize():
            return [
                (doc, score)
                for (
                    doc,
                    score,
                    doc_id,
                ) in self._similarity_search_with_score_id_with_vectorize(
                    query=query,
                    k=k,
                    filter=filter,
                )
            ]
        else:
            assert self.embedding is not None
            embedding_vector = self.embedding.embed_query(query)
            return self.similarity_search_with_score_by_vector(
                embedding_vector,
                k,
                filter=filter,
            )

    async def asimilarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query with score.

        Args:
            query: Query to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.

        Returns:
            The list of (Document, score), the most similar to the query vector.
        """
        if self._using_vectorize():
            return [
                (doc, score)
                for (
                    doc,
                    score,
                    doc_id,
                ) in await self._asimilarity_search_with_score_id_with_vectorize(
                    query=query,
                    k=k,
                    filter=filter,
                )
            ]
        else:
            assert self.embedding is not None
            embedding_vector = await self.embedding.aembed_query(query)
            return await self.asimilarity_search_with_score_by_vector(
                embedding_vector,
                k,
                filter=filter,
            )

    @staticmethod
    def _get_mmr_hits(
        embedding: List[float], k: int, lambda_mult: float, prefetch_hits: List[DocDict]
    ) -> List[Document]:
        mmr_chosen_indices = maximal_marginal_relevance(
            np.array(embedding, dtype=np.float32),
            [prefetch_hit["$vector"] for prefetch_hit in prefetch_hits],
            k=k,
            lambda_mult=lambda_mult,
        )
        mmr_hits = [
            prefetch_hit
            for prefetch_index, prefetch_hit in enumerate(prefetch_hits)
            if prefetch_index in mmr_chosen_indices
        ]
        return [
            Document(
                page_content=hit["content"],
                metadata=hit["metadata"],
            )
            for hit in mmr_hits
        ]

    def max_marginal_relevance_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.

        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
            lambda_mult: Number between 0 and 1 that determines the degree
                of diversity among the results with 0 corresponding
                to maximum diversity and 1 to minimum diversity.
            filter: Filter on the metadata to apply.

        Returns:
            The list of Documents selected by maximal marginal relevance.
        """
        self.astra_env.ensure_db_setup()
        metadata_parameter = self._filter_to_metadata(filter)

        prefetch_hits = list(
            # self.collection is not None (by _ensure_astra_db_client)
            self.collection.paginated_find(  # type: ignore[union-attr]
                filter=metadata_parameter,
                sort={"$vector": embedding},
                options={"limit": fetch_k, "includeSimilarity": True},
                projection={
                    "_id": 1,
                    "content": 1,
                    "metadata": 1,
                    "$vector": 1,
                },
            )
        )

        return self._get_mmr_hits(embedding, k, lambda_mult, prefetch_hits)

    async def amax_marginal_relevance_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.

        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
            lambda_mult: Number between 0 and 1 that determines the degree
                of diversity among the results with 0 corresponding
                to maximum diversity and 1 to minimum diversity.
            filter: Filter on the metadata to apply.

        Returns:
            The list of Documents selected by maximal marginal relevance.
        """
        await self.astra_env.aensure_db_setup()
        metadata_parameter = self._filter_to_metadata(filter)

        prefetch_hits = [
            hit
            async for hit in self.async_collection.paginated_find(
                filter=metadata_parameter,
                sort={"$vector": embedding},
                options={"limit": fetch_k, "includeSimilarity": True},
                projection={
                    "_id": 1,
                    "content": 1,
                    "metadata": 1,
                    "$vector": 1,
                },
            )
        ]

        return self._get_mmr_hits(embedding, k, lambda_mult, prefetch_hits)

    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.

        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.

        Args:
            query: Query to look up documents similar to.
            k: Number of Documents to return.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
            lambda_mult: Number between 0 and 1 that determines the degree
                of diversity among the results with 0 corresponding
                to maximum diversity and 1 to minimum diversity.
            filter: Filter on the metadata to apply.

        Returns:
            The list of Documents selected by maximal marginal relevance.
        """
        if self._using_vectorize():
            raise ValueError("MMR search is unsupported for server-side embeddings.")
        else:
            assert self.embedding is not None
            embedding_vector = self.embedding.embed_query(query)
            return self.max_marginal_relevance_search_by_vector(
                embedding_vector,
                k,
                fetch_k,
                lambda_mult=lambda_mult,
                filter=filter,
            )

    async def amax_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.

        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.

        Args:
            query: Query to look up documents similar to.
            k: Number of Documents to return.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
            lambda_mult: Number between 0 and 1 that determines the degree
                of diversity among the results with 0 corresponding
                to maximum diversity and 1 to minimum diversity.
            filter: Filter on the metadata to apply.

        Returns:
            The list of Documents selected by maximal marginal relevance.
        """
        if self._using_vectorize():
            raise ValueError("MMR search is unsupported for server-side embeddings.")
        else:
            assert self.embedding is not None
            embedding_vector = await self.embedding.aembed_query(query)
            return await self.amax_marginal_relevance_search_by_vector(
                embedding_vector,
                k,
                fetch_k,
                lambda_mult=lambda_mult,
                filter=filter,
            )

    @classmethod
    def _from_kwargs(
        cls: Type[AstraDBVectorStore],
        embedding: Optional[Embeddings] = None,
        collection_vector_service_options: Optional[
            CollectionVectorServiceOptions
        ] = None,
        **kwargs: Any,
    ) -> AstraDBVectorStore:
        known_kwargs = {
            "collection_name",
            "token",
            "api_endpoint",
            "astra_db_client",
            "async_astra_db_client",
            "namespace",
            "metric",
            "batch_size",
            "bulk_insert_batch_concurrency",
            "bulk_insert_overwrite_concurrency",
            "bulk_delete_concurrency",
            "batch_concurrency",
            "overwrite_concurrency",
        }
        if kwargs:
            unknown_kwargs = set(kwargs.keys()) - known_kwargs
            if unknown_kwargs:
                warnings.warn(
                    "Method 'from_texts' of AstraDBVectorStore vector store "
                    "invoked with unsupported arguments "
                    f"({', '.join(sorted(unknown_kwargs))}), "
                    "which will be ignored."
                )

        collection_name: str = kwargs["collection_name"]
        token = kwargs.get("token")
        api_endpoint = kwargs.get("api_endpoint")
        astra_db_client = kwargs.get("astra_db_client")
        async_astra_db_client = kwargs.get("async_astra_db_client")
        namespace = kwargs.get("namespace")
        metric = kwargs.get("metric")

        return cls(
            embedding=embedding,
            collection_name=collection_name,
            token=token,
            api_endpoint=api_endpoint,
            astra_db_client=astra_db_client,
            async_astra_db_client=async_astra_db_client,
            namespace=namespace,
            metric=metric,
            batch_size=kwargs.get("batch_size"),
            bulk_insert_batch_concurrency=kwargs.get("bulk_insert_batch_concurrency"),
            bulk_insert_overwrite_concurrency=kwargs.get(
                "bulk_insert_overwrite_concurrency"
            ),
            bulk_delete_concurrency=kwargs.get("bulk_delete_concurrency"),
            collection_vector_service_options=collection_vector_service_options,
        )

    @classmethod
    def from_texts(
        cls: Type[AstraDBVectorStore],
        texts: List[str],
        embedding: Optional[Embeddings] = None,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        collection_vector_service_options: Optional[
            CollectionVectorServiceOptions
        ] = None,
        **kwargs: Any,
    ) -> AstraDBVectorStore:
        """Create an Astra DB vectorstore from raw texts.

        Args:
            texts: the texts to insert.
            embedding: the embedding function to use in the store.
                This enables client-side embedding functions or calls to external
                embedding providers. Only one of `embedding` or
                `collection_vector_service_options` can be provided.
            metadatas: metadata dicts for the texts.
            ids: ids to associate to the texts.
            collection_vector_service_options: specifies the use of server-side
                embeddings within Astra DB. Only one of `embedding` or
                `collection_vector_service_options` can be provided.
            **kwargs: you can pass any argument that you would
                to :meth:`~add_texts` and/or to the 'AstraDBVectorStore' constructor
                (see these methods for details). These arguments will be
                routed to the respective methods as they are.

        Returns:
            an `AstraDBVectorStore` vectorstore.
        """
        astra_db_store = AstraDBVectorStore._from_kwargs(
            embedding=embedding,
            collection_vector_service_options=collection_vector_service_options,
            **kwargs,
        )
        astra_db_store.add_texts(
            texts=texts,
            metadatas=metadatas,
            ids=ids,
            batch_size=kwargs.get("batch_size"),
            batch_concurrency=kwargs.get("batch_concurrency"),
            overwrite_concurrency=kwargs.get("overwrite_concurrency"),
        )
        return astra_db_store

    @classmethod
    async def afrom_texts(
        cls: Type[AstraDBVectorStore],
        texts: List[str],
        embedding: Optional[Embeddings] = None,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        collection_vector_service_options: Optional[
            CollectionVectorServiceOptions
        ] = None,
        **kwargs: Any,
    ) -> AstraDBVectorStore:
        """Create an Astra DB vectorstore from raw texts.

        Args:
            texts: the texts to insert.
            embedding: the embedding function to use in the store.
            metadatas: metadata dicts for the texts.
            ids: ids to associate to the texts.
            collection_vector_service_options: specifies the use of server-side
                embeddings within Astra DB. Only one of `embedding` or
                `collection_vector_service_options` can be provided.
            **kwargs: you can pass any argument that you would
                to :meth:`~add_texts` and/or to the 'AstraDBVectorStore' constructor
                (see these methods for details). These arguments will be
                routed to the respective methods as they are.

        Returns:
            an `AstraDBVectorStore` vectorstore.
        """
        astra_db_store = AstraDBVectorStore._from_kwargs(
            embedding,
            collection_vector_service_options=collection_vector_service_options,
            **kwargs,
        )
        await astra_db_store.aadd_texts(
            texts=texts,
            metadatas=metadatas,
            ids=ids,
            batch_size=kwargs.get("batch_size"),
            batch_concurrency=kwargs.get("batch_concurrency"),
            overwrite_concurrency=kwargs.get("overwrite_concurrency"),
        )
        return astra_db_store

    @classmethod
    def from_documents(
        cls: Type[AstraDBVectorStore],
        documents: List[Document],
        embedding: Optional[Embeddings] = None,
        collection_vector_service_options: Optional[
            CollectionVectorServiceOptions
        ] = None,
        **kwargs: Any,
    ) -> AstraDBVectorStore:
        """Create an Astra DB vectorstore from a document list.

        Utility method that defers to 'from_texts' (see that one).

        Args: see 'from_texts', except here you have to supply 'documents'
            in place of 'texts' and 'metadatas'.

        Returns:
            an `AstraDBVectorStore` vectorstore.
        """
        texts = [d.page_content for d in documents]
        metadatas = [d.metadata for d in documents]
        return cls.from_texts(
            texts,
            embedding=embedding,
            metadatas=metadatas,
            collection_vector_service_options=collection_vector_service_options,
            **kwargs,
        )

    @classmethod
    async def afrom_documents(
        cls: Type[AstraDBVectorStore],
        documents: List[Document],
        embedding: Optional[Embeddings] = None,
        collection_vector_service_options: Optional[
            CollectionVectorServiceOptions
        ] = None,
        **kwargs: Any,
    ) -> AstraDBVectorStore:
        """Create an Astra DB vectorstore from a document list.

        Utility method that defers to 'afrom_texts' (see that one).

        Args: see 'afrom_texts', except here you have to supply 'documents'
            in place of 'texts' and 'metadatas'.

        Returns:
            an `AstraDBVectorStore` vectorstore.
        """
        texts = [d.page_content for d in documents]
        metadatas = [d.metadata for d in documents]
        return await cls.afrom_texts(
            texts,
            embedding=embedding,
            metadatas=metadatas,
            collection_vector_service_options=collection_vector_service_options,
            **kwargs,
        )
