langchain/libs/community/langchain_community/vectorstores/cassandra.py

from __future__ import annotations

import asyncio
import importlib.metadata
import typing
import uuid
import warnings
from typing import (
    Any,
    Awaitable,
    Callable,
    Dict,
    Iterable,
    List,
    Optional,
    Tuple,
    Type,
    TypeVar,
    Union,
)

import numpy as np
from packaging.version import Version  # this is a lancghain-core dependency

if typing.TYPE_CHECKING:
    from cassandra.cluster import Session

from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore, VectorStoreRetriever

from langchain_community.utilities.cassandra import SetupMode
from langchain_community.vectorstores.utils import maximal_marginal_relevance

CVST = TypeVar("CVST", bound="Cassandra")
MIN_CASSIO_VERSION = Version("0.1.10")


class Cassandra(VectorStore):
    _embedding_dimension: Union[int, None]

    def _get_embedding_dimension(self) -> int:
        if self._embedding_dimension is None:
            self._embedding_dimension = len(
                self.embedding.embed_query("This is a sample sentence.")
            )
        return self._embedding_dimension

    async def _aget_embedding_dimension(self) -> int:
        if self._embedding_dimension is None:
            self._embedding_dimension = len(
                await self.embedding.aembed_query("This is a sample sentence.")
            )
        return self._embedding_dimension

    def __init__(
        self,
        embedding: Embeddings,
        session: Optional[Session] = None,
        keyspace: Optional[str] = None,
        table_name: str = "",
        ttl_seconds: Optional[int] = None,
        *,
        body_index_options: Optional[List[Tuple[str, Any]]] = None,
        setup_mode: SetupMode = SetupMode.SYNC,
        metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
    ) -> None:
        """Apache Cassandra(R) for vector-store workloads.

        To use it, you need a recent installation of the `cassio` library
        and a Cassandra cluster / Astra DB instance supporting vector capabilities.

        Visit the cassio.org website for extensive quickstarts and code examples.

        Example:
            .. code-block:: python

                    from langchain_community.vectorstores import Cassandra
                    from langchain_openai import OpenAIEmbeddings

                    embeddings = OpenAIEmbeddings()
                    session = ...             # create your Cassandra session object
                    keyspace = 'my_keyspace'  # the keyspace should exist already
                    table_name = 'my_vector_store'
                    vectorstore = Cassandra(embeddings, session, keyspace, table_name)

        Args:
            embedding: Embedding function to use.
            session: Cassandra driver session. If not provided, it is resolved from
                cassio.
            keyspace: Cassandra keyspace. If not provided, it is resolved from cassio.
            table_name: Cassandra table (required).
            ttl_seconds: Optional time-to-live for the added texts.
            body_index_options: Optional options used to create the body index.
                Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
            setup_mode: mode used to create the Cassandra table (SYNC,
                ASYNC or OFF).
            metadata_indexing: Optional specification of a metadata indexing policy,
                i.e. to fine-tune which of the metadata fields are indexed.
                It can be a string ("all" or "none"), or a 2-tuple. The following
                means that all fields except 'f1', 'f2' ... are NOT indexed:
                    metadata_indexing=("allowlist", ["f1", "f2", ...])
                The following means all fields EXCEPT 'g1', 'g2', ... are indexed:
                    metadata_indexing("denylist", ["g1", "g2", ...])
                The default is to index every metadata field.
                Note: if you plan to have massive unique text metadata entries,
                consider not indexing them for performance
                (and to overcome max-length limitations).
        """
        try:
            from cassio.table import MetadataVectorCassandraTable
        except (ImportError, ModuleNotFoundError):
            raise ImportError(
                "Could not import cassio python package. "
                "Please install it with `pip install cassio`."
            )
        cassio_version = Version(importlib.metadata.version("cassio"))

        if cassio_version is not None and cassio_version < MIN_CASSIO_VERSION:
            msg = (
                "Cassio version not supported. Please upgrade cassio "
                f"to version {MIN_CASSIO_VERSION} or higher."
            )
            raise ImportError(msg)

        if not table_name:
            raise ValueError("Missing required parameter 'table_name'.")
        self.embedding = embedding
        self.session = session
        self.keyspace = keyspace
        self.table_name = table_name
        self.ttl_seconds = ttl_seconds
        #
        self._embedding_dimension = None
        #
        kwargs: Dict[str, Any] = {}
        if body_index_options is not None:
            kwargs["body_index_options"] = body_index_options
        if setup_mode == SetupMode.ASYNC:
            kwargs["async_setup"] = True

        embedding_dimension: Union[int, Awaitable[int], None] = None
        if setup_mode == SetupMode.ASYNC:
            embedding_dimension = self._aget_embedding_dimension()
        elif setup_mode == SetupMode.SYNC:
            embedding_dimension = self._get_embedding_dimension()

        self.table = MetadataVectorCassandraTable(
            session=session,
            keyspace=keyspace,
            table=table_name,
            vector_dimension=embedding_dimension,
            metadata_indexing=metadata_indexing,
            primary_key_type="TEXT",
            skip_provisioning=setup_mode == SetupMode.OFF,
            **kwargs,
        )

        if self.session is None:
            self.session = self.table.session

    @property
    def embeddings(self) -> Embeddings:
        return self.embedding

    def _select_relevance_score_fn(self) -> Callable[[float], float]:
        """
        The underlying VectorTable 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 delete_collection(self) -> None:
        """
        Just an alias for `clear`
        (to better align with other VectorStore implementations).
        """
        self.clear()

    async def adelete_collection(self) -> None:
        """
        Just an alias for `aclear`
        (to better align with other VectorStore implementations).
        """
        await self.aclear()

    def clear(self) -> None:
        """Empty the table."""
        self.table.clear()

    async def aclear(self) -> None:
        """Empty the table."""
        await self.table.aclear()

    def delete_by_document_id(self, document_id: str) -> None:
        """Delete by document ID.

        Args:
            document_id: the document ID to delete.
        """
        return self.table.delete(row_id=document_id)

    async def adelete_by_document_id(self, document_id: str) -> None:
        """Delete by document ID.

        Args:
            document_id: the document ID to delete.
        """
        return await self.table.adelete(row_id=document_id)

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

        Args:
            ids: List of ids to delete.

        Returns:
            Optional[bool]: True if deletion is successful,
            False otherwise, None if not implemented.
        """

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

        for document_id in ids:
            self.delete_by_document_id(document_id)
        return True

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

        Args:
            ids: List of ids to delete.

        Returns:
            Optional[bool]: True if deletion is successful,
            False otherwise, None if not implemented.
        """

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

        for document_id in ids:
            await self.adelete_by_document_id(document_id)
        return True

    def delete_by_metadata_filter(
        self,
        filter: dict[str, Any],
        *,
        batch_size: int = 50,
    ) -> int:
        """Delete all documents matching a certain metadata filtering condition.

        This operation does not use the vector embeddings in any way, it simply
        removes all documents whose metadata match the provided condition.

        Args:
            filter: Filter on the metadata to apply. The filter cannot be empty.
            batch_size: amount of deletions per each batch (until exhaustion of
                the matching documents).

        Returns:
            A number expressing the amount of deleted documents.
        """
        if not filter:
            msg = (
                "Method `delete_by_metadata_filter` does not accept an empty "
                "filter. Use the `clear()` method if you really want to empty "
                "the vector store."
            )
            raise ValueError(msg)

        return self.table.find_and_delete_entries(
            metadata=filter,
            batch_size=batch_size,
        )

    async def adelete_by_metadata_filter(
        self,
        filter: dict[str, Any],
        *,
        batch_size: int = 50,
    ) -> int:
        """Delete all documents matching a certain metadata filtering condition.

        This operation does not use the vector embeddings in any way, it simply
        removes all documents whose metadata match the provided condition.

        Args:
            filter: Filter on the metadata to apply. The filter cannot be empty.
            batch_size: amount of deletions per each batch (until exhaustion of
                the matching documents).

        Returns:
            A number expressing the amount of deleted documents.
        """
        if not filter:
            msg = (
                "Method `delete_by_metadata_filter` does not accept an empty "
                "filter. Use the `clear()` method if you really want to empty "
                "the vector store."
            )
            raise ValueError(msg)

        return await self.table.afind_and_delete_entries(
            metadata=filter,
            batch_size=batch_size,
        )

    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        batch_size: int = 16,
        ttl_seconds: Optional[int] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.

        Args:
            texts: Texts to add to the vectorstore.
            metadatas: Optional list of metadatas.
            ids: Optional list of IDs.
            batch_size: Number of concurrent requests to send to the server.
            ttl_seconds: Optional time-to-live for the added texts.

        Returns:
            List[str]: List of IDs of the added texts.
        """
        _texts = list(texts)
        ids = ids or [uuid.uuid4().hex for _ in _texts]
        metadatas = metadatas or [{}] * len(_texts)
        ttl_seconds = ttl_seconds or self.ttl_seconds
        embedding_vectors = self.embedding.embed_documents(_texts)

        for i in range(0, len(_texts), batch_size):
            batch_texts = _texts[i : i + batch_size]
            batch_embedding_vectors = embedding_vectors[i : i + batch_size]
            batch_ids = ids[i : i + batch_size]
            batch_metadatas = metadatas[i : i + batch_size]

            futures = [
                self.table.put_async(
                    row_id=text_id,
                    body_blob=text,
                    vector=embedding_vector,
                    metadata=metadata or {},
                    ttl_seconds=ttl_seconds,
                )
                for text, embedding_vector, text_id, metadata in zip(
                    batch_texts, batch_embedding_vectors, batch_ids, batch_metadatas
                )
            ]
            for future in futures:
                future.result()
        return ids

    async def aadd_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        concurrency: int = 16,
        ttl_seconds: Optional[int] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.

        Args:
            texts: Texts to add to the vectorstore.
            metadatas: Optional list of metadatas.
            ids: Optional list of IDs.
            concurrency: Number of concurrent queries to the database.
                Defaults to 16.
            ttl_seconds: Optional time-to-live for the added texts.

        Returns:
            List[str]: List of IDs of the added texts.
        """
        _texts = list(texts)
        ids = ids or [uuid.uuid4().hex for _ in _texts]
        _metadatas: List[dict] = metadatas or [{}] * len(_texts)
        ttl_seconds = ttl_seconds or self.ttl_seconds
        embedding_vectors = await self.embedding.aembed_documents(_texts)

        sem = asyncio.Semaphore(concurrency)

        async def send_concurrently(
            row_id: str, text: str, embedding_vector: List[float], metadata: dict
        ) -> None:
            async with sem:
                await self.table.aput(
                    row_id=row_id,
                    body_blob=text,
                    vector=embedding_vector,
                    metadata=metadata or {},
                    ttl_seconds=ttl_seconds,
                )

        for i in range(0, len(_texts)):
            tasks = [
                asyncio.create_task(
                    send_concurrently(
                        ids[i], _texts[i], embedding_vectors[i], _metadatas[i]
                    )
                )
            ]
            await asyncio.gather(*tasks)
        return ids

    def replace_metadata(
        self,
        id_to_metadata: dict[str, dict],
        *,
        batch_size: int = 50,
    ) -> None:
        """Replace the metadata of documents.

        For each document to update, identified by its ID, the new metadata
        dictionary completely replaces what is on the store. This includes
        passing empty metadata `{}` to erase the currently-stored information.

        Args:
            id_to_metadata: map from the Document IDs to modify to the
                new metadata for updating.
                Keys in this dictionary that do not correspond to an existing
                document will not cause an error, rather will result in new
                rows being written into the Cassandra table but without an
                associated vector: hence unreachable through vector search.
            batch_size: Number of concurrent requests to send to the server.

        Returns:
            None if the writes succeed (otherwise an error is raised).
        """
        ids_and_metadatas = list(id_to_metadata.items())
        for i in range(0, len(ids_and_metadatas), batch_size):
            batch_i_m = ids_and_metadatas[i : i + batch_size]
            futures = [
                self.table.put_async(
                    row_id=doc_id,
                    metadata=doc_md,
                )
                for doc_id, doc_md in batch_i_m
            ]
            for future in futures:
                future.result()
        return

    async def areplace_metadata(
        self,
        id_to_metadata: dict[str, dict],
        *,
        concurrency: int = 50,
    ) -> None:
        """Replace the metadata of documents.

        For each document to update, identified by its ID, the new metadata
        dictionary completely replaces what is on the store. This includes
        passing empty metadata `{}` to erase the currently-stored information.

        Args:
            id_to_metadata: map from the Document IDs to modify to the
                new metadata for updating.
                Keys in this dictionary that do not correspond to an existing
                document will not cause an error, rather will result in new
                rows being written into the Cassandra table but without an
                associated vector: hence unreachable through vector search.
            concurrency: Number of concurrent queries to the database.
                Defaults to 50.

        Returns:
            None if the writes succeed (otherwise an error is raised).
        """
        ids_and_metadatas = list(id_to_metadata.items())

        sem = asyncio.Semaphore(concurrency)

        async def send_concurrently(doc_id: str, doc_md: dict) -> None:
            async with sem:
                await self.table.aput(
                    row_id=doc_id,
                    metadata=doc_md,
                )

        for doc_id, doc_md in ids_and_metadatas:
            tasks = [asyncio.create_task(send_concurrently(doc_id, doc_md))]
            await asyncio.gather(*tasks)

        return

    @staticmethod
    def _row_to_document(row: Dict[str, Any]) -> Document:
        return Document(
            id=row["row_id"],
            page_content=row["body_blob"],
            metadata=row["metadata"],
        )

    def get_by_document_id(self, document_id: str) -> Document | None:
        """Retrieve a single document from the store, given its document ID.

        Args:
            document_id: The document ID

        Returns:
            The the document if it exists. Otherwise None.
        """
        row = self.table.get(row_id=document_id)
        if row is None:
            return None
        return self._row_to_document(row=row)

    async def aget_by_document_id(self, document_id: str) -> Document | None:
        """Retrieve a single document from the store, given its document ID.

        Args:
            document_id: The document ID

        Returns:
            The the document if it exists. Otherwise None.
        """
        row = await self.table.aget(row_id=document_id)
        if row is None:
            return None
        return self._row_to_document(row=row)

    def metadata_search(
        self,
        filter: dict[str, Any] = {},  # noqa: B006
        n: int = 5,
    ) -> Iterable[Document]:
        """Get documents via a metadata search.

        Args:
            filter: the metadata to query for.
            n: the maximum number of documents to return.
        """
        rows = self.table.find_entries(metadata=filter, n=n)
        return [self._row_to_document(row=row) for row in rows if row]

    async def ametadata_search(
        self,
        filter: dict[str, Any] = {},  # noqa: B006
        n: int = 5,
    ) -> Iterable[Document]:
        """Get documents via a metadata search.

        Args:
            filter: the metadata to query for.
            n: the maximum number of documents to return.
        """
        rows = await self.table.afind_entries(metadata=filter, n=n)
        return [self._row_to_document(row=row) for row in rows]

    async def asimilarity_search_with_embedding_id_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
        body_search: Optional[Union[str, List[str]]] = None,
    ) -> List[Tuple[Document, List[float], str]]:
        """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.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of (Document, embedding, id), the most similar to the query vector.
        """
        kwargs: Dict[str, Any] = {}
        if filter is not None:
            kwargs["metadata"] = filter
        if body_search is not None:
            kwargs["body_search"] = body_search

        hits = await self.table.aann_search(
            vector=embedding,
            n=k,
            **kwargs,
        )
        return [
            (
                self._row_to_document(row=hit),
                hit["vector"],
                hit["row_id"],
            )
            for hit in hits
        ]

    @staticmethod
    def _search_to_documents(
        hits: Iterable[Dict[str, Any]],
    ) -> List[Tuple[Document, float, str]]:
        # We stick to 'cos' distance as it can be normalized on a 0-1 axis
        # (1=most relevant), as required by this class' contract.
        return [
            (
                Cassandra._row_to_document(row=hit),
                0.5 + 0.5 * hit["distance"],
                hit["row_id"],
            )
            for hit in hits
        ]

    # id-returning search facilities
    def similarity_search_with_score_id_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
        body_search: Optional[Union[str, List[str]]] = None,
    ) -> List[Tuple[Document, float, str]]:
        """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.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of (Document, score, id), the most similar to the query vector.
        """
        kwargs: Dict[str, Any] = {}
        if filter is not None:
            kwargs["metadata"] = filter
        if body_search is not None:
            kwargs["body_search"] = body_search
        hits = self.table.metric_ann_search(
            vector=embedding,
            n=k,
            metric="cos",
            **kwargs,
        )
        return self._search_to_documents(hits)

    async def asimilarity_search_with_score_id_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
        body_search: Optional[Union[str, List[str]]] = None,
    ) -> List[Tuple[Document, float, str]]:
        """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.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of (Document, score, id), the most similar to the query vector.
        """
        kwargs: Dict[str, Any] = {}
        if filter is not None:
            kwargs["metadata"] = filter
        if body_search is not None:
            kwargs["body_search"] = body_search

        hits = await self.table.ametric_ann_search(
            vector=embedding,
            n=k,
            metric="cos",
            **kwargs,
        )
        return self._search_to_documents(hits)

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

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of (Document, score, id), the most similar to the query vector.
        """
        embedding_vector = self.embedding.embed_query(query)
        return self.similarity_search_with_score_id_by_vector(
            embedding=embedding_vector,
            k=k,
            filter=filter,
            body_search=body_search,
        )

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

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of (Document, score, id), the most similar to the query vector.
        """
        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,
            body_search=body_search,
        )

    # id-unaware search facilities
    def similarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
        body_search: Optional[Union[str, List[str]]] = None,
    ) -> List[Tuple[Document, float]]:
        """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.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of (Document, score), the most similar to the query vector.
        """
        return [
            (doc, score)
            for (doc, score, docId) in self.similarity_search_with_score_id_by_vector(
                embedding=embedding,
                k=k,
                filter=filter,
                body_search=body_search,
            )
        ]

    async def asimilarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
        body_search: Optional[Union[str, List[str]]] = None,
    ) -> List[Tuple[Document, float]]:
        """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.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of (Document, score), the most similar to the query vector.
        """
        return [
            (doc, score)
            for (
                doc,
                score,
                _,
            ) in await self.asimilarity_search_with_score_id_by_vector(
                embedding=embedding,
                k=k,
                filter=filter,
                body_search=body_search,
            )
        ]

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

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of Document, the most similar to the query vector.
        """
        embedding_vector = self.embedding.embed_query(query)
        return self.similarity_search_by_vector(
            embedding_vector,
            k,
            filter=filter,
            body_search=body_search,
        )

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

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of Document, the most similar to the query vector.
        """
        embedding_vector = await self.embedding.aembed_query(query)
        return await self.asimilarity_search_by_vector(
            embedding_vector,
            k,
            filter=filter,
            body_search=body_search,
        )

    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
        body_search: Optional[Union[str, List[str]]] = 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.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of Document, the most similar to the query vector.
        """
        return [
            doc
            for doc, _ in self.similarity_search_with_score_by_vector(
                embedding,
                k,
                filter=filter,
                body_search=body_search,
            )
        ]

    async def asimilarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, str]] = None,
        body_search: Optional[Union[str, List[str]]] = 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.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of Document, the most similar to the query vector.
        """
        return [
            doc
            for doc, _ in await self.asimilarity_search_with_score_by_vector(
                embedding,
                k,
                filter=filter,
                body_search=body_search,
            )
        ]

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

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of (Document, score), the most similar to the query vector.
        """
        embedding_vector = self.embedding.embed_query(query)
        return self.similarity_search_with_score_by_vector(
            embedding_vector,
            k,
            filter=filter,
            body_search=body_search,
        )

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

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter on the metadata to apply.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of (Document, score), the most similar to the query vector.
        """
        embedding_vector = await self.embedding.aembed_query(query)
        return await self.asimilarity_search_with_score_by_vector(
            embedding_vector,
            k,
            filter=filter,
            body_search=body_search,
        )

    @staticmethod
    def _mmr_search_to_documents(
        prefetch_hits: List[Dict[str, Any]],
        embedding: List[float],
        k: int,
        lambda_mult: float,
    ) -> List[Document]:
        # let the mmr utility pick the *indices* in the above array
        mmr_chosen_indices = maximal_marginal_relevance(
            np.array(embedding, dtype=np.float32),
            [pf_hit["vector"] for pf_hit in prefetch_hits],
            k=k,
            lambda_mult=lambda_mult,
        )
        mmr_hits = [
            pf_hit
            for pf_index, pf_hit in enumerate(prefetch_hits)
            if pf_index in mmr_chosen_indices
        ]
        return [Cassandra._row_to_document(row=hit) 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, str]] = None,
        body_search: Optional[Union[str, List[str]]] = 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. Defaults to 4.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
                Defaults to 20.
            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.
                Defaults to 0.5.
            filter: Filter on the metadata to apply.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        _kwargs: Dict[str, Any] = {}
        if filter is not None:
            _kwargs["metadata"] = filter
        if body_search is not None:
            _kwargs["body_search"] = body_search

        prefetch_hits = list(
            self.table.metric_ann_search(
                vector=embedding,
                n=fetch_k,
                metric="cos",
                **_kwargs,
            )
        )
        return self._mmr_search_to_documents(prefetch_hits, embedding, k, lambda_mult)

    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, str]] = None,
        body_search: Optional[Union[str, List[str]]] = 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. Defaults to 4.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
                Defaults to 20.
            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.
                Defaults to 0.5.
            filter: Filter on the metadata to apply.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        _kwargs: Dict[str, Any] = {}
        if filter is not None:
            _kwargs["metadata"] = filter
        if body_search is not None:
            _kwargs["body_search"] = body_search

        prefetch_hits = list(
            await self.table.ametric_ann_search(
                vector=embedding,
                n=fetch_k,
                metric="cos",
                **_kwargs,
            )
        )
        return self._mmr_search_to_documents(prefetch_hits, embedding, k, lambda_mult)

    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        filter: Optional[Dict[str, str]] = None,
        body_search: Optional[Union[str, List[str]]] = 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: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
                Defaults to 20.
            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.
                Defaults to 0.5.
            filter: Filter on the metadata to apply.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        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,
            body_search=body_search,
        )

    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, str]] = None,
        body_search: Optional[Union[str, List[str]]] = 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: Text 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.
                Defaults to 0.5.
            filter: Filter on the metadata to apply.
            body_search: Document textual search terms to apply.
                Only supported by Astra DB at the moment.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        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,
            body_search=body_search,
        )

    @staticmethod
    def _build_docs_from_texts(
        texts: List[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
    ) -> List[Document]:
        docs: List[Document] = []
        for i, text in enumerate(texts):
            doc = Document(
                page_content=text,
            )
            if metadatas is not None:
                doc.metadata = metadatas[i]
            if ids is not None:
                doc.id = ids[i]
            docs.append(doc)
        return docs

    @classmethod
    def from_texts(
        cls: Type[CVST],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        *,
        session: Optional[Session] = None,
        keyspace: Optional[str] = None,
        table_name: str = "",
        ids: Optional[List[str]] = None,
        ttl_seconds: Optional[int] = None,
        body_index_options: Optional[List[Tuple[str, Any]]] = None,
        metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
        **kwargs: Any,
    ) -> CVST:
        """Create a Cassandra vector store from raw texts.

        Args:
            texts: Texts to add to the vectorstore.
            embedding: Embedding function to use.
            metadatas: Optional list of metadatas associated with the texts.
            session: Cassandra driver session.
                If not provided, it is resolved from cassio.
            keyspace: Cassandra key space.
                If not provided, it is resolved from cassio.
            table_name: Cassandra table (required).
            ids: Optional list of IDs associated with the texts.
            ttl_seconds: Optional time-to-live for the added texts.
            body_index_options: Optional options used to create the body index.
                Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
            metadata_indexing: Optional specification of a metadata indexing policy,
                i.e. to fine-tune which of the metadata fields are indexed.
                It can be a string ("all" or "none"), or a 2-tuple. The following
                means that all fields except 'f1', 'f2' ... are NOT indexed:
                    metadata_indexing=("allowlist", ["f1", "f2", ...])
                The following means all fields EXCEPT 'g1', 'g2', ... are indexed:
                    metadata_indexing("denylist", ["g1", "g2", ...])
                The default is to index every metadata field.
                Note: if you plan to have massive unique text metadata entries,
                consider not indexing them for performance
                (and to overcome max-length limitations).

        Returns:
            a Cassandra vector store.
        """
        docs = cls._build_docs_from_texts(
            texts=texts,
            metadatas=metadatas,
            ids=ids,
        )

        return cls.from_documents(
            documents=docs,
            embedding=embedding,
            session=session,
            keyspace=keyspace,
            table_name=table_name,
            ttl_seconds=ttl_seconds,
            body_index_options=body_index_options,
            metadata_indexing=metadata_indexing,
            **kwargs,
        )

    @classmethod
    async def afrom_texts(
        cls: Type[CVST],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        *,
        session: Optional[Session] = None,
        keyspace: Optional[str] = None,
        table_name: str = "",
        ids: Optional[List[str]] = None,
        ttl_seconds: Optional[int] = None,
        body_index_options: Optional[List[Tuple[str, Any]]] = None,
        metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
        **kwargs: Any,
    ) -> CVST:
        """Create a Cassandra vector store from raw texts.

        Args:
            texts: Texts to add to the vectorstore.
            embedding: Embedding function to use.
            metadatas: Optional list of metadatas associated with the texts.
            session: Cassandra driver session.
                If not provided, it is resolved from cassio.
            keyspace: Cassandra key space.
                If not provided, it is resolved from cassio.
            table_name: Cassandra table (required).
            ids: Optional list of IDs associated with the texts.
            ttl_seconds: Optional time-to-live for the added texts.
            body_index_options: Optional options used to create the body index.
                Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
            metadata_indexing: Optional specification of a metadata indexing policy,
                i.e. to fine-tune which of the metadata fields are indexed.
                It can be a string ("all" or "none"), or a 2-tuple. The following
                means that all fields except 'f1', 'f2' ... are NOT indexed:
                    metadata_indexing=("allowlist", ["f1", "f2", ...])
                The following means all fields EXCEPT 'g1', 'g2', ... are indexed:
                    metadata_indexing("denylist", ["g1", "g2", ...])
                The default is to index every metadata field.
                Note: if you plan to have massive unique text metadata entries,
                consider not indexing them for performance
                (and to overcome max-length limitations).

        Returns:
            a Cassandra vector store.
        """
        docs = cls._build_docs_from_texts(
            texts=texts,
            metadatas=metadatas,
            ids=ids,
        )

        return await cls.afrom_documents(
            documents=docs,
            embedding=embedding,
            session=session,
            keyspace=keyspace,
            table_name=table_name,
            ttl_seconds=ttl_seconds,
            body_index_options=body_index_options,
            metadata_indexing=metadata_indexing,
            **kwargs,
        )

    @staticmethod
    def _add_ids_to_docs(
        docs: List[Document],
        ids: Optional[List[str]] = None,
    ) -> List[Document]:
        if ids is not None:
            for doc, doc_id in zip(docs, ids):
                doc.id = doc_id
        return docs

    @classmethod
    def from_documents(
        cls: Type[CVST],
        documents: List[Document],
        embedding: Embeddings,
        *,
        session: Optional[Session] = None,
        keyspace: Optional[str] = None,
        table_name: str = "",
        ids: Optional[List[str]] = None,
        ttl_seconds: Optional[int] = None,
        body_index_options: Optional[List[Tuple[str, Any]]] = None,
        metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
        **kwargs: Any,
    ) -> CVST:
        """Create a Cassandra vector store from a document list.

        Args:
            documents: Documents to add to the vectorstore.
            embedding: Embedding function to use.
            session: Cassandra driver session.
                If not provided, it is resolved from cassio.
            keyspace: Cassandra key space.
                If not provided, it is resolved from cassio.
            table_name: Cassandra table (required).
            ids: Optional list of IDs associated with the documents.
            ttl_seconds: Optional time-to-live for the added documents.
            body_index_options: Optional options used to create the body index.
                Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
            metadata_indexing: Optional specification of a metadata indexing policy,
                i.e. to fine-tune which of the metadata fields are indexed.
                It can be a string ("all" or "none"), or a 2-tuple. The following
                means that all fields except 'f1', 'f2' ... are NOT indexed:
                    metadata_indexing=("allowlist", ["f1", "f2", ...])
                The following means all fields EXCEPT 'g1', 'g2', ... are indexed:
                    metadata_indexing("denylist", ["g1", "g2", ...])
                The default is to index every metadata field.
                Note: if you plan to have massive unique text metadata entries,
                consider not indexing them for performance
                (and to overcome max-length limitations).

        Returns:
            a Cassandra vector store.
        """
        if ids is not None:
            warnings.warn(
                (
                    "Parameter `ids` to Cassandra's `from_documents` "
                    "method is deprecated. Please set the supplied documents' "
                    "`.id` attribute instead. The id attribute of Document "
                    "is ignored as long as the `ids` parameter is passed."
                ),
                DeprecationWarning,
                stacklevel=2,
            )

        store = cls(
            embedding=embedding,
            session=session,
            keyspace=keyspace,
            table_name=table_name,
            ttl_seconds=ttl_seconds,
            body_index_options=body_index_options,
            metadata_indexing=metadata_indexing,
            **kwargs,
        )
        store.add_documents(documents=cls._add_ids_to_docs(docs=documents, ids=ids))
        return store

    @classmethod
    async def afrom_documents(
        cls: Type[CVST],
        documents: List[Document],
        embedding: Embeddings,
        *,
        session: Optional[Session] = None,
        keyspace: Optional[str] = None,
        table_name: str = "",
        ids: Optional[List[str]] = None,
        ttl_seconds: Optional[int] = None,
        body_index_options: Optional[List[Tuple[str, Any]]] = None,
        metadata_indexing: Union[Tuple[str, Iterable[str]], str] = "all",
        **kwargs: Any,
    ) -> CVST:
        """Create a Cassandra vector store from a document list.

        Args:
            documents: Documents to add to the vectorstore.
            embedding: Embedding function to use.
            session: Cassandra driver session.
                If not provided, it is resolved from cassio.
            keyspace: Cassandra key space.
                If not provided, it is resolved from cassio.
            table_name: Cassandra table (required).
            ids: Optional list of IDs associated with the documents.
            ttl_seconds: Optional time-to-live for the added documents.
            body_index_options: Optional options used to create the body index.
                Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
            metadata_indexing: Optional specification of a metadata indexing policy,
                i.e. to fine-tune which of the metadata fields are indexed.
                It can be a string ("all" or "none"), or a 2-tuple. The following
                means that all fields except 'f1', 'f2' ... are NOT indexed:
                    metadata_indexing=("allowlist", ["f1", "f2", ...])
                The following means all fields EXCEPT 'g1', 'g2', ... are indexed:
                    metadata_indexing("denylist", ["g1", "g2", ...])
                The default is to index every metadata field.
                Note: if you plan to have massive unique text metadata entries,
                consider not indexing them for performance
                (and to overcome max-length limitations).

        Returns:
            a Cassandra vector store.
        """
        if ids is not None:
            warnings.warn(
                (
                    "Parameter `ids` to Cassandra's `afrom_documents` "
                    "method is deprecated. Please set the supplied documents' "
                    "`.id` attribute instead. The id attribute of Document "
                    "is ignored as long as the `ids` parameter is passed."
                ),
                DeprecationWarning,
                stacklevel=2,
            )

        store = cls(
            embedding=embedding,
            session=session,
            keyspace=keyspace,
            table_name=table_name,
            ttl_seconds=ttl_seconds,
            setup_mode=SetupMode.ASYNC,
            body_index_options=body_index_options,
            metadata_indexing=metadata_indexing,
            **kwargs,
        )
        await store.aadd_documents(
            documents=cls._add_ids_to_docs(docs=documents, ids=ids)
        )
        return store

    def as_retriever(
        self,
        search_type: str = "similarity",
        search_kwargs: Optional[Dict[str, Any]] = None,
        tags: Optional[List[str]] = None,
        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> VectorStoreRetriever:
        """Return VectorStoreRetriever initialized from this VectorStore.

        Args:
            search_type: Defines the type of search that
                the Retriever should perform.
                Can be "similarity" (default), "mmr", or
                "similarity_score_threshold".
            search_kwargs: Keyword arguments to pass to the
                search function. Can include things like:
                    k: Amount of documents to return (Default: 4)
                    score_threshold: Minimum relevance threshold
                        for similarity_score_threshold
                    fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
                    lambda_mult: Diversity of results returned by MMR;
                        1 for minimum diversity and 0 for maximum. (Default: 0.5)
                    filter: Filter by document metadata
            tags: List of tags associated with the retriever.
            metadata: Metadata associated with the retriever.
            kwargs: Other arguments passed to the VectorStoreRetriever init.

        Returns:
            Retriever for VectorStore.

        Examples:

        .. code-block:: python

            # Retrieve more documents with higher diversity
            # Useful if your dataset has many similar documents
            docsearch.as_retriever(
                search_type="mmr",
                search_kwargs={'k': 6, 'lambda_mult': 0.25}
            )

            # Fetch more documents for the MMR algorithm to consider
            # But only return the top 5
            docsearch.as_retriever(
                search_type="mmr",
                search_kwargs={'k': 5, 'fetch_k': 50}
            )

            # Only retrieve documents that have a relevance score
            # Above a certain threshold
            docsearch.as_retriever(
                search_type="similarity_score_threshold",
                search_kwargs={'score_threshold': 0.8}
            )

            # Only get the single most similar document from the dataset
            docsearch.as_retriever(search_kwargs={'k': 1})

            # Use a filter to only retrieve documents from a specific paper
            docsearch.as_retriever(
                search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
            )
        """
        _tags = tags or [] + self._get_retriever_tags()
        return VectorStoreRetriever(
            vectorstore=self,
            search_type=search_type,
            search_kwargs=search_kwargs or {},
            tags=_tags,
            metadata=metadata,
            **kwargs,
        )