return scored_documents
[docs]    def bulk_similarity_search(
        self,
        queries: Iterable[Union[str, Dict[str, float]]],
        k: int = 4,
        **kwargs: Any,
    ) -> List[List[Document]]:
        """Search the marqo index for the most similar documents in bulk with multiple
        queries.
        Args:
            queries (Iterable[Union[str, Dict[str, float]]]): An iterable of queries to
            execute in bulk, queries in the list can be strings or dictionaries of
            weighted queries.
            k (int, optional): The number of documents to return for each query.
            Defaults to 4.
        Returns:
            List[List[Document]]: A list of results for each query.
        """
        bulk_results = self.marqo_bulk_similarity_search(queries=queries, k=k)
        bulk_documents: List[List[Document]] = []
        for results in bulk_results["result"]:
            documents = self._construct_documents_from_results_without_score(results)
            bulk_documents.append(documents)
        return bulk_documents
[docs]    def bulk_similarity_search_with_score(
        self,
        queries: Iterable[Union[str, Dict[str, float]]],
        k: int = 4,
        **kwargs: Any,
    ) -> List[List[Tuple[Document, float]]]:
        """Return documents from Marqo that are similar to the query as well as
        their scores using a batch of queries.
        Args:
            query (Iterable[Union[str, Dict[str, float]]]): An iterable of queries
            to execute in bulk, queries in the list can be strings or dictionaries
            of weighted queries.
            k (int, optional): The number of documents to return. Defaults to 4.


k (int, optional): The number of documents to return. Defaults to 4.
        Returns:
            List[Tuple[Document, float]]: A list of lists of the matching
            documents and their scores for each query
        """
        bulk_results = self.marqo_bulk_similarity_search(queries=queries, k=k)
        bulk_documents: List[List[Tuple[Document, float]]] = []
        for results in bulk_results["result"]:
            documents = self._construct_documents_from_results_with_score(results)
            bulk_documents.append(documents)
        return bulk_documents
    def _construct_documents_from_results_with_score(
        self, results: Dict[str, List[Dict[str, str]]]
    ) -> List[Tuple[Document, Any]]:
        """Helper to convert Marqo results into documents.
        Args:
            results (List[dict]): A marqo results object with the 'hits'.
            include_scores (bool, optional): Include scores alongside documents.
            Defaults to False.
        Returns:
            Union[List[Document], List[Tuple[Document, float]]]: The documents or
            document score pairs if `include_scores` is true.
        """
        documents: List[Tuple[Document, Any]] = []
        for res in results["hits"]:
            if self.page_content_builder is None:
                text = res["text"]
            else:
                text = self.page_content_builder(res)
            metadata = json.loads(res.get("metadata", "{}"))
            documents.append(
                (Document(page_content=text, metadata=metadata), res["_score"])
            )
        return documents
    def _construct_documents_from_results_without_score(
        self, results: Dict[str, List[Dict[str, str]]]
    ) -> List[Document]:


) -> List[Document]:
        """Helper to convert Marqo results into documents.
        Args:
            results (List[dict]): A marqo results object with the 'hits'.
            include_scores (bool, optional): Include scores alongside documents.
            Defaults to False.
        Returns:
            Union[List[Document], List[Tuple[Document, float]]]: The documents or
            document score pairs if `include_scores` is true.
        """
        documents: List[Document] = []
        for res in results["hits"]:
            if self.page_content_builder is None:
                text = res["text"]
            else:
                text = self.page_content_builder(res)
            metadata = json.loads(res.get("metadata", "{}"))
            documents.append(Document(page_content=text, metadata=metadata))
        return documents
[docs]    def marqo_similarity_search(
        self,
        query: Union[str, Dict[str, float]],
        k: int = 4,
    ) -> Dict[str, List[Dict[str, str]]]:
        """Return documents from Marqo exposing Marqo's output directly
        Args:
            query (str): The query to search with.
            k (int, optional): The number of documents to return. Defaults to 4.
        Returns:
            List[Dict[str, Any]]: This hits from marqo.
        """
        results = self._client.index(self._index_name).search(
            q=query, searchable_attributes=self._searchable_attributes, limit=k
        )
        return results
[docs]    def marqo_bulk_similarity_search(
        self, queries: Iterable[Union[str, Dict[str, float]]], k: int = 4


) -> Dict[str, List[Dict[str, List[Dict[str, str]]]]]:
        """Return documents from Marqo using a bulk search, exposes Marqo's
        output directly
        Args:
            queries (Iterable[Union[str, Dict[str, float]]]): A list of queries.
            k (int, optional): The number of documents to return for each query.
            Defaults to 4.
        Returns:
            Dict[str, Dict[List[Dict[str, Dict[str, Any]]]]]: A bulk search results
            object
        """
        bulk_results = self._client.bulk_search(
            [
                {
                    "index": self._index_name,
                    "q": query,
                    "searchableAttributes": self._searchable_attributes,
                    "limit": k,
                }
                for query in queries
            ]
        )
        return bulk_results
[docs]    @classmethod
    def from_documents(
        cls: Type[Marqo],
        documents: List[Document],
        embedding: Union[Embeddings, None] = None,
        **kwargs: Any,
    ) -> Marqo:
        """Return VectorStore initialized from documents. Note that Marqo does not
        need embeddings, we retain the parameter to adhere to the Liskov substitution
        principle.
        Args:
            documents (List[Document]): Input documents
            embedding (Any, optional): Embeddings (not required). Defaults to None.
        Returns:
            VectorStore: A Marqo vectorstore
        """
        texts = [d.page_content for d in documents]
        metadatas = [d.metadata for d in documents]
        return cls.from_texts(texts, metadatas=metadatas, **kwargs)


return cls.from_texts(texts, metadatas=metadatas, **kwargs)
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Any = None,
        metadatas: Optional[List[dict]] = None,
        index_name: str = "",
        url: str = "http://localhost:8882",
        api_key: str = "",
        add_documents_settings: Optional[Dict[str, Any]] = {},
        searchable_attributes: Optional[List[str]] = None,
        page_content_builder: Optional[Callable[[Dict[str, str]], str]] = None,
        index_settings: Optional[Dict[str, Any]] = {},
        verbose: bool = True,
        **kwargs: Any,
    ) -> Marqo:
        """Return Marqo initialized from texts. Note that Marqo does not need
        embeddings, we retain the parameter to adhere to the Liskov
        substitution principle.
        This is a quick way to get started with marqo - simply provide your texts and
        metadatas and this will create an instance of the data store and index the
        provided data.
        To know the ids of your documents with this approach you will need to include
        them in under the key "_id" in your metadatas for each text
        Example:
        .. code-block:: python
                from langchain.vectorstores import Marqo
                datastore = Marqo(texts=['text'], index_name='my-first-index',
                url='http://localhost:8882')
        Args:
            texts (List[str]): A list of texts to index into marqo upon creation.
            embedding (Any, optional): Embeddings (not required). Defaults to None.


embedding (Any, optional): Embeddings (not required). Defaults to None.
            index_name (str, optional): The name of the index to use, if none is
            provided then one will be created with a UUID. Defaults to None.
            url (str, optional): The URL for Marqo. Defaults to "http://localhost:8882".
            api_key (str, optional): The API key for Marqo. Defaults to "".
            metadatas (Optional[List[dict]], optional): A list of metadatas, to
            accompany the texts. Defaults to None.
            this is only used when a new index is being created. Defaults to "cpu". Can
            be "cpu" or "cuda".
            add_documents_settings (Optional[Dict[str, Any]], optional): Settings
            for adding documents, see
            https://docs.marqo.ai/0.0.16/API-Reference/documents/#query-parameters.
            Defaults to {}.
            index_settings (Optional[Dict[str, Any]], optional): Index settings if
            the index doesn't exist, see
            https://docs.marqo.ai/0.0.16/API-Reference/indexes/#index-defaults-object.
            Defaults to {}.
        Returns:
            Marqo: An instance of the Marqo vector store
        """
        try:
            import marqo
        except ImportError:
            raise ValueError(
                "Could not import marqo python package. "
                "Please install it with `pip install marqo`."
            )
        if not index_name:
            index_name = str(uuid.uuid4())
        client = marqo.Client(url=url, api_key=api_key)
        try:
            client.create_index(index_name, settings_dict=index_settings)
            if verbose:


client.create_index(index_name, settings_dict=index_settings)
            if verbose:
                print(f"Created {index_name} successfully.")
        except Exception:
            if verbose:
                print(f"Index {index_name} exists.")
        instance: Marqo = cls(
            client,
            index_name,
            searchable_attributes=searchable_attributes,
            add_documents_settings=add_documents_settings,
            page_content_builder=page_content_builder,
        )
        instance.add_texts(texts, metadatas)
        return instance
[docs]    def get_indexes(self) -> List[Dict[str, str]]:
        """Helper to see your available indexes in marqo, useful if the
        from_texts method was used without an index name specified
        Returns:
            List[Dict[str, str]]: The list of indexes
        """
        return self._client.get_indexes()["results"]
[docs]    def get_number_of_documents(self) -> int:
        """Helper to see the number of documents in the index
        Returns:
            int: The number of documents
        """
        return self._client.index(self._index_name).get_stats()["numberOfDocuments"]


Source code for langchain.vectorstores.qdrant
"""Wrapper around Qdrant vector database."""
from __future__ import annotations
import asyncio
import functools
import uuid
import warnings
from itertools import islice
from operator import itemgetter
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Dict,
    Generator,
    Iterable,
    List,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
)
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
    from qdrant_client import grpc  # noqa
    from qdrant_client.conversions import common_types
    from qdrant_client.http import models as rest
    DictFilter = Dict[str, Union[str, int, bool, dict, list]]
    MetadataFilter = Union[DictFilter, common_types.Filter]
[docs]class QdrantException(Exception):
    """Base class for all the Qdrant related exceptions"""
[docs]def sync_call_fallback(method: Callable) -> Callable:
    """
    Decorator to call the synchronous method of the class if the async method is not
    implemented. This decorator might be only used for the methods that are defined
    as async in the class.
    """
    @functools.wraps(method)
    async def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
        try:
            return await method(self, *args, **kwargs)
        except NotImplementedError:
            # If the async method is not implemented, call the synchronous method


except NotImplementedError:
            # If the async method is not implemented, call the synchronous method
            # by removing the first letter from the method name. For example,
            # if the async method is called ``aaad_texts``, the synchronous method
            # will be called ``aad_texts``.
            sync_method = functools.partial(
                getattr(self, method.__name__[1:]), *args, **kwargs
            )
            return await asyncio.get_event_loop().run_in_executor(None, sync_method)
    return wrapper
[docs]class Qdrant(VectorStore):
    """Wrapper around Qdrant vector database.
    To use you should have the ``qdrant-client`` package installed.
    Example:
        .. code-block:: python
            from qdrant_client import QdrantClient
            from langchain import Qdrant
            client = QdrantClient()
            collection_name = "MyCollection"
            qdrant = Qdrant(client, collection_name, embedding_function)
    """
    CONTENT_KEY = "page_content"
    METADATA_KEY = "metadata"
    VECTOR_NAME = None
[docs]    def __init__(
        self,
        client: Any,
        collection_name: str,
        embeddings: Optional[Embeddings] = None,
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        distance_strategy: str = "COSINE",
        vector_name: Optional[str] = VECTOR_NAME,
        embedding_function: Optional[Callable] = None,  # deprecated
    ):
        """Initialize with necessary components."""
        try:
            import qdrant_client
        except ImportError:
            raise ValueError(
                "Could not import qdrant-client python package. "


raise ValueError(
                "Could not import qdrant-client python package. "
                "Please install it with `pip install qdrant-client`."
            )
        if not isinstance(client, qdrant_client.QdrantClient):
            raise ValueError(
                f"client should be an instance of qdrant_client.QdrantClient, "
                f"got {type(client)}"
            )
        if embeddings is None and embedding_function is None:
            raise ValueError(
                "`embeddings` value can't be None. Pass `Embeddings` instance."
            )
        if embeddings is not None and embedding_function is not None:
            raise ValueError(
                "Both `embeddings` and `embedding_function` are passed. "
                "Use `embeddings` only."
            )
        self._embeddings = embeddings
        self._embeddings_function = embedding_function
        self.client: qdrant_client.QdrantClient = client
        self.collection_name = collection_name
        self.content_payload_key = content_payload_key or self.CONTENT_KEY
        self.metadata_payload_key = metadata_payload_key or self.METADATA_KEY
        self.vector_name = vector_name or self.VECTOR_NAME
        if embedding_function is not None:
            warnings.warn(
                "Using `embedding_function` is deprecated. "
                "Pass `Embeddings` instance to `embeddings` instead."
            )
        if not isinstance(embeddings, Embeddings):
            warnings.warn(
                "`embeddings` should be an instance of `Embeddings`."
                "Using `embeddings` as `embedding_function` which is deprecated"
            )
            self._embeddings_function = embeddings
            self._embeddings = None
        self.distance_strategy = distance_strategy.upper()
    @property


self.distance_strategy = distance_strategy.upper()
    @property
    def embeddings(self) -> Optional[Embeddings]:
        return self._embeddings
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[Sequence[str]] = None,
        batch_size: int = 64,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
            ids:
                Optional list of ids to associate with the texts. Ids have to be
                uuid-like strings.
            batch_size:
                How many vectors upload per-request.
                Default: 64
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        added_ids = []
        for batch_ids, points in self._generate_rest_batches(
            texts, metadatas, ids, batch_size
        ):
            self.client.upsert(
                collection_name=self.collection_name, points=points, **kwargs
            )
            added_ids.extend(batch_ids)
        return added_ids
[docs]    @sync_call_fallback
    async def aadd_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[Sequence[str]] = None,
        batch_size: int = 64,
        **kwargs: Any,
    ) -> List[str]:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:


"""Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
            ids:
                Optional list of ids to associate with the texts. Ids have to be
                uuid-like strings.
            batch_size:
                How many vectors upload per-request.
                Default: 64
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        from qdrant_client import grpc  # noqa
        from qdrant_client.conversions.conversion import RestToGrpc
        added_ids = []
        for batch_ids, points in self._generate_rest_batches(
            texts, metadatas, ids, batch_size
        ):
            await self.client.async_grpc_points.Upsert(
                grpc.UpsertPoints(
                    collection_name=self.collection_name,
                    points=[RestToGrpc.convert_point_struct(point) for point in points],
                )
            )
            added_ids.extend(batch_ids)
        return added_ids
[docs]    def similarity_search(
        self,
        query: str,
        k: int = 4,
        filter: Optional[MetadataFilter] = None,
        search_params: Optional[common_types.SearchParams] = None,
        offset: int = 0,
        score_threshold: Optional[float] = None,
        consistency: Optional[common_types.ReadConsistency] = 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.


k: Number of Documents to return. Defaults to 4.
            filter: Filter by metadata. Defaults to None.
            search_params: Additional search params
            offset:
                Offset of the first result to return.
                May be used to paginate results.
                Note: large offset values may cause performance issues.
            score_threshold:
                Define a minimal score threshold for the result.
                If defined, less similar results will not be returned.
                Score of the returned result might be higher or smaller than the
                threshold depending on the Distance function used.
                E.g. for cosine similarity only higher scores will be returned.
            consistency:
                Read consistency of the search. Defines how many replicas should be
                queried before returning the result.
                Values:
                - int - number of replicas to query, values should present in all
                        queried replicas
                - 'majority' - query all replicas, but return values present in the
                               majority of replicas
                - 'quorum' - query the majority of replicas, return values present in
                             all of them
                - 'all' - query all replicas, and return values present in all replicas
        Returns:
            List of Documents most similar to the query.
        """
        results = self.similarity_search_with_score(
            query,
            k,
            filter=filter,
            search_params=search_params,
            offset=offset,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )
        return list(map(itemgetter(0), results))
[docs]    @sync_call_fallback
    async def asimilarity_search(
        self,
        query: str,
        k: int = 4,
        filter: Optional[MetadataFilter] = None,
        **kwargs: Any,


filter: Optional[MetadataFilter] = 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 by metadata. Defaults to None.
        Returns:
            List of Documents most similar to the query.
        """
        results = await self.asimilarity_search_with_score(query, k, filter, **kwargs)
        return list(map(itemgetter(0), results))
[docs]    def similarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        filter: Optional[MetadataFilter] = None,
        search_params: Optional[common_types.SearchParams] = None,
        offset: int = 0,
        score_threshold: Optional[float] = None,
        consistency: Optional[common_types.ReadConsistency] = None,
        **kwargs: Any,
    ) -> 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 by metadata. Defaults to None.
            search_params: Additional search params
            offset:
                Offset of the first result to return.
                May be used to paginate results.
                Note: large offset values may cause performance issues.
            score_threshold:
                Define a minimal score threshold for the result.
                If defined, less similar results will not be returned.
                Score of the returned result might be higher or smaller than the
                threshold depending on the Distance function used.


threshold depending on the Distance function used.
                E.g. for cosine similarity only higher scores will be returned.
            consistency:
                Read consistency of the search. Defines how many replicas should be
                queried before returning the result.
                Values:
                - int - number of replicas to query, values should present in all
                        queried replicas
                - 'majority' - query all replicas, but return values present in the
                               majority of replicas
                - 'quorum' - query the majority of replicas, return values present in
                             all of them
                - 'all' - query all replicas, and return values present in all replicas
        Returns:
            List of documents most similar to the query text and distance for each.
        """
        return self.similarity_search_with_score_by_vector(
            self._embed_query(query),
            k,
            filter=filter,
            search_params=search_params,
            offset=offset,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )
[docs]    @sync_call_fallback
    async def asimilarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        filter: Optional[MetadataFilter] = None,
        search_params: Optional[common_types.SearchParams] = None,
        offset: int = 0,
        score_threshold: Optional[float] = None,
        consistency: Optional[common_types.ReadConsistency] = None,
        **kwargs: Any,
    ) -> 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.


k: Number of Documents to return. Defaults to 4.
            filter: Filter by metadata. Defaults to None.
            search_params: Additional search params
            offset:
                Offset of the first result to return.
                May be used to paginate results.
                Note: large offset values may cause performance issues.
            score_threshold:
                Define a minimal score threshold for the result.
                If defined, less similar results will not be returned.
                Score of the returned result might be higher or smaller than the
                threshold depending on the Distance function used.
                E.g. for cosine similarity only higher scores will be returned.
            consistency:
                Read consistency of the search. Defines how many replicas should be
                queried before returning the result.
                Values:
                - int - number of replicas to query, values should present in all
                        queried replicas
                - 'majority' - query all replicas, but return values present in the
                               majority of replicas
                - 'quorum' - query the majority of replicas, return values present in
                             all of them
                - 'all' - query all replicas, and return values present in all replicas
        Returns:
            List of documents most similar to the query text and distance for each.
        """
        return await self.asimilarity_search_with_score_by_vector(
            self._embed_query(query),
            k,
            filter=filter,
            search_params=search_params,
            offset=offset,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )
[docs]    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[MetadataFilter] = None,


filter: Optional[MetadataFilter] = None,
        search_params: Optional[common_types.SearchParams] = None,
        offset: int = 0,
        score_threshold: Optional[float] = None,
        consistency: Optional[common_types.ReadConsistency] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector.
        Args:
            embedding: Embedding vector to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter by metadata. Defaults to None.
            search_params: Additional search params
            offset:
                Offset of the first result to return.
                May be used to paginate results.
                Note: large offset values may cause performance issues.
            score_threshold:
                Define a minimal score threshold for the result.
                If defined, less similar results will not be returned.
                Score of the returned result might be higher or smaller than the
                threshold depending on the Distance function used.
                E.g. for cosine similarity only higher scores will be returned.
            consistency:
                Read consistency of the search. Defines how many replicas should be
                queried before returning the result.
                Values:
                - int - number of replicas to query, values should present in all
                        queried replicas
                - 'majority' - query all replicas, but return values present in the
                               majority of replicas
                - 'quorum' - query the majority of replicas, return values present in
                             all of them
                - 'all' - query all replicas, and return values present in all replicas
        Returns:
            List of Documents most similar to the query.
        """
        results = self.similarity_search_with_score_by_vector(
            embedding,
            k,


embedding,
            k,
            filter=filter,
            search_params=search_params,
            offset=offset,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )
        return list(map(itemgetter(0), results))
[docs]    @sync_call_fallback
    async def asimilarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[MetadataFilter] = None,
        search_params: Optional[common_types.SearchParams] = None,
        offset: int = 0,
        score_threshold: Optional[float] = None,
        consistency: Optional[common_types.ReadConsistency] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector.
        Args:
            embedding: Embedding vector to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter by metadata. Defaults to None.
            search_params: Additional search params
            offset:
                Offset of the first result to return.
                May be used to paginate results.
                Note: large offset values may cause performance issues.
            score_threshold:
                Define a minimal score threshold for the result.
                If defined, less similar results will not be returned.
                Score of the returned result might be higher or smaller than the
                threshold depending on the Distance function used.
                E.g. for cosine similarity only higher scores will be returned.
            consistency:
                Read consistency of the search. Defines how many replicas should be
                queried before returning the result.
                Values:
                - int - number of replicas to query, values should present in all
                        queried replicas


- int - number of replicas to query, values should present in all
                        queried replicas
                - 'majority' - query all replicas, but return values present in the
                               majority of replicas
                - 'quorum' - query the majority of replicas, return values present in
                             all of them
                - 'all' - query all replicas, and return values present in all replicas
        Returns:
            List of Documents most similar to the query.
        """
        results = await self.asimilarity_search_with_score_by_vector(
            embedding,
            k,
            filter=filter,
            search_params=search_params,
            offset=offset,
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )
        return list(map(itemgetter(0), results))
[docs]    def similarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[MetadataFilter] = None,
        search_params: Optional[common_types.SearchParams] = None,
        offset: int = 0,
        score_threshold: Optional[float] = None,
        consistency: Optional[common_types.ReadConsistency] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to embedding vector.
        Args:
            embedding: Embedding vector to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter by metadata. Defaults to None.
            search_params: Additional search params
            offset:
                Offset of the first result to return.
                May be used to paginate results.
                Note: large offset values may cause performance issues.
            score_threshold:


Note: large offset values may cause performance issues.
            score_threshold:
                Define a minimal score threshold for the result.
                If defined, less similar results will not be returned.
                Score of the returned result might be higher or smaller than the
                threshold depending on the Distance function used.
                E.g. for cosine similarity only higher scores will be returned.
            consistency:
                Read consistency of the search. Defines how many replicas should be
                queried before returning the result.
                Values:
                - int - number of replicas to query, values should present in all
                        queried replicas
                - 'majority' - query all replicas, but return values present in the
                               majority of replicas
                - 'quorum' - query the majority of replicas, return values present in
                             all of them
                - 'all' - query all replicas, and return values present in all replicas
        Returns:
            List of documents most similar to the query text and distance for each.
        """
        if filter is not None and isinstance(filter, dict):
            warnings.warn(
                "Using dict as a `filter` is deprecated. Please use qdrant-client "
                "filters directly: "
                "https://qdrant.tech/documentation/concepts/filtering/",
                DeprecationWarning,
            )
            qdrant_filter = self._qdrant_filter_from_dict(filter)
        else:
            qdrant_filter = filter
        query_vector = embedding
        if self.vector_name is not None:
            query_vector = (self.vector_name, embedding)  # type: ignore[assignment]
        results = self.client.search(
            collection_name=self.collection_name,
            query_vector=query_vector,
            query_filter=qdrant_filter,
            search_params=search_params,
            limit=k,


search_params=search_params,
            limit=k,
            offset=offset,
            with_payload=True,
            with_vectors=False,  # Langchain does not expect vectors to be returned
            score_threshold=score_threshold,
            consistency=consistency,
            **kwargs,
        )
        return [
            (
                self._document_from_scored_point(
                    result, self.content_payload_key, self.metadata_payload_key
                ),
                result.score,
            )
            for result in results
        ]
[docs]    @sync_call_fallback
    async def asimilarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[MetadataFilter] = None,
        search_params: Optional[common_types.SearchParams] = None,
        offset: int = 0,
        score_threshold: Optional[float] = None,
        consistency: Optional[common_types.ReadConsistency] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to embedding vector.
        Args:
            embedding: Embedding vector to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: Filter by metadata. Defaults to None.
            search_params: Additional search params
            offset:
                Offset of the first result to return.
                May be used to paginate results.
                Note: large offset values may cause performance issues.
            score_threshold:
                Define a minimal score threshold for the result.
                If defined, less similar results will not be returned.
                Score of the returned result might be higher or smaller than the
                threshold depending on the Distance function used.


threshold depending on the Distance function used.
                E.g. for cosine similarity only higher scores will be returned.
            consistency:
                Read consistency of the search. Defines how many replicas should be
                queried before returning the result.
                Values:
                - int - number of replicas to query, values should present in all
                        queried replicas
                - 'majority' - query all replicas, but return values present in the
                               majority of replicas
                - 'quorum' - query the majority of replicas, return values present in
                             all of them
                - 'all' - query all replicas, and return values present in all replicas
        Returns:
            List of documents most similar to the query text and distance for each.
        """
        from qdrant_client import grpc  # noqa
        from qdrant_client.conversions.conversion import RestToGrpc
        from qdrant_client.http import models as rest
        if filter is not None and isinstance(filter, dict):
            warnings.warn(
                "Using dict as a `filter` is deprecated. Please use qdrant-client "
                "filters directly: "
                "https://qdrant.tech/documentation/concepts/filtering/",
                DeprecationWarning,
            )
            qdrant_filter = self._qdrant_filter_from_dict(filter)
        else:
            qdrant_filter = filter
        if qdrant_filter is not None and isinstance(qdrant_filter, rest.Filter):
            qdrant_filter = RestToGrpc.convert_filter(qdrant_filter)
        response = await self.client.async_grpc_points.Search(
            grpc.SearchPoints(
                collection_name=self.collection_name,
                vector_name=self.vector_name,
                vector=embedding,
                filter=qdrant_filter,
                params=search_params,
                limit=k,


filter=qdrant_filter,
                params=search_params,
                limit=k,
                offset=offset,
                with_payload=grpc.WithPayloadSelector(enable=True),
                with_vectors=grpc.WithVectorsSelector(enable=False),
                score_threshold=score_threshold,
                read_consistency=consistency,
                **kwargs,
            )
        )
        return [
            (
                self._document_from_scored_point_grpc(
                    result, self.content_payload_key, self.metadata_payload_key
                ),
                result.score,
            )
            for result in response.result
        ]
[docs]    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        **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.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        query_embedding = self._embed_query(query)
        return self.max_marginal_relevance_search_by_vector(
            query_embedding, k, fetch_k, lambda_mult, **kwargs
        )


query_embedding, k, fetch_k, lambda_mult, **kwargs
        )
[docs]    @sync_call_fallback
    async def amax_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        **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.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        query_embedding = self._embed_query(query)
        return await self.amax_marginal_relevance_search_by_vector(
            query_embedding, k, fetch_k, lambda_mult, **kwargs
        )
[docs]    def max_marginal_relevance_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        **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.


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.
            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.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        results = self.max_marginal_relevance_search_with_score_by_vector(
            embedding=embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, **kwargs
        )
        return list(map(itemgetter(0), results))
[docs]    @sync_call_fallback
    async def amax_marginal_relevance_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        **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.


to maximum diversity and 1 to minimum diversity.
                        Defaults to 0.5.
        Returns:
            List of Documents selected by maximal marginal relevance and distance for
            each.
        """
        results = await self.amax_marginal_relevance_search_with_score_by_vector(
            embedding, k, fetch_k, lambda_mult, **kwargs
        )
        return list(map(itemgetter(0), results))
[docs]    def max_marginal_relevance_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """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.
        Returns:
            List of Documents selected by maximal marginal relevance and distance for
            each.
        """
        query_vector = embedding
        if self.vector_name is not None:
            query_vector = (self.vector_name, query_vector)  # type: ignore[assignment]
        results = self.client.search(
            collection_name=self.collection_name,
            query_vector=query_vector,
            with_payload=True,


query_vector=query_vector,
            with_payload=True,
            with_vectors=True,
            limit=fetch_k,
        )
        embeddings = [
            result.vector.get(self.vector_name)  # type: ignore[index, union-attr]
            if self.vector_name is not None
            else result.vector
            for result in results
        ]
        mmr_selected = maximal_marginal_relevance(
            np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
        )
        return [
            (
                self._document_from_scored_point(
                    results[i], self.content_payload_key, self.metadata_payload_key
                ),
                results[i].score,
            )
            for i in mmr_selected
        ]
[docs]    @sync_call_fallback
    async def amax_marginal_relevance_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """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.
        Returns:


Defaults to 0.5.
        Returns:
            List of Documents selected by maximal marginal relevance and distance for
            each.
        """
        from qdrant_client import grpc  # noqa
        from qdrant_client.conversions.conversion import GrpcToRest
        response = await self.client.async_grpc_points.Search(
            grpc.SearchPoints(
                collection_name=self.collection_name,
                vector_name=self.vector_name,
                vector=embedding,
                with_payload=grpc.WithPayloadSelector(enable=True),
                with_vectors=grpc.WithVectorsSelector(enable=True),
                limit=fetch_k,
            )
        )
        results = [
            GrpcToRest.convert_vectors(result.vectors) for result in response.result
        ]
        embeddings: List[List[float]] = [
            result.get(self.vector_name)  # type: ignore
            if isinstance(result, dict)
            else result
            for result in results
        ]
        mmr_selected: List[int] = maximal_marginal_relevance(
            np.array(embedding),
            embeddings,
            k=k,
            lambda_mult=lambda_mult,
        )
        return [
            (
                self._document_from_scored_point_grpc(
                    response.result[i],
                    self.content_payload_key,
                    self.metadata_payload_key,
                ),
                response.result[i].score,
            )
            for i in mmr_selected
        ]
[docs]    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
        """Delete by vector ID or other criteria.
        Args:
            ids: List of ids to delete.
            **kwargs: Other keyword arguments that subclasses might use.
        Returns:
            Optional[bool]: True if deletion is successful,


Returns:
            Optional[bool]: True if deletion is successful,
            False otherwise, None if not implemented.
        """
        from qdrant_client.http import models as rest
        result = self.client.delete(
            collection_name=self.collection_name,
            points_selector=ids,
        )
        return result.status == rest.UpdateStatus.COMPLETED
[docs]    @classmethod
    def from_texts(
        cls: Type[Qdrant],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[Sequence[str]] = None,
        location: Optional[str] = None,
        url: Optional[str] = None,
        port: Optional[int] = 6333,
        grpc_port: int = 6334,
        prefer_grpc: bool = False,
        https: Optional[bool] = None,
        api_key: Optional[str] = None,
        prefix: Optional[str] = None,
        timeout: Optional[float] = None,
        host: Optional[str] = None,
        path: Optional[str] = None,
        collection_name: Optional[str] = None,
        distance_func: str = "Cosine",
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        vector_name: Optional[str] = VECTOR_NAME,
        batch_size: int = 64,
        shard_number: Optional[int] = None,
        replication_factor: Optional[int] = None,
        write_consistency_factor: Optional[int] = None,
        on_disk_payload: Optional[bool] = None,
        hnsw_config: Optional[common_types.HnswConfigDiff] = None,


hnsw_config: Optional[common_types.HnswConfigDiff] = None,
        optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
        wal_config: Optional[common_types.WalConfigDiff] = None,
        quantization_config: Optional[common_types.QuantizationConfig] = None,
        init_from: Optional[common_types.InitFrom] = None,
        on_disk: Optional[bool] = None,
        force_recreate: bool = False,
        **kwargs: Any,
    ) -> Qdrant:
        """Construct Qdrant wrapper from a list of texts.
        Args:
            texts: A list of texts to be indexed in Qdrant.
            embedding: A subclass of `Embeddings`, responsible for text vectorization.
            metadatas:
                An optional list of metadata. If provided it has to be of the same
                length as a list of texts.
            ids:
                Optional list of ids to associate with the texts. Ids have to be
                uuid-like strings.
            location:
                If `:memory:` - use in-memory Qdrant instance.
                If `str` - use it as a `url` parameter.
                If `None` - fallback to relying on `host` and `port` parameters.
            url: either host or str of "Optional[scheme], host, Optional[port],
                Optional[prefix]". Default: `None`
            port: Port of the REST API interface. Default: 6333
            grpc_port: Port of the gRPC interface. Default: 6334
            prefer_grpc:
                If true - use gPRC interface whenever possible in custom methods.
                Default: False
            https: If true - use HTTPS(SSL) protocol. Default: None


https: If true - use HTTPS(SSL) protocol. Default: None
            api_key: API key for authentication in Qdrant Cloud. Default: None
            prefix:
                If not None - add prefix to the REST URL path.
                Example: service/v1 will result in
                    http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
                Default: None
            timeout:
                Timeout for REST and gRPC API requests.
                Default: 5.0 seconds for REST and unlimited for gRPC
            host:
                Host name of Qdrant service. If url and host are None, set to
                'localhost'. Default: None
            path:
                Path in which the vectors will be stored while using local mode.
                Default: None
            collection_name:
                Name of the Qdrant collection to be used. If not provided,
                it will be created randomly. Default: None
            distance_func:
                Distance function. One of: "Cosine" / "Euclid" / "Dot".
                Default: "Cosine"
            content_payload_key:
                A payload key used to store the content of the document.
                Default: "page_content"
            metadata_payload_key:
                A payload key used to store the metadata of the document.
                Default: "metadata"
            vector_name:
                Name of the vector to be used internally in Qdrant.
                Default: None
            batch_size:
                How many vectors upload per-request.
                Default: 64
            shard_number: Number of shards in collection. Default is 1, minimum is 1.
            replication_factor:
                Replication factor for collection. Default is 1, minimum is 1.
                Defines how many copies of each shard will be created.


Defines how many copies of each shard will be created.
                Have effect only in distributed mode.
            write_consistency_factor:
                Write consistency factor for collection. Default is 1, minimum is 1.
                Defines how many replicas should apply the operation for us to consider
                it successful. Increasing this number will make the collection more
                resilient to inconsistencies, but will also make it fail if not enough
                replicas are available.
                Does not have any performance impact.
                Have effect only in distributed mode.
            on_disk_payload:
                If true - point`s payload will not be stored in memory.
                It will be read from the disk every time it is requested.
                This setting saves RAM by (slightly) increasing the response time.
                Note: those payload values that are involved in filtering and are
                indexed - remain in RAM.
            hnsw_config: Params for HNSW index
            optimizers_config: Params for optimizer
            wal_config: Params for Write-Ahead-Log
            quantization_config:
                Params for quantization, if None - quantization will be disabled
            init_from:
                Use data stored in another collection to initialize this collection
            force_recreate:
                Force recreating the collection
            **kwargs:
                Additional arguments passed directly into REST client initialization
        This is a user-friendly interface that:
        1. Creates embeddings, one for each text
        2. Initializes the Qdrant database as an in-memory docstore by default
           (and overridable to a remote docstore)
        3. Adds the text embeddings to the Qdrant database
        This is intended to be a quick way to get started.
        Example:
            .. code-block:: python
                from langchain import Qdrant


Example:
            .. code-block:: python
                from langchain import Qdrant
                from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
        """
        qdrant = cls._construct_instance(
            texts,
            embedding,
            location,
            url,
            port,
            grpc_port,
            prefer_grpc,
            https,
            api_key,
            prefix,
            timeout,
            host,
            path,
            collection_name,
            distance_func,
            content_payload_key,
            metadata_payload_key,
            vector_name,
            shard_number,
            replication_factor,
            write_consistency_factor,
            on_disk_payload,
            hnsw_config,
            optimizers_config,
            wal_config,
            quantization_config,
            init_from,
            on_disk,
            force_recreate,
            **kwargs,
        )
        qdrant.add_texts(texts, metadatas, ids, batch_size)
        return qdrant
[docs]    @classmethod
    @sync_call_fallback
    async def afrom_texts(
        cls: Type[Qdrant],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[Sequence[str]] = None,
        location: Optional[str] = None,
        url: Optional[str] = None,
        port: Optional[int] = 6333,
        grpc_port: int = 6334,
        prefer_grpc: bool = False,
        https: Optional[bool] = None,
        api_key: Optional[str] = None,


api_key: Optional[str] = None,
        prefix: Optional[str] = None,
        timeout: Optional[float] = None,
        host: Optional[str] = None,
        path: Optional[str] = None,
        collection_name: Optional[str] = None,
        distance_func: str = "Cosine",
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        vector_name: Optional[str] = VECTOR_NAME,
        batch_size: int = 64,
        shard_number: Optional[int] = None,
        replication_factor: Optional[int] = None,
        write_consistency_factor: Optional[int] = None,
        on_disk_payload: Optional[bool] = None,
        hnsw_config: Optional[common_types.HnswConfigDiff] = None,
        optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
        wal_config: Optional[common_types.WalConfigDiff] = None,
        quantization_config: Optional[common_types.QuantizationConfig] = None,
        init_from: Optional[common_types.InitFrom] = None,
        on_disk: Optional[bool] = None,
        force_recreate: bool = False,
        **kwargs: Any,
    ) -> Qdrant:
        """Construct Qdrant wrapper from a list of texts.
        Args:
            texts: A list of texts to be indexed in Qdrant.
            embedding: A subclass of `Embeddings`, responsible for text vectorization.
            metadatas:
                An optional list of metadata. If provided it has to be of the same
                length as a list of texts.
            ids:
                Optional list of ids to associate with the texts. Ids have to be


Optional list of ids to associate with the texts. Ids have to be
                uuid-like strings.
            location:
                If `:memory:` - use in-memory Qdrant instance.
                If `str` - use it as a `url` parameter.
                If `None` - fallback to relying on `host` and `port` parameters.
            url: either host or str of "Optional[scheme], host, Optional[port],
                Optional[prefix]". Default: `None`
            port: Port of the REST API interface. Default: 6333
            grpc_port: Port of the gRPC interface. Default: 6334
            prefer_grpc:
                If true - use gPRC interface whenever possible in custom methods.
                Default: False
            https: If true - use HTTPS(SSL) protocol. Default: None
            api_key: API key for authentication in Qdrant Cloud. Default: None
            prefix:
                If not None - add prefix to the REST URL path.
                Example: service/v1 will result in
                    http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
                Default: None
            timeout:
                Timeout for REST and gRPC API requests.
                Default: 5.0 seconds for REST and unlimited for gRPC
            host:
                Host name of Qdrant service. If url and host are None, set to
                'localhost'. Default: None
            path:
                Path in which the vectors will be stored while using local mode.
                Default: None
            collection_name:
                Name of the Qdrant collection to be used. If not provided,
                it will be created randomly. Default: None
            distance_func:


it will be created randomly. Default: None
            distance_func:
                Distance function. One of: "Cosine" / "Euclid" / "Dot".
                Default: "Cosine"
            content_payload_key:
                A payload key used to store the content of the document.
                Default: "page_content"
            metadata_payload_key:
                A payload key used to store the metadata of the document.
                Default: "metadata"
            vector_name:
                Name of the vector to be used internally in Qdrant.
                Default: None
            batch_size:
                How many vectors upload per-request.
                Default: 64
            shard_number: Number of shards in collection. Default is 1, minimum is 1.
            replication_factor:
                Replication factor for collection. Default is 1, minimum is 1.
                Defines how many copies of each shard will be created.
                Have effect only in distributed mode.
            write_consistency_factor:
                Write consistency factor for collection. Default is 1, minimum is 1.
                Defines how many replicas should apply the operation for us to consider
                it successful. Increasing this number will make the collection more
                resilient to inconsistencies, but will also make it fail if not enough
                replicas are available.
                Does not have any performance impact.
                Have effect only in distributed mode.
            on_disk_payload:
                If true - point`s payload will not be stored in memory.
                It will be read from the disk every time it is requested.
                This setting saves RAM by (slightly) increasing the response time.
                Note: those payload values that are involved in filtering and are
                indexed - remain in RAM.
            hnsw_config: Params for HNSW index
            optimizers_config: Params for optimizer


optimizers_config: Params for optimizer
            wal_config: Params for Write-Ahead-Log
            quantization_config:
                Params for quantization, if None - quantization will be disabled
            init_from:
                Use data stored in another collection to initialize this collection
            force_recreate:
                Force recreating the collection
            **kwargs:
                Additional arguments passed directly into REST client initialization
        This is a user-friendly interface that:
        1. Creates embeddings, one for each text
        2. Initializes the Qdrant database as an in-memory docstore by default
           (and overridable to a remote docstore)
        3. Adds the text embeddings to the Qdrant database
        This is intended to be a quick way to get started.
        Example:
            .. code-block:: python
                from langchain import Qdrant
                from langchain.embeddings import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                qdrant = await Qdrant.afrom_texts(texts, embeddings, "localhost")
        """
        qdrant = cls._construct_instance(
            texts,
            embedding,
            location,
            url,
            port,
            grpc_port,
            prefer_grpc,
            https,
            api_key,
            prefix,
            timeout,
            host,
            path,
            collection_name,
            distance_func,
            content_payload_key,
            metadata_payload_key,
            vector_name,
            shard_number,
            replication_factor,
            write_consistency_factor,
            on_disk_payload,
            hnsw_config,
            optimizers_config,
            wal_config,
            quantization_config,
            init_from,
            on_disk,
            force_recreate,
            **kwargs,
        )


on_disk,
            force_recreate,
            **kwargs,
        )
        await qdrant.aadd_texts(texts, metadatas, ids, batch_size)
        return qdrant
    @classmethod
    def _construct_instance(
        cls: Type[Qdrant],
        texts: List[str],
        embedding: Embeddings,
        location: Optional[str] = None,
        url: Optional[str] = None,
        port: Optional[int] = 6333,
        grpc_port: int = 6334,
        prefer_grpc: bool = False,
        https: Optional[bool] = None,
        api_key: Optional[str] = None,
        prefix: Optional[str] = None,
        timeout: Optional[float] = None,
        host: Optional[str] = None,
        path: Optional[str] = None,
        collection_name: Optional[str] = None,
        distance_func: str = "Cosine",
        content_payload_key: str = CONTENT_KEY,
        metadata_payload_key: str = METADATA_KEY,
        vector_name: Optional[str] = VECTOR_NAME,
        shard_number: Optional[int] = None,
        replication_factor: Optional[int] = None,
        write_consistency_factor: Optional[int] = None,
        on_disk_payload: Optional[bool] = None,
        hnsw_config: Optional[common_types.HnswConfigDiff] = None,
        optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
        wal_config: Optional[common_types.WalConfigDiff] = None,
        quantization_config: Optional[common_types.QuantizationConfig] = None,
        init_from: Optional[common_types.InitFrom] = None,


init_from: Optional[common_types.InitFrom] = None,
        on_disk: Optional[bool] = None,
        force_recreate: bool = False,
        **kwargs: Any,
    ) -> Qdrant:
        try:
            import qdrant_client
        except ImportError:
            raise ValueError(
                "Could not import qdrant-client python package. "
                "Please install it with `pip install qdrant-client`."
            )
        from grpc import RpcError
        from qdrant_client.http import models as rest
        from qdrant_client.http.exceptions import UnexpectedResponse
        # Just do a single quick embedding to get vector size
        partial_embeddings = embedding.embed_documents(texts[:1])
        vector_size = len(partial_embeddings[0])
        collection_name = collection_name or uuid.uuid4().hex
        distance_func = distance_func.upper()
        client = qdrant_client.QdrantClient(
            location=location,
            url=url,
            port=port,
            grpc_port=grpc_port,
            prefer_grpc=prefer_grpc,
            https=https,
            api_key=api_key,
            prefix=prefix,
            timeout=timeout,
            host=host,
            path=path,
            **kwargs,
        )
        try:
            # Skip any validation in case of forced collection recreate.
            if force_recreate:
                raise ValueError
            # Get the vector configuration of the existing collection and vector, if it
            # was specified. If the old configuration does not match the current one,
            # an exception is being thrown.
            collection_info = client.get_collection(collection_name=collection_name)
            current_vector_config = collection_info.config.params.vectors


current_vector_config = collection_info.config.params.vectors
            if isinstance(current_vector_config, dict) and vector_name is not None:
                if vector_name not in current_vector_config:
                    raise QdrantException(
                        f"Existing Qdrant collection {collection_name} does not "
                        f"contain vector named {vector_name}. Did you mean one of the "
                        f"existing vectors: {', '.join(current_vector_config.keys())}? "
                        f"If you want to recreate the collection, set `force_recreate` "
                        f"parameter to `True`."
                    )
                current_vector_config = current_vector_config.get(
                    vector_name
                )  # type: ignore[assignment]
            elif isinstance(current_vector_config, dict) and vector_name is None:
                raise QdrantException(
                    f"Existing Qdrant collection {collection_name} uses named vectors. "
                    f"If you want to reuse it, please set `vector_name` to any of the "
                    f"existing named vectors: "
                    f"{', '.join(current_vector_config.keys())}."  # noqa
                    f"If you want to recreate the collection, set `force_recreate` "
                    f"parameter to `True`."
                )
            elif (
                not isinstance(current_vector_config, dict) and vector_name is not None
            ):
                raise QdrantException(
                    f"Existing Qdrant collection {collection_name} doesn't use named "
                    f"vectors. If you want to reuse it, please set `vector_name` to "
                    f"`None`. If you want to recreate the collection, set "
                    f"`force_recreate` parameter to `True`."
                )
            # Check if the vector configuration has the same dimensionality.


)
            # Check if the vector configuration has the same dimensionality.
            if current_vector_config.size != vector_size:  # type: ignore[union-attr]
                raise QdrantException(
                    f"Existing Qdrant collection is configured for vectors with "
                    f"{current_vector_config.size} "  # type: ignore[union-attr]
                    f"dimensions. Selected embeddings are {vector_size}-dimensional. "
                    f"If you want to recreate the collection, set `force_recreate` "
                    f"parameter to `True`."
                )
            current_distance_func = (
                current_vector_config.distance.name.upper()  # type: ignore[union-attr]
            )
            if current_distance_func != distance_func:
                raise QdrantException(
                    f"Existing Qdrant collection is configured for "
                    f"{current_distance_func} similarity, but requested "
                    f"{distance_func}. Please set `distance_func` parameter to "
                    f"`{current_distance_func}` if you want to reuse it. "
                    f"If you want to recreate the collection, set `force_recreate` "
                    f"parameter to `True`."
                )
        except (UnexpectedResponse, RpcError, ValueError):
            vectors_config = rest.VectorParams(
                size=vector_size,
                distance=rest.Distance[distance_func],
                on_disk=on_disk,
            )
            # If vector name was provided, we're going to use the named vectors feature
            # with just a single vector.
            if vector_name is not None:
                vectors_config = {  # type: ignore[assignment]
                    vector_name: vectors_config,
                }
            client.recreate_collection(
                collection_name=collection_name,
                vectors_config=vectors_config,


collection_name=collection_name,
                vectors_config=vectors_config,
                shard_number=shard_number,
                replication_factor=replication_factor,
                write_consistency_factor=write_consistency_factor,
                on_disk_payload=on_disk_payload,
                hnsw_config=hnsw_config,
                optimizers_config=optimizers_config,
                wal_config=wal_config,
                quantization_config=quantization_config,
                init_from=init_from,
                timeout=timeout,  # type: ignore[arg-type]
            )
        qdrant = cls(
            client=client,
            collection_name=collection_name,
            embeddings=embedding,
            content_payload_key=content_payload_key,
            metadata_payload_key=metadata_payload_key,
            distance_strategy=distance_func,
            vector_name=vector_name,
        )
        return qdrant
    def _select_relevance_score_fn(self) -> Callable[[float], float]:
        """
        The 'correct' relevance function
        may differ depending on a few things, including:
        - the distance / similarity metric used by the VectorStore
        - the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
        - embedding dimensionality
        - etc.
        """
        if self.distance_strategy == "COSINE":
            return self._cosine_relevance_score_fn
        elif self.distance_strategy == "DOT":
            return self._max_inner_product_relevance_score_fn
        elif self.distance_strategy == "EUCLID":
            return self._euclidean_relevance_score_fn
        else:
            raise ValueError(
                "Unknown distance strategy, must be cosine, "
                "max_inner_product, or euclidean"
            )
    def _similarity_search_with_relevance_scores(


)
    def _similarity_search_with_relevance_scores(
        self,
        query: str,
        k: int = 4,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs and relevance scores in the range [0, 1].
        0 is dissimilar, 1 is most similar.
        Args:
            query: input text
            k: Number of Documents to return. Defaults to 4.
            **kwargs: kwargs to be passed to similarity search. Should include:
                score_threshold: Optional, a floating point value between 0 to 1 to
                    filter the resulting set of retrieved docs
        Returns:
            List of Tuples of (doc, similarity_score)
        """
        return self.similarity_search_with_score(query, k, **kwargs)
    @classmethod
    def _build_payloads(
        cls,
        texts: Iterable[str],
        metadatas: Optional[List[dict]],
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> List[dict]:
        payloads = []
        for i, text in enumerate(texts):
            if text is None:
                raise ValueError(
                    "At least one of the texts is None. Please remove it before "
                    "calling .from_texts or .add_texts on Qdrant instance."
                )
            metadata = metadatas[i] if metadatas is not None else None
            payloads.append(
                {
                    content_payload_key: text,
                    metadata_payload_key: metadata,
                }
            )
        return payloads
    @classmethod
    def _document_from_scored_point(
        cls,
        scored_point: Any,
        content_payload_key: str,


cls,
        scored_point: Any,
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> Document:
        return Document(
            page_content=scored_point.payload.get(content_payload_key),
            metadata=scored_point.payload.get(metadata_payload_key) or {},
        )
    @classmethod
    def _document_from_scored_point_grpc(
        cls,
        scored_point: Any,
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> Document:
        from qdrant_client.conversions.conversion import grpc_to_payload
        payload = grpc_to_payload(scored_point.payload)
        return Document(
            page_content=payload[content_payload_key],
            metadata=payload.get(metadata_payload_key) or {},
        )
    def _build_condition(self, key: str, value: Any) -> List[rest.FieldCondition]:
        from qdrant_client.http import models as rest
        out = []
        if isinstance(value, dict):
            for _key, value in value.items():
                out.extend(self._build_condition(f"{key}.{_key}", value))
        elif isinstance(value, list):
            for _value in value:
                if isinstance(_value, dict):
                    out.extend(self._build_condition(f"{key}[]", _value))
                else:
                    out.extend(self._build_condition(f"{key}", _value))
        else:
            out.append(
                rest.FieldCondition(
                    key=f"{self.metadata_payload_key}.{key}",
                    match=rest.MatchValue(value=value),
                )
            )
        return out
    def _qdrant_filter_from_dict(
        self, filter: Optional[DictFilter]
    ) -> Optional[rest.Filter]:


self, filter: Optional[DictFilter]
    ) -> Optional[rest.Filter]:
        from qdrant_client.http import models as rest
        if not filter:
            return None
        return rest.Filter(
            must=[
                condition
                for key, value in filter.items()
                for condition in self._build_condition(key, value)
            ]
        )
    def _embed_query(self, query: str) -> List[float]:
        """Embed query text.
        Used to provide backward compatibility with `embedding_function` argument.
        Args:
            query: Query text.
        Returns:
            List of floats representing the query embedding.
        """
        if self.embeddings is not None:
            embedding = self.embeddings.embed_query(query)
        else:
            if self._embeddings_function is not None:
                embedding = self._embeddings_function(query)
            else:
                raise ValueError("Neither of embeddings or embedding_function is set")
        return embedding.tolist() if hasattr(embedding, "tolist") else embedding
    def _embed_texts(self, texts: Iterable[str]) -> List[List[float]]:
        """Embed search texts.
        Used to provide backward compatibility with `embedding_function` argument.
        Args:
            texts: Iterable of texts to embed.
        Returns:
            List of floats representing the texts embedding.
        """
        if self.embeddings is not None:
            embeddings = self.embeddings.embed_documents(list(texts))
            if hasattr(embeddings, "tolist"):
                embeddings = embeddings.tolist()
        elif self._embeddings_function is not None:
            embeddings = []
            for text in texts:
                embedding = self._embeddings_function(text)
                if hasattr(embeddings, "tolist"):
                    embedding = embedding.tolist()


if hasattr(embeddings, "tolist"):
                    embedding = embedding.tolist()
                embeddings.append(embedding)
        else:
            raise ValueError("Neither of embeddings or embedding_function is set")
        return embeddings
    def _generate_rest_batches(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[Sequence[str]] = None,
        batch_size: int = 64,
    ) -> Generator[Tuple[List[str], List[rest.PointStruct]], None, None]:
        from qdrant_client.http import models as rest
        texts_iterator = iter(texts)
        metadatas_iterator = iter(metadatas or [])
        ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])
        while batch_texts := list(islice(texts_iterator, batch_size)):
            # Take the corresponding metadata and id for each text in a batch
            batch_metadatas = list(islice(metadatas_iterator, batch_size)) or None
            batch_ids = list(islice(ids_iterator, batch_size))
            # Generate the embeddings for all the texts in a batch
            batch_embeddings = self._embed_texts(batch_texts)
            points = [
                rest.PointStruct(
                    id=point_id,
                    vector=vector
                    if self.vector_name is None
                    else {self.vector_name: vector},
                    payload=payload,
                )
                for point_id, vector, payload in zip(
                    batch_ids,
                    batch_embeddings,
                    self._build_payloads(
                        batch_texts,
                        batch_metadatas,
                        self.content_payload_key,
                        self.metadata_payload_key,
                    ),
                )
            ]
            yield batch_ids, points


Source code for langchain.vectorstores.mongodb_atlas
from __future__ import annotations
import logging
from typing import (
    TYPE_CHECKING,
    Any,
    Dict,
    Generator,
    Iterable,
    List,
    Optional,
    Tuple,
    TypeVar,
    Union,
)
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
from langchain.vectorstores.utils import maximal_marginal_relevance
if TYPE_CHECKING:
    from pymongo.collection import Collection
MongoDBDocumentType = TypeVar("MongoDBDocumentType", bound=Dict[str, Any])
logger = logging.getLogger(__name__)
DEFAULT_INSERT_BATCH_SIZE = 100
[docs]class MongoDBAtlasVectorSearch(VectorStore):
    """Wrapper around MongoDB Atlas Vector Search.
    To use, you should have both:
    - the ``pymongo`` python package installed
    - a connection string associated with a MongoDB Atlas Cluster having deployed an
        Atlas Search index
    Example:
        .. code-block:: python
            from langchain.vectorstores import MongoDBAtlasVectorSearch
            from langchain.embeddings.openai import OpenAIEmbeddings
            from pymongo import MongoClient
            mongo_client = MongoClient("<YOUR-CONNECTION-STRING>")
            collection = mongo_client["<db_name>"]["<collection_name>"]
            embeddings = OpenAIEmbeddings()
            vectorstore = MongoDBAtlasVectorSearch(collection, embeddings)
    """
[docs]    def __init__(
        self,
        collection: Collection[MongoDBDocumentType],
        embedding: Embeddings,
        *,
        index_name: str = "default",
        text_key: str = "text",
        embedding_key: str = "embedding",
    ):


embedding_key: str = "embedding",
    ):
        """
        Args:
            collection: MongoDB collection to add the texts to.
            embedding: Text embedding model to use.
            text_key: MongoDB field that will contain the text for each
                document.
            embedding_key: MongoDB field that will contain the embedding for
                each document.
            index_name: Name of the Atlas Search index.
        """
        self._collection = collection
        self._embedding = embedding
        self._index_name = index_name
        self._text_key = text_key
        self._embedding_key = embedding_key
    @property
    def embeddings(self) -> Embeddings:
        return self._embedding
[docs]    @classmethod
    def from_connection_string(
        cls,
        connection_string: str,
        namespace: str,
        embedding: Embeddings,
        **kwargs: Any,
    ) -> MongoDBAtlasVectorSearch:
        try:
            from pymongo import MongoClient
        except ImportError:
            raise ImportError(
                "Could not import pymongo, please install it with "
                "`pip install pymongo`."
            )
        client: MongoClient = MongoClient(connection_string)
        db_name, collection_name = namespace.split(".")
        collection = client[db_name][collection_name]
        return cls(collection, embedding, **kwargs)
[docs]    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[Dict[str, Any]]] = None,
        **kwargs: Any,
    ) -> List:
        """Run more texts through the embeddings and add to the vectorstore.
        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.


metadatas: Optional list of metadatas associated with the texts.
        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        batch_size = kwargs.get("batch_size", DEFAULT_INSERT_BATCH_SIZE)
        _metadatas: Union[List, Generator] = metadatas or ({} for _ in texts)
        texts_batch = []
        metadatas_batch = []
        result_ids = []
        for i, (text, metadata) in enumerate(zip(texts, _metadatas)):
            texts_batch.append(text)
            metadatas_batch.append(metadata)
            if (i + 1) % batch_size == 0:
                result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
                texts_batch = []
                metadatas_batch = []
        if texts_batch:
            result_ids.extend(self._insert_texts(texts_batch, metadatas_batch))
        return result_ids
    def _insert_texts(self, texts: List[str], metadatas: List[Dict[str, Any]]) -> List:
        if not texts:
            return []
        # Embed and create the documents
        embeddings = self._embedding.embed_documents(texts)
        to_insert = [
            {self._text_key: t, self._embedding_key: embedding, **m}
            for t, m, embedding in zip(texts, metadatas, embeddings)
        ]
        # insert the documents in MongoDB Atlas
        insert_result = self._collection.insert_many(to_insert)  # type: ignore
        return insert_result.inserted_ids
    def _similarity_search_with_score(
        self,
        embedding: List[float],
        k: int = 4,
        pre_filter: Optional[dict] = None,


pre_filter: Optional[dict] = None,
        post_filter_pipeline: Optional[List[Dict]] = None,
    ) -> List[Tuple[Document, float]]:
        knn_beta = {
            "vector": embedding,
            "path": self._embedding_key,
            "k": k,
        }
        if pre_filter:
            knn_beta["filter"] = pre_filter
        pipeline = [
            {
                "$search": {
                    "index": self._index_name,
                    "knnBeta": knn_beta,
                }
            },
            {"$set": {"score": {"$meta": "searchScore"}}},
        ]
        if post_filter_pipeline is not None:
            pipeline.extend(post_filter_pipeline)
        cursor = self._collection.aggregate(pipeline)  # type: ignore[arg-type]
        docs = []
        for res in cursor:
            text = res.pop(self._text_key)
            score = res.pop("score")
            docs.append((Document(page_content=text, metadata=res), score))
        return docs
[docs]    def similarity_search_with_score(
        self,
        query: str,
        *,
        k: int = 4,
        pre_filter: Optional[dict] = None,
        post_filter_pipeline: Optional[List[Dict]] = None,
    ) -> List[Tuple[Document, float]]:
        """Return MongoDB documents most similar to query, along with scores.
        Use the knnBeta Operator available in MongoDB Atlas Search
        This feature is in early access and available only for evaluation purposes, to
        validate functionality, and to gather feedback from a small closed group of
        early access users. It is not recommended for production deployments as we
        may introduce breaking changes.


may introduce breaking changes.
        For more: https://www.mongodb.com/docs/atlas/atlas-search/knn-beta
        Args:
            query: Text to look up documents similar to.
            k: Optional Number of Documents to return. Defaults to 4.
            pre_filter: Optional Dictionary of argument(s) to prefilter on document
                fields.
            post_filter_pipeline: Optional Pipeline of MongoDB aggregation stages
                following the knnBeta search.
        Returns:
            List of Documents most similar to the query and score for each
        """
        embedding = self._embedding.embed_query(query)
        docs = self._similarity_search_with_score(
            embedding,
            k=k,
            pre_filter=pre_filter,
            post_filter_pipeline=post_filter_pipeline,
        )
        return docs
[docs]    def similarity_search(
        self,
        query: str,
        k: int = 4,
        pre_filter: Optional[dict] = None,
        post_filter_pipeline: Optional[List[Dict]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return MongoDB documents most similar to query.
        Use the knnBeta Operator available in MongoDB Atlas Search
        This feature is in early access and available only for evaluation purposes, to
        validate functionality, and to gather feedback from a small closed group of
        early access users. It is not recommended for production deployments as we may
        introduce breaking changes.
        For more: https://www.mongodb.com/docs/atlas/atlas-search/knn-beta
        Args:
            query: Text to look up documents similar to.
            k: Optional Number of Documents to return. Defaults to 4.
            pre_filter: Optional Dictionary of argument(s) to prefilter on document
                fields.


pre_filter: Optional Dictionary of argument(s) to prefilter on document
                fields.
            post_filter_pipeline: Optional Pipeline of MongoDB aggregation stages
                following the knnBeta search.
        Returns:
            List of Documents most similar to the query and score for each
        """
        docs_and_scores = self.similarity_search_with_score(
            query,
            k=k,
            pre_filter=pre_filter,
            post_filter_pipeline=post_filter_pipeline,
        )
        return [doc for doc, _ in docs_and_scores]
[docs]    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = 4,
        fetch_k: int = 20,
        lambda_mult: float = 0.5,
        pre_filter: Optional[dict] = None,
        post_filter_pipeline: Optional[List[Dict]] = 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: Optional Number of Documents to return. Defaults to 4.
            fetch_k: Optional Number of Documents to fetch before passing 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.
            pre_filter: Optional Dictionary of argument(s) to prefilter on document
                fields.
            post_filter_pipeline: Optional Pipeline of MongoDB aggregation stages
                following the knnBeta search.
        Returns:


following the knnBeta search.
        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        query_embedding = self._embedding.embed_query(query)
        docs = self._similarity_search_with_score(
            query_embedding,
            k=fetch_k,
            pre_filter=pre_filter,
            post_filter_pipeline=post_filter_pipeline,
        )
        mmr_doc_indexes = maximal_marginal_relevance(
            np.array(query_embedding),
            [doc.metadata[self._embedding_key] for doc, _ in docs],
            k=k,
            lambda_mult=lambda_mult,
        )
        mmr_docs = [docs[i][0] for i in mmr_doc_indexes]
        return mmr_docs
[docs]    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        collection: Optional[Collection[MongoDBDocumentType]] = None,
        **kwargs: Any,
    ) -> MongoDBAtlasVectorSearch:
        """Construct MongoDBAtlasVectorSearch wrapper from raw documents.
        This is a user-friendly interface that:
            1. Embeds documents.
            2. Adds the documents to a provided MongoDB Atlas Vector Search index
                (Lucene)
        This is intended to be a quick way to get started.
        Example:
            .. code-block:: python
                from pymongo import MongoClient
                from langchain.vectorstores import MongoDBAtlasVectorSearch
                from langchain.embeddings import OpenAIEmbeddings
                mongo_client = MongoClient("<YOUR-CONNECTION-STRING>")
                collection = mongo_client["<db_name>"]["<collection_name>"]
                embeddings = OpenAIEmbeddings()
                vectorstore = MongoDBAtlasVectorSearch.from_texts(


vectorstore = MongoDBAtlasVectorSearch.from_texts(
                    texts,
                    embeddings,
                    metadatas=metadatas,
                    collection=collection
                )
        """
        if collection is None:
            raise ValueError("Must provide 'collection' named parameter.")
        vectorstore = cls(collection, embedding, **kwargs)
        vectorstore.add_texts(texts, metadatas=metadatas)
        return vectorstore


Source code for langchain.vectorstores.hologres
"""VectorStore wrapper around a Hologres database."""
from __future__ import annotations
import json
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
from langchain.vectorstores.base import VectorStore
ADA_TOKEN_COUNT = 1536
_LANGCHAIN_DEFAULT_TABLE_NAME = "langchain_pg_embedding"
[docs]class HologresWrapper:
[docs]    def __init__(self, connection_string: str, ndims: int, table_name: str) -> None:
        import psycopg2
        self.table_name = table_name
        self.conn = psycopg2.connect(connection_string)
        self.cursor = self.conn.cursor()
        self.conn.autocommit = False
        self.ndims = ndims
[docs]    def create_vector_extension(self) -> None:
        self.cursor.execute("create extension if not exists proxima")
        self.conn.commit()
[docs]    def create_table(self, drop_if_exist: bool = True) -> None:
        if drop_if_exist:
            self.cursor.execute(f"drop table if exists {self.table_name}")
        self.conn.commit()
        self.cursor.execute(
            f"""create table if not exists {self.table_name} (
id text,
embedding float4[] check(array_ndims(embedding) = 1 and \
array_length(embedding, 1) = {self.ndims}),
metadata json,
document text);"""
        )
        self.cursor.execute(
            f"call set_table_property('{self.table_name}'"
            + """, 'proxima_vectors',
'{"embedding":{"algorithm":"Graph",


+ """, 'proxima_vectors',
'{"embedding":{"algorithm":"Graph",
"distance_method":"SquaredEuclidean",
"build_params":{"min_flush_proxima_row_count" : 1,
"min_compaction_proxima_row_count" : 1,
"max_total_size_to_merge_mb" : 2000}}}');"""
        )
        self.conn.commit()
[docs]    def get_by_id(self, id: str) -> List[Tuple]:
        statement = (
            f"select id, embedding, metadata, "
            f"document from {self.table_name} where id = %s;"
        )
        self.cursor.execute(
            statement,
            (id),
        )
        self.conn.commit()
        return self.cursor.fetchall()
[docs]    def insert(
        self,
        embedding: List[float],
        metadata: dict,
        document: str,
        id: Optional[str] = None,
    ) -> None:
        self.cursor.execute(
            f'insert into "{self.table_name}" '
            f"values (%s, array{json.dumps(embedding)}::float4[], %s, %s)",
            (id if id is not None else "null", json.dumps(metadata), document),
        )
        self.conn.commit()
[docs]    def query_nearest_neighbours(
        self, embedding: List[float], k: int, filter: Optional[Dict[str, str]] = None
    ) -> List[Tuple[str, str, float]]:
        params = []
        filter_clause = ""
        if filter is not None:
            conjuncts = []
            for key, val in filter.items():
                conjuncts.append("metadata->>%s=%s")
                params.append(key)


conjuncts.append("metadata->>%s=%s")
                params.append(key)
                params.append(val)
            filter_clause = "where " + " and ".join(conjuncts)
        sql = (
            f"select document, metadata::text, "
            f"pm_approx_squared_euclidean_distance(array{json.dumps(embedding)}"
            f"::float4[], embedding) as distance from"
            f" {self.table_name} {filter_clause} order by distance asc limit {k};"
        )
        self.cursor.execute(sql, tuple(params))
        self.conn.commit()
        return self.cursor.fetchall()
[docs]class Hologres(VectorStore):
    """VectorStore implementation using Hologres.
    - `connection_string` is a hologres connection string.
    - `embedding_function` any embedding function implementing
        `langchain.embeddings.base.Embeddings` interface.
    - `ndims` is the number of dimensions of the embedding output.
    - `table_name` is the name of the table to store embeddings and data.
        (default: langchain_pg_embedding)
        - NOTE: The table will be created when initializing the store (if not exists)
            So, make sure the user has the right permissions to create tables.
    - `pre_delete_table` if True, will delete the table if it exists.
        (default: False)
        - Useful for testing.
    """
[docs]    def __init__(
        self,
        connection_string: str,
        embedding_function: Embeddings,
        ndims: int = ADA_TOKEN_COUNT,
        table_name: str = _LANGCHAIN_DEFAULT_TABLE_NAME,
        pre_delete_table: bool = False,
        logger: Optional[logging.Logger] = None,
    ) -> None: