from __future__ import annotations

import logging
import operator
import os
import pickle
import uuid
import warnings
from pathlib import Path
from typing import (
    Any,
    Callable,
    Dict,
    Iterable,
    List,
    Optional,
    Sequence,
    Sized,
    Tuple,
    Union,
)

import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.runnables.config import run_in_executor
from langchain_core.vectorstores import VectorStore

from langchain_community.docstore.base import AddableMixin, Docstore
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain_community.vectorstores.utils import (
    DistanceStrategy,
    maximal_marginal_relevance,
)

logger = logging.getLogger(__name__)


def dependable_faiss_import(no_avx2: Optional[bool] = None) -> Any:
    """
    Import faiss if available, otherwise raise error.
    If FAISS_NO_AVX2 environment variable is set, it will be considered
    to load FAISS with no AVX2 optimization.

    Args:
        no_avx2: Load FAISS strictly with no AVX2 optimization
            so that the vectorstore is portable and compatible with other devices.
    """
    if no_avx2 is None and "FAISS_NO_AVX2" in os.environ:
        no_avx2 = bool(os.getenv("FAISS_NO_AVX2"))

    try:
        if no_avx2:
            from faiss import swigfaiss as faiss
        else:
            import faiss
    except ImportError:
        raise ImportError(
            "Could not import faiss python package. "
            "Please install it with `pip install faiss-gpu` (for CUDA supported GPU) "
            "or `pip install faiss-cpu` (depending on Python version)."
        )
    return faiss


def _len_check_if_sized(x: Any, y: Any, x_name: str, y_name: str) -> None:
    if isinstance(x, Sized) and isinstance(y, Sized) and len(x) != len(y):
        raise ValueError(
            f"{x_name} and {y_name} expected to be equal length but "
            f"len({x_name})={len(x)} and len({y_name})={len(y)}"
        )
    return


class FAISS(VectorStore):
    """FAISS vector store integration.

    See [The FAISS Library](https://arxiv.org/pdf/2401.08281) paper.

    Setup:
        Install ``langchain_community`` and ``faiss-cpu`` python packages.

        .. code-block:: bash

            pip install -qU langchain_community faiss-cpu

    Key init args — indexing params:
        embedding_function: Embeddings
            Embedding function to use.

    Key init args — client params:
        index: Any
            FAISS index to use.
        docstore: Docstore
            Docstore to use.
        index_to_docstore_id: Dict[int, str]
            Mapping of index to docstore id.

    Instantiate:
        .. code-block:: python

            import faiss
            from langchain_community.vectorstores import FAISS
            from langchain_community.docstore.in_memory import InMemoryDocstore
            from langchain_openai import OpenAIEmbeddings

            index = faiss.IndexFlatL2(len(OpenAIEmbeddings().embed_query("hello world")))

            vector_store = FAISS(
                embedding_function=OpenAIEmbeddings(),
                index=index,
                docstore= InMemoryDocstore(),
                index_to_docstore_id={}
            )

    Add Documents:
        .. code-block:: python

            from langchain_core.documents import Document

            document_1 = Document(page_content="foo", metadata={"baz": "bar"})
            document_2 = Document(page_content="thud", metadata={"bar": "baz"})
            document_3 = Document(page_content="i will be deleted :(")

            documents = [document_1, document_2, document_3]
            ids = ["1", "2", "3"]
            vector_store.add_documents(documents=documents, ids=ids)

    Delete Documents:
        .. code-block:: python

            vector_store.delete(ids=["3"])

    Search:
        .. code-block:: python

            results = vector_store.similarity_search(query="thud",k=1)
            for doc in results:
                print(f"* {doc.page_content} [{doc.metadata}]")

        .. code-block:: python

            * thud [{'bar': 'baz'}]

    Search with filter:
        .. code-block:: python

            results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
            for doc in results:
                print(f"* {doc.page_content} [{doc.metadata}]")

        .. code-block:: python

            * thud [{'bar': 'baz'}]

    Search with score:
        .. code-block:: python

            results = vector_store.similarity_search_with_score(query="qux",k=1)
            for doc, score in results:
                print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

        .. code-block:: python

            * [SIM=0.335304] foo [{'baz': 'bar'}]

    Async:
        .. code-block:: python

            # add documents
            # await vector_store.aadd_documents(documents=documents, ids=ids)

            # delete documents
            # await vector_store.adelete(ids=["3"])

            # search
            # results = vector_store.asimilarity_search(query="thud",k=1)

            # search with score
            results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
            for doc,score in results:
                print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

        .. code-block:: python

            * [SIM=0.335304] foo [{'baz': 'bar'}]

    Use as Retriever:
        .. code-block:: python

            retriever = vector_store.as_retriever(
                search_type="mmr",
                search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
            )
            retriever.invoke("thud")

        .. code-block:: python

            [Document(metadata={'bar': 'baz'}, page_content='thud')]

    """  # noqa: E501

    def __init__(
        self,
        embedding_function: Union[
            Callable[[str], List[float]],
            Embeddings,
        ],
        index: Any,
        docstore: Docstore,
        index_to_docstore_id: Dict[int, str],
        relevance_score_fn: Optional[Callable[[float], float]] = None,
        normalize_L2: bool = False,
        distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE,
    ):
        """Initialize with necessary components."""
        if not isinstance(embedding_function, Embeddings):
            logger.warning(
                "`embedding_function` is expected to be an Embeddings object, support "
                "for passing in a function will soon be removed."
            )
        self.embedding_function = embedding_function
        self.index = index
        self.docstore = docstore
        self.index_to_docstore_id = index_to_docstore_id
        self.distance_strategy = distance_strategy
        self.override_relevance_score_fn = relevance_score_fn
        self._normalize_L2 = normalize_L2
        if (
            self.distance_strategy != DistanceStrategy.EUCLIDEAN_DISTANCE
            and self._normalize_L2
        ):
            warnings.warn(
                "Normalizing L2 is not applicable for "
                f"metric type: {self.distance_strategy}"
            )

    @property
    def embeddings(self) -> Optional[Embeddings]:
        return (
            self.embedding_function
            if isinstance(self.embedding_function, Embeddings)
            else None
        )

    def _embed_documents(self, texts: List[str]) -> List[List[float]]:
        if isinstance(self.embedding_function, Embeddings):
            return self.embedding_function.embed_documents(texts)
        else:
            return [self.embedding_function(text) for text in texts]

    async def _aembed_documents(self, texts: List[str]) -> List[List[float]]:
        if isinstance(self.embedding_function, Embeddings):
            return await self.embedding_function.aembed_documents(texts)
        else:
            # return await asyncio.gather(
            #     [self.embedding_function(text) for text in texts]
            # )
            raise Exception(
                "`embedding_function` is expected to be an Embeddings object, support "
                "for passing in a function will soon be removed."
            )

    def _embed_query(self, text: str) -> List[float]:
        if isinstance(self.embedding_function, Embeddings):
            return self.embedding_function.embed_query(text)
        else:
            return self.embedding_function(text)

    async def _aembed_query(self, text: str) -> List[float]:
        if isinstance(self.embedding_function, Embeddings):
            return await self.embedding_function.aembed_query(text)
        else:
            # return await self.embedding_function(text)
            raise Exception(
                "`embedding_function` is expected to be an Embeddings object, support "
                "for passing in a function will soon be removed."
            )

    def __add(
        self,
        texts: Iterable[str],
        embeddings: Iterable[List[float]],
        metadatas: Optional[Iterable[dict]] = None,
        ids: Optional[List[str]] = None,
    ) -> List[str]:
        faiss = dependable_faiss_import()
        if not isinstance(self.docstore, AddableMixin):
            raise ValueError(
                "If trying to add texts, the underlying docstore should support "
                f"adding items, which {self.docstore} does not"
            )

        _len_check_if_sized(texts, metadatas, "texts", "metadatas")

        ids = ids or [str(uuid.uuid4()) for _ in texts]
        _len_check_if_sized(texts, ids, "texts", "ids")

        _metadatas = metadatas or ({} for _ in texts)
        documents = [
            Document(id=id_, page_content=t, metadata=m)
            for id_, t, m in zip(ids, texts, _metadatas)
        ]

        _len_check_if_sized(documents, embeddings, "documents", "embeddings")

        if ids and len(ids) != len(set(ids)):
            raise ValueError("Duplicate ids found in the ids list.")
        # Add to the index.
        vector = np.array(embeddings, dtype=np.float32)
        if self._normalize_L2:
            faiss.normalize_L2(vector)
        self.index.add(vector)

        # Add information to docstore and index.
        self.docstore.add({id_: doc for id_, doc in zip(ids, documents)})
        starting_len = len(self.index_to_docstore_id)
        index_to_id = {starting_len + j: id_ for j, id_ in enumerate(ids)}
        self.index_to_docstore_id.update(index_to_id)
        return ids

    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **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 unique IDs.

        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        texts = list(texts)
        embeddings = self._embed_documents(texts)
        return self.__add(texts, embeddings, metadatas=metadatas, ids=ids)

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

        Args:
            texts: Iterable of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
            ids: Optional list of unique IDs.

        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        texts = list(texts)
        embeddings = await self._aembed_documents(texts)
        return self.__add(texts, embeddings, metadatas=metadatas, ids=ids)

    def add_embeddings(
        self,
        text_embeddings: Iterable[Tuple[str, List[float]]],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[str]:
        """Add the given texts and embeddings to the vectorstore.

        Args:
            text_embeddings: Iterable pairs of string and embedding to
                add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
            ids: Optional list of unique IDs.

        Returns:
            List of ids from adding the texts into the vectorstore.
        """
        # Embed and create the documents.
        texts, embeddings = zip(*text_embeddings)
        return self.__add(texts, embeddings, metadatas=metadatas, ids=ids)

    def similarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Union[Callable, Dict[str, Any]]] = None,
        fetch_k: int = 20,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query.

        Args:
            embedding: Embedding vector to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter (Optional[Union[Callable, Dict[str, Any]]]): Filter by metadata.
                Defaults to None. If a callable, it must take as input the
                metadata dict of Document and return a bool.
            fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
                      Defaults to 20.
            **kwargs: kwargs to be passed to similarity search. Can include:
                score_threshold: Optional, a floating point value between 0 to 1 to
                    filter the resulting set of retrieved docs

        Returns:
            List of documents most similar to the query text and L2 distance
            in float for each. Lower score represents more similarity.
        """
        faiss = dependable_faiss_import()
        vector = np.array([embedding], dtype=np.float32)
        if self._normalize_L2:
            faiss.normalize_L2(vector)
        scores, indices = self.index.search(vector, k if filter is None else fetch_k)
        docs = []

        if filter is not None:
            filter_func = self._create_filter_func(filter)

        for j, i in enumerate(indices[0]):
            if i == -1:
                # This happens when not enough docs are returned.
                continue
            _id = self.index_to_docstore_id[i]
            doc = self.docstore.search(_id)
            if not isinstance(doc, Document):
                raise ValueError(f"Could not find document for id {_id}, got {doc}")
            if filter is not None:
                if filter_func(doc.metadata):
                    docs.append((doc, scores[0][j]))
            else:
                docs.append((doc, scores[0][j]))

        score_threshold = kwargs.get("score_threshold")
        if score_threshold is not None:
            cmp = (
                operator.ge
                if self.distance_strategy
                in (DistanceStrategy.MAX_INNER_PRODUCT, DistanceStrategy.JACCARD)
                else operator.le
            )
            docs = [
                (doc, similarity)
                for doc, similarity in docs
                if cmp(similarity, score_threshold)
            ]
        return docs[:k]

    async def asimilarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Union[Callable, Dict[str, Any]]] = None,
        fetch_k: int = 20,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query asynchronously.

        Args:
            embedding: Embedding vector to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter (Optional[Dict[str, Any]]): Filter by metadata.
                Defaults to None. If a callable, it must take as input the
                metadata dict of Document and return a bool.

            fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
                      Defaults to 20.
            **kwargs: kwargs to be passed to similarity search. Can include:
                score_threshold: Optional, a floating point value between 0 to 1 to
                    filter the resulting set of retrieved docs

        Returns:
            List of documents most similar to the query text and L2 distance
            in float for each. Lower score represents more similarity.
        """

        # This is a temporary workaround to make the similarity search asynchronous.
        return await run_in_executor(
            None,
            self.similarity_search_with_score_by_vector,
            embedding,
            k=k,
            filter=filter,
            fetch_k=fetch_k,
            **kwargs,
        )

    def similarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Union[Callable, Dict[str, Any]]] = None,
        fetch_k: int = 20,
        **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 (Optional[Dict[str, str]]): Filter by metadata.
                Defaults to None. If a callable, it must take as input the
                metadata dict of Document and return a bool.

            fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
                      Defaults to 20.

        Returns:
            List of documents most similar to the query text with
            L2 distance in float. Lower score represents more similarity.
        """
        embedding = self._embed_query(query)
        docs = self.similarity_search_with_score_by_vector(
            embedding,
            k,
            filter=filter,
            fetch_k=fetch_k,
            **kwargs,
        )
        return docs

    async def asimilarity_search_with_score(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Union[Callable, Dict[str, Any]]] = None,
        fetch_k: int = 20,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs most similar to query asynchronously.

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter (Optional[Dict[str, str]]): Filter by metadata.
                Defaults to None. If a callable, it must take as input the
                metadata dict of Document and return a bool.

            fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
                      Defaults to 20.

        Returns:
            List of documents most similar to the query text with
            L2 distance in float. Lower score represents more similarity.
        """
        embedding = await self._aembed_query(query)
        docs = await self.asimilarity_search_with_score_by_vector(
            embedding,
            k,
            filter=filter,
            fetch_k=fetch_k,
            **kwargs,
        )
        return docs

    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Dict[str, Any]] = None,
        fetch_k: int = 20,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter (Optional[Dict[str, str]]): Filter by metadata.
                Defaults to None. If a callable, it must take as input the
                metadata dict of Document and return a bool.

            fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
                      Defaults to 20.

        Returns:
            List of Documents most similar to the embedding.
        """
        docs_and_scores = self.similarity_search_with_score_by_vector(
            embedding,
            k,
            filter=filter,
            fetch_k=fetch_k,
            **kwargs,
        )
        return [doc for doc, _ in docs_and_scores]

    async def asimilarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        filter: Optional[Union[Callable, Dict[str, Any]]] = None,
        fetch_k: int = 20,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector asynchronously.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter (Optional[Dict[str, str]]): Filter by metadata.
                Defaults to None. If a callable, it must take as input the
                metadata dict of Document and return a bool.

            fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
                      Defaults to 20.

        Returns:
            List of Documents most similar to the embedding.
        """
        docs_and_scores = await self.asimilarity_search_with_score_by_vector(
            embedding,
            k,
            filter=filter,
            fetch_k=fetch_k,
            **kwargs,
        )
        return [doc for doc, _ in docs_and_scores]

    def similarity_search(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Union[Callable, Dict[str, Any]]] = None,
        fetch_k: int = 20,
        **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: (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
            fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
                      Defaults to 20.

        Returns:
            List of Documents most similar to the query.
        """
        docs_and_scores = self.similarity_search_with_score(
            query, k, filter=filter, fetch_k=fetch_k, **kwargs
        )
        return [doc for doc, _ in docs_and_scores]

    async def asimilarity_search(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Union[Callable, Dict[str, Any]]] = None,
        fetch_k: int = 20,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to query asynchronously.

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            filter: (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
            fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
                      Defaults to 20.

        Returns:
            List of Documents most similar to the query.
        """
        docs_and_scores = await self.asimilarity_search_with_score(
            query, k, filter=filter, fetch_k=fetch_k, **kwargs
        )
        return [doc for doc, _ in docs_and_scores]

    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,
        filter: Optional[Union[Callable, Dict[str, Any]]] = None,
    ) -> List[Tuple[Document, float]]:
        """Return docs and their similarity scores selected using the maximal marginal
            relevance.

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

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch before filtering 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 and similarity scores selected by maximal marginal
                relevance and score for each.
        """
        scores, indices = self.index.search(
            np.array([embedding], dtype=np.float32),
            fetch_k if filter is None else fetch_k * 2,
        )
        if filter is not None:
            filter_func = self._create_filter_func(filter)
            filtered_indices = []
            for i in indices[0]:
                if i == -1:
                    # This happens when not enough docs are returned.
                    continue
                _id = self.index_to_docstore_id[i]
                doc = self.docstore.search(_id)
                if not isinstance(doc, Document):
                    raise ValueError(f"Could not find document for id {_id}, got {doc}")
                if filter_func(doc.metadata):
                    filtered_indices.append(i)
            indices = np.array([filtered_indices])
        # -1 happens when not enough docs are returned.
        embeddings = [self.index.reconstruct(int(i)) for i in indices[0] if i != -1]
        mmr_selected = maximal_marginal_relevance(
            np.array([embedding], dtype=np.float32),
            embeddings,
            k=k,
            lambda_mult=lambda_mult,
        )

        docs_and_scores = []
        for i in mmr_selected:
            if indices[0][i] == -1:
                # This happens when not enough docs are returned.
                continue
            _id = self.index_to_docstore_id[indices[0][i]]
            doc = self.docstore.search(_id)
            if not isinstance(doc, Document):
                raise ValueError(f"Could not find document for id {_id}, got {doc}")
            docs_and_scores.append((doc, scores[0][i]))

        return docs_and_scores

    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,
        filter: Optional[Union[Callable, Dict[str, Any]]] = None,
    ) -> List[Tuple[Document, float]]:
        """Return docs and their similarity scores selected using the maximal marginal
            relevance asynchronously.

        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 before filtering 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 and similarity scores selected by maximal marginal
                relevance and score for each.
        """
        # This is a temporary workaround to make the similarity search asynchronous.
        return await run_in_executor(
            None,
            self.max_marginal_relevance_search_with_score_by_vector,
            embedding,
            k=k,
            fetch_k=fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
        )

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

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

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch before filtering 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.
        """
        docs_and_scores = self.max_marginal_relevance_search_with_score_by_vector(
            embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, filter=filter
        )
        return [doc for doc, _ in docs_and_scores]

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

        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 before filtering 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.
        """
        docs_and_scores = (
            await self.amax_marginal_relevance_search_with_score_by_vector(
                embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, filter=filter
            )
        )
        return [doc for doc, _ in docs_and_scores]

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

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

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch before filtering (if needed) 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.
        """
        embedding = self._embed_query(query)
        docs = self.max_marginal_relevance_search_by_vector(
            embedding,
            k=k,
            fetch_k=fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
            **kwargs,
        )
        return docs

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

        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 before filtering (if needed) 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.
        """
        embedding = await self._aembed_query(query)
        docs = await self.amax_marginal_relevance_search_by_vector(
            embedding,
            k=k,
            fetch_k=fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
            **kwargs,
        )
        return docs

    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
        """Delete by ID. These are the IDs in the vectorstore.

        Args:
            ids: List of ids to delete.

        Returns:
            Optional[bool]: True if deletion is successful,
            False otherwise, None if not implemented.
        """
        if ids is None:
            raise ValueError("No ids provided to delete.")
        missing_ids = set(ids).difference(self.index_to_docstore_id.values())
        if missing_ids:
            raise ValueError(
                f"Some specified ids do not exist in the current store. Ids not found: "
                f"{missing_ids}"
            )

        reversed_index = {id_: idx for idx, id_ in self.index_to_docstore_id.items()}
        index_to_delete = {reversed_index[id_] for id_ in ids}

        self.index.remove_ids(np.fromiter(index_to_delete, dtype=np.int64))
        self.docstore.delete(ids)

        remaining_ids = [
            id_
            for i, id_ in sorted(self.index_to_docstore_id.items())
            if i not in index_to_delete
        ]
        self.index_to_docstore_id = {i: id_ for i, id_ in enumerate(remaining_ids)}

        return True

    def merge_from(self, target: FAISS) -> None:
        """Merge another FAISS object with the current one.

        Add the target FAISS to the current one.

        Args:
            target: FAISS object you wish to merge into the current one

        Returns:
            None.
        """
        if not isinstance(self.docstore, AddableMixin):
            raise ValueError("Cannot merge with this type of docstore")
        # Numerical index for target docs are incremental on existing ones
        starting_len = len(self.index_to_docstore_id)

        # Merge two IndexFlatL2
        self.index.merge_from(target.index)

        # Get id and docs from target FAISS object
        full_info = []
        for i, target_id in target.index_to_docstore_id.items():
            doc = target.docstore.search(target_id)
            if not isinstance(doc, Document):
                raise ValueError("Document should be returned")
            full_info.append((starting_len + i, target_id, doc))

        # Add information to docstore and index_to_docstore_id.
        self.docstore.add({_id: doc for _, _id, doc in full_info})
        index_to_id = {index: _id for index, _id, _ in full_info}
        self.index_to_docstore_id.update(index_to_id)

    @classmethod
    def __from(
        cls,
        texts: Iterable[str],
        embeddings: List[List[float]],
        embedding: Embeddings,
        metadatas: Optional[Iterable[dict]] = None,
        ids: Optional[List[str]] = None,
        normalize_L2: bool = False,
        distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE,
        **kwargs: Any,
    ) -> FAISS:
        faiss = dependable_faiss_import()
        if distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
            index = faiss.IndexFlatIP(len(embeddings[0]))
        else:
            # Default to L2, currently other metric types not initialized.
            index = faiss.IndexFlatL2(len(embeddings[0]))
        docstore = kwargs.pop("docstore", InMemoryDocstore())
        index_to_docstore_id = kwargs.pop("index_to_docstore_id", {})
        vecstore = cls(
            embedding,
            index,
            docstore,
            index_to_docstore_id,
            normalize_L2=normalize_L2,
            distance_strategy=distance_strategy,
            **kwargs,
        )
        vecstore.__add(texts, embeddings, metadatas=metadatas, ids=ids)
        return vecstore

    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> FAISS:
        """Construct FAISS wrapper from raw documents.

        This is a user friendly interface that:
            1. Embeds documents.
            2. Creates an in memory docstore
            3. Initializes the FAISS database

        This is intended to be a quick way to get started.

        Example:
            .. code-block:: python

                from langchain_community.vectorstores import FAISS
                from langchain_community.embeddings import OpenAIEmbeddings

                embeddings = OpenAIEmbeddings()
                faiss = FAISS.from_texts(texts, embeddings)
        """
        embeddings = embedding.embed_documents(texts)
        return cls.__from(
            texts,
            embeddings,
            embedding,
            metadatas=metadatas,
            ids=ids,
            **kwargs,
        )

    @classmethod
    async def afrom_texts(
        cls,
        texts: list[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> FAISS:
        """Construct FAISS wrapper from raw documents asynchronously.

        This is a user friendly interface that:
            1. Embeds documents.
            2. Creates an in memory docstore
            3. Initializes the FAISS database

        This is intended to be a quick way to get started.

        Example:
            .. code-block:: python

                from langchain_community.vectorstores import FAISS
                from langchain_community.embeddings import OpenAIEmbeddings

                embeddings = OpenAIEmbeddings()
                faiss = await FAISS.afrom_texts(texts, embeddings)
        """
        embeddings = await embedding.aembed_documents(texts)
        return cls.__from(
            texts,
            embeddings,
            embedding,
            metadatas=metadatas,
            ids=ids,
            **kwargs,
        )

    @classmethod
    def from_embeddings(
        cls,
        text_embeddings: Iterable[Tuple[str, List[float]]],
        embedding: Embeddings,
        metadatas: Optional[Iterable[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> FAISS:
        """Construct FAISS wrapper from raw documents.

        This is a user friendly interface that:
            1. Embeds documents.
            2. Creates an in memory docstore
            3. Initializes the FAISS database

        This is intended to be a quick way to get started.

        Example:
            .. code-block:: python

                from langchain_community.vectorstores import FAISS
                from langchain_community.embeddings import OpenAIEmbeddings

                embeddings = OpenAIEmbeddings()
                text_embeddings = embeddings.embed_documents(texts)
                text_embedding_pairs = zip(texts, text_embeddings)
                faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings)
        """
        texts, embeddings = zip(*text_embeddings)
        return cls.__from(
            list(texts),
            list(embeddings),
            embedding,
            metadatas=metadatas,
            ids=ids,
            **kwargs,
        )

    @classmethod
    async def afrom_embeddings(
        cls,
        text_embeddings: Iterable[Tuple[str, List[float]]],
        embedding: Embeddings,
        metadatas: Optional[Iterable[dict]] = None,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> FAISS:
        """Construct FAISS wrapper from raw documents asynchronously."""
        return cls.from_embeddings(
            text_embeddings,
            embedding,
            metadatas=metadatas,
            ids=ids,
            **kwargs,
        )

    def save_local(self, folder_path: str, index_name: str = "index") -> None:
        """Save FAISS index, docstore, and index_to_docstore_id to disk.

        Args:
            folder_path: folder path to save index, docstore,
                and index_to_docstore_id to.
            index_name: for saving with a specific index file name
        """
        path = Path(folder_path)
        path.mkdir(exist_ok=True, parents=True)

        # save index separately since it is not picklable
        faiss = dependable_faiss_import()
        faiss.write_index(self.index, str(path / f"{index_name}.faiss"))

        # save docstore and index_to_docstore_id
        with open(path / f"{index_name}.pkl", "wb") as f:
            pickle.dump((self.docstore, self.index_to_docstore_id), f)

    @classmethod
    def load_local(
        cls,
        folder_path: str,
        embeddings: Embeddings,
        index_name: str = "index",
        *,
        allow_dangerous_deserialization: bool = False,
        **kwargs: Any,
    ) -> FAISS:
        """Load FAISS index, docstore, and index_to_docstore_id from disk.

        Args:
            folder_path: folder path to load index, docstore,
                and index_to_docstore_id from.
            embeddings: Embeddings to use when generating queries
            index_name: for saving with a specific index file name
            allow_dangerous_deserialization: whether to allow deserialization
                of the data which involves loading a pickle file.
                Pickle files can be modified by malicious actors to deliver a
                malicious payload that results in execution of
                arbitrary code on your machine.
        """
        if not allow_dangerous_deserialization:
            raise ValueError(
                "The de-serialization relies loading a pickle file. "
                "Pickle files can be modified to deliver a malicious payload that "
                "results in execution of arbitrary code on your machine."
                "You will need to set `allow_dangerous_deserialization` to `True` to "
                "enable deserialization. If you do this, make sure that you "
                "trust the source of the data. For example, if you are loading a "
                "file that you created, and know that no one else has modified the "
                "file, then this is safe to do. Do not set this to `True` if you are "
                "loading a file from an untrusted source (e.g., some random site on "
                "the internet.)."
            )
        path = Path(folder_path)
        # load index separately since it is not picklable
        faiss = dependable_faiss_import()
        index = faiss.read_index(str(path / f"{index_name}.faiss"))

        # load docstore and index_to_docstore_id
        with open(path / f"{index_name}.pkl", "rb") as f:
            (
                docstore,
                index_to_docstore_id,
            ) = pickle.load(  # ignore[pickle]: explicit-opt-in
                f
            )

        return cls(embeddings, index, docstore, index_to_docstore_id, **kwargs)

    def serialize_to_bytes(self) -> bytes:
        """Serialize FAISS index, docstore, and index_to_docstore_id to bytes."""
        return pickle.dumps((self.index, self.docstore, self.index_to_docstore_id))

    @classmethod
    def deserialize_from_bytes(
        cls,
        serialized: bytes,
        embeddings: Embeddings,
        *,
        allow_dangerous_deserialization: bool = False,
        **kwargs: Any,
    ) -> FAISS:
        """Deserialize FAISS index, docstore, and index_to_docstore_id from bytes."""
        if not allow_dangerous_deserialization:
            raise ValueError(
                "The de-serialization relies loading a pickle file. "
                "Pickle files can be modified to deliver a malicious payload that "
                "results in execution of arbitrary code on your machine."
                "You will need to set `allow_dangerous_deserialization` to `True` to "
                "enable deserialization. If you do this, make sure that you "
                "trust the source of the data. For example, if you are loading a "
                "file that you created, and know that no one else has modified the "
                "file, then this is safe to do. Do not set this to `True` if you are "
                "loading a file from an untrusted source (e.g., some random site on "
                "the internet.)."
            )
        (
            index,
            docstore,
            index_to_docstore_id,
        ) = pickle.loads(  # ignore[pickle]: explicit-opt-in
            serialized
        )
        return cls(embeddings, index, docstore, index_to_docstore_id, **kwargs)

    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.override_relevance_score_fn is not None:
            return self.override_relevance_score_fn

        # Default strategy is to rely on distance strategy provided in
        # vectorstore constructor
        if self.distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
            return self._max_inner_product_relevance_score_fn
        elif self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
            # Default behavior is to use euclidean distance relevancy
            return self._euclidean_relevance_score_fn
        elif self.distance_strategy == DistanceStrategy.COSINE:
            return self._cosine_relevance_score_fn
        else:
            raise ValueError(
                "Unknown distance strategy, must be cosine, max_inner_product,"
                " or euclidean"
            )

    def _similarity_search_with_relevance_scores(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Union[Callable, Dict[str, Any]]] = None,
        fetch_k: int = 20,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs and their similarity scores on a scale from 0 to 1."""
        # Pop score threshold so that only relevancy scores, not raw scores, are
        # filtered.
        relevance_score_fn = self._select_relevance_score_fn()
        if relevance_score_fn is None:
            raise ValueError(
                "relevance_score_fn must be provided to"
                " FAISS constructor to normalize scores"
            )
        docs_and_scores = self.similarity_search_with_score(
            query,
            k=k,
            filter=filter,
            fetch_k=fetch_k,
            **kwargs,
        )
        docs_and_rel_scores = [
            (doc, relevance_score_fn(score)) for doc, score in docs_and_scores
        ]
        return docs_and_rel_scores

    async def _asimilarity_search_with_relevance_scores(
        self,
        query: str,
        k: int = 4,
        filter: Optional[Union[Callable, Dict[str, Any]]] = None,
        fetch_k: int = 20,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs and their similarity scores on a scale from 0 to 1."""
        # Pop score threshold so that only relevancy scores, not raw scores, are
        # filtered.
        relevance_score_fn = self._select_relevance_score_fn()
        if relevance_score_fn is None:
            raise ValueError(
                "relevance_score_fn must be provided to"
                " FAISS constructor to normalize scores"
            )
        docs_and_scores = await self.asimilarity_search_with_score(
            query,
            k=k,
            filter=filter,
            fetch_k=fetch_k,
            **kwargs,
        )
        docs_and_rel_scores = [
            (doc, relevance_score_fn(score)) for doc, score in docs_and_scores
        ]
        return docs_and_rel_scores

    @staticmethod
    def _create_filter_func(
        filter: Optional[Union[Callable, Dict[str, Any]]],
    ) -> Callable[[Dict[str, Any]], bool]:
        """
        Create a filter function based on the provided filter.

        Args:
            filter: A callable or a dictionary representing the filter
            conditions for documents.

        Returns:
            A function that takes Document's metadata and returns True if it
            satisfies the filter conditions, otherwise False.

        Raises:
            ValueError: If the filter is invalid or contains unsupported operators.
        """
        if callable(filter):
            return filter

        if not isinstance(filter, dict):
            raise ValueError(
                f"filter must be a dict of metadata or a callable, not {type(filter)}"
            )

        from operator import eq, ge, gt, le, lt, ne

        COMPARISON_OPERATORS = {
            "$eq": eq,
            "$neq": ne,
            "$gt": gt,
            "$lt": lt,
            "$gte": ge,
            "$lte": le,
        }
        SEQUENCE_OPERATORS = {
            "$in": lambda a, b: a in b,
            "$nin": lambda a, b: a not in b,
        }
        OPERATIONS = COMPARISON_OPERATORS | SEQUENCE_OPERATORS
        VALID_OPERATORS = frozenset(list(OPERATIONS) + ["$and", "$or", "$not"])
        SET_CONVERT_THRESHOLD = 10

        # Validate top-level filter operators.
        for op in filter:
            if op and op.startswith("$") and op not in VALID_OPERATORS:
                raise ValueError(f"filter contains unsupported operator: {op}")

        def filter_func_cond(
            field: str, condition: Union[Dict[str, Any], List[Any], Any]
        ) -> Callable[[Dict[str, Any]], bool]:
            """
            Creates a filter function based on field and condition.

            Args:
                field: The document field to filter on
                condition: Filter condition (dict for operators, list for in,
                           or direct value for equality)

            Returns:
                A filter function that takes a document and returns boolean
            """
            if isinstance(condition, dict):
                operators = []
                for op, value in condition.items():
                    if op not in OPERATIONS:
                        raise ValueError(f"filter contains unsupported operator: {op}")
                    operators.append((OPERATIONS[op], value))

                def filter_fn(doc: Dict[str, Any]) -> bool:
                    """
                    Evaluates a document against a set of predefined operators
                    and their values. This function applies multiple
                    comparison/sequence operators to a specific field value
                    from the document. All conditions must be satisfied for the
                    function to return True.

                    Args:
                        doc (Dict[str, Any]): The document to evaluate, containing
                        key-value pairs where keys are field names and values
                        are the field values. The document must contain the field
                        being filtered.

                    Returns:
                        bool: True if the document's field value satisfies all
                            operator conditions, False otherwise.
                    """
                    doc_value = doc.get(field)
                    return all(op(doc_value, value) for op, value in operators)

                return filter_fn

            if isinstance(condition, list):
                if len(condition) > SET_CONVERT_THRESHOLD:
                    condition_set = frozenset(condition)
                    return lambda doc: doc.get(field) in condition_set
                return lambda doc: doc.get(field) in condition

            return lambda doc: doc.get(field) == condition

        def filter_func(filter: Dict[str, Any]) -> Callable[[Dict[str, Any]], bool]:
            """
            Creates a filter function that evaluates documents against specified
            filter conditions.

            This function processes a dictionary of filter conditions and returns
            a callable that can evaluate documents against these conditions. It
            supports logical operators ($and, $or, $not) and field-level filtering.

            Args:
                `dict` containing filter conditions.
                    Can include:
                        - Logical operators ($and, $or, $not) with lists of sub-filters
                        - Field-level conditions with comparison or sequence operators
                        - Direct field-value mappings for equality comparison

            Returns:
                Callable[[Dict[str, Any]], bool]: A function that takes a document
                (as a dictionary) and returns True if the document matches all
                filter conditions, False otherwise.
            """
            if "$and" in filter:
                filters = [filter_func(sub_filter) for sub_filter in filter["$and"]]
                return lambda doc: all(f(doc) for f in filters)

            if "$or" in filter:
                filters = [filter_func(sub_filter) for sub_filter in filter["$or"]]
                return lambda doc: any(f(doc) for f in filters)

            if "$not" in filter:
                cond = filter_func(filter["$not"])
                return lambda doc: not cond(doc)

            conditions = [
                filter_func_cond(field, condition)
                for field, condition in filter.items()
            ]
            return lambda doc: all(condition(doc) for condition in conditions)

        return filter_func(filter)

    def get_by_ids(self, ids: Sequence[str], /) -> list[Document]:
        docs = [self.docstore.search(id_) for id_ in ids]
        return [doc for doc in docs if isinstance(doc, Document)]
