from __future__ import annotations

import base64
import logging
import uuid
from copy import deepcopy
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Dict,
    Iterable,
    List,
    Literal,
    Optional,
    Sized,
    Tuple,
    Type,
    Union,
    get_args,
)

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

from langchain_community.vectorstores.utils import maximal_marginal_relevance

if TYPE_CHECKING:
    import vdms


DISTANCE_METRICS = Literal[
    "L2",  # Euclidean Distance
    "IP",  # Inner Product
]
AVAILABLE_DISTANCE_METRICS: List[DISTANCE_METRICS] = list(get_args(DISTANCE_METRICS))
ENGINES = Literal[
    "TileDBDense",  # TileDB Dense
    "TileDBSparse",  # TileDB Sparse
    "FaissFlat",  # FAISS IndexFlat
    "FaissIVFFlat",  # FAISS IndexIVFFlat
    "Flinng",  # FLINNG
]
AVAILABLE_ENGINES: List[ENGINES] = list(get_args(ENGINES))
DEFAULT_COLLECTION_NAME = "langchain"
DEFAULT_INSERT_BATCH_SIZE = 32
# Number of Documents to return.
DEFAULT_K = 3
# Number of Documents to fetch to pass to knn when filters applied.
DEFAULT_FETCH_K = DEFAULT_K * 5
DEFAULT_PROPERTIES = ["_distance", "id", "content"]
INVALID_DOC_METADATA_KEYS = ["_distance", "content", "blob"]
INVALID_METADATA_VALUE = ["Missing property", None, {}]  # type: List


logger = logging.getLogger(__name__)


def _len_check_if_sized(x: Any, y: Any, x_name: str, y_name: str) -> None:
    """
    Check that sizes of two variables are the same

    Args:
        x: Variable to compare
        y: Variable to compare
        x_name: Name for variable x
        y_name: Name for variable y
    """
    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


def VDMS_Client(host: str = "localhost", port: int = 55555) -> vdms.vdms:
    """
    Wrapper to initiate and connect a VDMS client to a VDMS server

    Args:
        host: IP or hostname of VDMS server
        port: Port to connect to VDMS server
    """
    try:
        import vdms
    except ImportError:
        raise ImportError(
            "Could not import vdms python package. "
            "Please install it with `pip install vdms."
        )

    client = vdms.vdms()
    client.connect(host, port)
    return client


class VDMS(VectorStore):
    """Wrapper around Intel Lab's VDMS for vector-store workloads.

    To use, you should have both:
    - the ``vdms`` python package installed
    - a host (str) and port (int) associated with a deployed VDMS Server

    Visit https://github.com/IntelLabs/vdms/wiki more information.

    IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.

    Args:
        client: VDMS Client used to connect to VDMS server
        collection_name: Name of data collection [Default: langchain]
        distance_strategy: Method used to calculate distances. VDMS supports
            "L2" (euclidean distance) or "IP" (inner product) [Default: L2]
        engine: Underlying implementation for indexing and computing distances.
            VDMS supports TileDBDense, TileDBSparse, FaissFlat, FaissIVFFlat,
            and Flinng [Default: FaissFlat]
        embedding: Any embedding function implementing
            `langchain_core.embeddings.Embeddings` interface.
        relevance_score_fn: Function for obtaining relevance score

    Example:
        .. code-block:: python

            from langchain_community.embeddings import HuggingFaceEmbeddings
            from langchain_community.vectorstores.vdms import VDMS, VDMS_Client

            vectorstore = VDMS(
                client=VDMS_Client("localhost", 55555),
                embedding=HuggingFaceEmbeddings(),
                collection_name="langchain-demo",
                distance_strategy="L2",
                engine="FaissFlat",
            )
    """

    def __init__(
        self,
        client: vdms.vdms,
        *,
        embedding: Optional[Embeddings] = None,
        collection_name: str = DEFAULT_COLLECTION_NAME,  # DescriptorSet name
        distance_strategy: DISTANCE_METRICS = "L2",
        engine: ENGINES = "FaissFlat",
        relevance_score_fn: Optional[Callable[[float], float]] = None,
    ) -> None:
        # Check required parameters
        self._client = client
        self.similarity_search_engine = engine
        self.distance_strategy = distance_strategy
        self.embedding = embedding
        self._check_required_inputs(collection_name)

        # Update other parameters
        self.override_relevance_score_fn = relevance_score_fn

        # Initialize collection
        self._collection_name = self.__add_set(
            collection_name,
            engine=self.similarity_search_engine,
            metric=self.distance_strategy,
        )

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

    def _embed_documents(self, texts: List[str]) -> List[List[float]]:
        if isinstance(self.embedding, Embeddings):
            return self.embedding.embed_documents(texts)
        else:
            p_str = "Must provide `embedding` which is expected"
            p_str += " to be an Embeddings object"
            raise ValueError(p_str)

    def _embed_image(self, uris: List[str]) -> List[List[float]]:
        if self.embedding is not None and hasattr(self.embedding, "embed_image"):
            return self.embedding.embed_image(uris=uris)
        else:
            raise ValueError(
                "Must provide `embedding` which has attribute `embed_image`"
            )

    def _embed_query(self, text: str) -> List[float]:
        if isinstance(self.embedding, Embeddings):
            return self.embedding.embed_query(text)
        else:
            raise ValueError(
                "Must provide `embedding` which is expected"
                " to be an Embeddings object"
            )

    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.lower() in ["ip", "l2"]:
            return lambda x: x
        else:
            raise ValueError(
                "No supported normalization function"
                f" for distance_strategy of {self.distance_strategy}."
                "Consider providing relevance_score_fn to VDMS constructor."
            )

    def _similarity_search_with_relevance_scores(
        self,
        query: str,
        k: int = DEFAULT_K,
        fetch_k: int = DEFAULT_FETCH_K,
        filter: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Return docs and their similarity scores on a scale from 0 to 1."""
        if self.override_relevance_score_fn is None:
            kwargs["normalize_distance"] = True
        docs_and_scores = self.similarity_search_with_score(
            query,
            k,
            fetch_k,
            filter,
            **kwargs,
        )

        docs_and_rel_scores: List[Any] = []
        for doc, score in docs_and_scores:
            if self.override_relevance_score_fn is None:
                docs_and_rel_scores.append((doc, score))
            else:
                docs_and_rel_scores.append(
                    (doc, self.override_relevance_score_fn(score))
                )
        return docs_and_rel_scores

    def __add(
        self,
        collection_name: str,
        texts: List[str],
        embeddings: List[List[float]],
        metadatas: Optional[Union[List[None], List[Dict[str, Any]]]] = None,
        ids: Optional[List[str]] = None,
    ) -> List:
        _len_check_if_sized(texts, embeddings, "texts", "embeddings")

        metadatas = metadatas if metadatas is not None else [None for _ in texts]
        _len_check_if_sized(texts, metadatas, "texts", "metadatas")

        ids = ids if ids is not None else [str(uuid.uuid1()) for _ in texts]
        _len_check_if_sized(texts, ids, "texts", "ids")

        all_queries: List[Any] = []
        all_blobs: List[Any] = []
        inserted_ids: List[Any] = []
        for meta, emb, doc, id in zip(metadatas, embeddings, texts, ids):
            query, blob = self.__get_add_query(
                collection_name, metadata=meta, embedding=emb, document=doc, id=id
            )

            if blob is not None:
                all_queries.append(query)
                all_blobs.append(blob)
                inserted_ids.append(id)

        response, response_array = self.__run_vdms_query(all_queries, all_blobs)

        return inserted_ids

    def __add_set(
        self,
        collection_name: str,
        engine: ENGINES = "FaissFlat",
        metric: DISTANCE_METRICS = "L2",
    ) -> str:
        query = _add_descriptorset(
            "AddDescriptorSet",
            collection_name,
            self.embedding_dimension,
            engine=getattr(engine, "value", engine),
            metric=getattr(metric, "value", metric),
        )

        response, _ = self.__run_vdms_query([query])

        if "FailedCommand" in response[0]:
            raise ValueError(f"Failed to add collection {collection_name}")

        return collection_name

    def __delete(
        self,
        collection_name: str,
        ids: Union[None, List[str]] = None,
        constraints: Union[None, Dict[str, Any]] = None,
    ) -> bool:
        """
        Deletes entire collection if id is not provided
        """
        all_queries: List[Any] = []
        all_blobs: List[Any] = []

        collection_properties = self.__get_properties(collection_name)
        results = {"list": collection_properties}

        if constraints is None:
            constraints = {"_deletion": ["==", 1]}
        else:
            constraints["_deletion"] = ["==", 1]

        if ids is not None:
            constraints["id"] = ["==", ids[0]]  # if len(ids) > 1 else ids[0]]

        query = _add_descriptor(
            "FindDescriptor",
            collection_name,
            label=None,
            ref=None,
            props=None,
            link=None,
            k_neighbors=None,
            constraints=constraints,
            results=results,
        )

        all_queries.append(query)
        response, response_array = self.__run_vdms_query(all_queries, all_blobs)
        return "FindDescriptor" in response[0]

    def __get_add_query(
        self,
        collection_name: str,
        metadata: Optional[Any] = None,
        embedding: Union[List[float], None] = None,
        document: Optional[Any] = None,
        id: Optional[str] = None,
    ) -> Tuple[Dict[str, Dict[str, Any]], Union[bytes, None]]:
        if id is None:
            props: Dict[str, Any] = {}
        else:
            props = {"id": id}
            id_exists, query = _check_descriptor_exists_by_id(
                self._client, collection_name, id
            )
            if id_exists:
                skipped_value = {
                    prop_key: prop_val[-1]
                    for prop_key, prop_val in query["FindDescriptor"][
                        "constraints"
                    ].items()
                }
                pstr = f"[!] Embedding with id ({id}) exists in DB;"
                pstr += "Therefore, skipped and not inserted"
                print(pstr)  # noqa: T201
                print(f"\tSkipped values are: {skipped_value}")  # noqa: T201
                return query, None

        if metadata:
            props.update(metadata)
        if document:
            props["content"] = document

        for k in props.keys():
            if k not in self.collection_properties:
                self.collection_properties.append(k)

        query = _add_descriptor(
            "AddDescriptor",
            collection_name,
            label=None,
            ref=None,
            props=props,
            link=None,
            k_neighbors=None,
            constraints=None,
            results=None,
        )

        blob = embedding2bytes(embedding)

        return (
            query,
            blob,
        )

    def __get_properties(
        self,
        collection_name: str,
        unique_entity: Optional[bool] = False,
        deletion: Optional[bool] = False,
    ) -> List[str]:
        find_query = _find_property_entity(
            collection_name, unique_entity=unique_entity, deletion=deletion
        )
        response, response_blob = self.__run_vdms_query([find_query])
        if len(response_blob) > 0:
            collection_properties = _bytes2str(response_blob[0]).split(",")
        else:
            collection_properties = deepcopy(DEFAULT_PROPERTIES)
        return collection_properties

    def __run_vdms_query(
        self,
        all_queries: List[Dict],
        all_blobs: Optional[List] = [],
        print_last_response: Optional[bool] = False,
    ) -> Tuple[Any, Any]:
        response, response_array = self._client.query(all_queries, all_blobs)

        _ = _check_valid_response(all_queries, response)
        if print_last_response:
            self._client.print_last_response()
        return response, response_array

    def __update(
        self,
        collection_name: str,
        ids: List[str],
        documents: List[str],
        embeddings: List[List[float]],
        metadatas: Optional[Union[List[None], List[Dict[str, Any]]]] = None,
    ) -> None:
        """
        Updates (find, delete, add) a collection based on id.
        If more than one collection returned with id, error occuers
        """
        _len_check_if_sized(ids, documents, "ids", "documents")

        _len_check_if_sized(ids, embeddings, "ids", "embeddings")

        metadatas = metadatas if metadatas is not None else [None for _ in ids]
        _len_check_if_sized(ids, metadatas, "ids", "metadatas")

        orig_props = self.__get_properties(collection_name)

        updated_ids: List[Any] = []
        for meta, emb, doc, id in zip(metadatas, embeddings, documents, ids):
            results = {"list": self.collection_properties}

            constraints = {"_deletion": ["==", 1]}

            if id is not None:
                constraints["id"] = ["==", id]

            query = _add_descriptor(
                "FindDescriptor",
                collection_name,
                label=None,
                ref=None,
                props=None,
                link=None,
                k_neighbors=None,
                constraints=constraints,
                results=results,
            )

            response, response_array = self.__run_vdms_query([query])

            query, blob = self.__get_add_query(
                collection_name,
                metadata=meta,
                embedding=emb,
                document=doc,
                id=id,
            )
            if blob is not None:
                response, response_array = self.__run_vdms_query([query], [blob])
                updated_ids.append(id)

        self.__update_properties(
            collection_name, orig_props, self.collection_properties
        )

    def __update_properties(
        self,
        collection_name: str,
        current_collection_properties: List,
        new_collection_properties: Optional[List],
    ) -> None:
        if new_collection_properties is not None:
            old_collection_properties = deepcopy(current_collection_properties)
            for prop in new_collection_properties:
                if prop not in current_collection_properties:
                    current_collection_properties.append(prop)

            if current_collection_properties != old_collection_properties:
                all_queries, blob_arr = _build_property_query(
                    collection_name,
                    command_type="update",
                    all_properties=current_collection_properties,
                )
                response, _ = self.__run_vdms_query(all_queries, [blob_arr])

    def add_images(
        self,
        uris: List[str],
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        batch_size: int = DEFAULT_INSERT_BATCH_SIZE,
        add_path: Optional[bool] = True,
        **kwargs: Any,
    ) -> List[str]:
        """Run more images through the embeddings and add to the vectorstore.

        Images are added as embeddings (AddDescriptor) instead of separate
        entity (AddImage) within VDMS to leverage similarity search capability

        Args:
            uris: List of paths to the images to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
            ids: Optional list of unique IDs.
            batch_size (int): Number of concurrent requests to send to the server.
            add_path: Bool to add image path as metadata

        Returns:
            List of ids from adding images into the vectorstore.
        """
        # Map from uris to blobs to base64
        b64_texts = [self.encode_image(image_path=uri) for uri in uris]

        if add_path and metadatas:
            for midx, uri in enumerate(uris):
                metadatas[midx]["image_path"] = uri
        elif add_path:
            metadatas = []
            for uri in uris:
                metadatas.append({"image_path": uri})

        # Populate IDs
        ids = ids if ids is not None else [str(uuid.uuid1()) for _ in uris]

        # Set embeddings
        embeddings = self._embed_image(uris=uris)

        if metadatas is None:
            metadatas = [{} for _ in uris]
        else:
            metadatas = [_validate_vdms_properties(m) for m in metadatas]

        self.__from(
            texts=b64_texts,
            embeddings=embeddings,
            ids=ids,
            metadatas=metadatas,
            batch_size=batch_size,
            **kwargs,
        )
        return ids

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

        Args:
            texts: List of strings to add to the vectorstore.
            metadatas: Optional list of metadatas associated with the texts.
            ids: Optional list of unique IDs.
            batch_size (int): Number of concurrent requests to send to the server.

        Returns:
            List of ids from adding the texts into the vectorstore.
        """

        texts = list(texts)
        if ids is None:
            ids = [str(uuid.uuid1()) for _ in texts]

        embeddings = self._embed_documents(texts)

        if metadatas is None:
            metadatas = [{} for _ in texts]
        else:
            metadatas = [_validate_vdms_properties(m) for m in metadatas]

        inserted_ids = self.__from(
            texts=texts,
            embeddings=embeddings,
            ids=ids,
            metadatas=metadatas,
            batch_size=batch_size,
            **kwargs,
        )
        return inserted_ids

    def __from(
        self,
        texts: List[str],
        embeddings: List[List[float]],
        ids: List[str],
        metadatas: Optional[List[dict]] = None,
        batch_size: int = DEFAULT_INSERT_BATCH_SIZE,
        **kwargs: Any,
    ) -> List[str]:
        # Get initial properties
        orig_props = self.__get_properties(self._collection_name)
        inserted_ids: List[str] = []
        for start_idx in range(0, len(texts), batch_size):
            end_idx = min(start_idx + batch_size, len(texts))

            batch_texts = texts[start_idx:end_idx]
            batch_embedding_vectors = embeddings[start_idx:end_idx]
            batch_ids = ids[start_idx:end_idx]
            if metadatas:
                batch_metadatas = metadatas[start_idx:end_idx]

            result = self.__add(
                self._collection_name,
                embeddings=batch_embedding_vectors,
                texts=batch_texts,
                metadatas=batch_metadatas,
                ids=batch_ids,
            )

            inserted_ids.extend(result)

        # Update Properties
        self.__update_properties(
            self._collection_name, orig_props, self.collection_properties
        )
        return inserted_ids

    def _check_required_inputs(self, collection_name: str) -> None:
        # Check connection to client
        if not self._client.is_connected():
            raise ValueError(
                "VDMS client must be connected to a VDMS server."
                + "Please use VDMS_Client to establish a connection"
            )

        # Check Distance Metric
        if self.distance_strategy not in AVAILABLE_DISTANCE_METRICS:
            raise ValueError("distance_strategy must be either 'L2' or 'IP'")

        # Check Engines
        if self.similarity_search_engine not in AVAILABLE_ENGINES:
            raise ValueError(
                "engine must be either 'TileDBDense', 'TileDBSparse', "
                + "'FaissFlat', 'FaissIVFFlat', or 'Flinng'"
            )

        # Check Embedding Func is provided and store dimension size
        if self.embedding is None:
            raise ValueError("Must provide embedding function")

        self.embedding_dimension = len(self._embed_query("This is a sample sentence."))

        # Check for properties
        current_props = self.__get_properties(collection_name)
        if hasattr(self, "collection_properties"):
            self.collection_properties.extend(current_props)
        else:
            self.collection_properties: List[str] = current_props

    def count(self, collection_name: str) -> int:
        all_queries: List[Any] = []
        all_blobs: List[Any] = []

        results = {"count": "", "list": ["id"]}  # collection_properties}
        query = _add_descriptor(
            "FindDescriptor",
            collection_name,
            label=None,
            ref=None,
            props=None,
            link=None,
            k_neighbors=None,
            constraints=None,
            results=results,
        )

        all_queries.append(query)

        response, response_array = self.__run_vdms_query(all_queries, all_blobs)
        return response[0]["FindDescriptor"]["returned"]

    def decode_image(self, base64_image: str) -> bytes:
        return base64.b64decode(base64_image)

    def delete(
        self,
        ids: Optional[List[str]] = None,
        collection_name: Optional[str] = None,
        constraints: Optional[Dict] = None,
        **kwargs: Any,
    ) -> 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.
        """
        name = collection_name if collection_name is not None else self._collection_name
        return self.__delete(name, ids=ids, constraints=constraints)

    def get_k_candidates(
        self,
        setname: str,
        fetch_k: Optional[int],
        results: Optional[Dict[str, Any]] = None,
        all_blobs: Optional[List] = None,
        normalize: Optional[bool] = False,
    ) -> Tuple[List[Dict[str, Any]], List, float]:
        max_dist = 1
        command_str = "FindDescriptor"
        query = _add_descriptor(
            command_str,
            setname,
            k_neighbors=fetch_k,
            results=results,
        )
        response, response_array = self.__run_vdms_query([query], all_blobs)

        if normalize:
            max_dist = response[0][command_str]["entities"][-1]["_distance"]

        return response, response_array, max_dist

    def get_descriptor_response(
        self,
        command_str: str,
        setname: str,
        k_neighbors: int = DEFAULT_K,
        fetch_k: int = DEFAULT_FETCH_K,
        constraints: Optional[dict] = None,
        results: Optional[Dict[str, Any]] = None,
        query_embedding: Optional[List[float]] = None,
        normalize_distance: bool = False,
    ) -> Tuple[List[Dict[str, Any]], List]:
        all_blobs: List[Any] = []
        blob = embedding2bytes(query_embedding)
        all_blobs.append(blob)

        if constraints is None:
            # K results returned
            response, response_array, max_dist = self.get_k_candidates(
                setname, k_neighbors, results, all_blobs, normalize=normalize_distance
            )
        else:
            if results is None:
                results = {"list": ["id"]}
            elif "list" not in results:
                results["list"] = ["id"]
            elif "id" not in results["list"]:
                results["list"].append("id")

            # (1) Find docs satisfy constraints
            query = _add_descriptor(
                command_str,
                setname,
                constraints=constraints,
                results=results,
            )
            response, response_array = self.__run_vdms_query([query])
            ids_of_interest = [
                ent["id"] for ent in response[0][command_str]["entities"]
            ]

            # (2) Find top fetch_k results
            response, response_array, max_dist = self.get_k_candidates(
                setname, fetch_k, results, all_blobs, normalize=normalize_distance
            )

            # (3) Intersection of (1) & (2) using ids
            new_entities: List[Dict] = []
            for ent in response[0][command_str]["entities"]:
                if ent["id"] in ids_of_interest:
                    new_entities.append(ent)
                if len(new_entities) == k_neighbors:
                    break
            response[0][command_str]["entities"] = new_entities
            response[0][command_str]["returned"] = len(new_entities)
            if len(new_entities) < k_neighbors:
                p_str = "Returned items < k_neighbors; Try increasing fetch_k"
                print(p_str)  # noqa: T201

        if normalize_distance:
            max_dist = 1.0 if max_dist == 0 else max_dist
            for ent_idx, ent in enumerate(response[0][command_str]["entities"]):
                ent["_distance"] = ent["_distance"] / max_dist
                response[0][command_str]["entities"][ent_idx]["_distance"] = ent[
                    "_distance"
                ]

        return response, response_array

    def encode_image(self, image_path: str) -> str:
        with open(image_path, "rb") as f:
            blob = f.read()
            return base64.b64encode(blob).decode("utf-8")

    @classmethod
    def from_documents(
        cls: Type[VDMS],
        documents: List[Document],
        embedding: Optional[Embeddings] = None,
        ids: Optional[List[str]] = None,
        batch_size: int = DEFAULT_INSERT_BATCH_SIZE,
        collection_name: str = DEFAULT_COLLECTION_NAME,  # Add this line
        **kwargs: Any,
    ) -> VDMS:
        """Create a VDMS vectorstore from a list of documents.

        Args:
            collection_name (str): Name of the collection to create.
            documents (List[Document]): List of documents to add to vectorstore.
            embedding (Embeddings): Embedding function. Defaults to None.
            ids (Optional[List[str]]): List of document IDs. Defaults to None.
            batch_size (int): Number of concurrent requests to send to the server.

        Returns:
            VDMS: VDMS vectorstore.
        """
        client: vdms.vdms = kwargs["client"]

        return cls.from_texts(
            client=client,
            texts=[doc.page_content for doc in documents],
            metadatas=[doc.metadata for doc in documents],
            embedding=embedding,
            ids=ids,
            batch_size=batch_size,
            collection_name=collection_name,
            # **kwargs,
        )

    @classmethod
    def from_texts(
        cls: Type[VDMS],
        texts: List[str],
        embedding: Optional[Embeddings] = None,
        metadatas: Optional[List[dict]] = None,
        ids: Optional[List[str]] = None,
        batch_size: int = DEFAULT_INSERT_BATCH_SIZE,
        collection_name: str = DEFAULT_COLLECTION_NAME,
        **kwargs: Any,
    ) -> VDMS:
        """Create a VDMS vectorstore from a raw documents.

        Args:
            texts (List[str]): List of texts to add to the collection.
            embedding (Embeddings): Embedding function. Defaults to None.
            metadatas (Optional[List[dict]]): List of metadatas. Defaults to None.
            ids (Optional[List[str]]): List of document IDs. Defaults to None.
            batch_size (int): Number of concurrent requests to send to the server.
            collection_name (str): Name of the collection to create.

        Returns:
            VDMS: VDMS vectorstore.
        """
        client: vdms.vdms = kwargs["client"]
        vdms_collection = cls(
            collection_name=collection_name,
            embedding=embedding,
            client=client,
            # **kwargs,
        )
        if ids is None:
            ids = [str(uuid.uuid1()) for _ in texts]
        vdms_collection.add_texts(
            texts=texts,
            metadatas=metadatas,
            ids=ids,
            batch_size=batch_size,  # **kwargs
        )
        return vdms_collection

    def get(
        self,
        collection_name: str,
        constraints: Optional[Dict] = None,
        limit: Optional[int] = None,
        include: List[str] = ["metadata"],
    ) -> Tuple[Any, Any]:
        """Gets the collection.
        Get embeddings and their associated data from the data store.
        If no constraints provided returns all embeddings up to limit.

        Args:
            constraints: A dict used to filter results by.
                   E.g. `{"color" : ["==", "red"], "price": [">", 4.00]}`. Optional.
            limit: The number of documents to return. Optional.
            include: A list of what to include in the results.
                     Can contain `"embeddings"`, `"metadatas"`, `"documents"`.
                     Ids are always included.
                     Defaults to `["metadatas", "documents"]`. Optional.
        """
        all_queries: List[Any] = []
        all_blobs: List[Any] = []

        results: Dict[str, Any] = {"count": ""}

        if limit is not None:
            results["limit"] = limit

        # Include metadata
        if "metadata" in include:
            collection_properties = self.__get_properties(collection_name)
            results["list"] = collection_properties

        # Include embedding
        if "embeddings" in include:
            results["blob"] = True

        query = _add_descriptor(
            "FindDescriptor",
            collection_name,
            k_neighbors=None,
            constraints=constraints,
            results=results,
        )

        all_queries.append(query)

        response, response_array = self.__run_vdms_query(all_queries, all_blobs)
        return response, response_array

    def max_marginal_relevance_search(
        self,
        query: str,
        k: int = DEFAULT_K,
        fetch_k: int = DEFAULT_FETCH_K,
        lambda_mult: float = 0.5,
        filter: Optional[Dict[str, List]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.
        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.

        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
            lambda_mult: Number between 0 and 1 that determines the degree
                        of diversity among the results with 0 corresponding
                        to maximum diversity and 1 to minimum diversity.
                        Defaults to 0.5.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        if self.embedding is None:
            raise ValueError(
                "For MMR search, you must specify an embedding function on" "creation."
            )

        embedding_vector: List[float] = self._embed_query(query)
        docs = self.max_marginal_relevance_search_by_vector(
            embedding_vector,
            k,
            fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
        )
        return docs

    def max_marginal_relevance_search_by_vector(
        self,
        embedding: List[float],
        k: int = DEFAULT_K,
        fetch_k: int = DEFAULT_FETCH_K,
        lambda_mult: float = 0.5,
        filter: Optional[Dict[str, List]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs selected using the maximal marginal relevance.
        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
            lambda_mult: Number between 0 and 1 that determines the degree
                        of diversity among the results with 0 corresponding
                        to maximum diversity and 1 to minimum diversity.
                        Defaults to 0.5.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        results = self.query_collection_embeddings(
            query_embeddings=[embedding],
            n_results=fetch_k,
            filter=filter,
            include=["metadatas", "documents", "distances", "embeddings"],
        )

        embedding_list = [list(_bytes2embedding(result)) for result in results[0][1]]

        mmr_selected = maximal_marginal_relevance(
            np.array(embedding, dtype=np.float32),
            embedding_list,
            k=k,
            lambda_mult=lambda_mult,
        )

        candidates = _results_to_docs(results)

        selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected]
        return selected_results

    def max_marginal_relevance_search_with_score(
        self,
        query: str,
        k: int = DEFAULT_K,
        fetch_k: int = DEFAULT_FETCH_K,
        lambda_mult: float = 0.5,
        filter: Optional[Dict[str, List]] = None,
        **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.
            lambda_mult: Number between 0 and 1 that determines the degree
                        of diversity among the results with 0 corresponding
                        to maximum diversity and 1 to minimum diversity.
                        Defaults to 0.5.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        if self.embedding is None:
            raise ValueError(
                "For MMR search, you must specify an embedding function on" "creation."
            )

        embedding = self._embed_query(query)
        docs = self.max_marginal_relevance_search_with_score_by_vector(
            embedding,
            k,
            fetch_k,
            lambda_mult=lambda_mult,
            filter=filter,
        )
        return docs

    def max_marginal_relevance_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = DEFAULT_K,
        fetch_k: int = DEFAULT_FETCH_K,
        lambda_mult: float = 0.5,
        filter: Optional[Dict[str, List]] = None,
        **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:
            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.
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

        Returns:
            List of Documents selected by maximal marginal relevance.
        """
        results = self.query_collection_embeddings(
            query_embeddings=[embedding],
            n_results=fetch_k,
            filter=filter,
            include=["metadatas", "documents", "distances", "embeddings"],
        )

        embedding_list = [list(_bytes2embedding(result)) for result in results[0][1]]

        mmr_selected = maximal_marginal_relevance(
            np.array(embedding, dtype=np.float32),
            embedding_list,
            k=k,
            lambda_mult=lambda_mult,
        )

        candidates = _results_to_docs_and_scores(results)

        selected_results = [
            (r, s) for i, (r, s) in enumerate(candidates) if i in mmr_selected
        ]
        return selected_results

    def query_collection_embeddings(
        self,
        query_embeddings: Optional[List[List[float]]] = None,
        collection_name: Optional[str] = None,
        n_results: int = DEFAULT_K,
        fetch_k: int = DEFAULT_FETCH_K,
        filter: Union[None, Dict[str, Any]] = None,
        results: Union[None, Dict[str, Any]] = None,
        normalize_distance: bool = False,
        **kwargs: Any,
    ) -> List[Tuple[Dict[str, Any], List]]:
        all_responses: List[Any] = []

        if collection_name is None:
            collection_name = self._collection_name

        if query_embeddings is None:
            return all_responses

        include = kwargs.get("include", ["metadatas"])
        if results is None and "metadatas" in include:
            results = {
                "list": self.collection_properties,
                "blob": "embeddings" in include,
            }

        for qemb in query_embeddings:
            response, response_array = self.get_descriptor_response(
                "FindDescriptor",
                collection_name,
                k_neighbors=n_results,
                fetch_k=fetch_k,
                constraints=filter,
                results=results,
                normalize_distance=normalize_distance,
                query_embedding=qemb,
            )
            all_responses.append([response, response_array])

        return all_responses

    def similarity_search(
        self,
        query: str,
        k: int = DEFAULT_K,
        fetch_k: int = DEFAULT_FETCH_K,
        filter: Optional[Dict[str, List]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Run similarity search with VDMS.

        Args:
            query (str): Query text to search for.
            k (int): Number of results to return. Defaults to 3.
            fetch_k (int): Number of candidates to fetch for knn (>= k).
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

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

    def similarity_search_by_vector(
        self,
        embedding: List[float],
        k: int = DEFAULT_K,
        fetch_k: int = DEFAULT_FETCH_K,
        filter: Optional[Dict[str, List]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs most similar to embedding vector.
        Args:
            embedding (List[float]): Embedding to look up documents similar to.
            k (int): Number of Documents to return. Defaults to 3.
            fetch_k (int): Number of candidates to fetch for knn (>= k).
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
        Returns:
            List of Documents most similar to the query vector.
        """
        results = self.query_collection_embeddings(
            query_embeddings=[embedding],
            n_results=k,
            fetch_k=fetch_k,
            filter=filter,
            **kwargs,
        )

        return _results_to_docs(results)

    def similarity_search_with_score(
        self,
        query: str,
        k: int = DEFAULT_K,
        fetch_k: int = DEFAULT_FETCH_K,
        filter: Optional[Dict[str, List]] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """Run similarity search with VDMS with distance.

        Args:
            query (str): Query text to search for.
            k (int): Number of results to return. Defaults to 3.
            fetch_k (int): Number of candidates to fetch for knn (>= k).
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

        Returns:
            List[Tuple[Document, float]]: List of documents most similar to
            the query text and cosine distance in float for each.
            Lower score represents more similarity.
        """
        if self.embedding is None:
            raise ValueError("Must provide embedding function")
        else:
            query_embedding: List[float] = self._embed_query(query)
            results = self.query_collection_embeddings(
                query_embeddings=[query_embedding],
                n_results=k,
                fetch_k=fetch_k,
                filter=filter,
                **kwargs,
            )

        return _results_to_docs_and_scores(results)

    def similarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = DEFAULT_K,
        fetch_k: int = DEFAULT_FETCH_K,
        filter: Optional[Dict[str, List]] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        """
        Return docs most similar to embedding vector and similarity score.

        Args:
            embedding (List[float]): Embedding to look up documents similar to.
            k (int): Number of Documents to return. Defaults to 3.
            fetch_k (int): Number of candidates to fetch for knn (>= k).
            filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

        Returns:
            List[Tuple[Document, float]]: List of documents most similar to
            the query text and cosine distance in float for each.
            Lower score represents more similarity.
        """
        kwargs["normalize_distance"] = True

        results = self.query_collection_embeddings(
            query_embeddings=[embedding],
            n_results=k,
            fetch_k=fetch_k,
            filter=filter,
            **kwargs,
        )
        return _results_to_docs_and_scores(results)

    def update_document(
        self, collection_name: str, document_id: str, document: Document
    ) -> None:
        """Update a document in the collection.

        Args:
            document_id (str): ID of the document to update.
            document (Document): Document to update.
        """
        return self.update_documents(collection_name, [document_id], [document])

    def update_documents(
        self, collection_name: str, ids: List[str], documents: List[Document]
    ) -> None:
        """Update a document in the collection.

        Args:
            ids (List[str]): List of ids of the document to update.
            documents (List[Document]): List of documents to update.
        """
        text = [document.page_content for document in documents]
        metadata = [
            _validate_vdms_properties(document.metadata) for document in documents
        ]
        embeddings = self._embed_documents(text)

        self.__update(
            collection_name,
            ids,
            metadatas=metadata,
            embeddings=embeddings,
            documents=text,
        )


# VDMS UTILITY


def _results_to_docs(results: Any) -> List[Document]:
    return [doc for doc, _ in _results_to_docs_and_scores(results)]


def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
    final_res: List[Any] = []
    responses, blobs = results[0]
    if (
        "FindDescriptor" in responses[0]
        and "entities" in responses[0]["FindDescriptor"]
    ):
        result_entities = responses[0]["FindDescriptor"]["entities"]
        # result_blobs = blobs
        for ent in result_entities:
            distance = ent["_distance"]
            txt_contents = ent["content"]
            for p in INVALID_DOC_METADATA_KEYS:
                if p in ent:
                    del ent[p]
            props = {
                mkey: mval
                for mkey, mval in ent.items()
                if mval not in INVALID_METADATA_VALUE
            }

            final_res.append(
                (Document(page_content=txt_contents, metadata=props), distance)
            )
    return final_res


def _add_descriptor(
    command_str: str,
    setname: str,
    label: Optional[str] = None,
    ref: Optional[int] = None,
    props: Optional[dict] = None,
    link: Optional[dict] = None,
    k_neighbors: Optional[int] = None,
    constraints: Optional[dict] = None,
    results: Optional[dict] = None,
) -> Dict[str, Dict[str, Any]]:
    entity: Dict[str, Any] = {"set": setname}

    if "Add" in command_str and label:
        entity["label"] = label

    if ref is not None:
        entity["_ref"] = ref

    if props not in INVALID_METADATA_VALUE:
        entity["properties"] = props

    if "Add" in command_str and link is not None:
        entity["link"] = link

    if "Find" in command_str and k_neighbors is not None:
        entity["k_neighbors"] = int(k_neighbors)

    if "Find" in command_str and constraints not in INVALID_METADATA_VALUE:
        entity["constraints"] = constraints

    if "Find" in command_str and results not in INVALID_METADATA_VALUE:
        entity["results"] = results

    query = {command_str: entity}
    return query


def _add_descriptorset(
    command_str: str,
    name: str,
    num_dims: Optional[int] = None,
    engine: Optional[str] = None,
    metric: Optional[str] = None,
    ref: Optional[int] = None,
    props: Optional[Dict] = None,
    link: Optional[Dict] = None,
    storeIndex: bool = False,
    constraints: Optional[Dict] = None,
    results: Optional[Dict] = None,
) -> Dict[str, Any]:
    if command_str == "AddDescriptorSet" and all(
        var is not None for var in [name, num_dims]
    ):
        entity: Dict[str, Any] = {
            "name": name,
            "dimensions": num_dims,
        }

        if engine is not None:
            entity["engine"] = engine

        if metric is not None:
            entity["metric"] = metric

        if ref is not None:
            entity["_ref"] = ref

        if props not in [None, {}]:
            entity["properties"] = props

        if link is not None:
            entity["link"] = link

    elif command_str == "FindDescriptorSet":
        entity = {"set": name}

        if storeIndex:
            entity["storeIndex"] = storeIndex

        if constraints not in [None, {}]:
            entity["constraints"] = constraints

        if results is not None:
            entity["results"] = results

    else:
        raise ValueError(f"Unknown command: {command_str}")

    query = {command_str: entity}
    return query


def _add_entity_with_blob(
    collection_name: str, all_properties: List
) -> Tuple[Dict[str, Any], bytes]:
    all_properties_str = ",".join(all_properties) if len(all_properties) > 0 else ""

    querytype = "AddEntity"
    entity: Dict[str, Any] = {}
    entity["class"] = "properties"
    entity["blob"] = True  # New

    props: Dict[str, Any] = {"name": collection_name}
    props["type"] = "queryable properties"
    props["content"] = all_properties_str
    entity["properties"] = props

    byte_data = _str2bytes(all_properties_str)

    query: Dict[str, Any] = {}
    query[querytype] = entity
    return query, byte_data


def _build_property_query(
    collection_name: str,
    command_type: str = "find",
    all_properties: List = [],
    ref: Optional[int] = None,
) -> Tuple[Any, Any]:
    all_queries: List[Any] = []
    blob_arr: List[Any] = []

    choices = ["find", "add", "update"]
    if command_type.lower() not in choices:
        raise ValueError("[!] Invalid type. Choices are : {}".format(",".join(choices)))

    if command_type.lower() == "find":
        query = _find_property_entity(collection_name, unique_entity=True)
        all_queries.append(query)

    elif command_type.lower() == "add":
        query, byte_data = _add_entity_with_blob(collection_name, all_properties)
        all_queries.append(query)
        blob_arr.append(byte_data)

    elif command_type.lower() == "update":
        # Find & Delete
        query = _find_property_entity(collection_name, deletion=True)
        all_queries.append(query)

        # Add
        query, byte_data = _add_entity_with_blob(collection_name, all_properties)
        all_queries.append(query)
        blob_arr.append(byte_data)

    return all_queries, blob_arr


def _bytes2embedding(blob: bytes) -> Any:
    emb = np.frombuffer(blob, dtype="float32")
    return emb


def _bytes2str(in_bytes: bytes) -> str:
    return in_bytes.decode()


def _get_cmds_from_query(all_queries: list) -> List[str]:
    return list(set([k for q in all_queries for k in q.keys()]))


def _check_valid_response(all_queries: List[dict], response: Any) -> bool:
    cmd_list = _get_cmds_from_query(all_queries)
    valid_res = isinstance(response, list) and any(
        cmd in response[0]
        and "returned" in response[0][cmd]
        and response[0][cmd]["returned"] > 0
        for cmd in cmd_list
    )
    return valid_res


def _check_descriptor_exists_by_id(
    client: vdms.vdms,
    setname: str,
    id: str,
) -> Tuple[bool, Any]:
    constraints = {"id": ["==", id]}
    findDescriptor = _add_descriptor(
        "FindDescriptor",
        setname,
        constraints=constraints,
        results={"list": ["id"], "count": ""},
    )
    all_queries = [findDescriptor]
    res, _ = client.query(all_queries)

    valid_res = _check_valid_response(all_queries, res)
    return valid_res, findDescriptor


def embedding2bytes(embedding: Union[List[float], None]) -> Union[bytes, None]:
    blob = None
    if embedding:
        emb = np.array(embedding, dtype="float32")
        blob = emb.tobytes()
    return blob


def _find_property_entity(
    collection_name: str,
    unique_entity: Optional[bool] = False,
    deletion: Optional[bool] = False,
) -> Dict[str, Dict[str, Any]]:
    querytype = "FindEntity"
    entity: Dict[str, Any] = {}
    entity["class"] = "properties"
    if unique_entity:
        entity["unique"] = unique_entity

    results: Dict[str, Any] = {}
    results["blob"] = True
    results["count"] = ""
    results["list"] = ["content"]
    entity["results"] = results

    constraints: Dict[str, Any] = {}
    if deletion:
        constraints["_deletion"] = ["==", 1]
    constraints["name"] = ["==", collection_name]
    entity["constraints"] = constraints

    query: Dict[str, Any] = {}
    query[querytype] = entity
    return query


def _str2bytes(in_str: str) -> bytes:
    return str.encode(in_str)


def _validate_vdms_properties(metadata: Dict[str, Any]) -> Dict:
    new_metadata: Dict[str, Any] = {}
    for key, value in metadata.items():
        if not isinstance(value, list):
            new_metadata[str(key)] = value
    return new_metadata