tasplanning_report/venv/lib/python3.12/site-packages/qdrant_client/conversions/conversion.py

from datetime import date, datetime, timezone
from typing import Any, Mapping, Optional, Sequence, Union, get_args

from google.protobuf.internal.containers import MessageMap
from google.protobuf.json_format import MessageToDict
from google.protobuf.timestamp_pb2 import Timestamp

try:
    from google.protobuf.pyext._message import MessageMapContainer  # type: ignore
except ImportError:
    pass

from qdrant_client import grpc
from qdrant_client.grpc import ListValue, NullValue, Struct, Value
from qdrant_client.http.models import models as rest
from qdrant_client._pydantic_compat import construct, to_jsonable_python
from qdrant_client.conversions.common_types import get_args_subscribed


def has_field(message: Any, field: str) -> bool:
    """
    Same as protobuf HasField, but also works for primitive values
    (https://stackoverflow.com/questions/51918871/check-if-a-field-has-been-set-in-protocol-buffer-3)

    Args:
        message (Any): protobuf message
        field (str): name of the field
    """
    try:
        return message.HasField(field)
    except ValueError:
        all_fields = set([descriptor.name for descriptor, _value in message.ListFields()])
        return field in all_fields


def json_to_value(payload: Any) -> Value:
    if payload is None:
        return Value(null_value=NullValue.NULL_VALUE)
    if isinstance(payload, bool):
        return Value(bool_value=payload)
    if isinstance(payload, int):
        return Value(integer_value=payload)
    if isinstance(payload, float):
        return Value(double_value=payload)
    if isinstance(payload, str):
        return Value(string_value=payload)
    if isinstance(payload, (list, tuple)):
        return Value(list_value=ListValue(values=[json_to_value(v) for v in payload]))
    if isinstance(payload, dict):
        return Value(
            struct_value=Struct(fields=dict((k, json_to_value(v)) for k, v in payload.items()))
        )
    if isinstance(payload, datetime) or isinstance(payload, date):
        return Value(string_value=to_jsonable_python(payload))
    raise ValueError(f"Not supported json value: {payload}")  # pragma: no cover


def value_to_json(value: Value) -> Any:
    if isinstance(value, Value):
        value_ = MessageToDict(value, preserving_proto_field_name=False)
    else:
        value_ = value

    if "integerValue" in value_:
        # by default int are represented as string for precision
        # But in python it is OK to just use `int`
        return int(value_["integerValue"])
    if "doubleValue" in value_:
        return value_["doubleValue"]
    if "stringValue" in value_:
        return value_["stringValue"]
    if "boolValue" in value_:
        return value_["boolValue"]
    if "structValue" in value_:
        if "fields" not in value_["structValue"]:
            return {}
        return dict(
            (key, value_to_json(val)) for key, val in value_["structValue"]["fields"].items()
        )
    if "listValue" in value_:
        if "values" in value_["listValue"]:
            return list(value_to_json(val) for val in value_["listValue"]["values"])
        else:
            return []
    if "nullValue" in value_:
        return None
    raise ValueError(f"Not supported value: {value_}")  # pragma: no cover


def payload_to_grpc(payload: dict[str, Any]) -> dict[str, Value]:
    return dict((key, json_to_value(val)) for key, val in payload.items())


def grpc_to_payload(grpc_: MessageMap[str, Value]) -> dict[str, Any]:
    return dict((key, value_to_json(val)) for key, val in grpc_.items())


def grpc_payload_schema_to_field_type(model: grpc.PayloadSchemaType) -> grpc.FieldType:
    if model == grpc.PayloadSchemaType.Keyword:
        return grpc.FieldType.FieldTypeKeyword
    if model == grpc.PayloadSchemaType.Integer:
        return grpc.FieldType.FieldTypeInteger
    if model == grpc.PayloadSchemaType.Float:
        return grpc.FieldType.FieldTypeFloat
    if model == grpc.PayloadSchemaType.Bool:
        return grpc.FieldType.FieldTypeBool
    if model == grpc.PayloadSchemaType.Geo:
        return grpc.FieldType.FieldTypeGeo
    if model == grpc.PayloadSchemaType.Text:
        return grpc.FieldType.FieldTypeText
    if model == grpc.PayloadSchemaType.Datetime:
        return grpc.FieldType.FieldTypeDatetime
    if model == grpc.PayloadSchemaType.Uuid:
        return grpc.FieldType.FieldTypeUuid

    raise ValueError(f"invalid PayloadSchemaType model: {model}")  # pragma: no cover


def grpc_field_type_to_payload_schema(model: grpc.FieldType) -> grpc.PayloadSchemaType:
    if model == grpc.FieldType.FieldTypeKeyword:
        return grpc.PayloadSchemaType.Keyword
    if model == grpc.FieldType.FieldTypeInteger:
        return grpc.PayloadSchemaType.Integer
    if model == grpc.FieldType.FieldTypeFloat:
        return grpc.PayloadSchemaType.Float
    if model == grpc.FieldType.FieldTypeBool:
        return grpc.PayloadSchemaType.Bool
    if model == grpc.FieldType.FieldTypeGeo:
        return grpc.PayloadSchemaType.Geo
    if model == grpc.FieldType.FieldTypeText:
        return grpc.PayloadSchemaType.Text
    if model == grpc.FieldType.FieldTypeDatetime:
        return grpc.PayloadSchemaType.Datetime
    if model == grpc.FieldType.FieldTypeUuid:
        return grpc.PayloadSchemaType.Uuid

    raise ValueError(f"invalid FieldType model: {model}")  # pragma: no cover


class GrpcToRest:
    @classmethod
    def convert_condition(cls, model: grpc.Condition) -> rest.Condition:
        name = model.WhichOneof("condition_one_of")
        if name is None:
            raise ValueError(f"invalid Condition model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "field":
            return cls.convert_field_condition(val)
        if name == "filter":
            return cls.convert_filter(val)
        if name == "has_id":
            return cls.convert_has_id_condition(val)
        if name == "has_vector":
            return cls.convert_has_vector_condition(val)
        if name == "is_empty":
            return cls.convert_is_empty_condition(val)
        if name == "is_null":
            return cls.convert_is_null_condition(val)
        if name == "nested":
            return cls.convert_nested_condition(val)

        raise ValueError(f"invalid Condition model: {model}")  # pragma: no cover

    @classmethod
    def convert_filter(cls, model: grpc.Filter) -> rest.Filter:
        return rest.Filter(
            must=[cls.convert_condition(condition) for condition in model.must],
            should=[cls.convert_condition(condition) for condition in model.should],
            must_not=[cls.convert_condition(condition) for condition in model.must_not],
            min_should=(
                rest.MinShould(
                    conditions=[
                        cls.convert_condition(condition)
                        for condition in model.min_should.conditions
                    ],
                    min_count=model.min_should.min_count,
                )
                if model.HasField("min_should")
                else None
            ),
        )

    @classmethod
    def convert_range(cls, model: grpc.Range) -> rest.Range:
        return rest.Range(
            gt=model.gt if model.HasField("gt") else None,
            gte=model.gte if model.HasField("gte") else None,
            lt=model.lt if model.HasField("lt") else None,
            lte=model.lte if model.HasField("lte") else None,
        )

    @classmethod
    def convert_timestamp(cls, model: Timestamp) -> datetime:
        return model.ToDatetime(tzinfo=timezone.utc)

    @classmethod
    def convert_datetime_range(cls, model: grpc.DatetimeRange) -> rest.DatetimeRange:
        return rest.DatetimeRange(
            gt=cls.convert_timestamp(model.gt) if model.HasField("gt") else None,
            gte=cls.convert_timestamp(model.gte) if model.HasField("gte") else None,
            lt=cls.convert_timestamp(model.lt) if model.HasField("lt") else None,
            lte=cls.convert_timestamp(model.lte) if model.HasField("lte") else None,
        )

    @classmethod
    def convert_geo_radius(cls, model: grpc.GeoRadius) -> rest.GeoRadius:
        return rest.GeoRadius(center=cls.convert_geo_point(model.center), radius=model.radius)

    @classmethod
    def convert_geo_line_string(cls, model: grpc.GeoLineString) -> rest.GeoLineString:
        return rest.GeoLineString(points=[cls.convert_geo_point(point) for point in model.points])

    @classmethod
    def convert_geo_polygon(cls, model: grpc.GeoPolygon) -> rest.GeoPolygon:
        return rest.GeoPolygon(
            exterior=cls.convert_geo_line_string(model.exterior),
            interiors=(
                [cls.convert_geo_line_string(interior) for interior in model.interiors]
                if model.interiors
                else None
            ),
        )

    @classmethod
    def convert_collection_description(
        cls, model: grpc.CollectionDescription
    ) -> rest.CollectionDescription:
        return rest.CollectionDescription(name=model.name)

    @classmethod
    def convert_collection_info(cls, model: grpc.CollectionInfo) -> rest.CollectionInfo:
        return rest.CollectionInfo(
            config=cls.convert_collection_config(model.config),
            optimizer_status=cls.convert_optimizer_status(model.optimizer_status),
            payload_schema=cls.convert_payload_schema(model.payload_schema),
            segments_count=model.segments_count,
            status=cls.convert_collection_status(model.status),
            vectors_count=model.vectors_count if model.HasField("vectors_count") else None,
            points_count=model.points_count,
            indexed_vectors_count=model.indexed_vectors_count or 0,
        )

    @classmethod
    def convert_optimizer_status(cls, model: grpc.OptimizerStatus) -> rest.OptimizersStatus:
        if model.ok:
            return rest.OptimizersStatusOneOf.OK
        else:
            return rest.OptimizersStatusOneOf1(error=model.error)

    @classmethod
    def convert_collection_config(cls, model: grpc.CollectionConfig) -> rest.CollectionConfig:
        return rest.CollectionConfig(
            hnsw_config=cls.convert_hnsw_config(model.hnsw_config),
            optimizer_config=cls.convert_optimizer_config(model.optimizer_config),
            params=cls.convert_collection_params(model.params),
            wal_config=cls.convert_wal_config(model.wal_config),
            quantization_config=(
                cls.convert_quantization_config(model.quantization_config)
                if model.HasField("quantization_config")
                else None
            ),
            strict_mode_config=(
                cls.convert_strict_mode_config_output(model.strict_mode_config)
                if model.HasField("strict_mode_config")
                else None
            ),
        )

    @classmethod
    def convert_hnsw_config_diff(cls, model: grpc.HnswConfigDiff) -> rest.HnswConfigDiff:
        return rest.HnswConfigDiff(
            ef_construct=model.ef_construct if model.HasField("ef_construct") else None,
            m=model.m if model.HasField("m") else None,
            full_scan_threshold=(
                model.full_scan_threshold if model.HasField("full_scan_threshold") else None
            ),
            max_indexing_threads=(
                model.max_indexing_threads if model.HasField("max_indexing_threads") else None
            ),
            on_disk=model.on_disk if model.HasField("on_disk") else None,
            payload_m=model.payload_m if model.HasField("payload_m") else None,
        )

    @classmethod
    def convert_hnsw_config(cls, model: grpc.HnswConfigDiff) -> rest.HnswConfig:
        return rest.HnswConfig(
            ef_construct=model.ef_construct if model.HasField("ef_construct") else None,
            m=model.m if model.HasField("m") else None,
            full_scan_threshold=(
                model.full_scan_threshold if model.HasField("full_scan_threshold") else None
            ),
            max_indexing_threads=(
                model.max_indexing_threads if model.HasField("max_indexing_threads") else None
            ),
            on_disk=model.on_disk if model.HasField("on_disk") else None,
            payload_m=model.payload_m if model.HasField("payload_m") else None,
        )

    @classmethod
    def convert_max_optimization_threads(
        cls, model: grpc.MaxOptimizationThreads
    ) -> rest.MaxOptimizationThreads:
        name = model.WhichOneof("variant")
        if name is None:
            raise ValueError(f"invalid MaxOptimizationThreads model: {model}")  # pragma: no cover

        if name == "setting":
            if model.setting == grpc.MaxOptimizationThreads.Setting.Auto:
                return rest.MaxOptimizationThreadsSetting.AUTO
            else:
                raise ValueError(
                    f"invalid MaxOptimizationThreads model: {model}"
                )  # pragma: no cover
        elif name == "value":
            return model.value
        else:
            raise ValueError(f"invalid MaxOptimizationThreads model: {model}")  # pragma: no cover

    @classmethod
    def convert_optimizer_config(cls, model: grpc.OptimizersConfigDiff) -> rest.OptimizersConfig:
        max_optimization_threads = None
        if model.HasField("deprecated_max_optimization_threads"):
            max_optimization_threads = model.deprecated_max_optimization_threads
        elif model.HasField("max_optimization_threads"):
            max_optimization_threads = cls.convert_max_optimization_threads(
                model.max_optimization_threads
            )
            if max_optimization_threads == rest.MaxOptimizationThreadsSetting.AUTO:
                max_optimization_threads = None

        return rest.OptimizersConfig(
            default_segment_number=(
                model.default_segment_number if model.HasField("default_segment_number") else None
            ),
            deleted_threshold=(
                model.deleted_threshold if model.HasField("deleted_threshold") else None
            ),
            flush_interval_sec=(
                model.flush_interval_sec if model.HasField("flush_interval_sec") else None
            ),
            indexing_threshold=(
                model.indexing_threshold if model.HasField("indexing_threshold") else None
            ),
            max_optimization_threads=max_optimization_threads,
            max_segment_size=(
                model.max_segment_size if model.HasField("max_segment_size") else None
            ),
            memmap_threshold=(
                model.memmap_threshold if model.HasField("memmap_threshold") else None
            ),
            vacuum_min_vector_number=(
                model.vacuum_min_vector_number
                if model.HasField("vacuum_min_vector_number")
                else None
            ),
        )

    @classmethod
    def convert_distance(cls, model: grpc.Distance) -> rest.Distance:
        if model == grpc.Distance.Cosine:
            return rest.Distance.COSINE
        elif model == grpc.Distance.Euclid:
            return rest.Distance.EUCLID
        elif model == grpc.Distance.Manhattan:
            return rest.Distance.MANHATTAN
        elif model == grpc.Distance.Dot:
            return rest.Distance.DOT
        else:
            raise ValueError(f"invalid Distance model: {model}")  # pragma: no cover

    @classmethod
    def convert_wal_config(cls, model: grpc.WalConfigDiff) -> rest.WalConfig:
        return rest.WalConfig(
            wal_capacity_mb=model.wal_capacity_mb if model.HasField("wal_capacity_mb") else None,
            wal_segments_ahead=(
                model.wal_segments_ahead if model.HasField("wal_segments_ahead") else None
            ),
        )

    @classmethod
    def convert_payload_schema(
        cls, model: dict[str, grpc.PayloadSchemaInfo]
    ) -> dict[str, rest.PayloadIndexInfo]:
        return {key: cls.convert_payload_schema_info(info) for key, info in model.items()}

    @classmethod
    def convert_payload_schema_info(cls, model: grpc.PayloadSchemaInfo) -> rest.PayloadIndexInfo:
        return rest.PayloadIndexInfo(
            data_type=cls.convert_payload_schema_type(model.data_type),
            params=(
                cls.convert_payload_schema_params(model.params)
                if model.HasField("params")
                else None
            ),
            points=model.points,
        )

    @classmethod
    def convert_payload_schema_params(
        cls, model: grpc.PayloadIndexParams
    ) -> rest.PayloadSchemaParams:
        if model.HasField("text_index_params"):
            text_index_params = model.text_index_params
            return cls.convert_text_index_params(text_index_params)
        if model.HasField("integer_index_params"):
            integer_index_params = model.integer_index_params
            return cls.convert_integer_index_params(integer_index_params)
        if model.HasField("keyword_index_params"):
            keyword_index_params = model.keyword_index_params
            return cls.convert_keyword_index_params(keyword_index_params)
        if model.HasField("float_index_params"):
            float_index_params = model.float_index_params
            return cls.convert_float_index_params(float_index_params)
        if model.HasField("geo_index_params"):
            geo_index_params = model.geo_index_params
            return cls.convert_geo_index_params(geo_index_params)
        if model.HasField("bool_index_params"):
            bool_index_params = model.bool_index_params
            return cls.convert_bool_index_params(bool_index_params)
        if model.HasField("datetime_index_params"):
            datetime_index_params = model.datetime_index_params
            return cls.convert_datetime_index_params(datetime_index_params)
        if model.HasField("uuid_index_params"):
            uuid_index_params = model.uuid_index_params
            return cls.convert_uuid_index_params(uuid_index_params)

        raise ValueError(f"invalid PayloadIndexParams model: {model}")  # pragma: no cover

    @classmethod
    def convert_payload_schema_type(cls, model: grpc.PayloadSchemaType) -> rest.PayloadSchemaType:
        if model == grpc.PayloadSchemaType.Float:
            return rest.PayloadSchemaType.FLOAT
        elif model == grpc.PayloadSchemaType.Geo:
            return rest.PayloadSchemaType.GEO
        elif model == grpc.PayloadSchemaType.Integer:
            return rest.PayloadSchemaType.INTEGER
        elif model == grpc.PayloadSchemaType.Keyword:
            return rest.PayloadSchemaType.KEYWORD
        elif model == grpc.PayloadSchemaType.Bool:
            return rest.PayloadSchemaType.BOOL
        elif model == grpc.PayloadSchemaType.Text:
            return rest.PayloadSchemaType.TEXT
        elif model == grpc.PayloadSchemaType.Datetime:
            return rest.PayloadSchemaType.DATETIME
        elif model == grpc.PayloadSchemaType.Uuid:
            return rest.PayloadSchemaType.UUID
        else:
            raise ValueError(f"invalid PayloadSchemaType model: {model}")  # pragma: no cover

    @classmethod
    def convert_collection_status(cls, model: grpc.CollectionStatus) -> rest.CollectionStatus:
        if model == grpc.CollectionStatus.Green:
            return rest.CollectionStatus.GREEN
        elif model == grpc.CollectionStatus.Yellow:
            return rest.CollectionStatus.YELLOW
        elif model == grpc.CollectionStatus.Red:
            return rest.CollectionStatus.RED
        elif model == grpc.CollectionStatus.Grey:
            return rest.CollectionStatus.GREY

        raise ValueError(f"invalid CollectionStatus model: {model}")  # pragma: no cover

    @classmethod
    def convert_update_result(cls, model: grpc.UpdateResult) -> rest.UpdateResult:
        return rest.UpdateResult(
            operation_id=model.operation_id,
            status=cls.convert_update_status(model.status),
        )

    @classmethod
    def convert_update_status(cls, model: grpc.UpdateStatus) -> rest.UpdateStatus:
        if model == grpc.UpdateStatus.Acknowledged:
            return rest.UpdateStatus.ACKNOWLEDGED
        elif model == grpc.UpdateStatus.Completed:
            return rest.UpdateStatus.COMPLETED
        else:
            raise ValueError(f"invalid UpdateStatus model: {model}")  # pragma: no cover

    @classmethod
    def convert_has_id_condition(cls, model: grpc.HasIdCondition) -> rest.HasIdCondition:
        return rest.HasIdCondition(has_id=[cls.convert_point_id(idx) for idx in model.has_id])

    @classmethod
    def convert_has_vector_condition(
        cls, model: grpc.HasVectorCondition
    ) -> rest.HasVectorCondition:
        return rest.HasVectorCondition(has_vector=model.has_vector)

    @classmethod
    def convert_point_id(cls, model: grpc.PointId) -> rest.ExtendedPointId:
        name = model.WhichOneof("point_id_options")

        if name == "num":
            return model.num
        if name == "uuid":
            return model.uuid
        raise ValueError(f"invalid PointId model: {model}")  # pragma: no cover

    @classmethod
    def convert_delete_alias(cls, model: grpc.DeleteAlias) -> rest.DeleteAlias:
        return rest.DeleteAlias(alias_name=model.alias_name)

    @classmethod
    def convert_rename_alias(cls, model: grpc.RenameAlias) -> rest.RenameAlias:
        return rest.RenameAlias(
            old_alias_name=model.old_alias_name, new_alias_name=model.new_alias_name
        )

    @classmethod
    def convert_is_empty_condition(cls, model: grpc.IsEmptyCondition) -> rest.IsEmptyCondition:
        return rest.IsEmptyCondition(is_empty=rest.PayloadField(key=model.key))

    @classmethod
    def convert_is_null_condition(cls, model: grpc.IsNullCondition) -> rest.IsNullCondition:
        return rest.IsNullCondition(is_null=rest.PayloadField(key=model.key))

    @classmethod
    def convert_nested_condition(cls, model: grpc.NestedCondition) -> rest.NestedCondition:
        return rest.NestedCondition(
            nested=rest.Nested(
                key=model.key,
                filter=cls.convert_filter(model.filter),
            )
        )

    @classmethod
    def convert_search_params(cls, model: grpc.SearchParams) -> rest.SearchParams:
        return rest.SearchParams(
            hnsw_ef=model.hnsw_ef if model.HasField("hnsw_ef") else None,
            exact=model.exact if model.HasField("exact") else None,
            quantization=(
                cls.convert_quantization_search_params(model.quantization)
                if model.HasField("quantization")
                else None
            ),
            indexed_only=model.indexed_only if model.HasField("indexed_only") else None,
        )

    @classmethod
    def convert_create_alias(cls, model: grpc.CreateAlias) -> rest.CreateAlias:
        return rest.CreateAlias(collection_name=model.collection_name, alias_name=model.alias_name)

    @classmethod
    def convert_order_value(cls, model: grpc.OrderValue) -> rest.OrderValue:
        name = model.WhichOneof("variant")
        if name is None:
            raise ValueError(f"invalid OrderValue model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "int":
            return val

        if name == "float":
            return val

        raise ValueError(f"invalid OrderValue model: {model}")  # pragma: no cover

    @classmethod
    def convert_scored_point(cls, model: grpc.ScoredPoint) -> rest.ScoredPoint:
        return construct(
            rest.ScoredPoint,
            id=cls.convert_point_id(model.id),
            payload=cls.convert_payload(model.payload) if has_field(model, "payload") else None,
            score=model.score,
            vector=(
                cls.convert_vectors_output(model.vectors) if model.HasField("vectors") else None
            ),
            version=model.version,
            shard_key=(
                cls.convert_shard_key(model.shard_key) if model.HasField("shard_key") else None
            ),
            order_value=(
                cls.convert_order_value(model.order_value)
                if model.HasField("order_value")
                else None
            ),
        )

    @classmethod
    def convert_payload(cls, model: "MessageMapContainer") -> rest.Payload:
        return dict((key, value_to_json(model[key])) for key in model)

    @classmethod
    def convert_values_count(cls, model: grpc.ValuesCount) -> rest.ValuesCount:
        return rest.ValuesCount(
            gt=model.gt if model.HasField("gt") else None,
            gte=model.gte if model.HasField("gte") else None,
            lt=model.lt if model.HasField("lt") else None,
            lte=model.lte if model.HasField("lte") else None,
        )

    @classmethod
    def convert_geo_bounding_box(cls, model: grpc.GeoBoundingBox) -> rest.GeoBoundingBox:
        return rest.GeoBoundingBox(
            bottom_right=cls.convert_geo_point(model.bottom_right),
            top_left=cls.convert_geo_point(model.top_left),
        )

    @classmethod
    def convert_point_struct(cls, model: grpc.PointStruct) -> rest.PointStruct:
        return rest.PointStruct(
            id=cls.convert_point_id(model.id),
            payload=cls.convert_payload(model.payload),
            vector=cls.convert_vectors(model.vectors) if model.HasField("vectors") else None,
        )

    @classmethod
    def convert_field_condition(cls, model: grpc.FieldCondition) -> rest.FieldCondition:
        geo_bounding_box = (
            cls.convert_geo_bounding_box(model.geo_bounding_box)
            if model.HasField("geo_bounding_box")
            else None
        )

        geo_radius = (
            cls.convert_geo_radius(model.geo_radius) if model.HasField("geo_radius") else None
        )

        geo_polygon = (
            cls.convert_geo_polygon(model.geo_polygon) if model.HasField("geo_polygon") else None
        )

        match = cls.convert_match(model.match) if model.HasField("match") else None

        range_: Optional[rest.RangeInterface] = None
        if model.HasField("range"):
            range_ = cls.convert_range(model.range)
        elif model.HasField("datetime_range"):
            range_ = cls.convert_datetime_range(model.datetime_range)

        values_count = (
            cls.convert_values_count(model.values_count)
            if model.HasField("values_count")
            else None
        )

        is_empty = model.is_empty if model.HasField("is_empty") else None

        is_null = model.is_null if model.HasField("is_null") else None

        return rest.FieldCondition(
            key=model.key,
            geo_bounding_box=geo_bounding_box,
            geo_radius=geo_radius,
            geo_polygon=geo_polygon,
            match=match,
            range=range_,
            values_count=values_count,
            is_empty=is_empty,
            is_null=is_null,
        )

    @classmethod
    def convert_match(cls, model: grpc.Match) -> rest.Match:
        name = model.WhichOneof("match_value")
        if name is None:
            raise ValueError(f"invalid Match model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "integer":
            return rest.MatchValue(value=val)
        if name == "boolean":
            return rest.MatchValue(value=val)
        if name == "keyword":
            return rest.MatchValue(value=val)
        if name == "text":
            return rest.MatchText(text=val)
        if name == "keywords":
            return rest.MatchAny(any=list(val.strings))
        if name == "integers":
            return rest.MatchAny(any=list(val.integers))
        if name == "except_keywords":
            return rest.MatchExcept(**{"except": list(val.strings)})
        if name == "except_integers":
            return rest.MatchExcept(**{"except": list(val.integers)})
        if name == "phrase":
            return rest.MatchPhrase(phrase=val)
        raise ValueError(f"invalid Match model: {model}")  # pragma: no cover

    @classmethod
    def convert_wal_config_diff(cls, model: grpc.WalConfigDiff) -> rest.WalConfigDiff:
        return rest.WalConfigDiff(
            wal_capacity_mb=model.wal_capacity_mb if model.HasField("wal_capacity_mb") else None,
            wal_segments_ahead=(
                model.wal_segments_ahead if model.HasField("wal_segments_ahead") else None
            ),
        )

    @classmethod
    def convert_collection_params(cls, model: grpc.CollectionParams) -> rest.CollectionParams:
        return rest.CollectionParams(
            vectors=(
                cls.convert_vectors_config(model.vectors_config)
                if model.HasField("vectors_config")
                else None
            ),
            shard_number=model.shard_number,
            on_disk_payload=model.on_disk_payload,
            replication_factor=(
                model.replication_factor if model.HasField("replication_factor") else None
            ),
            read_fan_out_factor=(
                model.read_fan_out_factor if model.HasField("read_fan_out_factor") else None
            ),
            write_consistency_factor=(
                model.write_consistency_factor
                if model.HasField("write_consistency_factor")
                else None
            ),
            sparse_vectors=(
                cls.convert_sparse_vector_config(model.sparse_vectors_config)
                if model.HasField("sparse_vectors_config")
                else None
            ),
            sharding_method=(
                cls.convert_sharding_method(model.sharding_method)
                if model.HasField("sharding_method")
                else None
            ),
        )

    @classmethod
    def convert_optimizers_config_diff(
        cls, model: grpc.OptimizersConfigDiff
    ) -> rest.OptimizersConfigDiff:
        max_optimization_threads = None
        if model.HasField("deprecated_max_optimization_threads"):
            max_optimization_threads = model.deprecated_max_optimization_threads
        elif model.HasField("max_optimization_threads"):
            max_optimization_threads = cls.convert_max_optimization_threads(
                model.max_optimization_threads
            )

        return rest.OptimizersConfigDiff(
            default_segment_number=(
                model.default_segment_number if model.HasField("default_segment_number") else None
            ),
            deleted_threshold=(
                model.deleted_threshold if model.HasField("deleted_threshold") else None
            ),
            flush_interval_sec=(
                model.flush_interval_sec if model.HasField("flush_interval_sec") else None
            ),
            indexing_threshold=(
                model.indexing_threshold if model.HasField("indexing_threshold") else None
            ),
            max_optimization_threads=max_optimization_threads,
            max_segment_size=(
                model.max_segment_size if model.HasField("max_segment_size") else None
            ),
            memmap_threshold=(
                model.memmap_threshold if model.HasField("memmap_threshold") else None
            ),
            vacuum_min_vector_number=(
                model.vacuum_min_vector_number
                if model.HasField("vacuum_min_vector_number")
                else None
            ),
        )

    @classmethod
    def convert_update_collection(cls, model: grpc.UpdateCollection) -> rest.UpdateCollection:
        return rest.UpdateCollection(
            vectors=(
                cls.convert_vectors_config_diff(model.vectors_config)
                if model.HasField("vectors_config")
                else None
            ),
            optimizers_config=(
                cls.convert_optimizers_config_diff(model.optimizers_config)
                if model.HasField("optimizers_config")
                else None
            ),
            params=(
                cls.convert_collection_params_diff(model.params)
                if model.HasField("params")
                else None
            ),
            hnsw_config=(
                cls.convert_hnsw_config_diff(model.hnsw_config)
                if model.HasField("hnsw_config")
                else None
            ),
            quantization_config=(
                cls.convert_quantization_config_diff(model.quantization_config)
                if model.HasField("quantization_config")
                else None
            ),
        )

    @classmethod
    def convert_geo_point(cls, model: grpc.GeoPoint) -> rest.GeoPoint:
        return rest.GeoPoint(
            lat=model.lat,
            lon=model.lon,
        )

    @classmethod
    def convert_alias_operations(cls, model: grpc.AliasOperations) -> rest.AliasOperations:
        name = model.WhichOneof("action")
        if name is None:
            raise ValueError(f"invalid AliasOperations model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "rename_alias":
            return rest.RenameAliasOperation(rename_alias=cls.convert_rename_alias(val))
        if name == "create_alias":
            return rest.CreateAliasOperation(create_alias=cls.convert_create_alias(val))
        if name == "delete_alias":
            return rest.DeleteAliasOperation(delete_alias=cls.convert_delete_alias(val))

        raise ValueError(f"invalid AliasOperations model: {model}")  # pragma: no cover

    @classmethod
    def convert_alias_description(cls, model: grpc.AliasDescription) -> rest.AliasDescription:
        return rest.AliasDescription(
            alias_name=model.alias_name,
            collection_name=model.collection_name,
        )

    @classmethod
    def convert_points_selector(
        cls,
        model: grpc.PointsSelector,
        shard_key_selector: Optional[grpc.ShardKeySelector] = None,
    ) -> rest.PointsSelector:
        name = model.WhichOneof("points_selector_one_of")
        if name is None:
            raise ValueError(f"invalid PointsSelector model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "points":
            return rest.PointIdsList(
                points=[cls.convert_point_id(point) for point in val.ids],
                shard_key=shard_key_selector,
            )
        if name == "filter":
            return rest.FilterSelector(
                filter=cls.convert_filter(val),
                shard_key=shard_key_selector,
            )
        raise ValueError(f"invalid PointsSelector model: {model}")  # pragma: no cover

    @classmethod
    def convert_with_payload_selector(
        cls, model: grpc.WithPayloadSelector
    ) -> rest.WithPayloadInterface:
        name = model.WhichOneof("selector_options")
        if name is None:
            raise ValueError(f"invalid WithPayloadSelector model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "enable":
            return val
        if name == "include":
            return list(val.fields)
        if name == "exclude":
            return rest.PayloadSelectorExclude(exclude=list(val.fields))

        raise ValueError(f"invalid WithPayloadSelector model: {model}")  # pragma: no cover

    @classmethod
    def convert_with_payload_interface(
        cls, model: grpc.WithPayloadSelector
    ) -> rest.WithPayloadInterface:
        return cls.convert_with_payload_selector(model)

    @classmethod
    def convert_retrieved_point(cls, model: grpc.RetrievedPoint) -> rest.Record:
        return rest.Record(
            id=cls.convert_point_id(model.id),
            payload=cls.convert_payload(model.payload),
            vector=(
                cls.convert_vectors_output(model.vectors) if model.HasField("vectors") else None
            ),
            shard_key=(
                cls.convert_shard_key(model.shard_key) if model.HasField("shard_key") else None
            ),
            order_value=(
                cls.convert_order_value(model.order_value)
                if model.HasField("order_value")
                else None
            ),
        )

    @classmethod
    def convert_record(cls, model: grpc.RetrievedPoint) -> rest.Record:
        return cls.convert_retrieved_point(model)

    @classmethod
    def convert_count_result(cls, model: grpc.CountResult) -> rest.CountResult:
        return rest.CountResult(count=model.count)

    @classmethod
    def convert_snapshot_description(
        cls, model: grpc.SnapshotDescription
    ) -> rest.SnapshotDescription:
        return rest.SnapshotDescription(
            name=model.name,
            creation_time=(
                model.creation_time.ToDatetime().isoformat()
                if model.HasField("creation_time")
                else None
            ),
            size=model.size,
        )

    @classmethod
    def convert_datatype(cls, model: grpc.Datatype) -> rest.Datatype:
        if model == grpc.Datatype.Float32:
            return rest.Datatype.FLOAT32
        elif model == grpc.Datatype.Uint8:
            return rest.Datatype.UINT8
        elif model == grpc.Datatype.Float16:
            return rest.Datatype.FLOAT16
        else:
            raise ValueError(f"invalid Datatype model: {model}")  # pragma: no cover

    @classmethod
    def convert_vector_params(cls, model: grpc.VectorParams) -> rest.VectorParams:
        return rest.VectorParams(
            size=model.size,
            distance=cls.convert_distance(model.distance),
            hnsw_config=(
                cls.convert_hnsw_config_diff(model.hnsw_config)
                if model.HasField("hnsw_config")
                else None
            ),
            quantization_config=(
                cls.convert_quantization_config(model.quantization_config)
                if model.HasField("quantization_config")
                else None
            ),
            on_disk=model.on_disk if model.HasField("on_disk") else None,
            datatype=cls.convert_datatype(model.datatype) if model.HasField("datatype") else None,
            multivector_config=(
                cls.convert_multivector_config(model.multivector_config)
                if model.HasField("multivector_config")
                else None
            ),
        )

    @classmethod
    def convert_multivector_config(cls, model: grpc.MultiVectorConfig) -> rest.MultiVectorConfig:
        return rest.MultiVectorConfig(
            comparator=cls.convert_multivector_comparator(model.comparator)
        )

    @classmethod
    def convert_multivector_comparator(
        cls, model: grpc.MultiVectorComparator
    ) -> rest.MultiVectorComparator:
        if model == grpc.MultiVectorComparator.MaxSim:
            return rest.MultiVectorComparator.MAX_SIM

        raise ValueError(f"invalid MultiVectorComparator model: {model}")  # pragma: no cover

    @classmethod
    def convert_vectors_config(cls, model: grpc.VectorsConfig) -> rest.VectorsConfig:
        name = model.WhichOneof("config")
        if name is None:
            raise ValueError(f"invalid VectorsConfig model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "params":
            return cls.convert_vector_params(val)
        if name == "params_map":
            return dict(
                (key, cls.convert_vector_params(vec_params)) for key, vec_params in val.map.items()
            )
        raise ValueError(f"invalid VectorsConfig model: {model}")  # pragma: no cover

    @classmethod
    def _convert_vector(
        cls, model: Union[grpc.Vector, grpc.VectorOutput]
    ) -> tuple[
        Optional[str],
        Union[
            list[float],
            list[list[float]],
            rest.SparseVector,
            grpc.Document,
            grpc.Image,
            grpc.InferenceObject,
        ],
    ]:
        """Parse common parts of vector structs

        Args:
            model: Vector or VectorOutput

        Returns:
            Tuple of name and value, name is None if the struct was parsed and returned with the converted value,
            otherwise it's propagated for further processing along with the raw value
        """
        name = model.WhichOneof("vector")
        if name is None:
            if model.HasField("indices"):
                return None, rest.SparseVector(indices=model.indices.data[:], values=model.data[:])
            if model.HasField("vectors_count"):
                vectors_count = model.vectors_count
                vectors = model.data
                step = len(vectors) // vectors_count
                return None, [vectors[i : i + step] for i in range(0, len(vectors), step)]

            return None, model.data[:]

        val = getattr(model, name)
        if name == "dense":
            return None, cls.convert_dense_vector(val)

        if name == "sparse":
            return None, cls.convert_sparse_vector(val)

        if name == "multi_dense":
            return None, cls.convert_multi_dense_vector(val)

        return name, val

    @classmethod
    def convert_vector(
        cls, model: grpc.Vector
    ) -> Union[
        list[float],
        list[list[float]],
        rest.SparseVector,
        rest.Document,
        rest.Image,
        rest.InferenceObject,
    ]:
        name, val = cls._convert_vector(model)

        if name is None:
            return val

        if name == "document":
            return cls.convert_document(val)

        if name == "image":
            return cls.convert_image(val)

        if name == "object":
            return cls.convert_inference_object(val)

        raise ValueError(f"invalid Vector model: {model}")  # pragma: no cover

    @classmethod
    def convert_vector_output(
        cls, model: grpc.VectorOutput
    ) -> Union[list[float], list[list[float]], rest.SparseVector]:
        name, val = cls._convert_vector(model)
        if name is None:
            return val
        raise ValueError(f"invalid Vector model: {model}")  # pragma: no cover

    @classmethod
    def convert_named_vectors(cls, model: grpc.NamedVectors) -> dict[str, rest.Vector]:
        vectors = {}
        for name, vector in model.vectors.items():
            vectors[name] = cls.convert_vector(vector)

        return vectors

    @classmethod
    def convert_named_vectors_output(
        cls, model: grpc.NamedVectorsOutput
    ) -> dict[str, rest.VectorOutput]:
        vectors = {}
        for name, vector in model.vectors.items():
            vectors[name] = cls.convert_vector_output(vector)

        return vectors

    @classmethod
    def convert_vectors(cls, model: grpc.Vectors) -> rest.VectorStruct:
        name = model.WhichOneof("vectors_options")
        if name is None:
            raise ValueError(f"invalid Vectors model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "vector":
            return cls.convert_vector(val)
        if name == "vectors":
            return cls.convert_named_vectors(val)
        raise ValueError(f"invalid Vectors model: {model}")  # pragma: no cover

    @classmethod
    def convert_vectors_output(cls, model: grpc.VectorsOutput) -> rest.VectorStructOutput:
        name = model.WhichOneof("vectors_options")
        if name is None:
            raise ValueError(f"invalid VectorsOutput model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "vector":
            return cls.convert_vector_output(val)
        if name == "vectors":
            return cls.convert_named_vectors_output(val)
        raise ValueError(f"invalid VectorsOutput model: {model}")  # pragma: no cover

    @classmethod
    def convert_dense_vector(cls, model: grpc.DenseVector) -> list[float]:
        return model.data[:]

    @classmethod
    def convert_sparse_vector(cls, model: grpc.SparseVector) -> rest.SparseVector:
        return rest.SparseVector(indices=model.indices[:], values=model.values[:])

    @classmethod
    def convert_multi_dense_vector(cls, model: grpc.MultiDenseVector) -> list[list[float]]:
        return [cls.convert_dense_vector(vector) for vector in model.vectors]

    @classmethod
    def convert_document(cls, model: grpc.Document) -> rest.Document:
        return rest.Document(
            text=model.text,
            model=model.model,
            options=grpc_to_payload(model.options),
        )

    @classmethod
    def convert_image(cls, model: grpc.Image) -> rest.Image:
        return rest.Image(
            image=value_to_json(model.image),
            model=model.model,
            options=grpc_to_payload(model.options),
        )

    @classmethod
    def convert_inference_object(cls, model: grpc.InferenceObject) -> rest.InferenceObject:
        return rest.InferenceObject(
            object=value_to_json(model.object),
            model=model.model,
            options=grpc_to_payload(model.options),
        )

    @classmethod
    def convert_vector_input(cls, model: grpc.VectorInput) -> rest.VectorInput:
        name = model.WhichOneof("variant")
        if name is None:
            raise ValueError(f"invalid VectorInput model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "id":
            return cls.convert_point_id(val)
        if name == "dense":
            return cls.convert_dense_vector(val)
        if name == "sparse":
            return cls.convert_sparse_vector(val)
        if name == "multi_dense":
            return cls.convert_multi_dense_vector(val)
        if name == "document":
            return cls.convert_document(val)
        if name == "image":
            return cls.convert_image(val)
        if name == "object":
            return cls.convert_inference_object(val)
        raise ValueError(f"invalid VectorInput model: {model}")  # pragma: no cover

    @classmethod
    def convert_recommend_input(cls, model: grpc.RecommendInput) -> rest.RecommendInput:
        return rest.RecommendInput(
            positive=[cls.convert_vector_input(vector) for vector in model.positive],
            negative=[cls.convert_vector_input(vector) for vector in model.negative],
            strategy=(
                cls.convert_recommend_strategy(model.strategy)
                if model.HasField("strategy")
                else None
            ),
        )

    @classmethod
    def convert_context_input_pair(cls, model: grpc.ContextInputPair) -> rest.ContextPair:
        return rest.ContextPair(
            positive=cls.convert_vector_input(model.positive),
            negative=cls.convert_vector_input(model.negative),
        )

    @classmethod
    def convert_context_input(cls, model: grpc.ContextInput) -> rest.ContextInput:
        return [cls.convert_context_input_pair(pair) for pair in model.pairs]

    @classmethod
    def convert_discover_input(cls, model: grpc.DiscoverInput) -> rest.DiscoverInput:
        return rest.DiscoverInput(
            target=cls.convert_vector_input(model.target),
            context=cls.convert_context_input(model.context),
        )

    @classmethod
    def convert_fusion(cls, model: grpc.Fusion) -> rest.Fusion:
        if model == grpc.Fusion.RRF:
            return rest.Fusion.RRF

        if model == grpc.Fusion.DBSF:
            return rest.Fusion.DBSF

        raise ValueError(f"invalid Fusion model: {model}")  # pragma: no cover

    @classmethod
    def convert_sample(cls, model: grpc.Sample) -> rest.Sample:
        if model == grpc.Sample.Random:
            return rest.Sample.RANDOM

        raise ValueError(f"invalid Sample model: {model}")  # pragma: no cover

    @classmethod
    def convert_formula_query(cls, model: grpc.Formula) -> rest.FormulaQuery:
        defaults = grpc_to_payload(model.defaults)
        return rest.FormulaQuery(
            formula=cls.convert_expression(model.expression), defaults=defaults
        )

    @classmethod
    def convert_expression(cls, model: grpc.Expression) -> rest.Expression:
        name = model.WhichOneof("variant")
        if name is None:
            raise ValueError(f"invalid Query model: {model}")  # pragma: no cover

        if name == "constant":
            return model.constant
        if name == "variable":
            return model.variable
        if name == "condition":
            return cls.convert_condition(model.condition)
        if name == "datetime":
            return rest.DatetimeExpression(datetime=model.datetime)
        if name == "datetime_key":
            return rest.DatetimeKeyExpression(datetime_key=model.datetime_key)
        if name == "sum":
            return cls.convert_sum_expression(model.sum)
        if name == "mult":
            return cls.convert_mult_expression(model.mult)
        if name == "div":
            return cls.convert_div_expression(model.div)
        if name == "abs":
            return rest.AbsExpression(abs=cls.convert_expression(model.abs))
        if name == "neg":
            return rest.NegExpression(neg=cls.convert_expression(model.neg))
        if name == "log10":
            return rest.Log10Expression(log10=cls.convert_expression(model.log10))
        if name == "ln":
            return rest.LnExpression(ln=cls.convert_expression(model.ln))
        if name == "sqrt":
            return rest.SqrtExpression(sqrt=cls.convert_expression(model.sqrt))
        if name == "exp":
            return rest.ExpExpression(exp=cls.convert_expression(model.exp))
        if name == "pow":
            return cls.convert_pow_expression(model.pow)
        if name == "geo_distance":
            return cls.convert_geo_distance(model.geo_distance)
        if name == "lin_decay":
            return rest.LinDecayExpression(
                lin_decay=cls.convert_decay_params_expression(model.lin_decay)
            )
        if name == "exp_decay":
            return rest.ExpDecayExpression(
                exp_decay=cls.convert_decay_params_expression(model.exp_decay)
            )
        if name == "gauss_decay":
            return rest.GaussDecayExpression(
                gauss_decay=cls.convert_decay_params_expression(model.gauss_decay)
            )

        raise ValueError(f"Unknown function name: {name}")

    @classmethod
    def convert_sum_expression(cls, model: grpc.SumExpression) -> rest.SumExpression:
        return rest.SumExpression(sum=[cls.convert_expression(expr) for expr in model.sum])

    @classmethod
    def convert_mult_expression(cls, model: grpc.MultExpression) -> rest.MultExpression:
        return rest.MultExpression(mult=[cls.convert_expression(expr) for expr in model.mult])

    @classmethod
    def convert_div_expression(cls, model: grpc.DivExpression) -> rest.DivExpression:
        left = cls.convert_expression(model.left)
        right = cls.convert_expression(model.right)
        by_zero_default = model.by_zero_default if model.HasField("by_zero_default") else None
        params = rest.DivParams(left=left, right=right, by_zero_default=by_zero_default)
        return rest.DivExpression(div=params)

    @classmethod
    def convert_pow_expression(cls, model: grpc.PowExpression) -> rest.PowExpression:
        base = cls.convert_expression(model.base)
        exponent = cls.convert_expression(model.exponent)
        params = rest.PowParams(base=base, exponent=exponent)
        return rest.PowExpression(pow=params)

    @classmethod
    def convert_geo_distance(cls, model: grpc.GeoDistance) -> rest.GeoDistance:
        origin = cls.convert_geo_point(model.origin)
        params = rest.GeoDistanceParams(origin=origin, to=model.to)
        return rest.GeoDistance(geo_distance=params)

    @classmethod
    def convert_decay_params_expression(
        cls, model: grpc.DecayParamsExpression
    ) -> rest.DecayParamsExpression:
        return rest.DecayParamsExpression(
            x=cls.convert_expression(model.x),
            target=cls.convert_expression(model.target) if model.HasField("target") else None,
            midpoint=model.midpoint if model.HasField("midpoint") else None,
            scale=model.scale if model.HasField("scale") else None,
        )

    @classmethod
    def convert_mmr(cls, model: grpc.Mmr) -> rest.Mmr:
        return rest.Mmr(
            diversity=model.diversity if model.HasField("diversity") else None,
            candidates_limit=model.candidates_limit
            if model.HasField("candidates_limit")
            else None,
        )

    @classmethod
    def convert_query(cls, model: grpc.Query) -> rest.Query:
        name = model.WhichOneof("variant")
        if name is None:
            raise ValueError(f"invalid Query model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "nearest":
            return rest.NearestQuery(nearest=cls.convert_vector_input(val))

        if name == "recommend":
            return rest.RecommendQuery(recommend=cls.convert_recommend_input(val))

        if name == "discover":
            return rest.DiscoverQuery(discover=cls.convert_discover_input(val))

        if name == "context":
            return rest.ContextQuery(context=cls.convert_context_input(val))

        if name == "order_by":
            return rest.OrderByQuery(order_by=cls.convert_order_by(val))

        if name == "fusion":
            return rest.FusionQuery(fusion=cls.convert_fusion(val))

        if name == "sample":
            return rest.SampleQuery(sample=cls.convert_sample(val))

        if name == "formula":
            return cls.convert_formula_query(val)

        if name == "nearest_with_mmr":
            val = model.nearest_with_mmr
            return rest.NearestQuery(
                nearest=cls.convert_vector_input(val.nearest), mmr=cls.convert_mmr(val.mmr)
            )

        raise ValueError(f"invalid Query model: {model}")  # pragma: no cover

    @classmethod
    def convert_prefetch_query(cls, model: grpc.PrefetchQuery) -> rest.Prefetch:
        return rest.Prefetch(
            prefetch=(
                [cls.convert_prefetch_query(prefetch) for prefetch in model.prefetch]
                if len(model.prefetch) != 0
                else None
            ),
            query=cls.convert_query(model.query) if model.HasField("query") else None,
            using=model.using if model.HasField("using") else None,
            filter=cls.convert_filter(model.filter) if model.HasField("filter") else None,
            params=cls.convert_search_params(model.params) if model.HasField("params") else None,
            score_threshold=model.score_threshold if model.HasField("score_threshold") else None,
            limit=model.limit if model.HasField("limit") else None,
            lookup_from=(
                cls.convert_lookup_location(model.lookup_from)
                if model.HasField("lookup_from")
                else None
            ),
        )

    @classmethod
    def convert_vectors_selector(cls, model: grpc.VectorsSelector) -> list[str]:
        return model.names[:]

    @classmethod
    def convert_with_vectors_selector(cls, model: grpc.WithVectorsSelector) -> rest.WithVector:
        name = model.WhichOneof("selector_options")
        if name is None:
            raise ValueError(f"invalid WithVectorsSelector model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "enable":
            return val
        if name == "include":
            return cls.convert_vectors_selector(val)
        raise ValueError(f"invalid WithVectorsSelector model: {model}")  # pragma: no cover

    @classmethod
    def convert_search_points(cls, model: grpc.SearchPoints) -> rest.SearchRequest:
        vector = (
            rest.NamedVector(name=model.vector_name, vector=model.vector[:])
            if not model.HasField("sparse_indices")
            else (
                rest.NamedSparseVector(
                    name=model.vector_name,
                    vector=rest.SparseVector(
                        indices=model.sparse_indices.data[:], values=model.vector[:]
                    ),
                )
            )
        )
        return rest.SearchRequest(
            vector=vector,
            filter=cls.convert_filter(model.filter) if model.HasField("filter") else None,
            limit=model.limit,
            with_payload=(
                cls.convert_with_payload_interface(model.with_payload)
                if model.HasField("with_payload")
                else None
            ),
            params=cls.convert_search_params(model.params) if model.HasField("params") else None,
            score_threshold=model.score_threshold if model.HasField("score_threshold") else None,
            offset=model.offset if model.HasField("offset") else None,
            with_vector=(
                cls.convert_with_vectors_selector(model.with_vectors)
                if model.HasField("with_vectors")
                else None
            ),
            shard_key=(
                cls.convert_shard_key_selector(model.shard_key_selector)
                if model.HasField("shard_key_selector")
                else None
            ),
        )

    @classmethod
    def convert_query_points(cls, model: grpc.QueryPoints) -> rest.QueryRequest:
        return rest.QueryRequest(
            shard_key=(
                cls.convert_shard_key_selector(model.shard_key_selector)
                if model.HasField("shard_key_selector")
                else None
            ),
            prefetch=(
                [cls.convert_prefetch_query(prefetch) for prefetch in model.prefetch]
                if len(model.prefetch) != 0
                else None
            ),
            query=cls.convert_query(model.query) if model.HasField("query") else None,
            using=model.using if model.HasField("using") else None,
            filter=cls.convert_filter(model.filter) if model.HasField("filter") else None,
            params=cls.convert_search_params(model.params) if model.HasField("params") else None,
            score_threshold=model.score_threshold if model.HasField("score_threshold") else None,
            limit=model.limit if model.HasField("limit") else None,
            offset=model.offset if model.HasField("offset") else None,
            with_vector=(
                cls.convert_with_vectors_selector(model.with_vectors)
                if model.HasField("with_vectors")
                else None
            ),
            with_payload=(
                cls.convert_with_payload_interface(model.with_payload)
                if model.HasField("with_payload")
                else None
            ),
            lookup_from=(
                cls.convert_lookup_location(model.lookup_from)
                if model.HasField("lookup_from")
                else None
            ),
        )

    @classmethod
    def convert_recommend_points(cls, model: grpc.RecommendPoints) -> rest.RecommendRequest:
        positive_ids = [cls.convert_point_id(point_id) for point_id in model.positive]
        negative_ids = [cls.convert_point_id(point_id) for point_id in model.negative]

        positive_vectors = [cls.convert_vector(vector) for vector in model.positive_vectors]
        negative_vectors = [cls.convert_vector(vector) for vector in model.negative_vectors]

        return rest.RecommendRequest(
            positive=positive_ids + positive_vectors,
            negative=negative_ids + negative_vectors,
            filter=cls.convert_filter(model.filter) if model.HasField("filter") else None,
            limit=model.limit,
            with_payload=(
                cls.convert_with_payload_interface(model.with_payload)
                if model.HasField("with_payload")
                else None
            ),
            params=cls.convert_search_params(model.params) if model.HasField("params") else None,
            score_threshold=model.score_threshold if model.HasField("score_threshold") else None,
            offset=model.offset if model.HasField("offset") else None,
            with_vector=(
                cls.convert_with_vectors_selector(model.with_vectors)
                if model.HasField("with_vectors")
                else None
            ),
            using=model.using,
            lookup_from=(
                cls.convert_lookup_location(model.lookup_from)
                if model.HasField("lookup_from")
                else None
            ),
            strategy=(
                cls.convert_recommend_strategy(model.strategy)
                if model.HasField("strategy")
                else None
            ),
            shard_key=(
                cls.convert_shard_key_selector(model.shard_key_selector)
                if model.HasField("shard_key_selector")
                else None
            ),
        )

    @classmethod
    def convert_discover_points(cls, model: grpc.DiscoverPoints) -> rest.DiscoverRequest:
        target = cls.convert_target_vector(model.target) if model.HasField("target") else None
        context = [cls.convert_context_example_pair(pair) for pair in model.context]
        return rest.DiscoverRequest(
            target=target,
            context=context,
            filter=cls.convert_filter(model.filter) if model.HasField("filter") else None,
            limit=model.limit,
            with_payload=(
                cls.convert_with_payload_interface(model.with_payload)
                if model.HasField("with_payload")
                else None
            ),
            params=cls.convert_search_params(model.params) if model.HasField("params") else None,
            offset=model.offset if model.HasField("offset") else None,
            with_vector=(
                cls.convert_with_vectors_selector(model.with_vectors)
                if model.HasField("with_vectors")
                else None
            ),
            using=model.using,
            lookup_from=(
                cls.convert_lookup_location(model.lookup_from)
                if model.HasField("lookup_from")
                else None
            ),
            shard_key=(
                cls.convert_shard_key_selector(model.shard_key_selector)
                if model.HasField("shard_key_selector")
                else None
            ),
        )

    @classmethod
    def convert_vector_example(cls, model: grpc.VectorExample) -> rest.RecommendExample:
        if model.HasField("vector"):
            return cls.convert_vector(model.vector)
        if model.HasField("id"):
            return cls.convert_point_id(model.id)

        raise ValueError(f"invalid VectorExample model: {model}")  # pragma: no cover

    @classmethod
    def convert_target_vector(cls, model: grpc.TargetVector) -> rest.RecommendExample:
        if model.HasField("single"):
            return cls.convert_vector_example(model.single)

        raise ValueError(f"invalid TargetVector model: {model}")  # pragma: no cover

    @classmethod
    def convert_context_example_pair(
        cls, model: grpc.ContextExamplePair
    ) -> rest.ContextExamplePair:
        return rest.ContextExamplePair(
            positive=cls.convert_vector_example(model.positive),
            negative=cls.convert_vector_example(model.negative),
        )

    @classmethod
    def convert_tokenizer_type(cls, model: grpc.TokenizerType) -> rest.TokenizerType:
        if model == grpc.Unknown:
            return None

        if model == grpc.Prefix:
            return rest.TokenizerType.PREFIX
        if model == grpc.Whitespace:
            return rest.TokenizerType.WHITESPACE
        if model == grpc.Word:
            return rest.TokenizerType.WORD
        if model == grpc.Multilingual:
            return rest.TokenizerType.MULTILINGUAL
        raise ValueError(f"invalid TokenizerType model: {model}")  # pragma: no cover

    @classmethod
    def convert_text_index_params(cls, model: grpc.TextIndexParams) -> rest.TextIndexParams:
        return rest.TextIndexParams(
            type="text",
            tokenizer=cls.convert_tokenizer_type(model.tokenizer),
            min_token_len=model.min_token_len if model.HasField("min_token_len") else None,
            max_token_len=model.max_token_len if model.HasField("max_token_len") else None,
            lowercase=model.lowercase if model.HasField("lowercase") else None,
            phrase_matching=model.phrase_matching if model.HasField("phrase_matching") else None,
            stopwords=cls.convert_stopwords(model.stopwords)
            if model.HasField("stopwords")
            else None,
            on_disk=model.on_disk if model.HasField("on_disk") else None,
            stemmer=cls.convert_stemmer(model.stemmer) if model.HasField("stemmer") else None,
        )

    @classmethod
    def convert_stopwords(cls, model: grpc.StopwordsSet) -> rest.StopwordsInterface:
        languages = model.languages[:]
        custom = model.custom[:]

        if len(languages) == 1 and not custom:
            return rest.Language(languages[0])
        return rest.StopwordsSet(languages=languages, custom=custom)

    @classmethod
    def convert_stemmer(cls, model: grpc.StemmingAlgorithm) -> rest.StemmingAlgorithm:
        name = model.WhichOneof("stemming_params")
        if name is None:
            raise ValueError(f"invalid StemmingAlgorithm model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "snowball":
            return cls.convert_snowball_parameters(val)
        raise ValueError(f"invalid StemmingAlgorithm model: {model}")  # pragma: no cover

    @classmethod
    def convert_snowball_parameters(cls, model: grpc.SnowballParams) -> rest.SnowballParams:
        return rest.SnowballParams(
            type=rest.Snowball.SNOWBALL, language=rest.SnowballLanguage(model.language)
        )

    @classmethod
    def convert_integer_index_params(
        cls, model: grpc.IntegerIndexParams
    ) -> rest.IntegerIndexParams:
        return rest.IntegerIndexParams(
            type=rest.IntegerIndexType.INTEGER,
            range=model.range,
            lookup=model.lookup,
            is_principal=model.is_principal if model.HasField("is_principal") else None,
            on_disk=model.on_disk if model.HasField("on_disk") else None,
        )

    @classmethod
    def convert_keyword_index_params(
        cls, model: grpc.KeywordIndexParams
    ) -> rest.KeywordIndexParams:
        return rest.KeywordIndexParams(
            type=rest.KeywordIndexType.KEYWORD,
            is_tenant=model.is_tenant if model.HasField("is_tenant") else None,
            on_disk=model.on_disk if model.HasField("on_disk") else None,
        )

    @classmethod
    def convert_float_index_params(cls, model: grpc.FloatIndexParams) -> rest.FloatIndexParams:
        return rest.FloatIndexParams(
            type=rest.FloatIndexType.FLOAT,
            is_principal=model.is_principal if model.HasField("is_principal") else None,
            on_disk=model.on_disk if model.HasField("on_disk") else None,
        )

    @classmethod
    def convert_geo_index_params(cls, model: grpc.GeoIndexParams) -> rest.GeoIndexParams:
        return rest.GeoIndexParams(
            type=rest.GeoIndexType.GEO,
            on_disk=model.on_disk if model.HasField("on_disk") else None,
        )

    @classmethod
    def convert_bool_index_params(cls, model: grpc.BoolIndexParams) -> rest.BoolIndexParams:
        return rest.BoolIndexParams(
            type=rest.BoolIndexType.BOOL,
            on_disk=model.on_disk if model.HasField("on_disk") else None,
        )

    @classmethod
    def convert_datetime_index_params(
        cls, model: grpc.DatetimeIndexParams
    ) -> rest.DatetimeIndexParams:
        return rest.DatetimeIndexParams(
            type=rest.DatetimeIndexType.DATETIME,
            is_principal=model.is_principal if model.HasField("is_principal") else None,
            on_disk=model.on_disk if model.HasField("on_disk") else None,
        )

    @classmethod
    def convert_uuid_index_params(cls, model: grpc.UuidIndexParams) -> rest.UuidIndexParams:
        return rest.UuidIndexParams(
            type=rest.UuidIndexType.UUID,
            is_tenant=model.is_tenant if model.HasField("is_tenant") else None,
            on_disk=model.on_disk if model.HasField("on_disk") else None,
        )

    @classmethod
    def convert_collection_params_diff(
        cls, model: grpc.CollectionParamsDiff
    ) -> rest.CollectionParamsDiff:
        return rest.CollectionParamsDiff(
            replication_factor=(
                model.replication_factor if model.HasField("replication_factor") else None
            ),
            write_consistency_factor=(
                model.write_consistency_factor
                if model.HasField("write_consistency_factor")
                else None
            ),
            read_fan_out_factor=(
                model.read_fan_out_factor if model.HasField("read_fan_out_factor") else None
            ),
            on_disk_payload=model.on_disk_payload if model.HasField("on_disk_payload") else None,
        )

    @classmethod
    def convert_lookup_location(cls, model: grpc.LookupLocation) -> rest.LookupLocation:
        return rest.LookupLocation(
            collection=model.collection_name,
            vector=model.vector_name if model.HasField("vector_name") else None,
        )

    @classmethod
    def convert_write_ordering(cls, model: grpc.WriteOrdering) -> rest.WriteOrdering:
        if model.type == grpc.WriteOrderingType.Weak:
            return rest.WriteOrdering.WEAK
        if model.type == grpc.WriteOrderingType.Medium:
            return rest.WriteOrdering.MEDIUM
        if model.type == grpc.WriteOrderingType.Strong:
            return rest.WriteOrdering.STRONG
        raise ValueError(f"invalid WriteOrdering model: {model}")  # pragma: no cover

    @classmethod
    def convert_read_consistency(cls, model: grpc.ReadConsistency) -> rest.ReadConsistency:
        name = model.WhichOneof("value")
        if name is None:
            raise ValueError(f"invalid ReadConsistency model: {model}")  # pragma: no cover
        val = getattr(model, name)
        if name == "factor":
            return val
        if name == "type":
            return cls.convert_read_consistency_type(val)
        raise ValueError(f"invalid ReadConsistency model: {model}")  # pragma: no cover

    @classmethod
    def convert_read_consistency_type(
        cls, model: grpc.ReadConsistencyType
    ) -> rest.ReadConsistencyType:
        if model == grpc.All:
            return rest.ReadConsistencyType.ALL
        if model == grpc.Majority:
            return rest.ReadConsistencyType.MAJORITY
        if model == grpc.Quorum:
            return rest.ReadConsistencyType.QUORUM
        raise ValueError(f"invalid ReadConsistencyType model: {model}")  # pragma: no cover

    @classmethod
    def convert_scalar_quantization_config(
        cls, model: grpc.ScalarQuantization
    ) -> rest.ScalarQuantizationConfig:
        return rest.ScalarQuantizationConfig(
            type=rest.ScalarType.INT8,
            quantile=model.quantile if model.HasField("quantile") else None,
            always_ram=model.always_ram if model.HasField("always_ram") else None,
        )

    @classmethod
    def convert_product_quantization_config(
        cls, model: grpc.ProductQuantization
    ) -> rest.ProductQuantizationConfig:
        return rest.ProductQuantizationConfig(
            compression=cls.convert_compression_ratio(model.compression),
            always_ram=model.always_ram if model.HasField("always_ram") else None,
        )

    @classmethod
    def convert_binary_quantization_config(
        cls, model: grpc.BinaryQuantization
    ) -> rest.BinaryQuantizationConfig:
        return rest.BinaryQuantizationConfig(
            always_ram=model.always_ram if model.HasField("always_ram") else None,
            encoding=cls.convert_binary_quantization_encoding(model.encoding)
            if model.HasField("encoding")
            else None,
            query_encoding=cls.convert_binary_quantization_query_encoding(model.query_encoding)
            if model.HasField("query_encoding")
            else None,
        )

    @classmethod
    def convert_binary_quantization_encoding(
        cls, model: grpc.BinaryQuantizationEncoding
    ) -> rest.BinaryQuantizationEncoding:
        if model == grpc.BinaryQuantizationEncoding.OneBit:
            return rest.BinaryQuantizationEncoding.ONE_BIT

        if model == grpc.BinaryQuantizationEncoding.TwoBits:
            return rest.BinaryQuantizationEncoding.TWO_BITS

        if model == grpc.BinaryQuantizationEncoding.OneAndHalfBits:
            return rest.BinaryQuantizationEncoding.ONE_AND_HALF_BITS

        raise ValueError(f"invalid BinaryQuantizationEncoding model: {model}")  # pragma: no cover

    @classmethod
    def convert_binary_quantization_query_encoding(
        cls, model: grpc.BinaryQuantizationQueryEncoding
    ) -> rest.BinaryQuantizationQueryEncoding:
        name = model.WhichOneof("variant")
        if name is None:
            raise ValueError(f"invalid BinaryQuantizationQueryEncoding model: {model}")

        val = getattr(model, name)
        if name == "setting":
            if val == grpc.BinaryQuantizationQueryEncoding.Setting.Default:
                return rest.BinaryQuantizationQueryEncoding.DEFAULT
            if val == grpc.BinaryQuantizationQueryEncoding.Setting.Binary:
                return rest.BinaryQuantizationQueryEncoding.BINARY
            if val == grpc.BinaryQuantizationQueryEncoding.Setting.Scalar4Bits:
                return rest.BinaryQuantizationQueryEncoding.SCALAR4BITS
            if val == grpc.BinaryQuantizationQueryEncoding.Setting.Scalar8Bits:
                return rest.BinaryQuantizationQueryEncoding.SCALAR8BITS
            raise ValueError(
                f"invalid BinaryQuantizationQueryEncoding setting: {val}"
            )  # pragma: no cover
        raise ValueError(
            f"invalid BinaryQuantizationQueryEncoding model: {model}"
        )  # pragma: no cover

    @classmethod
    def convert_compression_ratio(cls, model: grpc.CompressionRatio) -> rest.CompressionRatio:
        if model == grpc.x4:
            return rest.CompressionRatio.X4
        if model == grpc.x8:
            return rest.CompressionRatio.X8
        if model == grpc.x16:
            return rest.CompressionRatio.X16
        if model == grpc.x32:
            return rest.CompressionRatio.X32
        if model == grpc.x64:
            return rest.CompressionRatio.X64
        raise ValueError(f"invalid CompressionRatio model: {model}")  # pragma: no cover

    @classmethod
    def convert_quantization_config(
        cls, model: grpc.QuantizationConfig
    ) -> rest.QuantizationConfig:
        name = model.WhichOneof("quantization")
        if name is None:
            raise ValueError(f"invalid QuantizationConfig model: {model}")  # pragma: no cover
        val = getattr(model, name)
        if name == "scalar":
            return rest.ScalarQuantization(scalar=cls.convert_scalar_quantization_config(val))
        if name == "product":
            return rest.ProductQuantization(product=cls.convert_product_quantization_config(val))
        if name == "binary":
            return rest.BinaryQuantization(binary=cls.convert_binary_quantization_config(val))
        raise ValueError(f"invalid QuantizationConfig model: {model}")  # pragma: no cover

    @classmethod
    def convert_quantization_search_params(
        cls, model: grpc.QuantizationSearchParams
    ) -> rest.QuantizationSearchParams:
        return rest.QuantizationSearchParams(
            ignore=model.ignore if model.HasField("ignore") else None,
            rescore=model.rescore if model.HasField("rescore") else None,
            oversampling=model.oversampling if model.HasField("oversampling") else None,
        )

    @classmethod
    def convert_point_vectors(cls, model: grpc.PointVectors) -> rest.PointVectors:
        return rest.PointVectors(
            id=cls.convert_point_id(model.id),
            vector=cls.convert_vectors(model.vectors),
        )

    @classmethod
    def convert_groups_result(cls, model: grpc.GroupsResult) -> rest.GroupsResult:
        return rest.GroupsResult(
            groups=[cls.convert_point_group(group) for group in model.groups],
        )

    @classmethod
    def convert_point_group(cls, model: grpc.PointGroup) -> rest.PointGroup:
        return rest.PointGroup(
            id=cls.convert_group_id(model.id),
            hits=[cls.convert_scored_point(hit) for hit in model.hits],
            lookup=cls.convert_record(model.lookup) if model.HasField("lookup") else None,
        )

    @classmethod
    def convert_group_id(cls, model: grpc.GroupId) -> rest.GroupId:
        name = model.WhichOneof("kind")
        if name is None:
            raise ValueError(f"invalid GroupId model: {model}")  # pragma: no cover
        val = getattr(model, name)
        return val

    @classmethod
    def convert_with_lookup(cls, model: grpc.WithLookup) -> rest.WithLookup:
        return rest.WithLookup(
            collection=model.collection,
            with_payload=(
                cls.convert_with_payload_selector(model.with_payload)
                if model.HasField("with_payload")
                else None
            ),
            with_vectors=(
                cls.convert_with_vectors_selector(model.with_vectors)
                if model.HasField("with_vectors")
                else None
            ),
        )

    @classmethod
    def convert_quantization_config_diff(
        cls, model: grpc.QuantizationConfigDiff
    ) -> rest.QuantizationConfigDiff:
        name = model.WhichOneof("quantization")
        if name is None:
            raise ValueError(f"invalid QuantizationConfigDiff model: {model}")  # pragma: no cover
        val = getattr(model, name)
        if name == "scalar":
            return rest.ScalarQuantization(scalar=cls.convert_scalar_quantization_config(val))
        if name == "product":
            return rest.ProductQuantization(product=cls.convert_product_quantization_config(val))
        if name == "binary":
            return rest.BinaryQuantization(binary=cls.convert_binary_quantization_config(val))
        if name == "disabled":
            return rest.Disabled.DISABLED
        raise ValueError(f"invalid QuantizationConfigDiff model: {model}")  # pragma: no cover

    @classmethod
    def convert_vector_params_diff(cls, model: grpc.VectorParamsDiff) -> rest.VectorParamsDiff:
        return rest.VectorParamsDiff(
            hnsw_config=(
                cls.convert_hnsw_config_diff(model.hnsw_config)
                if model.HasField("hnsw_config")
                else None
            ),
            quantization_config=(
                cls.convert_quantization_config_diff(model.quantization_config)
                if model.HasField("quantization_config")
                else None
            ),
            on_disk=model.on_disk if model.HasField("on_disk") else None,
        )

    @classmethod
    def convert_vectors_config_diff(cls, model: grpc.VectorsConfigDiff) -> rest.VectorsConfigDiff:
        name = model.WhichOneof("config")
        if name is None:
            raise ValueError(f"invalid VectorsConfigDiff model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "params":
            return {"": cls.convert_vector_params_diff(val)}
        if name == "params_map":
            return dict(
                (key, cls.convert_vector_params_diff(vec_params))
                for key, vec_params in val.map.items()
            )
        raise ValueError(f"invalid VectorsConfigDiff model: {model}")  # pragma: no cover

    @classmethod
    def convert_points_update_operation(
        cls, model: grpc.PointsUpdateOperation
    ) -> rest.UpdateOperation:
        name = model.WhichOneof("operation")
        if name is None:
            raise ValueError(f"invalid PointsUpdateOperation model: {model}")  # pragma: no cover
        val = getattr(model, name)

        if name == "upsert":
            shard_key_selector = (
                cls.convert_shard_key(val.shard_key_selector)
                if val.HasField("shard_key_selector")
                else None
            )
            return rest.UpsertOperation(
                upsert=rest.PointsList(
                    points=[cls.convert_point_struct(point) for point in val.points],
                    shard_key=shard_key_selector,
                )
            )
        elif name == "delete_points":
            shard_key_selector = (
                val.shard_key_selector if val.HasField("shard_key_selector") else None
            )
            points_selector = cls.convert_points_selector(
                val.points, shard_key_selector=shard_key_selector
            )
            return rest.DeleteOperation(delete=points_selector)
        elif name == "set_payload":
            shard_key_selector = (
                val.shard_key_selector if val.HasField("shard_key_selector") else None
            )
            points_selector = cls.convert_points_selector(
                val.points_selector, shard_key_selector=shard_key_selector
            )
            points = None
            filter_ = None
            if isinstance(points_selector, rest.PointIdsList):
                points = points_selector.points
            elif isinstance(points_selector, rest.FilterSelector):
                filter_ = points_selector.filter
            else:
                raise ValueError(
                    f"invalid PointsSelector model: {points_selector}"
                )  # pragma: no cover

            return rest.SetPayloadOperation(
                set_payload=rest.SetPayload(
                    payload=cls.convert_payload(val.payload),
                    points=points,
                    filter=filter_,
                    key=val.key if val.HasField("key") else None,
                )
            )
        elif name == "overwrite_payload":
            shard_key_selector = (
                val.shard_key_selector if val.HasField("shard_key_selector") else None
            )
            points_selector = cls.convert_points_selector(
                val.points_selector, shard_key_selector=shard_key_selector
            )
            points = None
            filter_ = None
            if isinstance(points_selector, rest.PointIdsList):
                points = points_selector.points
            elif isinstance(points_selector, rest.FilterSelector):
                filter_ = points_selector.filter
            else:
                raise ValueError(
                    f"invalid PointsSelector model: {points_selector}"
                )  # pragma: no cover

            return rest.OverwritePayloadOperation(
                overwrite_payload=rest.SetPayload(
                    payload=cls.convert_payload(val.payload),
                    points=points,
                    filter=filter_,
                    key=val.key if val.HasField("key") else None,
                )
            )
        elif name == "delete_payload":
            shard_key_selector = (
                val.shard_key_selector if val.HasField("shard_key_selector") else None
            )
            points_selector = cls.convert_points_selector(
                val.points_selector, shard_key_selector=shard_key_selector
            )
            points = None
            filter_ = None
            if isinstance(points_selector, rest.PointIdsList):
                points = points_selector.points
            elif isinstance(points_selector, rest.FilterSelector):
                filter_ = points_selector.filter
            else:
                raise ValueError(
                    f"invalid PointsSelector model: {points_selector}"
                )  # pragma: no cover

            return rest.DeletePayloadOperation(
                delete_payload=rest.DeletePayload(
                    keys=[key for key in val.keys],
                    points=points,
                    filter=filter_,
                )
            )
        elif name == "clear_payload":
            shard_key_selector = (
                val.shard_key_selector if val.HasField("shard_key_selector") else None
            )
            points_selector = cls.convert_points_selector(
                val.points, shard_key_selector=shard_key_selector
            )
            return rest.ClearPayloadOperation(clear_payload=points_selector)
        elif name == "update_vectors":
            shard_key_selector = (
                cls.convert_shard_key(val.shard_key_selector)
                if val.HasField("shard_key_selector")
                else None
            )
            return rest.UpdateVectorsOperation(
                update_vectors=rest.UpdateVectors(
                    points=[cls.convert_point_vectors(point) for point in val.points],
                    shard_key=shard_key_selector,
                )
            )
        elif name == "delete_vectors":
            shard_key_selector = (
                val.shard_key_selector if val.HasField("shard_key_selector") else None
            )
            points_selector = cls.convert_points_selector(
                val.points_selector, shard_key_selector=shard_key_selector
            )
            points = None
            filter_ = None
            if isinstance(points_selector, rest.PointIdsList):
                points = points_selector.points
            elif isinstance(points_selector, rest.FilterSelector):
                filter_ = points_selector.filter
            else:
                raise ValueError(
                    f"invalid PointsSelector model: {points_selector}"
                )  # pragma: no cover

            return rest.DeleteVectorsOperation(
                delete_vectors=rest.DeleteVectors(
                    vector=[name for name in val.vectors.names],
                    points=points,
                    filter=filter_,
                )
            )
        else:
            raise ValueError(f"invalid UpdateOperation model: {model}")  # pragma: no cover

    @classmethod
    def convert_init_from(cls, model: str) -> rest.InitFrom:
        if isinstance(model, str):
            return rest.InitFrom(collection=model)
        raise ValueError(f"Invalid InitFrom model: {model}")  # pragma: no cover

    @classmethod
    def convert_recommend_strategy(cls, model: grpc.RecommendStrategy) -> rest.RecommendStrategy:
        if model == grpc.RecommendStrategy.AverageVector:
            return rest.RecommendStrategy.AVERAGE_VECTOR
        if model == grpc.RecommendStrategy.BestScore:
            return rest.RecommendStrategy.BEST_SCORE
        if model == grpc.RecommendStrategy.SumScores:
            return rest.RecommendStrategy.SUM_SCORES
        raise ValueError(f"invalid RecommendStrategy model: {model}")  # pragma: no cover

    @classmethod
    def convert_sparse_index_config(cls, model: grpc.SparseIndexConfig) -> rest.SparseIndexParams:
        return rest.SparseIndexParams(
            full_scan_threshold=(
                model.full_scan_threshold if model.HasField("full_scan_threshold") else None
            ),
            on_disk=model.on_disk if model.HasField("on_disk") else None,
            datatype=cls.convert_datatype(model.datatype) if model.HasField("datatype") else None,
        )

    @classmethod
    def convert_modifier(cls, model: grpc.Modifier) -> rest.Modifier:
        if model == grpc.Modifier.Idf:
            return rest.Modifier.IDF
        if model == getattr(grpc.Modifier, "None"):
            return rest.Modifier.NONE
        raise ValueError(f"invalid Modifier model: {model}")  # pragma: no cover

    @classmethod
    def convert_sparse_vector_params(
        cls, model: grpc.SparseVectorParams
    ) -> rest.SparseVectorParams:
        return rest.SparseVectorParams(
            index=(
                cls.convert_sparse_index_config(model.index)
                if model.HasField("index") is not None
                else None
            ),
            modifier=(
                cls.convert_modifier(model.modifier) if model.HasField("modifier") else None
            ),
        )

    @classmethod
    def convert_sparse_vector_config(
        cls, model: grpc.SparseVectorConfig
    ) -> dict[str, rest.SparseVectorParams]:
        return dict((key, cls.convert_sparse_vector_params(val)) for key, val in model.map.items())

    @classmethod
    def convert_shard_key(cls, model: grpc.ShardKey) -> rest.ShardKey:
        name = model.WhichOneof("key")
        if name is None:
            raise ValueError(f"invalid ShardKey model: {model}")  # pragma: no cover
        val = getattr(model, name)
        return val

    @classmethod
    def convert_shard_key_selector(cls, model: grpc.ShardKeySelector) -> rest.ShardKeySelector:
        if len(model.shard_keys) == 1:
            return cls.convert_shard_key(model.shard_keys[0])
        return [cls.convert_shard_key(shard_key) for shard_key in model.shard_keys]

    @classmethod
    def convert_sharding_method(cls, model: grpc.ShardingMethod) -> rest.ShardingMethod:
        if model == grpc.Auto:
            return rest.ShardingMethod.AUTO
        if model == grpc.Custom:
            return rest.ShardingMethod.CUSTOM
        raise ValueError(f"invalid ShardingMethod model: {model}")  # pragma: no cover

    @classmethod
    def convert_direction(cls, model: grpc.Direction) -> rest.Direction:
        if model == grpc.Asc:
            return rest.Direction.ASC
        if model == grpc.Desc:
            return rest.Direction.DESC
        raise ValueError(f"invalid Direction model: {model}")  # pragma: no cover

    @classmethod
    def convert_start_from(cls, model: grpc.StartFrom) -> rest.StartFrom:
        if model.HasField("integer"):
            return model.integer
        if model.HasField("float"):
            return model.float
        if model.HasField("timestamp"):
            dt = cls.convert_timestamp(model.timestamp)
            return dt
        if model.HasField("datetime"):
            return model.datetime

    @classmethod
    def convert_order_by(cls, model: grpc.OrderBy) -> rest.OrderBy:
        return rest.OrderBy(
            key=model.key,
            direction=(
                cls.convert_direction(model.direction) if model.HasField("direction") else None
            ),
            start_from=(
                cls.convert_start_from(model.start_from) if model.HasField("start_from") else None
            ),
        )

    @classmethod
    def convert_facet_value(cls, model: grpc.FacetValue) -> rest.FacetValue:
        name = model.WhichOneof("variant")
        if name is None:
            raise ValueError(f"invalid FacetValue model: {model}")  # pragma: no cover

        val = getattr(model, name)
        return val

    @classmethod
    def convert_facet_value_hit(cls, model: grpc.FacetHit) -> rest.FacetValueHit:
        return rest.FacetValueHit(
            value=cls.convert_facet_value(model.value),
            count=model.count,
        )

    @classmethod
    def convert_health_check_reply(cls, model: grpc.HealthCheckReply) -> rest.VersionInfo:
        return rest.VersionInfo(
            title=model.title,
            version=model.version,
            commit=model.commit if model.HasField("commit") else None,
        )

    @classmethod
    def convert_search_matrix_pair(cls, model: grpc.SearchMatrixPair) -> rest.SearchMatrixPair:
        return rest.SearchMatrixPair(
            a=cls.convert_point_id(model.a),
            b=cls.convert_point_id(model.b),
            score=model.score,
        )

    @classmethod
    def convert_search_matrix_pairs(
        cls, model: grpc.SearchMatrixPairs
    ) -> rest.SearchMatrixPairsResponse:
        return rest.SearchMatrixPairsResponse(
            pairs=[cls.convert_search_matrix_pair(pair) for pair in model.pairs],
        )

    @classmethod
    def convert_search_matrix_offsets(
        cls, model: grpc.SearchMatrixOffsets
    ) -> rest.SearchMatrixOffsetsResponse:
        return rest.SearchMatrixOffsetsResponse(
            offsets_row=list(model.offsets_row),
            offsets_col=list(model.offsets_col),
            scores=list(model.scores),
            ids=[cls.convert_point_id(p_id) for p_id in model.ids],
        )

    @classmethod
    def convert_strict_mode_multivector(
        cls, model: grpc.StrictModeMultivector
    ) -> rest.StrictModeMultivector:
        return rest.StrictModeMultivector(
            max_vectors=model.max_vectors if model.HasField("max_vectors") else None
        )

    @classmethod
    def convert_strict_mode_multivector_config(
        cls, model: grpc.StrictModeMultivectorConfig
    ) -> rest.StrictModeMultivectorConfig:
        return dict(
            (key, cls.convert_strict_mode_multivector(val))
            for key, val in model.multivector_config.items()
        )

    @classmethod
    def convert_strict_mode_sparse(cls, model: grpc.StrictModeSparse) -> rest.StrictModeSparse:
        return rest.StrictModeSparse(
            max_length=model.max_length if model.HasField("max_length") else None
        )

    @classmethod
    def convert_strict_mode_sparse_config(
        cls, model: grpc.StrictModeSparseConfig
    ) -> rest.StrictModeSparseConfig:
        return dict(
            (key, cls.convert_strict_mode_sparse(val)) for key, val in model.sparse_config.items()
        )

    @classmethod
    def convert_strict_mode_config(cls, model: grpc.StrictModeConfig) -> rest.StrictModeConfig:
        return rest.StrictModeConfig(
            enabled=model.enabled if model.HasField("enabled") else None,
            max_query_limit=model.max_query_limit if model.HasField("max_query_limit") else None,
            max_timeout=model.max_timeout if model.HasField("max_timeout") else None,
            unindexed_filtering_retrieve=(
                model.unindexed_filtering_retrieve
                if model.HasField("unindexed_filtering_retrieve")
                else None
            ),
            unindexed_filtering_update=(
                model.unindexed_filtering_update
                if model.HasField("unindexed_filtering_update")
                else None
            ),
            search_max_hnsw_ef=(
                model.search_max_hnsw_ef if model.HasField("search_max_hnsw_ef") else None
            ),
            search_allow_exact=(
                model.search_allow_exact if model.HasField("search_allow_exact") else None
            ),
            search_max_oversampling=(
                model.search_max_oversampling
                if model.HasField("search_max_oversampling")
                else None
            ),
            upsert_max_batchsize=(
                model.upsert_max_batchsize if model.HasField("upsert_max_batchsize") else None
            ),
            max_collection_vector_size_bytes=(
                model.max_collection_vector_size_bytes
                if model.HasField("max_collection_vector_size_bytes")
                else None
            ),
            read_rate_limit=model.read_rate_limit if model.HasField("read_rate_limit") else None,
            write_rate_limit=(
                model.write_rate_limit if model.HasField("write_rate_limit") else None
            ),
            max_collection_payload_size_bytes=(
                model.max_collection_payload_size_bytes
                if model.HasField("max_collection_payload_size_bytes")
                else None
            ),
            max_points_count=(
                model.max_points_count if model.HasField("max_points_count") else None
            ),
            filter_max_conditions=(
                model.filter_max_conditions if model.HasField("filter_max_conditions") else None
            ),
            condition_max_size=(
                model.condition_max_size if model.HasField("condition_max_size") else None
            ),
            multivector_config=(
                cls.convert_strict_mode_multivector_config(model.multivector_config)
                if model.HasField("multivector_config")
                else None
            ),
            sparse_config=(
                cls.convert_strict_mode_sparse_config(model.sparse_config)
                if model.HasField("sparse_config")
                else None
            ),
        )

    @classmethod
    def convert_strict_mode_config_output(
        cls, model: grpc.StrictModeConfig
    ) -> rest.StrictModeConfigOutput:
        return rest.StrictModeConfigOutput(
            enabled=model.enabled if model.HasField("enabled") else None,
            max_query_limit=model.max_query_limit if model.HasField("max_query_limit") else None,
            max_timeout=model.max_timeout if model.HasField("max_timeout") else None,
            unindexed_filtering_retrieve=(
                model.unindexed_filtering_retrieve
                if model.HasField("unindexed_filtering_retrieve")
                else None
            ),
            unindexed_filtering_update=(
                model.unindexed_filtering_update
                if model.HasField("unindexed_filtering_update")
                else None
            ),
            search_max_hnsw_ef=(
                model.search_max_hnsw_ef if model.HasField("search_max_hnsw_ef") else None
            ),
            search_allow_exact=(
                model.search_allow_exact if model.HasField("search_allow_exact") else None
            ),
            search_max_oversampling=(
                model.search_max_oversampling
                if model.HasField("search_max_oversampling")
                else None
            ),
            upsert_max_batchsize=(
                model.upsert_max_batchsize if model.HasField("upsert_max_batchsize") else None
            ),
            max_collection_vector_size_bytes=(
                model.max_collection_vector_size_bytes
                if model.HasField("max_collection_vector_size_bytes")
                else None
            ),
            read_rate_limit=model.read_rate_limit if model.HasField("read_rate_limit") else None,
            write_rate_limit=(
                model.write_rate_limit if model.HasField("write_rate_limit") else None
            ),
            max_collection_payload_size_bytes=(
                model.max_collection_payload_size_bytes
                if model.HasField("max_collection_payload_size_bytes")
                else None
            ),
            max_points_count=(
                model.max_points_count if model.HasField("max_points_count") else None
            ),
            filter_max_conditions=(
                model.filter_max_conditions if model.HasField("filter_max_conditions") else None
            ),
            condition_max_size=(
                model.condition_max_size if model.HasField("condition_max_size") else None
            ),
            multivector_config=(
                cls.convert_strict_mode_multivector_config_output(model.multivector_config)
                if model.HasField("multivector_config")
                else None
            ),
            sparse_config=(
                cls.convert_strict_mode_sparse_config_output(model.sparse_config)
                if model.HasField("sparse_config")
                else None
            ),
        )

    @classmethod
    def convert_strict_mode_multivector_config_output(
        cls, model: grpc.StrictModeMultivectorConfig
    ) -> rest.StrictModeMultivectorConfigOutput:
        return dict(
            (key, cls.convert_strict_mode_multivector_output(val))
            for key, val in model.multivector_config.items()
        )

    @classmethod
    def convert_strict_mode_sparse_config_output(
        cls, model: grpc.StrictModeSparseConfig
    ) -> rest.StrictModeSparseConfigOutput:
        return dict(
            (key, cls.convert_strict_mode_sparse_output(val))
            for key, val in model.sparse_config.items()
        )

    @classmethod
    def convert_strict_mode_sparse_output(
        cls, model: grpc.StrictModeSparse
    ) -> rest.StrictModeSparseOutput:
        return rest.StrictModeSparseOutput(
            max_length=model.max_length if model.HasField("max_length") else None
        )

    @classmethod
    def convert_strict_mode_multivector_output(
        cls, model: grpc.StrictModeMultivector
    ) -> rest.StrictModeMultivectorOutput:
        return rest.StrictModeMultivectorOutput(
            max_vectors=model.max_vectors if model.HasField("max_vectors") else None
        )


# ----------------------------------------
#
# ----------- REST TO gRPC ---------------
#
# ----------------------------------------


class RestToGrpc:
    @classmethod
    def convert_filter(cls, model: rest.Filter) -> grpc.Filter:
        def convert_conditions(
            conditions: Union[list[rest.Condition], rest.Condition],
        ) -> list[grpc.Condition]:
            if not isinstance(conditions, list):
                conditions = [conditions]
            return [cls.convert_condition(condition) for condition in conditions]

        return grpc.Filter(
            must=(convert_conditions(model.must) if model.must is not None else None),
            must_not=(convert_conditions(model.must_not) if model.must_not is not None else None),
            should=(convert_conditions(model.should) if model.should is not None else None),
            min_should=(
                grpc.MinShould(
                    conditions=convert_conditions(model.min_should.conditions),
                    min_count=model.min_should.min_count,
                )
                if model.min_should is not None
                else None
            ),
        )

    @classmethod
    def convert_range(cls, model: rest.Range) -> grpc.Range:
        return grpc.Range(
            lt=model.lt,
            gt=model.gt,
            gte=model.gte,
            lte=model.lte,
        )

    @classmethod
    def convert_datetime(cls, model: Union[datetime, date]) -> Timestamp:
        if isinstance(model, date) and not isinstance(model, datetime):
            model = datetime.combine(model, datetime.min.time())
        ts = Timestamp()
        ts.FromDatetime(model)
        return ts

    @classmethod
    def convert_datetime_range(cls, model: rest.DatetimeRange) -> grpc.DatetimeRange:
        return grpc.DatetimeRange(
            lt=cls.convert_datetime(model.lt) if model.lt is not None else None,
            gt=cls.convert_datetime(model.gt) if model.gt is not None else None,
            gte=cls.convert_datetime(model.gte) if model.gte is not None else None,
            lte=cls.convert_datetime(model.lte) if model.lte is not None else None,
        )

    @classmethod
    def convert_geo_radius(cls, model: rest.GeoRadius) -> grpc.GeoRadius:
        return grpc.GeoRadius(center=cls.convert_geo_point(model.center), radius=model.radius)

    @classmethod
    def convert_geo_line_string(cls, model: rest.GeoLineString) -> grpc.GeoLineString:
        return grpc.GeoLineString(points=[cls.convert_geo_point(point) for point in model.points])

    @classmethod
    def convert_geo_polygon(cls, model: rest.GeoPolygon) -> grpc.GeoPolygon:
        return grpc.GeoPolygon(
            exterior=cls.convert_geo_line_string(model.exterior),
            interiors=[cls.convert_geo_line_string(interior) for interior in model.interiors]
            if model.interiors
            else None,
        )

    @classmethod
    def convert_collection_description(
        cls, model: rest.CollectionDescription
    ) -> grpc.CollectionDescription:
        return grpc.CollectionDescription(name=model.name)

    @classmethod
    def convert_collection_info(cls, model: rest.CollectionInfo) -> grpc.CollectionInfo:
        return grpc.CollectionInfo(
            config=cls.convert_collection_config(model.config) if model.config else None,
            optimizer_status=cls.convert_optimizer_status(model.optimizer_status),
            payload_schema=(
                cls.convert_payload_schema(model.payload_schema)
                if model.payload_schema is not None
                else None
            ),
            segments_count=model.segments_count,
            status=cls.convert_collection_status(model.status),
            vectors_count=model.vectors_count if model.vectors_count is not None else None,
            points_count=model.points_count,
        )

    @classmethod
    def convert_collection_status(cls, model: rest.CollectionStatus) -> grpc.CollectionStatus:
        if model == rest.CollectionStatus.RED:
            return grpc.CollectionStatus.Red
        if model == rest.CollectionStatus.YELLOW:
            return grpc.CollectionStatus.Yellow
        if model == rest.CollectionStatus.GREEN:
            return grpc.CollectionStatus.Green
        if model == rest.CollectionStatus.GREY:
            return grpc.CollectionStatus.Grey

        raise ValueError(f"invalid CollectionStatus model: {model}")  # pragma: no cover

    @classmethod
    def convert_optimizer_status(cls, model: rest.OptimizersStatus) -> grpc.OptimizerStatus:
        if isinstance(model, rest.OptimizersStatusOneOf):
            return grpc.OptimizerStatus(
                ok=True,
            )
        if isinstance(model, rest.OptimizersStatusOneOf1):
            return grpc.OptimizerStatus(ok=False, error=model.error)
        raise ValueError(f"invalid OptimizersStatus model: {model}")  # pragma: no cover

    @classmethod
    def convert_payload_schema(
        cls, model: dict[str, rest.PayloadIndexInfo]
    ) -> dict[str, grpc.PayloadSchemaInfo]:
        return dict((key, cls.convert_payload_index_info(val)) for key, val in model.items())

    @classmethod
    def convert_payload_index_info(cls, model: rest.PayloadIndexInfo) -> grpc.PayloadSchemaInfo:
        params = model.params
        return grpc.PayloadSchemaInfo(
            data_type=cls.convert_payload_schema_type(model.data_type),
            params=cls.convert_payload_schema_params(params) if params is not None else None,
            points=model.points,
        )

    @classmethod
    def convert_payload_schema_params(
        cls, model: rest.PayloadSchemaParams
    ) -> grpc.PayloadIndexParams:
        if isinstance(model, rest.TextIndexParams):
            return grpc.PayloadIndexParams(text_index_params=cls.convert_text_index_params(model))

        if isinstance(model, rest.IntegerIndexParams):
            return grpc.PayloadIndexParams(
                integer_index_params=cls.convert_integer_index_params(model)
            )

        if isinstance(model, rest.KeywordIndexParams):
            return grpc.PayloadIndexParams(
                keyword_index_params=cls.convert_keyword_index_params(model)
            )

        if isinstance(model, rest.FloatIndexParams):
            return grpc.PayloadIndexParams(
                float_index_params=cls.convert_float_index_params(model)
            )

        if isinstance(model, rest.GeoIndexParams):
            return grpc.PayloadIndexParams(geo_index_params=cls.convert_geo_index_params(model))

        if isinstance(model, rest.BoolIndexParams):
            return grpc.PayloadIndexParams(bool_index_params=cls.convert_bool_index_params(model))

        if isinstance(model, rest.DatetimeIndexParams):
            return grpc.PayloadIndexParams(
                datetime_index_params=cls.convert_datetime_index_params(model)
            )

        if isinstance(model, rest.UuidIndexParams):
            return grpc.PayloadIndexParams(uuid_index_params=cls.convert_uuid_index_params(model))

        raise ValueError(f"invalid PayloadSchemaParams model: {model}")  # pragma: no cover

    @classmethod
    def convert_payload_schema_type(cls, model: rest.PayloadSchemaType) -> grpc.PayloadSchemaType:
        if model == rest.PayloadSchemaType.KEYWORD:
            return grpc.PayloadSchemaType.Keyword
        if model == rest.PayloadSchemaType.INTEGER:
            return grpc.PayloadSchemaType.Integer
        if model == rest.PayloadSchemaType.FLOAT:
            return grpc.PayloadSchemaType.Float
        if model == rest.PayloadSchemaType.BOOL:
            return grpc.PayloadSchemaType.Bool
        if model == rest.PayloadSchemaType.GEO:
            return grpc.PayloadSchemaType.Geo
        if model == rest.PayloadSchemaType.TEXT:
            return grpc.PayloadSchemaType.Text
        if model == rest.PayloadSchemaType.DATETIME:
            return grpc.PayloadSchemaType.Datetime
        if model == rest.PayloadSchemaType.UUID:
            return grpc.PayloadSchemaType.Uuid

        raise ValueError(f"invalid PayloadSchemaType model: {model}")  # pragma: no cover

    @classmethod
    def convert_update_result(cls, model: rest.UpdateResult) -> grpc.UpdateResult:
        return grpc.UpdateResult(
            operation_id=model.operation_id,
            status=cls.convert_update_stats(model.status),
        )

    @classmethod
    def convert_update_stats(cls, model: rest.UpdateStatus) -> grpc.UpdateStatus:
        if model == rest.UpdateStatus.COMPLETED:
            return grpc.UpdateStatus.Completed
        if model == rest.UpdateStatus.ACKNOWLEDGED:
            return grpc.UpdateStatus.Acknowledged

        raise ValueError(f"invalid UpdateStatus model: {model}")  # pragma: no cover

    @classmethod
    def convert_has_id_condition(cls, model: rest.HasIdCondition) -> grpc.HasIdCondition:
        return grpc.HasIdCondition(
            has_id=[cls.convert_extended_point_id(idx) for idx in model.has_id]
        )

    @classmethod
    def convert_has_vector_condition(
        cls, model: rest.HasVectorCondition
    ) -> grpc.HasVectorCondition:
        return grpc.HasVectorCondition(has_vector=model.has_vector)

    @classmethod
    def convert_delete_alias(cls, model: rest.DeleteAlias) -> grpc.DeleteAlias:
        return grpc.DeleteAlias(alias_name=model.alias_name)

    @classmethod
    def convert_rename_alias(cls, model: rest.RenameAlias) -> grpc.RenameAlias:
        return grpc.RenameAlias(
            old_alias_name=model.old_alias_name, new_alias_name=model.new_alias_name
        )

    @classmethod
    def convert_is_empty_condition(cls, model: rest.IsEmptyCondition) -> grpc.IsEmptyCondition:
        return grpc.IsEmptyCondition(key=model.is_empty.key)

    @classmethod
    def convert_is_null_condition(cls, model: rest.IsNullCondition) -> grpc.IsNullCondition:
        return grpc.IsNullCondition(key=model.is_null.key)

    @classmethod
    def convert_nested_condition(cls, model: rest.NestedCondition) -> grpc.NestedCondition:
        return grpc.NestedCondition(
            key=model.nested.key,
            filter=cls.convert_filter(model.nested.filter),
        )

    @classmethod
    def convert_search_params(cls, model: rest.SearchParams) -> grpc.SearchParams:
        return grpc.SearchParams(
            hnsw_ef=model.hnsw_ef,
            exact=model.exact,
            quantization=(
                cls.convert_quantization_search_params(model.quantization)
                if model.quantization is not None
                else None
            ),
            indexed_only=model.indexed_only,
        )

    @classmethod
    def convert_create_alias(cls, model: rest.CreateAlias) -> grpc.CreateAlias:
        return grpc.CreateAlias(collection_name=model.collection_name, alias_name=model.alias_name)

    @classmethod
    def convert_order_value(cls, model: rest.OrderValue) -> grpc.OrderValue:
        if isinstance(model, int):
            return grpc.OrderValue(int=model)
        if isinstance(model, float):
            return grpc.OrderValue(float=model)
        raise ValueError(f"invalid OrderValue model: {model}")  # pragma: no cover

    @classmethod
    def convert_scored_point(cls, model: rest.ScoredPoint) -> grpc.ScoredPoint:
        return grpc.ScoredPoint(
            id=cls.convert_extended_point_id(model.id),
            payload=cls.convert_payload(model.payload) if model.payload is not None else None,
            score=model.score,
            vectors=(
                cls.convert_vector_struct_output(model.vector)
                if model.vector is not None
                else None
            ),
            version=model.version,
            shard_key=cls.convert_shard_key(model.shard_key) if model.shard_key else None,
            order_value=cls.convert_order_value(model.order_value) if model.order_value else None,
        )

    @classmethod
    def convert_values_count(cls, model: rest.ValuesCount) -> grpc.ValuesCount:
        return grpc.ValuesCount(
            lt=model.lt,
            gt=model.gt,
            gte=model.gte,
            lte=model.lte,
        )

    @classmethod
    def convert_geo_bounding_box(cls, model: rest.GeoBoundingBox) -> grpc.GeoBoundingBox:
        return grpc.GeoBoundingBox(
            top_left=cls.convert_geo_point(model.top_left),
            bottom_right=cls.convert_geo_point(model.bottom_right),
        )

    @classmethod
    def convert_point_struct(cls, model: rest.PointStruct) -> grpc.PointStruct:
        return grpc.PointStruct(
            id=cls.convert_extended_point_id(model.id),
            vectors=cls.convert_vector_struct(model.vector),
            payload=cls.convert_payload(model.payload) if model.payload is not None else None,
        )

    @classmethod
    def convert_payload(cls, model: rest.Payload) -> dict[str, grpc.Value]:
        return dict((key, json_to_value(val)) for key, val in model.items())

    @classmethod
    def convert_hnsw_config_diff(cls, model: rest.HnswConfigDiff) -> grpc.HnswConfigDiff:
        return grpc.HnswConfigDiff(
            ef_construct=model.ef_construct,
            full_scan_threshold=model.full_scan_threshold,
            m=model.m,
            max_indexing_threads=model.max_indexing_threads,
            on_disk=model.on_disk,
            payload_m=model.payload_m,
        )

    @classmethod
    def convert_field_condition(cls, model: rest.FieldCondition) -> grpc.FieldCondition:
        if model.match is not None:
            return grpc.FieldCondition(key=model.key, match=cls.convert_match(model.match))
        if model.range is not None:
            if isinstance(model.range, rest.Range):
                return grpc.FieldCondition(key=model.key, range=cls.convert_range(model.range))
            if isinstance(model.range, rest.DatetimeRange):
                return grpc.FieldCondition(
                    key=model.key,
                    datetime_range=cls.convert_datetime_range(model.range),
                )
        if model.geo_bounding_box is not None:
            return grpc.FieldCondition(
                key=model.key,
                geo_bounding_box=cls.convert_geo_bounding_box(model.geo_bounding_box),
            )
        if model.geo_radius is not None:
            return grpc.FieldCondition(
                key=model.key, geo_radius=cls.convert_geo_radius(model.geo_radius)
            )
        if model.geo_polygon is not None:
            return grpc.FieldCondition(
                key=model.key, geo_polygon=cls.convert_geo_polygon(model.geo_polygon)
            )
        if model.values_count is not None:
            return grpc.FieldCondition(
                key=model.key, values_count=cls.convert_values_count(model.values_count)
            )
        if model.is_empty is not None:
            return grpc.FieldCondition(key=model.key, is_empty=model.is_empty)

        if model.is_null is not None:
            return grpc.FieldCondition(key=model.key, is_null=model.is_null)
        raise ValueError(f"invalid FieldCondition model: {model}")  # pragma: no cover

    @classmethod
    def convert_wal_config_diff(cls, model: rest.WalConfigDiff) -> grpc.WalConfigDiff:
        return grpc.WalConfigDiff(
            wal_capacity_mb=model.wal_capacity_mb,
            wal_segments_ahead=model.wal_segments_ahead,
        )

    @classmethod
    def convert_collection_config(cls, model: rest.CollectionConfig) -> grpc.CollectionConfig:
        return grpc.CollectionConfig(
            params=cls.convert_collection_params(model.params),
            hnsw_config=cls.convert_hnsw_config(model.hnsw_config),
            optimizer_config=cls.convert_optimizers_config(model.optimizer_config),
            wal_config=cls.convert_wal_config(model.wal_config),
            quantization_config=(
                cls.convert_quantization_config(model.quantization_config)
                if model.quantization_config is not None
                else None
            ),
            strict_mode_config=(
                cls.convert_strict_mode_config_output(model.strict_mode_config)
                if model.strict_mode_config is not None
                else None
            ),
        )

    @classmethod
    def convert_hnsw_config(cls, model: rest.HnswConfig) -> grpc.HnswConfigDiff:
        return grpc.HnswConfigDiff(
            ef_construct=model.ef_construct,
            full_scan_threshold=model.full_scan_threshold,
            m=model.m,
            max_indexing_threads=model.max_indexing_threads,
            on_disk=model.on_disk,
            payload_m=model.payload_m,
        )

    @classmethod
    def convert_wal_config(cls, model: rest.WalConfig) -> grpc.WalConfigDiff:
        return grpc.WalConfigDiff(
            wal_capacity_mb=model.wal_capacity_mb,
            wal_segments_ahead=model.wal_segments_ahead,
        )

    @classmethod
    def convert_distance(cls, model: rest.Distance) -> grpc.Distance:
        if model == rest.Distance.DOT:
            return grpc.Distance.Dot
        if model == rest.Distance.COSINE:
            return grpc.Distance.Cosine
        if model == rest.Distance.EUCLID:
            return grpc.Distance.Euclid
        if model == rest.Distance.MANHATTAN:
            return grpc.Distance.Manhattan

        raise ValueError(f"invalid Distance model: {model}")  # pragma: no cover

    @classmethod
    def convert_collection_params(cls, model: rest.CollectionParams) -> grpc.CollectionParams:
        return grpc.CollectionParams(
            vectors_config=(
                cls.convert_vectors_config(model.vectors) if model.vectors is not None else None
            ),
            shard_number=model.shard_number,
            on_disk_payload=model.on_disk_payload or False,
            write_consistency_factor=model.write_consistency_factor,
            replication_factor=model.replication_factor,
            read_fan_out_factor=model.read_fan_out_factor,
            sparse_vectors_config=(
                cls.convert_sparse_vector_config(model.sparse_vectors)
                if model.sparse_vectors is not None
                else None
            ),
            sharding_method=(
                cls.convert_sharding_method(model.sharding_method)
                if model.sharding_method is not None
                else None
            ),
        )

    @classmethod
    def convert_max_optimization_threads(
        cls, model: rest.MaxOptimizationThreads
    ) -> grpc.MaxOptimizationThreads:
        if model == rest.MaxOptimizationThreadsSetting.AUTO:
            return grpc.MaxOptimizationThreads(setting=grpc.MaxOptimizationThreads.Setting.Auto)
        elif isinstance(model, int):
            return grpc.MaxOptimizationThreads(value=model)
        raise ValueError(f"invalid MaxOptimizationThreads model: {model}")  # pragma: no cover

    @classmethod
    def convert_optimizers_config(cls, model: rest.OptimizersConfig) -> grpc.OptimizersConfigDiff:
        return grpc.OptimizersConfigDiff(
            default_segment_number=model.default_segment_number,
            deleted_threshold=model.deleted_threshold,
            flush_interval_sec=model.flush_interval_sec,
            indexing_threshold=model.indexing_threshold,
            max_optimization_threads=(
                cls.convert_max_optimization_threads(model.max_optimization_threads)
                if model.max_optimization_threads is not None
                else None
            ),
            max_segment_size=model.max_segment_size,
            memmap_threshold=model.memmap_threshold,
            vacuum_min_vector_number=model.vacuum_min_vector_number,
            deprecated_max_optimization_threads=model.max_optimization_threads,
        )

    @classmethod
    def convert_optimizers_config_diff(
        cls, model: rest.OptimizersConfigDiff
    ) -> grpc.OptimizersConfigDiff:
        deprecated_max_optimization_threads = None
        if isinstance(model.max_optimization_threads, int):
            deprecated_max_optimization_threads = model.max_optimization_threads

        return grpc.OptimizersConfigDiff(
            default_segment_number=model.default_segment_number,
            deleted_threshold=model.deleted_threshold,
            flush_interval_sec=model.flush_interval_sec,
            indexing_threshold=model.indexing_threshold,
            max_optimization_threads=(
                cls.convert_max_optimization_threads(model.max_optimization_threads)
                if model.max_optimization_threads is not None
                else None
            ),
            max_segment_size=model.max_segment_size,
            memmap_threshold=model.memmap_threshold,
            vacuum_min_vector_number=model.vacuum_min_vector_number,
            deprecated_max_optimization_threads=deprecated_max_optimization_threads,
        )

    @classmethod
    def convert_update_collection(
        cls, model: rest.UpdateCollection, collection_name: str
    ) -> grpc.UpdateCollection:
        return grpc.UpdateCollection(
            collection_name=collection_name,
            optimizers_config=(
                cls.convert_optimizers_config_diff(model.optimizers_config)
                if model.optimizers_config is not None
                else None
            ),
            vectors_config=(
                cls.convert_vectors_config_diff(model.vectors)
                if model.vectors is not None
                else None
            ),
            params=(
                cls.convert_collection_params_diff(model.params)
                if model.params is not None
                else None
            ),
            hnsw_config=(
                cls.convert_hnsw_config_diff(model.hnsw_config)
                if model.hnsw_config is not None
                else None
            ),
            quantization_config=(
                cls.convert_quantization_config_diff(model.quantization_config)
                if model.quantization_config is not None
                else None
            ),
        )

    @classmethod
    def convert_geo_point(cls, model: rest.GeoPoint) -> grpc.GeoPoint:
        return grpc.GeoPoint(lon=model.lon, lat=model.lat)

    @classmethod
    def convert_match(cls, model: rest.Match) -> grpc.Match:
        if isinstance(model, rest.MatchValue):
            if isinstance(model.value, bool):
                return grpc.Match(boolean=model.value)
            if isinstance(model.value, int):
                return grpc.Match(integer=model.value)
            if isinstance(model.value, str):
                return grpc.Match(keyword=model.value)
        if isinstance(model, rest.MatchText):
            return grpc.Match(text=model.text)
        if isinstance(model, rest.MatchAny):
            if len(model.any) == 0:
                return grpc.Match(keywords=grpc.RepeatedStrings(strings=[]))
            if isinstance(model.any[0], str):
                return grpc.Match(keywords=grpc.RepeatedStrings(strings=model.any))
            if isinstance(model.any[0], int):
                return grpc.Match(integers=grpc.RepeatedIntegers(integers=model.any))
            raise ValueError(f"invalid MatchAny model: {model}")  # pragma: no cover
        if isinstance(model, rest.MatchExcept):
            if len(model.except_) == 0:
                return grpc.Match(except_keywords=grpc.RepeatedStrings(strings=[]))
            if isinstance(model.except_[0], str):
                return grpc.Match(except_keywords=grpc.RepeatedStrings(strings=model.except_))
            if isinstance(model.except_[0], int):
                return grpc.Match(except_integers=grpc.RepeatedIntegers(integers=model.except_))
            raise ValueError(f"invalid MatchExcept model: {model}")  # pragma: no cover
        if isinstance(model, rest.MatchPhrase):
            return grpc.Match(phrase=model.phrase)
        raise ValueError(f"invalid Match model: {model}")  # pragma: no cover

    @classmethod
    def convert_alias_operations(cls, model: rest.AliasOperations) -> grpc.AliasOperations:
        if isinstance(model, rest.CreateAliasOperation):
            return grpc.AliasOperations(create_alias=cls.convert_create_alias(model.create_alias))
        if isinstance(model, rest.DeleteAliasOperation):
            return grpc.AliasOperations(delete_alias=cls.convert_delete_alias(model.delete_alias))
        if isinstance(model, rest.RenameAliasOperation):
            return grpc.AliasOperations(rename_alias=cls.convert_rename_alias(model.rename_alias))

        raise ValueError(f"invalid AliasOperations model: {model}")  # pragma: no cover

    @classmethod
    def convert_alias_description(cls, model: rest.AliasDescription) -> grpc.AliasDescription:
        return grpc.AliasDescription(
            alias_name=model.alias_name,
            collection_name=model.collection_name,
        )

    @classmethod
    def convert_recommend_examples_to_ids(
        cls, examples: Sequence[rest.RecommendExample]
    ) -> list[grpc.PointId]:
        ids: list[grpc.PointId] = []
        for example in examples:
            if isinstance(example, get_args_subscribed(rest.ExtendedPointId)):
                id_ = cls.convert_extended_point_id(example)
            elif isinstance(example, grpc.PointId):
                id_ = example
            else:
                continue

            ids.append(id_)

        return ids

    @classmethod
    def convert_recommend_examples_to_vectors(
        cls, examples: Sequence[rest.RecommendExample]
    ) -> list[grpc.Vector]:
        vectors: list[grpc.Vector] = []
        for example in examples:
            if isinstance(example, grpc.Vector):
                vector = example
            elif isinstance(example, list):
                vector = grpc.Vector(data=example)
            elif isinstance(example, rest.SparseVector):
                vector = cls.convert_sparse_vector_to_vector(example)
            else:
                continue

            vectors.append(vector)

        return vectors

    @classmethod
    def convert_vector_example(cls, model: rest.RecommendExample) -> grpc.VectorExample:
        return cls.convert_recommend_example(model)

    @classmethod
    def convert_recommend_example(cls, model: rest.RecommendExample) -> grpc.VectorExample:
        if isinstance(model, get_args_subscribed(rest.ExtendedPointId)):
            return grpc.VectorExample(id=cls.convert_extended_point_id(model))
        if isinstance(model, rest.SparseVector):
            return grpc.VectorExample(vector=cls.convert_sparse_vector_to_vector(model))
        if isinstance(model, list):
            return grpc.VectorExample(vector=grpc.Vector(data=model))

        raise ValueError(f"Invalid RecommendExample model: {model}")  # pragma: no cover

    @classmethod
    def convert_sparse_vector_to_vector(cls, model: rest.SparseVector) -> grpc.Vector:
        return grpc.Vector(
            data=model.values,
            indices=grpc.SparseIndices(data=model.indices),
        )

    @classmethod
    def convert_sparse_vector_to_vector_output(cls, model: rest.SparseVector) -> grpc.VectorOutput:
        return grpc.VectorOutput(
            data=model.values,
            indices=grpc.SparseIndices(data=model.indices),
        )

    @classmethod
    def convert_target_vector(cls, model: rest.RecommendExample) -> grpc.TargetVector:
        return grpc.TargetVector(single=cls.convert_recommend_example(model))

    @classmethod
    def convert_context_example_pair(
        cls,
        model: rest.ContextExamplePair,
    ) -> grpc.ContextExamplePair:
        return grpc.ContextExamplePair(
            positive=cls.convert_recommend_example(model.positive),
            negative=cls.convert_recommend_example(model.negative),
        )

    @classmethod
    def convert_extended_point_id(cls, model: rest.ExtendedPointId) -> grpc.PointId:
        if isinstance(model, int):
            return grpc.PointId(num=model)
        if isinstance(model, str):
            return grpc.PointId(uuid=model)
        raise ValueError(f"invalid ExtendedPointId model: {model}")  # pragma: no cover

    @classmethod
    def convert_points_selector(cls, model: rest.PointsSelector) -> grpc.PointsSelector:
        if isinstance(model, rest.PointIdsList):
            return grpc.PointsSelector(
                points=grpc.PointsIdsList(
                    ids=[cls.convert_extended_point_id(point) for point in model.points]
                )
            )
        if isinstance(model, rest.FilterSelector):
            return grpc.PointsSelector(filter=cls.convert_filter(model.filter))
        raise ValueError(f"invalid PointsSelector model: {model}")  # pragma: no cover

    @classmethod
    def convert_condition(cls, model: rest.Condition) -> grpc.Condition:
        if isinstance(model, rest.FieldCondition):
            return grpc.Condition(field=cls.convert_field_condition(model))
        if isinstance(model, rest.IsEmptyCondition):
            return grpc.Condition(is_empty=cls.convert_is_empty_condition(model))
        if isinstance(model, rest.IsNullCondition):
            return grpc.Condition(is_null=cls.convert_is_null_condition(model))
        if isinstance(model, rest.HasIdCondition):
            return grpc.Condition(has_id=cls.convert_has_id_condition(model))
        if isinstance(model, rest.HasVectorCondition):
            return grpc.Condition(has_vector=cls.convert_has_vector_condition(model))
        if isinstance(model, rest.Filter):
            return grpc.Condition(filter=cls.convert_filter(model))
        if isinstance(model, rest.NestedCondition):
            return grpc.Condition(nested=cls.convert_nested_condition(model))

        raise ValueError(f"invalid Condition model: {model}")  # pragma: no cover

    @classmethod
    def convert_payload_selector(cls, model: rest.PayloadSelector) -> grpc.WithPayloadSelector:
        if isinstance(model, rest.PayloadSelectorInclude):
            return grpc.WithPayloadSelector(
                include=grpc.PayloadIncludeSelector(fields=model.include)
            )
        if isinstance(model, rest.PayloadSelectorExclude):
            return grpc.WithPayloadSelector(
                exclude=grpc.PayloadExcludeSelector(fields=model.exclude)
            )
        raise ValueError(f"invalid PayloadSelector model: {model}")  # pragma: no cover

    @classmethod
    def convert_with_payload_selector(
        cls, model: rest.PayloadSelector
    ) -> grpc.WithPayloadSelector:
        return cls.convert_with_payload_interface(model)

    @classmethod
    def convert_with_payload_interface(
        cls, model: rest.WithPayloadInterface
    ) -> grpc.WithPayloadSelector:
        if isinstance(model, bool):
            return grpc.WithPayloadSelector(enable=model)
        elif isinstance(model, list):
            return grpc.WithPayloadSelector(include=grpc.PayloadIncludeSelector(fields=model))
        elif isinstance(model, get_args(rest.PayloadSelector)):
            return cls.convert_payload_selector(model)

        raise ValueError(f"invalid WithPayloadInterface model: {model}")  # pragma: no cover

    @classmethod
    def convert_start_from(cls, model: rest.StartFrom) -> grpc.StartFrom:
        if isinstance(model, int):
            return grpc.StartFrom(integer=model)
        if isinstance(model, float):
            return grpc.StartFrom(float=model)
        if isinstance(model, datetime):
            ts = cls.convert_datetime(model)
            return grpc.StartFrom(timestamp=ts)
        if isinstance(model, str):
            # Pydantic also accepts strings as datetime if they are correctly formatted
            return grpc.StartFrom(datetime=model)

        raise ValueError(f"invalid StartFrom model: {model}")  # pragma: no cover

    @classmethod
    def convert_direction(cls, model: rest.Direction) -> grpc.Direction:
        if model == rest.Direction.ASC:
            return grpc.Direction.Asc
        if model == rest.Direction.DESC:
            return grpc.Direction.Desc
        raise ValueError(f"invalid Direction model: {model}")  # pragma: no cover

    @classmethod
    def convert_order_by(cls, model: rest.OrderBy) -> grpc.OrderBy:
        return grpc.OrderBy(
            key=model.key,
            direction=(
                cls.convert_direction(model.direction) if model.direction is not None else None
            ),
            start_from=(
                cls.convert_start_from(model.start_from) if model.start_from is not None else None
            ),
        )

    @classmethod
    def convert_order_by_interface(cls, model: rest.OrderByInterface) -> grpc.OrderBy:
        # using no cover because there is no OrderByInterface in grpc
        if isinstance(model, str):
            return grpc.OrderBy(key=model)
        if isinstance(model, rest.OrderBy):
            return cls.convert_order_by(model)
        raise ValueError(f"invalid OrderByInterface model: {model}")  # pragma: no cover

    @classmethod
    def convert_facet_value(cls, model: rest.FacetValue) -> grpc.FacetValue:
        if isinstance(model, str):
            return grpc.FacetValue(string_value=model)
        if isinstance(model, int):
            return grpc.FacetValue(integer_value=model)

        raise ValueError(f"invalid FacetValue model: {model}")  # pragma: no cover

    @classmethod
    def convert_facet_value_hit(cls, model: rest.FacetValueHit) -> grpc.FacetHit:
        return grpc.FacetHit(
            value=cls.convert_facet_value(model.value),
            count=model.count,
        )

    @classmethod
    def convert_record(cls, model: rest.Record) -> grpc.RetrievedPoint:
        return grpc.RetrievedPoint(
            id=cls.convert_extended_point_id(model.id),
            payload=cls.convert_payload(model.payload),
            vectors=(
                cls.convert_vector_struct_output(model.vector)
                if model.vector is not None
                else None
            ),
            shard_key=cls.convert_shard_key(model.shard_key) if model.shard_key else None,
            order_value=cls.convert_order_value(model.order_value) if model.order_value else None,
        )

    @classmethod
    def convert_retrieved_point(cls, model: rest.Record) -> grpc.RetrievedPoint:
        return cls.convert_record(model)

    @classmethod
    def convert_count_result(cls, model: rest.CountResult) -> grpc.CountResult:
        return grpc.CountResult(count=model.count)

    @classmethod
    def convert_snapshot_description(
        cls, model: rest.SnapshotDescription
    ) -> grpc.SnapshotDescription:
        timestamp = Timestamp()
        timestamp.FromDatetime(datetime.fromisoformat(model.creation_time))
        return grpc.SnapshotDescription(
            name=model.name,
            creation_time=timestamp,
            size=model.size,
        )

    @classmethod
    def convert_datatype(cls, model: rest.Datatype) -> grpc.Datatype:
        if model == rest.Datatype.FLOAT32:
            return grpc.Datatype.Float32
        if model == rest.Datatype.UINT8:
            return grpc.Datatype.Uint8
        if model == rest.Datatype.FLOAT16:
            return grpc.Datatype.Float16

        raise ValueError(f"invalid Datatype model: {model}")  # pragma: no cover

    @classmethod
    def convert_vector_params(cls, model: rest.VectorParams) -> grpc.VectorParams:
        return grpc.VectorParams(
            size=model.size,
            distance=cls.convert_distance(model.distance),
            hnsw_config=(
                cls.convert_hnsw_config_diff(model.hnsw_config)
                if model.hnsw_config is not None
                else None
            ),
            quantization_config=(
                cls.convert_quantization_config(model.quantization_config)
                if model.quantization_config is not None
                else None
            ),
            on_disk=model.on_disk,
            datatype=cls.convert_datatype(model.datatype) if model.datatype is not None else None,
            multivector_config=(
                cls.convert_multivector_config(model.multivector_config)
                if model.multivector_config is not None
                else None
            ),
        )

    @classmethod
    def convert_multivector_config(cls, model: rest.MultiVectorConfig) -> grpc.MultiVectorConfig:
        return grpc.MultiVectorConfig(
            comparator=cls.convert_multivector_comparator(model.comparator)
        )

    @classmethod
    def convert_multivector_comparator(
        cls, model: rest.MultiVectorComparator
    ) -> grpc.MultiVectorComparator:
        if model == rest.MultiVectorComparator.MAX_SIM:
            return grpc.MultiVectorComparator.MaxSim

        raise ValueError(f"invalid MultiVectorComparator model: {model}")  # pragma: no cover

    @classmethod
    def convert_vectors_config(cls, model: rest.VectorsConfig) -> grpc.VectorsConfig:
        if isinstance(model, rest.VectorParams):
            return grpc.VectorsConfig(params=cls.convert_vector_params(model))
        elif isinstance(model, dict):
            return grpc.VectorsConfig(
                params_map=grpc.VectorParamsMap(
                    map=dict((key, cls.convert_vector_params(val)) for key, val in model.items())
                )
            )
        else:
            raise ValueError(f"invalid VectorsConfig model: {model}")  # pragma: no cover

    @classmethod
    def convert_vector_struct(cls, model: rest.VectorStruct) -> grpc.Vectors:
        def convert_vector(
            vector: Union[list[float], list[list[float]]],
        ) -> grpc.Vector:
            if len(vector) != 0 and isinstance(
                vector[0], list
            ):  # we can't say whether it is an empty dense or multi-dense vector
                return grpc.Vector(
                    data=[
                        inner_vector
                        for multi_vector in vector
                        for inner_vector in multi_vector  # type: ignore
                    ],
                    vectors_count=len(vector),
                )
            return grpc.Vector(data=vector)

        if isinstance(model, list):
            return grpc.Vectors(vector=convert_vector(model))
        elif isinstance(model, dict):
            vectors: dict = {}
            for key, val in model.items():
                if isinstance(val, list):
                    vectors.update({key: convert_vector(val)})
                elif isinstance(val, rest.SparseVector):
                    vectors.update({key: cls.convert_sparse_vector_to_vector(val)})
                elif isinstance(val, rest.Document):
                    vectors.update({key: grpc.Vector(document=cls.convert_document(val))})
                elif isinstance(val, rest.Image):
                    vectors.update({key: grpc.Vector(image=cls.convert_image(val))})
                elif isinstance(val, rest.InferenceObject):
                    vectors.update({key: grpc.Vector(object=cls.convert_inference_object(val))})
            return grpc.Vectors(vectors=grpc.NamedVectors(vectors=vectors))
        elif isinstance(model, rest.Document):
            return grpc.Vectors(vector=grpc.Vector(document=cls.convert_document(model)))
        elif isinstance(model, rest.Image):
            return grpc.Vectors(vector=grpc.Vector(image=cls.convert_image(model)))
        elif isinstance(model, rest.InferenceObject):
            return grpc.Vectors(vector=grpc.Vector(object=cls.convert_inference_object(model)))
        else:
            raise ValueError(f"invalid VectorStruct model: {model}")  # pragma: no cover

    @classmethod
    def convert_vector_struct_output(cls, model: rest.VectorStructOutput) -> grpc.VectorsOutput:
        def convert_vector(
            vector: Union[list[float], list[list[float]]],
        ) -> grpc.VectorOutput:
            if len(vector) != 0 and isinstance(
                vector[0], list
            ):  # we can't say whether it is an empty dense or multi-dense vector
                return grpc.VectorOutput(
                    data=[
                        inner_vector
                        for multi_vector in vector
                        for inner_vector in multi_vector  # type: ignore
                    ],
                    vectors_count=len(vector),
                )
            return grpc.VectorOutput(data=vector)

        if isinstance(model, list):
            return grpc.VectorsOutput(vector=convert_vector(model))
        elif isinstance(model, dict):
            vectors: dict = {}
            for key, val in model.items():
                if isinstance(val, list):
                    vectors.update({key: convert_vector(val)})
                elif isinstance(val, rest.SparseVector):
                    vectors.update({key: cls.convert_sparse_vector_to_vector_output(val)})
            return grpc.VectorsOutput(vectors=grpc.NamedVectorsOutput(vectors=vectors))
        else:
            raise ValueError(f"invalid VectorStructOutput model: {model}")  # pragma: no cover

    @classmethod
    def convert_with_vectors(cls, model: rest.WithVector) -> grpc.WithVectorsSelector:
        if isinstance(model, bool):
            return grpc.WithVectorsSelector(enable=model)
        elif isinstance(model, list):
            return grpc.WithVectorsSelector(include=grpc.VectorsSelector(names=model))
        else:
            raise ValueError(f"invalid WithVectors model: {model}")  # pragma: no cover

    @classmethod
    def convert_batch_vector_struct(
        cls, model: rest.BatchVectorStruct, num_records: int
    ) -> list[grpc.Vectors]:
        if isinstance(model, list):
            return [cls.convert_vector_struct(item) for item in model]
        elif isinstance(model, dict):
            result: list[dict] = [{} for _ in range(num_records)]
            for key, val in model.items():
                for i, item in enumerate(val):
                    result[i][key] = item
            return [cls.convert_vector_struct(item) for item in result]
        else:
            raise ValueError(f"invalid BatchVectorStruct model: {model}")  # pragma: no cover

    @classmethod
    def convert_named_vector_struct(
        cls, model: rest.NamedVectorStruct
    ) -> tuple[list[float], Optional[grpc.SparseIndices], Optional[str]]:
        if isinstance(model, list):
            return model, None, None
        elif isinstance(model, rest.NamedVector):
            return model.vector, None, model.name
        elif isinstance(model, rest.NamedSparseVector):
            return (
                model.vector.values,
                grpc.SparseIndices(data=model.vector.indices),
                model.name,
            )
        else:
            raise ValueError(f"invalid NamedVectorStruct model: {model}")  # pragma: no cover

    @classmethod
    def convert_dense_vector(cls, model: list[float]) -> grpc.DenseVector:
        return grpc.DenseVector(data=model)

    @classmethod
    def convert_sparse_vector(cls, model: rest.SparseVector) -> grpc.SparseVector:
        return grpc.SparseVector(values=model.values, indices=model.indices)

    @classmethod
    def convert_multi_dense_vector(cls, model: list[list[float]]) -> grpc.MultiDenseVector:
        return grpc.MultiDenseVector(
            vectors=[cls.convert_dense_vector(vector) for vector in model]
        )

    @classmethod
    def convert_document(cls, model: rest.Document) -> grpc.Document:
        return grpc.Document(
            text=model.text,
            model=model.model,
            options=payload_to_grpc(model.options) if model.options is not None else None,
        )

    @classmethod
    def convert_image(cls, model: rest.Image) -> grpc.Image:
        return grpc.Image(
            image=json_to_value(model.image),
            model=model.model,
            options=payload_to_grpc(model.options) if model.options is not None else None,
        )

    @classmethod
    def convert_inference_object(cls, model: rest.InferenceObject) -> grpc.InferenceObject:
        return grpc.InferenceObject(
            object=json_to_value(model.object),
            model=model.model,
            options=payload_to_grpc(model.options) if model.options is not None else None,
        )

    @classmethod
    def convert_vector_input(cls, model: rest.VectorInput) -> grpc.VectorInput:
        if isinstance(model, list):
            if len(model) != 0 and isinstance(
                model[0], list
            ):  # we can't say whether it is an empty dense or multi-dense vector
                return grpc.VectorInput(multi_dense=cls.convert_multi_dense_vector(model))
            return grpc.VectorInput(dense=cls.convert_dense_vector(model))
        if isinstance(model, rest.SparseVector):
            return grpc.VectorInput(sparse=cls.convert_sparse_vector(model))
        if isinstance(model, get_args_subscribed(rest.ExtendedPointId)):
            return grpc.VectorInput(id=cls.convert_extended_point_id(model))
        if isinstance(model, rest.Document):
            return grpc.VectorInput(document=cls.convert_document(model))
        if isinstance(model, rest.Image):
            return grpc.VectorInput(image=cls.convert_image(model))
        if isinstance(model, rest.InferenceObject):
            return grpc.VectorInput(object=cls.convert_inference_object(model))

        raise ValueError(f"invalid VectorInput model: {model}")  # pragma: no cover

    @classmethod
    def convert_recommend_input(cls, model: rest.RecommendInput) -> grpc.RecommendInput:
        return grpc.RecommendInput(
            positive=(
                [cls.convert_vector_input(vector) for vector in model.positive]
                if model.positive is not None
                else None
            ),
            negative=(
                [cls.convert_vector_input(vector) for vector in model.negative]
                if model.negative is not None
                else None
            ),
            strategy=(
                cls.convert_recommend_strategy(model.strategy)
                if model.strategy is not None
                else None
            ),
        )

    @classmethod
    def convert_context_input_pair(cls, model: rest.ContextPair) -> grpc.ContextInputPair:
        return grpc.ContextInputPair(
            positive=cls.convert_vector_input(model.positive),
            negative=cls.convert_vector_input(model.negative),
        )

    @classmethod
    def convert_context_input(cls, model: rest.ContextInput) -> grpc.ContextInput:
        if isinstance(model, list):
            return grpc.ContextInput(
                pairs=[cls.convert_context_input_pair(pair) for pair in model]
            )
        if isinstance(model, rest.ContextPair):
            return grpc.ContextInput(pairs=[cls.convert_context_input_pair(model)])

        raise ValueError(f"invalid ContextInput model: {model}")  # pragma: no cover

    @classmethod
    def convert_discover_input(cls, model: rest.DiscoverInput) -> grpc.DiscoverInput:
        return grpc.DiscoverInput(
            target=cls.convert_vector_input(model.target),
            context=cls.convert_context_input(model.context),
        )

    @classmethod
    def convert_fusion(cls, model: rest.Fusion) -> grpc.Fusion:
        if model == rest.Fusion.RRF:
            return grpc.Fusion.RRF

        if model == rest.Fusion.DBSF:
            return grpc.Fusion.DBSF

        raise ValueError(f"invalid Fusion model: {model}")  # pragma: no cover

    @classmethod
    def convert_sample(cls, model: rest.Sample) -> grpc.Sample:
        if model == rest.Sample.RANDOM:
            return grpc.Sample.Random

        raise ValueError(f"invalid Sample model: {model}")  # pragma: no cover

    @classmethod
    def convert_mmr(cls, model: rest.Mmr) -> grpc.Mmr:
        return grpc.Mmr(diversity=model.diversity, candidates_limit=model.candidates_limit)

    @classmethod
    def convert_query(cls, model: rest.Query) -> grpc.Query:
        if isinstance(model, rest.NearestQuery):
            if model.mmr is not None:
                nearest_with_mmr = grpc.NearestInputWithMmr(
                    nearest=cls.convert_vector_input(model.nearest), mmr=cls.convert_mmr(model.mmr)
                )
                return grpc.Query(nearest_with_mmr=nearest_with_mmr)

            return grpc.Query(nearest=cls.convert_vector_input(model.nearest))

        if isinstance(model, rest.RecommendQuery):
            return grpc.Query(recommend=cls.convert_recommend_input(model.recommend))

        if isinstance(model, rest.DiscoverQuery):
            return grpc.Query(discover=cls.convert_discover_input(model.discover))

        if isinstance(model, rest.ContextQuery):
            return grpc.Query(context=cls.convert_context_input(model.context))

        if isinstance(model, rest.OrderByQuery):
            return grpc.Query(order_by=cls.convert_order_by_interface(model.order_by))

        if isinstance(model, rest.FusionQuery):
            return grpc.Query(fusion=cls.convert_fusion(model.fusion))

        if isinstance(model, rest.SampleQuery):
            return grpc.Query(sample=cls.convert_sample(model.sample))

        if isinstance(model, rest.FormulaQuery):
            return grpc.Query(formula=cls.convert_formula_query(model))

        raise ValueError(f"invalid Query model: {model}")  # pragma: no cover

    @classmethod
    def convert_formula_query(cls, model: rest.FormulaQuery) -> grpc.Formula:
        defaults = payload_to_grpc(model.defaults) if model.defaults is not None else None
        expression = cls.convert_expression(model.formula)
        return grpc.Formula(defaults=defaults, expression=expression)

    @classmethod
    def convert_expression(cls, model: rest.Expression) -> grpc.Expression:
        if isinstance(model, float):
            return grpc.Expression(constant=model)
        if isinstance(model, str):
            return grpc.Expression(variable=model)
        if isinstance(model, get_args_subscribed(rest.Condition)):
            return grpc.Expression(condition=cls.convert_condition(model))
        if isinstance(model, rest.DatetimeExpression):
            return grpc.Expression(datetime=model.datetime)
        if isinstance(model, rest.DatetimeKeyExpression):
            return grpc.Expression(datetime_key=model.datetime_key)
        if isinstance(model, rest.NegExpression):
            return grpc.Expression(neg=cls.convert_expression(model.neg))
        if isinstance(model, rest.SumExpression):
            return grpc.Expression(sum=cls.convert_sum_expression(model))
        if isinstance(model, rest.MultExpression):
            return grpc.Expression(mult=cls.convert_mult_expression(model))
        if isinstance(model, rest.DivExpression):
            return grpc.Expression(div=cls.convert_div_expression(model))
        if isinstance(model, rest.PowExpression):
            return grpc.Expression(pow=cls.convert_pow_expression(model))
        if isinstance(model, rest.Log10Expression):
            return grpc.Expression(log10=cls.convert_expression(model.log10))
        if isinstance(model, rest.LnExpression):
            return grpc.Expression(ln=cls.convert_expression(model.ln))
        if isinstance(model, rest.AbsExpression):
            return grpc.Expression(abs=cls.convert_expression(model.abs))
        if isinstance(model, rest.SqrtExpression):
            return grpc.Expression(sqrt=cls.convert_expression(model.sqrt))
        if isinstance(model, rest.ExpExpression):
            return grpc.Expression(exp=cls.convert_expression(model.exp))
        if isinstance(model, rest.GeoDistance):
            return grpc.Expression(geo_distance=cls.convert_geo_distance(model))
        if isinstance(model, rest.LinDecayExpression):
            return grpc.Expression(lin_decay=cls.convert_decay_params_expression(model.lin_decay))
        if isinstance(model, rest.ExpDecayExpression):
            return grpc.Expression(exp_decay=cls.convert_decay_params_expression(model.exp_decay))
        if isinstance(model, rest.GaussDecayExpression):
            return grpc.Expression(
                gauss_decay=cls.convert_decay_params_expression(model.gauss_decay)
            )

        raise ValueError(f"invalid Expression model: {model}")  # pragma: no cover

    @classmethod
    def convert_sum_expression(cls, model: rest.SumExpression) -> grpc.SumExpression:
        return grpc.SumExpression(sum=[cls.convert_expression(expr) for expr in model.sum])

    @classmethod
    def convert_mult_expression(cls, model: rest.MultExpression) -> grpc.MultExpression:
        return grpc.MultExpression(mult=[cls.convert_expression(expr) for expr in model.mult])

    @classmethod
    def convert_div_expression(cls, model: rest.DivExpression) -> grpc.DivExpression:
        return grpc.DivExpression(
            left=cls.convert_expression(model.div.left),
            right=cls.convert_expression(model.div.right),
            by_zero_default=model.div.by_zero_default,
        )

    @classmethod
    def convert_pow_expression(cls, model: rest.PowExpression) -> grpc.PowExpression:
        return grpc.PowExpression(
            base=cls.convert_expression(model.pow.base),
            exponent=cls.convert_expression(model.pow.exponent),
        )

    @classmethod
    def convert_geo_distance(cls, model: rest.GeoDistance) -> grpc.GeoDistance:
        return grpc.GeoDistance(
            origin=cls.convert_geo_point(model.geo_distance.origin),
            to=model.geo_distance.to,
        )

    @classmethod
    def convert_decay_params_expression(
        cls, model: rest.DecayParamsExpression
    ) -> grpc.DecayParamsExpression:
        return grpc.DecayParamsExpression(
            x=cls.convert_expression(model.x),
            target=cls.convert_expression(model.target) if model.target is not None else None,
            midpoint=model.midpoint,
            scale=model.scale,
        )

    @classmethod
    def convert_query_interface(cls, model: rest.QueryInterface) -> grpc.Query:
        if isinstance(model, get_args_subscribed(rest.VectorInput)):
            return grpc.Query(nearest=cls.convert_vector_input(model))

        if isinstance(model, get_args(rest.Query)):
            return cls.convert_query(model)

        raise ValueError(f"invalid QueryInterface: {model}")  # pragma: no cover

    @classmethod
    def convert_prefetch_query(cls, model: rest.Prefetch) -> grpc.PrefetchQuery:
        prefetch = None
        if isinstance(model.prefetch, rest.Prefetch):
            prefetch = [cls.convert_prefetch_query(model.prefetch)]
        elif isinstance(model.prefetch, list):
            prefetch = [cls.convert_prefetch_query(prefetch) for prefetch in model.prefetch]

        return grpc.PrefetchQuery(
            prefetch=prefetch,
            query=cls.convert_query_interface(model.query) if model.query is not None else None,
            using=model.using if model.using is not None else None,
            filter=cls.convert_filter(model.filter) if model.filter is not None else None,
            params=cls.convert_search_params(model.params) if model.params is not None else None,
            score_threshold=model.score_threshold,
            limit=model.limit if model.limit is not None else None,
            lookup_from=(
                cls.convert_lookup_location(model.lookup_from)
                if model.lookup_from is not None
                else None
            ),
        )

    @classmethod
    def convert_search_request(
        cls, model: rest.SearchRequest, collection_name: str
    ) -> grpc.SearchPoints:
        vector, sparse_indices, name = cls.convert_named_vector_struct(model.vector)

        return grpc.SearchPoints(
            collection_name=collection_name,
            vector=vector,
            sparse_indices=sparse_indices,
            filter=cls.convert_filter(model.filter) if model.filter is not None else None,
            limit=model.limit,
            with_payload=(
                cls.convert_with_payload_interface(model.with_payload)
                if model.with_payload is not None
                else None
            ),
            params=cls.convert_search_params(model.params) if model.params is not None else None,
            score_threshold=model.score_threshold,
            offset=model.offset,
            vector_name=name,
            with_vectors=(
                cls.convert_with_vectors(model.with_vector)
                if model.with_vector is not None
                else None
            ),
            shard_key_selector=(
                cls.convert_shard_key_selector(model.shard_key) if model.shard_key else None
            ),
        )

    @classmethod
    def convert_search_points(
        cls, model: rest.SearchRequest, collection_name: str
    ) -> grpc.SearchPoints:
        return cls.convert_search_request(model, collection_name)

    @classmethod
    def convert_query_request(
        cls, model: rest.QueryRequest, collection_name: str
    ) -> grpc.QueryPoints:
        prefetch = (
            [model.prefetch] if isinstance(model.prefetch, rest.Prefetch) else model.prefetch
        )
        return grpc.QueryPoints(
            collection_name=collection_name,
            prefetch=(
                [cls.convert_prefetch_query(p) for p in prefetch]
                if model.prefetch is not None
                else None
            ),
            query=cls.convert_query_interface(model.query) if model.query is not None else None,
            using=model.using,
            filter=cls.convert_filter(model.filter) if model.filter is not None else None,
            params=cls.convert_search_params(model.params) if model.params is not None else None,
            score_threshold=model.score_threshold,
            limit=model.limit,
            offset=model.offset,
            with_vectors=(
                cls.convert_with_vectors(model.with_vector)
                if model.with_vector is not None
                else None
            ),
            with_payload=(
                cls.convert_with_payload_interface(model.with_payload)
                if model.with_payload is not None
                else None
            ),
            shard_key_selector=(
                cls.convert_shard_key_selector(model.shard_key)
                if model.shard_key is not None
                else None
            ),
            lookup_from=(
                cls.convert_lookup_location(model.lookup_from)
                if model.lookup_from is not None
                else None
            ),
        )

    @classmethod
    def convert_query_points(
        cls, model: rest.QueryRequest, collection_name: str
    ) -> grpc.QueryPoints:
        return cls.convert_query_request(model, collection_name)

    @classmethod
    def convert_recommend_request(
        cls, model: rest.RecommendRequest, collection_name: str
    ) -> grpc.RecommendPoints:
        positive_ids = cls.convert_recommend_examples_to_ids(model.positive)
        negative_ids = cls.convert_recommend_examples_to_ids(model.negative)

        positive_vectors = cls.convert_recommend_examples_to_vectors(model.positive)
        negative_vectors = cls.convert_recommend_examples_to_vectors(model.negative)

        return grpc.RecommendPoints(
            collection_name=collection_name,
            positive=positive_ids,
            negative=negative_ids,
            filter=cls.convert_filter(model.filter) if model.filter is not None else None,
            limit=model.limit,
            with_payload=(
                cls.convert_with_payload_interface(model.with_payload)
                if model.with_payload is not None
                else None
            ),
            params=cls.convert_search_params(model.params) if model.params is not None else None,
            score_threshold=model.score_threshold,
            offset=model.offset,
            with_vectors=(
                cls.convert_with_vectors(model.with_vector)
                if model.with_vector is not None
                else None
            ),
            using=model.using,
            lookup_from=(
                cls.convert_lookup_location(model.lookup_from)
                if model.lookup_from is not None
                else None
            ),
            strategy=(
                cls.convert_recommend_strategy(model.strategy)
                if model.strategy is not None
                else None
            ),
            positive_vectors=positive_vectors,
            negative_vectors=negative_vectors,
            shard_key_selector=(
                cls.convert_shard_key_selector(model.shard_key) if model.shard_key else None
            ),
        )

    @classmethod
    def convert_discover_points(
        cls, model: rest.DiscoverRequest, collection_name: str
    ) -> grpc.DiscoverPoints:
        return cls.convert_discover_request(model, collection_name)

    @classmethod
    def convert_discover_request(
        cls, model: rest.DiscoverRequest, collection_name: str
    ) -> grpc.DiscoverPoints:
        target = cls.convert_target_vector(model.target) if model.target is not None else None

        context = (
            [cls.convert_context_example_pair(pair) for pair in model.context]
            if model.context is not None
            else None
        )

        query_filter = None if model.filter is None else cls.convert_filter(model=model.filter)

        search_params = None if model.params is None else cls.convert_search_params(model.params)

        with_payload = (
            None
            if model.with_payload is None
            else cls.convert_with_payload_interface(model.with_payload)
        )

        with_vectors = (
            None if model.with_vector is None else cls.convert_with_vectors(model.with_vector)
        )

        lookup_from = (
            None if model.lookup_from is None else cls.convert_lookup_location(model.lookup_from)
        )

        shard_key_selector = (
            None if model.shard_key is None else cls.convert_shard_key_selector(model.shard_key)
        )

        return grpc.DiscoverPoints(
            collection_name=collection_name,
            target=target,
            context=context,
            filter=query_filter,
            limit=model.limit,
            offset=model.offset,
            with_vectors=with_vectors,
            with_payload=with_payload,
            params=search_params,
            using=model.using,
            lookup_from=lookup_from,
            shard_key_selector=shard_key_selector,
        )

    @classmethod
    def convert_recommend_points(
        cls, model: rest.RecommendRequest, collection_name: str
    ) -> grpc.RecommendPoints:
        return cls.convert_recommend_request(model, collection_name)

    @classmethod
    def convert_tokenizer_type(cls, model: rest.TokenizerType) -> grpc.TokenizerType:
        if model == rest.TokenizerType.WORD:
            return grpc.TokenizerType.Word
        elif model == rest.TokenizerType.WHITESPACE:
            return grpc.TokenizerType.Whitespace
        elif model == rest.TokenizerType.PREFIX:
            return grpc.TokenizerType.Prefix
        elif model == rest.TokenizerType.MULTILINGUAL:
            return grpc.TokenizerType.Multilingual
        else:
            raise ValueError(f"invalid TokenizerType model: {model}")  # pragma: no cover

    @classmethod
    def convert_text_index_params(cls, model: rest.TextIndexParams) -> grpc.TextIndexParams:
        return grpc.TextIndexParams(
            tokenizer=(
                cls.convert_tokenizer_type(model.tokenizer)
                if model.tokenizer is not None
                else None
            ),
            lowercase=model.lowercase,
            min_token_len=model.min_token_len,
            max_token_len=model.max_token_len,
            on_disk=model.on_disk,
            stopwords=cls.convert_stopwords(model.stopwords)
            if model.stopwords is not None
            else None,
            phrase_matching=model.phrase_matching,
            stemmer=cls.convert_stemmer(model.stemmer) if model.stemmer is not None else None,
        )

    @classmethod
    def convert_stopwords(cls, model: rest.StopwordsInterface) -> grpc.StopwordsSet:
        if isinstance(model, rest.Language):
            return grpc.StopwordsSet(languages=[model.value])

        if isinstance(model, rest.StopwordsSet):
            return grpc.StopwordsSet(
                languages=[lang for lang in model.languages] if model.languages else None,
                custom=model.custom,
            )

        raise ValueError(f"invalid StopwordsInterface model: {model}")  # pragma: no cover

    @classmethod
    def convert_stemmer(cls, model: rest.StemmingAlgorithm) -> grpc.StemmingAlgorithm:
        if isinstance(model, rest.SnowballParams):
            return grpc.StemmingAlgorithm(snowball=grpc.SnowballParams(language=model.language))

        raise ValueError(f"invalid StemmingAlgorithm model: {model}")  # pragma: no cover

    @classmethod
    def convert_integer_index_params(
        cls, model: rest.IntegerIndexParams
    ) -> grpc.IntegerIndexParams:
        return grpc.IntegerIndexParams(
            lookup=model.lookup,
            range=model.range,
            is_principal=model.is_principal,
            on_disk=model.on_disk,
        )

    @classmethod
    def convert_keyword_index_params(
        cls, model: rest.KeywordIndexParams
    ) -> grpc.KeywordIndexParams:
        return grpc.KeywordIndexParams(is_tenant=model.is_tenant, on_disk=model.on_disk)

    @classmethod
    def convert_float_index_params(cls, model: rest.FloatIndexParams) -> grpc.FloatIndexParams:
        return grpc.FloatIndexParams(is_principal=model.is_principal, on_disk=model.on_disk)

    @classmethod
    def convert_geo_index_params(cls, model: rest.GeoIndexParams) -> grpc.GeoIndexParams:
        return grpc.GeoIndexParams(on_disk=model.on_disk)

    @classmethod
    def convert_bool_index_params(cls, model: rest.BoolIndexParams) -> grpc.BoolIndexParams:
        return grpc.BoolIndexParams(on_disk=model.on_disk)

    @classmethod
    def convert_datetime_index_params(
        cls, model: rest.DatetimeIndexParams
    ) -> grpc.DatetimeIndexParams:
        return grpc.DatetimeIndexParams(is_principal=model.is_principal, on_disk=model.on_disk)

    @classmethod
    def convert_uuid_index_params(cls, model: rest.UuidIndexParams) -> grpc.UuidIndexParams:
        return grpc.UuidIndexParams(is_tenant=model.is_tenant, on_disk=model.on_disk)

    @classmethod
    def convert_collection_params_diff(
        cls, model: rest.CollectionParamsDiff
    ) -> grpc.CollectionParamsDiff:
        return grpc.CollectionParamsDiff(
            replication_factor=model.replication_factor,
            write_consistency_factor=model.write_consistency_factor,
            on_disk_payload=model.on_disk_payload,
            read_fan_out_factor=model.read_fan_out_factor,
        )

    @classmethod
    def convert_lookup_location(cls, model: rest.LookupLocation) -> grpc.LookupLocation:
        return grpc.LookupLocation(
            collection_name=model.collection,
            vector_name=model.vector,
        )

    @classmethod
    def convert_read_consistency(cls, model: rest.ReadConsistency) -> grpc.ReadConsistency:
        if isinstance(model, int):
            return grpc.ReadConsistency(
                factor=model,
            )
        elif isinstance(model, rest.ReadConsistencyType):
            return grpc.ReadConsistency(
                type=cls.convert_read_consistency_type(model),
            )
        else:
            raise ValueError(f"invalid ReadConsistency model: {model}")  # pragma: no cover

    @classmethod
    def convert_read_consistency_type(
        cls, model: rest.ReadConsistencyType
    ) -> grpc.ReadConsistencyType:
        if model == rest.ReadConsistencyType.MAJORITY:
            return grpc.ReadConsistencyType.Majority
        elif model == rest.ReadConsistencyType.ALL:
            return grpc.ReadConsistencyType.All
        elif model == rest.ReadConsistencyType.QUORUM:
            return grpc.ReadConsistencyType.Quorum
        else:
            raise ValueError(f"invalid ReadConsistencyType model: {model}")  # pragma: no cover

    @classmethod
    def convert_write_ordering(cls, model: rest.WriteOrdering) -> grpc.WriteOrdering:
        if model == rest.WriteOrdering.WEAK:
            return grpc.WriteOrdering(type=grpc.WriteOrderingType.Weak)
        elif model == rest.WriteOrdering.MEDIUM:
            return grpc.WriteOrdering(type=grpc.WriteOrderingType.Medium)
        elif model == rest.WriteOrdering.STRONG:
            return grpc.WriteOrdering(type=grpc.WriteOrderingType.Strong)
        else:
            raise ValueError(f"invalid WriteOrdering model: {model}")  # pragma: no cover

    @classmethod
    def convert_scalar_quantization_config(
        cls, model: rest.ScalarQuantizationConfig
    ) -> grpc.ScalarQuantization:
        return grpc.ScalarQuantization(
            type=grpc.QuantizationType.Int8,
            quantile=model.quantile,
            always_ram=model.always_ram,
        )

    @classmethod
    def convert_product_quantization_config(
        cls, model: rest.ProductQuantizationConfig
    ) -> grpc.ProductQuantization:
        return grpc.ProductQuantization(
            compression=cls.convert_compression_ratio(model.compression),
            always_ram=model.always_ram,
        )

    @classmethod
    def convert_binary_quantization_config(
        cls, model: rest.BinaryQuantizationConfig
    ) -> grpc.BinaryQuantization:
        return grpc.BinaryQuantization(
            always_ram=model.always_ram,
            encoding=cls.convert_binary_quantization_encoding(model.encoding)
            if model.encoding is not None
            else None,
            query_encoding=cls.convert_binary_quantization_query_encoding(model.query_encoding)
            if model.query_encoding is not None
            else None,
        )

    @classmethod
    def convert_binary_quantization_encoding(
        cls, model: rest.BinaryQuantizationEncoding
    ) -> grpc.BinaryQuantizationEncoding:
        if model == rest.BinaryQuantizationEncoding.ONE_BIT:
            return grpc.BinaryQuantizationEncoding.OneBit

        if model == rest.BinaryQuantizationEncoding.TWO_BITS:
            return grpc.BinaryQuantizationEncoding.TwoBits

        if model == rest.BinaryQuantizationEncoding.ONE_AND_HALF_BITS:
            return grpc.BinaryQuantizationEncoding.OneAndHalfBits

        raise ValueError(f"invalid BinaryQuantizationEncoding model: {model}")  # pragma: no cover

    @classmethod
    def convert_binary_quantization_query_encoding(
        cls, model: rest.BinaryQuantizationQueryEncoding
    ) -> grpc.BinaryQuantizationQueryEncoding:
        if model == rest.BinaryQuantizationQueryEncoding.DEFAULT:
            return grpc.BinaryQuantizationQueryEncoding(
                setting=grpc.BinaryQuantizationQueryEncoding.Setting.Default
            )

        if model == rest.BinaryQuantizationQueryEncoding.BINARY:
            return grpc.BinaryQuantizationQueryEncoding(
                setting=grpc.BinaryQuantizationQueryEncoding.Setting.Binary
            )

        if model == rest.BinaryQuantizationQueryEncoding.SCALAR4BITS:
            return grpc.BinaryQuantizationQueryEncoding(
                setting=grpc.BinaryQuantizationQueryEncoding.Setting.Scalar4Bits
            )

        if model == rest.BinaryQuantizationQueryEncoding.SCALAR8BITS:
            return grpc.BinaryQuantizationQueryEncoding(
                setting=grpc.BinaryQuantizationQueryEncoding.Setting.Scalar8Bits
            )

        raise ValueError(
            f"invalid BinaryQuantizationQueryEncoding model: {model}"
        )  # pragma: no cover

    @classmethod
    def convert_compression_ratio(cls, model: rest.CompressionRatio) -> grpc.CompressionRatio:
        if model == rest.CompressionRatio.X4:
            return grpc.CompressionRatio.x4
        elif model == rest.CompressionRatio.X8:
            return grpc.CompressionRatio.x8
        elif model == rest.CompressionRatio.X16:
            return grpc.CompressionRatio.x16
        elif model == rest.CompressionRatio.X32:
            return grpc.CompressionRatio.x32
        elif model == rest.CompressionRatio.X64:
            return grpc.CompressionRatio.x64
        else:
            raise ValueError(f"invalid CompressionRatio model: {model}")  # pragma: no cover

    @classmethod
    def convert_quantization_config(
        cls, model: rest.QuantizationConfig
    ) -> grpc.QuantizationConfig:
        if isinstance(model, rest.ScalarQuantization):
            return grpc.QuantizationConfig(
                scalar=cls.convert_scalar_quantization_config(model.scalar)
            )
        if isinstance(model, rest.ProductQuantization):
            return grpc.QuantizationConfig(
                product=cls.convert_product_quantization_config(model.product)
            )
        if isinstance(model, rest.BinaryQuantization):
            return grpc.QuantizationConfig(
                binary=cls.convert_binary_quantization_config(model.binary)
            )
        else:
            raise ValueError(f"invalid QuantizationConfig model: {model}")  # pragma: no cover

    @classmethod
    def convert_quantization_search_params(
        cls, model: rest.QuantizationSearchParams
    ) -> grpc.QuantizationSearchParams:
        return grpc.QuantizationSearchParams(
            ignore=model.ignore,
            rescore=model.rescore,
            oversampling=model.oversampling,
        )

    @classmethod
    def convert_point_vectors(cls, model: rest.PointVectors) -> grpc.PointVectors:
        return grpc.PointVectors(
            id=cls.convert_extended_point_id(model.id),
            vectors=cls.convert_vector_struct(model.vector),
        )

    @classmethod
    def convert_groups_result(cls, model: rest.GroupsResult) -> grpc.GroupsResult:
        return grpc.GroupsResult(
            groups=[cls.convert_point_group(group) for group in model.groups],
        )

    @classmethod
    def convert_point_group(cls, model: rest.PointGroup) -> grpc.PointGroup:
        return grpc.PointGroup(
            id=cls.convert_group_id(model.id),
            hits=[cls.convert_scored_point(point) for point in model.hits],
            lookup=cls.convert_record(model.lookup) if model.lookup is not None else None,
        )

    @classmethod
    def convert_group_id(cls, model: rest.GroupId) -> grpc.GroupId:
        if isinstance(model, str):
            return grpc.GroupId(
                string_value=model,
            )
        elif isinstance(model, int):
            if model >= 0:
                return grpc.GroupId(
                    unsigned_value=model,
                )
            else:
                return grpc.GroupId(
                    integer_value=model,
                )
        else:
            raise ValueError(f"invalid GroupId model: {model}")  # pragma: no cover

    @classmethod
    def convert_with_lookup(cls, model: rest.WithLookup) -> grpc.WithLookup:
        return grpc.WithLookup(
            collection=model.collection,
            with_vectors=(
                cls.convert_with_vectors(model.with_vectors)
                if model.with_vectors is not None
                else None
            ),
            with_payload=(
                cls.convert_with_payload_interface(model.with_payload)
                if model.with_payload is not None
                else None
            ),
        )

    @classmethod
    def convert_quantization_config_diff(
        cls, model: rest.QuantizationConfigDiff
    ) -> grpc.QuantizationConfigDiff:
        if isinstance(model, rest.ScalarQuantization):
            return grpc.QuantizationConfigDiff(
                scalar=cls.convert_scalar_quantization_config(model.scalar)
            )
        if isinstance(model, rest.ProductQuantization):
            return grpc.QuantizationConfigDiff(
                product=cls.convert_product_quantization_config(model.product)
            )
        if isinstance(model, rest.BinaryQuantization):
            return grpc.QuantizationConfigDiff(
                binary=cls.convert_binary_quantization_config(model.binary)
            )
        if model == rest.Disabled.DISABLED:
            return grpc.QuantizationConfigDiff(
                disabled=grpc.Disabled(),
            )
        else:
            raise ValueError(f"invalid QuantizationConfigDiff model: {model}")  # pragma: no cover

    @classmethod
    def convert_vector_params_diff(cls, model: rest.VectorParamsDiff) -> grpc.VectorParamsDiff:
        return grpc.VectorParamsDiff(
            hnsw_config=(
                cls.convert_hnsw_config_diff(model.hnsw_config)
                if model.hnsw_config is not None
                else None
            ),
            quantization_config=(
                cls.convert_quantization_config_diff(model.quantization_config)
                if model.quantization_config is not None
                else None
            ),
            on_disk=model.on_disk,
        )

    @classmethod
    def convert_vectors_config_diff(cls, model: rest.VectorsConfigDiff) -> grpc.VectorsConfigDiff:
        if isinstance(model, dict) and len(model) == 1 and "" in model:
            return grpc.VectorsConfigDiff(params=cls.convert_vector_params_diff(model[""]))
        elif isinstance(model, dict):
            return grpc.VectorsConfigDiff(
                params_map=grpc.VectorParamsDiffMap(
                    map=dict(
                        (key, cls.convert_vector_params_diff(val)) for key, val in model.items()
                    )
                )
            )
        else:
            raise ValueError(f"invalid VectorsConfigDiff model: {model}")  # pragma: no cover

    @classmethod
    def convert_point_insert_operation(
        cls, model: rest.PointInsertOperations
    ) -> list[grpc.PointStruct]:
        if isinstance(model, rest.PointsBatch):
            vectors_batch: list[grpc.Vectors] = cls.convert_batch_vector_struct(
                model.batch.vectors, len(model.batch.ids)
            )
            return [
                grpc.PointStruct(
                    id=RestToGrpc.convert_extended_point_id(model.batch.ids[idx]),
                    vectors=vectors_batch[idx],
                    payload=(
                        RestToGrpc.convert_payload(model.batch.payloads[idx])
                        if model.batch.payloads is not None
                        else None
                    ),
                )
                for idx in range(len(model.batch.ids))
            ]
        elif isinstance(model, rest.PointsList):
            return [cls.convert_point_struct(point) for point in model.points]
        else:
            raise ValueError(f"invalid PointInsertOperations model: {model}")  # pragma: no cover

    @classmethod
    def convert_update_operation(cls, model: rest.UpdateOperation) -> grpc.PointsUpdateOperation:
        return cls.convert_points_update_operation(model)

    @classmethod
    def convert_points_update_operation(
        cls, model: rest.UpdateOperation
    ) -> grpc.PointsUpdateOperation:
        if isinstance(model, rest.UpsertOperation):
            shard_key_selector = (
                cls.convert_shard_key_selector(model.upsert.shard_key)
                if model.upsert.shard_key
                else None
            )
            return grpc.PointsUpdateOperation(
                upsert=grpc.PointsUpdateOperation.PointStructList(
                    points=cls.convert_point_insert_operation(model.upsert),
                    shard_key_selector=shard_key_selector,
                )
            )
        elif isinstance(model, rest.DeleteOperation):
            shard_key_selector = (
                cls.convert_shard_key_selector(model.delete.shard_key)
                if model.delete.shard_key
                else None
            )
            points_selector = cls.convert_points_selector(model.delete)
            delete_points = grpc.PointsUpdateOperation.DeletePoints(
                points=points_selector,
                shard_key_selector=shard_key_selector,
            )
            return grpc.PointsUpdateOperation(
                delete_points=delete_points,
            )
        elif isinstance(model, rest.SetPayloadOperation):
            if model.set_payload.points:
                points_selector = rest.PointIdsList(points=model.set_payload.points)
            elif model.set_payload.filter:
                points_selector = rest.FilterSelector(filter=model.set_payload.filter)
            else:
                raise ValueError(f"invalid SetPayloadOperation model: {model}")  # pragma: no cover

            shard_key_selector = (
                cls.convert_shard_key_selector(model.set_payload.shard_key)
                if model.set_payload.shard_key
                else None
            )

            return grpc.PointsUpdateOperation(
                set_payload=grpc.PointsUpdateOperation.SetPayload(
                    payload=cls.convert_payload(model.set_payload.payload),
                    points_selector=cls.convert_points_selector(points_selector),
                    shard_key_selector=shard_key_selector,
                    key=model.set_payload.key,
                )
            )
        elif isinstance(model, rest.OverwritePayloadOperation):
            if model.overwrite_payload.points:
                points_selector = rest.PointIdsList(points=model.overwrite_payload.points)
            elif model.overwrite_payload.filter:
                points_selector = rest.FilterSelector(filter=model.overwrite_payload.filter)
            else:
                raise ValueError(
                    f"invalid OverwritePayloadOperation model: {model}"
                )  # pragma: no cover

            shard_key_selector = (
                cls.convert_shard_key_selector(model.overwrite_payload.shard_key)
                if model.overwrite_payload.shard_key
                else None
            )

            return grpc.PointsUpdateOperation(
                overwrite_payload=grpc.PointsUpdateOperation.OverwritePayload(
                    payload=cls.convert_payload(model.overwrite_payload.payload),
                    points_selector=cls.convert_points_selector(points_selector),
                    shard_key_selector=shard_key_selector,
                    key=model.overwrite_payload.key,
                )
            )
        elif isinstance(model, rest.DeletePayloadOperation):
            if model.delete_payload.points:
                points_selector = rest.PointIdsList(points=model.delete_payload.points)
            elif model.delete_payload.filter:
                points_selector = rest.FilterSelector(filter=model.delete_payload.filter)
            else:
                raise ValueError(
                    f"invalid DeletePayloadOperation model: {model}"
                )  # pragma: no cover

            shard_key_selector = (
                cls.convert_shard_key_selector(model.delete_payload.shard_key)
                if model.delete_payload.shard_key
                else None
            )

            return grpc.PointsUpdateOperation(
                delete_payload=grpc.PointsUpdateOperation.DeletePayload(
                    keys=model.delete_payload.keys,
                    points_selector=cls.convert_points_selector(points_selector),
                    shard_key_selector=shard_key_selector,
                )
            )
        elif isinstance(model, rest.ClearPayloadOperation):
            shard_key_selector = (
                cls.convert_shard_key_selector(model.clear_payload.shard_key)
                if model.clear_payload.shard_key
                else None
            )
            points_selector = cls.convert_points_selector(model.clear_payload)
            clear_payload = grpc.PointsUpdateOperation.ClearPayload(
                points=points_selector,
                shard_key_selector=shard_key_selector,
            )
            return grpc.PointsUpdateOperation(
                clear_payload=clear_payload,
            )
        elif isinstance(model, rest.UpdateVectorsOperation):
            shard_key_selector = (
                cls.convert_shard_key_selector(model.update_vectors.shard_key)
                if model.update_vectors.shard_key
                else None
            )

            return grpc.PointsUpdateOperation(
                update_vectors=grpc.PointsUpdateOperation.UpdateVectors(
                    points=[
                        cls.convert_point_vectors(point) for point in model.update_vectors.points
                    ],
                    shard_key_selector=shard_key_selector,
                )
            )
        elif isinstance(model, rest.DeleteVectorsOperation):
            if model.delete_vectors.points:
                points_selector = rest.PointIdsList(points=model.delete_vectors.points)
            elif model.delete_vectors.filter:
                points_selector = rest.FilterSelector(filter=model.delete_vectors.filter)
            else:
                raise ValueError(
                    f"invalid DeletePayloadOperation model: {model}"
                )  # pragma: no cover

            shard_key_selector = (
                cls.convert_shard_key_selector(model.delete_vectors.shard_key)
                if model.delete_vectors.shard_key
                else None
            )

            return grpc.PointsUpdateOperation(
                delete_vectors=grpc.PointsUpdateOperation.DeleteVectors(
                    points_selector=cls.convert_points_selector(points_selector),
                    vectors=grpc.VectorsSelector(names=model.delete_vectors.vector),
                    shard_key_selector=shard_key_selector,
                )
            )
        else:
            raise ValueError(f"invalid UpdateOperation model: {model}")  # pragma: no cover

    @classmethod
    def convert_init_from(cls, model: rest.InitFrom) -> str:
        if isinstance(model, rest.InitFrom):
            return model.collection
        else:
            raise ValueError(f"invalid InitFrom model: {model}")  # pragma: no cover

    @classmethod
    def convert_recommend_strategy(cls, model: rest.RecommendStrategy) -> grpc.RecommendStrategy:
        if model == rest.RecommendStrategy.AVERAGE_VECTOR:
            return grpc.RecommendStrategy.AverageVector
        elif model == rest.RecommendStrategy.BEST_SCORE:
            return grpc.RecommendStrategy.BestScore
        elif model == rest.RecommendStrategy.SUM_SCORES:
            return grpc.RecommendStrategy.SumScores
        else:
            raise ValueError(f"invalid RecommendStrategy model: {model}")  # pragma: no cover

    @classmethod
    def convert_sparse_index_params(cls, model: rest.SparseIndexParams) -> grpc.SparseIndexConfig:
        return grpc.SparseIndexConfig(
            full_scan_threshold=(
                model.full_scan_threshold if model.full_scan_threshold is not None else None
            ),
            on_disk=model.on_disk if model.on_disk is not None else None,
            datatype=cls.convert_datatype(model.datatype) if model.datatype is not None else None,
        )

    @classmethod
    def convert_modifier(cls, model: rest.Modifier) -> grpc.Modifier:
        if model == rest.Modifier.IDF:
            return grpc.Modifier.Idf
        elif model == rest.Modifier.NONE:
            return getattr(grpc.Modifier, "None")
        else:
            raise ValueError(f"invalid Modifier model: {model}")  # pragma: no cover

    @classmethod
    def convert_sparse_vector_params(
        cls, model: rest.SparseVectorParams
    ) -> grpc.SparseVectorParams:
        return grpc.SparseVectorParams(
            index=(
                cls.convert_sparse_index_params(model.index) if model.index is not None else None
            ),
            modifier=(
                cls.convert_modifier(model.modifier) if model.modifier is not None else None
            ),
        )

    @classmethod
    def convert_sparse_vector_config(
        cls, model: Mapping[str, rest.SparseVectorParams]
    ) -> grpc.SparseVectorConfig:
        return grpc.SparseVectorConfig(
            map=dict((key, cls.convert_sparse_vector_params(val)) for key, val in model.items())
        )

    @classmethod
    def convert_shard_key(cls, model: rest.ShardKey) -> grpc.ShardKey:
        if isinstance(model, int):
            return grpc.ShardKey(number=model)
        if isinstance(model, str):
            return grpc.ShardKey(keyword=model)

        raise ValueError(f"invalid ShardKey model: {model}")  # pragma: no cover

    @classmethod
    def convert_shard_key_selector(cls, model: rest.ShardKeySelector) -> grpc.ShardKeySelector:
        if isinstance(model, get_args_subscribed(rest.ShardKey)):
            return grpc.ShardKeySelector(shard_keys=[cls.convert_shard_key(model)])

        if isinstance(model, list):
            return grpc.ShardKeySelector(shard_keys=[cls.convert_shard_key(key) for key in model])

        raise ValueError(f"invalid ShardKeySelector model: {model}")  # pragma: no cover

    @classmethod
    def convert_sharding_method(cls, model: rest.ShardingMethod) -> grpc.ShardingMethod:
        if model == rest.ShardingMethod.AUTO:
            return grpc.Auto
        elif model == rest.ShardingMethod.CUSTOM:
            return grpc.Custom
        else:
            raise ValueError(f"invalid ShardingMethod model: {model}")  # pragma: no cover

    @classmethod
    def convert_health_check_reply(cls, model: rest.VersionInfo) -> grpc.HealthCheckReply:
        return grpc.HealthCheckReply(
            title=model.title,
            version=model.version,
            commit=model.commit,
        )

    @classmethod
    def convert_search_matrix_pair(cls, model: rest.SearchMatrixPair) -> grpc.SearchMatrixPair:
        return grpc.SearchMatrixPair(
            a=cls.convert_extended_point_id(model.a),
            b=cls.convert_extended_point_id(model.b),
            score=model.score,
        )

    @classmethod
    def convert_search_matrix_pairs(
        cls, model: rest.SearchMatrixPairsResponse
    ) -> grpc.SearchMatrixPairs:
        return grpc.SearchMatrixPairs(
            pairs=[cls.convert_search_matrix_pair(pair) for pair in model.pairs],
        )

    @classmethod
    def convert_search_matrix_offsets(
        cls, model: rest.SearchMatrixOffsetsResponse
    ) -> grpc.SearchMatrixOffsets:
        return grpc.SearchMatrixOffsets(
            offsets_row=list(model.offsets_row),
            offsets_col=list(model.offsets_col),
            scores=list(model.scores),
            ids=[cls.convert_extended_point_id(p_id) for p_id in model.ids],
        )

    @classmethod
    def convert_strict_mode_multivector(
        cls, model: rest.StrictModeMultivector
    ) -> grpc.StrictModeMultivector:
        return grpc.StrictModeMultivector(
            max_vectors=model.max_vectors,
        )

    @classmethod
    def convert_strict_mode_multivector_config(
        cls, model: rest.StrictModeMultivectorConfig
    ) -> grpc.StrictModeMultivectorConfig:
        return grpc.StrictModeMultivectorConfig(
            multivector_config=dict(
                (key, cls.convert_strict_mode_multivector(val)) for key, val in model.items()
            )
        )

    @classmethod
    def convert_strict_mode_sparse(cls, model: rest.StrictModeSparse) -> grpc.StrictModeSparse:
        return grpc.StrictModeSparse(
            max_length=model.max_length,
        )

    @classmethod
    def convert_strict_mode_sparse_config(
        cls, model: rest.StrictModeSparseConfig
    ) -> grpc.StrictModeSparseConfig:
        return grpc.StrictModeSparseConfig(
            sparse_config=dict(
                (key, cls.convert_strict_mode_sparse(val)) for key, val in model.items()
            )
        )

    @classmethod
    def convert_strict_mode_config(cls, model: rest.StrictModeConfig) -> grpc.StrictModeConfig:
        return grpc.StrictModeConfig(
            enabled=model.enabled,
            max_query_limit=model.max_query_limit,
            max_timeout=model.max_timeout,
            unindexed_filtering_retrieve=model.unindexed_filtering_retrieve,
            unindexed_filtering_update=model.unindexed_filtering_update,
            search_max_hnsw_ef=model.search_max_hnsw_ef,
            search_allow_exact=model.search_allow_exact,
            search_max_oversampling=model.search_max_oversampling,
            upsert_max_batchsize=model.upsert_max_batchsize,
            max_collection_vector_size_bytes=model.max_collection_vector_size_bytes,
            read_rate_limit=model.read_rate_limit,
            write_rate_limit=model.write_rate_limit,
            max_collection_payload_size_bytes=model.max_collection_payload_size_bytes,
            max_points_count=model.max_points_count,
            filter_max_conditions=model.filter_max_conditions,
            condition_max_size=model.condition_max_size,
            multivector_config=(
                cls.convert_strict_mode_multivector_config(model.multivector_config)
                if model.multivector_config
                else None
            ),
            sparse_config=(
                cls.convert_strict_mode_sparse_config(model.sparse_config)
                if model.sparse_config
                else None
            ),
        )

    @classmethod
    def convert_strict_mode_config_output(
        cls, model: rest.StrictModeConfigOutput
    ) -> grpc.StrictModeConfig:
        return grpc.StrictModeConfig(
            enabled=model.enabled,
            max_query_limit=model.max_query_limit,
            max_timeout=model.max_timeout,
            unindexed_filtering_retrieve=model.unindexed_filtering_retrieve,
            unindexed_filtering_update=model.unindexed_filtering_update,
            search_max_hnsw_ef=model.search_max_hnsw_ef,
            search_allow_exact=model.search_allow_exact,
            search_max_oversampling=model.search_max_oversampling,
            upsert_max_batchsize=model.upsert_max_batchsize,
            max_collection_vector_size_bytes=model.max_collection_vector_size_bytes,
            read_rate_limit=model.read_rate_limit,
            write_rate_limit=model.write_rate_limit,
            max_collection_payload_size_bytes=model.max_collection_payload_size_bytes,
            max_points_count=model.max_points_count,
            filter_max_conditions=model.filter_max_conditions,
            condition_max_size=model.condition_max_size,
            multivector_config=(
                cls.convert_strict_mode_multivector_config_output(model.multivector_config)
                if model.multivector_config
                else None
            ),
            sparse_config=(
                cls.convert_strict_mode_sparse_config_output(model.sparse_config)
                if model.sparse_config
                else None
            ),
        )

    @classmethod
    def convert_strict_mode_multivector_config_output(
        cls, model: rest.StrictModeMultivectorConfigOutput
    ) -> grpc.StrictModeMultivectorConfig:
        return grpc.StrictModeMultivectorConfig(
            multivector_config=dict(
                (key, cls.convert_strict_mode_multivector_output(val))
                for key, val in model.items()
            )
        )

    @classmethod
    def convert_strict_mode_sparse_config_output(
        cls, model: rest.StrictModeSparseConfigOutput
    ) -> grpc.StrictModeSparseConfig:
        return grpc.StrictModeSparseConfig(
            sparse_config=dict(
                (key, cls.convert_strict_mode_sparse_output(val)) for key, val in model.items()
            )
        )

    @classmethod
    def convert_strict_mode_multivector_output(
        cls, model: rest.StrictModeMultivectorOutput
    ) -> grpc.StrictModeMultivector:
        return grpc.StrictModeMultivector(
            max_vectors=model.max_vectors,
        )

    @classmethod
    def convert_strict_mode_sparse_output(
        cls, model: rest.StrictModeSparseOutput
    ) -> grpc.StrictModeSparse:
        return grpc.StrictModeSparse(
            max_length=model.max_length,
        )