from abc import abstractmethod
from typing import (
Awaitable,
Generic,
Optional,
Type,
Union,
)
from weaviate.collections.classes.config import (
CollectionConfig,
)
from weaviate.collections.classes.internal import References
from weaviate.collections.classes.types import (
Properties,
)
from weaviate.collections.collection import Collection, CollectionAsync
from weaviate.collections.collections.executor import _CollectionsExecutor
from weaviate.connect import executor
from weaviate.connect.v4 import ConnectionType
[docs]
class _CollectionsBase(Generic[ConnectionType], _CollectionsExecutor[ConnectionType]):
def __init__(self, connection: ConnectionType) -> None:
self._connection = connection
[docs]
@executor.no_wrapping
def get(
self,
name: str,
data_model_properties: Optional[Type[Properties]] = None,
data_model_references: Optional[Type[References]] = None,
skip_argument_validation: bool = False,
) -> Union[Collection[Properties, References], CollectionAsync[Properties, References]]:
"""Use this method to return a collection object to be used when interacting with your Weaviate collection.
This method does not send a request to Weaviate. It simply creates a Python object for you to use to make requests.
Args:
name: The name of the collection to get.
data_model_properties: The generic class that you want to use to represent the properties of objects in this collection when mutating objects through the `.query` namespace.
The generic provided in this argument will propagate to the methods in `.query` and allow you to do `mypy` static type checking on your codebase.
If you do not provide a generic, the methods in `.query` will return objects properties as `Dict[str, Any]`.
data_model_references: The generic class that you want to use to represent the objects of references in this collection when mutating objects through the `.query` namespace.
The generic provided in this argument will propagate to the methods in `.query` and allow you to do `mypy` static type checking on your codebase.
If you do not provide a generic, the methods in `.query` will return properties of referenced objects as `Dict[str, Any]`.
skip_argument_validation: If arguments to functions such as near_vector should be validated. Disable this if you need to squeeze out some extra performance.
Raises:
weaviate.exceptions.WeaviateInvalidInputError: If the input parameters are invalid.
weaviate.exceptions.InvalidDataModelException: If the data model is not a valid data model, i.e., it is not a `dict` nor a `TypedDict`.
"""
return self._use(
name=name,
data_model_properties=data_model_properties,
data_model_references=data_model_references,
skip_argument_validation=skip_argument_validation,
)
[docs]
@abstractmethod
def use(
self,
name: str,
data_model_properties: Optional[Type[Properties]] = None,
data_model_references: Optional[Type[References]] = None,
skip_argument_validation: bool = False,
) -> Union[Collection[Properties, References], CollectionAsync[Properties, References]]:
"""Use this method to return a collection object to be used when interacting with your Weaviate collection.
This method does not send a request to Weaviate. It simply creates a Python object for you to use to make requests.
Args:
name: The name of the collection to get.
data_model_properties: The generic class that you want to use to represent the properties of objects in this collection when mutating objects through the `.query` namespace.
The generic provided in this argument will propagate to the methods in `.query` and allow you to do `mypy` static type checking on your codebase.
If you do not provide a generic, the methods in `.query` will return objects properties as `Dict[str, Any]`.
data_model_references: The generic class that you want to use to represent the objects of references in this collection when mutating objects through the `.query` namespace.
The generic provided in this argument will propagate to the methods in `.query` and allow you to do `mypy` static type checking on your codebase.
If you do not provide a generic, the methods in `.query` will return properties of referenced objects as `Dict[str, Any]`.
skip_argument_validation: If arguments to functions such as near_vector should be validated. Disable this if you need to squeeze out some extra performance.
Raises:
weaviate.exceptions.WeaviateInvalidInputError: If the input parameters are invalid.
weaviate.exceptions.InvalidDataModelException: If the data model is not a valid data model, i.e., it is not a `dict` nor a `TypedDict`.
"""
raise NotImplementedError()
[docs]
@abstractmethod
def create_from_dict(
self, config: dict
) -> Union[
Collection[Properties, References],
Awaitable[CollectionAsync[Properties, References]],
]:
"""Use this method to create a collection in Weaviate and immediately return a collection object using a pre-defined Weaviate collection configuration dictionary object.
This method is helpful for those making the v3 -> v4 migration and for those interfacing with any experimental
Weaviate features that are not yet fully supported by the Weaviate Python client.
Args:
config: The dictionary representation of the collection's configuration.
Raises:
weaviate.exceptions.WeaviateConnectionError: If the network connection to Weaviate fails.
weaviate.exceptions.UnexpectedStatusCodeError: If Weaviate reports a non-OK status.
"""
raise NotImplementedError()
[docs]
@abstractmethod
def create_from_config(
self, config: CollectionConfig
) -> Union[
Collection[Properties, References],
Awaitable[CollectionAsync[Properties, References]],
]:
"""Use this method to create a collection in Weaviate and immediately return a collection object using a pre-defined Weaviate collection configuration object.
Args:
config: The collection's configuration.
Raises:
weaviate.exceptions.WeaviateConnectionError: If the network connection to Weaviate fails.
weaviate.exceptions.UnexpectedStatusCodeError: If Weaviate reports a non-OK status.
"""
raise NotImplementedError()