langchain_community.utilities.steam.SteamWebAPIWrapper

class langchain_community.utilities.steam.SteamWebAPIWrapper

Bases: BaseModel

Wrapper for Steam API.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param operations: List[dict] = [{'mode': 'get_game_details', 'name': 'Get Game Details', 'description': '\n    This tool is a wrapper around python-steam-api\'s steam.apps.search_games API and \n    steam.apps.get_app_details API, useful when you need to search for a game.\n    The input to this tool is a string specifying the name of the game you want to \n    search for. For example, to search for a game called "Counter-Strike: Global \n    Offensive", you would input "Counter-Strike: Global Offensive" as the game name.\n    This input will be passed into steam.apps.search_games to find the game id, link \n    and price, and then the game id will be passed into steam.apps.get_app_details to \n    get the detailed description and supported languages of the game. Finally the \n    results are combined and returned as a string.\n'}, {'mode': 'get_recommended_games', 'name': 'Get Recommended Games', 'description': '\n    This tool is a wrapper around python-steam-api\'s steam.users.get_owned_games API \n    and steamspypi\'s steamspypi.download API, useful when you need to get a list of \n    recommended games. The input to this tool is a string specifying the steam id of \n    the user you want to get recommended games for. For example, to get recommended \n    games for a user with steam id 76561197960435530, you would input \n    "76561197960435530" as the steam id.  This steamid is then utilized to form a \n    data_request sent to steamspypi\'s steamspypi.download to retrieve genres of user\'s \n    owned games. Then, calculates the frequency of each genre, identifying the most \n    popular one, and stored it in a dictionary. Subsequently, use steamspypi.download\n    to returns all games in this genre and return 5 most-played games that is not owned\n    by the user.\n\n'}]

param steam: Any = None

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

• include – fields to include in new model

• exclude – fields to exclude from new model, as with values this takes precedence over include

• update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep – set to True to make a deep copy of the model

Returns

new model instance

details_of_games(name: str) → str

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model

get_id_link_price(games: dict) → dict

The response may contain more than one game, so we need to choose the right one and return the id.

get_operations() → List[dict]

Return a list of operations.

get_steam_id(name: str) → str

get_users_games(steam_id: str) → List[str]

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

classmethod parse_obj(obj: Any) → Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

parse_to_str(details: dict) → str

Parse the details result.

recommended_games(steam_id: str) → str

remove_html_tags(html_string: str) → str

run(mode: str, game: str) → str

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model