viruPVS/langchain-docs huggingface dataset:
You can use these to eg identify a specific instance of a tool with its use case.
param name: str = 'close_multion_session'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param sessionId: str = ''¶
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the tool. Defaults to None
These tags will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
langchain_community.tools.file_management.read.ReadFileInput¶
class langchain_community.tools.file_management.read.ReadFileInput[source]¶
Bases: BaseModel
Input for ReadFileTool.
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 file_path: str [Required]¶
name of file
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
langchain_community.tools.powerbi.tool.ListPowerBITool¶
class langchain_community.tools.powerbi.tool.ListPowerBITool[source]¶
Bases: BaseTool
Tool for getting tables names.
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 args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Input is an empty string, output is a comma separated list of tables in the database.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param name: str = 'list_tables_powerbi'¶
The unique name of the tool that clearly communicates its purpose.
param powerbi: langchain_community.utilities.powerbi.PowerBIDataset [Required]¶
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param tags: Optional[List[str]] = None¶
langchain_community.tools.ainetwork.base.AINBaseTool¶
class langchain_community.tools.ainetwork.base.AINBaseTool[source]¶
Bases: BaseTool
Base class for the AINetwork tools.
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 args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str [Required]¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param interface: Ain [Optional]¶
The interface object for the AINetwork Blockchain.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param name: str [Required]¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the tool. Defaults to None
langchain_community.tools.slack.get_message.SlackGetMessage¶
class langchain_community.tools.slack.get_message.SlackGetMessage[source]¶
Bases: SlackBaseTool
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 args_schema: Type[langchain_community.tools.slack.get_message.SlackGetMessageSchema] = <class 'langchain_community.tools.slack.get_message.SlackGetMessageSchema'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param client: WebClient [Optional]¶
The WebClient object.
param description: str = 'Use this tool to get messages from a channel.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param name: str = 'get_messages'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
langchain_community.tools.sql_database.tool.ListSQLDatabaseTool¶
class langchain_community.tools.sql_database.tool.ListSQLDatabaseTool[source]¶
Bases: BaseSQLDatabaseTool, BaseTool
Tool for getting tables names.
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 args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param db: SQLDatabase [Required]¶
param description: str = 'Input is an empty string, output is a comma separated list of tables in the database.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param name: str = 'sql_db_list_tables'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param tags: Optional[List[str]] = None¶
langchain_community.tools.playwright.base.lazy_import_playwright_browsers¶
langchain_community.tools.playwright.base.lazy_import_playwright_browsers() → Tuple[Type[AsyncBrowser], Type[SyncBrowser]][source]¶
Lazy import playwright browsers.
Returns
AsyncBrowser and SyncBrowser classes.
Return type
Tuple[Type[AsyncBrowser], Type[SyncBrowser]]
langchain_community.tools.amadeus.flight_search.FlightSearchSchema¶
class langchain_community.tools.amadeus.flight_search.FlightSearchSchema[source]¶
Bases: BaseModel
Schema for the AmadeusFlightSearch tool.
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 departureDateTimeEarliest: str [Required]¶
The earliest departure datetime from the origin airport  for the flight search in the following format:  “YYYY-MM-DDTHH:MM”, where “T” separates the date and time  components. For example: “2023-06-09T10:30:00” represents  June 9th, 2023, at 10:30 AM.
param departureDateTimeLatest: str [Required]¶
The latest departure datetime from the origin airport  for the flight search in the following format:  “YYYY-MM-DDTHH:MM”, where “T” separates the date and time  components. For example: “2023-06-09T10:30:00” represents  June 9th, 2023, at 10:30 AM.
param destinationLocationCode: str [Required]¶
The three letter International Air Transport  Association (IATA) Location Identifier for the  search’s destination airport.
param originLocationCode: str [Required]¶
The three letter International Air Transport  Association (IATA) Location Identifier for the  search’s origin airport.
param page_number: int = 1¶
The specific page number of flight results to retrieve
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.
langchain_community.tools.office365.events_search.SearchEventsInput¶
class langchain_community.tools.office365.events_search.SearchEventsInput[source]¶
Bases: BaseModel
Input for SearchEmails Tool.
From https://learn.microsoft.com/en-us/graph/search-query-parameter
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 end_datetime: str [Required]¶
The end datetime for the search query in the following format:  YYYY-MM-DDTHH:MM:SS±hh:mm, where “T” separates the date and time  components, and the time zone offset is specified as ±hh:mm.  For example: “2023-06-09T10:30:00+03:00” represents June 9th,  2023, at 10:30 AM in a time zone with a positive offset of 3  hours from Coordinated Universal Time (UTC).
param max_results: int = 10¶
The maximum number of results to return.
param start_datetime: str [Required]¶
The start datetime for the search query in the following format:  YYYY-MM-DDTHH:MM:SS±hh:mm, where “T” separates the date and time  components, and the time zone offset is specified as ±hh:mm.  For example: “2023-06-09T10:30:00+03:00” represents June 9th,  2023, at 10:30 AM in a time zone with a positive offset of 3  hours from Coordinated Universal Time (UTC).
param truncate: bool = True¶
Whether the event’s body is truncated to meet token number limits. Set to False for searches that will retrieve small events, otherwise, set to True.
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
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¶
langchain_community.tools.shell.tool.ShellInput¶
class langchain_community.tools.shell.tool.ShellInput[source]¶
Bases: BaseModel
Commands for the Bash Shell tool.
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 commands: Union[str, List[str]] [Required]¶
List of shell commands to run.
List of shell commands to run. Deserialized using json.loads
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
langchain_community.tools.google_scholar.tool.GoogleScholarQueryRun¶
class langchain_community.tools.google_scholar.tool.GoogleScholarQueryRun[source]¶
Bases: BaseTool
Tool that queries the Google search 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 api_wrapper: langchain_community.utilities.google_scholar.GoogleScholarAPIWrapper [Required]¶
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A wrapper around Google Scholar Search. Useful for when you need to get information aboutresearch papers from Google ScholarInput should be a search query.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param name: str = 'google_scholar'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
langchain_community.tools.gmail.get_thread.GmailGetThread¶
class langchain_community.tools.gmail.get_thread.GmailGetThread[source]¶
Bases: GmailBaseTool
Tool that gets a thread by ID from Gmail.
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 api_resource: Resource [Optional]¶
param args_schema: Type[langchain_community.tools.gmail.get_thread.GetThreadSchema] = <class 'langchain_community.tools.gmail.get_thread.GetThreadSchema'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param name: str = 'get_gmail_thread'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
langchain_community.tools.shell.tool.ShellTool¶
class langchain_community.tools.shell.tool.ShellTool[source]¶
Bases: BaseTool
Tool to run shell commands.
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 args_schema: Type[pydantic.main.BaseModel] = <class 'langchain_community.tools.shell.tool.ShellInput'>¶
Schema for input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Run shell commands on this Linux machine.'¶
Description of tool.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param name: str = 'terminal'¶
Name of tool.
param process: Any [Optional]¶
Bash process to run commands.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the tool. Defaults to None
These tags will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) → Any¶
Run the tool asynchronously.