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3ce11860e1df-0 | langchain.tools.dataforseo_api_search.tool.DataForSeoAPISearchResults¶
class langchain.tools.dataforseo_api_search.tool.DataForSeoAPISearchResults(*, name: str = 'DataForSeo Results JSON', description: str = 'A comprehensive Google Search API provided by DataForSeo.This tool is useful for obtaining real-time data on current events or popular searches.The input should be a search query and the output is a JSON object of the query results.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, api_wrapper: DataForSeoAPIWrapper = None)[source]¶
Bases: BaseTool
Tool that queries the DataForSeo Google Search API
and get back json.
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.utilities.dataforseo_api_search.DataForSeoAPIWrapper [Optional]¶
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.dataforseo_api_search.tool.DataForSeoAPISearchResults.html |
3ce11860e1df-1 | param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A comprehensive Google Search API provided by DataForSeo.This tool is useful for obtaining real-time data on current events or popular searches.The input should be a search query and the output is a JSON object of the query results.'¶
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 = 'DataForSeo Results JSON'¶
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
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.dataforseo_api_search.tool.DataForSeoAPISearchResults.html |
3ce11860e1df-2 | Make tool callable.
async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.dataforseo_api_search.tool.DataForSeoAPISearchResults.html |
937ab655771f-0 | langchain.tools.gmail.utils.build_resource_service¶
langchain.tools.gmail.utils.build_resource_service(credentials: Optional[Credentials] = None, service_name: str = 'gmail', service_version: str = 'v1') → Resource[source]¶
Build a Gmail service.
Examples using build_resource_service¶
Gmail Toolkit | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.utils.build_resource_service.html |
a0ccf5abed14-0 | langchain.tools.youtube.search.YouTubeSearchTool¶
class langchain.tools.youtube.search.YouTubeSearchTool(*, name: str = 'youtube_search', description: str = 'search for youtube videos associated with a person. the input to this tool should be a comma separated list, the first part contains a person name and the second a number that is the maximum number of video results to return aka num_results. the second part is optional', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False)[source]¶
Bases: BaseTool
Tool that queries YouTube.
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 = 'search for youtube videos associated with a person. the input to this tool should be a comma separated list, the first part contains a person name and the second a number that is the maximum number of video results to return aka num_results. the second part is optional'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description. | https://api.python.langchain.com/en/latest/tools/langchain.tools.youtube.search.YouTubeSearchTool.html |
a0ccf5abed14-1 | 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 = 'youtube_search'¶
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
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.youtube.search.YouTubeSearchTool.html |
a0ccf5abed14-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶
Examples using YouTubeSearchTool¶
YouTubeSearchTool | https://api.python.langchain.com/en/latest/tools/langchain.tools.youtube.search.YouTubeSearchTool.html |
d2d76ce6414b-0 | langchain.tools.searx_search.tool.SearxSearchResults¶
class langchain.tools.searx_search.tool.SearxSearchResults(*, name: str = 'Searx Search Results', description: str = 'A meta search engine.Useful for when you need to answer questions about current events.Input should be a search query. Output is a JSON array of the query results', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, wrapper: SearxSearchWrapper, num_results: int = 4, kwargs: dict = None, **extra_data: Any)[source]¶
Bases: BaseTool
Tool that queries a Searx instance and gets back json.
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 = 'A meta search engine.Useful for when you need to answer questions about current events.Input should be a search query. Output is a JSON array of the query results'¶
Used to tell the model how/when/why to use the tool. | https://api.python.langchain.com/en/latest/tools/langchain.tools.searx_search.tool.SearxSearchResults.html |
d2d76ce6414b-1 | 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 kwargs: dict [Optional]¶
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 = 'Searx Search Results'¶
The unique name of the tool that clearly communicates its purpose.
param num_results: int = 4¶
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.
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.
param wrapper: langchain.utilities.searx_search.SearxSearchWrapper [Required]¶
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.searx_search.tool.SearxSearchResults.html |
d2d76ce6414b-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config[source]¶
Bases: object
Pydantic config.
extra = 'allow'¶
Examples using SearxSearchResults¶
SearxNG Search API | https://api.python.langchain.com/en/latest/tools/langchain.tools.searx_search.tool.SearxSearchResults.html |
a295100dbba5-0 | langchain.tools.openweathermap.tool.OpenWeatherMapQueryRun¶
class langchain.tools.openweathermap.tool.OpenWeatherMapQueryRun(*, name: str = 'OpenWeatherMap', description: str = 'A wrapper around OpenWeatherMap API. Useful for fetching current weather information for a specified location. Input should be a location string (e.g. London,GB).', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, api_wrapper: OpenWeatherMapAPIWrapper = None)[source]¶
Bases: BaseTool
Tool that queries the OpenWeatherMap 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.utilities.openweathermap.OpenWeatherMapAPIWrapper [Optional]¶
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 OpenWeatherMap API. Useful for fetching current weather information for a specified location. Input should be a location string (e.g. London,GB).'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description. | https://api.python.langchain.com/en/latest/tools/langchain.tools.openweathermap.tool.OpenWeatherMapQueryRun.html |
a295100dbba5-1 | 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 = 'OpenWeatherMap'¶
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
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.openweathermap.tool.OpenWeatherMapQueryRun.html |
a295100dbba5-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.openweathermap.tool.OpenWeatherMapQueryRun.html |
7b3e3f7ac461-0 | langchain.tools.base.BaseTool¶
class langchain.tools.base.BaseTool(*, name: str, description: str, args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False)[source]¶
Bases: BaseModel, Runnable[Union[str, Dict], Any]
Interface LangChain tools must implement.
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[pydantic.main.BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = 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[[langchain.tools.base.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, | https://api.python.langchain.com/en/latest/tools/langchain.tools.base.BaseTool.html |
7b3e3f7ac461-1 | 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
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[source]¶
Make tool callable.
async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any[source]¶
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, **kwargs: Any) → Any[source]¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any[source]¶
validator raise_deprecation » all fields[source]¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.base.BaseTool.html |
7b3e3f7ac461-2 | validator raise_deprecation » all fields[source]¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any[source]¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶
Examples using BaseTool¶
!pip install bs4
Defining Custom Tools
Combine agents and vector stores
Custom functions with OpenAI Functions Agent | https://api.python.langchain.com/en/latest/tools/langchain.tools.base.BaseTool.html |
276db6b890f0-0 | langchain.tools.powerbi.tool.InfoPowerBITool¶
class langchain.tools.powerbi.tool.InfoPowerBITool(*, name: str = 'schema_powerbi', description: str = '\n Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.\n Be sure that the tables actually exist by calling list_tables_powerbi first!\n\n Example Input: "table1, table2, table3"\n ', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, powerbi: PowerBIDataset)[source]¶
Bases: BaseTool
Tool for getting metadata about a PowerBI Dataset.
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 = '\n Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.\n Be sure that the tables actually exist by calling list_tables_powerbi first!\n\n Example Input: "table1, table2, table3"\n '¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.powerbi.tool.InfoPowerBITool.html |
276db6b890f0-1 | 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 = 'schema_powerbi'¶
The unique name of the tool that clearly communicates its purpose.
param powerbi: langchain.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¶
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.powerbi.tool.InfoPowerBITool.html |
276db6b890f0-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.powerbi.tool.InfoPowerBITool.html |
4e76d1d9ff18-0 | langchain.tools.base.tool¶
langchain.tools.base.tool(*args: Union[str, Callable], return_direct: bool = False, args_schema: Optional[Type[BaseModel]] = None, infer_schema: bool = True) → Callable[source]¶
Make tools out of functions, can be used with or without arguments.
Parameters
*args – The arguments to the tool.
return_direct – Whether to return directly from the tool rather
than continuing the agent loop.
args_schema – optional argument schema for user to specify
infer_schema – Whether to infer the schema of the arguments from
the function’s signature. This also makes the resultant tool
accept a dictionary input to its run() function.
Requires:
Function must be of type (str) -> str
Function must have a docstring
Examples
@tool
def search_api(query: str) -> str:
# Searches the API for the query.
return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
# Searches the API for the query.
return
Examples using tool¶
JSONFormer
Agent Trajectory
!pip install bs4
Defining Custom Tools | https://api.python.langchain.com/en/latest/tools/langchain.tools.base.tool.html |
e0b58b9a5a0a-0 | langchain.tools.google_search.tool.GoogleSearchResults¶
class langchain.tools.google_search.tool.GoogleSearchResults(*, name: str = 'Google Search Results JSON', description: str = 'A wrapper around Google Search. Useful for when you need to answer questions about current events. Input should be a search query. Output is a JSON array of the query results', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, num_results: int = 4, api_wrapper: GoogleSearchAPIWrapper)[source]¶
Bases: BaseTool
Tool that queries the Google Search API and gets back json.
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.utilities.google_search.GoogleSearchAPIWrapper [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 Search. Useful for when you need to answer questions about current events. Input should be a search query. Output is a JSON array of the query results'¶
Used to tell the model how/when/why to use the tool. | https://api.python.langchain.com/en/latest/tools/langchain.tools.google_search.tool.GoogleSearchResults.html |
e0b58b9a5a0a-1 | 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 Search Results JSON'¶
The unique name of the tool that clearly communicates its purpose.
param num_results: int = 4¶
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.
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.google_search.tool.GoogleSearchResults.html |
e0b58b9a5a0a-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.google_search.tool.GoogleSearchResults.html |
a4b0716e4916-0 | langchain.tools.google_serper.tool.GoogleSerperResults¶
class langchain.tools.google_serper.tool.GoogleSerperResults(*, name: str = 'google_serrper_results_json', description: str = 'A low-cost Google Search API.Useful for when you need to answer questions about current events.Input should be a search query. Output is a JSON object of the query results', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, api_wrapper: GoogleSerperAPIWrapper = None)[source]¶
Bases: BaseTool
Tool that queries the Serper.dev Google Search API
and get back json.
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.utilities.google_serper.GoogleSerperAPIWrapper [Optional]¶
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 low-cost Google Search API.Useful for when you need to answer questions about current events.Input should be a search query. Output is a JSON object of the query results'¶
Used to tell the model how/when/why to use the tool. | https://api.python.langchain.com/en/latest/tools/langchain.tools.google_serper.tool.GoogleSerperResults.html |
a4b0716e4916-1 | 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_serrper_results_json'¶
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
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.google_serper.tool.GoogleSerperResults.html |
a4b0716e4916-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.google_serper.tool.GoogleSerperResults.html |
6614926fd703-0 | langchain.tools.office365.create_draft_message.O365CreateDraftMessage¶
class langchain.tools.office365.create_draft_message.O365CreateDraftMessage(*, name: str = 'create_email_draft', description: str = 'Use this tool to create a draft email with the provided message fields.', args_schema: ~typing.Type[~langchain.tools.office365.create_draft_message.CreateDraftMessageSchema] = <class 'langchain.tools.office365.create_draft_message.CreateDraftMessageSchema'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, tags: ~typing.Optional[~typing.List[str]] = None, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, account: Account = None)[source]¶
Bases: O365BaseTool
Tool for creating a draft email in Office 365.
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 account: Account [Optional]¶
The account object for the Office 365 account.
param args_schema: Type[langchain.tools.office365.create_draft_message.CreateDraftMessageSchema] = <class 'langchain.tools.office365.create_draft_message.CreateDraftMessageSchema'>¶
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¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.create_draft_message.O365CreateDraftMessage.html |
6614926fd703-1 | Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this tool to create a draft email with the provided message fields.'¶
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 = 'create_email_draft'¶
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
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.create_draft_message.O365CreateDraftMessage.html |
6614926fd703-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.create_draft_message.O365CreateDraftMessage.html |
75f7eda27014-0 | langchain.tools.playwright.base.lazy_import_playwright_browsers¶
langchain.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]] | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.base.lazy_import_playwright_browsers.html |
fd6f989c8233-0 | langchain.tools.human.tool.HumanInputRun¶
class langchain.tools.human.tool.HumanInputRun(*, name: str = 'human', description: str = 'You can ask a human for guidance when you think you got stuck or you are not sure what to do next. The input should be a question for the human.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, prompt_func: Callable[[str], None] = None, input_func: Callable = None)[source]¶
Bases: BaseTool
Tool that asks user for input.
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 = 'You can ask a human for guidance when you think you got stuck or you are not sure what to do next. The input should be a question for the human.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description. | https://api.python.langchain.com/en/latest/tools/langchain.tools.human.tool.HumanInputRun.html |
fd6f989c8233-1 | 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 input_func: Callable [Optional]¶
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 = 'human'¶
The unique name of the tool that clearly communicates its purpose.
param prompt_func: Callable[[str], None] [Optional]¶
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.
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.human.tool.HumanInputRun.html |
fd6f989c8233-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶
Examples using HumanInputRun¶
Human as a tool
!pip install bs4 | https://api.python.langchain.com/en/latest/tools/langchain.tools.human.tool.HumanInputRun.html |
34eda6b88d15-0 | langchain.tools.playwright.click.ClickToolInput¶
class langchain.tools.playwright.click.ClickToolInput(*, selector: str)[source]¶
Bases: BaseModel
Input for ClickTool.
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 selector: str [Required]¶
CSS selector for the element to click | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.click.ClickToolInput.html |
25be963d0697-0 | langchain.tools.ddg_search.tool.DuckDuckGoSearchTool¶
langchain.tools.ddg_search.tool.DuckDuckGoSearchTool(*args: Any, **kwargs: Any) → DuckDuckGoSearchRun[source]¶
Deprecated. Use DuckDuckGoSearchRun instead.
Parameters
*args –
**kwargs –
Returns
DuckDuckGoSearchRun | https://api.python.langchain.com/en/latest/tools/langchain.tools.ddg_search.tool.DuckDuckGoSearchTool.html |
1efbbed8624d-0 | langchain.tools.office365.messages_search.SearchEmailsInput¶
class langchain.tools.office365.messages_search.SearchEmailsInput(*, folder: str = None, query: str, max_results: int = 10, truncate: bool = True)[source]¶
Bases: BaseModel
Input for SearchEmails 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 folder: str = None¶
If the user wants to search in only one folder, the name of the folder. Default folders are “inbox”, “drafts”, “sent items”, “deleted ttems”, but users can search custom folders as well.
param max_results: int = 10¶
The maximum number of results to return.
param query: str [Required]¶
The Microsoift Graph v1.0 $search query. Example filters include from:sender, from:sender, to:recipient, subject:subject, recipients:list_of_recipients, body:excitement, importance:high, received>2022-12-01, received<2021-12-01, sent>2022-12-01, sent<2021-12-01, hasAttachments:true attachment:api-catalog.md, cc:samanthab@contoso.com, bcc:samanthab@contoso.com, body:excitement date range example: received:2023-06-08..2023-06-09 matching example: from:amy OR from:david.
param truncate: bool = True¶
Whether the email body is truncated to meet token number limits. Set to False for searches that will retrieve very few results, otherwise, set to True | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.messages_search.SearchEmailsInput.html |
425297fb0660-0 | langchain.tools.shell.tool.ShellInput¶
class langchain.tools.shell.tool.ShellInput(*, commands: Union[str, List[str]])[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 | https://api.python.langchain.com/en/latest/tools/langchain.tools.shell.tool.ShellInput.html |
4fc161a6134b-0 | langchain.tools.gmail.search.GmailSearch¶
class langchain.tools.gmail.search.GmailSearch(*, name: str = 'search_gmail', description: str = 'Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.', args_schema: ~typing.Type[~langchain.tools.gmail.search.SearchArgsSchema] = <class 'langchain.tools.gmail.search.SearchArgsSchema'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, tags: ~typing.Optional[~typing.List[str]] = None, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, api_resource: Resource = None)[source]¶
Bases: GmailBaseTool
Tool that searches for messages or threads in 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.tools.gmail.search.SearchArgsSchema] = <class 'langchain.tools.gmail.search.SearchArgsSchema'>¶
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.GmailSearch.html |
4fc161a6134b-1 | param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.'¶
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 = 'search_gmail'¶
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
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.GmailSearch.html |
4fc161a6134b-2 | Make tool callable.
async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
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, **kwargs: Any) → Any¶
Run the tool asynchronously.
classmethod from_api_resource(api_resource: Resource) → GmailBaseTool¶
Create a tool from an api resource.
Parameters
api_resource – The api resource to use.
Returns
A tool.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.GmailSearch.html |
8f145970582c-0 | langchain.tools.zapier.tool.ZapierNLAListActions¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.zapier.tool.ZapierNLAListActions.html |
8f145970582c-1 | class langchain.tools.zapier.tool.ZapierNLAListActions(*, name: str = 'ZapierNLA_list_actions', description: str = 'A wrapper around Zapier NLA actions. The input to this tool is a natural language instruction, for example "get the latest email from my bank" or "send a slack message to the #general channel". Each tool will have params associated with it that are specified as a list. You MUST take into account the params when creating the instruction. For example, if the params are [\'Message_Text\', \'Channel\'], your instruction should be something like \'send a slack message to the #general channel with the text hello world\'. Another example: if the params are [\'Calendar\', \'Search_Term\'], your instruction should be something like \'find the meeting in my personal calendar at 3pm\'. Do not make up params, they will be explicitly specified in the tool description. If you do not have enough information to fill in the params, just say \'not enough information provided in the instruction, missing <param>\'. If you get a none or null response, STOP EXECUTION, do not try to another tool!This tool specifically used for: {zapier_description}, and has params: {params}This tool returns a list of the user\'s exposed actions.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, api_wrapper: ZapierNLAWrapper = None)[source]¶
Bases: BaseTool | https://api.python.langchain.com/en/latest/tools/langchain.tools.zapier.tool.ZapierNLAListActions.html |
8f145970582c-2 | Bases: BaseTool
Returns a list of all exposed (enabled) actions associated withcurrent user (associated with the set api_key). Change your exposed
actions here: https://nla.zapier.com/demo/start/
The return list can be empty if no actions exposed. Else will contain
a list of action objects:
[{“id”: str,
“description”: str,
“params”: Dict[str, str]
}]
params will always contain an instructions key, the only required
param. All others optional and if provided will override any AI guesses
(see “understanding the AI guessing flow” here:
https://nla.zapier.com/docs/using-the-api#ai-guessing)
Parameters
None –
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.utilities.zapier.ZapierNLAWrapper [Optional]¶
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.zapier.tool.ZapierNLAListActions.html |
8f145970582c-3 | param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A wrapper around Zapier NLA actions. The input to this tool is a natural language instruction, for example "get the latest email from my bank" or "send a slack message to the #general channel". Each tool will have params associated with it that are specified as a list. You MUST take into account the params when creating the instruction. For example, if the params are [\'Message_Text\', \'Channel\'], your instruction should be something like \'send a slack message to the #general channel with the text hello world\'. Another example: if the params are [\'Calendar\', \'Search_Term\'], your instruction should be something like \'find the meeting in my personal calendar at 3pm\'. Do not make up params, they will be explicitly specified in the tool description. If you do not have enough information to fill in the params, just say \'not enough information provided in the instruction, missing <param>\'. If you get a none or null response, STOP EXECUTION, do not try to another tool!This tool specifically used for: {zapier_description}, and has params: {params}This tool returns a list of the user\'s exposed actions.'¶
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.zapier.tool.ZapierNLAListActions.html |
8f145970582c-4 | 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 = 'ZapierNLA_list_actions'¶
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
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used. | https://api.python.langchain.com/en/latest/tools/langchain.tools.zapier.tool.ZapierNLAListActions.html |
8f145970582c-5 | Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.zapier.tool.ZapierNLAListActions.html |
db9b1b2e3924-0 | langchain.tools.office365.send_message.O365SendMessage¶
class langchain.tools.office365.send_message.O365SendMessage(*, name: str = 'send_email', description: str = 'Use this tool to send an email with the provided message fields.', args_schema: ~typing.Type[~langchain.tools.office365.send_message.SendMessageSchema] = <class 'langchain.tools.office365.send_message.SendMessageSchema'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, tags: ~typing.Optional[~typing.List[str]] = None, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, account: Account = None)[source]¶
Bases: O365BaseTool
Tool for sending an email in Office 365.
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 account: Account [Optional]¶
The account object for the Office 365 account.
param args_schema: Type[langchain.tools.office365.send_message.SendMessageSchema] = <class 'langchain.tools.office365.send_message.SendMessageSchema'>¶
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.send_message.O365SendMessage.html |
db9b1b2e3924-1 | param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this tool to send an email with the provided message fields.'¶
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 = 'send_email'¶
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
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.send_message.O365SendMessage.html |
db9b1b2e3924-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.send_message.O365SendMessage.html |
4ed48eb54594-0 | langchain.tools.plugin.AIPluginToolSchema¶
class langchain.tools.plugin.AIPluginToolSchema(*, tool_input: Optional[str] = '')[source]¶
Bases: BaseModel
Schema for AIPluginTool.
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 tool_input: Optional[str] = ''¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.plugin.AIPluginToolSchema.html |
2a1d76463f0d-0 | langchain.tools.sql_database.tool.QuerySQLCheckerTool¶
class langchain.tools.sql_database.tool.QuerySQLCheckerTool(*, name: str = 'sql_db_query_checker', description: str = '\n Use this tool to double check if your query is correct before executing it.\n Always use this tool before executing a query with query_sql_db!\n ', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, db: SQLDatabase, template: str = '\n{query}\nDouble check the {dialect} query above for common mistakes, including:\n- Using NOT IN with NULL values\n- Using UNION when UNION ALL should have been used\n- Using BETWEEN for exclusive ranges\n- Data type mismatch in predicates\n- Properly quoting identifiers\n- Using the correct number of arguments for functions\n- Casting to the correct data type\n- Using the proper columns for joins\n\nIf there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.\n\nOutput the final SQL query only.\n\nSQL Query: ', llm: BaseLanguageModel, llm_chain: LLMChain)[source]¶
Bases: BaseSQLDatabaseTool, BaseTool
Use an LLM to check if a query is correct.
Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/
Create a new model by parsing and validating input data from keyword arguments. | https://api.python.langchain.com/en/latest/tools/langchain.tools.sql_database.tool.QuerySQLCheckerTool.html |
2a1d76463f0d-1 | 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 = '\n Use this tool to double check if your query is correct before executing it.\n Always use this tool before executing a query with query_sql_db!\n '¶
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 llm: langchain.schema.language_model.BaseLanguageModel [Required]¶
param llm_chain: langchain.chains.llm.LLMChain [Required]¶
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_query_checker'¶
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.sql_database.tool.QuerySQLCheckerTool.html |
2a1d76463f0d-2 | 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.
You can use these to eg identify a specific instance of a tool with its use case.
param template: str = '\n{query}\nDouble check the {dialect} query above for common mistakes, including:\n- Using NOT IN with NULL values\n- Using UNION when UNION ALL should have been used\n- Using BETWEEN for exclusive ranges\n- Data type mismatch in predicates\n- Properly quoting identifiers\n- Using the correct number of arguments for functions\n- Casting to the correct data type\n- Using the proper columns for joins\n\nIf there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.\n\nOutput the final SQL query only.\n\nSQL Query: '¶
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
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, **kwargs: Any) → Any¶
Run the tool asynchronously. | https://api.python.langchain.com/en/latest/tools/langchain.tools.sql_database.tool.QuerySQLCheckerTool.html |
2a1d76463f0d-3 | Run the tool asynchronously.
validator initialize_llm_chain » all fields[source]¶
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: Config
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.sql_database.tool.QuerySQLCheckerTool.html |
54147cf52bf8-0 | langchain.tools.gmail.search.Resource¶
class langchain.tools.gmail.search.Resource(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Bases: str, Enum
Enumerator of Resources to search.
Methods
__init__(*args, **kwds)
capitalize()
Return a capitalized version of the string.
casefold()
Return a version of the string suitable for caseless comparisons.
center(width[, fillchar])
Return a centered string of length width.
count(sub[, start[, end]])
Return the number of non-overlapping occurrences of substring sub in string S[start:end].
encode([encoding, errors])
Encode the string using the codec registered for encoding.
endswith(suffix[, start[, end]])
Return True if S ends with the specified suffix, False otherwise.
expandtabs([tabsize])
Return a copy where all tab characters are expanded using spaces.
find(sub[, start[, end]])
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
format(*args, **kwargs)
Return a formatted version of S, using substitutions from args and kwargs.
format_map(mapping)
Return a formatted version of S, using substitutions from mapping.
index(sub[, start[, end]])
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end].
isalnum()
Return True if the string is an alpha-numeric string, False otherwise.
isalpha()
Return True if the string is an alphabetic string, False otherwise.
isascii()
Return True if all characters in the string are ASCII, False otherwise.
isdecimal()
Return True if the string is a decimal string, False otherwise.
isdigit() | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.Resource.html |
54147cf52bf8-1 | Return True if the string is a decimal string, False otherwise.
isdigit()
Return True if the string is a digit string, False otherwise.
isidentifier()
Return True if the string is a valid Python identifier, False otherwise.
islower()
Return True if the string is a lowercase string, False otherwise.
isnumeric()
Return True if the string is a numeric string, False otherwise.
isprintable()
Return True if the string is printable, False otherwise.
isspace()
Return True if the string is a whitespace string, False otherwise.
istitle()
Return True if the string is a title-cased string, False otherwise.
isupper()
Return True if the string is an uppercase string, False otherwise.
join(iterable, /)
Concatenate any number of strings.
ljust(width[, fillchar])
Return a left-justified string of length width.
lower()
Return a copy of the string converted to lowercase.
lstrip([chars])
Return a copy of the string with leading whitespace removed.
maketrans
Return a translation table usable for str.translate().
partition(sep, /)
Partition the string into three parts using the given separator.
removeprefix(prefix, /)
Return a str with the given prefix string removed if present.
removesuffix(suffix, /)
Return a str with the given suffix string removed if present.
replace(old, new[, count])
Return a copy with all occurrences of substring old replaced by new.
rfind(sub[, start[, end]])
Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
rindex(sub[, start[, end]])
Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.Resource.html |
54147cf52bf8-2 | rjust(width[, fillchar])
Return a right-justified string of length width.
rpartition(sep, /)
Partition the string into three parts using the given separator.
rsplit([sep, maxsplit])
Return a list of the substrings in the string, using sep as the separator string.
rstrip([chars])
Return a copy of the string with trailing whitespace removed.
split([sep, maxsplit])
Return a list of the substrings in the string, using sep as the separator string.
splitlines([keepends])
Return a list of the lines in the string, breaking at line boundaries.
startswith(prefix[, start[, end]])
Return True if S starts with the specified prefix, False otherwise.
strip([chars])
Return a copy of the string with leading and trailing whitespace removed.
swapcase()
Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()
Return a version of the string where each word is titlecased.
translate(table, /)
Replace each character in the string using the given translation table.
upper()
Return a copy of the string converted to uppercase.
zfill(width, /)
Pad a numeric string with zeros on the left, to fill a field of the given width.
Attributes
THREADS
MESSAGES
capitalize()¶
Return a capitalized version of the string.
More specifically, make the first character have upper case and the rest lower
case.
casefold()¶
Return a version of the string suitable for caseless comparisons.
center(width, fillchar=' ', /)¶
Return a centered string of length width.
Padding is done using the specified fill character (default is a space).
count(sub[, start[, end]]) → int¶
Return the number of non-overlapping occurrences of substring sub in | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.Resource.html |
54147cf52bf8-3 | Return the number of non-overlapping occurrences of substring sub in
string S[start:end]. Optional arguments start and end are
interpreted as in slice notation.
encode(encoding='utf-8', errors='strict')¶
Encode the string using the codec registered for encoding.
encodingThe encoding in which to encode the string.
errorsThe error handling scheme to use for encoding errors.
The default is ‘strict’ meaning that encoding errors raise a
UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and
‘xmlcharrefreplace’ as well as any other name registered with
codecs.register_error that can handle UnicodeEncodeErrors.
endswith(suffix[, start[, end]]) → bool¶
Return True if S ends with the specified suffix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
suffix can also be a tuple of strings to try.
expandtabs(tabsize=8)¶
Return a copy where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
find(sub[, start[, end]]) → int¶
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
format(*args, **kwargs) → str¶
Return a formatted version of S, using substitutions from args and kwargs.
The substitutions are identified by braces (‘{’ and ‘}’).
format_map(mapping) → str¶
Return a formatted version of S, using substitutions from mapping.
The substitutions are identified by braces (‘{’ and ‘}’).
index(sub[, start[, end]]) → int¶
Return the lowest index in S where substring sub is found, | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.Resource.html |
54147cf52bf8-4 | Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
isalnum()¶
Return True if the string is an alpha-numeric string, False otherwise.
A string is alpha-numeric if all characters in the string are alpha-numeric and
there is at least one character in the string.
isalpha()¶
Return True if the string is an alphabetic string, False otherwise.
A string is alphabetic if all characters in the string are alphabetic and there
is at least one character in the string.
isascii()¶
Return True if all characters in the string are ASCII, False otherwise.
ASCII characters have code points in the range U+0000-U+007F.
Empty string is ASCII too.
isdecimal()¶
Return True if the string is a decimal string, False otherwise.
A string is a decimal string if all characters in the string are decimal and
there is at least one character in the string.
isdigit()¶
Return True if the string is a digit string, False otherwise.
A string is a digit string if all characters in the string are digits and there
is at least one character in the string.
isidentifier()¶
Return True if the string is a valid Python identifier, False otherwise.
Call keyword.iskeyword(s) to test whether string s is a reserved identifier,
such as “def” or “class”.
islower()¶
Return True if the string is a lowercase string, False otherwise.
A string is lowercase if all cased characters in the string are lowercase and
there is at least one cased character in the string.
isnumeric()¶
Return True if the string is a numeric string, False otherwise. | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.Resource.html |
54147cf52bf8-5 | isnumeric()¶
Return True if the string is a numeric string, False otherwise.
A string is numeric if all characters in the string are numeric and there is at
least one character in the string.
isprintable()¶
Return True if the string is printable, False otherwise.
A string is printable if all of its characters are considered printable in
repr() or if it is empty.
isspace()¶
Return True if the string is a whitespace string, False otherwise.
A string is whitespace if all characters in the string are whitespace and there
is at least one character in the string.
istitle()¶
Return True if the string is a title-cased string, False otherwise.
In a title-cased string, upper- and title-case characters may only
follow uncased characters and lowercase characters only cased ones.
isupper()¶
Return True if the string is an uppercase string, False otherwise.
A string is uppercase if all cased characters in the string are uppercase and
there is at least one cased character in the string.
join(iterable, /)¶
Concatenate any number of strings.
The string whose method is called is inserted in between each given string.
The result is returned as a new string.
Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’
ljust(width, fillchar=' ', /)¶
Return a left-justified string of length width.
Padding is done using the specified fill character (default is a space).
lower()¶
Return a copy of the string converted to lowercase.
lstrip(chars=None, /)¶
Return a copy of the string with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
static maketrans()¶
Return a translation table usable for str.translate(). | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.Resource.html |
54147cf52bf8-6 | static maketrans()¶
Return a translation table usable for str.translate().
If there is only one argument, it must be a dictionary mapping Unicode
ordinals (integers) or characters to Unicode ordinals, strings or None.
Character keys will be then converted to ordinals.
If there are two arguments, they must be strings of equal length, and
in the resulting dictionary, each character in x will be mapped to the
character at the same position in y. If there is a third argument, it
must be a string, whose characters will be mapped to None in the result.
partition(sep, /)¶
Partition the string into three parts using the given separator.
This will search for the separator in the string. If the separator is found,
returns a 3-tuple containing the part before the separator, the separator
itself, and the part after it.
If the separator is not found, returns a 3-tuple containing the original string
and two empty strings.
removeprefix(prefix, /)¶
Return a str with the given prefix string removed if present.
If the string starts with the prefix string, return string[len(prefix):].
Otherwise, return a copy of the original string.
removesuffix(suffix, /)¶
Return a str with the given suffix string removed if present.
If the string ends with the suffix string and that suffix is not empty,
return string[:-len(suffix)]. Otherwise, return a copy of the original
string.
replace(old, new, count=- 1, /)¶
Return a copy with all occurrences of substring old replaced by new.
countMaximum number of occurrences to replace.
-1 (the default value) means replace all occurrences.
If the optional argument count is given, only the first count occurrences are
replaced.
rfind(sub[, start[, end]]) → int¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.Resource.html |
54147cf52bf8-7 | replaced.
rfind(sub[, start[, end]]) → int¶
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
rindex(sub[, start[, end]]) → int¶
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Raises ValueError when the substring is not found.
rjust(width, fillchar=' ', /)¶
Return a right-justified string of length width.
Padding is done using the specified fill character (default is a space).
rpartition(sep, /)¶
Partition the string into three parts using the given separator.
This will search for the separator in the string, starting at the end. If
the separator is found, returns a 3-tuple containing the part before the
separator, the separator itself, and the part after it.
If the separator is not found, returns a 3-tuple containing two empty strings
and the original string.
rsplit(sep=None, maxsplit=- 1)¶
Return a list of the substrings in the string, using sep as the separator string.
sepThe separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplitMaximum number of splits (starting from the left).
-1 (the default value) means no limit.
Splitting starts at the end of the string and works to the front.
rstrip(chars=None, /)¶
Return a copy of the string with trailing whitespace removed. | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.Resource.html |
54147cf52bf8-8 | rstrip(chars=None, /)¶
Return a copy of the string with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
split(sep=None, maxsplit=- 1)¶
Return a list of the substrings in the string, using sep as the separator string.
sepThe separator used to split the string.
When set to None (the default value), will split on any whitespace
character (including \n \r \t \f and spaces) and will discard
empty strings from the result.
maxsplitMaximum number of splits (starting from the left).
-1 (the default value) means no limit.
Note, str.split() is mainly useful for data that has been intentionally
delimited. With natural text that includes punctuation, consider using
the regular expression module.
splitlines(keepends=False)¶
Return a list of the lines in the string, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends is given and
true.
startswith(prefix[, start[, end]]) → bool¶
Return True if S starts with the specified prefix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
prefix can also be a tuple of strings to try.
strip(chars=None, /)¶
Return a copy of the string with leading and trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
swapcase()¶
Convert uppercase characters to lowercase and lowercase characters to uppercase.
title()¶
Return a version of the string where each word is titlecased.
More specifically, words start with uppercased characters and all remaining
cased characters have lower case.
translate(table, /)¶
Replace each character in the string using the given translation table. | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.Resource.html |
54147cf52bf8-9 | translate(table, /)¶
Replace each character in the string using the given translation table.
tableTranslation table, which must be a mapping of Unicode ordinals to
Unicode ordinals, strings, or None.
The table must implement lookup/indexing via __getitem__, for instance a
dictionary or list. If this operation raises LookupError, the character is
left untouched. Characters mapped to None are deleted.
upper()¶
Return a copy of the string converted to uppercase.
zfill(width, /)¶
Pad a numeric string with zeros on the left, to fill a field of the given width.
The string is never truncated.
MESSAGES = 'messages'¶
THREADS = 'threads'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.Resource.html |
05fcab72b540-0 | langchain.tools.gmail.utils.import_installed_app_flow¶
langchain.tools.gmail.utils.import_installed_app_flow() → InstalledAppFlow[source]¶
Import InstalledAppFlow class.
Returns
InstalledAppFlow class.
Return type
InstalledAppFlow | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.utils.import_installed_app_flow.html |
4a65ae8acb50-0 | langchain.tools.file_management.utils.is_relative_to¶
langchain.tools.file_management.utils.is_relative_to(path: Path, root: Path) → bool[source]¶
Check if path is relative to root. | https://api.python.langchain.com/en/latest/tools/langchain.tools.file_management.utils.is_relative_to.html |
5a61f73d4c40-0 | langchain.tools.azure_cognitive_services.form_recognizer.AzureCogsFormRecognizerTool¶
class langchain.tools.azure_cognitive_services.form_recognizer.AzureCogsFormRecognizerTool(*, name: str = 'azure_cognitive_services_form_recognizer', description: str = 'A wrapper around Azure Cognitive Services Form Recognizer. Useful for when you need to extract text, tables, and key-value pairs from documents. Input should be a url to a document.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, azure_cogs_key: str = '', azure_cogs_endpoint: str = '', doc_analysis_client: Any = None)[source]¶
Bases: BaseTool
Tool that queries the Azure Cognitive Services Form Recognizer API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/quickstarts/get-started-sdks-rest-api?view=form-recog-3.0.0&pivots=programming-language-python
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¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.azure_cognitive_services.form_recognizer.AzureCogsFormRecognizerTool.html |
5a61f73d4c40-1 | Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A wrapper around Azure Cognitive Services Form Recognizer. Useful for when you need to extract text, tables, and key-value pairs from documents. Input should be a url to a document.'¶
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 = 'azure_cognitive_services_form_recognizer'¶
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
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.azure_cognitive_services.form_recognizer.AzureCogsFormRecognizerTool.html |
5a61f73d4c40-2 | Make tool callable.
async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
validator validate_environment » all fields[source]¶
Validate that api key and endpoint exists in environment.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.azure_cognitive_services.form_recognizer.AzureCogsFormRecognizerTool.html |
68b82c4a364b-0 | langchain.tools.gmail.utils.import_googleapiclient_resource_builder¶
langchain.tools.gmail.utils.import_googleapiclient_resource_builder() → build_resource[source]¶
Import googleapiclient.discovery.build function.
Returns
googleapiclient.discovery.build function.
Return type
build_resource | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.utils.import_googleapiclient_resource_builder.html |
1b665e6f96bb-0 | langchain.tools.file_management.write.WriteFileInput¶
class langchain.tools.file_management.write.WriteFileInput(*, file_path: str, text: str, append: bool = False)[source]¶
Bases: BaseModel
Input for WriteFileTool.
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 append: bool = False¶
Whether to append to an existing file.
param file_path: str [Required]¶
name of file
param text: str [Required]¶
text to write to file | https://api.python.langchain.com/en/latest/tools/langchain.tools.file_management.write.WriteFileInput.html |
8092c10b0cc7-0 | langchain.tools.github.tool.GitHubAction¶
class langchain.tools.github.tool.GitHubAction(*, name: str = '', description: str = '', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, api_wrapper: GitHubAPIWrapper = None, mode: str)[source]¶
Bases: BaseTool
Tool for interacting with the GitHub 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.utilities.github.GitHubAPIWrapper [Optional]¶
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 = ''¶
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, | https://api.python.langchain.com/en/latest/tools/langchain.tools.github.tool.GitHubAction.html |
8092c10b0cc7-1 | 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 mode: str [Required]¶
param name: str = ''¶
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
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.github.tool.GitHubAction.html |
8092c10b0cc7-2 | validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶
Examples using GitHubAction¶
Github Toolkit | https://api.python.langchain.com/en/latest/tools/langchain.tools.github.tool.GitHubAction.html |
c7f34bf19949-0 | langchain.tools.office365.events_search.O365SearchEvents¶
class langchain.tools.office365.events_search.O365SearchEvents(*, name: str = 'events_search', description: str = " Use this tool to search for the user's calendar events. The input must be the start and end datetimes for the search query. The output is a JSON list of all the events in the user's calendar between the start and end times. You can assume that the user can not schedule any meeting over existing meetings, and that the user is busy during meetings. Any times without events are free for the user. ", args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.office365.events_search.SearchEventsInput'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, tags: ~typing.Optional[~typing.List[str]] = None, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, account: Account = None)[source]¶
Bases: O365BaseTool
Class for searching calendar events in Office 365
Free, but setup is required
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 account: Account [Optional]¶
The account object for the Office 365 account. | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.events_search.O365SearchEvents.html |
c7f34bf19949-1 | param account: Account [Optional]¶
The account object for the Office 365 account.
param args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.office365.events_search.SearchEventsInput'>¶
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 the user's calendar events. The input must be the start and end datetimes for the search query. The output is a JSON list of all the events in the user's calendar between the start and end times. You can assume that the user can not schedule any meeting over existing meetings, and that the user is busy during meetings. Any times without events are free for the user. "¶
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 = 'events_search'¶
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.events_search.O365SearchEvents.html |
c7f34bf19949-2 | 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.
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool. | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.events_search.O365SearchEvents.html |
c7f34bf19949-3 | Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config[source]¶
Bases: object
Configuration for this pydantic object.
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.events_search.O365SearchEvents.html |
248aa1eeec56-0 | langchain.tools.json.tool.JsonSpec¶
class langchain.tools.json.tool.JsonSpec(*, dict_: Dict, max_value_length: int = 200)[source]¶
Bases: BaseModel
Base class for JSON spec.
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 dict_: Dict [Required]¶
param max_value_length: int = 200¶
classmethod from_file(path: Path) → JsonSpec[source]¶
Create a JsonSpec from a file.
keys(text: str) → str[source]¶
Return the keys of the dict at the given path.
Parameters
text – Python representation of the path to the dict (e.g. data[“key1”][0][“key2”]).
value(text: str) → str[source]¶
Return the value of the dict at the given path.
Parameters
text – Python representation of the path to the dict (e.g. data[“key1”][0][“key2”]).
Examples using JsonSpec¶
JSON Agent
OpenAPI agents | https://api.python.langchain.com/en/latest/tools/langchain.tools.json.tool.JsonSpec.html |
7266636d41da-0 | langchain.tools.shell.tool.ShellTool¶
class langchain.tools.shell.tool.ShellTool(*, name: str = 'terminal', description: str = 'Run shell commands on this Linux machine.', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.shell.tool.ShellInput'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, tags: ~typing.Optional[~typing.List[str]] = None, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, process: ~langchain.utilities.bash.BashProcess = None)[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.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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.shell.tool.ShellTool.html |
7266636d41da-1 | 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: langchain.utilities.bash.BashProcess [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.
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
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, **kwargs: Any) → Any¶
Run the tool asynchronously. | https://api.python.langchain.com/en/latest/tools/langchain.tools.shell.tool.ShellTool.html |
7266636d41da-2 | Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶
Examples using ShellTool¶
Shell Tool
Human-in-the-loop Tool Validation | https://api.python.langchain.com/en/latest/tools/langchain.tools.shell.tool.ShellTool.html |
9f8d7791f5a6-0 | langchain.tools.azure_cognitive_services.text2speech.AzureCogsText2SpeechTool¶
class langchain.tools.azure_cognitive_services.text2speech.AzureCogsText2SpeechTool(*, name: str = 'azure_cognitive_services_text2speech', description: str = 'A wrapper around Azure Cognitive Services Text2Speech. Useful for when you need to convert text to speech. ', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, azure_cogs_key: str = '', azure_cogs_region: str = '', speech_language: str = 'en-US', speech_config: Any = None)[source]¶
Bases: BaseTool
Tool that queries the Azure Cognitive Services Text2Speech API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/get-started-text-to-speech?pivots=programming-language-python
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.azure_cognitive_services.text2speech.AzureCogsText2SpeechTool.html |
9f8d7791f5a6-1 | param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A wrapper around Azure Cognitive Services Text2Speech. Useful for when you need to convert text to speech. '¶
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 = 'azure_cognitive_services_text2speech'¶
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
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.azure_cognitive_services.text2speech.AzureCogsText2SpeechTool.html |
9f8d7791f5a6-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
validator validate_environment » all fields[source]¶
Validate that api key and endpoint exists in environment.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.azure_cognitive_services.text2speech.AzureCogsText2SpeechTool.html |
c727325e30c2-0 | langchain.tools.wikipedia.tool.WikipediaQueryRun¶
class langchain.tools.wikipedia.tool.WikipediaQueryRun(*, name: str = 'Wikipedia', description: str = 'A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, api_wrapper: WikipediaAPIWrapper)[source]¶
Bases: BaseTool
Tool that searches the Wikipedia 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.utilities.wikipedia.WikipediaAPIWrapper [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 Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input 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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.wikipedia.tool.WikipediaQueryRun.html |
c727325e30c2-1 | 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 = 'Wikipedia'¶
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
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.wikipedia.tool.WikipediaQueryRun.html |
c727325e30c2-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶
Examples using WikipediaQueryRun¶
Wikipedia | https://api.python.langchain.com/en/latest/tools/langchain.tools.wikipedia.tool.WikipediaQueryRun.html |
fe6a3912ab8f-0 | langchain.tools.playwright.utils.aget_current_page¶
async langchain.tools.playwright.utils.aget_current_page(browser: AsyncBrowser) → AsyncPage[source]¶
Asynchronously get the current page of the browser.
Parameters
browser – The browser (AsyncBrowser) to get the current page from.
Returns
The current page.
Return type
AsyncPage | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.utils.aget_current_page.html |
b8470a3211d3-0 | langchain.tools.gmail.search.SearchArgsSchema¶
class langchain.tools.gmail.search.SearchArgsSchema(*, query: str, resource: Resource = Resource.MESSAGES, max_results: int = 10)[source]¶
Bases: BaseModel
Input for SearchGmailTool.
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 max_results: int = 10¶
The maximum number of results to return.
param query: str [Required]¶
The Gmail query. Example filters include from:sender, to:recipient, subject:subject, -filtered_term, in:folder, is:important|read|starred, after:year/mo/date, before:year/mo/date, label:label_name “exact phrase”. Search newer/older than using d (day), m (month), and y (year): newer_than:2d, older_than:1y. Attachments with extension example: filename:pdf. Multiple term matching example: from:amy OR from:david.
param resource: langchain.tools.gmail.search.Resource = Resource.MESSAGES¶
Whether to search for threads or messages. | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.search.SearchArgsSchema.html |
ef1d6cd916ec-0 | langchain.tools.file_management.copy.CopyFileTool¶
class langchain.tools.file_management.copy.CopyFileTool(*, name: str = 'copy_file', description: str = 'Create a copy of a file in a specified location', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.file_management.copy.FileCopyInput'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, tags: ~typing.Optional[~typing.List[str]] = None, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, root_dir: ~typing.Optional[str] = None)[source]¶
Bases: BaseFileToolMixin, BaseTool
Tool that copies a file.
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.tools.file_management.copy.FileCopyInput'>¶
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 = 'Create a copy of a file in a specified location'¶
Used to tell the model how/when/why to use the tool. | https://api.python.langchain.com/en/latest/tools/langchain.tools.file_management.copy.CopyFileTool.html |
ef1d6cd916ec-1 | 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 = 'copy_file'¶
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 root_dir: Optional[str] = None¶
The final path will be chosen relative to root_dir if specified.
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 ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.file_management.copy.CopyFileTool.html |
ef1d6cd916ec-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
get_relative_path(file_path: str) → Path¶
Get the relative path, returning an error if unsupported.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶
Examples using CopyFileTool¶
File System Tools | https://api.python.langchain.com/en/latest/tools/langchain.tools.file_management.copy.CopyFileTool.html |
c7173d5ed96e-0 | langchain.tools.searx_search.tool.SearxSearchRun¶
class langchain.tools.searx_search.tool.SearxSearchRun(*, name: str = 'searx_search', description: str = 'A meta search engine.Useful for when you need to answer questions about current events.Input should be a search query.', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, wrapper: SearxSearchWrapper, kwargs: dict = None)[source]¶
Bases: BaseTool
Tool that queries a Searx instance.
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 = 'A meta search engine.Useful for when you need to answer questions about current events.Input 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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.searx_search.tool.SearxSearchRun.html |
c7173d5ed96e-1 | Handle the content of the ToolException thrown.
param kwargs: dict [Optional]¶
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 = 'searx_search'¶
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
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.
param wrapper: langchain.utilities.searx_search.SearxSearchWrapper [Required]¶
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.searx_search.tool.SearxSearchRun.html |
c7173d5ed96e-2 | 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, **kwargs: Any) → Any¶
Run the tool asynchronously.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.searx_search.tool.SearxSearchRun.html |
92f7af325029-0 | langchain.tools.gmail.get_thread.GmailGetThread¶
class langchain.tools.gmail.get_thread.GmailGetThread(*, name: str = 'get_gmail_thread', 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.', args_schema: ~typing.Type[~langchain.tools.gmail.get_thread.GetThreadSchema] = <class 'langchain.tools.gmail.get_thread.GetThreadSchema'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, tags: ~typing.Optional[~typing.List[str]] = None, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, api_resource: Resource = None)[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.tools.gmail.get_thread.GetThreadSchema] = <class 'langchain.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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.get_thread.GmailGetThread.html |
92f7af325029-1 | 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
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.
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.get_thread.GmailGetThread.html |
92f7af325029-2 | Make tool callable.
async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
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, **kwargs: Any) → Any¶
Run the tool asynchronously.
classmethod from_api_resource(api_resource: Resource) → GmailBaseTool¶
Create a tool from an api resource.
Parameters
api_resource – The api resource to use.
Returns
A tool.
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run(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, **kwargs: Any) → Any¶
Run the tool.
property args: dict¶
property is_single_input: bool¶
Whether the tool only accepts a single input.
model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.get_thread.GmailGetThread.html |
b2c0e1034450-0 | langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksTool¶
class langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksTool(*, name: str = 'extract_hyperlinks', description: str = 'Extract all hyperlinks on the current webpage', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksToolInput'>, return_direct: bool = False, verbose: bool = False, callbacks: ~typing.Optional[~typing.Union[~typing.List[~langchain.callbacks.base.BaseCallbackHandler], ~langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: ~typing.Optional[~langchain.callbacks.base.BaseCallbackManager] = None, tags: ~typing.Optional[~typing.List[str]] = None, metadata: ~typing.Optional[~typing.Dict[str, ~typing.Any]] = None, handle_tool_error: ~typing.Optional[~typing.Union[bool, str, ~typing.Callable[[~langchain.tools.base.ToolException], str]]] = False, sync_browser: Optional['SyncBrowser'] = None, async_browser: Optional['AsyncBrowser'] = None)[source]¶
Bases: BaseBrowserTool
Extract all hyperlinks on the page.
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[BaseModel] = <class 'langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksToolInput'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param async_browser: Optional['AsyncBrowser'] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution. | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksTool.html |
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