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507b0427204c-4 | Pydantic model class to validate and parse the tool’s input arguments.
param base_prompt: 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}'¶
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 | https://api.python.langchain.com/en/latest/tools/langchain.tools.zapier.tool.ZapierNLARunAction.html |
507b0427204c-5 | 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 = ''¶
The unique name of the tool that clearly communicates its purpose.
param params: Optional[dict] = None¶
param params_schema: Dict[str, str] [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.
param zapier_description: str [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¶
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.zapier.tool.ZapierNLARunAction.html |
507b0427204c-6 | 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 set_name_description » all fields[source]¶
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 ZapierNLARunAction¶
Zapier Natural Language Actions API | https://api.python.langchain.com/en/latest/tools/langchain.tools.zapier.tool.ZapierNLARunAction.html |
5a247fdaa27f-0 | langchain.tools.file_management.read.ReadFileInput¶
class langchain.tools.file_management.read.ReadFileInput(*, file_path: str)[source]¶
Bases: BaseModel
Input for ReadFileTool.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param file_path: str [Required]¶
name of file | https://api.python.langchain.com/en/latest/tools/langchain.tools.file_management.read.ReadFileInput.html |
993acdb78008-0 | langchain.tools.sql_database.tool.QuerySQLDataBaseTool¶
class langchain.tools.sql_database.tool.QuerySQLDataBaseTool(*, name: str = 'sql_db_query', description: str = '\n Input to this tool is a detailed and correct SQL query, output is a result from the database.\n If the query is not correct, an error message will be returned.\n If an error is returned, rewrite the query, check the query, and try again.\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)[source]¶
Bases: BaseSQLDatabaseTool, BaseTool
Tool for querying a SQL database.
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: langchain.utilities.sql_database.SQLDatabase [Required]¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.sql_database.tool.QuerySQLDataBaseTool.html |
993acdb78008-1 | param db: langchain.utilities.sql_database.SQLDatabase [Required]¶
param description: str = '\n Input to this tool is a detailed and correct SQL query, output is a result from the database.\n If the query is not correct, an error message will be returned.\n If an error is returned, rewrite the query, check the query, and try again.\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 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'¶
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¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.sql_database.tool.QuerySQLDataBaseTool.html |
993acdb78008-2 | 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: 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.QuerySQLDataBaseTool.html |
82b2c57e304d-0 | langchain.tools.python.tool.sanitize_input¶
langchain.tools.python.tool.sanitize_input(query: str) → str[source]¶
Sanitize input to the python REPL.
Remove whitespace, backtick & python (if llm mistakes python console as terminal)
Parameters
query – The query to sanitize
Returns
The sanitized query
Return type
str | https://api.python.langchain.com/en/latest/tools/langchain.tools.python.tool.sanitize_input.html |
77e6d19d58e1-0 | langchain.tools.ddg_search.tool.DuckDuckGoSearchRun¶
class langchain.tools.ddg_search.tool.DuckDuckGoSearchRun(*, name: str = 'duckduckgo_search', description: str = 'A wrapper around DuckDuckGo Search. 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, api_wrapper: DuckDuckGoSearchAPIWrapper = None)[source]¶
Bases: BaseTool
Tool that queries the DuckDuckGo search API.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_wrapper: langchain.utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper [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 DuckDuckGo Search. 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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.ddg_search.tool.DuckDuckGoSearchRun.html |
77e6d19d58e1-1 | 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 = 'duckduckgo_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.ddg_search.tool.DuckDuckGoSearchRun.html |
77e6d19d58e1-2 | 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 DuckDuckGoSearchRun¶
DuckDuckGo Search
Github Toolkit
!pip install bs4 | https://api.python.langchain.com/en/latest/tools/langchain.tools.ddg_search.tool.DuckDuckGoSearchRun.html |
d0e39fdc5e35-0 | langchain.tools.base.Tool¶
class langchain.tools.base.Tool(name: str, func: Callable, 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, coroutine: Optional[Callable[[...], Awaitable[str]]] = None)[source]¶
Bases: BaseTool
Tool that takes in function or coroutine directly.
Initialize tool.
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 coroutine: Optional[Callable[[...], Awaitable[str]]] = None¶
The asynchronous version of the function.
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 func: Callable[[...], str] [Required]¶
The function to run when the tool is called.
param handle_tool_error: Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]] = False¶
Handle the content of the ToolException thrown. | https://api.python.langchain.com/en/latest/tools/langchain.tools.base.Tool.html |
d0e39fdc5e35-1 | 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 [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¶
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¶
Run the tool asynchronously. | https://api.python.langchain.com/en/latest/tools/langchain.tools.base.Tool.html |
d0e39fdc5e35-2 | Run the tool asynchronously.
classmethod from_function(func: Callable, name: str, description: str, return_direct: bool = False, args_schema: Optional[Type[BaseModel]] = None, **kwargs: Any) → Tool[source]¶
Initialize tool from a function.
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¶
The tool’s input arguments.
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 Tool¶
DataForSeo API Wrapper
Google Serper API
SerpAPI
Google Search
Python REPL
Zep Memory
Dynamodb Chat Message History
Google Serper
Document Comparison
Natural Language APIs
Github Toolkit
Comparing Chain Outputs
Agent VectorDB Question Answering Benchmarking
AutoGPT
BabyAGI with Tools
Plug-and-Plai
Wikibase Agent
SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base
Custom Agent with PlugIn Retrieval
Agent Debates with Tools
Adding Message Memory backed by a database to an Agent
How to add Memory to an Agent | https://api.python.langchain.com/en/latest/tools/langchain.tools.base.Tool.html |
d0e39fdc5e35-3 | Adding Message Memory backed by a database to an Agent
How to add Memory to an Agent
Multi-Input Tools
Defining Custom Tools
Self ask with search
ReAct document store
OpenAI Multi Functions Agent
Combine agents and vector stores
Custom MRKL agent
Handle parsing errors
Shared memory across agents and tools
Custom multi-action agent
Running Agent as an Iterator
Timeouts for agents
Add Memory to OpenAI Functions Agent
Cap the max number of iterations
Custom agent
Use ToolKits with OpenAI Functions
Custom agent with tool retrieval | https://api.python.langchain.com/en/latest/tools/langchain.tools.base.Tool.html |
9be43092a485-0 | langchain.tools.vectorstore.tool.BaseVectorStoreTool¶
class langchain.tools.vectorstore.tool.BaseVectorStoreTool(*, vectorstore: VectorStore, llm: BaseLanguageModel = None)[source]¶
Bases: BaseModel
Base class for tools that use a VectorStore.
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 llm: langchain.schema.language_model.BaseLanguageModel [Optional]¶
param vectorstore: langchain.vectorstores.base.VectorStore [Required]¶
model Config[source]¶
Bases: Config
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.vectorstore.tool.BaseVectorStoreTool.html |
0bf110c9a3d5-0 | langchain.tools.office365.send_event.SendEventSchema¶
class langchain.tools.office365.send_event.SendEventSchema(*, body: str, attendees: List[str], subject: str, start_datetime: str, end_datetime: str)[source]¶
Bases: BaseModel
Input for CreateEvent 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 attendees: List[str] [Required]¶
The list of attendees for the event.
param body: str [Required]¶
The message body to include in the event.
param end_datetime: str [Required]¶
The end datetime for the event in the following format: YYYY-MM-DDTHH:MM:SS±hh:mm, where “T” separates the date and time components, and the time zone offset is specified as ±hh:mm. For example: “2023-06-09T10:30:00+03:00” represents June 9th, 2023, at 10:30 AM in a time zone with a positive offset of 3 hours from Coordinated Universal Time (UTC).
param start_datetime: str [Required]¶
The start datetime for the event in the following format: YYYY-MM-DDTHH:MM:SS±hh:mm, where “T” separates the date and time components, and the time zone offset is specified as ±hh:mm. For example: “2023-06-09T10:30:00+03:00” represents June 9th, 2023, at 10:30 AM in a time zone with a positive offset of 3 hours from Coordinated Universal Time (UTC).
param subject: str [Required]¶
The subject of the event. | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.send_event.SendEventSchema.html |
24f00d4112b3-0 | langchain.tools.plugin.AIPluginTool¶
class langchain.tools.plugin.AIPluginTool(*, name: str, description: str, args_schema: ~typing.Type[~langchain.tools.plugin.AIPluginToolSchema] = <class 'langchain.tools.plugin.AIPluginToolSchema'>, 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, plugin: ~langchain.tools.plugin.AIPlugin, api_spec: str)[source]¶
Bases: BaseTool
Tool for getting the OpenAPI spec for an AI Plugin.
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_spec: str [Required]¶
param args_schema: Type[AIPluginToolSchema] = <class 'langchain.tools.plugin.AIPluginToolSchema'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str [Required]¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description. | https://api.python.langchain.com/en/latest/tools/langchain.tools.plugin.AIPluginTool.html |
24f00d4112b3-1 | 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 [Required]¶
The unique name of the tool that clearly communicates its purpose.
param plugin: AIPlugin [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.plugin.AIPluginTool.html |
24f00d4112b3-2 | 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.
classmethod from_plugin_url(url: str) → AIPluginTool[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: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶
Examples using AIPluginTool¶
ChatGPT Plugins | https://api.python.langchain.com/en/latest/tools/langchain.tools.plugin.AIPluginTool.html |
985834be17ff-0 | langchain.tools.amadeus.base.AmadeusBaseTool¶
class langchain.tools.amadeus.base.AmadeusBaseTool(*, 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, client: Client = None)[source]¶
Bases: BaseTool
Base Tool for Amadeus.
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 client: Client [Optional]¶
param description: str [Required]¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param 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.amadeus.base.AmadeusBaseTool.html |
985834be17ff-1 | 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¶
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.amadeus.base.AmadeusBaseTool.html |
985834be17ff-2 | 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.amadeus.base.AmadeusBaseTool.html |
62c437df95fb-0 | langchain.tools.file_management.list_dir.DirectoryListingInput¶
class langchain.tools.file_management.list_dir.DirectoryListingInput(*, dir_path: str = '.')[source]¶
Bases: BaseModel
Input for ListDirectoryTool.
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 dir_path: str = '.'¶
Subdirectory to list. | https://api.python.langchain.com/en/latest/tools/langchain.tools.file_management.list_dir.DirectoryListingInput.html |
b42d75b88672-0 | langchain.tools.vectorstore.tool.VectorStoreQAWithSourcesTool¶
class langchain.tools.vectorstore.tool.VectorStoreQAWithSourcesTool(*, 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, vectorstore: VectorStore, llm: BaseLanguageModel = None)[source]¶
Bases: BaseVectorStoreTool, BaseTool
Tool for the VectorDBQAWithSources chain.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str [Required]¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param llm: langchain.schema.language_model.BaseLanguageModel [Optional]¶
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None | https://api.python.langchain.com/en/latest/tools/langchain.tools.vectorstore.tool.VectorStoreQAWithSourcesTool.html |
b42d75b88672-1 | Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param name: str [Required]¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the tool. Defaults to None
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 vectorstore: langchain.vectorstores.base.VectorStore [Required]¶
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.
static get_description(name: str, description: str) → str[source]¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.vectorstore.tool.VectorStoreQAWithSourcesTool.html |
b42d75b88672-2 | static get_description(name: str, description: str) → str[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.vectorstore.tool.VectorStoreQAWithSourcesTool.html |
9ab5c54da1be-0 | langchain.tools.playwright.utils.get_current_page¶
langchain.tools.playwright.utils.get_current_page(browser: SyncBrowser) → SyncPage[source]¶
Get the current page of the browser.
:param browser: The browser to get the current page from.
Returns
The current page.
Return type
SyncPage | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.utils.get_current_page.html |
0c75ed994450-0 | langchain.tools.steamship_image_generation.utils.make_image_public¶
langchain.tools.steamship_image_generation.utils.make_image_public(client: Steamship, block: Block) → str[source]¶
Upload a block to a signed URL and return the public URL. | https://api.python.langchain.com/en/latest/tools/langchain.tools.steamship_image_generation.utils.make_image_public.html |
e01ede8cf53d-0 | langchain.tools.python.tool.PythonREPLTool¶
class langchain.tools.python.tool.PythonREPLTool(*, name: str = 'Python_REPL', description: str = 'A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.', 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, python_repl: PythonREPL = None, sanitize_input: bool = True)[source]¶
Bases: BaseTool
A tool for running python code in a REPL.
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 Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.'¶
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.python.tool.PythonREPLTool.html |
e01ede8cf53d-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 = 'Python_REPL'¶
The unique name of the tool that clearly communicates its purpose.
param python_repl: langchain.utilities.python.PythonREPL [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 sanitize_input: bool = True¶
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.python.tool.PythonREPLTool.html |
e01ede8cf53d-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 PythonREPLTool¶
Python Agent | https://api.python.langchain.com/en/latest/tools/langchain.tools.python.tool.PythonREPLTool.html |
1d2e1992d9ab-0 | langchain.tools.plugin.marshal_spec¶
langchain.tools.plugin.marshal_spec(txt: str) → dict[source]¶
Convert the yaml or json serialized spec to a dict.
Parameters
txt – The yaml or json serialized spec.
Returns
The spec as a dict.
Return type
dict | https://api.python.langchain.com/en/latest/tools/langchain.tools.plugin.marshal_spec.html |
6a8d707f00c9-0 | langchain.tools.pubmed.tool.PubmedQueryRun¶
class langchain.tools.pubmed.tool.PubmedQueryRun(*, name: str = 'PubMed', description: str = 'A wrapper around PubMed.org Useful for when you need to answer questions about Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance, Statistics, Electrical Engineering, and Economics from scientific articles on PubMed.org. 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: PubMedAPIWrapper = None)[source]¶
Bases: BaseTool
Tool that searches the PubMed 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.pupmed.PubMedAPIWrapper [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 PubMed.org Useful for when you need to answer questions about Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance, Statistics, Electrical Engineering, and Economics from scientific articles on PubMed.org. Input should be a search query.'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.pubmed.tool.PubmedQueryRun.html |
6a8d707f00c9-1 | 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 = 'PubMed'¶
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.pubmed.tool.PubmedQueryRun.html |
6a8d707f00c9-2 | 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 PubmedQueryRun¶
PubMed Tool | https://api.python.langchain.com/en/latest/tools/langchain.tools.pubmed.tool.PubmedQueryRun.html |
067f9c1da49c-0 | langchain.tools.base.ToolMetaclass¶
class langchain.tools.base.ToolMetaclass(name: str, bases: Tuple[Type, ...], dct: dict)[source]¶
Bases: ModelMetaclass
Metaclass for BaseTool to ensure the provided args_schema
doesn’t silently ignored.
Create the definition of the new tool class.
Methods
__init__(*args, **kwargs)
mro()
Return a type's method resolution order.
register(subclass)
Register a virtual subclass of an ABC.
__call__(*args, **kwargs)¶
Call self as a function.
mro()¶
Return a type’s method resolution order.
register(subclass)¶
Register a virtual subclass of an ABC.
Returns the subclass, to allow usage as a class decorator. | https://api.python.langchain.com/en/latest/tools/langchain.tools.base.ToolMetaclass.html |
d94b9bd94e03-0 | langchain.tools.json.tool.JsonGetValueTool¶
class langchain.tools.json.tool.JsonGetValueTool(*, name: str = 'json_spec_get_value', description: str = '\n Can be used to see value in string format at a given path.\n Before calling this you should be SURE that the path to this exists.\n The input is a text representation of the path to the dict in Python syntax (e.g. data["key1"][0]["key2"]).\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, spec: JsonSpec)[source]¶
Bases: BaseTool
Tool for getting a value in a 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 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.json.tool.JsonGetValueTool.html |
d94b9bd94e03-1 | param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = '\n Can be used to see value in string format at a given path.\n Before calling this you should be SURE that the path to this exists.\n The input is a text representation of the path to the dict in Python syntax (e.g. data["key1"][0]["key2"]).\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 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 = 'json_spec_get_value'¶
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 spec: JsonSpec [Required]¶
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.json.tool.JsonGetValueTool.html |
d94b9bd94e03-2 | 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.
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.json.tool.JsonGetValueTool.html |
32dc2b7a63f1-0 | langchain.tools.office365.send_message.SendMessageSchema¶
class langchain.tools.office365.send_message.SendMessageSchema(*, body: str, to: List[str], subject: str, cc: Optional[List[str]] = None, bcc: Optional[List[str]] = None)[source]¶
Bases: BaseModel
Input for SendMessageTool.
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 bcc: Optional[List[str]] = None¶
The list of BCC recipients.
param body: str [Required]¶
The message body to be sent.
param cc: Optional[List[str]] = None¶
The list of CC recipients.
param subject: str [Required]¶
The subject of the message.
param to: List[str] [Required]¶
The list of recipients. | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.send_message.SendMessageSchema.html |
db275430bce8-0 | langchain.tools.google_serper.tool.GoogleSerperRun¶
class langchain.tools.google_serper.tool.GoogleSerperRun(*, name: str = 'google_serper', 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.', 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)[source]¶
Bases: BaseTool
Tool that queries the Serper.dev Google search API.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_wrapper: langchain.utilities.google_serper.GoogleSerperAPIWrapper [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 low-cost Google Search API.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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.google_serper.tool.GoogleSerperRun.html |
db275430bce8-1 | 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 = 'google_serper'¶
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.GoogleSerperRun.html |
db275430bce8-2 | 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.GoogleSerperRun.html |
fa2a552bd9f2-0 | langchain.tools.playwright.get_elements.GetElementsToolInput¶
class langchain.tools.playwright.get_elements.GetElementsToolInput(*, selector: str, attributes: List[str] = None)[source]¶
Bases: BaseModel
Input for GetElementsTool.
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 attributes: List[str] [Optional]¶
Set of attributes to retrieve for each element
param selector: str [Required]¶
CSS selector, such as ‘*’, ‘div’, ‘p’, ‘a’, #id, .classname | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.get_elements.GetElementsToolInput.html |
d845a6877f48-0 | langchain.tools.wolfram_alpha.tool.WolframAlphaQueryRun¶
class langchain.tools.wolfram_alpha.tool.WolframAlphaQueryRun(*, name: str = 'wolfram_alpha', description: str = 'A wrapper around Wolfram Alpha. Useful for when you need to answer questions about Math, Science, Technology, Culture, Society and Everyday Life. 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: WolframAlphaAPIWrapper)[source]¶
Bases: BaseTool
Tool that queries using the Wolfram Alpha SDK.
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.wolfram_alpha.WolframAlphaAPIWrapper [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 Wolfram Alpha. Useful for when you need to answer questions about Math, Science, Technology, Culture, Society and Everyday Life. Input should be a search query.'¶
Used to tell the model how/when/why to use the tool. | https://api.python.langchain.com/en/latest/tools/langchain.tools.wolfram_alpha.tool.WolframAlphaQueryRun.html |
d845a6877f48-1 | 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 = 'wolfram_alpha'¶
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.wolfram_alpha.tool.WolframAlphaQueryRun.html |
d845a6877f48-2 | 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.wolfram_alpha.tool.WolframAlphaQueryRun.html |
11dc6dbe5b8f-0 | langchain.tools.ifttt.IFTTTWebhook¶
class langchain.tools.ifttt.IFTTTWebhook(*, 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, url: str)[source]¶
Bases: BaseTool
IFTTT Webhook.
Parameters
name – name of the tool
description – description of the tool
url – url to hit with the json event.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str [Required]¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param 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.ifttt.IFTTTWebhook.html |
11dc6dbe5b8f-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 url: str [Required]¶
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.ifttt.IFTTTWebhook.html |
11dc6dbe5b8f-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 IFTTTWebhook¶
IFTTT WebHooks | https://api.python.langchain.com/en/latest/tools/langchain.tools.ifttt.IFTTTWebhook.html |
0879c88cbaf1-0 | langchain.tools.azure_cognitive_services.utils.download_audio_from_url¶
langchain.tools.azure_cognitive_services.utils.download_audio_from_url(audio_url: str) → str[source]¶
Download audio from url to local. | https://api.python.langchain.com/en/latest/tools/langchain.tools.azure_cognitive_services.utils.download_audio_from_url.html |
be227c5bbd40-0 | langchain.tools.gmail.get_message.GmailGetMessage¶
class langchain.tools.gmail.get_message.GmailGetMessage(*, name: str = 'get_gmail_message', description: str = 'Use this tool to fetch an email by message ID. Returns the thread ID, snippet, body, subject, and sender.', args_schema: ~typing.Type[~langchain.tools.gmail.get_message.SearchArgsSchema] = <class 'langchain.tools.gmail.get_message.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 gets a message 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_message.SearchArgsSchema] = <class 'langchain.tools.gmail.get_message.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.get_message.GmailGetMessage.html |
be227c5bbd40-1 | param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this tool to fetch an email by message ID. Returns the thread ID, snippet, body, subject, and sender.'¶
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_message'¶
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.gmail.get_message.GmailGetMessage.html |
be227c5bbd40-2 | 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.
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_message.GmailGetMessage.html |
acc94ef2a3d9-0 | langchain.tools.file_management.list_dir.ListDirectoryTool¶
class langchain.tools.file_management.list_dir.ListDirectoryTool(*, name: str = 'list_directory', description: str = 'List files and directories in a specified folder', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, 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 lists files and directories in a specified folder.
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.list_dir.DirectoryListingInput'>¶
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 = 'List files and directories in a specified folder'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.file_management.list_dir.ListDirectoryTool.html |
acc94ef2a3d9-1 | param description: str = 'List files and directories in a specified folder'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param name: str = 'list_directory'¶
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.list_dir.ListDirectoryTool.html |
acc94ef2a3d9-2 | 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 ListDirectoryTool¶
File System Tools | https://api.python.langchain.com/en/latest/tools/langchain.tools.file_management.list_dir.ListDirectoryTool.html |
836a67ced5fe-0 | langchain.tools.office365.messages_search.O365SearchEmails¶
class langchain.tools.office365.messages_search.O365SearchEmails(*, name: str = 'messages_search', description: str = 'Use this tool to search for email messages. The input must be a valid Microsoft Graph v1.0 $search query. The output is a JSON list of the requested resource.', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.office365.messages_search.SearchEmailsInput'>, 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 email messages 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.
param args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.office365.messages_search.SearchEmailsInput'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead. | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.messages_search.O365SearchEmails.html |
836a67ced5fe-1 | Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this tool to search for email messages. The input must be a valid Microsoft Graph v1.0 $search 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 = 'messages_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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.messages_search.O365SearchEmails.html |
836a67ced5fe-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[source]¶
Bases: object
Configuration for this pydantic object.
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.messages_search.O365SearchEmails.html |
9b04b1303118-0 | langchain.tools.interaction.tool.StdInInquireTool¶
langchain.tools.interaction.tool.StdInInquireTool(*args: Any, **kwargs: Any) → HumanInputRun[source]¶
Tool for asking the user for input. | https://api.python.langchain.com/en/latest/tools/langchain.tools.interaction.tool.StdInInquireTool.html |
2e4ccf039da5-0 | langchain.tools.jira.tool.JiraAction¶
class langchain.tools.jira.tool.JiraAction(*, 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: JiraAPIWrapper = None, mode: str)[source]¶
Bases: BaseTool
Tool that queries the Atlassian Jira 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.jira.JiraAPIWrapper [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.jira.tool.JiraAction.html |
2e4ccf039da5-1 | 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.jira.tool.JiraAction.html |
2e4ccf039da5-2 | 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'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.jira.tool.JiraAction.html |
38ebe70c1fa4-0 | langchain.tools.amadeus.flight_search.FlightSearchSchema¶
class langchain.tools.amadeus.flight_search.FlightSearchSchema(*, originLocationCode: str, destinationLocationCode: str, departureDateTimeEarliest: str, departureDateTimeLatest: str, page_number: int = 1)[source]¶
Bases: BaseModel
Schema for the AmadeusFlightSearch tool.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param departureDateTimeEarliest: str [Required]¶
The earliest departure datetime from the origin airport for the flight search in the following format: “YYYY-MM-DDTHH:MM”, where “T” separates the date and time components. For example: “2023-06-09T10:30:00” represents June 9th, 2023, at 10:30 AM.
param departureDateTimeLatest: str [Required]¶
The latest departure datetime from the origin airport for the flight search in the following format: “YYYY-MM-DDTHH:MM”, where “T” separates the date and time components. For example: “2023-06-09T10:30:00” represents June 9th, 2023, at 10:30 AM.
param destinationLocationCode: str [Required]¶
The three letter International Air Transport Association (IATA) Location Identifier for the search’s destination airport.
param originLocationCode: str [Required]¶
The three letter International Air Transport Association (IATA) Location Identifier for the search’s origin airport.
param page_number: int = 1¶
The specific page number of flight results to retrieve | https://api.python.langchain.com/en/latest/tools/langchain.tools.amadeus.flight_search.FlightSearchSchema.html |
2cf86c88e2ad-0 | langchain.tools.openapi.utils.api_models.APIRequestBodyProperty¶
class langchain.tools.openapi.utils.api_models.APIRequestBodyProperty(*, name: str, required: bool, type: Union[str, Type, tuple, None, Enum] = None, default: Optional[Any] = None, description: Optional[str] = None, properties: List[APIRequestBodyProperty], references_used: List[str])[source]¶
Bases: APIPropertyBase
A model for a request body property.
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 default: Optional[Any] = None¶
The default value of the property.
param description: Optional[str] = None¶
The description of the property.
param name: str [Required]¶
The name of the property.
param properties: List[langchain.tools.openapi.utils.api_models.APIRequestBodyProperty] [Required]¶
The sub-properties of the property.
param references_used: List[str] [Required]¶
The references used by the property.
param required: bool [Required]¶
Whether the property is required.
param type: Union[str, Type, tuple, None, enum.Enum] = None¶
The type of the property.
Either a primitive type, a component/parameter type,
or an array or ‘object’ (dict) of the above.
classmethod from_schema(schema: Schema, name: str, required: bool, spec: OpenAPISpec, references_used: Optional[List[str]] = None) → APIRequestBodyProperty[source]¶
Recursively populate from an OpenAPI Schema. | https://api.python.langchain.com/en/latest/tools/langchain.tools.openapi.utils.api_models.APIRequestBodyProperty.html |
a166f2b3f89d-0 | langchain.tools.playwright.extract_text.ExtractTextTool¶
class langchain.tools.playwright.extract_text.ExtractTextTool(*, name: str = 'extract_text', description: str = 'Extract all the text on the current webpage', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'pydantic.main.BaseModel'>, 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
Tool for extracting all the text on the current webpage.
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 'pydantic.main.BaseModel'>¶
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.
param description: str = 'Extract all the text on the current webpage'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.extract_text.ExtractTextTool.html |
a166f2b3f89d-1 | param description: str = 'Extract all the text on the current webpage'¶
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 = 'extract_text'¶
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 sync_browser: Optional['SyncBrowser'] = None¶
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.playwright.extract_text.ExtractTextTool.html |
a166f2b3f89d-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.
validator check_acheck_bs_importrgs » all fields[source]¶
Check that the arguments are valid.
classmethod from_browser(sync_browser: Optional[SyncBrowser] = None, async_browser: Optional[AsyncBrowser] = None) → BaseBrowserTool¶
Instantiate the 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.
validator validate_browser_provided » all fields¶
Check that the arguments are valid.
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.playwright.extract_text.ExtractTextTool.html |
c8a0d6b7666c-0 | langchain.tools.sleep.tool.SleepInput¶
class langchain.tools.sleep.tool.SleepInput(*, sleep_time: int)[source]¶
Bases: BaseModel
Input for CopyFileTool.
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 sleep_time: int [Required]¶
Time to sleep in seconds | https://api.python.langchain.com/en/latest/tools/langchain.tools.sleep.tool.SleepInput.html |
b339871f0759-0 | langchain.tools.office365.create_draft_message.CreateDraftMessageSchema¶
class langchain.tools.office365.create_draft_message.CreateDraftMessageSchema(*, body: str, to: List[str], subject: str, cc: Optional[List[str]] = None, bcc: Optional[List[str]] = None)[source]¶
Bases: BaseModel
Input for SendMessageTool.
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 bcc: Optional[List[str]] = None¶
The list of BCC recipients.
param body: str [Required]¶
The message body to include in the draft.
param cc: Optional[List[str]] = None¶
The list of CC recipients.
param subject: str [Required]¶
The subject of the message.
param to: List[str] [Required]¶
The list of recipients. | https://api.python.langchain.com/en/latest/tools/langchain.tools.office365.create_draft_message.CreateDraftMessageSchema.html |
7fcca5f96543-0 | langchain.tools.azure_cognitive_services.image_analysis.AzureCogsImageAnalysisTool¶
class langchain.tools.azure_cognitive_services.image_analysis.AzureCogsImageAnalysisTool(*, name: str = 'azure_cognitive_services_image_analysis', description: str = 'A wrapper around Azure Cognitive Services Image Analysis. Useful for when you need to analyze images. Input should be a url to an image.', 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 = '', vision_service: Any = None, analysis_options: Any = None)[source]¶
Bases: BaseTool
Tool that queries the Azure Cognitive Services Image Analysis API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/quickstarts-sdk/image-analysis-client-library-40
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.image_analysis.AzureCogsImageAnalysisTool.html |
7fcca5f96543-1 | param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'A wrapper around Azure Cognitive Services Image Analysis. Useful for when you need to analyze images. Input should be a url to an image.'¶
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_image_analysis'¶
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.image_analysis.AzureCogsImageAnalysisTool.html |
7fcca5f96543-2 | 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.image_analysis.AzureCogsImageAnalysisTool.html |
f08330105508-0 | langchain.tools.metaphor_search.tool.MetaphorSearchResults¶
class langchain.tools.metaphor_search.tool.MetaphorSearchResults(*, name: str = 'metaphor_search_results_json', description: str = 'A wrapper around Metaphor Search. Input should be a Metaphor-optimized 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, api_wrapper: MetaphorSearchAPIWrapper)[source]¶
Bases: BaseTool
Tool that queries the Metaphor 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.metaphor_search.MetaphorSearchAPIWrapper [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 Metaphor Search. Input should be a Metaphor-optimized 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.metaphor_search.tool.MetaphorSearchResults.html |
f08330105508-1 | 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 = 'metaphor_search_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.metaphor_search.tool.MetaphorSearchResults.html |
f08330105508-2 | 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 MetaphorSearchResults¶
Metaphor Search | https://api.python.langchain.com/en/latest/tools/langchain.tools.metaphor_search.tool.MetaphorSearchResults.html |
5d1135f66210-0 | langchain.tools.file_management.move.FileMoveInput¶
class langchain.tools.file_management.move.FileMoveInput(*, source_path: str, destination_path: str)[source]¶
Bases: BaseModel
Input for MoveFileTool.
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 destination_path: str [Required]¶
New path for the moved file
param source_path: str [Required]¶
Path of the file to move | https://api.python.langchain.com/en/latest/tools/langchain.tools.file_management.move.FileMoveInput.html |
ff1abc6a31e6-0 | langchain.tools.golden_query.tool.GoldenQueryRun¶
class langchain.tools.golden_query.tool.GoldenQueryRun(*, name: str = 'Golden Query', description: str = "A wrapper around Golden Query API. Useful for getting entities that match a natural language query from Golden's Knowledge Base.\nExample queries:\n- companies in nanotech\n- list of cloud providers starting in 2019\nInput should be the natural language query.\nOutput is a paginated list of results or an error object in JSON format.", 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: GoldenQueryAPIWrapper)[source]¶
Bases: BaseTool
Tool that adds the capability to query using the Golden 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.golden_query.GoldenQueryAPIWrapper [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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.golden_query.tool.GoldenQueryRun.html |
ff1abc6a31e6-1 | param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = "A wrapper around Golden Query API. Useful for getting entities that match a natural language query from Golden's Knowledge Base.\nExample queries:\n- companies in nanotech\n- list of cloud providers starting in 2019\nInput should be the natural language query.\nOutput is a paginated list of results or an error object in JSON format."¶
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 = 'Golden Query'¶
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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.golden_query.tool.GoldenQueryRun.html |
ff1abc6a31e6-2 | 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.
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.golden_query.tool.GoldenQueryRun.html |
2528ba170a81-0 | langchain.tools.gmail.utils.import_google¶
langchain.tools.gmail.utils.import_google() → Tuple[Request, Credentials][source]¶
Import google libraries.
Returns
Request and Credentials classes.
Return type
Tuple[Request, Credentials] | https://api.python.langchain.com/en/latest/tools/langchain.tools.gmail.utils.import_google.html |
48a349a42c51-0 | langchain.tools.sql_database.tool.ListSQLDatabaseTool¶
class langchain.tools.sql_database.tool.ListSQLDatabaseTool(*, name: str = 'sql_db_list_tables', description: str = 'Input is an empty string, output is a comma separated list of tables in the database.', 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)[source]¶
Bases: BaseSQLDatabaseTool, BaseTool
Tool for getting tables names.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param db: langchain.utilities.sql_database.SQLDatabase [Required]¶
param description: str = 'Input is an empty string, output is a comma separated list of tables in the database.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown. | https://api.python.langchain.com/en/latest/tools/langchain.tools.sql_database.tool.ListSQLDatabaseTool.html |
48a349a42c51-1 | 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 = 'sql_db_list_tables'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
param tags: Optional[List[str]] = None¶
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.sql_database.tool.ListSQLDatabaseTool.html |
48a349a42c51-2 | 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: 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.ListSQLDatabaseTool.html |
d602628d1ddd-0 | langchain.tools.openapi.utils.api_models.APIProperty¶
class langchain.tools.openapi.utils.api_models.APIProperty(*, name: str, required: bool, type: Union[str, Type, tuple, None, Enum] = None, default: Optional[Any] = None, description: Optional[str] = None, location: APIPropertyLocation)[source]¶
Bases: APIPropertyBase
A model for a property in the query, path, header, or cookie params.
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 default: Optional[Any] = None¶
The default value of the property.
param description: Optional[str] = None¶
The description of the property.
param location: langchain.tools.openapi.utils.api_models.APIPropertyLocation [Required]¶
The path/how it’s being passed to the endpoint.
param name: str [Required]¶
The name of the property.
param required: bool [Required]¶
Whether the property is required.
param type: Union[str, Type, tuple, None, enum.Enum] = None¶
The type of the property.
Either a primitive type, a component/parameter type,
or an array or ‘object’ (dict) of the above.
classmethod from_parameter(parameter: Parameter, spec: OpenAPISpec) → APIProperty[source]¶
Instantiate from an OpenAPI Parameter.
static is_supported_location(location: str) → bool[source]¶
Return whether the provided location is supported. | https://api.python.langchain.com/en/latest/tools/langchain.tools.openapi.utils.api_models.APIProperty.html |
055187a9141c-0 | langchain.tools.base.SchemaAnnotationError¶
class langchain.tools.base.SchemaAnnotationError[source]¶
Bases: TypeError
Raised when ‘args_schema’ is missing or has an incorrect type annotation.
add_note()¶
Exception.add_note(note) –
add a note to the exception
with_traceback()¶
Exception.with_traceback(tb) –
set self.__traceback__ to tb and return self.
args¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.base.SchemaAnnotationError.html |
2afd0357ca0e-0 | langchain.tools.playwright.current_page.CurrentWebPageTool¶
class langchain.tools.playwright.current_page.CurrentWebPageTool(*, name: str = 'current_webpage', description: str = 'Returns the URL of the current page', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'pydantic.main.BaseModel'>, 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
Tool for getting the URL of the current webpage.
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 'pydantic.main.BaseModel'>¶
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.
param description: str = 'Returns the URL of the current page'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.current_page.CurrentWebPageTool.html |
2afd0357ca0e-1 | param description: str = 'Returns the URL of the current page'¶
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 = 'current_webpage'¶
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 sync_browser: Optional['SyncBrowser'] = None¶
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.playwright.current_page.CurrentWebPageTool.html |
2afd0357ca0e-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.
classmethod from_browser(sync_browser: Optional[SyncBrowser] = None, async_browser: Optional[AsyncBrowser] = None) → BaseBrowserTool¶
Instantiate the 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.
validator validate_browser_provided » all fields¶
Check that the arguments are valid.
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.playwright.current_page.CurrentWebPageTool.html |
52769f4deae3-0 | langchain.tools.requests.tool.RequestsPostTool¶
class langchain.tools.requests.tool.RequestsPostTool(*, name: str = 'requests_post', description: str = 'Use this when you want to POST to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to POST to the url.\n Be careful to always use double quotes for strings in the json string\n The output will be the text response of the POST request.\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, requests_wrapper: TextRequestsWrapper)[source]¶
Bases: BaseRequestsTool, BaseTool
Tool for making a POST request to an API endpoint.
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.requests.tool.RequestsPostTool.html |
52769f4deae3-1 | param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this when you want to POST to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to POST to the url.\n Be careful to always use double quotes for strings in the json string\n The output will be the text response of the POST request.\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 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 = 'requests_post'¶
The unique name of the tool that clearly communicates its purpose.
param requests_wrapper: langchain.utilities.requests.TextRequestsWrapper [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. | https://api.python.langchain.com/en/latest/tools/langchain.tools.requests.tool.RequestsPostTool.html |
52769f4deae3-2 | 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.
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¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.requests.tool.RequestsPostTool.html |
52769f4deae3-3 | Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.requests.tool.RequestsPostTool.html |
2c907b35ddcf-0 | langchain.tools.playwright.utils.create_sync_playwright_browser¶
langchain.tools.playwright.utils.create_sync_playwright_browser(headless: bool = True) → SyncBrowser[source]¶
Create a playwright browser.
Parameters
headless – Whether to run the browser in headless mode. Defaults to True.
Returns
The playwright browser.
Return type
SyncBrowser
Examples using create_sync_playwright_browser¶
PlayWright Browser Toolkit | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.utils.create_sync_playwright_browser.html |
7a80ffc6a0ca-0 | langchain.tools.playwright.click.ClickTool¶
class langchain.tools.playwright.click.ClickTool(*, name: str = 'click_element', description: str = 'Click on an element with the given CSS selector', args_schema: ~typing.Type[~pydantic.main.BaseModel] = <class 'langchain.tools.playwright.click.ClickToolInput'>, 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, visible_only: bool = True, playwright_strict: bool = False, playwright_timeout: float = 1000)[source]¶
Bases: BaseBrowserTool
Tool for clicking on an element with the given CSS selector.
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.click.ClickToolInput'>¶
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¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.click.ClickTool.html |
7a80ffc6a0ca-1 | Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Click on an element with the given CSS selector'¶
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 = 'click_element'¶
The unique name of the tool that clearly communicates its purpose.
param playwright_strict: bool = False¶
Whether to employ Playwright’s strict mode when clicking on elements.
param playwright_timeout: float = 1000¶
Timeout (in ms) for Playwright to wait for element to be ready.
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 sync_browser: Optional['SyncBrowser'] = None¶
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 visible_only: bool = True¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.click.ClickTool.html |
7a80ffc6a0ca-2 | Whether to log the tool’s progress.
param visible_only: bool = True¶
Whether to consider only visible elements.
__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.
classmethod from_browser(sync_browser: Optional[SyncBrowser] = None, async_browser: Optional[AsyncBrowser] = None) → BaseBrowserTool¶
Instantiate the 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.
validator validate_browser_provided » all fields¶
Check that the arguments are valid.
property args: dict¶
property is_single_input: bool¶ | https://api.python.langchain.com/en/latest/tools/langchain.tools.playwright.click.ClickTool.html |
7a80ffc6a0ca-3 | 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.playwright.click.ClickTool.html |
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