viruPVS/langchain-docs huggingface dataset:
langchain_community.tools.amadeus.base.AmadeusBaseTool¶
class langchain_community.tools.amadeus.base.AmadeusBaseTool[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.
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,
langchain_community.tools.reddit_search.tool.RedditSearchSchema¶
class langchain_community.tools.reddit_search.tool.RedditSearchSchema[source]¶
Bases: BaseModel
Input for Reddit search.
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 limit: str [Required]¶
a positive integer indicating the maximum number         of results to return
param query: str [Required]¶
should be query string that post title should         contain, or ‘*’ if anything is allowed.
param sort: str [Required]¶
should be sort method, which is one of: “relevance”         , “hot”, “top”, “new”, or “comments”.
param subreddit: str [Required]¶
should be name of subreddit, like “all” for         r/all
param time_filter: str [Required]¶
should be time period to filter by, which is         one of “all”, “day”, “hour”, “month”, “week”, or “year”
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
langchain_community.tools.gmail.search.GmailSearch¶
class langchain_community.tools.gmail.search.GmailSearch[source]¶
Bases: GmailBaseTool
Tool that searches for messages or threads in Gmail.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param api_resource: Resource [Optional]¶
param args_schema: Type[langchain_community.tools.gmail.search.SearchArgsSchema] = <class 'langchain_community.tools.gmail.search.SearchArgsSchema'>¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = 'Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.'¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param name: str = 'search_gmail'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
langchain_community.tools.sql_database.tool.InfoSQLDatabaseTool¶
class langchain_community.tools.sql_database.tool.InfoSQLDatabaseTool[source]¶
Bases: BaseSQLDatabaseTool, BaseTool
Tool for getting metadata about 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: SQLDatabase [Required]¶
param description: str = '\n    Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.    \n\n    Example Input: "table1, table2, table3"\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_schema'¶
The unique name of the tool that clearly communicates its purpose.
param return_direct: bool = False¶
Whether to return the tool’s output directly. Setting this to True means
langchain.tools.retriever.create_retriever_tool¶
langchain.tools.retriever.create_retriever_tool(retriever: BaseRetriever, name: str, description: str) → Tool[source]¶
Create a tool to do retrieval of documents.
Parameters
retriever – The retriever to use for the retrieval
name – The name for the tool. This will be passed to the language model,
so should be unique and somewhat descriptive.
description – The description for the tool. This will be passed to the language
model, so should be descriptive.
Returns
Tool class to pass to an agent
Examples using create_retriever_tool¶
Xata chat memory
Conversational Retrieval Agent
Set env var OPENAI_API_KEY or load from a .env file
SQL
langchain_community.tools.edenai.audio_speech_to_text.EdenAiSpeechToTextTool¶
class langchain_community.tools.edenai.audio_speech_to_text.EdenAiSpeechToTextTool[source]¶
Bases: EdenaiTool
Tool that queries the Eden AI Speech To Text API.
for api reference check edenai documentation:
https://app.edenai.run/bricks/speech/asynchronous-speech-to-text.
To use, you should have
the environment variable EDENAI_API_KEY set with your API token.
You can find your token here: https://app.edenai.run/admin/account/settings
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 custom_vocabulary: Optional[List[str]] = None¶
param description: str = 'A wrapper around edenai Services speech to text Useful for when you have to convert audio to text.Input should be a url to an audio file.'¶
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 edenai_api_key: Optional[str] = None¶
param feature: str = 'audio'¶
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param is_async: bool = True¶
param language: Optional[str] = 'en'¶
param language: Optional[str] = 'en'¶
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 = 'edenai_speech_to_text'¶
The unique name of the tool that clearly communicates its purpose.
param profanity_filter: bool = False¶
param providers: List[str] [Required]¶
provider to use for the API call.
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 speakers: Optional[int] = None¶
param subfeature: str = 'speech_to_text_async'¶
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the tool. Defaults to None
These tags will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property is_single_input: bool¶
Whether the tool only accepts a single input.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using EdenAiSpeechToTextTool¶
Eden AI
langchain_core.tools.ToolException¶
class langchain_core.tools.ToolException[source]¶
An optional exception that tool throws when execution error occurs.
When this exception is thrown, the agent will not stop working,
but will handle the exception according to the handle_tool_error
variable of the tool, and the processing result will be returned
to the agent as observation, and printed in red on the console.
Examples using ToolException¶
Defining Custom Tools
langchain_community.tools.eleven_labs.models.ElevenLabsModel¶
class langchain_community.tools.eleven_labs.models.ElevenLabsModel(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Models available for Eleven Labs Text2Speech.
MULTI_LINGUAL = 'eleven_multilingual_v1'¶
MONO_LINGUAL = 'eleven_monolingual_v1'¶
langchain_community.tools.playwright.navigate_back.NavigateBackTool¶
class langchain_community.tools.playwright.navigate_back.NavigateBackTool[source]¶
Bases: BaseBrowserTool
Navigate back to the previous page in the browser history.
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 = 'Navigate back to the previous page in the browser history'¶
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 = 'previous_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¶
langchain_community.tools.arxiv.tool.ArxivInput¶
class langchain_community.tools.arxiv.tool.ArxivInput[source]¶
Bases: BaseModel
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 query: str [Required]¶
search query to look up
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
langchain_community.tools.playwright.get_elements.GetElementsToolInput¶
class langchain_community.tools.playwright.get_elements.GetElementsToolInput[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
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
langchain_community.tools.edenai.text_moderation.EdenAiTextModerationTool¶
class langchain_community.tools.edenai.text_moderation.EdenAiTextModerationTool[source]¶
Bases: EdenaiTool
Tool that queries the Eden AI Explicit text detection.
for api reference check edenai documentation:
https://docs.edenai.co/reference/image_explicit_content_create.
To use, you should have
the environment variable EDENAI_API_KEY set with your API token.
You can find your token here: https://app.edenai.run/admin/account/settings
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 callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param description: str = "A wrapper around edenai Services explicit content detection for text. Useful for when you have to scan text for offensive, \n        sexually explicit or suggestive content,\n        it checks also if there is any content of self-harm,\n        violence, racist or hate speech.the structure of the output is : \n        'the type of the explicit content : the likelihood of it being explicit'\n        the likelihood is a number \n        between 1 and 5, 1 being the lowest and 5 the highest.\n        something is explicit if the likelihood is equal or higher than 3.\n        for example : \n        nsfw_likelihood: 1\n        this is not explicit.\n        for example : \n        nsfw_likelihood: 3\n        this is explicit.\n        Input should be a string."¶
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 edenai_api_key: Optional[str] = None¶
param feature: str = 'text'¶
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param is_async: bool = False¶
param language: str [Required]¶
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param name: str = 'edenai_explicit_content_detection_text'¶
param name: str = 'edenai_explicit_content_detection_text'¶
The unique name of the tool that clearly communicates its purpose.
param providers: List[str] [Required]¶
provider to use for the API call.
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 subfeature: str = 'moderation'¶
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the tool. Defaults to None
These tags will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a tool with its use case.
param verbose: bool = False¶
Whether to log the tool’s progress.
__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str¶
Make tool callable.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
The tool’s input schema.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
static get_user_agent() → str¶
invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any¶
Transform a single input into an output. Override to implement.
Parameters
input – The input to the runnable.