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1ace37f9e39e-17 | callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
num_results (int) β
api_wrapper (langchain.utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper [Optional]ο
attribute num_results: int = 4ο
class langchain.tools.DuckDuckGoSearchRun(*, name='duckduckgo_search', description='A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper=None)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that adds the capability to query the DuckDuckGo search API.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper [Optional]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-18 | class langchain.tools.ExtractHyperlinksTool(*, name='extract_hyperlinks', description='Extract all hyperlinks on the current webpage', args_schema=<class 'langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksToolInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, sync_browser=None, async_browser=None)[source]ο
Bases: langchain.tools.playwright.base.BaseBrowserTool
Extract all hyperlinks on the page.
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
sync_browser (Optional['SyncBrowser']) β
async_browser (Optional['AsyncBrowser']) β
Return type
None
attribute args_schema: Type[BaseModel] = <class 'langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksToolInput'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Extract all hyperlinks on the current webpage'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'extract_hyperlinks'ο
The unique name of the tool that clearly communicates its purpose.
static scrape_page(page, html_content, absolute_urls)[source]ο
Parameters
page (Any) β
html_content (str) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-19 | Parameters
page (Any) β
html_content (str) β
absolute_urls (bool) β
Return type
str
class langchain.tools.ExtractTextTool(*, name='extract_text', description='Extract all the text on the current webpage', args_schema=<class 'pydantic.main.BaseModel'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, sync_browser=None, async_browser=None)[source]ο
Bases: langchain.tools.playwright.base.BaseBrowserTool
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
sync_browser (Optional['SyncBrowser']) β
async_browser (Optional['AsyncBrowser']) β
Return type
None
attribute args_schema: Type[BaseModel] = <class 'pydantic.main.BaseModel'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Extract all the text on the current webpage'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'extract_text'ο
The unique name of the tool that clearly communicates its purpose. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-20 | The unique name of the tool that clearly communicates its purpose.
class langchain.tools.FileSearchTool(*, name='file_search', description='Recursively search for files in a subdirectory that match the regex pattern', args_schema=<class 'langchain.tools.file_management.file_search.FileSearchInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, root_dir=None)[source]ο
Bases: langchain.tools.file_management.utils.BaseFileToolMixin, langchain.tools.base.BaseTool
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
root_dir (Optional[str]) β
Return type
None
attribute args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.file_search.FileSearchInput'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Recursively search for files in a subdirectory that match the regex pattern'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'file_search'ο
The unique name of the tool that clearly communicates its purpose. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-21 | The unique name of the tool that clearly communicates its purpose.
class langchain.tools.GetElementsTool(*, name='get_elements', description='Retrieve elements in the current web page matching the given CSS selector', args_schema=<class 'langchain.tools.playwright.get_elements.GetElementsToolInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, sync_browser=None, async_browser=None)[source]ο
Bases: langchain.tools.playwright.base.BaseBrowserTool
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
sync_browser (Optional['SyncBrowser']) β
async_browser (Optional['AsyncBrowser']) β
Return type
None
attribute args_schema: Type[BaseModel] = <class 'langchain.tools.playwright.get_elements.GetElementsToolInput'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Retrieve elements in the current web page matching the given CSS selector'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'get_elements'ο
The unique name of the tool that clearly communicates its purpose. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-22 | The unique name of the tool that clearly communicates its purpose.
class langchain.tools.GmailCreateDraft(*, name='create_gmail_draft', description='Use this tool to create a draft email with the provided message fields.', args_schema=<class 'langchain.tools.gmail.create_draft.CreateDraftSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_resource=None)[source]ο
Bases: langchain.tools.gmail.base.GmailBaseTool
Parameters
name (str) β
description (str) β
args_schema (Type[langchain.tools.gmail.create_draft.CreateDraftSchema]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_resource (Resource) β
Return type
None
attribute args_schema: Type[langchain.tools.gmail.create_draft.CreateDraftSchema] = <class 'langchain.tools.gmail.create_draft.CreateDraftSchema'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Use this tool to create a draft email with the provided message fields.'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'create_gmail_draft'ο
The unique name of the tool that clearly communicates its purpose. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-23 | The unique name of the tool that clearly communicates its purpose.
class langchain.tools.GmailGetMessage(*, name='get_gmail_message', description='Use this tool to fetch an email by message ID. Returns the thread ID, snipet, body, subject, and sender.', args_schema=<class 'langchain.tools.gmail.get_message.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_resource=None)[source]ο
Bases: langchain.tools.gmail.base.GmailBaseTool
Parameters
name (str) β
description (str) β
args_schema (Type[langchain.tools.gmail.get_message.SearchArgsSchema]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_resource (Resource) β
Return type
None
attribute args_schema: Type[langchain.tools.gmail.get_message.SearchArgsSchema] = <class 'langchain.tools.gmail.get_message.SearchArgsSchema'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Use this tool to fetch an email by message ID. Returns the thread ID, snipet, body, subject, and sender.'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'get_gmail_message'ο
The unique name of the tool that clearly communicates its purpose. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-24 | The unique name of the tool that clearly communicates its purpose.
class langchain.tools.GmailGetThread(*, name='get_gmail_thread', description='Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.', args_schema=<class 'langchain.tools.gmail.get_thread.GetThreadSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_resource=None)[source]ο
Bases: langchain.tools.gmail.base.GmailBaseTool
Parameters
name (str) β
description (str) β
args_schema (Type[langchain.tools.gmail.get_thread.GetThreadSchema]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_resource (Resource) β
Return type
None
attribute args_schema: Type[langchain.tools.gmail.get_thread.GetThreadSchema] = <class 'langchain.tools.gmail.get_thread.GetThreadSchema'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'get_gmail_thread'ο
The unique name of the tool that clearly communicates its purpose. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-25 | The unique name of the tool that clearly communicates its purpose.
class langchain.tools.GmailSearch(*, name='search_gmail', description='Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.', args_schema=<class 'langchain.tools.gmail.search.SearchArgsSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_resource=None)[source]ο
Bases: langchain.tools.gmail.base.GmailBaseTool
Parameters
name (str) β
description (str) β
args_schema (Type[langchain.tools.gmail.search.SearchArgsSchema]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_resource (Resource) β
Return type
None
attribute args_schema: Type[langchain.tools.gmail.search.SearchArgsSchema] = <class 'langchain.tools.gmail.search.SearchArgsSchema'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute 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.'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'search_gmail'ο
The unique name of the tool that clearly communicates its purpose. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-26 | The unique name of the tool that clearly communicates its purpose.
class langchain.tools.GmailSendMessage(*, name='send_gmail_message', description='Use this tool to send email messages. The input is the message, recipents', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_resource=None)[source]ο
Bases: langchain.tools.gmail.base.GmailBaseTool
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_resource (Resource) β
Return type
None
attribute description: str = 'Use this tool to send email messages. The input is the message, recipents'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'send_gmail_message'ο
The unique name of the tool that clearly communicates its purpose.
class langchain.tools.GooglePlacesTool(*, name='google_places', description='A wrapper around Google Places. Useful for when you need to validate or discover addressed from ambiguous text. Input should be a search query.', args_schema=<class 'langchain.tools.google_places.tool.GooglePlacesSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper=None)[source]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-27 | Bases: langchain.tools.base.BaseTool
Tool that adds the capability to query the Google places API.
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.google_places_api.GooglePlacesAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.google_places_api.GooglePlacesAPIWrapper [Optional]ο
attribute args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.google_places.tool.GooglePlacesSchema'>ο
Pydantic model class to validate and parse the toolβs input arguments.
class langchain.tools.GoogleSearchResults(*, name='Google Search Results JSON', description='A wrapper around Google Search. Useful for when you need to answer questions about current events. Input should be a search query. Output is a JSON array of the query results', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, num_results=4, api_wrapper)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that has capability to query the Google Search API and get back json.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-28 | return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
num_results (int) β
api_wrapper (langchain.utilities.google_search.GoogleSearchAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.google_search.GoogleSearchAPIWrapper [Required]ο
attribute num_results: int = 4ο
class langchain.tools.GoogleSearchRun(*, name='google_search', description='A wrapper around Google Search. Useful for when you need to answer questions about current events. Input should be a search query.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that adds the capability to query the Google search API.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.google_search.GoogleSearchAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.google_search.GoogleSearchAPIWrapper [Required]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-29 | attribute api_wrapper: langchain.utilities.google_search.GoogleSearchAPIWrapper [Required]ο
class langchain.tools.GoogleSerperResults(*, name='Google Serrper Results JSON', description='A low-cost Google Search API.Useful for when you need to answer questions about current events.Input should be a search query. Output is a JSON object of the query results', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper=None)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that has capability to query the Serper.dev Google Search API
and get back json.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.google_serper.GoogleSerperAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.google_serper.GoogleSerperAPIWrapper [Optional]ο
class langchain.tools.GoogleSerperRun(*, name='google_serper', description='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=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that adds the capability to query the Serper.dev Google search API.
Parameters | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-30 | Tool that adds the capability to query the Serper.dev Google search API.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.google_serper.GoogleSerperAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.google_serper.GoogleSerperAPIWrapper [Required]ο
class langchain.tools.HumanInputRun(*, name='human', description='You can ask a human for guidance when you think you got stuck or you are not sure what to do next. The input should be a question for the human.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, prompt_func=None, input_func=None)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that adds the capability to ask user for input.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-31 | callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
prompt_func (Callable[[str], None]) β
input_func (Callable) β
Return type
None
attribute input_func: Callable [Optional]ο
attribute prompt_func: Callable[[str], None] [Optional]ο
class langchain.tools.IFTTTWebhook(*, name, description, args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, url)[source]ο
Bases: langchain.tools.base.BaseTool
IFTTT Webhook.
Parameters
name (str) β name of the tool
description (str) β description of the tool
url (str) β url to hit with the json event.
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
Return type
None
attribute url: str [Required]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-32 | Return type
None
attribute url: str [Required]ο
class langchain.tools.InfoPowerBITool(*, name='schema_powerbi', description='\nΒ Β Β Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.\nΒ Β Β Be sure that the tables actually exist by calling list_tables_powerbi first!\n\nΒ Β Β Example Input: "table1, table2, table3"\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, powerbi)[source]ο
Bases: langchain.tools.base.BaseTool
Tool for getting metadata about a PowerBI Dataset.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
powerbi (langchain.utilities.powerbi.PowerBIDataset) β
Return type
None
attribute powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]ο
class langchain.tools.InfoSQLDatabaseTool(*, name='sql_db_schema', description='\nΒ Β Β Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.Β Β Β \n\nΒ Β Β Example Input: "table1, table2, table3"\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, db)[source]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-33 | Bases: langchain.tools.sql_database.tool.BaseSQLDatabaseTool, langchain.tools.base.BaseTool
Tool for getting metadata about a SQL database.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
db (langchain.sql_database.SQLDatabase) β
Return type
None
class langchain.tools.InfoSparkSQLTool(*, name='schema_sql_db', description='\nΒ Β Β Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.\nΒ Β Β Be sure that the tables actually exist by calling list_tables_sql_db first!\n\nΒ Β Β Example Input: "table1, table2, table3"\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, db)[source]ο
Bases: langchain.tools.spark_sql.tool.BaseSparkSQLTool, langchain.tools.base.BaseTool
Tool for getting metadata about a Spark SQL.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-34 | callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
db (langchain.utilities.spark_sql.SparkSQL) β
Return type
None
class langchain.tools.JiraAction(*, name='', description='', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper=None, mode)[source]ο
Bases: langchain.tools.base.BaseTool
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.jira.JiraAPIWrapper) β
mode (str) β
Return type
None
attribute api_wrapper: langchain.utilities.jira.JiraAPIWrapper [Optional]ο
attribute mode: str [Required]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-35 | attribute mode: str [Required]ο
class langchain.tools.JsonGetValueTool(*, name='json_spec_get_value', description='\nΒ Β Β Can be used to see value in string format at a given path.\nΒ Β Β Before calling this you should be SURE that the path to this exists.\nΒ Β Β The input is a text representation of the path to the dict in Python syntax (e.g. data["key1"][0]["key2"]).\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, spec)[source]ο
Bases: langchain.tools.base.BaseTool
Tool for getting a value in a JSON spec.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
spec (langchain.tools.json.tool.JsonSpec) β
Return type
None
attribute spec: JsonSpec [Required]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-36 | Return type
None
attribute spec: JsonSpec [Required]ο
class langchain.tools.JsonListKeysTool(*, name='json_spec_list_keys', description='\nΒ Β Β Can be used to list all keys at a given path. \nΒ Β Β Before calling this you should be SURE that the path to this exists.\nΒ Β Β The input is a text representation of the path to the dict in Python syntax (e.g. data["key1"][0]["key2"]).\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, spec)[source]ο
Bases: langchain.tools.base.BaseTool
Tool for listing keys in a JSON spec.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
spec (langchain.tools.json.tool.JsonSpec) β
Return type
None
attribute spec: JsonSpec [Required]ο
class langchain.tools.ListDirectoryTool(*, name='list_directory', description='List files and directories in a specified folder', args_schema=<class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, root_dir=None)[source]ο
Bases: langchain.tools.file_management.utils.BaseFileToolMixin, langchain.tools.base.BaseTool
Parameters
name (str) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-37 | Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
root_dir (Optional[str]) β
Return type
None
attribute args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'List files and directories in a specified folder'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'list_directory'ο
The unique name of the tool that clearly communicates its purpose.
class langchain.tools.ListPowerBITool(*, name='list_tables_powerbi', description='Input is an empty string, output is a comma separated list of tables in the database.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, powerbi)[source]ο
Bases: langchain.tools.base.BaseTool
Tool for getting tables names.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-38 | return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
powerbi (langchain.utilities.powerbi.PowerBIDataset) β
Return type
None
attribute powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]ο
class langchain.tools.ListSQLDatabaseTool(*, name='sql_db_list_tables', description='Input is an empty string, output is a comma separated list of tables in the database.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, db)[source]ο
Bases: langchain.tools.sql_database.tool.BaseSQLDatabaseTool, langchain.tools.base.BaseTool
Tool for getting tables names.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
db (langchain.sql_database.SQLDatabase) β
Return type
None | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-39 | db (langchain.sql_database.SQLDatabase) β
Return type
None
class langchain.tools.ListSparkSQLTool(*, name='list_tables_sql_db', description='Input is an empty string, output is a comma separated list of tables in the Spark SQL.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, db)[source]ο
Bases: langchain.tools.spark_sql.tool.BaseSparkSQLTool, langchain.tools.base.BaseTool
Tool for getting tables names.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
db (langchain.utilities.spark_sql.SparkSQL) β
Return type
None
class langchain.tools.MetaphorSearchResults(*, name='metaphor_search_results_json', description='A wrapper around Metaphor Search. Input should be a Metaphor-optimized query. Output is a JSON array of the query results', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that has capability to query the Metaphor Search API and get back json.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-40 | args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.metaphor_search.MetaphorSearchAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.metaphor_search.MetaphorSearchAPIWrapper [Required]ο
class langchain.tools.MoveFileTool(*, name='move_file', description='Move or rename a file from one location to another', args_schema=<class 'langchain.tools.file_management.move.FileMoveInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, root_dir=None)[source]ο
Bases: langchain.tools.file_management.utils.BaseFileToolMixin, langchain.tools.base.BaseTool
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
root_dir (Optional[str]) β
Return type
None | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-41 | root_dir (Optional[str]) β
Return type
None
attribute args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.move.FileMoveInput'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Move or rename a file from one location to another'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'move_file'ο
The unique name of the tool that clearly communicates its purpose.
class langchain.tools.NavigateBackTool(*, name='previous_webpage', description='Navigate back to the previous page in the browser history', args_schema=<class 'pydantic.main.BaseModel'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, sync_browser=None, async_browser=None)[source]ο
Bases: langchain.tools.playwright.base.BaseBrowserTool
Navigate back to the previous page in the browser history.
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
sync_browser (Optional['SyncBrowser']) β
async_browser (Optional['AsyncBrowser']) β
Return type
None
attribute args_schema: Type[BaseModel] = <class 'pydantic.main.BaseModel'>ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-42 | Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Navigate back to the previous page in the browser history'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'previous_webpage'ο
The unique name of the tool that clearly communicates its purpose.
class langchain.tools.NavigateTool(*, name='navigate_browser', description='Navigate a browser to the specified URL', args_schema=<class 'langchain.tools.playwright.navigate.NavigateToolInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, sync_browser=None, async_browser=None)[source]ο
Bases: langchain.tools.playwright.base.BaseBrowserTool
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
sync_browser (Optional['SyncBrowser']) β
async_browser (Optional['AsyncBrowser']) β
Return type
None
attribute args_schema: Type[BaseModel] = <class 'langchain.tools.playwright.navigate.NavigateToolInput'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Navigate a browser to the specified URL'ο
Used to tell the model how/when/why to use the tool. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-43 | Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'navigate_browser'ο
The unique name of the tool that clearly communicates its purpose.
class langchain.tools.OpenAPISpec(*, openapi='3.1.0', info, jsonSchemaDialect=None, servers=[Server(url='/', description=None, variables=None)], paths=None, webhooks=None, components=None, security=None, tags=None, externalDocs=None)[source]ο
Bases: openapi_schema_pydantic.v3.v3_1_0.open_api.OpenAPI
OpenAPI Model that removes misformatted parts of the spec.
Parameters
openapi (str) β
info (openapi_schema_pydantic.v3.v3_1_0.info.Info) β
jsonSchemaDialect (Optional[str]) β
servers (List[openapi_schema_pydantic.v3.v3_1_0.server.Server]) β
paths (Optional[Dict[str, openapi_schema_pydantic.v3.v3_1_0.path_item.PathItem]]) β
webhooks (Optional[Dict[str, Union[openapi_schema_pydantic.v3.v3_1_0.path_item.PathItem, openapi_schema_pydantic.v3.v3_1_0.reference.Reference]]]) β
components (Optional[openapi_schema_pydantic.v3.v3_1_0.components.Components]) β
security (Optional[List[Dict[str, List[str]]]]) β
tags (Optional[List[openapi_schema_pydantic.v3.v3_1_0.tag.Tag]]) β
externalDocs (Optional[openapi_schema_pydantic.v3.v3_1_0.external_documentation.ExternalDocumentation]) β
Return type
None | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-44 | Return type
None
classmethod from_file(path)[source]ο
Get an OpenAPI spec from a file path.
Parameters
path (Union[str, pathlib.Path]) β
Return type
langchain.utilities.openapi.OpenAPISpec
classmethod from_spec_dict(spec_dict)[source]ο
Get an OpenAPI spec from a dict.
Parameters
spec_dict (dict) β
Return type
langchain.utilities.openapi.OpenAPISpec
classmethod from_text(text)[source]ο
Get an OpenAPI spec from a text.
Parameters
text (str) β
Return type
langchain.utilities.openapi.OpenAPISpec
classmethod from_url(url)[source]ο
Get an OpenAPI spec from a URL.
Parameters
url (str) β
Return type
langchain.utilities.openapi.OpenAPISpec
static get_cleaned_operation_id(operation, path, method)[source]ο
Get a cleaned operation id from an operation id.
Parameters
operation (openapi_schema_pydantic.v3.v3_1_0.operation.Operation) β
path (str) β
method (str) β
Return type
str
get_methods_for_path(path)[source]ο
Return a list of valid methods for the specified path.
Parameters
path (str) β
Return type
List[str]
get_operation(path, method)[source]ο
Get the operation object for a given path and HTTP method.
Parameters
path (str) β
method (str) β
Return type
openapi_schema_pydantic.v3.v3_1_0.operation.Operation
get_parameters_for_operation(operation)[source]ο
Get the components for a given operation.
Parameters
operation (openapi_schema_pydantic.v3.v3_1_0.operation.Operation) β
Return type | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-45 | Return type
List[openapi_schema_pydantic.v3.v3_1_0.parameter.Parameter]
get_parameters_for_path(path)[source]ο
Parameters
path (str) β
Return type
List[openapi_schema_pydantic.v3.v3_1_0.parameter.Parameter]
get_referenced_schema(ref)[source]ο
Get a schema (or nested reference) or err.
Parameters
ref (openapi_schema_pydantic.v3.v3_1_0.reference.Reference) β
Return type
openapi_schema_pydantic.v3.v3_1_0.schema.Schema
get_request_body_for_operation(operation)[source]ο
Get the request body for a given operation.
Parameters
operation (openapi_schema_pydantic.v3.v3_1_0.operation.Operation) β
Return type
Optional[openapi_schema_pydantic.v3.v3_1_0.request_body.RequestBody]
get_schema(schema)[source]ο
Parameters
schema (Union[openapi_schema_pydantic.v3.v3_1_0.reference.Reference, openapi_schema_pydantic.v3.v3_1_0.schema.Schema]) β
Return type
openapi_schema_pydantic.v3.v3_1_0.schema.Schema
classmethod parse_obj(obj)[source]ο
Parameters
obj (dict) β
Return type
langchain.utilities.openapi.OpenAPISpec
property base_url: strο
Get the base url. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-46 | property base_url: strο
Get the base url.
class langchain.tools.OpenWeatherMapQueryRun(*, name='OpenWeatherMap', description='A wrapper around OpenWeatherMap API. Useful for fetching current weather information for a specified location. Input should be a location string (e.g. London,GB).', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper=None)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that adds the capability to query using the OpenWeatherMap API.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.openweathermap.OpenWeatherMapAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.openweathermap.OpenWeatherMapAPIWrapper [Optional]ο
class langchain.tools.PubmedQueryRun(*, name='PubMed', description='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=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper=None)[source]ο
Bases: langchain.tools.base.BaseTool | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-47 | Bases: langchain.tools.base.BaseTool
Tool that adds the capability to search using the PubMed API.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.pupmed.PubMedAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.pupmed.PubMedAPIWrapper [Optional]ο
class langchain.tools.PythonAstREPLTool(*, name='python_repl_ast', description='A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, globals=None, locals=None, sanitize_input=True)[source]ο
Bases: langchain.tools.base.BaseTool
A tool for running python code in a REPL.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-48 | callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
globals (Optional[Dict]) β
locals (Optional[Dict]) β
sanitize_input (bool) β
Return type
None
attribute globals: Optional[Dict] [Optional]ο
attribute locals: Optional[Dict] [Optional]ο
attribute sanitize_input: bool = Trueο
class langchain.tools.PythonREPLTool(*, name='Python_REPL', description='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=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, python_repl=None, sanitize_input=True)[source]ο
Bases: langchain.tools.base.BaseTool
A tool for running python code in a REPL.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
python_repl (langchain.utilities.python.PythonREPL) β
sanitize_input (bool) β
Return type
None
attribute python_repl: langchain.utilities.python.PythonREPL [Optional]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-49 | attribute python_repl: langchain.utilities.python.PythonREPL [Optional]ο
attribute sanitize_input: bool = Trueο
class langchain.tools.QueryCheckerTool(*, name='query_checker_sql_db', description='\nΒ Β Β Use this tool to double check if your query is correct before executing it.\nΒ Β Β Always use this tool before executing a query with query_sql_db!\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, db, template='\n{query}\nDouble check the Spark SQL query above for common mistakes, including:\n- Using NOT IN with NULL values\n- Using UNION when UNION ALL should have been used\n- Using BETWEEN for exclusive ranges\n- Data type mismatch in predicates\n- Properly quoting identifiers\n- Using the correct number of arguments for functions\n- Casting to the correct data type\n- Using the proper columns for joins\n\nIf there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.', llm, llm_chain)[source]ο
Bases: langchain.tools.spark_sql.tool.BaseSparkSQLTool, langchain.tools.base.BaseTool
Use an LLM to check if a query is correct.
Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-50 | callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
db (langchain.utilities.spark_sql.SparkSQL) β
template (str) β
llm (langchain.base_language.BaseLanguageModel) β
llm_chain (langchain.chains.llm.LLMChain) β
Return type
None
attribute llm: langchain.base_language.BaseLanguageModel [Required]ο
attribute llm_chain: langchain.chains.llm.LLMChain [Required]ο
attribute template: str = '\n{query}\nDouble check the Spark SQL query above for common mistakes, including:\n- Using NOT IN with NULL values\n- Using UNION when UNION ALL should have been used\n- Using BETWEEN for exclusive ranges\n- Data type mismatch in predicates\n- Properly quoting identifiers\n- Using the correct number of arguments for functions\n- Casting to the correct data type\n- Using the proper columns for joins\n\nIf there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.'ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-51 | class langchain.tools.QueryPowerBITool(*, name='query_powerbi', description='\nΒ Β Β Input to this tool is a detailed question about the dataset, output is a result from the dataset. It will try to answer the question using the dataset, and if it cannot, it will ask for clarification.\n\nΒ Β Β Example Input: "How many rows are in table1?"\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, llm_chain, powerbi, template='\nAnswer the question below with a DAX query that can be sent to Power BI. DAX queries have a simple syntax comprised of just one required keyword, EVALUATE, and several optional keywords: ORDER BY, START AT, DEFINE, MEASURE, VAR, TABLE, and COLUMN. Each keyword defines a statement used for the duration of the query. Any time < or > are used in the text below it means that those values need to be replaced by table, columns or other things. If the question is not something you can answer with a DAX query, reply with "I cannot answer this" and the question will be escalated to a human.\n\nSome DAX functions return a table instead of a scalar, and must be wrapped in a function that evaluates the table and returns a scalar; unless the table is a single column, single row table, then it is treated as a scalar value. Most DAX functions require one or more arguments, which can include tables, columns, expressions, and values. However, some functions, such as PI, do not require any arguments, but always require parentheses to indicate the null argument. For example, you must always type PI(), not PI. You can also nest functions within other functions. \n\nSome commonly used functions are:\nEVALUATE <table> - At the most basic level, a DAX query is an EVALUATE statement | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-52 | <table> - At the most basic level, a DAX query is an EVALUATE statement containing a table expression. At least one EVALUATE statement is required, however, a query can contain any number of EVALUATE statements.\nEVALUATE <table> ORDER BY <expression> ASC or DESC - The optional ORDER BY keyword defines one or more expressions used to sort query results. Any expression that can be evaluated for each row of the result is valid.\nEVALUATE <table> ORDER BY <expression> ASC or DESC START AT <value> or <parameter> - The optional START AT keyword is used inside an ORDER BY clause. It defines the value at which the query results begin.\nDEFINE MEASURE | VAR; EVALUATE <table> - The optional DEFINE keyword introduces one or more calculated entity definitions that exist only for the duration of the query. Definitions precede the EVALUATE statement and are valid for all EVALUATE statements in the query. Definitions can be variables, measures, tables1, and columns1. Definitions can reference other definitions that appear before or after the current definition. At least one definition is required if the DEFINE keyword is included in a query.\nMEASURE <table name>[<measure name>] = <scalar expression> - Introduces a measure definition in a DEFINE statement of a DAX query.\nVAR <name> = <expression> - Stores the result of an expression as a named variable, which can then be passed as an argument to other measure expressions. Once resultant values have been calculated for a variable expression, those values do not change, even if the variable is referenced in another expression.\n\nFILTER(<table>,<filter>) - Returns a table that represents a subset of another table or expression, where <filter> is a Boolean expression that is to be evaluated for each row of the table. For example, [Amount] > 0 or [Region] = | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-53 | each row of the table. For example, [Amount] > 0 or [Region] = "France"\nROW(<name>, <expression>) - Returns a table with a single row containing values that result from the expressions given to each column.\nDISTINCT(<column>) - Returns a one-column table that contains the distinct values from the specified column. In other words, duplicate values are removed and only unique values are returned. This function cannot be used to Return values into a cell or column on a worksheet; rather, you nest the DISTINCT function within a formula, to get a list of distinct values that can be passed to another function and then counted, summed, or used for other operations.\nDISTINCT(<table>) - Returns a table by removing duplicate rows from another table or expression.\n\nAggregation functions, names with a A in it, handle booleans and empty strings in appropriate ways, while the same function without A only uses the numeric values in a column. Functions names with an X in it can include a expression as an argument, this will be evaluated for each row in the table and the result will be used in the regular function calculation, these are the functions:\nCOUNT(<column>), COUNTA(<column>), COUNTX(<table>,<expression>), COUNTAX(<table>,<expression>), COUNTROWS([<table>]), COUNTBLANK(<column>), DISTINCTCOUNT(<column>), DISTINCTCOUNTNOBLANK (<column>) - these are all variantions of count functions.\nAVERAGE(<column>), AVERAGEA(<column>), AVERAGEX(<table>,<expression>) - these are all variantions of average functions.\nMAX(<column>), MAXA(<column>), MAXX(<table>,<expression>) - these are all variantions of max functions.\nMIN(<column>), MINA(<column>), MINX(<table>,<expression>) - these are all variantions | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-54 | MINA(<column>), MINX(<table>,<expression>) - these are all variantions of min functions.\nPRODUCT(<column>), PRODUCTX(<table>,<expression>) - these are all variantions of product functions.\nSUM(<column>), SUMX(<table>,<expression>) - these are all variantions of sum functions.\n\nDate and time functions:\nDATE(year, month, day) - Returns a date value that represents the specified year, month, and day.\nDATEDIFF(date1, date2, <interval>) - Returns the difference between two date values, in the specified interval, that can be SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR.\nDATEVALUE(<date_text>) - Returns a date value that represents the specified date.\nYEAR(<date>), QUARTER(<date>), MONTH(<date>), DAY(<date>), HOUR(<date>), MINUTE(<date>), SECOND(<date>) - Returns the part of the date for the specified date.\n\nFinally, make sure to escape double quotes with a single backslash, and make sure that only table names have single quotes around them, while names of measures or the values of columns that you want to compare against are in escaped double quotes. Newlines are not necessary and can be skipped. The queries are serialized as json and so will have to fit be compliant with json syntax. Sometimes you will get a question, a DAX query and a error, in that case you need to rewrite the DAX query to get the correct answer.\n\nThe following tables exist: {tables}\n\nand the schema\'s for some are given here:\n{schemas}\n\nExamples:\n{examples}\n\nQuestion: {tool_input}\nDAX: \n', examples='\nQuestion: How many rows are in the table <table>?\nDAX: | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-55 | examples='\nQuestion: How many rows are in the table <table>?\nDAX: EVALUATE ROW("Number of rows", COUNTROWS(<table>))\n----\nQuestion: How many rows are in the table <table> where <column> is not empty?\nDAX: EVALUATE ROW("Number of rows", COUNTROWS(FILTER(<table>, <table>[<column>] <> "")))\n----\nQuestion: What was the average of <column> in <table>?\nDAX: EVALUATE ROW("Average", AVERAGE(<table>[<column>]))\n----\n', session_cache=None, max_iterations=5)[source]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-56 | Bases: langchain.tools.base.BaseTool
Tool for querying a Power BI Dataset.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
llm_chain (langchain.chains.llm.LLMChain) β
powerbi (langchain.utilities.powerbi.PowerBIDataset) β
template (Optional[str]) β
examples (Optional[str]) β
session_cache (Dict[str, Any]) β
max_iterations (int) β
Return type
None
attribute examples: Optional[str] = '\nQuestion: How many rows are in the table <table>?\nDAX: EVALUATE ROW("Number of rows", COUNTROWS(<table>))\n----\nQuestion: How many rows are in the table <table> where <column> is not empty?\nDAX: EVALUATE ROW("Number of rows", COUNTROWS(FILTER(<table>, <table>[<column>] <> "")))\n----\nQuestion: What was the average of <column> in <table>?\nDAX: EVALUATE ROW("Average", AVERAGE(<table>[<column>]))\n----\n'ο
attribute llm_chain: langchain.chains.llm.LLMChain [Required]ο
attribute max_iterations: int = 5ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-57 | attribute max_iterations: int = 5ο
attribute powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]ο
attribute session_cache: Dict[str, Any] [Optional]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-58 | attribute template: Optional[str] = '\nAnswer the question below with a DAX query that can be sent to Power BI. DAX queries have a simple syntax comprised of just one required keyword, EVALUATE, and several optional keywords: ORDER BY, START AT, DEFINE, MEASURE, VAR, TABLE, and COLUMN. Each keyword defines a statement used for the duration of the query. Any time < or > are used in the text below it means that those values need to be replaced by table, columns or other things. If the question is not something you can answer with a DAX query, reply with "I cannot answer this" and the question will be escalated to a human.\n\nSome DAX functions return a table instead of a scalar, and must be wrapped in a function that evaluates the table and returns a scalar; unless the table is a single column, single row table, then it is treated as a scalar value. Most DAX functions require one or more arguments, which can include tables, columns, expressions, and values. However, some functions, such as PI, do not require any arguments, but always require parentheses to indicate the null argument. For example, you must always type PI(), not PI. You can also nest functions within other functions. \n\nSome commonly used functions are:\nEVALUATE <table> - At the most basic level, a DAX query is an EVALUATE statement containing a table expression. At least one EVALUATE statement is required, however, a query can contain any number of EVALUATE statements.\nEVALUATE <table> ORDER BY <expression> ASC or DESC - The optional ORDER BY keyword defines one or more expressions used to sort query results. Any expression that can be evaluated for each row of the result is valid.\nEVALUATE <table> ORDER BY <expression> ASC or DESC START AT <value> or <parameter> - The optional | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-59 | ORDER BY <expression> ASC or DESC START AT <value> or <parameter> - The optional START AT keyword is used inside an ORDER BY clause. It defines the value at which the query results begin.\nDEFINE MEASURE | VAR; EVALUATE <table> - The optional DEFINE keyword introduces one or more calculated entity definitions that exist only for the duration of the query. Definitions precede the EVALUATE statement and are valid for all EVALUATE statements in the query. Definitions can be variables, measures, tables1, and columns1. Definitions can reference other definitions that appear before or after the current definition. At least one definition is required if the DEFINE keyword is included in a query.\nMEASURE <table name>[<measure name>] = <scalar expression> - Introduces a measure definition in a DEFINE statement of a DAX query.\nVAR <name> = <expression> - Stores the result of an expression as a named variable, which can then be passed as an argument to other measure expressions. Once resultant values have been calculated for a variable expression, those values do not change, even if the variable is referenced in another expression.\n\nFILTER(<table>,<filter>) - Returns a table that represents a subset of another table or expression, where <filter> is a Boolean expression that is to be evaluated for each row of the table. For example, [Amount] > 0 or [Region] = "France"\nROW(<name>, <expression>) - Returns a table with a single row containing values that result from the expressions given to each column.\nDISTINCT(<column>) - Returns a one-column table that contains the distinct values from the specified column. In other words, duplicate values are removed and only unique values are returned. This function cannot be used to Return values into a cell or column on a worksheet; rather, you nest the DISTINCT function within a formula, to get a list of distinct values that can be passed | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-60 | you nest the DISTINCT function within a formula, to get a list of distinct values that can be passed to another function and then counted, summed, or used for other operations.\nDISTINCT(<table>) - Returns a table by removing duplicate rows from another table or expression.\n\nAggregation functions, names with a A in it, handle booleans and empty strings in appropriate ways, while the same function without A only uses the numeric values in a column. Functions names with an X in it can include a expression as an argument, this will be evaluated for each row in the table and the result will be used in the regular function calculation, these are the functions:\nCOUNT(<column>), COUNTA(<column>), COUNTX(<table>,<expression>), COUNTAX(<table>,<expression>), COUNTROWS([<table>]), COUNTBLANK(<column>), DISTINCTCOUNT(<column>), DISTINCTCOUNTNOBLANK (<column>) - these are all variantions of count functions.\nAVERAGE(<column>), AVERAGEA(<column>), AVERAGEX(<table>,<expression>) - these are all variantions of average functions.\nMAX(<column>), MAXA(<column>), MAXX(<table>,<expression>) - these are all variantions of max functions.\nMIN(<column>), MINA(<column>), MINX(<table>,<expression>) - these are all variantions of min functions.\nPRODUCT(<column>), PRODUCTX(<table>,<expression>) - these are all variantions of product functions.\nSUM(<column>), SUMX(<table>,<expression>) - these are all variantions of sum functions.\n\nDate and time functions:\nDATE(year, month, day) - Returns a date value that represents the specified year, month, and day.\nDATEDIFF(date1, date2, <interval>) - Returns the difference between two date values, in the specified | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-61 | date2, <interval>) - Returns the difference between two date values, in the specified interval, that can be SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR.\nDATEVALUE(<date_text>) - Returns a date value that represents the specified date.\nYEAR(<date>), QUARTER(<date>), MONTH(<date>), DAY(<date>), HOUR(<date>), MINUTE(<date>), SECOND(<date>) - Returns the part of the date for the specified date.\n\nFinally, make sure to escape double quotes with a single backslash, and make sure that only table names have single quotes around them, while names of measures or the values of columns that you want to compare against are in escaped double quotes. Newlines are not necessary and can be skipped. The queries are serialized as json and so will have to fit be compliant with json syntax. Sometimes you will get a question, a DAX query and a error, in that case you need to rewrite the DAX query to get the correct answer.\n\nThe following tables exist: {tables}\n\nand the schema\'s for some are given here:\n{schemas}\n\nExamples:\n{examples}\n\nQuestion: {tool_input}\nDAX: \n'ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-62 | class langchain.tools.QuerySQLCheckerTool(*, name='sql_db_query_checker', description='\nΒ Β Β Use this tool to double check if your query is correct before executing it.\nΒ Β Β Always use this tool before executing a query with query_sql_db!\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, db, template='\n{query}\nDouble check the {dialect} query above for common mistakes, including:\n- Using NOT IN with NULL values\n- Using UNION when UNION ALL should have been used\n- Using BETWEEN for exclusive ranges\n- Data type mismatch in predicates\n- Properly quoting identifiers\n- Using the correct number of arguments for functions\n- Casting to the correct data type\n- Using the proper columns for joins\n\nIf there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.', llm, llm_chain)[source]ο
Bases: langchain.tools.sql_database.tool.BaseSQLDatabaseTool, langchain.tools.base.BaseTool
Use an LLM to check if a query is correct.
Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
db (langchain.sql_database.SQLDatabase) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-63 | db (langchain.sql_database.SQLDatabase) β
template (str) β
llm (langchain.base_language.BaseLanguageModel) β
llm_chain (langchain.chains.llm.LLMChain) β
Return type
None
attribute llm: langchain.base_language.BaseLanguageModel [Required]ο
attribute llm_chain: langchain.chains.llm.LLMChain [Required]ο
attribute template: str = '\n{query}\nDouble check the {dialect} query above for common mistakes, including:\n- Using NOT IN with NULL values\n- Using UNION when UNION ALL should have been used\n- Using BETWEEN for exclusive ranges\n- Data type mismatch in predicates\n- Properly quoting identifiers\n- Using the correct number of arguments for functions\n- Casting to the correct data type\n- Using the proper columns for joins\n\nIf there are any of the above mistakes, rewrite the query. If there are no mistakes, just reproduce the original query.'ο
class langchain.tools.QuerySQLDataBaseTool(*, name='sql_db_query', description='\nΒ Β Β Input to this tool is a detailed and correct SQL query, output is a result from the database.\nΒ Β Β If the query is not correct, an error message will be returned.\nΒ Β Β If an error is returned, rewrite the query, check the query, and try again.\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, db)[source]ο
Bases: langchain.tools.sql_database.tool.BaseSQLDatabaseTool, langchain.tools.base.BaseTool
Tool for querying a SQL database.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-64 | return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
db (langchain.sql_database.SQLDatabase) β
Return type
None
class langchain.tools.QuerySparkSQLTool(*, name='query_sql_db', description='\nΒ Β Β Input to this tool is a detailed and correct SQL query, output is a result from the Spark SQL.\nΒ Β Β If the query is not correct, an error message will be returned.\nΒ Β Β If an error is returned, rewrite the query, check the query, and try again.\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, db)[source]ο
Bases: langchain.tools.spark_sql.tool.BaseSparkSQLTool, langchain.tools.base.BaseTool
Tool for querying a Spark SQL.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
db (langchain.utilities.spark_sql.SparkSQL) β
Return type
None | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-65 | db (langchain.utilities.spark_sql.SparkSQL) β
Return type
None
class langchain.tools.ReadFileTool(*, name='read_file', description='Read file from disk', args_schema=<class 'langchain.tools.file_management.read.ReadFileInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, root_dir=None)[source]ο
Bases: langchain.tools.file_management.utils.BaseFileToolMixin, langchain.tools.base.BaseTool
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
root_dir (Optional[str]) β
Return type
None
attribute args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.read.ReadFileInput'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Read file from disk'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'read_file'ο
The unique name of the tool that clearly communicates its purpose. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-66 | The unique name of the tool that clearly communicates its purpose.
class langchain.tools.RequestsDeleteTool(*, name='requests_delete', description='A portal to the internet. Use this when you need to make a DELETE request to a URL. Input should be a specific url, and the output will be the text response of the DELETE request.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, requests_wrapper)[source]ο
Bases: langchain.tools.requests.tool.BaseRequestsTool, langchain.tools.base.BaseTool
Tool for making a DELETE request to an API endpoint.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
requests_wrapper (langchain.requests.TextRequestsWrapper) β
Return type
None
class langchain.tools.RequestsGetTool(*, name='requests_get', description='A portal to the internet. Use this when you need to get specific content from a website. Input should be aΒ url (i.e. https://www.google.com). The output will be the text response of the GET request.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, requests_wrapper)[source]ο
Bases: langchain.tools.requests.tool.BaseRequestsTool, langchain.tools.base.BaseTool
Tool for making a GET request to an API endpoint.
Parameters | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-67 | Tool for making a GET request to an API endpoint.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
requests_wrapper (langchain.requests.TextRequestsWrapper) β
Return type
None
class langchain.tools.RequestsPatchTool(*, name='requests_patch', description='Use this when you want to PATCH to a website.\nΒ Β Β Input should be a json string with two keys: "url" and "data".\nΒ Β Β The value of "url" should be a string, and the value of "data" should be a dictionary of \nΒ Β Β key-value pairs you want to PATCH to the url.\nΒ Β Β Be careful to always use double quotes for strings in the json string\nΒ Β Β The output will be the text response of the PATCH request.\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, requests_wrapper)[source]ο
Bases: langchain.tools.requests.tool.BaseRequestsTool, langchain.tools.base.BaseTool
Tool for making a PATCH request to an API endpoint.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-68 | return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
requests_wrapper (langchain.requests.TextRequestsWrapper) β
Return type
None
class langchain.tools.RequestsPostTool(*, name='requests_post', description='Use this when you want to POST to a website.\nΒ Β Β Input should be a json string with two keys: "url" and "data".\nΒ Β Β The value of "url" should be a string, and the value of "data" should be a dictionary of \nΒ Β Β key-value pairs you want to POST to the url.\nΒ Β Β Be careful to always use double quotes for strings in the json string\nΒ Β Β The output will be the text response of the POST request.\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, requests_wrapper)[source]ο
Bases: langchain.tools.requests.tool.BaseRequestsTool, langchain.tools.base.BaseTool
Tool for making a POST request to an API endpoint.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-69 | requests_wrapper (langchain.requests.TextRequestsWrapper) β
Return type
None
class langchain.tools.RequestsPutTool(*, name='requests_put', description='Use this when you want to PUT to a website.\nΒ Β Β Input should be a json string with two keys: "url" and "data".\nΒ Β Β The value of "url" should be a string, and the value of "data" should be a dictionary of \nΒ Β Β key-value pairs you want to PUT to the url.\nΒ Β Β Be careful to always use double quotes for strings in the json string.\nΒ Β Β The output will be the text response of the PUT request.\nΒ Β Β ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, requests_wrapper)[source]ο
Bases: langchain.tools.requests.tool.BaseRequestsTool, langchain.tools.base.BaseTool
Tool for making a PUT request to an API endpoint.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
requests_wrapper (langchain.requests.TextRequestsWrapper) β
Return type
None | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-70 | requests_wrapper (langchain.requests.TextRequestsWrapper) β
Return type
None
class langchain.tools.SceneXplainTool(*, name='image_explainer', description='An Image Captioning Tool: Use this tool to generate a detailed caption for an image. The input can be an image file of any format, and the output will be a text description that covers every detail of the image.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper=None)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that adds the capability to explain images.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.scenexplain.SceneXplainAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.scenexplain.SceneXplainAPIWrapper [Optional]ο
class langchain.tools.SearxSearchResults(*, name='Searx Search Results', description='A meta search engine.Useful for when you need to answer questions about current events.Input should be a search query. Output is a JSON array of the query results', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, wrapper, num_results=4, kwargs=None, **extra_data)[source]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-71 | Bases: langchain.tools.base.BaseTool
Tool that has the capability to query a Searx instance and get back json.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
wrapper (langchain.utilities.searx_search.SearxSearchWrapper) β
num_results (int) β
kwargs (dict) β
extra_data (Any) β
Return type
None
attribute kwargs: dict [Optional]ο
attribute num_results: int = 4ο
attribute wrapper: langchain.utilities.searx_search.SearxSearchWrapper [Required]ο
class langchain.tools.SearxSearchRun(*, name='searx_search', description='A meta search engine.Useful for when you need to answer questions about current events.Input should be a search query.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, wrapper, kwargs=None)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that adds the capability to query a Searx instance.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-72 | return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
wrapper (langchain.utilities.searx_search.SearxSearchWrapper) β
kwargs (dict) β
Return type
None
attribute kwargs: dict [Optional]ο
attribute wrapper: langchain.utilities.searx_search.SearxSearchWrapper [Required]ο
class langchain.tools.ShellTool(*, name='terminal', description='Run shell commands on this Linux machine.', args_schema=<class 'langchain.tools.shell.tool.ShellInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, process=None)[source]ο
Bases: langchain.tools.base.BaseTool
Tool to run shell commands.
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
process (langchain.utilities.bash.BashProcess) β
Return type
None
attribute args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.shell.tool.ShellInput'>ο
Schema for input arguments. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-73 | Schema for input arguments.
attribute description: str = 'Run shell commands on this Linux machine.'ο
Description of tool.
attribute name: str = 'terminal'ο
Name of tool.
attribute process: langchain.utilities.bash.BashProcess [Optional]ο
Bash process to run commands.
class langchain.tools.SleepTool(*, name='sleep', description='Make agent sleep for a specified number of seconds.', args_schema=<class 'langchain.tools.sleep.tool.SleepInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that adds the capability to sleep.
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
Return type
None
attribute args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.sleep.tool.SleepInput'>ο
Pydantic model class to validate and parse the toolβs input arguments.
langchain.tools.StdInInquireTool(*args, **kwargs)[source]ο
Tool for asking the user for input.
Parameters
args (Any) β
kwargs (Any) β
Return type
langchain.tools.human.tool.HumanInputRun | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-74 | Return type
langchain.tools.human.tool.HumanInputRun
class langchain.tools.SteamshipImageGenerationTool(*, name='GenerateImage', description='Useful for when you need to generate an image.Input: A detailed text-2-image prompt describing an imageOutput: the UUID of a generated image', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, model_name, size='512x512', steamship, return_urls=False)[source]ο
Bases: langchain.tools.base.BaseTool
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
model_name (langchain.tools.steamship_image_generation.tool.ModelName) β
size (Optional[str]) β
steamship (Steamship) β
return_urls (Optional[bool]) β
Return type
None
attribute model_name: ModelName [Required]ο
attribute return_urls: Optional[bool] = Falseο
attribute size: Optional[str] = '512x512'ο
attribute steamship: Steamship [Required]ο
class langchain.tools.StructuredTool(*, name, description='', args_schema, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, func, coroutine=None)[source]ο
Bases: langchain.tools.base.BaseTool | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-75 | Bases: langchain.tools.base.BaseTool
Tool that can operate on any number of inputs.
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
func (Callable[[...], Any]) β
coroutine (Optional[Callable[[...], Awaitable[Any]]]) β
Return type
None
attribute args_schema: Type[pydantic.main.BaseModel] [Required]ο
The input argumentsβ schema.
The tool schema.
attribute coroutine: Optional[Callable[[...], Awaitable[Any]]] = Noneο
The asynchronous version of the function.
attribute description: str = ''ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute func: Callable[[...], Any] [Required]ο
The function to run when the tool is called.
classmethod from_function(func, name=None, description=None, return_direct=False, args_schema=None, infer_schema=True, **kwargs)[source]ο
Create tool from a given function.
A classmethod that helps to create a tool from a function.
Parameters
func (Callable) β The function from which to create a tool
name (Optional[str]) β The name of the tool. Defaults to the function name
description (Optional[str]) β The description of the tool. Defaults to the function docstring | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-76 | description (Optional[str]) β The description of the tool. Defaults to the function docstring
return_direct (bool) β Whether to return the result directly or as a callback
args_schema (Optional[Type[pydantic.main.BaseModel]]) β The schema of the toolβs input arguments
infer_schema (bool) β Whether to infer the schema from the functionβs signature
**kwargs β Additional arguments to pass to the tool
kwargs (Any) β
Returns
The tool
Return type
langchain.tools.base.StructuredTool
Examples
β¦ code-block:: python
def add(a: int, b: int) -> int:βββAdd two numbersβββ
return a + b
tool = StructuredTool.from_function(add)
tool.run(1, 2) # 3
property args: dictο
The toolβs input arguments.
class langchain.tools.Tool(name, func, description, *, args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, coroutine=None)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that takes in function or coroutine directly.
Parameters
name (str) β
func (Callable[[...], str]) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
coroutine (Optional[Callable[[...], Awaitable[str]]]) β
Return type
None | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-77 | Return type
None
attribute args_schema: Optional[Type[pydantic.main.BaseModel]] = Noneο
Pydantic model class to validate and parse the toolβs input arguments.
attribute callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = Noneο
Deprecated. Please use callbacks instead.
attribute callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = Noneο
Callbacks to be called during tool execution.
attribute coroutine: Optional[Callable[[...], Awaitable[str]]] = Noneο
The asynchronous version of the function.
attribute description: str = ''ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute func: Callable[[...], str] [Required]ο
The function to run when the tool is called.
attribute handle_tool_error: Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]] = Falseο
Handle the content of the ToolException thrown.
attribute name: str [Required]ο
The unique name of the tool that clearly communicates its purpose.
attribute return_direct: bool = Falseο
Whether to return the toolβs output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
attribute verbose: bool = Falseο
Whether to log the toolβs progress.
classmethod from_function(func, name, description, return_direct=False, args_schema=None, **kwargs)[source]ο
Initialize tool from a function.
Parameters
func (Callable) β
name (str) β
description (str) β
return_direct (bool) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-78 | args_schema (Optional[Type[pydantic.main.BaseModel]]) β
kwargs (Any) β
Return type
langchain.tools.base.Tool
property args: dictο
The toolβs input arguments.
class langchain.tools.VectorStoreQATool(*, name, description, args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, vectorstore, llm=None)[source]ο
Bases: langchain.tools.vectorstore.tool.BaseVectorStoreTool, langchain.tools.base.BaseTool
Tool for the VectorDBQA chain. To be initialized with name and chain.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
vectorstore (langchain.vectorstores.base.VectorStore) β
llm (langchain.base_language.BaseLanguageModel) β
Return type
None
static get_description(name, description)[source]ο
Parameters
name (str) β
description (str) β
Return type
str
class langchain.tools.VectorStoreQAWithSourcesTool(*, name, description, args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, vectorstore, llm=None)[source]ο
Bases: langchain.tools.vectorstore.tool.BaseVectorStoreTool, langchain.tools.base.BaseTool | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-79 | Tool for the VectorDBQAWithSources chain.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
vectorstore (langchain.vectorstores.base.VectorStore) β
llm (langchain.base_language.BaseLanguageModel) β
Return type
None
static get_description(name, description)[source]ο
Parameters
name (str) β
description (str) β
Return type
str
class langchain.tools.WikipediaQueryRun(*, name='Wikipedia', description='A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that adds the capability to search using the Wikipedia API.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-80 | callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.wikipedia.WikipediaAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.wikipedia.WikipediaAPIWrapper [Required]ο
class langchain.tools.WolframAlphaQueryRun(*, name='wolfram_alpha', description='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=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that adds the capability to query using the Wolfram Alpha SDK.
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.wolfram_alpha.WolframAlphaAPIWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.wolfram_alpha.WolframAlphaAPIWrapper [Required]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-81 | class langchain.tools.WriteFileTool(*, name='write_file', description='Write file to disk', args_schema=<class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, root_dir=None)[source]ο
Bases: langchain.tools.file_management.utils.BaseFileToolMixin, langchain.tools.base.BaseTool
Parameters
name (str) β
description (str) β
args_schema (Type[pydantic.main.BaseModel]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
root_dir (Optional[str]) β
Return type
None
attribute args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.write.WriteFileInput'>ο
Pydantic model class to validate and parse the toolβs input arguments.
attribute description: str = 'Write file to disk'ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
attribute name: str = 'write_file'ο
The unique name of the tool that clearly communicates its purpose. | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-82 | The unique name of the tool that clearly communicates its purpose.
class langchain.tools.YouTubeSearchTool(*, name='youtube_search', description='search for youtube videos associated with a person. the input to this tool should be a comma separated list, the first part contains a person name and the second a number that is the maximum number of video results to return aka num_results. the second part is optional', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False)[source]ο
Bases: langchain.tools.base.BaseTool
Parameters
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
Return type
None | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-83 | Return type
None
class langchain.tools.ZapierNLAListActions(*, name='ZapierNLA_list_actions', description='A wrapper around Zapier NLA actions. The input to this tool is a natural language instruction, for example "get the latest email from my bank" or "send a slack message to the #general channel". Each tool will have params associated with it that are specified as a list. You MUST take into account the params when creating the instruction. For example, if the params are [\'Message_Text\', \'Channel\'], your instruction should be something like \'send a slack message to the #general channel with the text hello world\'. Another example: if the params are [\'Calendar\', \'Search_Term\'], your instruction should be something like \'find the meeting in my personal calendar at 3pm\'. Do not make up params, they will be explicitly specified in the tool description. If you do not have enough information to fill in the params, just say \'not enough information provided in the instruction, missing <param>\'. If you get a none or null response, STOP EXECUTION, do not try to another tool!This tool specifically used for: {zapier_description}, and has params: {params}This tool returns a list of the user\'s exposed actions.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper=None)[source]ο
Bases: langchain.tools.base.BaseTool
Returns a list of all exposed (enabled) actions associated withcurrent user (associated with the set api_key). Change your exposed
actions here: https://nla.zapier.com/demo/start/
The return list can be empty if no actions exposed. Else will contain
a list of action objects:
[{βidβ: str,
βdescriptionβ: str,
βparamsβ: Dict[str, str] | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-84 | βdescriptionβ: str,
βparamsβ: Dict[str, str]
}]
params will always contain an instructions key, the only required
param. All others optional and if provided will override any AI guesses
(see βunderstanding the AI guessing flowβ here:
https://nla.zapier.com/api/v1/docs)
Parameters
None β
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.zapier.ZapierNLAWrapper) β
Return type
None
attribute api_wrapper: langchain.utilities.zapier.ZapierNLAWrapper [Optional]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-85 | class langchain.tools.ZapierNLARunAction(*, name='', description='', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, api_wrapper=None, action_id, params=None, base_prompt='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}', zapier_description, params_schema=None)[source]ο
Bases: langchain.tools.base.BaseTool
Executes an action that is identified by action_id, must be exposed(enabled) by the current user (associated with the set api_key). Change
your exposed actions here: https://nla.zapier.com/demo/start/
The return JSON is guaranteed to be less than ~500 words (350
tokens) making it safe to inject into the prompt of another LLM
call.
Parameters | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-86 | tokens) making it safe to inject into the prompt of another LLM
call.
Parameters
action_id (str) β a specific action ID (from list actions) of the action to execute
(the set api_key must be associated with the action owner)
instructions β a natural language instruction string for using the action
(eg. βget the latest email from Mike Knoopβ for βGmail: find emailβ action)
params (Optional[dict]) β a dict, optional. Any params provided will override AI guesses
from instructions (see βunderstanding the AI guessing flowβ here:
https://nla.zapier.com/api/v1/docs)
name (str) β
description (str) β
args_schema (Optional[Type[pydantic.main.BaseModel]]) β
return_direct (bool) β
verbose (bool) β
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) β
api_wrapper (langchain.utilities.zapier.ZapierNLAWrapper) β
base_prompt (str) β
zapier_description (str) β
params_schema (Dict[str, str]) β
Return type
None
attribute action_id: str [Required]ο
attribute api_wrapper: langchain.utilities.zapier.ZapierNLAWrapper [Optional]ο | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-87 | attribute 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}'ο
attribute params: Optional[dict] = Noneο
attribute params_schema: Dict[str, str] [Optional]ο
attribute zapier_description: str [Required]ο
langchain.tools.format_tool_to_openai_function(tool)[source]ο
Format tool into the OpenAI function API.
Parameters
tool (langchain.tools.base.BaseTool) β
Return type
langchain.tools.convert_to_openai.FunctionDescription
langchain.tools.tool(*args, return_direct=False, args_schema=None, infer_schema=True)[source]ο
Make tools out of functions, can be used with or without arguments.
Parameters
*args β The arguments to the tool.
return_direct (bool) β Whether to return directly from the tool rather | https://api.python.langchain.com/en/latest/modules/tools.html |
1ace37f9e39e-88 | return_direct (bool) β Whether to return directly from the tool rather
than continuing the agent loop.
args_schema (Optional[Type[pydantic.main.BaseModel]]) β optional argument schema for user to specify
infer_schema (bool) β Whether to infer the schema of the arguments from
the functionβs signature. This also makes the resultant tool
accept a dictionary input to its run() function.
args (Union[str, Callable]) β
Return type
Callable
Requires:
Function must be of type (str) -> str
Function must have a docstring
Examples
@tool
def search_api(query: str) -> str:
# Searches the API for the query.
return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
# Searches the API for the query.
return | https://api.python.langchain.com/en/latest/modules/tools.html |
1a644f703b6b-0 | Callbacksο
Callback handlers that allow listening to events in LangChain.
class langchain.callbacks.AimCallbackHandler(repo=None, experiment_name=None, system_tracking_interval=10, log_system_params=True)[source]ο
Bases: langchain.callbacks.aim_callback.BaseMetadataCallbackHandler, langchain.callbacks.base.BaseCallbackHandler
Callback Handler that logs to Aim.
Parameters
repo (str, optional) β Aim repository path or Repo object to which
Run object is bound. If skipped, default Repo is used.
experiment_name (str, optional) β Sets Runβs experiment property.
βdefaultβ if not specified. Can be used later to query runs/sequences.
system_tracking_interval (int, optional) β Sets the tracking interval
in seconds for system usage metrics (CPU, Memory, etc.). Set to None
to disable system metrics tracking.
log_system_params (bool, optional) β Enable/Disable logging of system
params such as installed packages, git info, environment variables, etc.
Return type
None
This handler will utilize the associated callback method called and formats
the input of each callback function with metadata regarding the state of LLM run
and then logs the response to Aim.
setup(**kwargs)[source]ο
Parameters
kwargs (Any) β
Return type
None
on_llm_start(serialized, prompts, **kwargs)[source]ο
Run when LLM starts.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_end(response, **kwargs)[source]ο
Run when LLM ends running.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-1 | Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Run when LLM generates a new token.
Parameters
token (str) β
kwargs (Any) β
Return type
None
on_llm_error(error, **kwargs)[source]ο
Run when LLM errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_chain_start(serialized, inputs, **kwargs)[source]ο
Run when chain starts running.
Parameters
serialized (Dict[str, Any]) β
inputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_end(outputs, **kwargs)[source]ο
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_error(error, **kwargs)[source]ο
Run when chain errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_tool_start(serialized, input_str, **kwargs)[source]ο
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) β
input_str (str) β
kwargs (Any) β
Return type
None
on_tool_end(output, **kwargs)[source]ο
Run when tool ends running.
Parameters
output (str) β
kwargs (Any) β
Return type
None
on_tool_error(error, **kwargs)[source]ο
Run when tool errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_text(text, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-2 | Return type
None
on_text(text, **kwargs)[source]ο
Run when agent is ending.
Parameters
text (str) β
kwargs (Any) β
Return type
None
on_agent_finish(finish, **kwargs)[source]ο
Run when agent ends running.
Parameters
finish (langchain.schema.AgentFinish) β
kwargs (Any) β
Return type
None
on_agent_action(action, **kwargs)[source]ο
Run on agent action.
Parameters
action (langchain.schema.AgentAction) β
kwargs (Any) β
Return type
Any
flush_tracker(repo=None, experiment_name=None, system_tracking_interval=10, log_system_params=True, langchain_asset=None, reset=True, finish=False)[source]ο
Flush the tracker and reset the session.
Parameters
repo (str, optional) β Aim repository path or Repo object to which
Run object is bound. If skipped, default Repo is used.
experiment_name (str, optional) β Sets Runβs experiment property.
βdefaultβ if not specified. Can be used later to query runs/sequences.
system_tracking_interval (int, optional) β Sets the tracking interval
in seconds for system usage metrics (CPU, Memory, etc.). Set to None
to disable system metrics tracking.
log_system_params (bool, optional) β Enable/Disable logging of system
params such as installed packages, git info, environment variables, etc.
langchain_asset (Any) β The langchain asset to save.
reset (bool) β Whether to reset the session.
finish (bool) β Whether to finish the run.
Returns β None
Return type
None
class langchain.callbacks.ArgillaCallbackHandler(dataset_name, workspace_name=None, api_url=None, api_key=None)[source]ο | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-3 | Bases: langchain.callbacks.base.BaseCallbackHandler
Callback Handler that logs into Argilla.
Parameters
dataset_name (str) β name of the FeedbackDataset in Argilla. Note that it must
exist in advance. If you need help on how to create a FeedbackDataset in
Argilla, please visit
https://docs.argilla.io/en/latest/guides/llms/practical_guides/use_argilla_callback_in_langchain.html.
workspace_name (Optional[str]) β name of the workspace in Argilla where the specified
FeedbackDataset lives in. Defaults to None, which means that the
default workspace will be used.
api_url (Optional[str]) β URL of the Argilla Server that we want to use, and where the
FeedbackDataset lives in. Defaults to None, which means that either
ARGILLA_API_URL environment variable or the default http://localhost:6900
will be used.
api_key (Optional[str]) β API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
argilla.apikey will be used.
Raises
ImportError β if the argilla package is not installed.
ConnectionError β if the connection to Argilla fails.
FileNotFoundError β if the FeedbackDataset retrieval from Argilla fails.
Return type
None
Examples
>>> from langchain.llms import OpenAI
>>> from langchain.callbacks import ArgillaCallbackHandler
>>> argilla_callback = ArgillaCallbackHandler(
... dataset_name="my-dataset",
... workspace_name="my-workspace",
... api_url="http://localhost:6900",
... api_key="argilla.apikey",
... )
>>> llm = OpenAI(
... temperature=0,
... callbacks=[argilla_callback],
... verbose=True, | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-4 | ... callbacks=[argilla_callback],
... verbose=True,
... openai_api_key="API_KEY_HERE",
... )
>>> llm.generate([
... "What is the best NLP-annotation tool out there? (no bias at all)",
... ])
"Argilla, no doubt about it."
on_llm_start(serialized, prompts, **kwargs)[source]ο
Save the prompts in memory when an LLM starts.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Do nothing when a new token is generated.
Parameters
token (str) β
kwargs (Any) β
Return type
None
on_llm_end(response, **kwargs)[source]ο
Log records to Argilla when an LLM ends.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
on_llm_error(error, **kwargs)[source]ο
Do nothing when LLM outputs an error.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_chain_start(serialized, inputs, **kwargs)[source]ο
If the key input is in inputs, then save it in self.prompts using
either the parent_run_id or the run_id as the key. This is done so that
we donβt log the same input prompt twice, once when the LLM starts and once
when the chain starts.
Parameters
serialized (Dict[str, Any]) β
inputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-5 | kwargs (Any) β
Return type
None
on_chain_end(outputs, **kwargs)[source]ο
If either the parent_run_id or the run_id is in self.prompts, then
log the outputs to Argilla, and pop the run from self.prompts. The behavior
differs if the output is a list or not.
Parameters
outputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_error(error, **kwargs)[source]ο
Do nothing when LLM chain outputs an error.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_tool_start(serialized, input_str, **kwargs)[source]ο
Do nothing when tool starts.
Parameters
serialized (Dict[str, Any]) β
input_str (str) β
kwargs (Any) β
Return type
None
on_agent_action(action, **kwargs)[source]ο
Do nothing when agent takes a specific action.
Parameters
action (langchain.schema.AgentAction) β
kwargs (Any) β
Return type
Any
on_tool_end(output, observation_prefix=None, llm_prefix=None, **kwargs)[source]ο
Do nothing when tool ends.
Parameters
output (str) β
observation_prefix (Optional[str]) β
llm_prefix (Optional[str]) β
kwargs (Any) β
Return type
None
on_tool_error(error, **kwargs)[source]ο
Do nothing when tool outputs an error.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_text(text, **kwargs)[source]ο
Do nothing
Parameters
text (str) β
kwargs (Any) β | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-6 | Do nothing
Parameters
text (str) β
kwargs (Any) β
Return type
None
on_agent_finish(finish, **kwargs)[source]ο
Do nothing
Parameters
finish (langchain.schema.AgentFinish) β
kwargs (Any) β
Return type
None
class langchain.callbacks.ArizeCallbackHandler(model_id=None, model_version=None, SPACE_KEY=None, API_KEY=None)[source]ο
Bases: langchain.callbacks.base.BaseCallbackHandler
Callback Handler that logs to Arize.
Parameters
model_id (Optional[str]) β
model_version (Optional[str]) β
SPACE_KEY (Optional[str]) β
API_KEY (Optional[str]) β
Return type
None
on_llm_start(serialized, prompts, **kwargs)[source]ο
Run when LLM starts running.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Do nothing.
Parameters
token (str) β
kwargs (Any) β
Return type
None
on_llm_end(response, **kwargs)[source]ο
Run when LLM ends running.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
on_llm_error(error, **kwargs)[source]ο
Do nothing.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_chain_start(serialized, inputs, **kwargs)[source]ο
Run when chain starts running.
Parameters
serialized (Dict[str, Any]) β
inputs (Dict[str, Any]) β
kwargs (Any) β | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-7 | inputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_end(outputs, **kwargs)[source]ο
Do nothing.
Parameters
outputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_error(error, **kwargs)[source]ο
Do nothing.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_tool_start(serialized, input_str, **kwargs)[source]ο
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) β
input_str (str) β
kwargs (Any) β
Return type
None
on_agent_action(action, **kwargs)[source]ο
Do nothing.
Parameters
action (langchain.schema.AgentAction) β
kwargs (Any) β
Return type
Any
on_tool_end(output, observation_prefix=None, llm_prefix=None, **kwargs)[source]ο
Run when tool ends running.
Parameters
output (str) β
observation_prefix (Optional[str]) β
llm_prefix (Optional[str]) β
kwargs (Any) β
Return type
None
on_tool_error(error, **kwargs)[source]ο
Run when tool errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_text(text, **kwargs)[source]ο
Run on arbitrary text.
Parameters
text (str) β
kwargs (Any) β
Return type
None
on_agent_finish(finish, **kwargs)[source]ο
Run on agent end.
Parameters
finish (langchain.schema.AgentFinish) β
kwargs (Any) β
Return type
None | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-8 | kwargs (Any) β
Return type
None
class langchain.callbacks.AsyncIteratorCallbackHandler[source]ο
Bases: langchain.callbacks.base.AsyncCallbackHandler
Callback handler that returns an async iterator.
Return type
None
property always_verbose: boolο
queue: asyncio.queues.Queue[str]ο
done: asyncio.locks.Eventο
async on_llm_start(serialized, prompts, **kwargs)[source]ο
Run when LLM starts running.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
async on_llm_new_token(token, **kwargs)[source]ο
Run on new LLM token. Only available when streaming is enabled.
Parameters
token (str) β
kwargs (Any) β
Return type
None
async on_llm_end(response, **kwargs)[source]ο
Run when LLM ends running.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
async on_llm_error(error, **kwargs)[source]ο
Run when LLM errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
async aiter()[source]ο
Return type
AsyncIterator[str]
class langchain.callbacks.ClearMLCallbackHandler(task_type='inference', project_name='langchain_callback_demo', tags=None, task_name=None, visualize=False, complexity_metrics=False, stream_logs=False)[source]ο
Bases: langchain.callbacks.utils.BaseMetadataCallbackHandler, langchain.callbacks.base.BaseCallbackHandler
Callback Handler that logs to ClearML.
Parameters | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-9 | Callback Handler that logs to ClearML.
Parameters
job_type (str) β The type of clearml task such as βinferenceβ, βtestingβ or βqcβ
project_name (str) β The clearml project name
tags (list) β Tags to add to the task
task_name (str) β Name of the clearml task
visualize (bool) β Whether to visualize the run.
complexity_metrics (bool) β Whether to log complexity metrics
stream_logs (bool) β Whether to stream callback actions to ClearML
task_type (Optional[str]) β
Return type
None
This handler will utilize the associated callback method and formats
the input of each callback function with metadata regarding the state of LLM run,
and adds the response to the list of records for both the {method}_records and
action. It then logs the response to the ClearML console.
on_llm_start(serialized, prompts, **kwargs)[source]ο
Run when LLM starts.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Run when LLM generates a new token.
Parameters
token (str) β
kwargs (Any) β
Return type
None
on_llm_end(response, **kwargs)[source]ο
Run when LLM ends running.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
on_llm_error(error, **kwargs)[source]ο
Run when LLM errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-10 | kwargs (Any) β
Return type
None
on_chain_start(serialized, inputs, **kwargs)[source]ο
Run when chain starts running.
Parameters
serialized (Dict[str, Any]) β
inputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_end(outputs, **kwargs)[source]ο
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_error(error, **kwargs)[source]ο
Run when chain errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_tool_start(serialized, input_str, **kwargs)[source]ο
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) β
input_str (str) β
kwargs (Any) β
Return type
None
on_tool_end(output, **kwargs)[source]ο
Run when tool ends running.
Parameters
output (str) β
kwargs (Any) β
Return type
None
on_tool_error(error, **kwargs)[source]ο
Run when tool errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_text(text, **kwargs)[source]ο
Run when agent is ending.
Parameters
text (str) β
kwargs (Any) β
Return type
None
on_agent_finish(finish, **kwargs)[source]ο
Run when agent ends running.
Parameters
finish (langchain.schema.AgentFinish) β
kwargs (Any) β
Return type
None
on_agent_action(action, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-11 | Return type
None
on_agent_action(action, **kwargs)[source]ο
Run on agent action.
Parameters
action (langchain.schema.AgentAction) β
kwargs (Any) β
Return type
Any
analyze_text(text)[source]ο
Analyze text using textstat and spacy.
Parameters
text (str) β The text to analyze.
Returns
A dictionary containing the complexity metrics.
Return type
(dict)
flush_tracker(name=None, langchain_asset=None, finish=False)[source]ο
Flush the tracker and setup the session.
Everything after this will be a new table.
Parameters
name (Optional[str]) β Name of the preformed session so far so it is identifyable
langchain_asset (Any) β The langchain asset to save.
finish (bool) β Whether to finish the run.
Returns β None
Return type
None
class langchain.callbacks.CometCallbackHandler(task_type='inference', workspace=None, project_name=None, tags=None, name=None, visualizations=None, complexity_metrics=False, custom_metrics=None, stream_logs=True)[source]ο
Bases: langchain.callbacks.utils.BaseMetadataCallbackHandler, langchain.callbacks.base.BaseCallbackHandler
Callback Handler that logs to Comet.
Parameters
job_type (str) β The type of comet_ml task such as βinferenceβ,
βtestingβ or βqcβ
project_name (str) β The comet_ml project name
tags (list) β Tags to add to the task
task_name (str) β Name of the comet_ml task
visualize (bool) β Whether to visualize the run.
complexity_metrics (bool) β Whether to log complexity metrics
stream_logs (bool) β Whether to stream callback actions to Comet
task_type (Optional[str]) β
workspace (Optional[str]) β
name (Optional[str]) β | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-12 | workspace (Optional[str]) β
name (Optional[str]) β
visualizations (Optional[List[str]]) β
custom_metrics (Optional[Callable]) β
Return type
None
This handler will utilize the associated callback method and formats
the input of each callback function with metadata regarding the state of LLM run,
and adds the response to the list of records for both the {method}_records and
action. It then logs the response to Comet.
on_llm_start(serialized, prompts, **kwargs)[source]ο
Run when LLM starts.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Run when LLM generates a new token.
Parameters
token (str) β
kwargs (Any) β
Return type
None
on_llm_end(response, **kwargs)[source]ο
Run when LLM ends running.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
on_llm_error(error, **kwargs)[source]ο
Run when LLM errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_chain_start(serialized, inputs, **kwargs)[source]ο
Run when chain starts running.
Parameters
serialized (Dict[str, Any]) β
inputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_end(outputs, **kwargs)[source]ο
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-13 | kwargs (Any) β
Return type
None
on_chain_error(error, **kwargs)[source]ο
Run when chain errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_tool_start(serialized, input_str, **kwargs)[source]ο
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) β
input_str (str) β
kwargs (Any) β
Return type
None
on_tool_end(output, **kwargs)[source]ο
Run when tool ends running.
Parameters
output (str) β
kwargs (Any) β
Return type
None
on_tool_error(error, **kwargs)[source]ο
Run when tool errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_text(text, **kwargs)[source]ο
Run when agent is ending.
Parameters
text (str) β
kwargs (Any) β
Return type
None
on_agent_finish(finish, **kwargs)[source]ο
Run when agent ends running.
Parameters
finish (langchain.schema.AgentFinish) β
kwargs (Any) β
Return type
None
on_agent_action(action, **kwargs)[source]ο
Run on agent action.
Parameters
action (langchain.schema.AgentAction) β
kwargs (Any) β
Return type
Any
flush_tracker(langchain_asset=None, task_type='inference', workspace=None, project_name='comet-langchain-demo', tags=None, name=None, visualizations=None, complexity_metrics=False, custom_metrics=None, finish=False, reset=False)[source]ο
Flush the tracker and setup the session.
Everything after this will be a new table. | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-14 | Flush the tracker and setup the session.
Everything after this will be a new table.
Parameters
name (Optional[str]) β Name of the preformed session so far so it is identifyable
langchain_asset (Any) β The langchain asset to save.
finish (bool) β Whether to finish the run.
Returns β None
task_type (Optional[str]) β
workspace (Optional[str]) β
project_name (Optional[str]) β
tags (Optional[Sequence]) β
visualizations (Optional[List[str]]) β
complexity_metrics (bool) β
custom_metrics (Optional[Callable]) β
reset (bool) β
Return type
None
class langchain.callbacks.FileCallbackHandler(filename, mode='a', color=None)[source]ο
Bases: langchain.callbacks.base.BaseCallbackHandler
Callback Handler that writes to a file.
Parameters
filename (str) β
mode (str) β
color (Optional[str]) β
Return type
None
on_chain_start(serialized, inputs, **kwargs)[source]ο
Print out that we are entering a chain.
Parameters
serialized (Dict[str, Any]) β
inputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_end(outputs, **kwargs)[source]ο
Print out that we finished a chain.
Parameters
outputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_agent_action(action, color=None, **kwargs)[source]ο
Run on agent action.
Parameters
action (langchain.schema.AgentAction) β
color (Optional[str]) β
kwargs (Any) β
Return type
Any | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-15 | color (Optional[str]) β
kwargs (Any) β
Return type
Any
on_tool_end(output, color=None, observation_prefix=None, llm_prefix=None, **kwargs)[source]ο
If not the final action, print out observation.
Parameters
output (str) β
color (Optional[str]) β
observation_prefix (Optional[str]) β
llm_prefix (Optional[str]) β
kwargs (Any) β
Return type
None
on_text(text, color=None, end='', **kwargs)[source]ο
Run when agent ends.
Parameters
text (str) β
color (Optional[str]) β
end (str) β
kwargs (Any) β
Return type
None
on_agent_finish(finish, color=None, **kwargs)[source]ο
Run on agent end.
Parameters
finish (langchain.schema.AgentFinish) β
color (Optional[str]) β
kwargs (Any) β
Return type
None
class langchain.callbacks.FinalStreamingStdOutCallbackHandler(*, answer_prefix_tokens=None, strip_tokens=True, stream_prefix=False)[source]ο
Bases: langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler
Callback handler for streaming in agents.
Only works with agents using LLMs that support streaming.
Only the final output of the agent will be streamed.
Parameters
answer_prefix_tokens (Optional[List[str]]) β
strip_tokens (bool) β
stream_prefix (bool) β
Return type
None
append_to_last_tokens(token)[source]ο
Parameters
token (str) β
Return type
None
check_if_answer_reached()[source]ο
Return type
bool
on_llm_start(serialized, prompts, **kwargs)[source]ο
Run when LLM starts running.
Parameters | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-16 | Run when LLM starts running.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Run on new LLM token. Only available when streaming is enabled.
Parameters
token (str) β
kwargs (Any) β
Return type
None
class langchain.callbacks.HumanApprovalCallbackHandler(approve=<function _default_approve>, should_check=<function _default_true>)[source]ο
Bases: langchain.callbacks.base.BaseCallbackHandler
Callback for manually validating values.
Parameters
approve (Callable[[Any], bool]) β
should_check (Callable[[Dict[str, Any]], bool]) β
raise_error: bool = Trueο
on_tool_start(serialized, input_str, *, run_id, parent_run_id=None, **kwargs)[source]ο
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) β
input_str (str) β
run_id (uuid.UUID) β
parent_run_id (Optional[uuid.UUID]) β
kwargs (Any) β
Return type
Any
class langchain.callbacks.InfinoCallbackHandler(model_id=None, model_version=None, verbose=False)[source]ο
Bases: langchain.callbacks.base.BaseCallbackHandler
Callback Handler that logs to Infino.
Parameters
model_id (Optional[str]) β
model_version (Optional[str]) β
verbose (bool) β
Return type
None
on_llm_start(serialized, prompts, **kwargs)[source]ο
Log the prompts to Infino, and set start time and error flag.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-17 | serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Do nothing when a new token is generated.
Parameters
token (str) β
kwargs (Any) β
Return type
None
on_llm_end(response, **kwargs)[source]ο
Log the latency, error, token usage, and response to Infino.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
on_llm_error(error, **kwargs)[source]ο
Set the error flag.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_chain_start(serialized, inputs, **kwargs)[source]ο
Do nothing when LLM chain starts.
Parameters
serialized (Dict[str, Any]) β
inputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_end(outputs, **kwargs)[source]ο
Do nothing when LLM chain ends.
Parameters
outputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_error(error, **kwargs)[source]ο
Need to log the error.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_tool_start(serialized, input_str, **kwargs)[source]ο
Do nothing when tool starts.
Parameters
serialized (Dict[str, Any]) β
input_str (str) β
kwargs (Any) β
Return type
None
on_agent_action(action, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-18 | Return type
None
on_agent_action(action, **kwargs)[source]ο
Do nothing when agent takes a specific action.
Parameters
action (langchain.schema.AgentAction) β
kwargs (Any) β
Return type
Any
on_tool_end(output, observation_prefix=None, llm_prefix=None, **kwargs)[source]ο
Do nothing when tool ends.
Parameters
output (str) β
observation_prefix (Optional[str]) β
llm_prefix (Optional[str]) β
kwargs (Any) β
Return type
None
on_tool_error(error, **kwargs)[source]ο
Do nothing when tool outputs an error.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_text(text, **kwargs)[source]ο
Do nothing.
Parameters
text (str) β
kwargs (Any) β
Return type
None
on_agent_finish(finish, **kwargs)[source]ο
Do nothing.
Parameters
finish (langchain.schema.AgentFinish) β
kwargs (Any) β
Return type
None
class langchain.callbacks.MlflowCallbackHandler(name='langchainrun-%', experiment='langchain', tags={}, tracking_uri=None)[source]ο
Bases: langchain.callbacks.utils.BaseMetadataCallbackHandler, langchain.callbacks.base.BaseCallbackHandler
Callback Handler that logs metrics and artifacts to mlflow server.
Parameters
name (str) β Name of the run.
experiment (str) β Name of the experiment.
tags (dict) β Tags to be attached for the run.
tracking_uri (str) β MLflow tracking server uri.
Return type
None
This handler will utilize the associated callback method called and formats
the input of each callback function with metadata regarding the state of LLM run, | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-19 | the input of each callback function with metadata regarding the state of LLM run,
and adds the response to the list of records for both the {method}_records and
action. It then logs the response to mlflow server.
on_llm_start(serialized, prompts, **kwargs)[source]ο
Run when LLM starts.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Run when LLM generates a new token.
Parameters
token (str) β
kwargs (Any) β
Return type
None
on_llm_end(response, **kwargs)[source]ο
Run when LLM ends running.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
on_llm_error(error, **kwargs)[source]ο
Run when LLM errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_chain_start(serialized, inputs, **kwargs)[source]ο
Run when chain starts running.
Parameters
serialized (Dict[str, Any]) β
inputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_end(outputs, **kwargs)[source]ο
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_error(error, **kwargs)[source]ο
Run when chain errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-20 | kwargs (Any) β
Return type
None
on_tool_start(serialized, input_str, **kwargs)[source]ο
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) β
input_str (str) β
kwargs (Any) β
Return type
None
on_tool_end(output, **kwargs)[source]ο
Run when tool ends running.
Parameters
output (str) β
kwargs (Any) β
Return type
None
on_tool_error(error, **kwargs)[source]ο
Run when tool errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_text(text, **kwargs)[source]ο
Run when agent is ending.
Parameters
text (str) β
kwargs (Any) β
Return type
None
on_agent_finish(finish, **kwargs)[source]ο
Run when agent ends running.
Parameters
finish (langchain.schema.AgentFinish) β
kwargs (Any) β
Return type
None
on_agent_action(action, **kwargs)[source]ο
Run on agent action.
Parameters
action (langchain.schema.AgentAction) β
kwargs (Any) β
Return type
Any
flush_tracker(langchain_asset=None, finish=False)[source]ο
Parameters
langchain_asset (Any) β
finish (bool) β
Return type
None
class langchain.callbacks.OpenAICallbackHandler[source]ο
Bases: langchain.callbacks.base.BaseCallbackHandler
Callback Handler that tracks OpenAI info.
total_tokens: int = 0ο
prompt_tokens: int = 0ο
completion_tokens: int = 0ο
successful_requests: int = 0ο | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-21 | completion_tokens: int = 0ο
successful_requests: int = 0ο
total_cost: float = 0.0ο
property always_verbose: boolο
Whether to call verbose callbacks even if verbose is False.
on_llm_start(serialized, prompts, **kwargs)[source]ο
Print out the prompts.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Print out the token.
Parameters
token (str) β
kwargs (Any) β
Return type
None
on_llm_end(response, **kwargs)[source]ο
Collect token usage.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
class langchain.callbacks.StdOutCallbackHandler(color=None)[source]ο
Bases: langchain.callbacks.base.BaseCallbackHandler
Callback Handler that prints to std out.
Parameters
color (Optional[str]) β
Return type
None
on_llm_start(serialized, prompts, **kwargs)[source]ο
Print out the prompts.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_end(response, **kwargs)[source]ο
Do nothing.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Do nothing.
Parameters
token (str) β
kwargs (Any) β
Return type
None
on_llm_error(error, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-22 | Return type
None
on_llm_error(error, **kwargs)[source]ο
Do nothing.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_chain_start(serialized, inputs, **kwargs)[source]ο
Print out that we are entering a chain.
Parameters
serialized (Dict[str, Any]) β
inputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_end(outputs, **kwargs)[source]ο
Print out that we finished a chain.
Parameters
outputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_error(error, **kwargs)[source]ο
Do nothing.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_tool_start(serialized, input_str, **kwargs)[source]ο
Do nothing.
Parameters
serialized (Dict[str, Any]) β
input_str (str) β
kwargs (Any) β
Return type
None
on_agent_action(action, color=None, **kwargs)[source]ο
Run on agent action.
Parameters
action (langchain.schema.AgentAction) β
color (Optional[str]) β
kwargs (Any) β
Return type
Any
on_tool_end(output, color=None, observation_prefix=None, llm_prefix=None, **kwargs)[source]ο
If not the final action, print out observation.
Parameters
output (str) β
color (Optional[str]) β
observation_prefix (Optional[str]) β
llm_prefix (Optional[str]) β
kwargs (Any) β
Return type
None | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-23 | kwargs (Any) β
Return type
None
on_tool_error(error, **kwargs)[source]ο
Do nothing.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_text(text, color=None, end='', **kwargs)[source]ο
Run when agent ends.
Parameters
text (str) β
color (Optional[str]) β
end (str) β
kwargs (Any) β
Return type
None
on_agent_finish(finish, color=None, **kwargs)[source]ο
Run on agent end.
Parameters
finish (langchain.schema.AgentFinish) β
color (Optional[str]) β
kwargs (Any) β
Return type
None
class langchain.callbacks.StreamingStdOutCallbackHandler[source]ο
Bases: langchain.callbacks.base.BaseCallbackHandler
Callback handler for streaming. Only works with LLMs that support streaming.
on_llm_start(serialized, prompts, **kwargs)[source]ο
Run when LLM starts running.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Run on new LLM token. Only available when streaming is enabled.
Parameters
token (str) β
kwargs (Any) β
Return type
None
on_llm_end(response, **kwargs)[source]ο
Run when LLM ends running.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
on_llm_error(error, **kwargs)[source]ο
Run when LLM errors.
Parameters | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-24 | Run when LLM errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_chain_start(serialized, inputs, **kwargs)[source]ο
Run when chain starts running.
Parameters
serialized (Dict[str, Any]) β
inputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_end(outputs, **kwargs)[source]ο
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) β
kwargs (Any) β
Return type
None
on_chain_error(error, **kwargs)[source]ο
Run when chain errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_tool_start(serialized, input_str, **kwargs)[source]ο
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) β
input_str (str) β
kwargs (Any) β
Return type
None
on_agent_action(action, **kwargs)[source]ο
Run on agent action.
Parameters
action (langchain.schema.AgentAction) β
kwargs (Any) β
Return type
Any
on_tool_end(output, **kwargs)[source]ο
Run when tool ends running.
Parameters
output (str) β
kwargs (Any) β
Return type
None
on_tool_error(error, **kwargs)[source]ο
Run when tool errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_text(text, **kwargs)[source]ο
Run on arbitrary text.
Parameters
text (str) β
kwargs (Any) β
Return type | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-25 | Parameters
text (str) β
kwargs (Any) β
Return type
None
on_agent_finish(finish, **kwargs)[source]ο
Run on agent end.
Parameters
finish (langchain.schema.AgentFinish) β
kwargs (Any) β
Return type
None
langchain.callbacks.StreamlitCallbackHandler(parent_container, *, max_thought_containers=4, expand_new_thoughts=True, collapse_completed_thoughts=True, thought_labeler=None)[source]ο
Construct a new StreamlitCallbackHandler. This CallbackHandler is geared towards
use with a LangChain Agent; it displays the Agentβs LLM and tool-usage βthoughtsβ
inside a series of Streamlit expanders.
Parameters
parent_container (DeltaGenerator) β The st.container that will contain all the Streamlit elements that the
Handler creates.
max_thought_containers (int) β The max number of completed LLM thought containers to show at once. When this
threshold is reached, a new thought will cause the oldest thoughts to be
collapsed into a βHistoryβ expander. Defaults to 4.
expand_new_thoughts (bool) β Each LLM βthoughtβ gets its own st.expander. This param controls whether that
expander is expanded by default. Defaults to True.
collapse_completed_thoughts (bool) β If True, LLM thought expanders will be collapsed when completed.
Defaults to True.
thought_labeler (Optional[LLMThoughtLabeler]) β An optional custom LLMThoughtLabeler instance. If unspecified, the handler
will use the default thought labeling logic. Defaults to None.
Returns
A new StreamlitCallbackHandler instance.
Note that this is an βauto-updatingβ API (if the installed version of Streamlit)
has a more recent StreamlitCallbackHandler implementation, an instance of that class | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-26 | has a more recent StreamlitCallbackHandler implementation, an instance of that class
will be used.
Return type
BaseCallbackHandler
class langchain.callbacks.LLMThoughtLabeler[source]ο
Bases: object
Generates markdown labels for LLMThought containers. Pass a custom
subclass of this to StreamlitCallbackHandler to override its default
labeling logic.
get_initial_label()[source]ο
Return the markdown label for a new LLMThought that doesnβt have
an associated tool yet.
Return type
str
get_tool_label(tool, is_complete)[source]ο
Return the label for an LLMThought that has an associated
tool.
Parameters
tool (langchain.callbacks.streamlit.streamlit_callback_handler.ToolRecord) β The toolβs ToolRecord
is_complete (bool) β True if the thought is complete; False if the thought
is still receiving input.
Return type
The markdown label for the thoughtβs container.
get_history_label()[source]ο
Return a markdown label for the special βhistoryβ container
that contains overflow thoughts.
Return type
str
get_final_agent_thought_label()[source]ο
Return the markdown label for the agentβs final thought -
the βNow I have the answerβ thought, that doesnβt involve
a tool.
Return type
str
class langchain.callbacks.WandbCallbackHandler(job_type=None, project='langchain_callback_demo', entity=None, tags=None, group=None, name=None, notes=None, visualize=False, complexity_metrics=False, stream_logs=False)[source]ο
Bases: langchain.callbacks.utils.BaseMetadataCallbackHandler, langchain.callbacks.base.BaseCallbackHandler
Callback Handler that logs to Weights and Biases.
Parameters
job_type (str) β The type of job.
project (str) β The project to log to. | https://api.python.langchain.com/en/latest/modules/callbacks.html |
1a644f703b6b-27 | project (str) β The project to log to.
entity (str) β The entity to log to.
tags (list) β The tags to log.
group (str) β The group to log to.
name (str) β The name of the run.
notes (str) β The notes to log.
visualize (bool) β Whether to visualize the run.
complexity_metrics (bool) β Whether to log complexity metrics.
stream_logs (bool) β Whether to stream callback actions to W&B
Return type
None
This handler will utilize the associated callback method called and formats
the input of each callback function with metadata regarding the state of LLM run,
and adds the response to the list of records for both the {method}_records and
action. It then logs the response using the run.log() method to Weights and Biases.
on_llm_start(serialized, prompts, **kwargs)[source]ο
Run when LLM starts.
Parameters
serialized (Dict[str, Any]) β
prompts (List[str]) β
kwargs (Any) β
Return type
None
on_llm_new_token(token, **kwargs)[source]ο
Run when LLM generates a new token.
Parameters
token (str) β
kwargs (Any) β
Return type
None
on_llm_end(response, **kwargs)[source]ο
Run when LLM ends running.
Parameters
response (langchain.schema.LLMResult) β
kwargs (Any) β
Return type
None
on_llm_error(error, **kwargs)[source]ο
Run when LLM errors.
Parameters
error (Union[Exception, KeyboardInterrupt]) β
kwargs (Any) β
Return type
None
on_chain_start(serialized, inputs, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/callbacks.html |
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