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class langchain.utilities.JiraAPIWrapper(*, jira=None, confluence=None, jira_username=None, jira_api_token=None, jira_instance_url=None, operations=[{'mode': 'jql', 'name': 'JQL Query', 'description': '\n    This tool is a wrapper around atlassian-python-api\'s Jira jql API, useful when you need to search for Jira issu...
https://api.python.langchain.com/en/stable/modules/utilities.html
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low priority task called "test issue" with description "test description", you would pass in the following dictionary: \n    {{"summary": "test issue", "description": "test description", "issuetype": {{"name": "Task"}}, "priority": {{"name": "Low"}}}}\n    '}, {'mode': 'other', 'name': 'Catch all Jira API call', 'descr...
https://api.python.langchain.com/en/stable/modules/utilities.html
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the DEMO space titled "This is the title" with body "This is the body. You can use \n<strong>HTML tags</strong>!", you would pass in the following dictionary: {{"space": "DEMO", "title":"This is the \ntitle","body":"This is the body. You can use <strong>HTML tags</strong>!"}} '}])[source]
https://api.python.langchain.com/en/stable/modules/utilities.html
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Bases: pydantic.main.BaseModel Wrapper for Jira API. Parameters jira (Any) – confluence (Any) – jira_username (Optional[str]) – jira_api_token (Optional[str]) – jira_instance_url (Optional[str]) – operations (List[Dict]) – Return type None attribute confluence: Any = None attribute jira_api_token: Optional[str] ...
https://api.python.langchain.com/en/stable/modules/utilities.html
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attribute operations: List[Dict] = [{'mode': 'jql', 'name': 'JQL Query', 'description': '\n    This tool is a wrapper around atlassian-python-api\'s Jira jql API, useful when you need to search for Jira issues.\n    The input to this tool is a JQL query string, and will be passed into atlassian-python-api\'s Jira `jql`...
https://api.python.langchain.com/en/stable/modules/utilities.html
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"test issue", "description": "test description", "issuetype": {{"name": "Task"}}, "priority": {{"name": "Low"}}}}\n    '}, {'mode': 'other', 'name': 'Catch all Jira API call', 'description': '\n    This tool is a wrapper around atlassian-python-api\'s Jira API.\n    There are other dedicated tools for fetching all proj...
https://api.python.langchain.com/en/stable/modules/utilities.html
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you would pass in the following dictionary: {{"space": "DEMO", "title":"This is the \ntitle","body":"This is the body. You can use <strong>HTML tags</strong>!"}} '}]
https://api.python.langchain.com/en/stable/modules/utilities.html
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issue_create(query)[source] Parameters query (str) – Return type str list()[source] Return type List[Dict] other(query)[source] Parameters query (str) – Return type str page_create(query)[source] Parameters query (str) – Return type str parse_issues(issues)[source] Parameters issues (Dict) – Return type List[d...
https://api.python.langchain.com/en/stable/modules/utilities.html
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run(query)[source] Invoke Lambda function and parse result. Parameters query (str) – Return type str class langchain.utilities.MaxComputeAPIWrapper(client)[source] Bases: object Interface for querying Alibaba Cloud MaxCompute tables. Parameters client (ODPS) – classmethod from_params(endpoint, project, *, access_id...
https://api.python.langchain.com/en/stable/modules/utilities.html
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attribute metaphor_api_key: str [Required] results(query, num_results, include_domains=None, exclude_domains=None, start_crawl_date=None, end_crawl_date=None, start_published_date=None, end_published_date=None)[source] Run query through Metaphor Search and return metadata. Parameters query (str) – The query to search...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Bases: pydantic.main.BaseModel Wrapper for OpenWeatherMap API using PyOWM. Docs for using: Go to OpenWeatherMap and sign up for an API key Save your API KEY into OPENWEATHERMAP_API_KEY env variable pip install pyowm Parameters owm (Any) – openweathermap_api_key (Optional[str]) – Return type None attribute openweather...
https://api.python.langchain.com/en/stable/modules/utilities.html
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aiosession (Optional[aiohttp.client.ClientSession]) – Return type None attribute aiosession: Optional[aiohttp.ClientSession] = None attribute credential: Optional[TokenCredential] = None attribute dataset_id: str [Required] attribute group_id: Optional[str] = None attribute impersonated_user_name: Optional[str] = ...
https://api.python.langchain.com/en/stable/modules/utilities.html
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property table_info: str Information about all tables in the database. class langchain.utilities.PubMedAPIWrapper(*, top_k_results=3, load_max_docs=25, doc_content_chars_max=2000, load_all_available_meta=False, email='your_email@example.com', base_url_esearch='https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi...
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attribute load_all_available_meta: bool = False attribute load_max_docs: int = 25 attribute top_k_results: int = 3 load(query)[source] Search PubMed for documents matching the query. Return a list of dictionaries containing the document metadata. Parameters query (str) – Return type List[dict] load_docs(query)[sou...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Wrapper for SceneXplain API. In order to set this up, you need API key for the SceneXplain API. You can obtain a key by following the steps below. - Sign up for a free account at https://scenex.jina.ai/. - Navigate to the API Access page (https://scenex.jina.ai/api) and create a new API key. Show JSON schema{ "title...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Fields scenex_api_key (str) scenex_api_url (str) attribute scenex_api_key: str [Required] attribute scenex_api_url: str = 'https://us-central1-causal-diffusion.cloudfunctions.net/describe' run(image)[source] Run SceneXplain image explainer. Parameters image (str) – Return type str validator validate_environment  » ...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Parameters searx_host (str) – unsecure (bool) – params (dict) – headers (Optional[dict]) – engines (Optional[List[str]]) – categories (Optional[List[str]]) – query_suffix (Optional[str]) – k (int) – aiosession (Optional[Any]) – Return type None attribute aiosession: Optional[Any] = None attribute categories: ...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Parameters query (str) – The query to search for. query_suffix (Optional[str]) – Extra suffix appended to the query. num_results (int) – Limit the number of results to return. engines (Optional[List[str]]) – List of engines to use for the query. categories (Optional[List[str]]) – List of categories to use for the query...
https://api.python.langchain.com/en/stable/modules/utilities.html
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searx.run("what is the weather in France ?", engine="qwant") # the same result can be achieved using the `!` syntax of searx # to select the engine using `query_suffix` searx.run("what is the weather in France ?", query_suffix="!qwant") class langchain.utilities.SerpAPIWrapper(*, search_engine=None, params={'engine': '...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Run query through SerpAPI and parse result async. Parameters query (str) – kwargs (Any) – Return type str get_params(query)[source] Get parameters for SerpAPI. Parameters query (str) – Return type Dict[str, str] results(query)[source] Run query through SerpAPI and return the raw result. Parameters query (str) – R...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Return type str run(command, fetch='all')[source] Parameters command (str) – fetch (str) – Return type str get_table_info_no_throw(table_names=None)[source] Get information about specified tables. Follows best practices as specified in: Rajkumar et al, 2022 (https://arxiv.org/abs/2204.00498) If sample_rows_in_table...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Parameters url (str) – kwargs (Any) – Return type str async aget(url, **kwargs)[source] GET the URL and return the text asynchronously. Parameters url (str) – kwargs (Any) – Return type str async apatch(url, data, **kwargs)[source] PATCH the URL and return the text asynchronously. Parameters url (str) – data (Di...
https://api.python.langchain.com/en/stable/modules/utilities.html
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POST to the URL and return the text. Parameters url (str) – data (Dict[str, Any]) – kwargs (Any) – Return type str put(url, data, **kwargs)[source] PUT the URL and return the text. Parameters url (str) – data (Dict[str, Any]) – kwargs (Any) – Return type str property requests: langchain.requests.Requests class ...
https://api.python.langchain.com/en/stable/modules/utilities.html
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format, an [alphanumeric sender ID](https://www.twilio.com/docs/sms/send-messages#use-an-alphanumeric-sender-id), or a [Channel Endpoint address](https://www.twilio.com/docs/sms/channels#channel-addresses) that is enabled for the type of message you want to send. Phone numbers or [short codes](https://www.twilio.com/do...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Parameters wiki_client (Any) – top_k_results (int) – lang (str) – load_all_available_meta (bool) – doc_content_chars_max (int) – Return type None attribute doc_content_chars_max: int = 4000 attribute lang: str = 'en' attribute load_all_available_meta: bool = False attribute top_k_results: int = 3 load(query)[s...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Parameters query (str) – Return type str class langchain.utilities.ZapierNLAWrapper(*, zapier_nla_api_key, zapier_nla_oauth_access_token, zapier_nla_api_base='https://nla.zapier.com/api/v1/')[source] Bases: pydantic.main.BaseModel Wrapper for Zapier NLA. Full docs here: https://nla.zapier.com/start/ This wrapper supp...
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actions here: https://nla.zapier.com/demo/start/ The return list can be empty if no actions exposed. Else will contain a list of action objects: [{“id”: str, “description”: str, “params”: Dict[str, str] }] params will always contain an instructions key, the only required param. All others optional and if provided will ...
https://api.python.langchain.com/en/stable/modules/utilities.html
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call. Parameters action_id (str) – instructions (str) – params (Optional[Dict]) – Return type Dict async arun_as_str(*args, **kwargs)[source] Same as run, but returns a stringified version of the JSON for insertting back into an LLM. Return type str list()[source] Returns a list of all exposed (enabled) actions as...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Same as preview, but returns a stringified version of the JSON for insertting back into an LLM. Return type str run(action_id, instructions, params=None)[source] 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 acti...
https://api.python.langchain.com/en/stable/modules/utilities.html
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Prompt Templates Prompt template classes. class langchain.prompts.AIMessagePromptTemplate(*, prompt, additional_kwargs=None)[source] Bases: langchain.prompts.chat.BaseStringMessagePromptTemplate Parameters prompt (langchain.prompts.base.StringPromptTemplate) – additional_kwargs (dict) – Return type None format(**kw...
https://api.python.langchain.com/en/stable/modules/prompts.html
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Parameters input_variables (List[str]) – output_parser (Optional[langchain.schema.BaseOutputParser]) – partial_variables (Mapping[str, Union[str, Callable[[], str]]]) – Return type None attribute input_variables: List[str] [Required] A list of the names of the variables the prompt template expects. attribute output...
https://api.python.langchain.com/en/stable/modules/prompts.html
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property lc_serializable: bool Return whether or not the class is serializable. class langchain.prompts.ChatMessagePromptTemplate(*, prompt, additional_kwargs=None, role)[source] Bases: langchain.prompts.chat.BaseStringMessagePromptTemplate Parameters prompt (langchain.prompts.base.StringPromptTemplate) – additional...
https://api.python.langchain.com/en/stable/modules/prompts.html
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Parameters kwargs (Any) – Return type List[langchain.schema.BaseMessage] classmethod from_messages(messages)[source] Parameters messages (Sequence[Union[langchain.prompts.chat.BaseMessagePromptTemplate, langchain.schema.BaseMessage]]) – Return type langchain.prompts.chat.ChatPromptTemplate classmethod from_role_stri...
https://api.python.langchain.com/en/stable/modules/prompts.html
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Prompt template that contains few shot examples. Parameters input_variables (List[str]) – output_parser (Optional[langchain.schema.BaseOutputParser]) – partial_variables (Mapping[str, Union[str, Callable[[], str]]]) – examples (Optional[List[dict]]) – example_selector (Optional[langchain.prompts.example_selector.ba...
https://api.python.langchain.com/en/stable/modules/prompts.html
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attribute validate_template: bool = True Whether or not to try validating the template. dict(**kwargs)[source] Return a dictionary of the prompt. Parameters kwargs (Any) – Return type Dict format(**kwargs)[source] Format the prompt with the inputs. Parameters kwargs (Any) – Any arguments to be passed to the prompt ...
https://api.python.langchain.com/en/stable/modules/prompts.html
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PromptTemplate used to format an individual example. attribute example_selector: Optional[langchain.prompts.example_selector.base.BaseExampleSelector] = None ExampleSelector to choose the examples to format into the prompt. Either this or examples should be provided. attribute example_separator: str = '\n\n' String s...
https://api.python.langchain.com/en/stable/modules/prompts.html
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additional_kwargs (dict) – Return type None format(**kwargs)[source] To a BaseMessage. Parameters kwargs (Any) – Return type langchain.schema.BaseMessage class langchain.prompts.LengthBasedExampleSelector(*, examples, example_prompt, get_text_length=<function _get_length_based>, max_length=2048, example_text_lengths...
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input_variables (Dict[str, str]) – Return type List[dict] class langchain.prompts.MaxMarginalRelevanceExampleSelector(*, vectorstore, k=4, example_keys=None, input_keys=None, fetch_k=20)[source] Bases: langchain.prompts.example_selector.semantic_similarity.SemanticSimilarityExampleSelector ExampleSelector that select...
https://api.python.langchain.com/en/stable/modules/prompts.html
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Parameters examples (List[dict]) – List of examples to use in the prompt. embeddings (langchain.embeddings.base.Embeddings) – An iniialized embedding API interface, e.g. OpenAIEmbeddings(). vectorstore_cls (Type[langchain.vectorstores.base.VectorStore]) – A vector store DB interface class, e.g. FAISS. k (int) – Number ...
https://api.python.langchain.com/en/stable/modules/prompts.html
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Select and order examples based on ngram overlap score (sentence_bleu score). https://www.nltk.org/_modules/nltk/translate/bleu_score.html https://aclanthology.org/P02-1040.pdf Parameters examples (List[dict]) – example_prompt (langchain.prompts.prompt.PromptTemplate) – threshold (float) – Return type None attribute...
https://api.python.langchain.com/en/stable/modules/prompts.html
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A prompt template for composing multiple prompts together. This can be useful when you want to reuse parts of prompts. A PipelinePrompt consists of two main parts: final_prompt: This is the final prompt that is returned pipeline_prompts: This is a list of tuples, consistingof a string (name) and a Prompt Template. Each...
https://api.python.langchain.com/en/stable/modules/prompts.html
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Schema to represent a prompt for an LLM. Example from langchain import PromptTemplate prompt = PromptTemplate(input_variables=["foo"], template="Say {foo}") Parameters input_variables (List[str]) – output_parser (Optional[langchain.schema.BaseOutputParser]) – partial_variables (Mapping[str, Union[str, Callable[[], st...
https://api.python.langchain.com/en/stable/modules/prompts.html
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will expect. example_separator (str) – The separator to use in between examples. Defaults to two new line characters. prefix (str) – String that should go before any examples. Generally includes examples. Default to an empty string. kwargs (Any) – Returns The final prompt generated. Return type langchain.prompts.promp...
https://api.python.langchain.com/en/stable/modules/prompts.html
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Return type None attribute example_keys: Optional[List[str]] = None Optional keys to filter examples to. attribute input_keys: Optional[List[str]] = None Optional keys to filter input to. If provided, the search is based on the input variables instead of all variables. attribute k: int = 4 Number of examples to sele...
https://api.python.langchain.com/en/stable/modules/prompts.html
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input_variables (Dict[str, str]) – Return type List[dict] class langchain.prompts.StringPromptTemplate(*, input_variables, output_parser=None, partial_variables=None)[source] Bases: langchain.prompts.base.BasePromptTemplate, abc.ABC String prompt should expose the format method, returning a prompt. Parameters input_v...
https://api.python.langchain.com/en/stable/modules/prompts.html
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Tools Core toolkit implementations. class langchain.tools.AIPluginTool(*, name, description, args_schema=<class 'langchain.tools.plugin.AIPluginToolSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, plugin, api_spec)[source] Bases: langchain.tools.base.BaseTo...
https://api.python.langchain.com/en/stable/modules/tools.html
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base_url (str) – path (str) – method (langchain.utilities.openapi.HTTPVerb) – properties (Sequence[langchain.tools.openapi.utils.api_models.APIProperty]) – request_body (Optional[langchain.tools.openapi.utils.api_models.APIRequestBody]) – Return type None attribute base_url: str [Required] The base URL of the ope...
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Return type str static ts_type_from_python(type_)[source] Parameters type_ (Union[str, Type, tuple, None, enum.Enum]) – Return type str property body_params: List[str] property path_params: List[str] property query_params: List[str] class langchain.tools.ArxivQueryRun(*, name='arxiv', description='A wrapper around...
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attribute api_wrapper: langchain.utilities.arxiv.ArxivAPIWrapper [Optional] class langchain.tools.AzureCogsFormRecognizerTool(*, name='azure_cognitive_services_form_recognizer', description='A wrapper around Azure Cognitive Services Form Recognizer. Useful for when you need to extract text, tables, and key-value pairs...
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doc_analysis_client (Any) – Return type None class langchain.tools.AzureCogsImageAnalysisTool(*, name='azure_cognitive_services_image_analysis', description='A wrapper around Azure Cognitive Services Image Analysis. Useful for when you need to analyze images. Input should be a url to an image.', args_schema=None, retu...
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analysis_options (Any) – Return type None class langchain.tools.AzureCogsSpeech2TextTool(*, name='azure_cognitive_services_speech2text', description='A wrapper around Azure Cognitive Services Speech2Text. Useful for when you need to transcribe audio to text. Input should be a url to an audio file.', args_schema=None, ...
https://api.python.langchain.com/en/stable/modules/tools.html
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speech_config (Any) – Return type None class langchain.tools.AzureCogsText2SpeechTool(*, name='azure_cognitive_services_text2speech', description='A wrapper around Azure Cognitive Services Text2Speech. Useful for when you need to convert text to speech. ', args_schema=None, return_direct=False, verbose=False, callback...
https://api.python.langchain.com/en/stable/modules/tools.html
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speech_config (Any) – Return type None class langchain.tools.BaseGraphQLTool(*, name='query_graphql', description="    Input to this tool is a detailed and correct GraphQL query, output is a result from the API.\n    If the query is not correct, an error message will be returned.\n    If an error is returned with 'Bad...
https://api.python.langchain.com/en/stable/modules/tools.html
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Parameters requests_wrapper (langchain.requests.TextRequestsWrapper) – Return type None attribute requests_wrapper: langchain.requests.TextRequestsWrapper [Required] class langchain.tools.BaseSQLDatabaseTool(*, db)[source] Bases: pydantic.main.BaseModel Base tool for interacting with a SQL database. Parameters db (l...
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attribute args_schema: Optional[Type[pydantic.main.BaseModel]] = None Pydantic model class to validate and parse the tool’s input arguments. attribute callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None Deprecated. Please use callbacks instead. attribute callbacks: Optional[Union[List[lang...
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kwargs (Any) – Return type Any run(tool_input, verbose=None, start_color='green', color='green', callbacks=None, **kwargs)[source] Run the tool. Parameters tool_input (Union[str, Dict]) – verbose (Optional[bool]) – start_color (Optional[str]) – color (Optional[str]) – callbacks (Optional[Union[List[langchain.call...
https://api.python.langchain.com/en/stable/modules/tools.html
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num_results (int) – api_wrapper (langchain.utilities.bing_search.BingSearchAPIWrapper) – Return type None attribute api_wrapper: langchain.utilities.bing_search.BingSearchAPIWrapper [Required] attribute num_results: int = 4 class langchain.tools.BingSearchRun(*, name='bing_search', description='A wrapper around Bin...
https://api.python.langchain.com/en/stable/modules/tools.html
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Bases: langchain.tools.base.BaseTool Parameters name (str) – description (str) – args_schema (Optional[Type[pydantic.main.BaseModel]]) – return_direct (bool) – verbose (bool) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – callback_...
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callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) – sync_browser (Optional['SyncBrowser']) – async_browser (Optional['AsyncBrowser']) – visible_only (bool) – playwright_strict (bool) – pla...
https://api.python.langchain.com/en/stable/modules/tools.html
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Parameters name (str) – description (str) – args_schema (Type[pydantic.main.BaseModel]) – return_direct (bool) – verbose (bool) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – callback_manager (Optional[langchain.callbacks.base.Base...
https://api.python.langchain.com/en/stable/modules/tools.html
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return_direct (bool) – verbose (bool) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.b...
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callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) – root_dir (Optional[str]) – Return type None attribute args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management....
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callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) – num_results (int) – api_wrapper (langchain.utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper) – Return type None attribute api_wrappe...
https://api.python.langchain.com/en/stable/modules/tools.html
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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_er...
https://api.python.langchain.com/en/stable/modules/tools.html
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Parameters page (Any) – html_content (str) – absolute_urls (bool) – 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_mana...
https://api.python.langchain.com/en/stable/modules/tools.html
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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...
https://api.python.langchain.com/en/stable/modules/tools.html
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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=Fals...
https://api.python.langchain.com/en/stable/modules/tools.html
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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, ...
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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.SearchArgsSc...
https://api.python.langchain.com/en/stable/modules/tools.html
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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_threa...
https://api.python.langchain.com/en/stable/modules/tools.html
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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.too...
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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, ha...
https://api.python.langchain.com/en/stable/modules/tools.html
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Bases: langchain.tools.base.BaseTool Tool that adds the capability to query the Google places API. Parameters name (str) – description (str) – args_schema (Type[pydantic.main.BaseModel]) – return_direct (bool) – verbose (bool) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchai...
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return_direct (bool) – verbose (bool) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.b...
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attribute api_wrapper: langchain.utilities.google_search.GoogleSearchAPIWrapper [Required] 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 ...
https://api.python.langchain.com/en/stable/modules/tools.html
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Tool that adds the capability to query the Serper.dev Google search API. Parameters name (str) – description (str) – args_schema (Optional[Type[pydantic.main.BaseModel]]) – return_direct (bool) – verbose (bool) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base...
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callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) – prompt_func (Callable[[str], None]) – input_func (Callable) – Return type None attribute input_func: Callable [Optional] attribute prompt...
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Return type None attribute url: str [Required] class langchain.tools.InfoPowerBITool(*, name='schema_powerbi', description='\n    Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.\n    Be sure that the tables actually exist by calling list_tables_powerbi fir...
https://api.python.langchain.com/en/stable/modules/tools.html
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Bases: langchain.tools.sql_database.tool.BaseSQLDatabaseTool, langchain.tools.base.BaseTool Tool for getting metadata about a SQL database. Parameters name (str) – description (str) – args_schema (Optional[Type[pydantic.main.BaseModel]]) – return_direct (bool) – verbose (bool) – callbacks (Optional[Union[List[lang...
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callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) – db (langchain.utilities.spark_sql.SparkSQL) – Return type None class langchain.tools.JiraAction(*, name='', description='', args_schema=Non...
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attribute mode: str [Required] class langchain.tools.JsonGetValueTool(*, name='json_spec_get_value', description='\n    Can be used to see value in string format at a given path.\n    Before calling this you should be SURE that the path to this exists.\n    The input is a text representation of the path to the dict in...
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Return type None attribute spec: JsonSpec [Required] class langchain.tools.JsonListKeysTool(*, name='json_spec_list_keys', description='\n    Can be used to list all keys at a given path. \n    Before calling this you should be SURE that the path to this exists.\n    The input is a text representation of the path to t...
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Parameters name (str) – description (str) – args_schema (Type[pydantic.main.BaseModel]) – return_direct (bool) – verbose (bool) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – callback_manager (Optional[langchain.callbacks.base.Base...
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return_direct (bool) – verbose (bool) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.b...
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db (langchain.sql_database.SQLDatabase) – 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=No...
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args_schema (Optional[Type[pydantic.main.BaseModel]]) – return_direct (bool) – verbose (bool) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – handle_tool_err...
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root_dir (Optional[str]) – Return type None attribute args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.move.FileMoveInput'> Pydantic model class to validate and parse the tool’s input arguments. attribute description: str = 'Move or rename a file from one location to another' Used...
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Pydantic model class to validate and parse the tool’s input arguments. attribute description: str = 'Navigate back to the previous page in the browser history' Used to tell the model how/when/why to use the tool. You can provide few-shot examples as a part of the description. attribute name: str = 'previous_webpage' ...
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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' The unique name of the tool that clearly communicates its purpose. class langchain.tools.OpenAPISpec(*, openapi='3.1.0', info, jsonSchemaDialect=None, servers=[S...
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Return type None classmethod from_file(path)[source] Get an OpenAPI spec from a file path. Parameters path (Union[str, pathlib.Path]) – Return type langchain.utilities.openapi.OpenAPISpec classmethod from_spec_dict(spec_dict)[source] Get an OpenAPI spec from a dict. Parameters spec_dict (dict) – Return type langcha...
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Return type List[openapi_schema_pydantic.v3.v3_1_0.parameter.Parameter] get_parameters_for_path(path)[source] Parameters path (str) – Return type List[openapi_schema_pydantic.v3.v3_1_0.parameter.Parameter] get_referenced_schema(ref)[source] Get a schema (or nested reference) or err. Parameters ref (openapi_schema_py...
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property base_url: str 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_dire...
https://api.python.langchain.com/en/stable/modules/tools.html
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Bases: langchain.tools.base.BaseTool Tool that adds the capability to search using the PubMed API. Parameters name (str) – description (str) – args_schema (Optional[Type[pydantic.main.BaseModel]]) – return_direct (bool) – verbose (bool) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler]...
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callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) – globals (Optional[Dict]) – locals (Optional[Dict]) – sanitize_input (bool) – Return type None attribute globals: Optional[Dict] [Optional...
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attribute python_repl: langchain.utilities.python.PythonREPL [Optional] attribute sanitize_input: bool = True class langchain.tools.QueryCheckerTool(*, name='query_checker_sql_db', description='\n    Use this tool to double check if your query is correct before executing it.\n    Always use this tool before executing...
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callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – handle_tool_error (Optional[Union[bool, str, Callable[[langchain.tools.base.ToolException], str]]]) – db (langchain.utilities.spark_sql.SparkSQL) – template (str) – llm (langchain.base_language.BaseLanguageModel) – llm_chain (langchain.cha...
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class langchain.tools.QueryPowerBITool(*, name='query_powerbi', description='\n    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\n    Example Input: "How many rows...
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<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 expr...
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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...
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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, m...
https://api.python.langchain.com/en/stable/modules/tools.html