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4cb173dbed63-40 | tells American Billionaires.', 'link': 'https://thebharatexpressnews.com/i-think-people-can-get-by-with-999-million-bernie-sanders-tells-american-billionaires-heres-how-the-ultra-rich-can-pay-less-income-tax-than-you-legally/', 'snippet': 'The report noted that in 2007 and 2011, Amazon.com Inc. ' 'founder Jeff Bezos “did not pay a dime in federal ... ' 'If you want to bet on Musk, check out Tesla.', 'date': '11 mins ago', 'source': 'THE BHARAT EXPRESS NEWS', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR_X9qqSwVFBBdos2CK5ky5IWIE3aJPCQeRYR9O1Jz4t-MjaEYBuwK7AU3AJQ&s', 'position': 3}]}Some examples of the tbs parameter:qdr:h (past hour) | https://python.langchain.com/docs/integrations/tools/google_serper |
4cb173dbed63-41 | qdr:d (past day)
qdr:w (past week)
qdr:m (past month)
qdr:y (past year)You can specify intermediate time periods by adding a number:
qdr:h12 (past 12 hours)
qdr:d3 (past 3 days)
qdr:w2 (past 2 weeks)
qdr:m6 (past 6 months) | https://python.langchain.com/docs/integrations/tools/google_serper |
4cb173dbed63-42 | qdr:m2 (past 2 years)For all supported filters simply go to Google Search, search for something, click on "Tools", add your date filter and check the URL for "tbs=".Searching for Google Places​We can also query Google Places using this wrapper. For example:search = GoogleSerperAPIWrapper(type="places")results = search.results("Italian restaurants in Upper East Side")pprint.pp(results) {'searchParameters': {'q': 'Italian restaurants in Upper East Side', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'places'}, 'places': [{'position': 1, 'title': "L'Osteria", 'address': '1219 Lexington Ave', 'latitude': 40.777154599999996, 'longitude': -73.9571363, 'thumbnailUrl': | https://python.langchain.com/docs/integrations/tools/google_serper |
4cb173dbed63-43 | 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNjU7BWEq_aYQANBCbX52Kb0lDpd_lFIx5onw40=w92-h92-n-k-no', 'rating': 4.7, 'ratingCount': 91, 'category': 'Italian'}, {'position': 2, 'title': "Tony's Di Napoli", 'address': '1081 3rd Ave', 'latitude': 40.7643567, 'longitude': -73.9642373, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNbNv6jZkJ9nyVi60__8c1DQbe_eEbugRAhIYye=w92-h92-n-k-no', 'rating': 4.5, 'ratingCount': 2265, 'category': | https://python.langchain.com/docs/integrations/tools/google_serper |
4cb173dbed63-44 | 'category': 'Italian'}, {'position': 3, 'title': 'Caravaggio', 'address': '23 E 74th St', 'latitude': 40.773412799999996, 'longitude': -73.96473379999999, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPDGchokDvppoLfmVEo6X_bWd3Fz0HyxIHTEe9V=w92-h92-n-k-no', 'rating': 4.5, 'ratingCount': 276, 'category': 'Italian'}, {'position': 4, 'title': 'Luna Rossa', 'address': '347 E 85th St', 'latitude': 40.776593999999996, | https://python.langchain.com/docs/integrations/tools/google_serper |
4cb173dbed63-45 | 40.776593999999996, 'longitude': -73.950351, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNPCpCPuqPAb1Mv6_fOP7cjb8Wu1rbqbk2sMBlh=w92-h92-n-k-no', 'rating': 4.5, 'ratingCount': 140, 'category': 'Italian'}, {'position': 5, 'title': "Paola's", 'address': '1361 Lexington Ave', 'latitude': 40.7822019, 'longitude': -73.9534096, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPJr2Vcx-B6K-GNQa4koOTffggTePz8TKRTnWi3=w92-h92-n-k-no', 'rating': 4.5, | https://python.langchain.com/docs/integrations/tools/google_serper |
4cb173dbed63-46 | 4.5, 'ratingCount': 344, 'category': 'Italian'}, {'position': 6, 'title': 'Come Prima', 'address': '903 Madison Ave', 'latitude': 40.772124999999996, 'longitude': -73.965012, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNrX19G0NVdtDyMovCQ-M-m0c_gLmIxrWDQAAbz=w92-h92-n-k-no', 'rating': 4.5, 'ratingCount': 176, 'category': 'Italian'}, {'position': 7, 'title': 'Botte UES', 'address': '1606 1st Ave.', 'latitude': | https://python.langchain.com/docs/integrations/tools/google_serper |
4cb173dbed63-47 | 'latitude': 40.7750785, 'longitude': -73.9504801, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPPN5GXxfH3NDacBc0Pt3uGAInd9OChS5isz9RF=w92-h92-n-k-no', 'rating': 4.4, 'ratingCount': 152, 'category': 'Italian'}, {'position': 8, 'title': 'Piccola Cucina Uptown', 'address': '106 E 60th St', 'latitude': 40.7632468, 'longitude': -73.9689825, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPifIgzOCD5SjgzzqBzGkdZCBp0MQsK5k7M7znn=w92-h92-n-k-no', | https://python.langchain.com/docs/integrations/tools/google_serper |
4cb173dbed63-48 | 'rating': 4.6, 'ratingCount': 941, 'category': 'Italian'}, {'position': 9, 'title': 'Pinocchio Restaurant', 'address': '300 E 92nd St', 'latitude': 40.781453299999995, 'longitude': -73.9486788, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNtxlIyEEJHtDtFtTR9nB38S8A2VyMu-mVVz72A=w92-h92-n-k-no', 'rating': 4.5, 'ratingCount': 113, 'category': 'Italian'}, {'position': 10, 'title': 'Barbaresco', 'address': | https://python.langchain.com/docs/integrations/tools/google_serper |
4cb173dbed63-49 | 'address': '843 Lexington Ave #1', 'latitude': 40.7654332, 'longitude': -73.9656873, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipMb9FbPuXF_r9g5QseOHmReejxSHgSahPMPJ9-8=w92-h92-n-k-no', 'rating': 4.3, 'ratingCount': 122, 'locationHint': 'In The Touraine', 'category': 'Italian'}]}PreviousGoogle SearchNextGradio ToolsAs part of a Self Ask With Search ChainObtaining results with metadataSearching for Google ImagesSearching for Google NewsSearching for Google PlacesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/google_serper |
38560e9e30b6-0 | Google Places | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/google_places |
38560e9e30b6-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsGoogle PlacesGoogle PlacesThis notebook goes through how to use Google Places API#!pip install googlemapsimport osos.environ["GPLACES_API_KEY"] = ""from langchain.tools import GooglePlacesToolplaces = GooglePlacesTool()places.run("al fornos") "1. Delfina Restaurant\nAddress: 3621 18th St, San Francisco, CA 94110, USA\nPhone: (415) 552-4055\nWebsite: https://www.delfinasf.com/\n\n\n2. Piccolo Forno\nAddress: 725 Columbus Ave, San Francisco, CA 94133, USA\nPhone: (415) 757-0087\nWebsite: https://piccolo-forno-sf.com/\n\n\n3. L'Osteria del Forno\nAddress: 519 Columbus Ave, San Francisco, CA 94133, USA\nPhone: (415) 982-1124\nWebsite: Unknown\n\n\n4. Il Fornaio\nAddress: 1265 Battery | https://python.langchain.com/docs/integrations/tools/google_places |
38560e9e30b6-2 | Unknown\n\n\n4. Il Fornaio\nAddress: 1265 Battery St, San Francisco, CA 94111, USA\nPhone: (415) 986-0100\nWebsite: https://www.ilfornaio.com/\n\n"PreviousGolden QueryNextGoogle SearchCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/google_places |
3d93859559e5-0 | Brave Search | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/brave_search |
3d93859559e5-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsBrave SearchBrave SearchThis notebook goes over how to use the Brave Search tool.from langchain.tools import BraveSearchapi_key = "BSAv1neIuQOsxqOyy0sEe_ie2zD_n_V"tool = BraveSearch.from_api_key(api_key=api_key, search_kwargs={"count": 3})tool.run("obama middle name") '[{"title": "Obama\'s Middle Name -- My Last Name -- is \'Hussein.\' So?", "link": "https://www.cair.com/cair_in_the_news/obamas-middle-name-my-last-name-is-hussein-so/", "snippet": "I wasn\\u2019t sure whether to laugh or cry a few days back listening to radio talk show host Bill Cunningham repeatedly scream Barack <strong>Obama</strong>\\u2019<strong>s</strong> <strong>middle</strong> <strong>name</strong> \\u2014 my last <strong>name</strong> \\u2014 as | https://python.langchain.com/docs/integrations/tools/brave_search |
3d93859559e5-2 | \\u2014 my last <strong>name</strong> \\u2014 as if he had anti-Muslim Tourette\\u2019s. \\u201cHussein,\\u201d Cunningham hissed like he was beckoning Satan when shouting the ..."}, {"title": "What\'s up with Obama\'s middle name? - Quora", "link": "https://www.quora.com/Whats-up-with-Obamas-middle-name", "snippet": "Answer (1 of 15): A better question would be, \\u201cWhat\\u2019s up with <strong>Obama</strong>\\u2019s first <strong>name</strong>?\\u201d President Barack Hussein <strong>Obama</strong>\\u2019s father\\u2019s <strong>name</strong> was Barack Hussein <strong>Obama</strong>. He was <strong>named</strong> after his father. Hussein, <strong>Obama</strong>\\u2019<strong>s</strong> <strong>middle</strong> <strong>name</strong>, is a very common Arabic <strong>name</strong>, meaning "good," "handsome," or ..."}, {"title": "Barack Obama | Biography, Parents, Education, Presidency, Books, ...", "link": "https://www.britannica.com/biography/Barack-Obama", "snippet": "Barack <strong>Obama</strong>, in full Barack Hussein <strong>Obama</strong> II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009\\u201317) and the first African American to hold the office. Before winning the presidency, <strong>Obama</strong> represented Illinois in the U.S."}]'PreviousBing | https://python.langchain.com/docs/integrations/tools/brave_search |
3d93859559e5-3 | <strong>Obama</strong> represented Illinois in the U.S."}]'PreviousBing SearchNextChatGPT PluginsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/brave_search |
aaa7758ce7d9-0 | Apify | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/apify |
aaa7758ce7d9-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsApifyApifyThis notebook shows how to use the Apify integration for LangChain.Apify is a cloud platform for web scraping and data extraction,
which provides an ecosystem of more than a thousand
ready-made apps called Actors for various web scraping, crawling, and data extraction use cases.
For example, you can use it to extract Google Search results, Instagram and Facebook profiles, products from Amazon or Shopify, Google Maps reviews, etc. etc.In this example, we'll use the Website Content Crawler Actor,
which can deeply crawl websites such as documentation, knowledge bases, help centers, or blogs, | https://python.langchain.com/docs/integrations/tools/apify |
aaa7758ce7d9-2 | and extract text content from the web pages. Then we feed the documents into a vector index and answer questions from it.#!pip install apify-client openai langchain chromadb tiktokenFirst, import ApifyWrapper into your source code:from langchain.document_loaders.base import Documentfrom langchain.indexes import VectorstoreIndexCreatorfrom langchain.utilities import ApifyWrapperInitialize it using your Apify API token and for the purpose of this example, also with your OpenAI API key:import osos.environ["OPENAI_API_KEY"] = "Your OpenAI API key"os.environ["APIFY_API_TOKEN"] = "Your Apify API token"apify = ApifyWrapper()Then run the Actor, wait for it to finish, and fetch its results from the Apify dataset into a LangChain document loader.Note that if you already have some results in an Apify dataset, you can load them directly using ApifyDatasetLoader, as shown in this notebook. In that notebook, you'll also find the explanation of the dataset_mapping_function, which is used to map fields from the Apify dataset records to LangChain Document fields.loader = apify.call_actor( actor_id="apify/website-content-crawler", run_input={"startUrls": [{"url": "https://python.langchain.com/en/latest/"}]}, dataset_mapping_function=lambda item: Document( page_content=item["text"] or "", metadata={"source": item["url"]} ),)Initialize the vector index from the crawled documents:index = VectorstoreIndexCreator().from_loaders([loader])And finally, query the vector index:query = "What is LangChain?"result = index.query_with_sources(query)print(result["answer"])print(result["sources"]) LangChain is a standard interface through which you can interact with a variety of large language models (LLMs). It provides modules that | https://python.langchain.com/docs/integrations/tools/apify |
aaa7758ce7d9-3 | through which you can interact with a variety of large language models (LLMs). It provides modules that can be used to build language model applications, and it also provides chains and agents with memory capabilities. https://python.langchain.com/en/latest/modules/models/llms.html, https://python.langchain.com/en/latest/getting_started/getting_started.htmlPreviousToolsNextArXiv API ToolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/apify |
f64ed59ac595-0 | Google Search | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/google_search |
f64ed59ac595-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsGoogle SearchOn this pageGoogle SearchThis notebook goes over how to use the google search component.First, you need to set up the proper API keys and environment variables. To set it up, create the GOOGLE_API_KEY in the Google Cloud credential console (https://console.cloud.google.com/apis/credentials) and a GOOGLE_CSE_ID using the Programmable Search Enginge (https://programmablesearchengine.google.com/controlpanel/create). Next, it is good to follow the instructions found here.Then we will need to set some environment variables.import osos.environ["GOOGLE_CSE_ID"] = ""os.environ["GOOGLE_API_KEY"] = ""from langchain.tools import Toolfrom langchain.utilities import GoogleSearchAPIWrappersearch = GoogleSearchAPIWrapper()tool = Tool( name="Google Search", description="Search Google for recent results.", func=search.run,)tool.run("Obama's first name?") "STATE OF HAWAII. 1 Child's First Name. (Type or print). | https://python.langchain.com/docs/integrations/tools/google_search |
f64ed59ac595-2 | "STATE OF HAWAII. 1 Child's First Name. (Type or print). 2. Sex. BARACK. 3. This Birth. CERTIFICATE OF LIVE BIRTH. FILE. NUMBER 151 le. lb. Middle Name. Barack Hussein Obama II is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic\xa0... When Barack Obama was elected president in 2008, he became the first African American to hold ... The Middle East remained a key foreign policy challenge. Jan 19, 2017 ... Jordan Barack Treasure, New York City, born in 2008 ... Jordan Barack Treasure made national news when he was the focus of a New York newspaper\xa0... Portrait of George Washington, the 1st President of the United States ... Portrait of Barack Obama, the 44th President of the United States\xa0... His full name is Barack Hussein Obama II. Since the “II� is simply because he was named for his father, his last name is Obama. Mar 22, 2008 ... Barry Obama decided that he didn't like his nickname. A few of his friends at Occidental College had already begun to call him Barack (his\xa0... Aug 18, 2017 ... It took him several seconds and multiple clues to remember former President Barack Obama's first name. Miller knew that every answer had to\xa0... Feb 9, 2015 ... Michael Jordan misspelled Barack Obama's first name on 50th-birthday gift ... Knowing Obama is a Chicagoan and huge basketball fan,\xa0... 4 days ago ... Barack Obama, in full Barack Hussein Obama II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009–17) and\xa0..."Number of | https://python.langchain.com/docs/integrations/tools/google_search |
f64ed59ac595-3 | president of the United States (2009–17) and\xa0..."Number of Results​You can use the k parameter to set the number of resultssearch = GoogleSearchAPIWrapper(k=1)tool = Tool( name="I'm Feeling Lucky", description="Search Google and return the first result.", func=search.run,)tool.run("python") 'The official home of the Python Programming Language.''The official home of the Python Programming Language.'Metadata Results​Run query through GoogleSearch and return snippet, title, and link metadata.Snippet: The description of the result.Title: The title of the result.Link: The link to the result.search = GoogleSearchAPIWrapper()def top5_results(query): return search.results(query, 5)tool = Tool( name="Google Search Snippets", description="Search Google for recent results.", func=top5_results,)PreviousGoogle PlacesNextGoogle Serper APINumber of ResultsMetadata ResultsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/google_search |
3ddbf3c7de75-0 | Wikipedia | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/wikipedia |
3ddbf3c7de75-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsWikipediaWikipediaWikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.First, you need to install wikipedia python package.pip install wikipediafrom langchain.tools import WikipediaQueryRunfrom langchain.utilities import WikipediaAPIWrapperwikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())wikipedia.run("HUNTER X HUNTER") 'Page: Hunter × Hunter\nSummary: Hunter × Hunter (stylized as HUNTER×HUNTER and pronounced "hunter hunter") is a Japanese manga series written and illustrated by Yoshihiro Togashi. It has been serialized in Shueisha\'s sh�nen manga magazine Weekly Sh�nen Jump since March 1998, although the manga has frequently gone on extended hiatuses since 2006. Its chapters have been collected in 37 tank�bon | https://python.langchain.com/docs/integrations/tools/wikipedia |
3ddbf3c7de75-2 | hiatuses since 2006. Its chapters have been collected in 37 tank�bon volumes as of November 2022. The story focuses on a young boy named Gon Freecss who discovers that his father, who left him at a young age, is actually a world-renowned Hunter, a licensed professional who specializes in fantastical pursuits such as locating rare or unidentified animal species, treasure hunting, surveying unexplored enclaves, or hunting down lawless individuals. Gon departs on a journey to become a Hunter and eventually find his father. Along the way, Gon meets various other Hunters and encounters the paranormal.\nHunter × Hunter was adapted into a 62-episode anime television series produced by Nippon Animation and directed by Kazuhiro Furuhashi, which ran on Fuji Television from October 1999 to March 2001. Three separate original video animations (OVAs) totaling 30 episodes were subsequently produced by Nippon Animation and released in Japan from 2002 to 2004. A second anime television series by Madhouse aired on Nippon Television from October 2011 to September 2014, totaling 148 episodes, with two animated theatrical films released in 2013. There are also numerous audio albums, video games, musicals, and other media based on Hunter × Hunter.\nThe manga has been translated into English and released in North America by Viz Media since April 2005. Both television series have been also licensed by Viz Media, with the first series having aired on the Funimation Channel in 2009 and the second series broadcast on Adult Swim\'s Toonami programming block from April 2016 to June 2019.\nHunter × Hunter has been a huge critical and financial success and has become one of the best-selling manga series of all time, having over 84 million copies in circulation by July 2022.\n\nPage: Hunter × Hunter | https://python.langchain.com/docs/integrations/tools/wikipedia |
3ddbf3c7de75-3 | 84 million copies in circulation by July 2022.\n\nPage: Hunter × Hunter (2011 TV series)\nSummary: Hunter × Hunter is an anime television series that aired from 2011 to 2014 based on Yoshihiro Togashi\'s manga series Hunter × Hunter. The story begins with a young boy named Gon Freecss, who one day discovers that the father who he thought was dead, is in fact alive and well. He learns that his father, Ging, is a legendary "Hunter", an individual who has proven themselves an elite member of humanity. Despite the fact that Ging left his son with his relatives in order to pursue his own dreams, Gon becomes determined to follow in his father\'s footsteps, pass the rigorous "Hunter Examination", and eventually find his father to become a Hunter in his own right.\nThis new Hunter × Hunter anime was announced on July 24, 2011. It is a complete reboot starting from the beginning of the original manga, with no connection to the first anime television series from 1999. Produced by Nippon TV, VAP, Shueisha and Madhouse, the series is directed by Hiroshi K�jina, with Atsushi Maekawa and Tsutomu Kamishiro handling series composition, Takahiro Yoshimatsu designing the characters and Yoshihisa Hirano composing the music. Instead of having the old cast reprise their roles for the new adaptation, the series features an entirely new cast to voice the characters. The new series premiered airing weekly on Nippon TV and the nationwide Nippon News Network from October 2, 2011. The series started to be collected in both DVD and Blu-ray format on January 25, 2012. Viz Media has licensed the anime for a DVD/Blu-ray release in North America with an English dub. On television, the series began airing on Adult | https://python.langchain.com/docs/integrations/tools/wikipedia |
3ddbf3c7de75-4 | release in North America with an English dub. On television, the series began airing on Adult Swim\'s Toonami programming block on April 17, 2016, and ended on June 23, 2019.The anime series\' opening theme is alternated between the song "Departure!" and an alternate version titled "Departure! -Second Version-" both sung by Galneryus\' voc'PreviousTwilioNextWolfram AlphaCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/wikipedia |
45a026b49057-0 | Lemon AI NLP Workflow Automation | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/lemonai |
45a026b49057-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsLemon AI NLP Workflow AutomationOn this pageLemon AI NLP Workflow Automation\ | https://python.langchain.com/docs/integrations/tools/lemonai |
45a026b49057-2 | Full docs are available at: https://github.com/felixbrock/lemonai-py-clientLemon AI helps you build powerful AI assistants in minutes and automate workflows by allowing for accurate and reliable read and write operations in tools like Airtable, Hubspot, Discord, Notion, Slack and Github.Most connectors available today are focused on read-only operations, limiting the potential of LLMs. Agents, on the other hand, have a tendency to hallucinate from time to time due to missing context or instructions.With Lemon AI, it is possible to give your agents access to well-defined APIs for reliable read and write operations. In addition, Lemon AI functions allow you to further reduce the risk of hallucinations by providing a way to statically define workflows that the model can rely on in case of uncertainty.Quick Start​The following quick start demonstrates how to use Lemon AI in combination with Agents to automate workflows that involve interaction with internal tooling.1. Install Lemon AI​Requires Python 3.8.1 and above.To use Lemon AI in your Python project run pip install lemonaiThis will install the corresponding Lemon AI client which you can then import into your script.The tool uses Python packages langchain and loguru. In case of any installation errors with Lemon AI, install both packages first and then install the Lemon AI package.2. Launch the Server​The interaction of your agents and all tools provided by Lemon AI is handled by the Lemon AI Server. To use Lemon AI you need to run the server on your local machine so the Lemon AI Python client can connect to it.3. Use Lemon AI with Langchain​Lemon AI automatically solves given tasks by finding the right combination of relevant tools or uses Lemon AI Functions as an alternative. The following example demonstrates how to retrieve a user from Hackernews and write it to a table in Airtable:(Optional) Define your Lemon AI Functions​Similar | https://python.langchain.com/docs/integrations/tools/lemonai |
45a026b49057-3 | it to a table in Airtable:(Optional) Define your Lemon AI Functions​Similar to OpenAI functions, Lemon AI provides the option to define workflows as reusable functions. These functions can be defined for use cases where it is especially important to move as close as possible to near-deterministic behavior. Specific workflows can be defined in a separate lemonai.json:[ { "name": "Hackernews Airtable User Workflow", "description": "retrieves user data from Hackernews and appends it to a table in Airtable", "tools": ["hackernews-get-user", "airtable-append-data"] }]Your model will have access to these functions and will prefer them over self-selecting tools to solve a given task. All you have to do is to let the agent know that it should use a given function by including the function name in the prompt.Include Lemon AI in your Langchain project​import osfrom lemonai import execute_workflowfrom langchain import OpenAILoad API Keys and Access Tokens​To use tools that require authentication, you have to store the corresponding access credentials in your environment in the format "{tool name}_{authentication string}" where the authentication string is one of ["API_KEY", "SECRET_KEY", "SUBSCRIPTION_KEY", "ACCESS_KEY"] for API keys or ["ACCESS_TOKEN", "SECRET_TOKEN"] for authentication tokens. Examples are "OPENAI_API_KEY", "BING_SUBSCRIPTION_KEY", "AIRTABLE_ACCESS_TOKEN".""" Load all relevant API Keys and Access Tokens into your environment variables """os.environ["OPENAI_API_KEY"] = "*INSERT OPENAI API KEY HERE*"os.environ["AIRTABLE_ACCESS_TOKEN"] = "*INSERT AIRTABLE TOKEN HERE*"hackernews_username = "*INSERT HACKERNEWS USERNAME HERE*"airtable_base_id = "*INSERT BASE ID HERE*"airtable_table_id = "*INSERT TABLE ID HERE*"""" Define your instruction | https://python.langchain.com/docs/integrations/tools/lemonai |
45a026b49057-4 | BASE ID HERE*"airtable_table_id = "*INSERT TABLE ID HERE*"""" Define your instruction to be given to your LLM """prompt = f"""Read information from Hackernews for user {hackernews_username} and then write the results toAirtable (baseId: {airtable_base_id}, tableId: {airtable_table_id}). Only write the fields "username", "karma"and "created_at_i". Please make sure that Airtable does NOT automatically convert the field types.""""""Use the Lemon AI execute_workflow wrapper to run your Langchain agent in combination with Lemon AI """model = OpenAI(temperature=0)execute_workflow(llm=model, prompt_string=prompt)4. Gain transparency on your Agent's decision making​To gain transparency on how your Agent interacts with Lemon AI tools to solve a given task, all decisions made, tools used and operations performed are written to a local lemonai.log file. Every time your LLM agent is interacting with the Lemon AI tool stack a corresponding log entry is created.2023-06-26T11:50:27.708785+0100 - b5f91c59-8487-45c2-800a-156eac0c7dae - hackernews-get-user2023-06-26T11:50:39.624035+0100 - b5f91c59-8487-45c2-800a-156eac0c7dae - airtable-append-data2023-06-26T11:58:32.925228+0100 - 5efe603c-9898-4143-b99a-55b50007ed9d - hackernews-get-user2023-06-26T11:58:43.988788+0100 - 5efe603c-9898-4143-b99a-55b50007ed9d - | https://python.langchain.com/docs/integrations/tools/lemonai |
45a026b49057-5 | - airtable-append-dataBy using the Lemon AI Analytics Tool you can easily gain a better understanding of how frequently and in which order tools are used. As a result, you can identify weak spots in your agent’s decision-making capabilities and move to a more deterministic behavior by defining Lemon AI functions.PreviousIFTTT WebHooksNextMetaphor SearchQuick Start1. Install Lemon AI2. Launch the Server3. Use Lemon AI with Langchain4. Gain transparency on your Agent's decision makingCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/lemonai |
2c20df94b52c-0 | YouTubeSearchTool | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/youtube |
2c20df94b52c-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsYouTubeSearchToolYouTubeSearchToolThis notebook shows how to use a tool to search YouTubeAdapted from https://github.com/venuv/langchain_yt_tools#! pip install youtube_searchfrom langchain.tools import YouTubeSearchTooltool = YouTubeSearchTool()tool.run("lex friedman") "['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu']"You can also specify the number of results that are returnedtool.run("lex friedman,5") "['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=YVJ8gTnDC4Y&pp=ygUMbGV4IGZyaWVkbWFu', | https://python.langchain.com/docs/integrations/tools/youtube |
2c20df94b52c-2 | '/watch?v=Udh22kuLebg&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=L_Guz73e6fw&pp=ygUMbGV4IGZyaWVkbWFu']"PreviousWolfram AlphaNextZapier Natural Language Actions APICommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/youtube |
670c9c742069-0 | Zapier Natural Language Actions API | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/zapier |
670c9c742069-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsZapier Natural Language Actions APIOn this pageZapier Natural Language Actions API\ | https://python.langchain.com/docs/integrations/tools/zapier |
670c9c742069-2 | Full docs here: https://nla.zapier.com/start/Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface.NLA supports apps like Gmail, Salesforce, Trello, Slack, Asana, HubSpot, Google Sheets, Microsoft Teams, and thousands more apps: https://zapier.com/appsZapier NLA handles ALL the underlying API auth and translation from natural language --> underlying API call --> return simplified output for LLMs. The key idea is you, or your users, expose a set of actions via an oauth-like setup window, which you can then query and execute via a REST API.NLA offers both API Key and OAuth for signing NLA API requests.Server-side (API Key): for quickly getting started, testing, and production scenarios where LangChain will only use actions exposed in the developer's Zapier account (and will use the developer's connected accounts on Zapier.com)User-facing (Oauth): for production scenarios where you are deploying an end-user facing application and LangChain needs access to end-user's exposed actions and connected accounts on Zapier.comThis quick start will focus mostly on the server-side use case for brevity. Jump to Example Using OAuth Access Token to see a short example how to set up Zapier for user-facing situations. Review full docs for full user-facing oauth developer support.This example goes over how to use the Zapier integration with a SimpleSequentialChain, then an Agent. | https://python.langchain.com/docs/integrations/tools/zapier |
670c9c742069-3 | In code, below:import os# get from https://platform.openai.com/os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "")# get from https://nla.zapier.com/docs/authentication/ after logging in):os.environ["ZAPIER_NLA_API_KEY"] = os.environ.get("ZAPIER_NLA_API_KEY", "")Example with Agent​Zapier tools can be used with an agent. See the example below.from langchain.llms import OpenAIfrom langchain.agents import initialize_agentfrom langchain.agents.agent_toolkits import ZapierToolkitfrom langchain.agents import AgentTypefrom langchain.utilities.zapier import ZapierNLAWrapper## step 0. expose gmail 'find email' and slack 'send channel message' actions# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields "Have AI guess"# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through firstllm = OpenAI(temperature=0)zapier = ZapierNLAWrapper()toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)agent = initialize_agent( toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run( "Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.") > Entering new AgentExecutor chain... I need to find the email and summarize it. Action: Gmail: Find Email Action Input: Find the latest email from Silicon Valley Bank Observation: | https://python.langchain.com/docs/integrations/tools/zapier |
670c9c742069-4 | Email Action Input: Find the latest email from Silicon Valley Bank Observation: {"from__name": "Silicon Valley Bridge Bank, N.A.", "from__email": "[email protected]", "body_plain": "Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG", "reply_to__email": "[email protected]", "subject": "Meet the new CEO Tim Mayopoulos", "date": "Tue, 14 Mar 2023 23:42:29 -0500 (CDT)", "message_url": "https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a", "attachment_count": "0", "to__emails": "[email protected]", "message_id": "186e393b13cfdf0a", "labels": "IMPORTANT, CATEGORY_UPDATES, INBOX"} Thought: I need to summarize the email and send it to the #test-zapier channel in Slack. Action: Slack: Send Channel Message Action Input: Send a slack message to the #test-zapier channel with the text "Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild." Observation: {"message__text": "Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for | https://python.langchain.com/docs/integrations/tools/zapier |
670c9c742069-5 | is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.", "message__permalink": "https://langchain.slack.com/archives/C04TSGU0RA7/p1678859932375259", "channel": "C04TSGU0RA7", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:58:52Z", "message__bot_profile__icons__image_36": "https://avatars.slack-edge.com/2022-08-02/3888649620612_f864dc1bb794cf7d82b0_36.png", "message__blocks[]block_id": "kdZZ", "message__blocks[]elements[]type": "['rich_text_section']"} Thought: I now know the final answer. Final Answer: I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack. > Finished chain. 'I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.'Example with SimpleSequentialChain​If you need more explicit control, use a chain, like below.from langchain.llms import OpenAIfrom langchain.chains import LLMChain, TransformChain, SimpleSequentialChainfrom langchain.prompts import PromptTemplatefrom langchain.tools.zapier.tool import ZapierNLARunActionfrom langchain.utilities.zapier import | https://python.langchain.com/docs/integrations/tools/zapier |
670c9c742069-6 | import ZapierNLARunActionfrom langchain.utilities.zapier import ZapierNLAWrapper## step 0. expose gmail 'find email' and slack 'send direct message' actions# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields "Have AI guess"# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through firstactions = ZapierNLAWrapper().list()## step 1. gmail find emailGMAIL_SEARCH_INSTRUCTIONS = "Grab the latest email from Silicon Valley Bank"def nla_gmail(inputs): action = next( (a for a in actions if a["description"].startswith("Gmail: Find Email")), None ) return { "email_data": ZapierNLARunAction( action_id=action["id"], zapier_description=action["description"], params_schema=action["params"], ).run(inputs["instructions"]) }gmail_chain = TransformChain( input_variables=["instructions"], output_variables=["email_data"], transform=nla_gmail,)## step 2. generate draft replytemplate = """You are an assisstant who drafts replies to an incoming email. Output draft reply in plain text (not JSON).Incoming email:{email_data}Draft email reply:"""prompt_template = PromptTemplate(input_variables=["email_data"], template=template)reply_chain = LLMChain(llm=OpenAI(temperature=0.7), prompt=prompt_template)## step | https://python.langchain.com/docs/integrations/tools/zapier |
670c9c742069-7 | prompt=prompt_template)## step 3. send draft reply via a slack direct messageSLACK_HANDLE = "@Ankush Gola"def nla_slack(inputs): action = next( ( a for a in actions if a["description"].startswith("Slack: Send Direct Message") ), None, ) instructions = f'Send this to {SLACK_HANDLE} in Slack: {inputs["draft_reply"]}' return { "slack_data": ZapierNLARunAction( action_id=action["id"], zapier_description=action["description"], params_schema=action["params"], ).run(instructions) }slack_chain = TransformChain( input_variables=["draft_reply"], output_variables=["slack_data"], transform=nla_slack,)## finally, executeoverall_chain = SimpleSequentialChain( chains=[gmail_chain, reply_chain, slack_chain], verbose=True)overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS) > Entering new SimpleSequentialChain chain... {"from__name": "Silicon Valley Bridge Bank, N.A.", "from__email": "[email protected]", "body_plain": "Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have | https://python.langchain.com/docs/integrations/tools/zapier |
670c9c742069-8 | we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG", "reply_to__email": "[email protected]", "subject": "Meet the new CEO Tim Mayopoulos", "date": "Tue, 14 Mar 2023 23:42:29 -0500 (CDT)", "message_url": "https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a", "attachment_count": "0", "to__emails": "[email protected]", "message_id": "186e393b13cfdf0a", "labels": "IMPORTANT, CATEGORY_UPDATES, INBOX"} Dear Silicon Valley Bridge Bank, Thank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. Best regards, [Your Name] {"message__text": "Dear Silicon Valley Bridge Bank, \n\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \n\nBest regards, \n[Your Name]", "message__permalink": "https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629", "channel": "D04TKF5BBHU", "message__bot_profile__name": | https://python.langchain.com/docs/integrations/tools/zapier |
670c9c742069-9 | "channel": "D04TKF5BBHU", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:59:28Z", "message__blocks[]block_id": "p7i", "message__blocks[]elements[]elements[]type": "[['text']]", "message__blocks[]elements[]type": "['rich_text_section']"} > Finished chain. '{"message__text": "Dear Silicon Valley Bridge Bank, \\n\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\n\\nBest regards, \\n[Your Name]", "message__permalink": "https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629", "channel": "D04TKF5BBHU", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:59:28Z", "message__blocks[]block_id": "p7i", "message__blocks[]elements[]elements[]type": "[[\'text\']]", | https://python.langchain.com/docs/integrations/tools/zapier |
670c9c742069-10 | "message__blocks[]elements[]elements[]type": "[[\'text\']]", "message__blocks[]elements[]type": "[\'rich_text_section\']"}'Example Using OAuth Access Token​The below snippet shows how to initialize the wrapper with a procured OAuth access token. Note the argument being passed in as opposed to setting an environment variable. Review the authentication docs for full user-facing oauth developer support.The developer is tasked with handling the OAuth handshaking to procure and refresh the access token.llm = OpenAI(temperature=0)zapier = ZapierNLAWrapper(zapier_nla_oauth_access_token="<fill in access token here>")toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)agent = initialize_agent( toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run( "Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.")PreviousYouTubeSearchToolNextVector storesExample with AgentExample with SimpleSequentialChainExample Using OAuth Access TokenCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/zapier |
1dd275105260-0 | Shell Tool | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/bash |
1dd275105260-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsShell ToolOn this pageShell ToolGiving agents access to the shell is powerful (though risky outside a sandboxed environment).The LLM can use it to execute any shell commands. A common use case for this is letting the LLM interact with your local file system.from langchain.tools import ShellToolshell_tool = ShellTool()print(shell_tool.run({"commands": ["echo 'Hello World!'", "time"]})) Hello World! real 0m0.000s user 0m0.000s sys 0m0.000s /Users/wfh/code/lc/lckg/langchain/tools/shell/tool.py:34: UserWarning: The shell tool has no safeguards by default. Use at your own risk. warnings.warn(Use with Agents​As with all tools, these can be given to an agent to accomplish more complex tasks. Let's have the agent | https://python.langchain.com/docs/integrations/tools/bash |
1dd275105260-2 | tools, these can be given to an agent to accomplish more complex tasks. Let's have the agent fetch some links from a web page.from langchain.chat_models import ChatOpenAIfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypellm = ChatOpenAI(temperature=0)shell_tool.description = shell_tool.description + f"args {shell_tool.args}".replace( "{", "{{").replace("}", "}}")self_ask_with_search = initialize_agent( [shell_tool], llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)self_ask_with_search.run( "Download the langchain.com webpage and grep for all urls. Return only a sorted list of them. Be sure to use double quotes.") > Entering new AgentExecutor chain... Question: What is the task? Thought: We need to download the langchain.com webpage and extract all the URLs from it. Then we need to sort the URLs and return them. Action: ``` { "action": "shell", "action_input": { "commands": [ "curl -s https://langchain.com | grep -o 'http[s]*://[^\" ]*' | sort" ] } } ``` /Users/wfh/code/lc/lckg/langchain/tools/shell/tool.py:34: UserWarning: The shell tool has no safeguards by default. Use at your own risk. warnings.warn( Observation: https://blog.langchain.dev/ | https://python.langchain.com/docs/integrations/tools/bash |
1dd275105260-3 | Observation: https://blog.langchain.dev/ https://discord.gg/6adMQxSpJS https://docs.langchain.com/docs/ https://github.com/hwchase17/chat-langchain https://github.com/hwchase17/langchain https://github.com/hwchase17/langchainjs https://github.com/sullivan-sean/chat-langchainjs https://js.langchain.com/docs/ https://python.langchain.com/en/latest/ https://twitter.com/langchainai Thought:The URLs have been successfully extracted and sorted. We can return the list of URLs as the final answer. Final Answer: ["https://blog.langchain.dev/", "https://discord.gg/6adMQxSpJS", "https://docs.langchain.com/docs/", "https://github.com/hwchase17/chat-langchain", "https://github.com/hwchase17/langchain", "https://github.com/hwchase17/langchainjs", "https://github.com/sullivan-sean/chat-langchainjs", "https://js.langchain.com/docs/", "https://python.langchain.com/en/latest/", "https://twitter.com/langchainai"] > Finished chain. '["https://blog.langchain.dev/", "https://discord.gg/6adMQxSpJS", "https://docs.langchain.com/docs/", "https://github.com/hwchase17/chat-langchain", "https://github.com/hwchase17/langchain", "https://github.com/hwchase17/langchainjs", "https://github.com/sullivan-sean/chat-langchainjs", "https://js.langchain.com/docs/", "https://python.langchain.com/en/latest/", | https://python.langchain.com/docs/integrations/tools/bash |
1dd275105260-4 | "https://js.langchain.com/docs/", "https://python.langchain.com/en/latest/", "https://twitter.com/langchainai"]'PreviousawslambdaNextBing SearchUse with AgentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/bash |
a963b14eed7f-0 | OpenWeatherMap API | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/openweathermap |
a963b14eed7f-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsOpenWeatherMap APIOn this pageOpenWeatherMap APIThis notebook goes over how to use the OpenWeatherMap component to fetch weather information.First, you need to sign up for an OpenWeatherMap API key:Go to OpenWeatherMap and sign up for an API key herepip install pyowmThen we will need to set some environment variables:Save your API KEY into OPENWEATHERMAP_API_KEY env variableUse the wrapper​from langchain.utilities import OpenWeatherMapAPIWrapperimport osos.environ["OPENWEATHERMAP_API_KEY"] = ""weather = OpenWeatherMapAPIWrapper()weather_data = weather.run("London,GB")print(weather_data) In London,GB, the current weather is as follows: Detailed status: broken clouds Wind speed: 2.57 m/s, direction: 240° Humidity: 55% Temperature: - Current: 20.12°C - High: | https://python.langchain.com/docs/integrations/tools/openweathermap |
a963b14eed7f-2 | - Current: 20.12°C - High: 21.75°C - Low: 18.68°C - Feels like: 19.62°C Rain: {} Heat index: None Cloud cover: 75%Use the tool​from langchain.llms import OpenAIfrom langchain.agents import load_tools, initialize_agent, AgentTypeimport osos.environ["OPENAI_API_KEY"] = ""os.environ["OPENWEATHERMAP_API_KEY"] = ""llm = OpenAI(temperature=0)tools = load_tools(["openweathermap-api"], llm)agent_chain = initialize_agent( tools=tools, llm=llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent_chain.run("What's the weather like in London?") > Entering new AgentExecutor chain... I need to find out the current weather in London. Action: OpenWeatherMap Action Input: London,GB Observation: In London,GB, the current weather is as follows: Detailed status: broken clouds Wind speed: 2.57 m/s, direction: 240° Humidity: 56% Temperature: - Current: 20.11°C - High: 21.75°C - Low: 18.68°C - Feels like: 19.64°C Rain: {} Heat index: None Cloud cover: 75% Thought: I now know the current weather | https://python.langchain.com/docs/integrations/tools/openweathermap |
a963b14eed7f-3 | Cloud cover: 75% Thought: I now know the current weather in London. Final Answer: The current weather in London is broken clouds, with a wind speed of 2.57 m/s, direction 240°, humidity of 56%, temperature of 20.11°C, high of 21.75°C, low of 18.68°C, and a heat index of None. > Finished chain. 'The current weather in London is broken clouds, with a wind speed of 2.57 m/s, direction 240°, humidity of 56%, temperature of 20.11°C, high of 21.75°C, low of 18.68°C, and a heat index of None.'PreviousMetaphor SearchNextPubMed ToolUse the wrapperUse the toolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/openweathermap |
cf95c1c238cf-0 | Golden Query | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/golden_query |
cf95c1c238cf-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsGolden QueryGolden QueryThis notebook goes over how to use the golden-query tool.Go to the Golden API docs to get an overview about the Golden API.Create a Golden account if you don't have one on the Golden Website.Get your API key from the Golden API Settings page.Save your API key into GOLDEN_API_KEY env variableimport osos.environ["GOLDEN_API_KEY"] = ""from langchain.utilities.golden_query import GoldenQueryAPIWrappergolden_query = GoldenQueryAPIWrapper()import jsonjson.loads(golden_query.run("companies in nanotech")) {'results': [{'id': 4673886, 'latestVersionId': 60276991, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Samsung', 'citations': []}]}]}, {'id': 7008, 'latestVersionId': 61087416, | https://python.langchain.com/docs/integrations/tools/golden_query |
cf95c1c238cf-2 | 'latestVersionId': 61087416, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Intel', 'citations': []}]}]}, {'id': 24193, 'latestVersionId': 60274482, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Texas Instruments', 'citations': []}]}]}, {'id': 1142, 'latestVersionId': 61406205, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Advanced Micro Devices', 'citations': []}]}]}, {'id': 193948, 'latestVersionId': 58326582, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Freescale Semiconductor', 'citations': []}]}]}, {'id': 91316, 'latestVersionId': 60387380, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Agilent Technologies', 'citations': []}]}]}, {'id': 90014, 'latestVersionId': 60388078, 'properties': [{'predicateId': 'name', 'instances': [{'value': | https://python.langchain.com/docs/integrations/tools/golden_query |
cf95c1c238cf-3 | 'name', 'instances': [{'value': 'Novartis', 'citations': []}]}]}, {'id': 237458, 'latestVersionId': 61406160, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Analog Devices', 'citations': []}]}]}, {'id': 3941943, 'latestVersionId': 60382250, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'AbbVie Inc.', 'citations': []}]}]}, {'id': 4178762, 'latestVersionId': 60542667, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'IBM', 'citations': []}]}]}], 'next': 'https://golden.com/api/v2/public/queries/59044/results/?cursor=eyJwb3NpdGlvbiI6IFsxNzYxNiwgIklCTS04M1lQM1oiXX0%3D&pageSize=10', 'previous': None}PreviousFile System ToolsNextGoogle PlacesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/golden_query |
cc03c8da7f6b-0 | Search Tools | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/search_tools |
cc03c8da7f6b-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsSearch ToolsOn this pageSearch ToolsThis notebook shows off usage of various search tools.from langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypefrom langchain.llms import OpenAIllm = OpenAI(temperature=0)Google Serper API Wrapper​First, let's try to use the Google Serper API tool.tools = load_tools(["google-serper"], llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the weather in Pomfret?") > Entering new AgentExecutor chain... I should look up the current weather conditions. Action: Search Action Input: "weather in Pomfret" Observation: 37°F Thought: I now know the current temperature in Pomfret. Final | https://python.langchain.com/docs/integrations/tools/search_tools |
cc03c8da7f6b-2 | Thought: I now know the current temperature in Pomfret. Final Answer: The current temperature in Pomfret is 37°F. > Finished chain. 'The current temperature in Pomfret is 37°F.'SerpAPI​Now, let's use the SerpAPI tool.tools = load_tools(["serpapi"], llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the weather in Pomfret?") > Entering new AgentExecutor chain... I need to find out what the current weather is in Pomfret. Action: Search Action Input: "weather in Pomfret" Observation: Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 ... Thought: I now know the current weather in Pomfret. Final Answer: Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 mph. > Finished chain. 'Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 mph.'GoogleSearchAPIWrapper​Now, let's use the official Google Search API Wrapper.tools = load_tools(["google-search"], llm=llm)agent = initialize_agent( tools, llm, | https://python.langchain.com/docs/integrations/tools/search_tools |
cc03c8da7f6b-3 | llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the weather in Pomfret?") > Entering new AgentExecutor chain... I should look up the current weather conditions. Action: Google Search Action Input: "weather in Pomfret" Observation: Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days. Hourly Weather-Pomfret, CT. As of 12:52 am EST. Special Weather Statement +2 ... Hazardous Weather Conditions. Special Weather Statement ... Pomfret CT. Tonight ... National Digital Forecast Database Maximum Temperature Forecast. Pomfret Center Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & tropical weather conditions for ... Pomfret, CT 12 hour by hour weather forecast includes precipitation, temperatures, sky conditions, rain chance, dew-point, relative humidity, wind direction ... North Pomfret Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & tropical weather conditions for ... Today's Weather - Pomfret, CT. Dec 31, 2022 4:00 PM. Putnam MS. --. Weather forecast icon. Feels like --. Hi --. Lo --. Pomfret, CT temperature trend for the next 14 Days. Find daytime highs and nighttime lows from TheWeatherNetwork.com. Pomfret, MD Weather Forecast Date: 332 PM EST Wed | https://python.langchain.com/docs/integrations/tools/search_tools |
cc03c8da7f6b-4 | from TheWeatherNetwork.com. Pomfret, MD Weather Forecast Date: 332 PM EST Wed Dec 28 2022. The area/counties/county of: Charles, including the cites of: St. Charles and Waldorf. Thought: I now know the current weather conditions in Pomfret. Final Answer: Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%. > Finished AgentExecutor chain. 'Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%.'SearxNG Meta Search Engine​Here we will be using a self hosted SearxNG meta search engine.tools = load_tools(["searx-search"], searx_host="http://localhost:8888", llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the weather in Pomfret") > Entering new AgentExecutor chain... I should look up the current weather Action: SearX Search Action Input: "weather in Pomfret" Observation: Mainly cloudy with snow showers around in the morning. High around 40F. Winds NNW at 5 to 10 mph. Chance of snow 40%. Snow accumulations less than one inch. 10 Day Weather - Pomfret, MD As of 1:37 pm EST Today 49°/ 41° 52% Mon 27 | | https://python.langchain.com/docs/integrations/tools/search_tools |
cc03c8da7f6b-5 | pm EST Today 49°/ 41° 52% Mon 27 | Day 49° 52% SE 14 mph Cloudy with occasional rain showers. High 49F. Winds SE at 10 to 20 mph. Chance of rain 50%.... 10 Day Weather - Pomfret, VT As of 3:51 am EST Special Weather Statement Today 39°/ 32° 37% Wed 01 | Day 39° 37% NE 4 mph Cloudy with snow showers developing for the afternoon. High 39F.... Pomfret, CT ; Current Weather. 1:06 AM. 35°F · RealFeel® 32° ; TODAY'S WEATHER FORECAST. 3/3. 44°Hi. RealFeel® 50° ; TONIGHT'S WEATHER FORECAST. 3/3. 32°Lo. Pomfret, MD Forecast Today Hourly Daily Morning 41° 1% Afternoon 43° 0% Evening 35° 3% Overnight 34° 2% Don't Miss Finally, Here’s Why We Get More Colds and Flu When It’s Cold Coast-To-Coast... Pomfret, MD Weather Forecast | AccuWeather Current Weather 5:35 PM 35° F RealFeel® 36° RealFeel Shade™ 36° Air Quality Excellent Wind E 3 mph Wind Gusts 5 mph Cloudy More Details WinterCast... Pomfret, VT Weather Forecast | AccuWeather Current Weather 11:21 AM 23° F RealFeel® | https://python.langchain.com/docs/integrations/tools/search_tools |
cc03c8da7f6b-6 | | AccuWeather Current Weather 11:21 AM 23° F RealFeel® 27° RealFeel Shade™ 25° Air Quality Fair Wind ESE 3 mph Wind Gusts 7 mph Cloudy More Details WinterCast... Pomfret Center, CT Weather Forecast | AccuWeather Daily Current Weather 6:50 PM 39° F RealFeel® 36° Air Quality Fair Wind NW 6 mph Wind Gusts 16 mph Mostly clear More Details WinterCast... 12:00 pm · Feels Like36° · WindN 5 mph · Humidity43% · UV Index3 of 10 · Cloud Cover65% · Rain Amount0 in ... Pomfret Center, CT Weather Conditions | Weather Underground star Popular Cities San Francisco, CA 49 °F Clear Manhattan, NY 37 °F Fair Schiller Park, IL (60176) warning39 °F Mostly Cloudy... Thought: I now know the final answer Final Answer: The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%. > Finished chain. 'The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.'PreviousSceneXplainNextSearxNG Search APIGoogle Serper API WrapperSerpAPIGoogleSearchAPIWrapperSearxNG Meta Search EngineCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/search_tools |
44e5491cc7fd-0 | SceneXplain | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/sceneXplain |
44e5491cc7fd-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsSceneXplainOn this pageSceneXplainSceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool.To use this tool, you'll need to make an account and fetch your API Token from the website. Then you can instantiate the tool.import osos.environ["SCENEX_API_KEY"] = "<YOUR_API_KEY>"from langchain.agents import load_toolstools = load_tools(["sceneXplain"])Or directly instantiate the tool.from langchain.tools import SceneXplainTooltool = SceneXplainTool()Usage in an Agent​The tool can be used in any LangChain agent as follows:from langchain.llms import OpenAIfrom langchain.agents import initialize_agentfrom langchain.memory import ConversationBufferMemoryllm = OpenAI(temperature=0)memory = ConversationBufferMemory(memory_key="chat_history")agent = initialize_agent( tools, llm, memory=memory, agent="conversational-react-description", verbose=True)output = agent.run( input=( | https://python.langchain.com/docs/integrations/tools/sceneXplain |
44e5491cc7fd-2 | verbose=True)output = agent.run( input=( "What is in this image https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png. " "Is it movie or a game? If it is a movie, what is the name of the movie?" ))print(output) > Entering new AgentExecutor chain... Thought: Do I need to use a tool? Yes Action: Image Explainer Action Input: https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png Observation: In a charmingly whimsical scene, a young girl is seen braving the rain alongside her furry companion, the lovable Totoro. The two are depicted standing on a bustling street corner, where they are sheltered from the rain by a bright yellow umbrella. The girl, dressed in a cheerful yellow frock, holds onto the umbrella with both hands while gazing up at Totoro with an expression of wonder and delight. Totoro, meanwhile, stands tall and proud beside his young friend, holding his own umbrella aloft to protect them both from the downpour. His furry body is rendered in rich shades of grey and white, while his large ears and wide eyes lend him an endearing charm. In the background of the scene, a street sign can be seen jutting out from the pavement amidst a flurry of raindrops. A sign with Chinese characters adorns its surface, adding to the sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy | https://python.langchain.com/docs/integrations/tools/sceneXplain |
44e5491cc7fd-3 | sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy and camaraderie in this heartwarming image. Thought: Do I need to use a tool? No AI: This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro. > Finished chain. This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.PreviousRequestsNextSearch ToolsUsage in an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/sceneXplain |
a8101e8d1f7c-0 | Wolfram Alpha | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/wolfram_alpha |
a8101e8d1f7c-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsWolfram AlphaWolfram AlphaThis notebook goes over how to use the wolfram alpha component.First, you need to set up your Wolfram Alpha developer account and get your APP ID:Go to wolfram alpha and sign up for a developer account hereCreate an app and get your APP IDpip install wolframalphaThen we will need to set some environment variables:Save your APP ID into WOLFRAM_ALPHA_APPID env variablepip install wolframalphaimport osos.environ["WOLFRAM_ALPHA_APPID"] = ""from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapperwolfram = WolframAlphaAPIWrapper()wolfram.run("What is 2x+5 = -3x + 7?") 'x = 2/5'PreviousWikipediaNextYouTubeSearchToolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/wolfram_alpha |
edc4b158d1e3-0 | ChatGPT Plugins | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/chatgpt_plugins |
edc4b158d1e3-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsChatGPT PluginsChatGPT PluginsThis example shows how to use ChatGPT Plugins within LangChain abstractions.Note 1: This currently only works for plugins with no auth.Note 2: There are almost certainly other ways to do this, this is just a first pass. If you have better ideas, please open a PR!from langchain.chat_models import ChatOpenAIfrom langchain.agents import load_tools, initialize_agentfrom langchain.agents import AgentTypefrom langchain.tools import AIPluginTooltool = AIPluginTool.from_plugin_url("https://www.klarna.com/.well-known/ai-plugin.json")llm = ChatOpenAI(temperature=0)tools = load_tools(["requests_all"])tools += [tool]agent_chain = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent_chain.run("what t shirts are available in klarna?") > Entering new AgentExecutor chain... | https://python.langchain.com/docs/integrations/tools/chatgpt_plugins |
edc4b158d1e3-2 | > Entering new AgentExecutor chain... I need to check the Klarna Shopping API to see if it has information on available t shirts. Action: KlarnaProducts Action Input: None Observation: Usage Guide: Use the Klarna plugin to get relevant product suggestions for any shopping or researching purpose. The query to be sent should not include stopwords like articles, prepositions and determinants. The api works best when searching for words that are related to products, like their name, brand, model or category. Links will always be returned and should be shown to the user. OpenAPI Spec: {'openapi': '3.0.1', 'info': {'version': 'v0', 'title': 'Open AI Klarna product Api'}, 'servers': [{'url': 'https://www.klarna.com/us/shopping'}], 'tags': [{'name': 'open-ai-product-endpoint', 'description': 'Open AI Product Endpoint. Query for products.'}], 'paths': {'/public/openai/v0/products': {'get': {'tags': ['open-ai-product-endpoint'], 'summary': 'API for fetching Klarna product information', 'operationId': 'productsUsingGET', 'parameters': [{'name': 'q', 'in': 'query', 'description': 'query, must be between 2 and 100 characters', 'required': True, 'schema': {'type': 'string'}}, {'name': 'size', 'in': 'query', 'description': 'number of products returned', 'required': False, 'schema': {'type': 'integer'}}, {'name': 'budget', 'in': 'query', 'description': 'maximum price of the matching product in local currency, filters results', 'required': False, 'schema': {'type': 'integer'}}], | https://python.langchain.com/docs/integrations/tools/chatgpt_plugins |
edc4b158d1e3-3 | currency, filters results', 'required': False, 'schema': {'type': 'integer'}}], 'responses': {'200': {'description': 'Products found', 'content': {'application/json': {'schema': {'$ref': '#/components/schemas/ProductResponse'}}}}, '503': {'description': 'one or more services are unavailable'}}, 'deprecated': False}}}, 'components': {'schemas': {'Product': {'type': 'object', 'properties': {'attributes': {'type': 'array', 'items': {'type': 'string'}}, 'name': {'type': 'string'}, 'price': {'type': 'string'}, 'url': {'type': 'string'}}, 'title': 'Product'}, 'ProductResponse': {'type': 'object', 'properties': {'products': {'type': 'array', 'items': {'$ref': '#/components/schemas/Product'}}}, 'title': 'ProductResponse'}}}} Thought:I need to use the Klarna Shopping API to search for t shirts. Action: requests_get Action Input: https://www.klarna.com/us/shopping/public/openai/v0/products?q=t%20shirts Observation: {"products":[{"name":"Lacoste Men's Pack of Plain T-Shirts","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202043025/Clothing/Lacoste-Men-s-Pack-of-Plain-T-Shirts/?utm_source=openai","price":"$26.60","attributes":["Material:Cotton","Target Group:Man","Color:White,Black"]},{"name":"Hanes Men's Ultimate 6pk. Crewneck | https://python.langchain.com/docs/integrations/tools/chatgpt_plugins |
edc4b158d1e3-4 | Men's Ultimate 6pk. Crewneck T-Shirts","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201808270/Clothing/Hanes-Men-s-Ultimate-6pk.-Crewneck-T-Shirts/?utm_source=openai","price":"$13.82","attributes":["Material:Cotton","Target Group:Man","Color:White"]},{"name":"Nike Boy's Jordan Stretch T-shirts","url":"https://www.klarna.com/us/shopping/pl/cl359/3201863202/Children-s-Clothing/Nike-Boy-s-Jordan-Stretch-T-shirts/?utm_source=openai","price":"$14.99","attributes":["Material:Cotton","Color:White,Green","Model:Boy","Size (Small-Large):S,XL,L,M"]},{"name":"Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203028500/Clothing/Polo-Classic-Fit-Cotton-V-Neck-T-Shirts-3-Pack/?utm_source=openai","price":"$29.95","attributes":["Material:Cotton","Target Group:Man","Color:White,Blue,Black"]},{"name":"adidas Comfort T-shirts Men's 3-pack","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202640533/Clothing/adidas-Comfort-T-shirts-Men-s-3-pack/?utm_source=openai","price":"$14.99","attributes":["Material:Cotton","Target Group:Man","Color:White,Black","Neckline:Round"]}]} Thought:The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton | https://python.langchain.com/docs/integrations/tools/chatgpt_plugins |
edc4b158d1e3-5 | Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack. Final Answer: The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack. > Finished chain. "The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack."PreviousBrave SearchNextDataForSeo API WrapperCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/chatgpt_plugins |
25af4539c135-0 | Bing Search | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/bing_search |
25af4539c135-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsBing SearchOn this pageBing SearchThis notebook goes over how to use the bing search component.First, you need to set up the proper API keys and environment variables. To set it up, follow the instructions found here.Then we will need to set some environment variables.import osos.environ["BING_SUBSCRIPTION_KEY"] = "<key>"os.environ["BING_SEARCH_URL"] = "https://api.bing.microsoft.com/v7.0/search"from langchain.utilities import BingSearchAPIWrappersearch = BingSearchAPIWrapper()search.run("python") 'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor. <b>Python</b> releases by version number: Release version Release date Click for more. | https://python.langchain.com/docs/integrations/tools/bing_search |
25af4539c135-2 | <b>Python</b> releases by version number: Release version Release date Click for more. <b>Python</b> 3.11.1 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.10.9 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.9.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.8.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.7.16 Dec. 6, 2022 Download Release Notes. In this lesson, we will look at the += operator in <b>Python</b> and see how it works with several simple examples.. The operator ‘+=’ is a shorthand for the addition assignment operator.It adds two values and assigns the sum to a variable (left operand). W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, <b>Python</b>, SQL, Java, and many, many more. This tutorial introduces the reader informally to the basic concepts and features of the <b>Python</b> language and system. It helps to have a <b>Python</b> interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well. For a description of standard objects and modules, see The <b>Python</b> Standard ... <b>Python</b> is a general-purpose, versatile, and powerful programming language. It's a great first language because <b>Python</b> code is concise and easy to read. Whatever you want to do, <b>python</b> can do it. From web | https://python.langchain.com/docs/integrations/tools/bing_search |
25af4539c135-3 | Whatever you want to do, <b>python</b> can do it. From web development to machine learning to data science, <b>Python</b> is the language for you. To install <b>Python</b> using the Microsoft Store: Go to your Start menu (lower left Windows icon), type "Microsoft Store", select the link to open the store. Once the store is open, select Search from the upper-right menu and enter "<b>Python</b>". Select which version of <b>Python</b> you would like to use from the results under Apps. Under the “<b>Python</b> Releases for Mac OS X� heading, click the link for the Latest <b>Python</b> 3 Release - <b>Python</b> 3.x.x. As of this writing, the latest version was <b>Python</b> 3.8.4. Scroll to the bottom and click macOS 64-bit installer to start the download. When the installer is finished downloading, move on to the next step. Step 2: Run the Installer'Number of results​You can use the k parameter to set the number of resultssearch = BingSearchAPIWrapper(k=1)search.run("python") 'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor.'Metadata Results​Run query through BingSearch and return snippet, title, and link metadata.Snippet: The description of the result.Title: The title of the result.Link: The link to the result.search = | https://python.langchain.com/docs/integrations/tools/bing_search |
25af4539c135-4 | description of the result.Title: The title of the result.Link: The link to the result.search = BingSearchAPIWrapper()search.results("apples", 5) [{'snippet': 'Lady Alice. Pink Lady <b>apples</b> aren’t the only lady in the apple family. Lady Alice <b>apples</b> were discovered growing, thanks to bees pollinating, in Washington. They are smaller and slightly more stout in appearance than other varieties. Their skin color appears to have red and yellow stripes running from stem to butt.', 'title': '25 Types of Apples - Jessica Gavin', 'link': 'https://www.jessicagavin.com/types-of-apples/'}, {'snippet': '<b>Apples</b> can do a lot for you, thanks to plant chemicals called flavonoids. And they have pectin, a fiber that breaks down in your gut. If you take off the apple’s skin before eating it, you won ...', 'title': 'Apples: Nutrition & Health Benefits - WebMD', 'link': 'https://www.webmd.com/food-recipes/benefits-apples'}, {'snippet': '<b>Apples</b> boast many vitamins and minerals, though not in high amounts. However, <b>apples</b> are usually a good source of vitamin C. Vitamin C. Also called ascorbic acid, this vitamin is a common ...', 'title': 'Apples 101: Nutrition Facts and Health Benefits', 'link': 'https://www.healthline.com/nutrition/foods/apples'}, {'snippet': 'Weight management. The fibers in <b>apples</b> | https://python.langchain.com/docs/integrations/tools/bing_search |
25af4539c135-5 | {'snippet': 'Weight management. The fibers in <b>apples</b> can slow digestion, helping one to feel greater satisfaction after eating. After following three large prospective cohorts of 133,468 men and women for 24 years, researchers found that higher intakes of fiber-rich fruits with a low glycemic load, particularly <b>apples</b> and pears, were associated with the least amount of weight gain over time.', 'title': 'Apples | The Nutrition Source | Harvard T.H. Chan School of Public Health', 'link': 'https://www.hsph.harvard.edu/nutritionsource/food-features/apples/'}]PreviousShell ToolNextBrave SearchNumber of resultsMetadata ResultsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/bing_search |
97ea4f0b0726-0 | SerpAPI | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/serpapi |
97ea4f0b0726-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsSerpAPIOn this pageSerpAPIThis notebook goes over how to use the SerpAPI component to search the web.from langchain.utilities import SerpAPIWrappersearch = SerpAPIWrapper()search.run("Obama's first name?") 'Barack Hussein Obama II'Custom Parameters​You can also customize the SerpAPI wrapper with arbitrary parameters. For example, in the below example we will use bing instead of google.params = { "engine": "bing", "gl": "us", "hl": "en",}search = SerpAPIWrapper(params=params)search.run("Obama's first name?") 'Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American presi…New content will be added above the current area of focus upon selectionBarack Hussein Obama II is an | https://python.langchain.com/docs/integrations/tools/serpapi |
97ea4f0b0726-2 | content will be added above the current area of focus upon selectionBarack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004, and previously worked as a civil rights lawyer before entering politics.Wikipediabarackobama.com'from langchain.agents import Tool# You can create the tool to pass to an agentrepl_tool = Tool( 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(...)`.", func=search.run,)PreviousSearxNG Search APINextTwilioCustom ParametersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/serpapi |
abd99c17dffe-0 | Twilio | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/twilio |
abd99c17dffe-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsTwilioOn this pageTwilioThis notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels.Twilio Messaging Channels facilitates integrations with 3rd party messaging apps and lets you send messages through WhatsApp Business Platform (GA), Facebook Messenger (Public Beta) and Google Business Messages (Private Beta).Setup​To use this tool you need to install the Python Twilio package twilio# !pip install twilioYou'll also need to set up a Twilio account and get your credentials. You'll need your Account String Identifier (SID) and your Auth Token. You'll also need a number to send messages from.You can either pass these in to the TwilioAPIWrapper as named parameters account_sid, auth_token, from_number, or you can set the environment variables TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, TWILIO_FROM_NUMBER.Sending an SMS​from langchain.utilities.twilio import TwilioAPIWrappertwilio = TwilioAPIWrapper( | https://python.langchain.com/docs/integrations/tools/twilio |
abd99c17dffe-2 | import TwilioAPIWrappertwilio = TwilioAPIWrapper( # account_sid="foo", # auth_token="bar", # from_number="baz,")twilio.run("hello world", "+16162904619")Sending a WhatsApp Message​You'll need to link your WhatsApp Business Account with Twilio. You'll also need to make sure that the number to send messages from is configured as a WhatsApp Enabled Sender on Twilio and registered with WhatsApp.from langchain.utilities.twilio import TwilioAPIWrappertwilio = TwilioAPIWrapper( # account_sid="foo", # auth_token="bar", # from_number="whatsapp: baz,")twilio.run("hello world", "whatsapp: +16162904619")PreviousSerpAPINextWikipediaSetupSending an SMSSending a WhatsApp MessageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/tools/twilio |
a6197e18da7f-0 | Requests | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/tools/requests |
a6197e18da7f-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsRequestsOn this pageRequestsThe web contains a lot of information that LLMs do not have access to. In order to easily let LLMs interact with that information, we provide a wrapper around the Python Requests module that takes in a URL and fetches data from that URL.from langchain.agents import load_toolsrequests_tools = load_tools(["requests_all"])requests_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 a 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, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)), RequestsPostTool(name='requests_post', description='Use this when you want to POST to a website.\n Input should be a json string with two keys: "url" and "data".\n | https://python.langchain.com/docs/integrations/tools/requests |
a6197e18da7f-2 | Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to POST to the url.\n Be careful to always use double quotes for strings in the json string\n The output will be the text response of the POST request.\n ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)), RequestsPatchTool(name='requests_patch', description='Use this when you want to PATCH to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to PATCH to the url.\n Be careful to always use double quotes for strings in the json string\n The output will be the text response of the PATCH request.\n ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)), RequestsPutTool(name='requests_put', description='Use this when you want to PUT to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to PUT to the url.\n Be careful to always use double quotes for strings in the json | https://python.langchain.com/docs/integrations/tools/requests |
a6197e18da7f-3 | to the url.\n Be careful to always use double quotes for strings in the json string.\n The output will be the text response of the PUT request.\n ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)), 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, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None))]Inside the tool​Each requests tool contains a requests wrapper. You can work with these wrappers directly below# Each tool wrapps a requests wrapperrequests_tools[0].requests_wrapper TextRequestsWrapper(headers=None, aiosession=None)from langchain.utilities import TextRequestsWrapperrequests = TextRequestsWrapper()requests.get("https://www.google.com") '<!doctype html><html itemscope="" itemtype="http://schema.org/WebPage" lang="en"><head><meta content="Search the world\'s information, including webpages, images, videos and more. Google has many special features to help you find exactly what you\'re looking for." name="description"><meta content="noodp" name="robots"><meta content="text/html; charset=UTF-8" http-equiv="Content-Type"><meta content="/images/branding/googleg/1x/googleg_standard_color_128dp.png" itemprop="image"><title>Google</title><script | https://python.langchain.com/docs/integrations/tools/requests |
a6197e18da7f-4 | nonce="MXrF0nnIBPkxBza4okrgPA">(function(){window.google={kEI:\'TA9QZOa5EdTakPIPuIad-Ac\',kEXPI:\'0,1359409,6059,206,4804,2316,383,246,5,1129120,1197768,626,380097,16111,28687,22431,1361,12319,17581,4997,13228,37471,7692,2891,3926,213,7615,606,50058,8228,17728,432,3,346,1244,1,16920,2648,4,1528,2304,29062,9871,3194,13658,2980,1457,16786,5803,2554,4094,7596,1,42154,2,14022,2373,342,23024,6699,3112 | https://python.langchain.com/docs/integrations/tools/requests |
a6197e18da7f-5 | ,342,23024,6699,31123,4568,6258,23418,1252,5835,14967,4333,4239,3245,445,2,2,1,26632,239,7916,7321,60,2,3,15965,872,7830,1796,10008,7,1922,9779,36154,6305,2007,17765,427,20136,14,82,2730,184,13600,3692,109,2412,1548,4308,3785,15175,3888,1515,3030,5628,478,4,9706,1804,7734,2738,1853,1032,9480,2995,576,1041,5648,3722,2058,3048,2130,2365,662,476,958,87,111,5807,2,975,1167,891,3580,1439,1128,7343,426, | https://python.langchain.com/docs/integrations/tools/requests |
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