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3,000 | 'and sells a range of related\n' 'services.', 'title': 'AAPL.O - | Stock Price & Latest News - Reuters', 'link': 'https://www.reuters.com/markets/companies/AAPL.O/'}, {'question': 'What is the full form of Apple Inc?', 'snippet': '(formerly Apple Computer Inc.) is an American ' 'computer and consumer electronics\n' 'company famous for creating the iPhone, iPad ' 'and Macintosh computers.', 'title': 'What is Apple? An products and history overview ' '- TechTarget', 'link': 'https://www.techtarget.com/whatis/definition/Apple'}, {'question': 'What is Apple Inc iPhone?', 'snippet': 'Apple Inc (Apple) designs, manufactures, and ' 'markets smartphones, tablets,\n' 'personal computers, and wearable devices. The ' 'company also offers software\n' 'applications and related services, ' 'accessories, and third-party digital content.\n' "Apple's product portfolio includes iPhone, " 'iPad, Mac, iPod, Apple Watch, and\n' 'Apple TV.', 'title': 'Apple Inc Company Profile - Apple Inc Overview - ' 'GlobalData', 'link': 'https://www.globaldata.com/company-profile/apple-inc/'}, {'question': 'Who runs Apple Inc?', 'snippet': 'Timothy Donald Cook (born November 1, 1960) is ' 'an American business executive\n' | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: 'and sells a range of related\n' 'services.', 'title': 'AAPL.O - | Stock Price & Latest News - Reuters', 'link': 'https://www.reuters.com/markets/companies/AAPL.O/'}, {'question': 'What is the full form of Apple Inc?', 'snippet': '(formerly Apple Computer Inc.) is an American ' 'computer and consumer electronics\n' 'company famous for creating the iPhone, iPad ' 'and Macintosh computers.', 'title': 'What is Apple? An products and history overview ' '- TechTarget', 'link': 'https://www.techtarget.com/whatis/definition/Apple'}, {'question': 'What is Apple Inc iPhone?', 'snippet': 'Apple Inc (Apple) designs, manufactures, and ' 'markets smartphones, tablets,\n' 'personal computers, and wearable devices. The ' 'company also offers software\n' 'applications and related services, ' 'accessories, and third-party digital content.\n' "Apple's product portfolio includes iPhone, " 'iPad, Mac, iPod, Apple Watch, and\n' 'Apple TV.', 'title': 'Apple Inc Company Profile - Apple Inc Overview - ' 'GlobalData', 'link': 'https://www.globaldata.com/company-profile/apple-inc/'}, {'question': 'Who runs Apple Inc?', 'snippet': 'Timothy Donald Cook (born November 1, 1960) is ' 'an American business executive\n' |
3,001 | 'an American business executive\n' 'who has been the chief executive officer of ' 'Apple Inc. since 2011. Cook\n' "previously served as the company's chief " 'operating officer under its co-founder\n' 'Steve Jobs. He is the first CEO of any Fortune ' '500 company who is openly gay.', 'title': 'Tim Cook - Wikipedia', 'link': 'https://en.wikipedia.org/wiki/Tim_Cook'}], 'relatedSearches': [{'query': 'Who invented the iPhone'}, {'query': 'Apple iPhone'}, {'query': 'History of Apple company PDF'}, {'query': 'Apple company history'}, {'query': 'Apple company introduction'}, {'query': 'Apple India'}, {'query': 'What does Apple Inc own'}, {'query': 'Apple Inc After Steve'}, {'query': 'Apple Watch'}, {'query': 'Apple App Store'}]}Searching for Google Images‚ÄãWe can also query Google Images using this wrapper. For example:search = GoogleSerperAPIWrapper(type="images")results = search.results("Lion")pprint.pp(results) {'searchParameters': {'q': 'Lion', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'images'}, 'images': [{'title': 'Lion - Wikipedia', 'imageUrl': 'https://upload.wikimedia.org/wikipedia/commons/thumb/7/73/Lion_waiting_in_Namibia.jpg/1200px-Lion_waiting_in_Namibia.jpg', 'imageWidth': 1200, 'imageHeight': 900, 'thumbnailUrl': | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: 'an American business executive\n' 'who has been the chief executive officer of ' 'Apple Inc. since 2011. Cook\n' "previously served as the company's chief " 'operating officer under its co-founder\n' 'Steve Jobs. He is the first CEO of any Fortune ' '500 company who is openly gay.', 'title': 'Tim Cook - Wikipedia', 'link': 'https://en.wikipedia.org/wiki/Tim_Cook'}], 'relatedSearches': [{'query': 'Who invented the iPhone'}, {'query': 'Apple iPhone'}, {'query': 'History of Apple company PDF'}, {'query': 'Apple company history'}, {'query': 'Apple company introduction'}, {'query': 'Apple India'}, {'query': 'What does Apple Inc own'}, {'query': 'Apple Inc After Steve'}, {'query': 'Apple Watch'}, {'query': 'Apple App Store'}]}Searching for Google Images‚ÄãWe can also query Google Images using this wrapper. For example:search = GoogleSerperAPIWrapper(type="images")results = search.results("Lion")pprint.pp(results) {'searchParameters': {'q': 'Lion', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'images'}, 'images': [{'title': 'Lion - Wikipedia', 'imageUrl': 'https://upload.wikimedia.org/wikipedia/commons/thumb/7/73/Lion_waiting_in_Namibia.jpg/1200px-Lion_waiting_in_Namibia.jpg', 'imageWidth': 1200, 'imageHeight': 900, 'thumbnailUrl': |
3,002 | 900, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRye79ROKwjfb6017jr0iu8Bz2E1KKuHg-A4qINJaspyxkZrkw&s', 'thumbnailWidth': 259, 'thumbnailHeight': 194, 'source': 'Wikipedia', 'domain': 'en.wikipedia.org', 'link': 'https://en.wikipedia.org/wiki/Lion', 'position': 1}, {'title': 'Lion | Characteristics, Habitat, & Facts | Britannica', 'imageUrl': 'https://cdn.britannica.com/55/2155-050-604F5A4A/lion.jpg', 'imageWidth': 754, 'imageHeight': 752, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS3fnDub1GSojI0hJ-ZGS8Tv-hkNNloXh98DOwXZoZ_nUs3GWSd&s', 'thumbnailWidth': 225, 'thumbnailHeight': 224, 'source': 'Encyclopedia Britannica', 'domain': 'www.britannica.com', 'link': 'https://www.britannica.com/animal/lion', 'position': 2}, {'title': 'African lion, facts and photos', 'imageUrl': 'https://i.natgeofe.com/n/487a0d69-8202-406f-a6a0-939ed3704693/african-lion.JPG', 'imageWidth': 3072, 'imageHeight': 2043, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTPlTarrtDbyTiEm-VI_PML9VtOTVPuDXJ5ybDf_lN11H2mShk&s', 'thumbnailWidth': 275, 'thumbnailHeight': 183, 'source': 'National Geographic', 'domain': 'www.nationalgeographic.com', 'link': 'https://www.nationalgeographic.com/animals/mammals/facts/african-lion', 'position': 3}, {'title': 'Saint Louis Zoo | African Lion', 'imageUrl': | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: 900, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRye79ROKwjfb6017jr0iu8Bz2E1KKuHg-A4qINJaspyxkZrkw&s', 'thumbnailWidth': 259, 'thumbnailHeight': 194, 'source': 'Wikipedia', 'domain': 'en.wikipedia.org', 'link': 'https://en.wikipedia.org/wiki/Lion', 'position': 1}, {'title': 'Lion | Characteristics, Habitat, & Facts | Britannica', 'imageUrl': 'https://cdn.britannica.com/55/2155-050-604F5A4A/lion.jpg', 'imageWidth': 754, 'imageHeight': 752, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS3fnDub1GSojI0hJ-ZGS8Tv-hkNNloXh98DOwXZoZ_nUs3GWSd&s', 'thumbnailWidth': 225, 'thumbnailHeight': 224, 'source': 'Encyclopedia Britannica', 'domain': 'www.britannica.com', 'link': 'https://www.britannica.com/animal/lion', 'position': 2}, {'title': 'African lion, facts and photos', 'imageUrl': 'https://i.natgeofe.com/n/487a0d69-8202-406f-a6a0-939ed3704693/african-lion.JPG', 'imageWidth': 3072, 'imageHeight': 2043, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTPlTarrtDbyTiEm-VI_PML9VtOTVPuDXJ5ybDf_lN11H2mShk&s', 'thumbnailWidth': 275, 'thumbnailHeight': 183, 'source': 'National Geographic', 'domain': 'www.nationalgeographic.com', 'link': 'https://www.nationalgeographic.com/animals/mammals/facts/african-lion', 'position': 3}, {'title': 'Saint Louis Zoo | African Lion', 'imageUrl': |
3,003 | Zoo | African Lion', 'imageUrl': 'https://optimise2.assets-servd.host/maniacal-finch/production/animals/african-lion-01-01.jpg?w=1200&auto=compress%2Cformat&fit=crop&dm=1658933674&s=4b63f926a0f524f2087a8e0613282bdb', 'imageWidth': 1200, 'imageHeight': 1200, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTlewcJ5SwC7yKup6ByaOjTnAFDeoOiMxyJTQaph2W_I3dnks4&s', 'thumbnailWidth': 225, 'thumbnailHeight': 225, 'source': 'St. Louis Zoo', 'domain': 'stlzoo.org', 'link': 'https://stlzoo.org/animals/mammals/carnivores/lion', 'position': 4}, {'title': 'How to Draw a Realistic Lion like an Artist - Studio ' 'Wildlife', 'imageUrl': 'https://studiowildlife.com/wp-content/uploads/2021/10/245528858_183911853822648_6669060845725210519_n.jpg', 'imageWidth': 1431, 'imageHeight': 2048, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTmn5HayVj3wqoBDQacnUtzaDPZzYHSLKUlIEcni6VB8w0mVeA&s', 'thumbnailWidth': 188, 'thumbnailHeight': 269, 'source': 'Studio Wildlife', 'domain': 'studiowildlife.com', 'link': 'https://studiowildlife.com/how-to-draw-a-realistic-lion-like-an-artist/', 'position': 5}, {'title': 'Lion | Characteristics, Habitat, & Facts | Britannica', 'imageUrl': 'https://cdn.britannica.com/29/150929-050-547070A1/lion-Kenya-Masai-Mara-National-Reserve.jpg', 'imageWidth': 1600, 'imageHeight': 1085, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSCqaKY_THr0IBZN8c-2VApnnbuvKmnsWjfrwKoWHFR9w3eN5o&s', 'thumbnailWidth': 273, 'thumbnailHeight': | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: Zoo | African Lion', 'imageUrl': 'https://optimise2.assets-servd.host/maniacal-finch/production/animals/african-lion-01-01.jpg?w=1200&auto=compress%2Cformat&fit=crop&dm=1658933674&s=4b63f926a0f524f2087a8e0613282bdb', 'imageWidth': 1200, 'imageHeight': 1200, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTlewcJ5SwC7yKup6ByaOjTnAFDeoOiMxyJTQaph2W_I3dnks4&s', 'thumbnailWidth': 225, 'thumbnailHeight': 225, 'source': 'St. Louis Zoo', 'domain': 'stlzoo.org', 'link': 'https://stlzoo.org/animals/mammals/carnivores/lion', 'position': 4}, {'title': 'How to Draw a Realistic Lion like an Artist - Studio ' 'Wildlife', 'imageUrl': 'https://studiowildlife.com/wp-content/uploads/2021/10/245528858_183911853822648_6669060845725210519_n.jpg', 'imageWidth': 1431, 'imageHeight': 2048, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTmn5HayVj3wqoBDQacnUtzaDPZzYHSLKUlIEcni6VB8w0mVeA&s', 'thumbnailWidth': 188, 'thumbnailHeight': 269, 'source': 'Studio Wildlife', 'domain': 'studiowildlife.com', 'link': 'https://studiowildlife.com/how-to-draw-a-realistic-lion-like-an-artist/', 'position': 5}, {'title': 'Lion | Characteristics, Habitat, & Facts | Britannica', 'imageUrl': 'https://cdn.britannica.com/29/150929-050-547070A1/lion-Kenya-Masai-Mara-National-Reserve.jpg', 'imageWidth': 1600, 'imageHeight': 1085, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSCqaKY_THr0IBZN8c-2VApnnbuvKmnsWjfrwKoWHFR9w3eN5o&s', 'thumbnailWidth': 273, 'thumbnailHeight': |
3,004 | 273, 'thumbnailHeight': 185, 'source': 'Encyclopedia Britannica', 'domain': 'www.britannica.com', 'link': 'https://www.britannica.com/animal/lion', 'position': 6}, {'title': "Where do lions live? Facts about lions' habitats and " 'other cool facts', 'imageUrl': 'https://www.gannett-cdn.com/-mm-/b2b05a4ab25f4fca0316459e1c7404c537a89702/c=0-0-1365-768/local/-/media/2022/03/16/USATODAY/usatsports/imageForEntry5-ODq.jpg?width=1365&height=768&fit=crop&format=pjpg&auto=webp', 'imageWidth': 1365, 'imageHeight': 768, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTc_4vCHscgvFvYy3PSrtIOE81kNLAfhDK8F3mfOuotL0kUkbs&s', 'thumbnailWidth': 299, 'thumbnailHeight': 168, 'source': 'USA Today', 'domain': 'www.usatoday.com', 'link': 'https://www.usatoday.com/story/news/2023/01/08/where-do-lions-live-habitat/10927718002/', 'position': 7}, {'title': 'Lion', 'imageUrl': 'https://i.natgeofe.com/k/1d33938b-3d02-4773-91e3-70b113c3b8c7/lion-male-roar_square.jpg', 'imageWidth': 3072, 'imageHeight': 3072, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQqLfnBrBLcTiyTZynHH3FGbBtX2bd1ScwpcuOLnksTyS9-4GM&s', 'thumbnailWidth': 225, 'thumbnailHeight': 225, 'source': 'National Geographic Kids', 'domain': 'kids.nationalgeographic.com', 'link': 'https://kids.nationalgeographic.com/animals/mammals/facts/lion', 'position': 8}, {'title': "Lion | Smithsonian's National Zoo", 'imageUrl': | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: 273, 'thumbnailHeight': 185, 'source': 'Encyclopedia Britannica', 'domain': 'www.britannica.com', 'link': 'https://www.britannica.com/animal/lion', 'position': 6}, {'title': "Where do lions live? Facts about lions' habitats and " 'other cool facts', 'imageUrl': 'https://www.gannett-cdn.com/-mm-/b2b05a4ab25f4fca0316459e1c7404c537a89702/c=0-0-1365-768/local/-/media/2022/03/16/USATODAY/usatsports/imageForEntry5-ODq.jpg?width=1365&height=768&fit=crop&format=pjpg&auto=webp', 'imageWidth': 1365, 'imageHeight': 768, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTc_4vCHscgvFvYy3PSrtIOE81kNLAfhDK8F3mfOuotL0kUkbs&s', 'thumbnailWidth': 299, 'thumbnailHeight': 168, 'source': 'USA Today', 'domain': 'www.usatoday.com', 'link': 'https://www.usatoday.com/story/news/2023/01/08/where-do-lions-live-habitat/10927718002/', 'position': 7}, {'title': 'Lion', 'imageUrl': 'https://i.natgeofe.com/k/1d33938b-3d02-4773-91e3-70b113c3b8c7/lion-male-roar_square.jpg', 'imageWidth': 3072, 'imageHeight': 3072, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQqLfnBrBLcTiyTZynHH3FGbBtX2bd1ScwpcuOLnksTyS9-4GM&s', 'thumbnailWidth': 225, 'thumbnailHeight': 225, 'source': 'National Geographic Kids', 'domain': 'kids.nationalgeographic.com', 'link': 'https://kids.nationalgeographic.com/animals/mammals/facts/lion', 'position': 8}, {'title': "Lion | Smithsonian's National Zoo", 'imageUrl': |
3,005 | National Zoo", 'imageUrl': 'https://nationalzoo.si.edu/sites/default/files/styles/1400_scale/public/animals/exhibit/africanlion-005.jpg?itok=6wA745g_', 'imageWidth': 1400, 'imageHeight': 845, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSgB3z_D4dMEOWJ7lajJk4XaQSL4DdUvIRj4UXZ0YoE5fGuWuo&s', 'thumbnailWidth': 289, 'thumbnailHeight': 174, 'source': "Smithsonian's National Zoo", 'domain': 'nationalzoo.si.edu', 'link': 'https://nationalzoo.si.edu/animals/lion', 'position': 9}, {'title': "Zoo's New Male Lion Explores Habitat for the First Time " '- Virginia Zoo', 'imageUrl': 'https://virginiazoo.org/wp-content/uploads/2022/04/ZOO_0056-scaled.jpg', 'imageWidth': 2560, 'imageHeight': 2141, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTDCG7XvXRCwpe_-Vy5mpvrQpVl5q2qwgnDklQhrJpQzObQGz4&s', 'thumbnailWidth': 246, 'thumbnailHeight': 205, 'source': 'Virginia Zoo', 'domain': 'virginiazoo.org', 'link': 'https://virginiazoo.org/zoos-new-male-lion-explores-habitat-for-thefirst-time/', 'position': 10}]}Searching for Google News‚ÄãWe can also query Google News using this wrapper. For example:search = GoogleSerperAPIWrapper(type="news")results = search.results("Tesla Inc.")pprint.pp(results) {'searchParameters': {'q': 'Tesla Inc.', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'news'}, 'news': [{'title': 'ISS recommends Tesla investors vote against re-election ' 'of Robyn Denholm', 'link': | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: National Zoo", 'imageUrl': 'https://nationalzoo.si.edu/sites/default/files/styles/1400_scale/public/animals/exhibit/africanlion-005.jpg?itok=6wA745g_', 'imageWidth': 1400, 'imageHeight': 845, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSgB3z_D4dMEOWJ7lajJk4XaQSL4DdUvIRj4UXZ0YoE5fGuWuo&s', 'thumbnailWidth': 289, 'thumbnailHeight': 174, 'source': "Smithsonian's National Zoo", 'domain': 'nationalzoo.si.edu', 'link': 'https://nationalzoo.si.edu/animals/lion', 'position': 9}, {'title': "Zoo's New Male Lion Explores Habitat for the First Time " '- Virginia Zoo', 'imageUrl': 'https://virginiazoo.org/wp-content/uploads/2022/04/ZOO_0056-scaled.jpg', 'imageWidth': 2560, 'imageHeight': 2141, 'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTDCG7XvXRCwpe_-Vy5mpvrQpVl5q2qwgnDklQhrJpQzObQGz4&s', 'thumbnailWidth': 246, 'thumbnailHeight': 205, 'source': 'Virginia Zoo', 'domain': 'virginiazoo.org', 'link': 'https://virginiazoo.org/zoos-new-male-lion-explores-habitat-for-thefirst-time/', 'position': 10}]}Searching for Google News‚ÄãWe can also query Google News using this wrapper. For example:search = GoogleSerperAPIWrapper(type="news")results = search.results("Tesla Inc.")pprint.pp(results) {'searchParameters': {'q': 'Tesla Inc.', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'news'}, 'news': [{'title': 'ISS recommends Tesla investors vote against re-election ' 'of Robyn Denholm', 'link': |
3,006 | 'of Robyn Denholm', 'link': 'https://www.reuters.com/business/autos-transportation/iss-recommends-tesla-investors-vote-against-re-election-robyn-denholm-2023-05-04/', 'snippet': 'Proxy advisory firm ISS on Wednesday recommended Tesla ' 'investors vote against re-election of board chair Robyn ' 'Denholm, citing "concerns on...', 'date': '5 mins ago', 'source': 'Reuters', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcROdETe_GUyp1e8RHNhaRM8Z_vfxCvdfinZwzL1bT1ZGSYaGTeOojIdBoLevA&s', 'position': 1}, {'title': 'Global companies by market cap: Tesla fell most in April', 'link': 'https://www.reuters.com/markets/global-companies-by-market-cap-tesla-fell-most-april-2023-05-02/', 'snippet': 'Tesla Inc was the biggest loser among top companies by ' 'market capitalisation in April, hit by disappointing ' 'quarterly earnings after it...', 'date': '1 day ago', 'source': 'Reuters', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ4u4CP8aOdGyRFH6o4PkXi-_eZDeY96vLSag5gDjhKMYf98YBER2cZPbkStQ&s', 'position': 2}, {'title': 'Tesla Wanted an EV Price War. Ford Showed Up.', 'link': 'https://www.bloomberg.com/opinion/articles/2023-05-03/tesla-wanted-an-ev-price-war-ford-showed-up', 'snippet': 'The legacy automaker is paring back the cost of its ' 'Mustang Mach-E model after Tesla discounted its ' 'competing EVs, portending tighter...', 'date': '6 hours ago', 'source': 'Bloomberg.com', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS_3Eo4VI0H-nTeIbYc5DaQn5ep7YrWnmhx6pv8XddFgNF5zRC9gEpHfDq8yQ&s', | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: 'of Robyn Denholm', 'link': 'https://www.reuters.com/business/autos-transportation/iss-recommends-tesla-investors-vote-against-re-election-robyn-denholm-2023-05-04/', 'snippet': 'Proxy advisory firm ISS on Wednesday recommended Tesla ' 'investors vote against re-election of board chair Robyn ' 'Denholm, citing "concerns on...', 'date': '5 mins ago', 'source': 'Reuters', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcROdETe_GUyp1e8RHNhaRM8Z_vfxCvdfinZwzL1bT1ZGSYaGTeOojIdBoLevA&s', 'position': 1}, {'title': 'Global companies by market cap: Tesla fell most in April', 'link': 'https://www.reuters.com/markets/global-companies-by-market-cap-tesla-fell-most-april-2023-05-02/', 'snippet': 'Tesla Inc was the biggest loser among top companies by ' 'market capitalisation in April, hit by disappointing ' 'quarterly earnings after it...', 'date': '1 day ago', 'source': 'Reuters', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ4u4CP8aOdGyRFH6o4PkXi-_eZDeY96vLSag5gDjhKMYf98YBER2cZPbkStQ&s', 'position': 2}, {'title': 'Tesla Wanted an EV Price War. Ford Showed Up.', 'link': 'https://www.bloomberg.com/opinion/articles/2023-05-03/tesla-wanted-an-ev-price-war-ford-showed-up', 'snippet': 'The legacy automaker is paring back the cost of its ' 'Mustang Mach-E model after Tesla discounted its ' 'competing EVs, portending tighter...', 'date': '6 hours ago', 'source': 'Bloomberg.com', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS_3Eo4VI0H-nTeIbYc5DaQn5ep7YrWnmhx6pv8XddFgNF5zRC9gEpHfDq8yQ&s', |
3,007 | 'position': 3}, {'title': 'Joby Aviation to get investment from Tesla shareholder ' 'Baillie Gifford', 'link': 'https://finance.yahoo.com/news/joby-aviation-investment-tesla-shareholder-204450712.html', 'snippet': 'This comes days after Joby clinched a $55 million ' 'contract extension to deliver up to nine air taxis to ' 'the U.S. Air Force,...', 'date': '4 hours ago', 'source': 'Yahoo Finance', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQO0uVn297LI-xryrPNqJ-apUOulj4ohM-xkN4OfmvMOYh1CPdUEBbYx6hviw&s', 'position': 4}, {'title': 'Tesla resumes U.S. orders for a Model 3 version at lower ' 'price, range', 'link': 'https://finance.yahoo.com/news/tesla-resumes-us-orders-model-045736115.html', 'snippet': '(Reuters) -Tesla Inc has resumed taking orders for its ' 'Model 3 long-range vehicle in the United States, the ' "company's website showed late on...", 'date': '19 hours ago', 'source': 'Yahoo Finance', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTIZetJ62sQefPfbQ9KKDt6iH7Mc0ylT5t_hpgeeuUkHhJuAx2FOJ4ZTRVDFg&s', 'position': 5}, {'title': 'The Tesla Model 3 Long Range AWD Is Now Available in the ' 'U.S. With 325 Miles of Range', 'link': 'https://www.notateslaapp.com/news/1393/tesla-reopens-orders-for-model-3-long-range-after-months-of-unavailability', 'snippet': 'Tesla has reopened orders for the Model 3 Long Range ' 'RWD, which has been unavailable for months due to high ' 'demand.', 'date': '7 hours ago', 'source': 'Not a Tesla App', | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: 'position': 3}, {'title': 'Joby Aviation to get investment from Tesla shareholder ' 'Baillie Gifford', 'link': 'https://finance.yahoo.com/news/joby-aviation-investment-tesla-shareholder-204450712.html', 'snippet': 'This comes days after Joby clinched a $55 million ' 'contract extension to deliver up to nine air taxis to ' 'the U.S. Air Force,...', 'date': '4 hours ago', 'source': 'Yahoo Finance', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQO0uVn297LI-xryrPNqJ-apUOulj4ohM-xkN4OfmvMOYh1CPdUEBbYx6hviw&s', 'position': 4}, {'title': 'Tesla resumes U.S. orders for a Model 3 version at lower ' 'price, range', 'link': 'https://finance.yahoo.com/news/tesla-resumes-us-orders-model-045736115.html', 'snippet': '(Reuters) -Tesla Inc has resumed taking orders for its ' 'Model 3 long-range vehicle in the United States, the ' "company's website showed late on...", 'date': '19 hours ago', 'source': 'Yahoo Finance', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTIZetJ62sQefPfbQ9KKDt6iH7Mc0ylT5t_hpgeeuUkHhJuAx2FOJ4ZTRVDFg&s', 'position': 5}, {'title': 'The Tesla Model 3 Long Range AWD Is Now Available in the ' 'U.S. With 325 Miles of Range', 'link': 'https://www.notateslaapp.com/news/1393/tesla-reopens-orders-for-model-3-long-range-after-months-of-unavailability', 'snippet': 'Tesla has reopened orders for the Model 3 Long Range ' 'RWD, which has been unavailable for months due to high ' 'demand.', 'date': '7 hours ago', 'source': 'Not a Tesla App', |
3,008 | 'source': 'Not a Tesla App', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSecrgxZpRj18xIJY-nDHljyP-A4ejEkswa9eq77qhMNrScnVIqe34uql5U4w&s', 'position': 6}, {'title': 'Tesla Cybertruck alpha prototype spotted at the Fremont ' 'factory in new pics and videos', 'link': 'https://www.teslaoracle.com/2023/05/03/tesla-cybertruck-alpha-prototype-interior-and-exterior-spotted-at-the-fremont-factory-in-new-pics-and-videos/', 'snippet': 'A Tesla Cybertruck alpha prototype goes to Fremont, ' 'California for another round of testing before going to ' 'production later this year (pics...', 'date': '14 hours ago', 'source': 'Tesla Oracle', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRO7M5ZLQE-Zo4-_5dv9hNAQZ3wSqfvYCuKqzxHG-M6CgLpwPMMG_ssebdcMg&s', 'position': 7}, {'title': 'Tesla putting facility in new part of country - Austin ' 'Business Journal', 'link': 'https://www.bizjournals.com/austin/news/2023/05/02/tesla-leases-building-seattle-area.html', 'snippet': 'Check out what Puget Sound Business Journal has to ' "report about the Austin-based company's real estate " 'footprint in the Pacific Northwest.', 'date': '22 hours ago', 'source': 'The Business Journals', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR9kIEHWz1FcHKDUtGQBS0AjmkqtyuBkQvD8kyIY3kpaPrgYaN7I_H2zoOJsA&s', 'position': 8}, {'title': 'Tesla (TSLA) Resumes Orders for Model 3 Long Range After ' 'Backlog', 'link': 'https://www.bloomberg.com/news/articles/2023-05-03/tesla-resumes-orders-for-popular-model-3-long-range-at-47-240', | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: 'source': 'Not a Tesla App', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSecrgxZpRj18xIJY-nDHljyP-A4ejEkswa9eq77qhMNrScnVIqe34uql5U4w&s', 'position': 6}, {'title': 'Tesla Cybertruck alpha prototype spotted at the Fremont ' 'factory in new pics and videos', 'link': 'https://www.teslaoracle.com/2023/05/03/tesla-cybertruck-alpha-prototype-interior-and-exterior-spotted-at-the-fremont-factory-in-new-pics-and-videos/', 'snippet': 'A Tesla Cybertruck alpha prototype goes to Fremont, ' 'California for another round of testing before going to ' 'production later this year (pics...', 'date': '14 hours ago', 'source': 'Tesla Oracle', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRO7M5ZLQE-Zo4-_5dv9hNAQZ3wSqfvYCuKqzxHG-M6CgLpwPMMG_ssebdcMg&s', 'position': 7}, {'title': 'Tesla putting facility in new part of country - Austin ' 'Business Journal', 'link': 'https://www.bizjournals.com/austin/news/2023/05/02/tesla-leases-building-seattle-area.html', 'snippet': 'Check out what Puget Sound Business Journal has to ' "report about the Austin-based company's real estate " 'footprint in the Pacific Northwest.', 'date': '22 hours ago', 'source': 'The Business Journals', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR9kIEHWz1FcHKDUtGQBS0AjmkqtyuBkQvD8kyIY3kpaPrgYaN7I_H2zoOJsA&s', 'position': 8}, {'title': 'Tesla (TSLA) Resumes Orders for Model 3 Long Range After ' 'Backlog', 'link': 'https://www.bloomberg.com/news/articles/2023-05-03/tesla-resumes-orders-for-popular-model-3-long-range-at-47-240', |
3,009 | 'snippet': 'Tesla Inc. has resumed taking orders for its Model 3 ' 'Long Range edition with a starting price of $47240, ' 'according to its website.', 'date': '5 hours ago', 'source': 'Bloomberg.com', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTWWIC4VpMTfRvSyqiomODOoLg0xhoBf-Tc1qweKnSuaiTk-Y1wMJZM3jct0w&s', 'position': 9}]}If you want to only receive news articles published in the last hour, you can do the following:search = GoogleSerperAPIWrapper(type="news", tbs="qdr:h")results = search.results("Tesla Inc.")pprint.pp(results) {'searchParameters': {'q': 'Tesla Inc.', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'news', 'tbs': 'qdr:h'}, 'news': [{'title': 'Oklahoma Gov. Stitt sees growing foreign interest in ' 'investments in ...', 'link': 'https://www.reuters.com/world/us/oklahoma-gov-stitt-sees-growing-foreign-interest-investments-state-2023-05-04/', 'snippet': 'T)), a battery supplier to electric vehicle maker Tesla ' 'Inc (TSLA.O), said on Sunday it is considering building ' 'a battery plant in Oklahoma, its third in...', 'date': '53 mins ago', 'source': 'Reuters', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSSTcsXeenqmEKdiekvUgAmqIPR4nlAmgjTkBqLpza-lLfjX1CwB84MoNVj0Q&s', 'position': 1}, {'title': 'Ryder lanza solución llave en mano para vehículos ' 'eléctricos en EU', 'link': 'https://www.tyt.com.mx/nota/ryder-lanza-solucion-llave-en-mano-para-vehiculos-electricos-en-eu', 'snippet': 'Ryder System Inc. presentó RyderElectric+ TM como su ' | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: 'snippet': 'Tesla Inc. has resumed taking orders for its Model 3 ' 'Long Range edition with a starting price of $47240, ' 'according to its website.', 'date': '5 hours ago', 'source': 'Bloomberg.com', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTWWIC4VpMTfRvSyqiomODOoLg0xhoBf-Tc1qweKnSuaiTk-Y1wMJZM3jct0w&s', 'position': 9}]}If you want to only receive news articles published in the last hour, you can do the following:search = GoogleSerperAPIWrapper(type="news", tbs="qdr:h")results = search.results("Tesla Inc.")pprint.pp(results) {'searchParameters': {'q': 'Tesla Inc.', 'gl': 'us', 'hl': 'en', 'num': 10, 'type': 'news', 'tbs': 'qdr:h'}, 'news': [{'title': 'Oklahoma Gov. Stitt sees growing foreign interest in ' 'investments in ...', 'link': 'https://www.reuters.com/world/us/oklahoma-gov-stitt-sees-growing-foreign-interest-investments-state-2023-05-04/', 'snippet': 'T)), a battery supplier to electric vehicle maker Tesla ' 'Inc (TSLA.O), said on Sunday it is considering building ' 'a battery plant in Oklahoma, its third in...', 'date': '53 mins ago', 'source': 'Reuters', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSSTcsXeenqmEKdiekvUgAmqIPR4nlAmgjTkBqLpza-lLfjX1CwB84MoNVj0Q&s', 'position': 1}, {'title': 'Ryder lanza solución llave en mano para vehículos ' 'eléctricos en EU', 'link': 'https://www.tyt.com.mx/nota/ryder-lanza-solucion-llave-en-mano-para-vehiculos-electricos-en-eu', 'snippet': 'Ryder System Inc. presentó RyderElectric+ TM como su ' |
3,010 | presentó RyderElectric+ TM como su ' 'nueva solución llave en mano ... Ryder también tiene ' 'reservados los semirremolques Tesla y continúa...', 'date': '56 mins ago', 'source': 'Revista Transportes y Turismo', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQJhXTQQtjSUZf9YPM235WQhFU5_d7lEA76zB8DGwZfixcgf1_dhPJyKA1Nbw&s', 'position': 2}, {'title': '"I think people can get by with $999 million," Bernie ' 'Sanders 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) | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: presentó RyderElectric+ TM como su ' 'nueva solución llave en mano ... Ryder también tiene ' 'reservados los semirremolques Tesla y continúa...', 'date': '56 mins ago', 'source': 'Revista Transportes y Turismo', 'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQJhXTQQtjSUZf9YPM235WQhFU5_d7lEA76zB8DGwZfixcgf1_dhPJyKA1Nbw&s', 'position': 2}, {'title': '"I think people can get by with $999 million," Bernie ' 'Sanders 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) |
3,011 | 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) | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: 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) |
3,012 | 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‚ÄãWe 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://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': '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, | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: 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‚ÄãWe 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://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': '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, |
3,013 | {'position': 4, 'title': 'Luna Rossa', 'address': '347 E 85th St', 'latitude': 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, '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': 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': | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: {'position': 4, 'title': 'Luna Rossa', 'address': '347 E 85th St', 'latitude': 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, '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': 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': |
3,014 | -73.9689825, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPifIgzOCD5SjgzzqBzGkdZCBp0MQsK5k7M7znn=w92-h92-n-k-no', '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': '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 SearchNextGradioAs part of a Self Ask With Search ChainObtaining results with metadataSearching for Google ImagesSearching for Google NewsSearching for Google PlacesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. | This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at serper.dev and get your api key. ->: -73.9689825, 'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPifIgzOCD5SjgzzqBzGkdZCBp0MQsK5k7M7znn=w92-h92-n-k-no', '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': '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 SearchNextGradioAs part of a Self Ask With Search ChainObtaining results with metadataSearching for Google ImagesSearching for Google NewsSearching for Google PlacesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,015 | Google Drive | ü¶úÔ∏èüîó Langchain | This notebook walks through connecting a LangChain to the Google Drive API. | This notebook walks through connecting a LangChain to the Google Drive API. ->: Google Drive | ü¶úÔ∏èüîó Langchain |
3,016 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGoogle DriveOn this pageGoogle DriveThis notebook walks through connecting a LangChain to the Google Drive API.Prerequisites‚ÄãCreate a Google Cloud project or use an existing projectEnable the Google Drive APIAuthorize credentials for desktop apppip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlibInstructions for retrieving your Google Docs data‚ÄãBy default, the GoogleDriveTools and GoogleDriveWrapper expects the credentials.json file to be ~/.credentials/credentials.json, but this is configurable using the GOOGLE_ACCOUNT_FILE environment variable. | This notebook walks through connecting a LangChain to the Google Drive API. | This notebook walks through connecting a LangChain to the Google Drive API. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGoogle DriveOn this pageGoogle DriveThis notebook walks through connecting a LangChain to the Google Drive API.Prerequisites‚ÄãCreate a Google Cloud project or use an existing projectEnable the Google Drive APIAuthorize credentials for desktop apppip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlibInstructions for retrieving your Google Docs data‚ÄãBy default, the GoogleDriveTools and GoogleDriveWrapper expects the credentials.json file to be ~/.credentials/credentials.json, but this is configurable using the GOOGLE_ACCOUNT_FILE environment variable. |
3,017 | The location of token.json use the same directory (or use the parameter token_path). Note that token.json will be created automatically the first time you use the tool.GoogleDriveSearchTool can retrieve a selection of files with some requests. By default, If you use a folder_id, all the files inside this folder can be retrieved to Document, if the name match the query.#!pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlibYou can obtain your folder and document id from the URL:Folder: https://drive.google.com/drive/u/0/folders/1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5 -> folder id is "1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5"Document: https://docs.google.com/document/d/1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw/edit -> document id is "1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw"The special value root is for your personal home.folder_id="root"#folder_id='1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5'By default, all files with these mime-type can be converted to Document.text/texttext/plaintext/htmltext/csvtext/markdownimage/pngimage/jpegapplication/epub+zipapplication/pdfapplication/rtfapplication/vnd.google-apps.document (GDoc)application/vnd.google-apps.presentation (GSlide)application/vnd.google-apps.spreadsheet (GSheet)application/vnd.google.colaboratory (Notebook colab)application/vnd.openxmlformats-officedocument.presentationml.presentation (PPTX)application/vnd.openxmlformats-officedocument.wordprocessingml.document (DOCX)It's possible to update or customize this. See the documentation of GoogleDriveAPIWrapper.But, the corresponding packages must installed.#!pip install unstructuredfrom langchain.utilities.google_drive import GoogleDriveAPIWrapperfrom langchain.tools.google_drive.tool import GoogleDriveSearchTool# By default, search only in the filename.tool = GoogleDriveSearchTool( api_wrapper=GoogleDriveAPIWrapper( folder_id=folder_id, num_results=2, template="gdrive-query-in-folder", # Search in the body of documents | This notebook walks through connecting a LangChain to the Google Drive API. | This notebook walks through connecting a LangChain to the Google Drive API. ->: The location of token.json use the same directory (or use the parameter token_path). Note that token.json will be created automatically the first time you use the tool.GoogleDriveSearchTool can retrieve a selection of files with some requests. By default, If you use a folder_id, all the files inside this folder can be retrieved to Document, if the name match the query.#!pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlibYou can obtain your folder and document id from the URL:Folder: https://drive.google.com/drive/u/0/folders/1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5 -> folder id is "1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5"Document: https://docs.google.com/document/d/1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw/edit -> document id is "1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw"The special value root is for your personal home.folder_id="root"#folder_id='1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5'By default, all files with these mime-type can be converted to Document.text/texttext/plaintext/htmltext/csvtext/markdownimage/pngimage/jpegapplication/epub+zipapplication/pdfapplication/rtfapplication/vnd.google-apps.document (GDoc)application/vnd.google-apps.presentation (GSlide)application/vnd.google-apps.spreadsheet (GSheet)application/vnd.google.colaboratory (Notebook colab)application/vnd.openxmlformats-officedocument.presentationml.presentation (PPTX)application/vnd.openxmlformats-officedocument.wordprocessingml.document (DOCX)It's possible to update or customize this. See the documentation of GoogleDriveAPIWrapper.But, the corresponding packages must installed.#!pip install unstructuredfrom langchain.utilities.google_drive import GoogleDriveAPIWrapperfrom langchain.tools.google_drive.tool import GoogleDriveSearchTool# By default, search only in the filename.tool = GoogleDriveSearchTool( api_wrapper=GoogleDriveAPIWrapper( folder_id=folder_id, num_results=2, template="gdrive-query-in-folder", # Search in the body of documents |
3,018 | # Search in the body of documents ))import logginglogging.basicConfig(level=logging.INFO)tool.run("machine learning")tool.descriptionfrom langchain.agents import load_toolstools = load_tools(["google-drive-search"], folder_id=folder_id, template="gdrive-query-in-folder", )Use within an Agent​from langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=tools, llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,)agent.run( "Search in google drive, who is 'Yann LeCun' ?")PreviousGolden QueryNextGoogle PlacesPrerequisitesInstructions for retrieving your Google Docs dataUse within an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook walks through connecting a LangChain to the Google Drive API. | This notebook walks through connecting a LangChain to the Google Drive API. ->: # Search in the body of documents ))import logginglogging.basicConfig(level=logging.INFO)tool.run("machine learning")tool.descriptionfrom langchain.agents import load_toolstools = load_tools(["google-drive-search"], folder_id=folder_id, template="gdrive-query-in-folder", )Use within an Agent​from langchain.llms import OpenAIfrom langchain.agents import initialize_agent, AgentTypellm = OpenAI(temperature=0)agent = initialize_agent( tools=tools, llm=llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,)agent.run( "Search in google drive, who is 'Yann LeCun' ?")PreviousGolden QueryNextGoogle PlacesPrerequisitesInstructions for retrieving your Google Docs dataUse within an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,019 | Shell (bash) | ü¶úÔ∏èüîó Langchain | Giving agents access to the shell is powerful (though risky outside a sandboxed environment). | Giving agents access to the shell is powerful (though risky outside a sandboxed environment). ->: Shell (bash) | ü¶úÔ∏èüîó Langchain |
3,020 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsShell (bash)On this pageShell (bash)Giving 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.Note: Shell tool does not work with Windows OS.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‚ÄãAs with all 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("}", | Giving agents access to the shell is powerful (though risky outside a sandboxed environment). | Giving agents access to the shell is powerful (though risky outside a sandboxed environment). ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsShell (bash)On this pageShell (bash)Giving 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.Note: Shell tool does not work with Windows OS.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‚ÄãAs with all 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("}", |
3,021 | "{", "{{").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://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/", | Giving agents access to the shell is powerful (though risky outside a sandboxed environment). | Giving agents access to the shell is powerful (though risky outside a sandboxed environment). ->: "{", "{{").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://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/", |
3,022 | "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"]'PreviousAWS LambdaNextBearly Code InterpreterUse with AgentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Giving agents access to the shell is powerful (though risky outside a sandboxed environment). | Giving agents access to the shell is powerful (though risky outside a sandboxed environment). ->: "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"]'PreviousAWS LambdaNextBearly Code InterpreterUse with AgentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,023 | SerpAPI | ü¶úÔ∏èüîó Langchain | This notebook goes over how to use the SerpAPI component to search the web. | This notebook goes over how to use the SerpAPI component to search the web. ->: SerpAPI | ü¶úÔ∏èüîó Langchain |
3,024 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsSerpAPIOn 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‚ÄãYou 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‚ĶNew 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 | This notebook goes over how to use the SerpAPI component to search the web. | This notebook goes over how to use the SerpAPI component to search the web. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsSerpAPIOn 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‚ÄãYou 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‚ĶNew 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 |
3,025 | 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 SearchNextTwilioCustom ParametersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook goes over how to use the SerpAPI component to search the web. | This notebook goes over how to use the SerpAPI component to search the web. ->: 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 SearchNextTwilioCustom ParametersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,026 | Dall-E Image Generator | ü¶úÔ∏èüîó Langchain | This notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM. | This notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM. ->: Dall-E Image Generator | ü¶úÔ∏èüîó Langchain |
3,027 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsDall-E Image GeneratorOn this pageDall-E Image GeneratorThis notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM.# Needed if you would like to display images in the notebookpip install opencv-python scikit-imagefrom langchain.llms import OpenAIimport osos.environ["OPENAI_API_KEY"] = "<your-key-here>"Run as a chain‚Äãfrom langchain.utilities.dalle_image_generator import DallEAPIWrapperfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain.llms import OpenAIllm = OpenAI(temperature=0.9)prompt = PromptTemplate( input_variables=["image_desc"], template="Generate a detailed prompt to generate an image based on the following description: {image_desc}",)chain = LLMChain(llm=llm, prompt=prompt)image_url = DallEAPIWrapper().run(chain.run("halloween night at a haunted museum"))image_url | This notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM. | This notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsDall-E Image GeneratorOn this pageDall-E Image GeneratorThis notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM.# Needed if you would like to display images in the notebookpip install opencv-python scikit-imagefrom langchain.llms import OpenAIimport osos.environ["OPENAI_API_KEY"] = "<your-key-here>"Run as a chain‚Äãfrom langchain.utilities.dalle_image_generator import DallEAPIWrapperfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain.llms import OpenAIllm = OpenAI(temperature=0.9)prompt = PromptTemplate( input_variables=["image_desc"], template="Generate a detailed prompt to generate an image based on the following description: {image_desc}",)chain = LLMChain(llm=llm, prompt=prompt)image_url = DallEAPIWrapper().run(chain.run("halloween night at a haunted museum"))image_url |
3,028 | night at a haunted museum"))image_url 'https://oaidalleapiprodscus.blob.core.windows.net/private/org-i0zjYONU3PemzJ222esBaAzZ/user-f6uEIOFxoiUZivy567cDSWni/img-i7Z2ZxvJ4IbbdAiO6OXJgS3v.png?st=2023-08-11T14%3A03%3A14Z&se=2023-08-11T16%3A03%3A14Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-08-10T20%3A58%3A32Z&ske=2023-08-11T20%3A58%3A32Z&sks=b&skv=2021-08-06&sig=/sECe7C0EAq37ssgBm7g7JkVIM/Q1W3xOstd0Go6slA%3D'# You can click on the link above to display the image # Or you can try the options below to display the image inline in this notebooktry: import google.colab IN_COLAB = Trueexcept: IN_COLAB = Falseif IN_COLAB: from google.colab.patches import cv2_imshow # for image display from skimage import io image = io.imread(image_url) cv2_imshow(image)else: import cv2 from skimage import io image = io.imread(image_url) cv2.imshow('image', image) cv2.waitKey(0) #wait for a keyboard input cv2.destroyAllWindows()Run as a tool with an agent‚Äãfrom langchain.agents import load_toolsfrom langchain.agents import initialize_agenttools = load_tools(['dalle-image-generator'])agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)output = agent.run("Create an image of a halloween night at a haunted museum") > Entering new AgentExecutor chain... What is the best way to turn this description into an image? Action: Dall-E Image Generator Action Input: A spooky Halloween night at a haunted | This notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM. | This notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM. ->: night at a haunted museum"))image_url 'https://oaidalleapiprodscus.blob.core.windows.net/private/org-i0zjYONU3PemzJ222esBaAzZ/user-f6uEIOFxoiUZivy567cDSWni/img-i7Z2ZxvJ4IbbdAiO6OXJgS3v.png?st=2023-08-11T14%3A03%3A14Z&se=2023-08-11T16%3A03%3A14Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-08-10T20%3A58%3A32Z&ske=2023-08-11T20%3A58%3A32Z&sks=b&skv=2021-08-06&sig=/sECe7C0EAq37ssgBm7g7JkVIM/Q1W3xOstd0Go6slA%3D'# You can click on the link above to display the image # Or you can try the options below to display the image inline in this notebooktry: import google.colab IN_COLAB = Trueexcept: IN_COLAB = Falseif IN_COLAB: from google.colab.patches import cv2_imshow # for image display from skimage import io image = io.imread(image_url) cv2_imshow(image)else: import cv2 from skimage import io image = io.imread(image_url) cv2.imshow('image', image) cv2.waitKey(0) #wait for a keyboard input cv2.destroyAllWindows()Run as a tool with an agent‚Äãfrom langchain.agents import load_toolsfrom langchain.agents import initialize_agenttools = load_tools(['dalle-image-generator'])agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)output = agent.run("Create an image of a halloween night at a haunted museum") > Entering new AgentExecutor chain... What is the best way to turn this description into an image? Action: Dall-E Image Generator Action Input: A spooky Halloween night at a haunted |
3,029 | Input: A spooky Halloween night at a haunted museumhttps://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D Observation: https://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D Thought: With the image generated, I can now make my final answer. Final Answer: An image of a Halloween night at a haunted museum can be seen here: https://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22 > Finished chain.PreviousChatGPT PluginsNextDataForSeoRun as a chainRun as a tool with an agentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM. | This notebook shows how you can generate images from a prompt synthesized using an OpenAI LLM. The images are generated using Dall-E, which uses the same OpenAI API key as the LLM. ->: Input: A spooky Halloween night at a haunted museumhttps://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D Observation: https://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22%3A19%3A36Z&ske=2023-01-31T22%3A19%3A36Z&sks=b&skv=2021-08-06&sig=XsomxxBfu2CP78SzR9lrWUlbask4wBNnaMsHamy4VvU%3D Thought: With the image generated, I can now make my final answer. Final Answer: An image of a Halloween night at a haunted museum can be seen here: https://oaidalleapiprodscus.blob.core.windows.net/private/org-rocrupyvzgcl4yf25rqq6d1v/user-WsxrbKyP2c8rfhCKWDyMfe8N/img-ogKfqxxOS5KWVSj4gYySR6FY.png?st=2023-01-31T07%3A38%3A25Z&se=2023-01-31T09%3A38%3A25Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-01-30T22 > Finished chain.PreviousChatGPT PluginsNextDataForSeoRun as a chainRun as a tool with an agentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,030 | Alpha Vantage | 🦜�🔗 Langchain
Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsAlpha VantageAlpha VantageAlpha Vantage Alpha Vantage provides realtime and historical financial market data through a set of powerful and developer-friendly data APIs and spreadsheets. Use the AlphaVantageAPIWrapper to get currency exchange rates.import getpassimport osos.environ["ALPHAVANTAGE_API_KEY"] = getpass.getpass() ········from langchain.utilities.alpha_vantage import AlphaVantageAPIWrapperalpha_vantage = AlphaVantageAPIWrapper()alpha_vantage.run("USD", "JPY") {'1. From_Currency Code': 'USD', '2. From_Currency Name': 'United States Dollar', '3. To_Currency Code': 'JPY', '4. To_Currency Name': 'Japanese Yen', '5. Exchange Rate': '144.93000000', '6. Last Refreshed': '2023-08-11 21:31:01', '7. Time Zone': 'UTC', '8. Bid Price': '144.92600000', '9. Ask Price': '144.93400000'}PreviousToolsNextApifyCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Alpha Vantage Alpha Vantage provides realtime and historical financial market data through a set of powerful and developer-friendly data APIs and spreadsheets. | Alpha Vantage Alpha Vantage provides realtime and historical financial market data through a set of powerful and developer-friendly data APIs and spreadsheets. ->: Alpha Vantage | 🦜�🔗 Langchain
Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsAlpha VantageAlpha VantageAlpha Vantage Alpha Vantage provides realtime and historical financial market data through a set of powerful and developer-friendly data APIs and spreadsheets. Use the AlphaVantageAPIWrapper to get currency exchange rates.import getpassimport osos.environ["ALPHAVANTAGE_API_KEY"] = getpass.getpass() ········from langchain.utilities.alpha_vantage import AlphaVantageAPIWrapperalpha_vantage = AlphaVantageAPIWrapper()alpha_vantage.run("USD", "JPY") {'1. From_Currency Code': 'USD', '2. From_Currency Name': 'United States Dollar', '3. To_Currency Code': 'JPY', '4. To_Currency Name': 'Japanese Yen', '5. Exchange Rate': '144.93000000', '6. Last Refreshed': '2023-08-11 21:31:01', '7. Time Zone': 'UTC', '8. Bid Price': '144.92600000', '9. Ask Price': '144.93400000'}PreviousToolsNextApifyCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,031 | Search Tools | 🦜�🔗 Langchain | This notebook shows off usage of various search tools. | This notebook shows off usage of various search tools. ->: Search Tools | 🦜�🔗 Langchain |
3,032 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsSearch 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 Answer: The current temperature in Pomfret is 37°F. > Finished chain. 'The current temperature in Pomfret is 37°F.'SearchApi​Second, let's try SearchApi tool.tools = load_tools(["searchapi"], llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the | This notebook shows off usage of various search tools. | This notebook shows off usage of various search tools. ->: Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsSearch 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 Answer: The current temperature in Pomfret is 37°F. > Finished chain. 'The current temperature in Pomfret is 37°F.'SearchApi​Second, let's try SearchApi tool.tools = load_tools(["searchapi"], llm=llm)agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the |
3,033 | verbose=True)agent.run("What is the weather in Pomfret?") > Entering new AgentExecutor chain... I need to find out the current weather in Pomfret. Action: searchapi Action Input: "weather in Pomfret" Observation: Thu 14 | Day ... Some clouds this morning will give way to generally sunny skies for the afternoon. High 73F. Winds NW at 5 to 10 mph. Hourly Weather-Pomfret, CT · 1 pm. 71°. 0%. Sunny. Feels Like71°. WindNW 9 mph · 2 pm. 72°. 0%. Sunny. Feels Like72°. WindNW 9 mph · 3 pm. 72°. 0%. Sunny. Feels ... 10 Day Weather-Pomfret, VT. As of 4:28 am EDT. Today. 68°/48°. 4%. Thu 14 | Day. 68°. 4%. WNW 10 mph. Some clouds this morning will give way to generally ... Be prepared with the most accurate 10-day forecast for Pomfret, MD with highs, lows, chance of precipitation from The Weather Channel and Weather.com. Current Weather. 10:00 PM. 65°F. RealFeel® 67°. Mostly cloudy. LOCAL HURRICANE TRACKER. Category2. Lee. Late Friday Night - Saturday Afternoon. 10 Day Weather-Pomfret, NY. As of 5:09 pm EDT. Tonight. --/55°. 10%. Wed 13 | Night. 55°. 10%. NW 11 mph. Some clouds. Low near 55F. Pomfret CT. Overnight. Overnight: Patchy fog before 3am, then patchy fog after 4am. Otherwise, mostly. Patchy Fog. Low: 58 °F. Thursday. Isolated showers. Mostly cloudy, with a high near 76. Calm wind. Chance of precipitation is 20%. Tonight. Mostly Cloudy. Mostly cloudy, with a ... Partly sunny, with a high near 67. Breezy, with a north wind 18 to 22 mph, with gusts as high as 34 mph. Chance of precipitation is 30%. ... A chance of showers ... Today's Weather - Pomfret, CT ... Patchy fog. Showers. Lows in the upper 50s. Northwest winds around 5 mph. Chance of rain near 100 percent. ... Sunny. Patchy fog ... Thought: I now know the final answer Final Answer: The current weather in Pomfret is mostly cloudy with a high near 67 and a chance of showers. Winds are from the north at 18 to 22 mph with gusts up to 34 mph. | This notebook shows off usage of various search tools. | This notebook shows off usage of various search tools. ->: verbose=True)agent.run("What is the weather in Pomfret?") > Entering new AgentExecutor chain... I need to find out the current weather in Pomfret. Action: searchapi Action Input: "weather in Pomfret" Observation: Thu 14 | Day ... Some clouds this morning will give way to generally sunny skies for the afternoon. High 73F. Winds NW at 5 to 10 mph. Hourly Weather-Pomfret, CT · 1 pm. 71°. 0%. Sunny. Feels Like71°. WindNW 9 mph · 2 pm. 72°. 0%. Sunny. Feels Like72°. WindNW 9 mph · 3 pm. 72°. 0%. Sunny. Feels ... 10 Day Weather-Pomfret, VT. As of 4:28 am EDT. Today. 68°/48°. 4%. Thu 14 | Day. 68°. 4%. WNW 10 mph. Some clouds this morning will give way to generally ... Be prepared with the most accurate 10-day forecast for Pomfret, MD with highs, lows, chance of precipitation from The Weather Channel and Weather.com. Current Weather. 10:00 PM. 65°F. RealFeel® 67°. Mostly cloudy. LOCAL HURRICANE TRACKER. Category2. Lee. Late Friday Night - Saturday Afternoon. 10 Day Weather-Pomfret, NY. As of 5:09 pm EDT. Tonight. --/55°. 10%. Wed 13 | Night. 55°. 10%. NW 11 mph. Some clouds. Low near 55F. Pomfret CT. Overnight. Overnight: Patchy fog before 3am, then patchy fog after 4am. Otherwise, mostly. Patchy Fog. Low: 58 °F. Thursday. Isolated showers. Mostly cloudy, with a high near 76. Calm wind. Chance of precipitation is 20%. Tonight. Mostly Cloudy. Mostly cloudy, with a ... Partly sunny, with a high near 67. Breezy, with a north wind 18 to 22 mph, with gusts as high as 34 mph. Chance of precipitation is 30%. ... A chance of showers ... Today's Weather - Pomfret, CT ... Patchy fog. Showers. Lows in the upper 50s. Northwest winds around 5 mph. Chance of rain near 100 percent. ... Sunny. Patchy fog ... Thought: I now know the final answer Final Answer: The current weather in Pomfret is mostly cloudy with a high near 67 and a chance of showers. Winds are from the north at 18 to 22 mph with gusts up to 34 mph. |
3,034 | north at 18 to 22 mph with gusts up to 34 mph. > Finished chain. 'The current weather in Pomfret is mostly cloudy with a high near 67 and a chance of showers. Winds are from the north at 18 to 22 mph with gusts up to 34 mph.'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: {'type': 'weather_result', 'temperature': '69', 'unit': 'Fahrenheit', 'precipitation': '2%', 'humidity': '90%', 'wind': '1 mph', 'location': 'Pomfret, CT', 'date': 'Sunday 9:00 PM', 'weather': 'Clear'} Thought: I now know the current weather in Pomfret. Final Answer: The current weather in Pomfret is 69 degrees Fahrenheit, 2% precipitation, 90% humidity, and 1 mph wind. It is currently clear. > Finished chain. 'The current weather in Pomfret is 69 degrees Fahrenheit, 2% precipitation, 90% humidity, and 1 mph wind. It is currently clear.'GoogleSearchAPIWrapper​Now, let's use the official Google Search API Wrapper.tools = load_tools(["google-search"], 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 | This notebook shows off usage of various search tools. | This notebook shows off usage of various search tools. ->: north at 18 to 22 mph with gusts up to 34 mph. > Finished chain. 'The current weather in Pomfret is mostly cloudy with a high near 67 and a chance of showers. Winds are from the north at 18 to 22 mph with gusts up to 34 mph.'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: {'type': 'weather_result', 'temperature': '69', 'unit': 'Fahrenheit', 'precipitation': '2%', 'humidity': '90%', 'wind': '1 mph', 'location': 'Pomfret, CT', 'date': 'Sunday 9:00 PM', 'weather': 'Clear'} Thought: I now know the current weather in Pomfret. Final Answer: The current weather in Pomfret is 69 degrees Fahrenheit, 2% precipitation, 90% humidity, and 1 mph wind. It is currently clear. > Finished chain. 'The current weather in Pomfret is 69 degrees Fahrenheit, 2% precipitation, 90% humidity, and 1 mph wind. It is currently clear.'GoogleSearchAPIWrapper​Now, let's use the official Google Search API Wrapper.tools = load_tools(["google-search"], 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 |
3,035 | 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 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: | This notebook shows off usage of various search tools. | This notebook shows off usage of various search tools. ->: 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 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: |
3,036 | 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 | 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® 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 | This notebook shows off usage of various search tools. | This notebook shows off usage of various search tools. ->: 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 | 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® 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 |
3,037 | 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%.'PreviousSceneXplainNextSearchApiGoogle Serper API WrapperSearchApiSerpAPIGoogleSearchAPIWrapperSearxNG Meta Search EngineCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook shows off usage of various search tools. | This notebook shows off usage of various search tools. ->: 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%.'PreviousSceneXplainNextSearchApiGoogle Serper API WrapperSearchApiSerpAPIGoogleSearchAPIWrapperSearxNG Meta Search EngineCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,038 | Zapier Natural Language Actions | ü¶úÔ∏èüîó Langchain | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. ->: Zapier Natural Language Actions | ü¶úÔ∏èüîó Langchain |
3,039 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsZapier Natural Language ActionsOn this pageZapier Natural Language ActionsZapier 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/apps | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsZapier Natural Language ActionsOn this pageZapier Natural Language ActionsZapier 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/apps |
3,040 | Zapier 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 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. | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. ->: Zapier 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 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. |
3,041 | 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‚ÄãZapier 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: {"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)", | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. ->: 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‚ÄãZapier 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: {"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)", |
3,042 | "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 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‚ÄãIf 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 | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. ->: "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 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‚ÄãIf 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 |
3,043 | langchain.prompts import PromptTemplatefrom langchain.tools.zapier.tool 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 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"], | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. ->: langchain.prompts import PromptTemplatefrom langchain.tools.zapier.tool 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 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"], |
3,044 | 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 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": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. ->: 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 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": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", |
3,045 | "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\']]", "message__blocks[]elements[]type": "[\'rich_text_section\']"}'Example Using OAuth Access Token‚ÄãThe 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.")PreviousYouTubeNextAgents | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. ->: "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\']]", "message__blocks[]elements[]type": "[\'rich_text_section\']"}'Example Using OAuth Access Token‚ÄãThe 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.")PreviousYouTubeNextAgents |
3,046 | channel in slack.")PreviousYouTubeNextAgents and toolkitsExample with AgentExample with SimpleSequentialChainExample Using OAuth Access TokenCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. | Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface. ->: channel in slack.")PreviousYouTubeNextAgents and toolkitsExample with AgentExample with SimpleSequentialChainExample Using OAuth Access TokenCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,047 | SearxNG Search | ü¶úÔ∏èüîó Langchain | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: SearxNG Search | ü¶úÔ∏èüîó Langchain |
3,048 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsSearxNG SearchOn this pageSearxNG SearchThis notebook goes over how to use a self hosted SearxNG search API to search the web.You can check this link for more informations about Searx API parameters.import pprintfrom langchain.utilities import SearxSearchWrappersearch = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")For some engines, if a direct answer is available the warpper will print the answer instead of the full list of search results. You can use the results method of the wrapper if you want to obtain all the results.search.run("What is the capital of France") 'Paris is the capital of France, the largest country of Europe with 550 000 km2 (65 millions inhabitants). Paris has 2.234 million inhabitants end 2011. She is the core of Ile de France region (12 million people).'Custom Parameters‚ÄãSearxNG supports 135 search engines. You can also customize the Searx wrapper with arbitrary named parameters that will be passed to the Searx search API . In the below example we will making a more interesting use of custom search parameters from searx search api.In this example we will be using the engines | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsSearxNG SearchOn this pageSearxNG SearchThis notebook goes over how to use a self hosted SearxNG search API to search the web.You can check this link for more informations about Searx API parameters.import pprintfrom langchain.utilities import SearxSearchWrappersearch = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")For some engines, if a direct answer is available the warpper will print the answer instead of the full list of search results. You can use the results method of the wrapper if you want to obtain all the results.search.run("What is the capital of France") 'Paris is the capital of France, the largest country of Europe with 550 000 km2 (65 millions inhabitants). Paris has 2.234 million inhabitants end 2011. She is the core of Ile de France region (12 million people).'Custom Parameters‚ÄãSearxNG supports 135 search engines. You can also customize the Searx wrapper with arbitrary named parameters that will be passed to the Searx search API . In the below example we will making a more interesting use of custom search parameters from searx search api.In this example we will be using the engines |
3,049 | api.In this example we will be using the engines parameters to query wikipediasearch = SearxSearchWrapper( searx_host="http://127.0.0.1:8888", k=5) # k is for max number of itemssearch.run("large language model ", engines=["wiki"]) 'Large language models (LLMs) represent a major advancement in AI, with the promise of transforming domains through learned knowledge. LLM sizes have been increasing 10X every year for the last few years, and as these models grow in complexity and size, so do their capabilities.\n\nGPT-3 can translate language, write essays, generate computer code, and more — all with limited to no supervision. In July 2020, OpenAI unveiled GPT-3, a language model that was easily the largest known at the time. Put simply, GPT-3 is trained to predict the next word in a sentence, much like how a text message autocomplete feature works.\n\nA large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Large language models are among the most successful applications of transformer models.\n\nAll of today’s well-known language models—e.g., GPT-3 from OpenAI, PaLM or LaMDA from Google, Galactica or OPT from Meta, Megatron-Turing from Nvidia/Microsoft, Jurassic-1 from AI21 Labs—are...\n\nLarge language models (LLMs) such as GPT-3are increasingly being used to generate text. These tools should be used with care, since they can generate content that is biased, non-verifiable, constitutes original research, or violates copyrights.'Passing other Searx parameters for searx like languagesearch = SearxSearchWrapper(searx_host="http://127.0.0.1:8888", k=1)search.run("deep learning", language="es", engines=["wiki"]) 'Aprendizaje profundo (en inglés, deep learning) es un conjunto de algoritmos de aprendizaje automático (en inglés, machine learning) que intenta modelar abstracciones de alto nivel en datos usando | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: api.In this example we will be using the engines parameters to query wikipediasearch = SearxSearchWrapper( searx_host="http://127.0.0.1:8888", k=5) # k is for max number of itemssearch.run("large language model ", engines=["wiki"]) 'Large language models (LLMs) represent a major advancement in AI, with the promise of transforming domains through learned knowledge. LLM sizes have been increasing 10X every year for the last few years, and as these models grow in complexity and size, so do their capabilities.\n\nGPT-3 can translate language, write essays, generate computer code, and more — all with limited to no supervision. In July 2020, OpenAI unveiled GPT-3, a language model that was easily the largest known at the time. Put simply, GPT-3 is trained to predict the next word in a sentence, much like how a text message autocomplete feature works.\n\nA large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Large language models are among the most successful applications of transformer models.\n\nAll of today’s well-known language models—e.g., GPT-3 from OpenAI, PaLM or LaMDA from Google, Galactica or OPT from Meta, Megatron-Turing from Nvidia/Microsoft, Jurassic-1 from AI21 Labs—are...\n\nLarge language models (LLMs) such as GPT-3are increasingly being used to generate text. These tools should be used with care, since they can generate content that is biased, non-verifiable, constitutes original research, or violates copyrights.'Passing other Searx parameters for searx like languagesearch = SearxSearchWrapper(searx_host="http://127.0.0.1:8888", k=1)search.run("deep learning", language="es", engines=["wiki"]) 'Aprendizaje profundo (en inglés, deep learning) es un conjunto de algoritmos de aprendizaje automático (en inglés, machine learning) que intenta modelar abstracciones de alto nivel en datos usando |
3,050 | abstracciones de alto nivel en datos usando arquitecturas computacionales que admiten transformaciones no lineales múltiples e iterativas de datos expresados en forma matricial o tensorial. 1'Obtaining results with metadata​In this example we will be looking for scientific paper using the categories parameter and limiting the results to a time_range (not all engines support the time range option).We also would like to obtain the results in a structured way including metadata. For this we will be using the results method of the wrapper.search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")results = search.results( "Large Language Model prompt", num_results=5, categories="science", time_range="year",)pprint.pp(results) [{'snippet': '… on natural language instructions, large language models (… the ' 'prompt used to steer the model, and most effective prompts … to ' 'prompt engineering, we propose Automatic Prompt …', 'title': 'Large language models are human-level prompt engineers', 'link': 'https://arxiv.org/abs/2211.01910', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… Large language models (LLMs) have introduced new possibilities ' 'for prototyping with AI [18]. Pre-trained on a large amount of ' 'text data, models … language instructions called prompts. …', 'title': 'Promptchainer: Chaining large language model prompts through ' 'visual programming', 'link': 'https://dl.acm.org/doi/abs/10.1145/3491101.3519729', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… can introspect the large prompt model. We derive the view ' 'ϕ0(X) and the model h0 from T01. However, instead of fully ' 'fine-tuning T0 during co-training, we focus on soft prompt ' 'tuning, …', 'title': 'Co-training improves prompt-based | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: abstracciones de alto nivel en datos usando arquitecturas computacionales que admiten transformaciones no lineales múltiples e iterativas de datos expresados en forma matricial o tensorial. 1'Obtaining results with metadata​In this example we will be looking for scientific paper using the categories parameter and limiting the results to a time_range (not all engines support the time range option).We also would like to obtain the results in a structured way including metadata. For this we will be using the results method of the wrapper.search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")results = search.results( "Large Language Model prompt", num_results=5, categories="science", time_range="year",)pprint.pp(results) [{'snippet': '… on natural language instructions, large language models (… the ' 'prompt used to steer the model, and most effective prompts … to ' 'prompt engineering, we propose Automatic Prompt …', 'title': 'Large language models are human-level prompt engineers', 'link': 'https://arxiv.org/abs/2211.01910', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… Large language models (LLMs) have introduced new possibilities ' 'for prototyping with AI [18]. Pre-trained on a large amount of ' 'text data, models … language instructions called prompts. …', 'title': 'Promptchainer: Chaining large language model prompts through ' 'visual programming', 'link': 'https://dl.acm.org/doi/abs/10.1145/3491101.3519729', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… can introspect the large prompt model. We derive the view ' 'ϕ0(X) and the model h0 from T01. However, instead of fully ' 'fine-tuning T0 during co-training, we focus on soft prompt ' 'tuning, …', 'title': 'Co-training improves prompt-based |
3,051 | 'title': 'Co-training improves prompt-based learning for large language ' 'models', 'link': 'https://proceedings.mlr.press/v162/lang22a.html', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… With the success of large language models (LLMs) of code and ' 'their use as … prompt design process become important. In this ' 'work, we propose a framework called Repo-Level Prompt …', 'title': 'Repository-level prompt generation for large language models of ' 'code', 'link': 'https://arxiv.org/abs/2206.12839', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… Figure 2 | The benefits of different components of a prompt ' 'for the largest language model (Gopher), as estimated from ' 'hierarchical logistic regression. Each point estimates the ' 'unique …', 'title': 'Can language models learn from explanations in context?', 'link': 'https://arxiv.org/abs/2204.02329', 'engines': ['google scholar'], 'category': 'science'}]Get papers from arxivresults = search.results( "Large Language Model prompt", num_results=5, engines=["arxiv"])pprint.pp(results) [{'snippet': 'Thanks to the advanced improvement of large pre-trained language ' 'models, prompt-based fine-tuning is shown to be effective on a ' 'variety of downstream tasks. Though many prompting methods have ' 'been investigated, it remains unknown which type of prompts are ' 'the most effective among three types of prompts (i.e., ' 'human-designed prompts, schema prompts and null prompts). In ' 'this work, we empirically compare the three types of prompts ' 'under both few-shot and fully-supervised settings. Our ' 'experimental results show that schema prompts are the most ' | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: 'title': 'Co-training improves prompt-based learning for large language ' 'models', 'link': 'https://proceedings.mlr.press/v162/lang22a.html', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… With the success of large language models (LLMs) of code and ' 'their use as … prompt design process become important. In this ' 'work, we propose a framework called Repo-Level Prompt …', 'title': 'Repository-level prompt generation for large language models of ' 'code', 'link': 'https://arxiv.org/abs/2206.12839', 'engines': ['google scholar'], 'category': 'science'}, {'snippet': '… Figure 2 | The benefits of different components of a prompt ' 'for the largest language model (Gopher), as estimated from ' 'hierarchical logistic regression. Each point estimates the ' 'unique …', 'title': 'Can language models learn from explanations in context?', 'link': 'https://arxiv.org/abs/2204.02329', 'engines': ['google scholar'], 'category': 'science'}]Get papers from arxivresults = search.results( "Large Language Model prompt", num_results=5, engines=["arxiv"])pprint.pp(results) [{'snippet': 'Thanks to the advanced improvement of large pre-trained language ' 'models, prompt-based fine-tuning is shown to be effective on a ' 'variety of downstream tasks. Though many prompting methods have ' 'been investigated, it remains unknown which type of prompts are ' 'the most effective among three types of prompts (i.e., ' 'human-designed prompts, schema prompts and null prompts). In ' 'this work, we empirically compare the three types of prompts ' 'under both few-shot and fully-supervised settings. Our ' 'experimental results show that schema prompts are the most ' |
3,052 | results show that schema prompts are the most ' 'effective in general. Besides, the performance gaps tend to ' 'diminish when the scale of training data grows large.', 'title': 'Do Prompts Solve NLP Tasks Using Natural Language?', 'link': 'http://arxiv.org/abs/2203.00902v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Cross-prompt automated essay scoring (AES) requires the system ' 'to use non target-prompt essays to award scores to a ' 'target-prompt essay. Since obtaining a large quantity of ' 'pre-graded essays to a particular prompt is often difficult and ' 'unrealistic, the task of cross-prompt AES is vital for the ' 'development of real-world AES systems, yet it remains an ' 'under-explored area of research. Models designed for ' 'prompt-specific AES rely heavily on prompt-specific knowledge ' 'and perform poorly in the cross-prompt setting, whereas current ' 'approaches to cross-prompt AES either require a certain quantity ' 'of labelled target-prompt essays or require a large quantity of ' 'unlabelled target-prompt essays to perform transfer learning in ' 'a multi-step manner. To address these issues, we introduce ' 'Prompt Agnostic Essay Scorer (PAES) for cross-prompt AES. Our ' 'method requires no access to labelled or unlabelled ' 'target-prompt data during training and is a single-stage ' 'approach. PAES is easy to apply in practice and achieves ' 'state-of-the-art performance on the Automated Student Assessment ' 'Prize (ASAP) dataset.', 'title': 'Prompt Agnostic Essay Scorer: A Domain Generalization Approach to ' 'Cross-prompt Automated Essay Scoring', 'link': | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: results show that schema prompts are the most ' 'effective in general. Besides, the performance gaps tend to ' 'diminish when the scale of training data grows large.', 'title': 'Do Prompts Solve NLP Tasks Using Natural Language?', 'link': 'http://arxiv.org/abs/2203.00902v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Cross-prompt automated essay scoring (AES) requires the system ' 'to use non target-prompt essays to award scores to a ' 'target-prompt essay. Since obtaining a large quantity of ' 'pre-graded essays to a particular prompt is often difficult and ' 'unrealistic, the task of cross-prompt AES is vital for the ' 'development of real-world AES systems, yet it remains an ' 'under-explored area of research. Models designed for ' 'prompt-specific AES rely heavily on prompt-specific knowledge ' 'and perform poorly in the cross-prompt setting, whereas current ' 'approaches to cross-prompt AES either require a certain quantity ' 'of labelled target-prompt essays or require a large quantity of ' 'unlabelled target-prompt essays to perform transfer learning in ' 'a multi-step manner. To address these issues, we introduce ' 'Prompt Agnostic Essay Scorer (PAES) for cross-prompt AES. Our ' 'method requires no access to labelled or unlabelled ' 'target-prompt data during training and is a single-stage ' 'approach. PAES is easy to apply in practice and achieves ' 'state-of-the-art performance on the Automated Student Assessment ' 'Prize (ASAP) dataset.', 'title': 'Prompt Agnostic Essay Scorer: A Domain Generalization Approach to ' 'Cross-prompt Automated Essay Scoring', 'link': |
3,053 | Automated Essay Scoring', 'link': 'http://arxiv.org/abs/2008.01441v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Research on prompting has shown excellent performance with ' 'little or even no supervised training across many tasks. ' 'However, prompting for machine translation is still ' 'under-explored in the literature. We fill this gap by offering a ' 'systematic study on prompting strategies for translation, ' 'examining various factors for prompt template and demonstration ' 'example selection. We further explore the use of monolingual ' 'data and the feasibility of cross-lingual, cross-domain, and ' 'sentence-to-document transfer learning in prompting. Extensive ' 'experiments with GLM-130B (Zeng et al., 2022) as the testbed ' 'show that 1) the number and the quality of prompt examples ' 'matter, where using suboptimal examples degenerates translation; ' '2) several features of prompt examples, such as semantic ' 'similarity, show significant Spearman correlation with their ' 'prompting performance; yet, none of the correlations are strong ' 'enough; 3) using pseudo parallel prompt examples constructed ' 'from monolingual data via zero-shot prompting could improve ' 'translation; and 4) improved performance is achievable by ' 'transferring knowledge from prompt examples selected in other ' 'settings. We finally provide an analysis on the model outputs ' 'and discuss several problems that prompting still suffers from.', 'title': 'Prompting Large Language Model for Machine Translation: A Case ' 'Study', 'link': 'http://arxiv.org/abs/2301.07069v2', 'engines': ['arxiv'], | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: Automated Essay Scoring', 'link': 'http://arxiv.org/abs/2008.01441v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Research on prompting has shown excellent performance with ' 'little or even no supervised training across many tasks. ' 'However, prompting for machine translation is still ' 'under-explored in the literature. We fill this gap by offering a ' 'systematic study on prompting strategies for translation, ' 'examining various factors for prompt template and demonstration ' 'example selection. We further explore the use of monolingual ' 'data and the feasibility of cross-lingual, cross-domain, and ' 'sentence-to-document transfer learning in prompting. Extensive ' 'experiments with GLM-130B (Zeng et al., 2022) as the testbed ' 'show that 1) the number and the quality of prompt examples ' 'matter, where using suboptimal examples degenerates translation; ' '2) several features of prompt examples, such as semantic ' 'similarity, show significant Spearman correlation with their ' 'prompting performance; yet, none of the correlations are strong ' 'enough; 3) using pseudo parallel prompt examples constructed ' 'from monolingual data via zero-shot prompting could improve ' 'translation; and 4) improved performance is achievable by ' 'transferring knowledge from prompt examples selected in other ' 'settings. We finally provide an analysis on the model outputs ' 'and discuss several problems that prompting still suffers from.', 'title': 'Prompting Large Language Model for Machine Translation: A Case ' 'Study', 'link': 'http://arxiv.org/abs/2301.07069v2', 'engines': ['arxiv'], |
3,054 | 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Large language models can perform new tasks in a zero-shot ' 'fashion, given natural language prompts that specify the desired ' 'behavior. Such prompts are typically hand engineered, but can ' 'also be learned with gradient-based methods from labeled data. ' 'However, it is underexplored what factors make the prompts ' 'effective, especially when the prompts are natural language. In ' 'this paper, we investigate common attributes shared by effective ' 'prompts. We first propose a human readable prompt tuning method ' '(F LUENT P ROMPT) based on Langevin dynamics that incorporates a ' 'fluency constraint to find a diverse distribution of effective ' 'and fluent prompts. Our analysis reveals that effective prompts ' 'are topically related to the task domain and calibrate the prior ' 'probability of label words. Based on these findings, we also ' 'propose a method for generating prompts using only unlabeled ' 'data, outperforming strong baselines by an average of 7.0% ' 'accuracy across three tasks.', 'title': "Toward Human Readable Prompt Tuning: Kubrick's The Shining is a " 'good movie, and a good prompt too?', 'link': 'http://arxiv.org/abs/2212.10539v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Prevailing methods for mapping large generative language models ' "to supervised tasks may fail to sufficiently probe models' novel " 'capabilities. Using GPT-3 as a case study, we show that 0-shot ' 'prompts can significantly outperform few-shot prompts. We ' 'suggest that the function of few-shot examples in these cases is ' | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Large language models can perform new tasks in a zero-shot ' 'fashion, given natural language prompts that specify the desired ' 'behavior. Such prompts are typically hand engineered, but can ' 'also be learned with gradient-based methods from labeled data. ' 'However, it is underexplored what factors make the prompts ' 'effective, especially when the prompts are natural language. In ' 'this paper, we investigate common attributes shared by effective ' 'prompts. We first propose a human readable prompt tuning method ' '(F LUENT P ROMPT) based on Langevin dynamics that incorporates a ' 'fluency constraint to find a diverse distribution of effective ' 'and fluent prompts. Our analysis reveals that effective prompts ' 'are topically related to the task domain and calibrate the prior ' 'probability of label words. Based on these findings, we also ' 'propose a method for generating prompts using only unlabeled ' 'data, outperforming strong baselines by an average of 7.0% ' 'accuracy across three tasks.', 'title': "Toward Human Readable Prompt Tuning: Kubrick's The Shining is a " 'good movie, and a good prompt too?', 'link': 'http://arxiv.org/abs/2212.10539v1', 'engines': ['arxiv'], 'category': 'science'}, {'snippet': 'Prevailing methods for mapping large generative language models ' "to supervised tasks may fail to sufficiently probe models' novel " 'capabilities. Using GPT-3 as a case study, we show that 0-shot ' 'prompts can significantly outperform few-shot prompts. We ' 'suggest that the function of few-shot examples in these cases is ' |
3,055 | examples in these cases is ' 'better described as locating an already learned task rather than ' 'meta-learning. This analysis motivates rethinking the role of ' 'prompts in controlling and evaluating powerful language models. ' 'In this work, we discuss methods of prompt programming, ' 'emphasizing the usefulness of considering prompts through the ' 'lens of natural language. We explore techniques for exploiting ' 'the capacity of narratives and cultural anchors to encode ' 'nuanced intentions and techniques for encouraging deconstruction ' 'of a problem into components before producing a verdict. ' 'Informed by this more encompassing theory of prompt programming, ' 'we also introduce the idea of a metaprompt that seeds the model ' 'to generate its own natural language prompts for a range of ' 'tasks. Finally, we discuss how these more general methods of ' 'interacting with language models can be incorporated into ' 'existing and future benchmarks and practical applications.', 'title': 'Prompt Programming for Large Language Models: Beyond the Few-Shot ' 'Paradigm', 'link': 'http://arxiv.org/abs/2102.07350v1', 'engines': ['arxiv'], 'category': 'science'}]In this example we query for large language models under the it category. We then filter the results that come from github.results = search.results("large language model", num_results=20, categories="it")pprint.pp(list(filter(lambda r: r["engines"][0] == "github", results))) [{'snippet': 'Guide to using pre-trained large language models of source code', 'title': 'Code-LMs', 'link': 'https://github.com/VHellendoorn/Code-LMs', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Dramatron uses large language models to | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: examples in these cases is ' 'better described as locating an already learned task rather than ' 'meta-learning. This analysis motivates rethinking the role of ' 'prompts in controlling and evaluating powerful language models. ' 'In this work, we discuss methods of prompt programming, ' 'emphasizing the usefulness of considering prompts through the ' 'lens of natural language. We explore techniques for exploiting ' 'the capacity of narratives and cultural anchors to encode ' 'nuanced intentions and techniques for encouraging deconstruction ' 'of a problem into components before producing a verdict. ' 'Informed by this more encompassing theory of prompt programming, ' 'we also introduce the idea of a metaprompt that seeds the model ' 'to generate its own natural language prompts for a range of ' 'tasks. Finally, we discuss how these more general methods of ' 'interacting with language models can be incorporated into ' 'existing and future benchmarks and practical applications.', 'title': 'Prompt Programming for Large Language Models: Beyond the Few-Shot ' 'Paradigm', 'link': 'http://arxiv.org/abs/2102.07350v1', 'engines': ['arxiv'], 'category': 'science'}]In this example we query for large language models under the it category. We then filter the results that come from github.results = search.results("large language model", num_results=20, categories="it")pprint.pp(list(filter(lambda r: r["engines"][0] == "github", results))) [{'snippet': 'Guide to using pre-trained large language models of source code', 'title': 'Code-LMs', 'link': 'https://github.com/VHellendoorn/Code-LMs', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Dramatron uses large language models to |
3,056 | 'Dramatron uses large language models to generate coherent ' 'scripts and screenplays.', 'title': 'dramatron', 'link': 'https://github.com/deepmind/dramatron', 'engines': ['github'], 'category': 'it'}]We could also directly query for results from github and other source forges.results = search.results( "large language model", num_results=20, engines=["github", "gitlab"])pprint.pp(results) [{'snippet': "Implementation of 'A Watermark for Large Language Models' paper " 'by Kirchenbauer & Geiping et. al.', 'title': 'Peutlefaire / LMWatermark', 'link': 'https://gitlab.com/BrianPulfer/LMWatermark', 'engines': ['gitlab'], 'category': 'it'}, {'snippet': 'Guide to using pre-trained large language models of source code', 'title': 'Code-LMs', 'link': 'https://github.com/VHellendoorn/Code-LMs', 'engines': ['github'], 'category': 'it'}, {'snippet': '', 'title': 'Simen Burud / Large-scale Language Models for Conversational ' 'Speech Recognition', 'link': 'https://gitlab.com/BrianPulfer', 'engines': ['gitlab'], 'category': 'it'}, {'snippet': 'Dramatron uses large language models to generate coherent ' 'scripts and screenplays.', 'title': 'dramatron', 'link': 'https://github.com/deepmind/dramatron', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code for loralib, an implementation of "LoRA: Low-Rank ' 'Adaptation of Large Language Models"', 'title': 'LoRA', 'link': 'https://github.com/microsoft/LoRA', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code for the paper "Evaluating Large Language Models Trained on ' 'Code"', 'title': 'human-eval', 'link': 'https://github.com/openai/human-eval', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A trend starts from "Chain of Thought Prompting Elicits ' | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: 'Dramatron uses large language models to generate coherent ' 'scripts and screenplays.', 'title': 'dramatron', 'link': 'https://github.com/deepmind/dramatron', 'engines': ['github'], 'category': 'it'}]We could also directly query for results from github and other source forges.results = search.results( "large language model", num_results=20, engines=["github", "gitlab"])pprint.pp(results) [{'snippet': "Implementation of 'A Watermark for Large Language Models' paper " 'by Kirchenbauer & Geiping et. al.', 'title': 'Peutlefaire / LMWatermark', 'link': 'https://gitlab.com/BrianPulfer/LMWatermark', 'engines': ['gitlab'], 'category': 'it'}, {'snippet': 'Guide to using pre-trained large language models of source code', 'title': 'Code-LMs', 'link': 'https://github.com/VHellendoorn/Code-LMs', 'engines': ['github'], 'category': 'it'}, {'snippet': '', 'title': 'Simen Burud / Large-scale Language Models for Conversational ' 'Speech Recognition', 'link': 'https://gitlab.com/BrianPulfer', 'engines': ['gitlab'], 'category': 'it'}, {'snippet': 'Dramatron uses large language models to generate coherent ' 'scripts and screenplays.', 'title': 'dramatron', 'link': 'https://github.com/deepmind/dramatron', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code for loralib, an implementation of "LoRA: Low-Rank ' 'Adaptation of Large Language Models"', 'title': 'LoRA', 'link': 'https://github.com/microsoft/LoRA', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code for the paper "Evaluating Large Language Models Trained on ' 'Code"', 'title': 'human-eval', 'link': 'https://github.com/openai/human-eval', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A trend starts from "Chain of Thought Prompting Elicits ' |
3,057 | from "Chain of Thought Prompting Elicits ' 'Reasoning in Large Language Models".', 'title': 'Chain-of-ThoughtsPapers', 'link': 'https://github.com/Timothyxxx/Chain-of-ThoughtsPapers', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Mistral: A strong, northwesterly wind: Framework for transparent ' 'and accessible large-scale language model training, built with ' 'Hugging Face ü§ó Transformers.', 'title': 'mistral', 'link': 'https://github.com/stanford-crfm/mistral', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A prize for finding tasks that cause large language models to ' 'show inverse scaling', 'title': 'prize', 'link': 'https://github.com/inverse-scaling/prize', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Optimus: the first large-scale pre-trained VAE language model', 'title': 'Optimus', 'link': 'https://github.com/ChunyuanLI/Optimus', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Seminar on Large Language Models (COMP790-101 at UNC Chapel ' 'Hill, Fall 2022)', 'title': 'llm-seminar', 'link': 'https://github.com/craffel/llm-seminar', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A central, open resource for data and tools related to ' 'chain-of-thought reasoning in large language models. Developed @ ' 'Samwald research group: https://samwald.info/', 'title': 'ThoughtSource', 'link': 'https://github.com/OpenBioLink/ThoughtSource', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A comprehensive list of papers using large language/multi-modal ' 'models for Robotics/RL, including papers, codes, and related ' 'websites', 'title': 'Awesome-LLM-Robotics', 'link': 'https://github.com/GT-RIPL/Awesome-LLM-Robotics', | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: from "Chain of Thought Prompting Elicits ' 'Reasoning in Large Language Models".', 'title': 'Chain-of-ThoughtsPapers', 'link': 'https://github.com/Timothyxxx/Chain-of-ThoughtsPapers', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Mistral: A strong, northwesterly wind: Framework for transparent ' 'and accessible large-scale language model training, built with ' 'Hugging Face ü§ó Transformers.', 'title': 'mistral', 'link': 'https://github.com/stanford-crfm/mistral', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A prize for finding tasks that cause large language models to ' 'show inverse scaling', 'title': 'prize', 'link': 'https://github.com/inverse-scaling/prize', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Optimus: the first large-scale pre-trained VAE language model', 'title': 'Optimus', 'link': 'https://github.com/ChunyuanLI/Optimus', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Seminar on Large Language Models (COMP790-101 at UNC Chapel ' 'Hill, Fall 2022)', 'title': 'llm-seminar', 'link': 'https://github.com/craffel/llm-seminar', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A central, open resource for data and tools related to ' 'chain-of-thought reasoning in large language models. Developed @ ' 'Samwald research group: https://samwald.info/', 'title': 'ThoughtSource', 'link': 'https://github.com/OpenBioLink/ThoughtSource', 'engines': ['github'], 'category': 'it'}, {'snippet': 'A comprehensive list of papers using large language/multi-modal ' 'models for Robotics/RL, including papers, codes, and related ' 'websites', 'title': 'Awesome-LLM-Robotics', 'link': 'https://github.com/GT-RIPL/Awesome-LLM-Robotics', |
3,058 | 'engines': ['github'], 'category': 'it'}, {'snippet': 'Tools for curating biomedical training data for large-scale ' 'language modeling', 'title': 'biomedical', 'link': 'https://github.com/bigscience-workshop/biomedical', 'engines': ['github'], 'category': 'it'}, {'snippet': 'ChatGPT @ Home: Large Language Model (LLM) chatbot application, ' 'written by ChatGPT', 'title': 'ChatGPT-at-Home', 'link': 'https://github.com/Sentdex/ChatGPT-at-Home', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Design and Deploy Large Language Model Apps', 'title': 'dust', 'link': 'https://github.com/dust-tt/dust', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Polyglot: Large Language Models of Well-balanced Competence in ' 'Multi-languages', 'title': 'polyglot', 'link': 'https://github.com/EleutherAI/polyglot', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code release for "Learning Video Representations from Large ' 'Language Models"', 'title': 'LaViLa', 'link': 'https://github.com/facebookresearch/LaViLa', 'engines': ['github'], 'category': 'it'}, {'snippet': 'SmoothQuant: Accurate and Efficient Post-Training Quantization ' 'for Large Language Models', 'title': 'smoothquant', 'link': 'https://github.com/mit-han-lab/smoothquant', 'engines': ['github'], 'category': 'it'}, {'snippet': 'This repository contains the code, data, and models of the paper ' 'titled "XL-Sum: Large-Scale Multilingual Abstractive ' 'Summarization for 44 Languages" published in Findings of the ' 'Association for Computational Linguistics: ACL-IJCNLP 2021.', 'title': 'xl-sum', 'link': 'https://github.com/csebuetnlp/xl-sum', 'engines': ['github'], 'category': | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: 'engines': ['github'], 'category': 'it'}, {'snippet': 'Tools for curating biomedical training data for large-scale ' 'language modeling', 'title': 'biomedical', 'link': 'https://github.com/bigscience-workshop/biomedical', 'engines': ['github'], 'category': 'it'}, {'snippet': 'ChatGPT @ Home: Large Language Model (LLM) chatbot application, ' 'written by ChatGPT', 'title': 'ChatGPT-at-Home', 'link': 'https://github.com/Sentdex/ChatGPT-at-Home', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Design and Deploy Large Language Model Apps', 'title': 'dust', 'link': 'https://github.com/dust-tt/dust', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Polyglot: Large Language Models of Well-balanced Competence in ' 'Multi-languages', 'title': 'polyglot', 'link': 'https://github.com/EleutherAI/polyglot', 'engines': ['github'], 'category': 'it'}, {'snippet': 'Code release for "Learning Video Representations from Large ' 'Language Models"', 'title': 'LaViLa', 'link': 'https://github.com/facebookresearch/LaViLa', 'engines': ['github'], 'category': 'it'}, {'snippet': 'SmoothQuant: Accurate and Efficient Post-Training Quantization ' 'for Large Language Models', 'title': 'smoothquant', 'link': 'https://github.com/mit-han-lab/smoothquant', 'engines': ['github'], 'category': 'it'}, {'snippet': 'This repository contains the code, data, and models of the paper ' 'titled "XL-Sum: Large-Scale Multilingual Abstractive ' 'Summarization for 44 Languages" published in Findings of the ' 'Association for Computational Linguistics: ACL-IJCNLP 2021.', 'title': 'xl-sum', 'link': 'https://github.com/csebuetnlp/xl-sum', 'engines': ['github'], 'category': |
3,059 | 'engines': ['github'], 'category': 'it'}]PreviousSearchApiNextSerpAPICustom ParametersObtaining results with metadataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook goes over how to use a self hosted SearxNG search API to search the web. | This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: 'engines': ['github'], 'category': 'it'}]PreviousSearchApiNextSerpAPICustom ParametersObtaining results with metadataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,060 | Google Places | ü¶úÔ∏èüîó Langchain
Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGoogle 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 St, San Francisco, CA 94111, USA\nPhone: (415) 986-0100\nWebsite: https://www.ilfornaio.com/\n\n"PreviousGoogle DriveNextGoogle SearchCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc. | This notebook goes through how to use Google Places API | This notebook goes through how to use Google Places API ->: Google Places | ü¶úÔ∏èüîó Langchain
Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGoogle 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 St, San Francisco, CA 94111, USA\nPhone: (415) 986-0100\nWebsite: https://www.ilfornaio.com/\n\n"PreviousGoogle DriveNextGoogle SearchCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc. |
3,061 | Requests | ü¶úÔ∏èüîó Langchain | The 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. | The 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. ->: Requests | ü¶úÔ∏èüîó Langchain |
3,062 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsRequestsOn 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 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 | The 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. | The 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. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsRequestsOn 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 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 |
3,063 | 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 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 | The 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. | The 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. ->: 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 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 |
3,064 | aiosession=None))]Inside the tool‚ÄãEach 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 | The 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. | The 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. ->: aiosession=None))]Inside the tool‚ÄãEach 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 |
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3,071 | - <a href="/intl/en/policies/terms/">Terms</a></p></span></center><script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){window.google.cdo={height:757,width:1440};(function(){var a=window.innerWidth,b=window.innerHeight;if(!a||!b){var c=window.document,d="CSS1Compat"==c.compatMode?c.documentElement:c.body;a=d.clientWidth;b=d.clientHeight}a&&b&&(a!=google.cdo.width||b!=google.cdo.height)&&google.log("","","/client_204?&atyp=i&biw="+a+"&bih="+b+"&ei="+google.kEI);}).call(this);})();</script> <script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){google.xjs={ck:\'xjs.hp.vUsZk7fd8do.L.X.O\',cs:\'ACT90oF8ktm8JGoaZ23megDhHoJku7YaGw\',excm:[]};})();</script> <script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){var u=\'/xjs/_/js/k\\x3dxjs.hp.en.q0lHXBfs9JY.O/am\\x3dAAAA6AQAUABgAQ/d\\x3d1/ed\\x3d1/rs\\x3dACT90oE3ek6-fjkab6CsTH0wUEUUPhnExg/m\\x3dsb_he,d\';var amd=0;\nvar e=this||self,f=function(c){return c};var h;var n=function(c,g){this.g=g===l?c:""};n.prototype.toString=function(){return this.g+""};var l={};\nfunction p(){var c=u,g=function(){};google.lx=google.stvsc?g:function(){google.timers&&google.timers.load&&google.tick&&google.tick("load","xjsls");var a=document;var b="SCRIPT";"application/xhtml+xml"===a.contentType&&(b=b.toLowerCase());b=a.createElement(b);a=null===c?"null":void 0===c?"undefined":c;if(void 0===h){var d=null;var m=e.trustedTypes;if(m&&m.createPolicy){try{d=m.createPolicy("goog#html",{createHTML:f,createScript:f,createScriptURL:f})}catch(r){e.console&&e.console.error(r.message)}h=\nd}else h=d}a=(d=h)?d.createScriptURL(a):a;a=new n(a,l);b.src=a instanceof n&&a.constructor===n?a.g:"type_error:TrustedResourceUrl";var k,q;(k=(a=null==(q=(k=(b.ownerDocument&&b.ownerDocument.defaultView||window).document).querySelector)?void | The 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. | The 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. ->: - <a href="/intl/en/policies/terms/">Terms</a></p></span></center><script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){window.google.cdo={height:757,width:1440};(function(){var a=window.innerWidth,b=window.innerHeight;if(!a||!b){var c=window.document,d="CSS1Compat"==c.compatMode?c.documentElement:c.body;a=d.clientWidth;b=d.clientHeight}a&&b&&(a!=google.cdo.width||b!=google.cdo.height)&&google.log("","","/client_204?&atyp=i&biw="+a+"&bih="+b+"&ei="+google.kEI);}).call(this);})();</script> <script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){google.xjs={ck:\'xjs.hp.vUsZk7fd8do.L.X.O\',cs:\'ACT90oF8ktm8JGoaZ23megDhHoJku7YaGw\',excm:[]};})();</script> <script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){var u=\'/xjs/_/js/k\\x3dxjs.hp.en.q0lHXBfs9JY.O/am\\x3dAAAA6AQAUABgAQ/d\\x3d1/ed\\x3d1/rs\\x3dACT90oE3ek6-fjkab6CsTH0wUEUUPhnExg/m\\x3dsb_he,d\';var amd=0;\nvar e=this||self,f=function(c){return c};var h;var n=function(c,g){this.g=g===l?c:""};n.prototype.toString=function(){return this.g+""};var l={};\nfunction p(){var c=u,g=function(){};google.lx=google.stvsc?g:function(){google.timers&&google.timers.load&&google.tick&&google.tick("load","xjsls");var a=document;var b="SCRIPT";"application/xhtml+xml"===a.contentType&&(b=b.toLowerCase());b=a.createElement(b);a=null===c?"null":void 0===c?"undefined":c;if(void 0===h){var d=null;var m=e.trustedTypes;if(m&&m.createPolicy){try{d=m.createPolicy("goog#html",{createHTML:f,createScript:f,createScriptURL:f})}catch(r){e.console&&e.console.error(r.message)}h=\nd}else h=d}a=(d=h)?d.createScriptURL(a):a;a=new n(a,l);b.src=a instanceof n&&a.constructor===n?a.g:"type_error:TrustedResourceUrl";var k,q;(k=(a=null==(q=(k=(b.ownerDocument&&b.ownerDocument.defaultView||window).document).querySelector)?void |
3,072 | 0:q.call(k,"script[nonce]"))?a.nonce||a.getAttribute("nonce")||"":"")&&b.setAttribute("nonce",k);document.body.appendChild(b);google.psa=!0;google.lx=g};google.bx||google.lx()};google.xjsu=u;e._F_jsUrl=u;setTimeout(function(){0<amd?google.caft(function(){return p()},amd):p()},0);})();window._ = window._ || {};window._DumpException = _._DumpException = function(e){throw e;};window._s = window._s || {};_s._DumpException = _._DumpException;window._qs = window._qs || {};_qs._DumpException = _._DumpException;function _F_installCss(c){}\n(function(){google.jl={blt:\'none\',chnk:0,dw:false,dwu:true,emtn:0,end:0,ico:false,ikb:0,ine:false,injs:\'none\',injt:0,injth:0,injv2:false,lls:\'default\',pdt:0,rep:0,snet:true,strt:0,ubm:false,uwp:true};})();(function(){var pmc=\'{\\x22d\\x22:{},\\x22sb_he\\x22:{\\x22agen\\x22:true,\\x22cgen\\x22:true,\\x22client\\x22:\\x22heirloom-hp\\x22,\\x22dh\\x22:true,\\x22ds\\x22:\\x22\\x22,\\x22fl\\x22:true,\\x22host\\x22:\\x22google.com\\x22,\\x22jsonp\\x22:true,\\x22msgs\\x22:{\\x22cibl\\x22:\\x22Clear Search\\x22,\\x22dym\\x22:\\x22Did you mean:\\x22,\\x22lcky\\x22:\\x22I\\\\u0026#39;m Feeling Lucky\\x22,\\x22lml\\x22:\\x22Learn more\\x22,\\x22psrc\\x22:\\x22This search was removed from your \\\\u003Ca href\\x3d\\\\\\x22/history\\\\\\x22\\\\u003EWeb History\\\\u003C/a\\\\u003E\\x22,\\x22psrl\\x22:\\x22Remove\\x22,\\x22sbit\\x22:\\x22Search by image\\x22,\\x22srch\\x22:\\x22Google Search\\x22},\\x22ovr\\x22:{},\\x22pq\\x22:\\x22\\x22,\\x22rfs\\x22:[],\\x22sbas\\x22:\\x220 3px 8px 0 rgba(0,0,0,0.2),0 0 0 1px rgba(0,0,0,0.08)\\x22,\\x22stok\\x22:\\x22C3TIBpTor6RHJfEIn2nbidnhv50\\x22}}\';google.pmc=JSON.parse(pmc);})();</script> </body></html>'PreviousPubMedNextSceneXplainInside the toolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | The 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. | The 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. ->: 0:q.call(k,"script[nonce]"))?a.nonce||a.getAttribute("nonce")||"":"")&&b.setAttribute("nonce",k);document.body.appendChild(b);google.psa=!0;google.lx=g};google.bx||google.lx()};google.xjsu=u;e._F_jsUrl=u;setTimeout(function(){0<amd?google.caft(function(){return p()},amd):p()},0);})();window._ = window._ || {};window._DumpException = _._DumpException = function(e){throw e;};window._s = window._s || {};_s._DumpException = _._DumpException;window._qs = window._qs || {};_qs._DumpException = _._DumpException;function _F_installCss(c){}\n(function(){google.jl={blt:\'none\',chnk:0,dw:false,dwu:true,emtn:0,end:0,ico:false,ikb:0,ine:false,injs:\'none\',injt:0,injth:0,injv2:false,lls:\'default\',pdt:0,rep:0,snet:true,strt:0,ubm:false,uwp:true};})();(function(){var pmc=\'{\\x22d\\x22:{},\\x22sb_he\\x22:{\\x22agen\\x22:true,\\x22cgen\\x22:true,\\x22client\\x22:\\x22heirloom-hp\\x22,\\x22dh\\x22:true,\\x22ds\\x22:\\x22\\x22,\\x22fl\\x22:true,\\x22host\\x22:\\x22google.com\\x22,\\x22jsonp\\x22:true,\\x22msgs\\x22:{\\x22cibl\\x22:\\x22Clear Search\\x22,\\x22dym\\x22:\\x22Did you mean:\\x22,\\x22lcky\\x22:\\x22I\\\\u0026#39;m Feeling Lucky\\x22,\\x22lml\\x22:\\x22Learn more\\x22,\\x22psrc\\x22:\\x22This search was removed from your \\\\u003Ca href\\x3d\\\\\\x22/history\\\\\\x22\\\\u003EWeb History\\\\u003C/a\\\\u003E\\x22,\\x22psrl\\x22:\\x22Remove\\x22,\\x22sbit\\x22:\\x22Search by image\\x22,\\x22srch\\x22:\\x22Google Search\\x22},\\x22ovr\\x22:{},\\x22pq\\x22:\\x22\\x22,\\x22rfs\\x22:[],\\x22sbas\\x22:\\x220 3px 8px 0 rgba(0,0,0,0.2),0 0 0 1px rgba(0,0,0,0.08)\\x22,\\x22stok\\x22:\\x22C3TIBpTor6RHJfEIn2nbidnhv50\\x22}}\';google.pmc=JSON.parse(pmc);})();</script> </body></html>'PreviousPubMedNextSceneXplainInside the toolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,073 | PubMed | ü¶úÔ∏èüîó Langchain | PubMed¬Æ comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites. | PubMed¬Æ comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites. ->: PubMed | ü¶úÔ∏èüîó Langchain |
3,074 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsPubMedPubMedPubMed¬Æ comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.This notebook goes over how to use PubMed as a tool.from langchain.tools import PubmedQueryRuntool = PubmedQueryRun()tool.run("chatgpt") 'Published: <Year>2023</Year><Month>May</Month><Day>31</Day>\nTitle: Dermatology in the wake of an AI revolution: who gets a say?\nSummary: \n\nPublished: <Year>2023</Year><Month>May</Month><Day>30</Day>\nTitle: What is ChatGPT and what do we do with it? Implications of the age of AI for nursing and midwifery practice and education: An editorial.\nSummary: \n\nPublished: <Year>2023</Year><Month>Jun</Month><Day>02</Day>\nTitle: The Impact of ChatGPT on the Nursing Profession: Revolutionizing Patient Care and Education.\nSummary: The nursing field has undergone notable changes over time and is projected to undergo further modifications in the future, owing to the advent of sophisticated technologies and growing healthcare needs. The advent of | PubMed¬Æ comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites. | PubMed¬Æ comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsPubMedPubMedPubMed¬Æ comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites.This notebook goes over how to use PubMed as a tool.from langchain.tools import PubmedQueryRuntool = PubmedQueryRun()tool.run("chatgpt") 'Published: <Year>2023</Year><Month>May</Month><Day>31</Day>\nTitle: Dermatology in the wake of an AI revolution: who gets a say?\nSummary: \n\nPublished: <Year>2023</Year><Month>May</Month><Day>30</Day>\nTitle: What is ChatGPT and what do we do with it? Implications of the age of AI for nursing and midwifery practice and education: An editorial.\nSummary: \n\nPublished: <Year>2023</Year><Month>Jun</Month><Day>02</Day>\nTitle: The Impact of ChatGPT on the Nursing Profession: Revolutionizing Patient Care and Education.\nSummary: The nursing field has undergone notable changes over time and is projected to undergo further modifications in the future, owing to the advent of sophisticated technologies and growing healthcare needs. The advent of |
3,075 | and growing healthcare needs. The advent of ChatGPT, an AI-powered language model, is expected to exert a significant influence on the nursing profession, specifically in the domains of patient care and instruction. The present article delves into the ramifications of ChatGPT within the nursing domain and accentuates its capacity and constraints to transform the discipline.'PreviousOpenWeatherMapNextRequestsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | PubMed® comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites. | PubMed® comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full text content from PubMed Central and publisher web sites. ->: and growing healthcare needs. The advent of ChatGPT, an AI-powered language model, is expected to exert a significant influence on the nursing profession, specifically in the domains of patient care and instruction. The present article delves into the ramifications of ChatGPT within the nursing domain and accentuates its capacity and constraints to transform the discipline.'PreviousOpenWeatherMapNextRequestsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,076 | ChatGPT Plugins | ü¶úÔ∏èüîó Langchain | This example shows how to use ChatGPT Plugins within LangChain abstractions. | This example shows how to use ChatGPT Plugins within LangChain abstractions. ->: ChatGPT Plugins | ü¶úÔ∏èüîó Langchain |
3,077 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsChatGPT 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... 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. | This example shows how to use ChatGPT Plugins within LangChain abstractions. | This example shows how to use ChatGPT Plugins within LangChain abstractions. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsChatGPT 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... 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. |
3,078 | 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'}}], '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': {'$ref': '#/components/schemas/Product'}}}, 'title': 'ProductResponse'}}}} Thought:I need to use the Klarna Shopping API to search for t shirts. Action: requests_get Action Input: | This example shows how to use ChatGPT Plugins within LangChain abstractions. | This example shows how to use ChatGPT Plugins within LangChain abstractions. ->: for any shopping or researching purpose. The query to be sent should not include stopwords like articles, prepositions and determinants. The api works best when searching for words that are related to products, like their name, brand, model or category. Links will always be returned and should be shown to the user. OpenAPI Spec: {'openapi': '3.0.1', 'info': {'version': 'v0', 'title': 'Open AI Klarna product Api'}, 'servers': [{'url': 'https://www.klarna.com/us/shopping'}], 'tags': [{'name': 'open-ai-product-endpoint', 'description': 'Open AI Product Endpoint. Query for products.'}], 'paths': {'/public/openai/v0/products': {'get': {'tags': ['open-ai-product-endpoint'], 'summary': 'API for fetching Klarna product information', 'operationId': 'productsUsingGET', 'parameters': [{'name': 'q', 'in': 'query', 'description': 'query, must be between 2 and 100 characters', 'required': True, 'schema': {'type': 'string'}}, {'name': 'size', 'in': 'query', 'description': 'number of products returned', 'required': False, 'schema': {'type': 'integer'}}, {'name': 'budget', 'in': 'query', 'description': 'maximum price of the matching product in local currency, filters results', 'required': False, 'schema': {'type': 'integer'}}], 'responses': {'200': {'description': 'Products found', 'content': {'application/json': {'schema': {'$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: |
3,079 | 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 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 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, | This example shows how to use ChatGPT Plugins within LangChain abstractions. | This example shows how to use ChatGPT Plugins within LangChain abstractions. ->: 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 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 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, |
3,080 | 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 SearchNextDall-E Image GeneratorCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This example shows how to use ChatGPT Plugins within LangChain abstractions. | This example shows how to use ChatGPT Plugins within LangChain abstractions. ->: 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 SearchNextDall-E Image GeneratorCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,081 | Golden Query | ü¶úÔ∏èüîó Langchain | Golden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities. | Golden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities. ->: Golden Query | ü¶úÔ∏èüîó Langchain |
3,082 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGolden QueryGolden QueryGolden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities.The golden-query langchain tool is a wrapper on top of the Golden Query API which enables programmatic access to these results. | Golden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities. | Golden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsGolden QueryGolden QueryGolden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities.The golden-query langchain tool is a wrapper on top of the Golden Query API which enables programmatic access to these results. |
3,083 | See the Golden Query API docs for more information.This notebook goes over how to use the golden-query tool.Go to the Golden API docs to get an overview about the Golden API.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, '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': 'Novartis', 'citations': []}]}]}, {'id': 237458, 'latestVersionId': 61406160, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Analog Devices', 'citations': []}]}]}, {'id': 3941943, 'latestVersionId': 60382250, 'properties': [{'predicateId': 'name', 'instances': | Golden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities. | Golden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities. ->: See the Golden Query API docs for more information.This notebook goes over how to use the golden-query tool.Go to the Golden API docs to get an overview about the Golden API.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, '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': 'Novartis', 'citations': []}]}]}, {'id': 237458, 'latestVersionId': 61406160, 'properties': [{'predicateId': 'name', 'instances': [{'value': 'Analog Devices', 'citations': []}]}]}, {'id': 3941943, 'latestVersionId': 60382250, 'properties': [{'predicateId': 'name', 'instances': |
3,084 | [{'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 SystemNextGoogle DriveCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Golden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities. | Golden provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: Products from OpenAI, Generative ai companies with series a funding, and rappers who invest can be used to retrieve structured data about relevant entities. ->: [{'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 SystemNextGoogle DriveCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,085 | IFTTT WebHooks | ü¶úÔ∏èüîó Langchain | This notebook shows how to use IFTTT Webhooks. | This notebook shows how to use IFTTT Webhooks. ->: IFTTT WebHooks | ü¶úÔ∏èüîó Langchain |
3,086 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsIFTTT WebHooksOn this pageIFTTT WebHooksThis notebook shows how to use IFTTT Webhooks.From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.Creating a webhook‚ÄãGo to https://ifttt.com/createConfiguring the "If This"‚ÄãClick on the "If This" button in the IFTTT interface.Search for "Webhooks" in the search bar.Choose the first option for "Receive a web request with a JSON payload."Choose an Event Name that is specific to the service you plan to connect to.
This will make it easier for you to manage the webhook URL.
For example, if you're connecting to Spotify, you could use "Spotify" as your
Event Name.Click the "Create Trigger" button to save your settings and create your webhook.Configuring the "Then That"‚ÄãTap on the "Then That" button in the IFTTT interface.Search for the service you want to connect, such as Spotify.Choose an action from the service, such as "Add track to a playlist".Configure the action by specifying the necessary details, such as the playlist name,
e.g., "Songs from AI".Reference the JSON Payload received by the Webhook in your action. For the Spotify | This notebook shows how to use IFTTT Webhooks. | This notebook shows how to use IFTTT Webhooks. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsIFTTT WebHooksOn this pageIFTTT WebHooksThis notebook shows how to use IFTTT Webhooks.From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.Creating a webhook‚ÄãGo to https://ifttt.com/createConfiguring the "If This"‚ÄãClick on the "If This" button in the IFTTT interface.Search for "Webhooks" in the search bar.Choose the first option for "Receive a web request with a JSON payload."Choose an Event Name that is specific to the service you plan to connect to.
This will make it easier for you to manage the webhook URL.
For example, if you're connecting to Spotify, you could use "Spotify" as your
Event Name.Click the "Create Trigger" button to save your settings and create your webhook.Configuring the "Then That"‚ÄãTap on the "Then That" button in the IFTTT interface.Search for the service you want to connect, such as Spotify.Choose an action from the service, such as "Add track to a playlist".Configure the action by specifying the necessary details, such as the playlist name,
e.g., "Songs from AI".Reference the JSON Payload received by the Webhook in your action. For the Spotify |
3,087 | scenario, choose "{{JsonPayload}}" as your search query.Tap the "Create Action" button to save your action settings.Once you have finished configuring your action, click the "Finish" button to
complete the setup.Congratulations! You have successfully connected the Webhook to the desired
service, and you're ready to start receiving data and triggering actions üéâFinishing up‚ÄãTo get your webhook URL go to https://ifttt.com/maker_webhooks/settingsCopy the IFTTT key value from there. The URL is of the form
https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.from langchain.tools.ifttt import IFTTTWebhookimport oskey = os.environ["IFTTTKey"]url = f"https://maker.ifttt.com/trigger/spotify/json/with/key/{key}"tool = IFTTTWebhook( name="Spotify", description="Add a song to spotify playlist", url=url)tool.run("taylor swift") "Congratulations! You've fired the spotify JSON event"PreviousHuman as a toolNextLemon AgentCreating a webhookConfiguring the "If This"Configuring the "Then That"Finishing upCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook shows how to use IFTTT Webhooks. | This notebook shows how to use IFTTT Webhooks. ->: scenario, choose "{{JsonPayload}}" as your search query.Tap the "Create Action" button to save your action settings.Once you have finished configuring your action, click the "Finish" button to
complete the setup.Congratulations! You have successfully connected the Webhook to the desired
service, and you're ready to start receiving data and triggering actions üéâFinishing up‚ÄãTo get your webhook URL go to https://ifttt.com/maker_webhooks/settingsCopy the IFTTT key value from there. The URL is of the form
https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.from langchain.tools.ifttt import IFTTTWebhookimport oskey = os.environ["IFTTTKey"]url = f"https://maker.ifttt.com/trigger/spotify/json/with/key/{key}"tool = IFTTTWebhook( name="Spotify", description="Add a song to spotify playlist", url=url)tool.run("taylor swift") "Congratulations! You've fired the spotify JSON event"PreviousHuman as a toolNextLemon AgentCreating a webhookConfiguring the "If This"Configuring the "Then That"Finishing upCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
3,088 | SearchApi | ü¶úÔ∏èüîó Langchain | This notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key. | This notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key. ->: SearchApi | ü¶úÔ∏èüîó Langchain |
3,089 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsSearchApiOn this pageSearchApiThis notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key.import osos.environ["SEARCHAPI_API_KEY"] = ""from langchain.utilities import SearchApiAPIWrappersearch = SearchApiAPIWrapper()search.run("Obama's first name?") 'Barack Hussein Obama II'Using as part of a Self Ask With Search Chain‚Äãos.environ["OPENAI_API_KEY"] = ""from langchain.utilities import SearchApiAPIWrapperfrom langchain.llms.openai import OpenAIfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypellm = OpenAI(temperature=0)search = SearchApiAPIWrapper()tools = [ Tool( name="Intermediate Answer", func=search.run, description="useful for when you need to ask with search" )]self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)self_ask_with_search.run("Who lived longer: Plato, Socrates, or Aristotle?") > Entering new AgentExecutor chain... Yes. Follow up: How old was Plato when he died? | This notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key. | This notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAlpha VantageApifyArXivAWS LambdaShell (bash)Bearly Code InterpreterBing SearchBrave SearchChatGPT PluginsDall-E Image GeneratorDataForSeoDuckDuckGo SearchEden AIEleven Labs Text2SpeechFile SystemGolden QueryGoogle DriveGoogle PlacesGoogle SearchGoogle SerperGradioGraphQLHuggingFace Hub ToolsHuman as a toolIFTTT WebHooksLemon AgentMetaphor SearchNuclia UnderstandingOpenWeatherMapPubMedRequestsSceneXplainSearch ToolsSearchApiSearxNG SearchSerpAPITwilioWikipediaWolfram AlphaYahoo Finance NewsYouTubeZapier Natural Language ActionsAgents and toolkitsMemoryCallbacksChat loadersComponentsToolsSearchApiOn this pageSearchApiThis notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key.import osos.environ["SEARCHAPI_API_KEY"] = ""from langchain.utilities import SearchApiAPIWrappersearch = SearchApiAPIWrapper()search.run("Obama's first name?") 'Barack Hussein Obama II'Using as part of a Self Ask With Search Chain‚Äãos.environ["OPENAI_API_KEY"] = ""from langchain.utilities import SearchApiAPIWrapperfrom langchain.llms.openai import OpenAIfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentTypellm = OpenAI(temperature=0)search = SearchApiAPIWrapper()tools = [ Tool( name="Intermediate Answer", func=search.run, description="useful for when you need to ask with search" )]self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)self_ask_with_search.run("Who lived longer: Plato, Socrates, or Aristotle?") > Entering new AgentExecutor chain... Yes. Follow up: How old was Plato when he died? |
3,090 | Follow up: How old was Plato when he died? Intermediate answer: eighty Follow up: How old was Socrates when he died? Intermediate answer: | Socrates | | -------- | | Born | c. 470 BC Deme Alopece, Athens | | Died | 399 BC (aged approximately 71) Athens | | Cause of death | Execution by forced suicide by poisoning | | Spouse(s) | Xanthippe, Myrto | Follow up: How old was Aristotle when he died? Intermediate answer: 62 years So the final answer is: Plato > Finished chain. 'Plato'Custom parameters​SearchApi wrapper can be customized to use different engines like Google News, Google Jobs, Google Scholar, or others which can be found in SearchApi documentation. All parameters supported by SearchApi can be passed when executing the query. search = SearchApiAPIWrapper(engine="google_jobs")search.run("AI Engineer", location="Portugal", gl="pt")[0:500] 'Azure AI Engineer Be an XpanderCandidatar-meCandidatar-meCandidatar-me\n\nShare:\n\nAzure AI Engineer\n\nA área Digital Xperience da Xpand IT é uma equipa tecnológica de rápido crescimento que se concentra em tecnologias Microsoft e Mobile. A sua principal missão é fornecer soluções de software de alta qualidade que atendam às necessidades do utilizador final, num mundo tecnológico continuamente exigente e em ritmo acelerado, proporcionando a melhor experiência em termos de personalização, performance'Getting results with metadata​import pprintsearch = SearchApiAPIWrapper(engine="google_scholar")results = search.results("Large Language Models")pprint.pp(results) {'search_metadata': {'id': 'search_qVdXG2jzvrlqTzayeYoaOb8A', 'status': 'Success', 'created_at': '2023-09-25T15:22:30Z', 'request_time_taken': 3.21, 'parsing_time_taken': 0.03, 'total_time_taken': 3.24, 'request_url': | This notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key. | This notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key. ->: Follow up: How old was Plato when he died? Intermediate answer: eighty Follow up: How old was Socrates when he died? Intermediate answer: | Socrates | | -------- | | Born | c. 470 BC Deme Alopece, Athens | | Died | 399 BC (aged approximately 71) Athens | | Cause of death | Execution by forced suicide by poisoning | | Spouse(s) | Xanthippe, Myrto | Follow up: How old was Aristotle when he died? Intermediate answer: 62 years So the final answer is: Plato > Finished chain. 'Plato'Custom parameters​SearchApi wrapper can be customized to use different engines like Google News, Google Jobs, Google Scholar, or others which can be found in SearchApi documentation. All parameters supported by SearchApi can be passed when executing the query. search = SearchApiAPIWrapper(engine="google_jobs")search.run("AI Engineer", location="Portugal", gl="pt")[0:500] 'Azure AI Engineer Be an XpanderCandidatar-meCandidatar-meCandidatar-me\n\nShare:\n\nAzure AI Engineer\n\nA área Digital Xperience da Xpand IT é uma equipa tecnológica de rápido crescimento que se concentra em tecnologias Microsoft e Mobile. A sua principal missão é fornecer soluções de software de alta qualidade que atendam às necessidades do utilizador final, num mundo tecnológico continuamente exigente e em ritmo acelerado, proporcionando a melhor experiência em termos de personalização, performance'Getting results with metadata​import pprintsearch = SearchApiAPIWrapper(engine="google_scholar")results = search.results("Large Language Models")pprint.pp(results) {'search_metadata': {'id': 'search_qVdXG2jzvrlqTzayeYoaOb8A', 'status': 'Success', 'created_at': '2023-09-25T15:22:30Z', 'request_time_taken': 3.21, 'parsing_time_taken': 0.03, 'total_time_taken': 3.24, 'request_url': |
3,091 | 3.24, 'request_url': 'https://scholar.google.com/scholar?q=Large+Language+Models&hl=en', 'html_url': 'https://www.searchapi.io/api/v1/searches/search_qVdXG2jzvrlqTzayeYoaOb8A.html', 'json_url': 'https://www.searchapi.io/api/v1/searches/search_qVdXG2jzvrlqTzayeYoaOb8A'}, 'search_parameters': {'engine': 'google_scholar', 'q': 'Large Language Models', 'hl': 'en'}, 'search_information': {'query_displayed': 'Large Language Models', 'total_results': 6420000, 'page': 1, 'time_taken_displayed': 0.06}, 'organic_results': [{'position': 1, 'title': 'ChatGPT for good? On opportunities and ' 'challenges of large language models for ' 'education', 'data_cid': 'uthwmf2nU3EJ', 'link': 'https://www.sciencedirect.com/science/article/pii/S1041608023000195', 'publication': 'E Kasneci, K Seßler, S Küchemann, M ' 'Bannert… - Learning and individual …, ' '2023 - Elsevier', 'snippet': '… state of large language models and their ' 'applications. We then highlight how these ' 'models can be … With regard to challenges, ' 'we argue that large language models in ' 'education require …', 'inline_links': {'cited_by': {'cites_id': '8166055256995715258', 'total': 410, 'link': | This notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key. | This notebook shows examples of how to use SearchApi to search the web. Go to https://www.searchapi.io/ to sign up for a free account and get API key. ->: 3.24, 'request_url': 'https://scholar.google.com/scholar?q=Large+Language+Models&hl=en', 'html_url': 'https://www.searchapi.io/api/v1/searches/search_qVdXG2jzvrlqTzayeYoaOb8A.html', 'json_url': 'https://www.searchapi.io/api/v1/searches/search_qVdXG2jzvrlqTzayeYoaOb8A'}, 'search_parameters': {'engine': 'google_scholar', 'q': 'Large Language Models', 'hl': 'en'}, 'search_information': {'query_displayed': 'Large Language Models', 'total_results': 6420000, 'page': 1, 'time_taken_displayed': 0.06}, 'organic_results': [{'position': 1, 'title': 'ChatGPT for good? On opportunities and ' 'challenges of large language models for ' 'education', 'data_cid': 'uthwmf2nU3EJ', 'link': 'https://www.sciencedirect.com/science/article/pii/S1041608023000195', 'publication': 'E Kasneci, K Seßler, S Küchemann, M ' 'Bannert… - Learning and individual …, ' '2023 - Elsevier', 'snippet': '… state of large language models and their ' 'applications. We then highlight how these ' 'models can be … With regard to challenges, ' 'we argue that large language models in ' 'education require …', 'inline_links': {'cited_by': {'cites_id': '8166055256995715258', 'total': 410, 'link': |
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