Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsGolden QueryGolden QueryThis notebook goes over how to use the golden-query tool.Go to the Golden API docs to get an overview about the Golden API.Create a Golden account if you don't have one on the Golden Website.Get your API key from the Golden API Settings page.Save your API key into GOLDEN_API_KEY env variableimport osos.environ["GOLDEN_API_KEY"] = ""from langchain.utilities.golden_query import GoldenQueryAPIWrappergolden_query = GoldenQueryAPIWrapper()import jsonjson.loads(golden_query.run("companies in nanotech"))    {'results': [{'id': 4673886,       'latestVersionId': 60276991,       'properties': [{'predicateId': 'name',         'instances': [{'value': 'Samsung', 'citations': []}]}]},      {'id': 7008,       'latestVersionId': 61087416,


'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':


'name',         'instances': [{'value': 'Novartis', 'citations': []}]}]},      {'id': 237458,       'latestVersionId': 61406160,       'properties': [{'predicateId': 'name',         'instances': [{'value': 'Analog Devices', 'citations': []}]}]},      {'id': 3941943,       'latestVersionId': 60382250,       'properties': [{'predicateId': 'name',         'instances': [{'value': 'AbbVie Inc.', 'citations': []}]}]},      {'id': 4178762,       'latestVersionId': 60542667,       'properties': [{'predicateId': 'name',         'instances': [{'value': 'IBM', 'citations': []}]}]}],     'next': 'https://golden.com/api/v2/public/queries/59044/results/?cursor=eyJwb3NpdGlvbiI6IFsxNzYxNiwgIklCTS04M1lQM1oiXX0%3D&pageSize=10',     'previous': None}PreviousFile System ToolsNextGoogle PlacesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


Search Tools | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsSearch ToolsOn this pageSearch ToolsThis notebook shows off usage of various search tools.from langchain.agents import load_toolsfrom langchain.agents import initialize_agentfrom langchain.agents import AgentTypefrom langchain.llms import OpenAIllm = OpenAI(temperature=0)Google Serper API Wrapperâ€‹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: 37Â°F    Thought: I now know the current temperature in Pomfret.    Final


Thought: I now know the current temperature in Pomfret.    Final Answer: The current temperature in Pomfret is 37Â°F.        > Finished chain.    'The current temperature in Pomfret is 37Â°F.'SerpAPIâ€‹Now, let's use the SerpAPI tool.tools = load_tools(["serpapi"], llm=llm)agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the weather in Pomfret?")            > Entering new AgentExecutor chain...     I need to find out what the current weather is in Pomfret.    Action: Search    Action Input: "weather in Pomfret"    Observation: Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 ...    Thought: I now know the current weather in Pomfret.    Final Answer: Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 mph.        > Finished chain.    'Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 mph.'GoogleSearchAPIWrapperâ€‹Now, let's use the official Google Search API Wrapper.tools = load_tools(["google-search"], llm=llm)agent = initialize_agent(    tools, llm,


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 +2Â ... 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 forÂ ... Pomfret, CT 12 hour by hour weather forecast includes precipitation, temperatures, sky conditions, rain chance, dew-point, relative humidity, wind directionÂ ... North Pomfret Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & tropical weather conditions forÂ ... 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


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 Engineâ€‹Here we will be using a self hosted SearxNG meta search engine.tools = load_tools(["searx-search"], searx_host="http://localhost:8888", llm=llm)agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("What is the weather in Pomfret")            > Entering new AgentExecutor chain...     I should look up the current weather    Action: SearX Search    Action Input: "weather in Pomfret"    Observation: Mainly cloudy with snow showers around in the morning. High around 40F. Winds NNW at 5 to 10 mph. Chance of snow 40%. Snow accumulations less than one inch.        10 Day Weather - Pomfret, MD As of 1:37 pm EST Today 49Â°/ 41Â° 52% Mon 27 |


pm EST Today 49Â°/ 41Â° 52% Mon 27 | Day 49Â° 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 39Â°/ 32Â° 37% Wed 01 | Day 39Â° 37% NE 4 mph Cloudy with snow showers developing for the afternoon. High 39F....        Pomfret, CT ; Current Weather. 1:06 AM. 35Â°F Â· RealFeelÂ® 32Â° ; TODAY'S WEATHER FORECAST. 3/3. 44Â°Hi. RealFeelÂ® 50Â° ; TONIGHT'S WEATHER FORECAST. 3/3. 32Â°Lo.        Pomfret, MD Forecast Today Hourly Daily Morning 41Â° 1% Afternoon 43Â° 0% Evening 35Â° 3% Overnight 34Â° 2% Don't Miss Finally, Hereâ€™s Why We Get More Colds and Flu When Itâ€™s Cold Coast-To-Coast...        Pomfret, MD Weather Forecast | AccuWeather Current Weather 5:35 PM 35Â° F RealFeelÂ® 36Â° RealFeel Shadeâ„¢ 36Â° 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 23Â° F RealFeelÂ®


| AccuWeather Current Weather 11:21 AM 23Â° F RealFeelÂ® 27Â° RealFeel Shadeâ„¢ 25Â° 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 39Â° F RealFeelÂ® 36Â° Air Quality Fair Wind NW 6 mph Wind Gusts 16 mph Mostly clear More Details WinterCast...        12:00 pm Â· Feels Like36Â° Â· WindN 5 mph Â· Humidity43% Â· UV Index3 of 10 Â· Cloud Cover65% Â· Rain Amount0 in ...        Pomfret Center, CT Weather Conditions | Weather Underground star Popular Cities San Francisco, CA 49 Â°F Clear Manhattan, NY 37 Â°F Fair Schiller Park, IL (60176) warning39 Â°F Mostly Cloudy...    Thought: I now know the final answer    Final Answer: The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.        > Finished chain.    'The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.'PreviousSceneXplainNextSearxNG Search APIGoogle Serper API WrapperSerpAPIGoogleSearchAPIWrapperSearxNG Meta Search EngineCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


SceneXplain | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsSceneXplainOn this pageSceneXplainSceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool.To use this tool, you'll need to make an account and fetch your API Token from the website. Then you can instantiate the tool.import osos.environ["SCENEX_API_KEY"] = "<YOUR_API_KEY>"from langchain.agents import load_toolstools = load_tools(["sceneXplain"])Or directly instantiate the tool.from langchain.tools import SceneXplainTooltool = SceneXplainTool()Usage in an Agentâ€‹The tool can be used in any LangChain agent as follows:from langchain.llms import OpenAIfrom langchain.agents import initialize_agentfrom langchain.memory import ConversationBufferMemoryllm = OpenAI(temperature=0)memory = ConversationBufferMemory(memory_key="chat_history")agent = initialize_agent(    tools, llm, memory=memory, agent="conversational-react-description", verbose=True)output = agent.run(    input=(


verbose=True)output = agent.run(    input=(        "What is in this image https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png. "        "Is it movie or a game? If it is a movie, what is the name of the movie?"    ))print(output)            > Entering new AgentExecutor chain...        Thought: Do I need to use a tool? Yes    Action: Image Explainer    Action Input: https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png    Observation: In a charmingly whimsical scene, a young girl is seen braving the rain alongside her furry companion, the lovable Totoro. The two are depicted standing on a bustling street corner, where they are sheltered from the rain by a bright yellow umbrella. The girl, dressed in a cheerful yellow frock, holds onto the umbrella with both hands while gazing up at Totoro with an expression of wonder and delight.        Totoro, meanwhile, stands tall and proud beside his young friend, holding his own umbrella aloft to protect them both from the downpour. His furry body is rendered in rich shades of grey and white, while his large ears and wide eyes lend him an endearing charm.        In the background of the scene, a street sign can be seen jutting out from the pavement amidst a flurry of raindrops. A sign with Chinese characters adorns its surface, adding to the sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy


sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy and camaraderie in this heartwarming image.    Thought: Do I need to use a tool? No    AI: This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.        > Finished chain.    This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.PreviousRequestsNextSearch ToolsUsage in an AgentCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


Wolfram Alpha | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsWolfram AlphaWolfram AlphaThis notebook goes over how to use the wolfram alpha component.First, you need to set up your Wolfram Alpha developer account and get your APP ID:Go to wolfram alpha and sign up for a developer account hereCreate an app and get your APP IDpip install wolframalphaThen we will need to set some environment variables:Save your APP ID into WOLFRAM_ALPHA_APPID env variablepip install wolframalphaimport osos.environ["WOLFRAM_ALPHA_APPID"] = ""from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapperwolfram = WolframAlphaAPIWrapper()wolfram.run("What is 2x+5 = -3x + 7?")    'x = 2/5'PreviousWikipediaNextYouTubeSearchToolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


ChatGPT Plugins | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsChatGPT PluginsChatGPT PluginsThis example shows how to use ChatGPT Plugins within LangChain abstractions.Note 1: This currently only works for plugins with no auth.Note 2: There are almost certainly other ways to do this, this is just a first pass. If you have better ideas, please open a PR!from langchain.chat_models import ChatOpenAIfrom langchain.agents import load_tools, initialize_agentfrom langchain.agents import AgentTypefrom langchain.tools import AIPluginTooltool = AIPluginTool.from_plugin_url("https://www.klarna.com/.well-known/ai-plugin.json")llm = ChatOpenAI(temperature=0)tools = load_tools(["requests_all"])tools += [tool]agent_chain = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent_chain.run("what t shirts are available in klarna?")            > Entering new AgentExecutor chain...


> Entering new AgentExecutor chain...    I need to check the Klarna Shopping API to see if it has information on available t shirts.    Action: KlarnaProducts    Action Input: None    Observation: Usage Guide: Use the Klarna plugin to get relevant product suggestions for any shopping or researching purpose. The query to be sent should not include stopwords like articles, prepositions and determinants. The api works best when searching for words that are related to products, like their name, brand, model or category. Links will always be returned and should be shown to the user.        OpenAPI Spec: {'openapi': '3.0.1', 'info': {'version': 'v0', 'title': 'Open AI Klarna product Api'}, 'servers': [{'url': 'https://www.klarna.com/us/shopping'}], 'tags': [{'name': 'open-ai-product-endpoint', 'description': 'Open AI Product Endpoint. Query for products.'}], 'paths': {'/public/openai/v0/products': {'get': {'tags': ['open-ai-product-endpoint'], 'summary': 'API for fetching Klarna product information', 'operationId': 'productsUsingGET', 'parameters': [{'name': 'q', 'in': 'query', 'description': 'query, must be between 2 and 100 characters', 'required': True, 'schema': {'type': 'string'}}, {'name': 'size', 'in': 'query', 'description': 'number of products returned', 'required': False, 'schema': {'type': 'integer'}}, {'name': 'budget', 'in': 'query', 'description': 'maximum price of the matching product in local currency, filters results', 'required': False, 'schema': {'type': 'integer'}}],


currency, filters results', 'required': False, 'schema': {'type': 'integer'}}], 'responses': {'200': {'description': 'Products found', 'content': {'application/json': {'schema': {'$ref': '#/components/schemas/ProductResponse'}}}}, '503': {'description': 'one or more services are unavailable'}}, 'deprecated': False}}}, 'components': {'schemas': {'Product': {'type': 'object', 'properties': {'attributes': {'type': 'array', 'items': {'type': 'string'}}, 'name': {'type': 'string'}, 'price': {'type': 'string'}, 'url': {'type': 'string'}}, 'title': 'Product'}, 'ProductResponse': {'type': 'object', 'properties': {'products': {'type': 'array', 'items': {'$ref': '#/components/schemas/Product'}}}, 'title': 'ProductResponse'}}}}    Thought:I need to use the Klarna Shopping API to search for t shirts.    Action: requests_get    Action Input: https://www.klarna.com/us/shopping/public/openai/v0/products?q=t%20shirts    Observation: {"products":[{"name":"Lacoste Men's Pack of Plain T-Shirts","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202043025/Clothing/Lacoste-Men-s-Pack-of-Plain-T-Shirts/?utm_source=openai","price":"$26.60","attributes":["Material:Cotton","Target Group:Man","Color:White,Black"]},{"name":"Hanes Men's Ultimate 6pk. Crewneck


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


Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.    Final Answer: The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.        > Finished chain.    "The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack."PreviousBrave SearchNextDataForSeo API WrapperCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


Bing Search | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsBing SearchOn this pageBing SearchThis notebook goes over how to use the bing search component.First, you need to set up the proper API keys and environment variables. To set it up, follow the instructions found here.Then we will need to set some environment variables.import osos.environ["BING_SUBSCRIPTION_KEY"] = "<key>"os.environ["BING_SEARCH_URL"] = "https://api.bing.microsoft.com/v7.0/search"from langchain.utilities import BingSearchAPIWrappersearch = BingSearchAPIWrapper()search.run("python")    'Thanks to the flexibility of Python and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with Python by Dan Taylor. Python releases by version number: Release version Release date Click for more.


Python releases by version number: Release version Release date Click for more. Python 3.11.1 Dec. 6, 2022 Download Release Notes. Python 3.10.9 Dec. 6, 2022 Download Release Notes. Python 3.9.16 Dec. 6, 2022 Download Release Notes. Python 3.8.16 Dec. 6, 2022 Download Release Notes. Python 3.7.16 Dec. 6, 2022 Download Release Notes. In this lesson, we will look at the += operator in Python and see how it works with several simple examples.. The operator â€˜+=â€™ is a shorthand for the addition assignment operator.It adds two values and assigns the sum to a variable (left operand). W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more. This tutorial introduces the reader informally to the basic concepts and features of the Python language and system. It helps to have a Python interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well. For a description of standard objects and modules, see The Python Standard ... Python is a general-purpose, versatile, and powerful programming language. It's a great first language because Python code is concise and easy to read. Whatever you want to do, python can do it. From web


Whatever you want to do, python can do it. From web development to machine learning to data science, Python is the language for you. To install Python using the Microsoft Store: Go to your Start menu (lower left Windows icon), type "Microsoft Store", select the link to open the store. Once the store is open, select Search from the upper-right menu and enter "Python". Select which version of Python you would like to use from the results under Apps. Under the â€œPython Releases for Mac OS Xâ€� heading, click the link for the Latest Python 3 Release - Python 3.x.x. As of this writing, the latest version was Python 3.8.4. Scroll to the bottom and click macOS 64-bit installer to start the download. When the installer is finished downloading, move on to the next step. Step 2: Run the Installer'Number of resultsâ€‹You can use the k parameter to set the number of resultssearch = BingSearchAPIWrapper(k=1)search.run("python")    'Thanks to the flexibility of Python and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with Python by Dan Taylor.'Metadata Resultsâ€‹Run query through BingSearch and return snippet, title, and link metadata.Snippet: The description of the result.Title: The title of the result.Link: The link to the result.search =


description of the result.Title: The title of the result.Link: The link to the result.search = BingSearchAPIWrapper()search.results("apples", 5)    [{'snippet': 'Lady Alice. Pink Lady apples arenâ€™t the only lady in the apple family. Lady Alice apples were discovered growing, thanks to bees pollinating, in Washington. They are smaller and slightly more stout in appearance than other varieties. Their skin color appears to have red and yellow stripes running from stem to butt.',      'title': '25 Types of Apples - Jessica Gavin',      'link': 'https://www.jessicagavin.com/types-of-apples/'},     {'snippet': 'Apples can do a lot for you, thanks to plant chemicals called flavonoids. And they have pectin, a fiber that breaks down in your gut. If you take off the appleâ€™s skin before eating it, you won ...',      'title': 'Apples: Nutrition & Health Benefits - WebMD',      'link': 'https://www.webmd.com/food-recipes/benefits-apples'},     {'snippet': 'Apples boast many vitamins and minerals, though not in high amounts. However, apples are usually a good source of vitamin C. Vitamin C. Also called ascorbic acid, this vitamin is a common ...',      'title': 'Apples 101: Nutrition Facts and Health Benefits',      'link': 'https://www.healthline.com/nutrition/foods/apples'},     {'snippet': 'Weight management. The fibers in apples


{'snippet': 'Weight management. The fibers in apples can slow digestion, helping one to feel greater satisfaction after eating. After following three large prospective cohorts of 133,468 men and women for 24 years, researchers found that higher intakes of fiber-rich fruits with a low glycemic load, particularly apples and pears, were associated with the least amount of weight gain over time.',      'title': 'Apples | The Nutrition Source | Harvard T.H. Chan School of Public Health',      'link': 'https://www.hsph.harvard.edu/nutritionsource/food-features/apples/'}]PreviousShell ToolNextBrave SearchNumber of resultsMetadata ResultsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


SerpAPI | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsSerpAPIOn this pageSerpAPIThis notebook goes over how to use the SerpAPI component to search the web.from langchain.utilities import SerpAPIWrappersearch = SerpAPIWrapper()search.run("Obama's first name?")    'Barack Hussein Obama II'Custom Parametersâ€‹You can also customize the SerpAPI wrapper with arbitrary parameters. For example, in the below example we will use bing instead of google.params = {    "engine": "bing",    "gl": "us",    "hl": "en",}search = SerpAPIWrapper(params=params)search.run("Obama's first name?")    'Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American presiâ€¦New content will be added above the current area of focus upon selectionBarack Hussein Obama II is an


content will be added above the current area of focus upon selectionBarack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004, and previously worked as a civil rights lawyer before entering politics.Wikipediabarackobama.com'from langchain.agents import Tool# You can create the tool to pass to an agentrepl_tool = Tool(    name="python_repl",    description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",    func=search.run,)PreviousSearxNG Search APINextTwilioCustom ParametersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


Twilio | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsTwilioOn this pageTwilioThis notebook goes over how to use the Twilio API wrapper to send a message through SMS or Twilio Messaging Channels.Twilio Messaging Channels facilitates integrations with 3rd party messaging apps and lets you send messages through WhatsApp Business Platform (GA), Facebook Messenger (Public Beta) and Google Business Messages (Private Beta).Setupâ€‹To use this tool you need to install the Python Twilio package twilio# !pip install twilioYou'll also need to set up a Twilio account and get your credentials. You'll need your Account String Identifier (SID) and your Auth Token. You'll also need a number to send messages from.You can either pass these in to the TwilioAPIWrapper as named parameters account_sid, auth_token, from_number, or you can set the environment variables TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, TWILIO_FROM_NUMBER.Sending an SMSâ€‹from langchain.utilities.twilio import TwilioAPIWrappertwilio = TwilioAPIWrapper(


import TwilioAPIWrappertwilio = TwilioAPIWrapper(    #     account_sid="foo",    #     auth_token="bar",    #     from_number="baz,")twilio.run("hello world", "+16162904619")Sending a WhatsApp Messageâ€‹You'll need to link your WhatsApp Business Account with Twilio. You'll also need to make sure that the number to send messages from is configured as a WhatsApp Enabled Sender on Twilio and registered with WhatsApp.from langchain.utilities.twilio import TwilioAPIWrappertwilio = TwilioAPIWrapper(    #     account_sid="foo",    #     auth_token="bar",    #     from_number="whatsapp: baz,")twilio.run("hello world", "whatsapp: +16162904619")PreviousSerpAPINextWikipediaSetupSending an SMSSending a WhatsApp MessageCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


Requests | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsRequestsOn this pageRequestsThe web contains a lot of information that LLMs do not have access to. In order to easily let LLMs interact with that information, we provide a wrapper around the Python Requests module that takes in a URL and fetches data from that URL.from langchain.agents import load_toolsrequests_tools = load_tools(["requests_all"])requests_tools    [RequestsGetTool(name='requests_get', description='A portal to the internet. Use this when you need to get specific content from a website. Input should be a  url (i.e. https://www.google.com). The output will be the text response of the GET request.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)),     RequestsPostTool(name='requests_post', description='Use this when you want to POST to a website.\n    Input should be a json string with two keys: "url" and "data".\n


Input should be a json string with two keys: "url" and "data".\n    The value of "url" should be a string, and the value of "data" should be a dictionary of \n    key-value pairs you want to POST to the url.\n    Be careful to always use double quotes for strings in the json string\n    The output will be the text response of the POST request.\n    ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)),     RequestsPatchTool(name='requests_patch', description='Use this when you want to PATCH to a website.\n    Input should be a json string with two keys: "url" and "data".\n    The value of "url" should be a string, and the value of "data" should be a dictionary of \n    key-value pairs you want to PATCH to the url.\n    Be careful to always use double quotes for strings in the json string\n    The output will be the text response of the PATCH request.\n    ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)),     RequestsPutTool(name='requests_put', description='Use this when you want to PUT to a website.\n    Input should be a json string with two keys: "url" and "data".\n    The value of "url" should be a string, and the value of "data" should be a dictionary of \n    key-value pairs you want to PUT to the url.\n    Be careful to always use double quotes for strings in the json


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 toolâ€‹Each requests tool contains a requests wrapper. You can work with these wrappers directly below# Each tool wrapps a requests wrapperrequests_tools[0].requests_wrapper    TextRequestsWrapper(headers=None, aiosession=None)from langchain.utilities import TextRequestsWrapperrequests = TextRequestsWrapper()requests.get("https://www.google.com")    '<!doctype html><html itemscope="" itemtype="http://schema.org/WebPage" lang="en"><head><meta content="Search the world\'s information, including webpages, images, videos and more. Google has many special features to help you find exactly what you\'re looking for." name="description"><meta content="noodp" name="robots"><meta content="text/html; charset=UTF-8" http-equiv="Content-Type"><meta content="/images/branding/googleg/1x/googleg_standard_color_128dp.png" itemprop="image"><title>Google</title><script


!important;}#gbar{height:22px}#guser{padding-bottom:7px !important;text-align:right}.gbh,.gbd{border-top:1px solid #c9d7f1;font-size:1px}.gbh{height:0;position:absolute;top:24px;width:100%}@media all{.gb1{height:22px;margin-right:.5em;vertical-align:top}#gbar{float:left}}a.gb1,a.gb4{text-decoration:underline !important}a.gb1,a.gb4{color:#00c !important}.gbi .gb4{color:#dd8e27 !important}.gbf .gb4{color:#900 !important}\n</style><style>body,td,a,p,.h{font-family:arial,sans-serif}body{margin:0;overflow-y:scroll}#gog{padding:3px 8px 0}td{line-height:.8em}.gac_m td{line-height:17px}form{margin-bottom:20px}.h{color:#1558d6}em{font-weight:bold;font-style:normal}.lst{height:25px;width:496px}.gsfi,.lst{font:18px arial,sans-serif}.gsfs{font:17px arial,sans-serif}.ds{display:inline-box;display:inline-block;margin:3px 0 4px;margin-left:4px}input{font-family:inherit}body{background:#fff;color:#000}a{color:#4b11a8;text-decoration:none}a:hover,a:active{text-decoration:underline}.fl a{color:#1558d6}a:visited{color:#4b11a8}.sblc{padding-top:5px}.sblc a{display:block;margin:2px


a{display:block;margin:2px 0;margin-left:13px;font-size:11px}.lsbb{background:#f8f9fa;border:solid 1px;border-color:#dadce0 #70757a #70757a #dadce0;height:30px}.lsbb{display:block}#WqQANb a{display:inline-block;margin:0 12px}.lsb{background:url(/images/nav_logo229.png) 0 -261px repeat-x;border:none;color:#000;cursor:pointer;height:30px;margin:0;outline:0;font:15px arial,sans-serif;vertical-align:top}.lsb:active{background:#dadce0}.lst:focus{outline:none}</style><script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){window.google.erd={jsr:1,bv:1785,de:true};\nvar h=this||self;var k,l=null!=(k=h.mei)?k:1,n,p=null!=(n=h.sdo)?n:!0,q=0,r,t=google.erd,v=t.jsr;google.ml=function(a,b,d,m,e){e=void 0===e?2:e;b&&(r=a&&a.message);if(google.dl)return google.dl(a,e,d),null;if(0>v){window.console&&console.error(a,d);if(-2===v)throw a;b=!1}else b=!a||!a.message||"Error loading script"===a.message||q>=l&&!m?!1:!0;if(!b)return null;q++;d=d||{};b=encodeURIComponent;var


null;q++;d=d||{};b=encodeURIComponent;var c="/gen_204?atyp=i&ei="+b(google.kEI);google.kEXPI&&(c+="&jexpid="+b(google.kEXPI));c+="&srcpg="+b(google.sn)+"&jsr="+b(t.jsr)+"&bver="+b(t.bv);var f=a.lineNumber;void 0!==f&&(c+="&line="+f);var g=\na.fileName;g&&(0<g.indexOf("-extension:/")&&(e=3),c+="&script="+b(g),f&&g===window.location.href&&(f=document.documentElement.outerHTML.split("\\n")[f],c+="&cad="+b(f?f.substring(0,300):"No script found.")));c+="&jsel="+e;for(var u in d)c+="&",c+=b(u),c+="=",c+=b(d[u]);c=c+"&emsg="+b(a.name+": "+a.message);c=c+"&jsst="+b(a.stack||"N/A");12288<=c.length&&(c=c.substr(0,12288));a=c;m||google.log(0,"",a);return a};window.onerror=function(a,b,d,m,e){r!==a&&(a=e instanceof Error?e:Error(a),void 0===d||"lineNumber"in a||(a.lineNumber=d),void 0===b||"fileName"in a||(a.fileName=b),google.ml(a,!1,void 0,!1,"SyntaxError"===a.name||"SyntaxError"===a.message.substring(0,11)||-1!==a.message.indexOf("Script error")?3:0));r=null;p&&q>=l&&(window.onerror=null)};})();</script></head><body bgcolor="#fff"><script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){var


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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>'PreviousPubMed ToolNextSceneXplainInside the toolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


Human as a tool | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsHuman as a toolOn this pageHuman as a toolHuman are AGI so they can certainly be used as a tool to help out AI agent
when it is confused.from langchain.chat_models import ChatOpenAIfrom langchain.llms import OpenAIfrom langchain.agents import load_tools, initialize_agentfrom langchain.agents import AgentTypellm = ChatOpenAI(temperature=0.0)math_llm = OpenAI(temperature=0.0)tools = load_tools(    ["human", "llm-math"],    llm=math_llm,)agent_chain = initialize_agent(    tools,    llm,    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,    verbose=True,)In the above code you can see the tool takes input directly from command line.


You can customize prompt_func and input_func according to your need (as shown below).agent_chain.run("What's my friend Eric's surname?")# Answer with 'Zhu'            > Entering new AgentExecutor chain...    I don't know Eric's surname, so I should ask a human for guidance.    Action: Human    Action Input: "What is Eric's surname?"        What is Eric's surname?     Zhu        Observation: Zhu    Thought:I now know Eric's surname is Zhu.    Final Answer: Eric's surname is Zhu.        > Finished chain.    "Eric's surname is Zhu."Configuring the Input Functionâ€‹By default, the HumanInputRun tool uses the python input function to get input from the user.
You can customize the input_func to be anything you'd like.


For instance, if you want to accept multi-line input, you could do the following:def get_input() -> str:    print("Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.")    contents = []    while True:        try:            line = input()        except EOFError:            break        if line == "q":            break        contents.append(line)    return "\n".join(contents)# You can modify the tool when loadingtools = load_tools(["human", "ddg-search"], llm=math_llm, input_func=get_input)# Or you can directly instantiate the toolfrom langchain.tools import HumanInputRuntool = HumanInputRun(input_func=get_input)agent_chain = initialize_agent(    tools,    llm,    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,    verbose=True,)agent_chain.run("I need help attributing a quote")            > Entering new AgentExecutor chain...    I should ask a human for guidance    Action: Human    Action Input: "Can you help me attribute a quote?"        Can you help me attribute a quote?    Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.     vini     vidi     vici     q        Observation: vini    vidi    vici


Observation: vini    vidi    vici    Thought:I need to provide more context about the quote    Action: Human    Action Input: "The quote is 'Veni, vidi, vici'"        The quote is 'Veni, vidi, vici'    Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.     oh who said it      q        Observation: oh who said it     Thought:I can use DuckDuckGo Search to find out who said the quote    Action: DuckDuckGo Search    Action Input: "Who said 'Veni, vidi, vici'?"    Observation: Updated on September 06, 2019. "Veni, vidi, vici" is a famous phrase said to have been spoken by the Roman Emperor Julius Caesar (100-44 BCE) in a bit of stylish bragging that impressed many of the writers of his day and beyond. The phrase means roughly "I came, I saw, I conquered" and it could be pronounced approximately Vehnee, Veedee ... Veni, vidi, vici (Classical Latin: [weË�niË� wiË�diË� wiË�kiË�], Ecclesiastical Latin: [Ëˆveni Ëˆvidi ËˆvitÊƒi]; "I came; I saw; I conquered") is a Latin phrase used to refer to a swift, conclusive victory.The phrase is popularly attributed to Julius Caesar who, according to Appian, used the phrase in a letter to the Roman Senate around 47 BC after he had achieved a quick victory


the phrase in a letter to the Roman Senate around 47 BC after he had achieved a quick victory in his short ... veni, vidi, vici Latin quotation from Julius Caesar veÂ· ni, viÂ· di, viÂ· ci ËŒwÄ�-nÄ“ ËŒwÄ“-dÄ“ ËˆwÄ“-kÄ“ ËŒvÄ�-nÄ“ ËŒvÄ“-dÄ“ ËˆvÄ“-chÄ“ : I came, I saw, I conquered Articles Related to veni, vidi, vici 'In Vino Veritas' and Other Latin... Dictionary Entries Near veni, vidi, vici Venite veni, vidi, vici VenizÃ©los See More Nearby Entries Cite this Entry Style The simplest explanation for why veni, vidi, vici is a popular saying is that it comes from Julius Caesar, one of history's most famous figures, and has a simple, strong meaning: I'm powerful and fast. But it's not just the meaning that makes the phrase so powerful. Caesar was a gifted writer, and the phrase makes use of Latin grammar to ... One of the best known and most frequently quoted Latin expression, veni, vidi, vici may be found hundreds of times throughout the centuries used as an expression of triumph. The words are said to have been used by Caesar as he was enjoying a triumph.    Thought:I now know the final answer    Final Answer: Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered".        > Finished chain.    'Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I


"Veni, vidi, vici" which means "I came, I saw, I conquered".'Previoushuggingface_toolsNextIFTTT WebHooksConfiguring the Input FunctionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


DataForSeo API Wrapper | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsDataForSeo API WrapperOn this pageDataForSeo API WrapperThis notebook demonstrates how to use the DataForSeo API wrapper to obtain search engine results. The DataForSeo API allows users to retrieve SERP from most popular search engines like Google, Bing, Yahoo. It also allows to get SERPs from different search engine types like Maps, News, Events, etc.from langchain.utilities import DataForSeoAPIWrapperSetting up the API wrapper with your credentialsâ€‹You can obtain your API credentials by registering on the DataForSeo website.import osos.environ["DATAFORSEO_LOGIN"] = "your_api_access_username"os.environ["DATAFORSEO_PASSWORD"] = "your_api_access_password"wrapper = DataForSeoAPIWrapper()The run method will return the first result snippet from one of the following elements: answer_box, knowledge_graph, featured_snippet, shopping, organic.wrapper.run("Weather in Los Angeles")The Difference Between run and resultsâ€‹run and results are two methods provided by the


Difference Between run and resultsâ€‹run and results are two methods provided by the DataForSeoAPIWrapper class.The run method executes the search and returns the first result snippet from the answer box, knowledge graph, featured snippet, shopping, or organic results. These elements are sorted by priority from highest to lowest.The results method returns a JSON response configured according to the parameters set in the wrapper. This allows for more flexibility in terms of what data you want to return from the API.Getting Results as JSONâ€‹You can customize the result types and fields you want to return in the JSON response. You can also set a maximum count for the number of top results to return.json_wrapper = DataForSeoAPIWrapper(    json_result_types=["organic", "knowledge_graph", "answer_box"],    json_result_fields=["type", "title", "description", "text"],    top_count=3,)json_wrapper.results("Bill Gates")Customizing Location and Languageâ€‹You can specify the location and language of your search results by passing additional parameters to the API wrapper.customized_wrapper = DataForSeoAPIWrapper(    top_count=10,    json_result_types=["organic", "local_pack"],    json_result_fields=["title", "description", "type"],    params={"location_name": "Germany", "language_code": "en"},)customized_wrapper.results("coffee near me")Customizing the Search Engineâ€‹You can also specify the search engine you want to use.customized_wrapper = DataForSeoAPIWrapper(    top_count=10,    json_result_types=["organic", "local_pack"],    json_result_fields=["title", "description", "type"],    params={"location_name": "Germany", "language_code": "en", "se_name":


params={"location_name": "Germany", "language_code": "en", "se_name": "bing"},)customized_wrapper.results("coffee near me")Customizing the Search Typeâ€‹The API wrapper also allows you to specify the type of search you want to perform. For example, you can perform a maps search.maps_search = DataForSeoAPIWrapper(    top_count=10,    json_result_fields=["title", "value", "address", "rating", "type"],    params={        "location_coordinate": "52.512,13.36,12z",        "language_code": "en",        "se_type": "maps",    },)maps_search.results("coffee near me")Integration with Langchain Agentsâ€‹You can use the Tool class from the langchain.agents module to integrate the DataForSeoAPIWrapper with a langchain agent. The Tool class encapsulates a function that the agent can call.from langchain.agents import Toolsearch = DataForSeoAPIWrapper(    top_count=3,    json_result_types=["organic"],    json_result_fields=["title", "description", "type"],)tool = Tool(    name="google-search-answer",    description="My new answer tool",    func=search.run,)json_tool = Tool(    name="google-search-json",    description="My new json tool",    func=search.results,)PreviousChatGPT PluginsNextDuckDuckGo SearchSetting up the API wrapper with your credentialsThe Difference Between run and resultsGetting Results as JSONCustomizing Location and LanguageCustomizing the Search EngineCustomizing the Search TypeIntegration with Langchain AgentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â©


with Langchain AgentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


PubMed Tool | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsPubMed ToolPubMed ToolThis notebook goes over how to use PubMed as a toolPubMedÂ® 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.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:


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 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.'PreviousOpenWeatherMap APINextRequestsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


SearxNG Search API | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsSearxNG Search APIOn this pageSearxNG Search APIThis 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 Parametersâ€‹SearxNG supports 135 search engines. You can also customize


Parametersâ€‹SearxNG 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 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 â€” 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 todayâ€™s well-known language modelsâ€”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 Labsâ€”are...\n\nLarge language models (LLMs) such


from AI21 Labsâ€”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 inglÃ©s, deep learning) es un conjunto de algoritmos de aprendizaje automÃ¡tico (en inglÃ©s, machine learning) que intenta modelar abstracciones de alto nivel en datos usando arquitecturas computacionales que admiten transformaciones no lineales mÃºltiples e iterativas de datos expresados en forma matricial o tensorial. 1'Obtaining results with metadataâ€‹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': 'â€¦ on natural language instructions, large language models (â€¦ the '                 'prompt used to steer the model,


'prompt used to steer the model, and most effective prompts â€¦ to '                 'prompt engineering, we propose Automatic Prompt â€¦',      'title': 'Large language models are human-level prompt engineers',      'link': 'https://arxiv.org/abs/2211.01910',      'engines': ['google scholar'],      'category': 'science'},     {'snippet': 'â€¦ Large language models (LLMs) have introduced new possibilities '                 'for prototyping with AI [18]. Pre-trained on a large amount of '                 'text data, models â€¦ language instructions called prompts. â€¦',      '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': 'â€¦ can introspect the large prompt model. We derive the view '                 'Ï•0(X) and the model h0 from T01. However, instead of fully '                 'fine-tuning T0 during co-training, we focus on soft prompt


'fine-tuning T0 during co-training, we focus on soft prompt '                 'tuning, â€¦',      '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': 'â€¦ With the success of large language models (LLMs) of code and '                 'their use as â€¦ prompt design process become important. In this '                 'work, we propose a framework called Repo-Level Prompt â€¦',      'title': 'Repository-level prompt generation for large language models of '               'code',      'link': 'https://arxiv.org/abs/2206.12839',      'engines': ['google scholar'],      'category': 'science'},     {'snippet': 'â€¦ 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


the '                 'unique â€¦',      '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 '                 'effective in general. Besides,


'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


'                 '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': 'http://arxiv.org/abs/2008.01441v1',      'engines': ['arxiv'],      'category': 'science'},     {'snippet':


'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 '


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'],      '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


'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 '                 '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, '


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'],


'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 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',


'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':


'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 '                 '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 ğŸ¤— Transformers.',      'title': 'mistral',      'link': 'https://github.com/stanford-crfm/mistral',      'engines': ['github'],      'category': 'it'},     {'snippet': 'A prize for finding tasks that cause large language


'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',


'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',      '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


'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 '


{'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': 'it'}]PreviousSearch ToolsNextSerpAPICustom ParametersObtaining results with metadataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


awslambda | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsawslambdaOn this pageawslambdaAWS Lambda APIâ€‹This notebook goes over how to use the AWS Lambda Tool component.AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS), designed to allow developers to build and run applications and services without the need for provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.By including a awslambda in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need.When an Agent uses the awslambda tool, it will provide an argument of type string which will in turn be passed into the Lambda function via the event parameter.First, you need to install boto3 python package.pip install boto3 > /dev/nullIn order for an agent to use the tool, you must provide it with the name and description that match the functionality of you lambda function's logic. You


must provide it with the name and description that match the functionality of you lambda function's logic. You must also provide the name of your function. Note that because this tool is effectively just a wrapper around the boto3 library, you will need to run aws configure in order to make use of the tool. For more detail, see herefrom langchain import OpenAIfrom langchain.agents import load_tools, AgentTypellm = OpenAI(temperature=0)tools = load_tools(    ["awslambda"],    awslambda_tool_name="email-sender",    awslambda_tool_description="sends an email with the specified content to test@testing123.com",    function_name="testFunction1",)agent = initialize_agent(    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)agent.run("Send an email to test@testing123.com saying hello world.")PreviousArXiv API ToolNextShell ToolAWS Lambda APICommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


IFTTT WebHooks | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsIFTTT 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 webhookâ€‹Go to https://ifttt.com/createConfiguring the "If This"â€‹Click 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


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"â€‹Tap 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
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 ğŸ�‰Finishing upâ€‹To 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 AI NLP Workflow AutomationCreating a webhookConfiguring the "If This"Configuring the "Then That"Finishing upCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


Metaphor Search | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsApifyArXiv API ToolawslambdaShell ToolBing SearchBrave SearchChatGPT PluginsDataForSeo API WrapperDuckDuckGo SearchFile System ToolsGolden QueryGoogle PlacesGoogle SearchGoogle Serper APIGradio ToolsGraphQL toolhuggingface_toolsHuman as a toolIFTTT WebHooksLemon AI NLP Workflow AutomationMetaphor SearchOpenWeatherMap APIPubMed ToolRequestsSceneXplainSearch ToolsSearxNG Search APISerpAPITwilioWikipediaWolfram AlphaYouTubeSearchToolZapier Natural Language Actions APIVector storesGrouped by providerIntegrationsToolsMetaphor SearchMetaphor SearchMetaphor is a search engine fully designed to be used by LLMs. You can search and then get the contents for any page.This notebook goes over how to use Metaphor search.First, you need to set up the proper API keys and environment variables. Get 1000 free searches/month here.Then enter your API key as an environment variable.import osos.environ["METAPHOR_API_KEY"] = ""from langchain.utilities import MetaphorSearchAPIWrappersearch = MetaphorSearchAPIWrapper()Call the APIresults takes in a Metaphor-optimized search query and a number of results (up to 500). It returns a list of results with title, url, author, and creation date.search.results("The best blog post about AI safety is definitely this: ", 10)Adding filtersWe can also add filters to our search. include_domains: Optional[List[str]] - List of domains to include in the search. If specified, results will only come from these domains. Only one of include_domains and


the search. If specified, results will only come from these domains. Only one of include_domains and exclude_domains should be specified.exclude_domains: Optional[List[str]] - List of domains to exclude in the search. If specified, results will only come from these domains. Only one of include_domains and exclude_domains should be specified.start_crawl_date: Optional[str] - "Crawl date" refers to the date that Metaphor discovered a link, which is more granular and can be more useful than published date. If start_crawl_date is specified, results will only include links that were crawled after start_crawl_date. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ)end_crawl_date: Optional[str] - "Crawl date" refers to the date that Metaphor discovered a link, which is more granular and can be more useful than published date. If endCrawlDate is specified, results will only include links that were crawled before end_crawl_date. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ)start_published_date: Optional[str] - If specified, only links with a published date after start_published_date will be returned. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ). Note that for some links, we have no published date, and these links will be excluded from the results if start_published_date is specified.end_published_date: Optional[str] - If specified, only links with a published date before end_published_date will be returned. Must be specified in ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ). Note that for some links, we have no published date, and these links will be excluded from the results if end_published_date is specified.See full docs here.search.results(    "The best blog post about AI safety is definitely this: ",


here.search.results(    "The best blog post about AI safety is definitely this: ",    10,    include_domains=["lesswrong.com"],    start_published_date="2019-01-01",)Use Metaphor as a toolMetaphor can be used as a tool that gets URLs that other tools such as browsing tools.from langchain.agents.agent_toolkits import PlayWrightBrowserToolkitfrom langchain.tools.playwright.utils import (    create_async_playwright_browser,  # A synchronous browser is available, though it isn't compatible with jupyter.)async_browser = create_async_playwright_browser()toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)tools = toolkit.get_tools()tools_by_name = {tool.name: tool for tool in tools}print(tools_by_name.keys())navigate_tool = tools_by_name["navigate_browser"]extract_text = tools_by_name["extract_text"]from langchain.agents import initialize_agent, AgentTypefrom langchain.chat_models import ChatOpenAIfrom langchain.tools import MetaphorSearchResultsllm = ChatOpenAI(model_name="gpt-4", temperature=0.7)metaphor_tool = MetaphorSearchResults(api_wrapper=search)agent_chain = initialize_agent(    [metaphor_tool, extract_text, navigate_tool],    llm,    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,    verbose=True,)agent_chain.run(    "find me an interesting tweet about AI safety using Metaphor, then tell me the first sentence in the post. Do not finish until able to retrieve the first sentence.")PreviousLemon AI NLP Workflow AutomationNextOpenWeatherMap APICommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.


Grouped by provider | ğŸ¦œï¸�ğŸ”— Langchain


Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerWandB TracingAI21 LabsAimAirbyteAirtableAleph AlphaAlibaba Cloud OpensearchAmazon API GatewayAnalyticDBAnnoyAnyscaleApifyArangoDBArgillaArthurArxivAtlasDBAwaDBAWS S3 DirectoryAZLyricsAzure Blob StorageAzure Cognitive SearchAzure OpenAIBananaBasetenBeamBedrockBiliBiliBlackboardBrave SearchCassandraCerebriumAIChaindeskChromaClarifaiClearMLCnosDBCohereCollege ConfidentialCometConfluenceC TransformersDatabricksDatadog TracingDatadog LogsDataForSEODeepInfraDeep LakeDiffbotDiscordDocugamiDuckDBElasticsearchEverNoteFacebook ChatFigmaFlyteForefrontAIGitGitBookGoldenGoogle BigQueryGoogle Cloud StorageGoogle DriveGoogle SearchGoogle SerperGooseAIGPT4AllGraphsignalGrobidGutenbergHacker NewsHazy ResearchHeliconeHologresHugging FaceiFixitIMSDbInfinoJinaLanceDBLangChain Decorators âœ¨Llama.cppMarqoMediaWikiDumpMetalMicrosoft OneDriveMicrosoft PowerPointMicrosoft WordMilvusMLflow AI GatewayMLflowModalModelScopeModern TreasuryMomentoMotherduckMyScaleNLPCloudNotion DBObsidianOpenAIOpenLLMOpenSearchOpenWeatherMapPetalsPGVectorPineconePipelineAIPortkeyPredibasePrediction GuardPromptLayerPsychicQdrantRay ServeRebuffRedditRedisReplicateRoamRocksetRunhouseRWKV-4SageMaker EndpointSearxNG Search APISerpAPIShale


EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerGrouped by providerğŸ“„ï¸� WandB TracingThere are two recommended ways to trace your LangChains:ğŸ“„ï¸� AI21 LabsThis page covers how to use the AI21 ecosystem within LangChain.ğŸ“„ï¸� AimAim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents.ğŸ“„ï¸� AirbyteAirbyte is a data integration platform for ELT pipelines from APIs,ğŸ“„ï¸� AirtableAirtable is a cloud collaboration service.ğŸ“„ï¸� Aleph AlphaAleph Alpha was founded in 2019 with the mission to research and build the foundational technology for an era of strong AI. The team of international scientists, engineers, and innovators researches, develops, and deploys transformative AI like large language and multimodal models and runs the fastest European commercial AI cluster.ğŸ“„ï¸� Alibaba Cloud OpensearchAlibaba Cloud Opensearch OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O,