Make AI Web Search

the {{product name}} ai web search app is currently in beta there may be changes to functionality or pricing {{product name}} ai web search is a built in app that lets you find and retrieve up to date information from the internet directly within your {{product name}} {{scenario plural lowercase}} it searches across websites, news articles, and other online sources to provide relevant, structured results — perfect for enriching workflows with fresh data, fact checking, or content discovery all searches run securely within {{product name}} , without depending on external third party integrations key benefits no external accounts no need to connect or manage a separate account everything works within your existing environment privacy focused your data is never stored or used for ai training instant setup start using the app immediately in your platform there is no need to create any connection credit usage the {{product name}} ai web search app has a specific credit usage for each module you can find the details below generate a response this module uses 1 credit for 900 tokens processed example if you use the generate a response module for a question of 10000 characters, or about 1500 words, the module uses 2170 input tokens and 145 output tokens, totalling 2315 tokens this in total uses the following token based credits 2315/900 = 2 57 credits total credit usage per run = token based credits + 1 credit for an operation in this example, you used 3 57 total credits (2 57 + 1 00) for more detailed information about credits, refer to the credits documentation {{product name}} ai web search modules you can use the following module to build your {{scenario plural lowercase}} the current features and functionality are still in beta and are actively being improved as such, pricing is subject to change and will be updated as the app evolves generate a response generates a response from the input provided field description text enter your search query for the ai you can include details on how you want the response to be formatted parse json in response enable to parse the ai’s output as json if parsing fails, the raw text response will be returned city enter a city to refine the search location for example, san francisco country select or map a country to prioritize search results relevant to that country content from the selected country will appear higher in web search results to map a country manually, enter its two letter iso code for example, us region enter a region to narrow the search location for example, california timezone select or map a timezone to adjust results based on local time for example, america/los angeles example retrieve information from a website in this use case, you’ll use the make ai web search app to retrieve information from a website you’ll find the most recent posts on the make blog, collect their urls and publication dates, and extract their content the scenario returns all data in a structured json format step 1 set up your first make ai web search module first, you need to add the make ai web search app to your {{scenario singular lowercase}} and configure it to retrieve one of today’s blog post urls add a make ai web search > generate a response module to your {{scenario singular lowercase}} in the text field, enter the prompt to retrieve the information you need example prompt get one of today's blog post urls nest these items under the urls get the date of the last blog post formatted yyyy mm dd, blog title output in json wrapped by \[startjson] and \[endjson] do not ask for clarification and do it now https //www make com/en/blog switch on the advanced settings option in the parse json in response field, select yes to format the response as json step 2 add an iterator click save next, add an iterator module to your {{scenario singular lowercase}} to handle each blog post result as a separate item add the iterator module to your {{scenario singular lowercase}} in the array field, map the result from the previous make ai web search module c lick save step 3 extract the blog post content finally, add another make ai web search module to extract the content of the blog post add a make ai web search > generate a response module to your {{scenario singular lowercase}} after the iterator in the text field, enter the prompt to extract the content from the url example prompt extract the blog post body after entering the prompt, map the url value from the iterator module to the text field to extract the retrieved content click save you’ve now set up the {{scenario singular lowercase}} to retrieve and extract content from a website, with the data processed step by step for each url and returned in json format templates you can look for {{product name}} ai web search templates in make's template gallery , where you'll find thousands of pre created {{scenario plural lowercase}}