# Google Search ![](./imgs/google_search_1.png) ![](./imgs/google_search_2.png) As shown in the above images, with the help of the HTTP module, you can easily integrate a search engine. Here, we take calling the Google Search API as an example. ## Register Google Search API [Refer to this article to register the Google Search API](https://zhuanlan.zhihu.com/p/174666017) ## Create a Google Search interface [Here, we use laf to quickly implement an interface, which can be written and published without deployment. Click to open laf cloud](https://laf.dev/), make sure to open the POST request method. ```ts import cloud from '@lafjs/cloud'; const googleSearchKey = ''; const googleCxId = ''; const baseurl = 'https://www.googleapis.com/customsearch/v1'; export default async function (ctx: FunctionContext) { const { searchKey } = ctx.body; if (!searchKey) { return { prompt: '' }; } try { const { data } = await cloud.fetch.get(baseurl, { params: { q: searchKey, cx: googleCxId, key: googleSearchKey, c2coff: 1, start: 1, num: 5, dateRestrict: 'm[1]' } }); const result = data.items.map((item) => item.snippet).join('\n'); return { prompt: `Here are the search results from Google: ${result}`, searchKey: `\nSearch term: ${searchKey}` }; } catch (err) { console.log(err); return { prompt: '' }; } } ``` ## Workflow Drag out a FastGPT workflow as shown in the image, where the request URL of the HTTP module is the interface address, and the input and output parameters are as follows: **Input** ``` searchKey: Search Key Word ``` **Output** ``` prompt: Search Result ``` - The HTTP module will send the searchKey to laf, and laf will perform a Google search based on the received input. It will then return the search results through the prompt parameter. - After receiving the response, the HTTP module connects to the prompt of the "AI Dialogue" to guide the model in providing an answer.