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71 lines
2.0 KiB
Markdown
71 lines
2.0 KiB
Markdown
# Google Search
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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.
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## Register Google Search API
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[Refer to this article to register the Google Search API](https://zhuanlan.zhihu.com/p/174666017)
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## Create a Google Search interface
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[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.
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```ts
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import cloud from '@lafjs/cloud';
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const googleSearchKey = '';
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const googleCxId = '';
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const baseurl = 'https://www.googleapis.com/customsearch/v1';
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export default async function (ctx: FunctionContext) {
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const { searchKey } = ctx.body;
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if (!searchKey) {
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return {
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prompt: ''
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};
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}
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try {
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const { data } = await cloud.fetch.get(baseurl, {
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params: {
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q: searchKey,
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cx: googleCxId,
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key: googleSearchKey,
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c2coff: 1,
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start: 1,
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num: 5,
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dateRestrict: 'm[1]'
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}
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});
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const result = data.items.map((item) => item.snippet).join('\n');
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return {
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prompt: `Here are the search results from Google: ${result}`,
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searchKey: `\nSearch term: ${searchKey}`
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};
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} catch (err) {
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console.log(err);
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return {
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prompt: ''
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};
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}
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}
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```
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## Workflow
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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:
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**Input**
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```
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searchKey: Search Key Word
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```
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**Output**
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```
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prompt: Search Result
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```
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- 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.
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- After receiving the response, the HTTP module connects to the prompt of the "AI Dialogue" to guide the model in providing an answer.
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