83 lines
2.2 KiB
TypeScript
83 lines
2.2 KiB
TypeScript
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import FirecrawlApp, { JobStatusResponse } from '@mendable/firecrawl-js';
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import { z } from "zod";
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const app = new FirecrawlApp({apiKey: "fc-YOUR_API_KEY"});
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// Scrape a website:
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const scrapeResult = await app.scrapeUrl('firecrawl.dev');
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console.log(scrapeResult.data.content)
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// Crawl a website:
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const crawlResult = await app.crawlUrl('mendable.ai', {crawlerOptions: {excludes: ['blog/*'], limit: 5}}, false);
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console.log(crawlResult)
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const jobId: string = await crawlResult['jobId'];
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console.log(jobId);
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let job: JobStatusResponse;
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while (true) {
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job = await app.checkCrawlStatus(jobId);
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if (job.status === 'completed') {
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break;
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}
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await new Promise(resolve => setTimeout(resolve, 1000)); // wait 1 second
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}
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console.log(job.data[0].content);
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// Search for a query:
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const query = 'what is mendable?'
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const searchResult = await app.search(query)
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console.log(searchResult)
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// LLM Extraction:
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// Define schema to extract contents into using zod schema
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const zodSchema = z.object({
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top: z
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.array(
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z.object({
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title: z.string(),
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points: z.number(),
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by: z.string(),
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commentsURL: z.string(),
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})
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)
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.length(5)
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.describe("Top 5 stories on Hacker News"),
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});
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let llmExtractionResult = await app.scrapeUrl("https://news.ycombinator.com", {
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extractorOptions: { extractionSchema: zodSchema },
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});
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console.log(llmExtractionResult.data.llm_extraction);
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// Define schema to extract contents into using json schema
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const jsonSchema = {
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"type": "object",
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"properties": {
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"top": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"title": {"type": "string"},
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"points": {"type": "number"},
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"by": {"type": "string"},
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"commentsURL": {"type": "string"}
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},
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"required": ["title", "points", "by", "commentsURL"]
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},
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"minItems": 5,
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"maxItems": 5,
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"description": "Top 5 stories on Hacker News"
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}
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},
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"required": ["top"]
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}
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llmExtractionResult = await app.scrapeUrl("https://news.ycombinator.com", {
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extractorOptions: { extractionSchema: jsonSchema },
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});
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console.log(llmExtractionResult.data.llm_extraction);
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