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Integrate Evocrawl with LlamaIndex to build AI applications with vector search and embeddings powered by web content.

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Setup

npm install llamaindex @llamaindex/openai @mendable/firecrawl-js
Create .env file: 
FIRECRAWL_API_KEY=your_firecrawl_key
OPENAI_API_KEY=your_openai_key
Note: If using Node < 20, install dotenv and add import 'dotenv/config' to your code.
This example demonstrates how to use LlamaIndex with Evocrawl to crawl a website, create embeddings, and query the content using RAG. 
import Evocrawl from '@mendable/firecrawl-js';
import { Document, VectorStoreIndex, Settings } from 'llamaindex';
import { OpenAI, OpenAIEmbedding } from '@llamaindex/openai';

Settings.llm = new OpenAI({ model: "gpt-4o" });
Settings.embedModel = new OpenAIEmbedding({ model: "text-embedding-3-small" });

const firecrawl = new Evocrawl({ apiKey: process.env.FIRECRAWL_API_KEY });
const crawlResult = await firecrawl.crawl('https://evocrawl.com', {
  limit: 10,
  scrapeOptions: { formats: ['markdown'] }
});
console.log(`Crawled ${crawlResult.data.length } pages`);

const documents = crawlResult.data.map((page: any, i: number) =>
  new Document({
    text: page.markdown,
    id_: `page-${i}`,
    metadata: { url: page.metadata?.sourceURL }
  })
);

const index = await VectorStoreIndex.fromDocuments(documents);
console.log('Vector index created with embeddings');

const queryEngine = index.asQueryEngine();
const response = await queryEngine.query({ query: 'What is Evocrawl and how does it work?' });

console.log('\nAnswer:', response.toString());
For more examples, check the LlamaIndex documentation.