# LlamaIndex (/pt-BR/developer-guides/llm-sdks-and-frameworks/llamaindex)

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Related: [OpenAI](/pt-BR/developer-guides/llm-sdks-and-frameworks/openai.md), [Anthropic](/pt-BR/developer-guides/llm-sdks-and-frameworks/anthropic.md), [Gemini](/pt-BR/developer-guides/llm-sdks-and-frameworks/gemini.md), [Agent Development Kit (ADK)](/pt-BR/developer-guides/llm-sdks-and-frameworks/google-adk.md), [Vercel AI SDK](/pt-BR/developer-guides/llm-sdks-and-frameworks/vercel-ai-sdk.md), [LangChain](/pt-BR/developer-guides/llm-sdks-and-frameworks/langchain.md)

Integre o Firecrawl ao LlamaIndex para criar aplicações de IA com busca vetorial e embeddings baseados em conteúdo da web.

<div id="setup">
  ## Configuração [#configuração]
</div>

```bash
npm install llamaindex @llamaindex/openai firecrawl
```

Crie o arquivo `.env`:

```bash
FIRECRAWL_API_KEY=your_firecrawl_key
OPENAI_API_KEY=your_openai_key
```

> **Observação:** Se estiver usando Node \< 20, instale `dotenv` e adicione `import 'dotenv/config'` ao seu código.

<div id="rag-with-vector-search">
  ## RAG com Busca Vetorial [#rag-com-busca-vetorial]
</div>

Este exemplo demonstra como usar LlamaIndex com Firecrawl para rastrear um site, criar embeddings e consultar o conteúdo por meio de RAG.

```typescript
import { Firecrawl } from 'firecrawl';
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 Firecrawl({ apiKey: process.env.FIRECRAWL_API_KEY });
const crawlResult = await firecrawl.crawl('https://firecrawl.dev', {
  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 Firecrawl and how does it work?' });

console.log('\nAnswer:', response.toString());
```

Para mais exemplos, consulte a [documentação do LlamaIndex](https://ts.llamaindex.ai/).