LlamaIndex Scraper
Extract data connector docs, index types, query engine references, and tutorial guides from the LlamaIndex framework. Built on spider-browser .
- target
- llamaindex.ai
- success rate
- 99.9%
- latency
- ~4ms
/fetch/llamaindex.ai/ curl -X POST https://api.spider.cloud/fetch/llamaindex.ai/ \ -H "Authorization: Bearer $SPIDER_API_KEY" \ -H "Content-Type: application/json" \ -d '{"return_format": "json"}'
{
"url": "https://llamaindex.ai/",
"status": 200,
"data": {
"component_name": "string",
"description": "string",
"parameters": "string",
"code_example": "string",
"return_type": "string",
"category": "string",
"related_modules": "string",
"version": "string"
}
} # LlamaIndex Scraper
**Component name**: string
**Description**: string
**Parameters**: string
**Code example**: string
**Return type**: string
**Category**: string
**Related modules**: string
**Version**: string Extract data in minutes.
Structured JSON from llamaindex.ai with a single POST. AI-resolved selectors, cached on the first call.
import { SpiderBrowser } from "spider-browser";
const spider = new SpiderBrowser({
apiKey: process.env.SPIDER_API_KEY!,
stealth: 2,
});
await spider.connect();
const page = spider.page!;
await page.goto("https://docs.llamaindex.ai/en/stable/");
await page.content(10000);
const data = await page.evaluate(`(() => {
const title = document.querySelector("h1")?.textContent?.trim();
const sections = [];
document.querySelectorAll("article h2, article h3").forEach(h => {
const heading = h.textContent?.trim();
const content = h.nextElementSibling?.textContent?.trim();
if (heading) sections.push({ heading, content: content?.slice(0, 300) });
});
const codeBlocks = [...document.querySelectorAll("pre code")].map(c => c.textContent?.trim().slice(0, 200));
return JSON.stringify({ title, sections: sections.slice(0, 10), codeBlocks: codeBlocks.slice(0, 5) });
})()`);
console.log(JSON.parse(data));
await spider.close(); curl -X POST https://api.spider.cloud/fetch/llamaindex.ai/ \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"return_format": "json"}' import requests
resp = requests.post(
"https://api.spider.cloud/fetch/llamaindex.ai/",
headers={
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json",
},
json={"return_format": "json"},
)
print(resp.json()) const resp = await fetch("https://api.spider.cloud/fetch/llamaindex.ai/", {
method: "POST",
headers: {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({ return_format: "json" }),
});
const data = await resp.json();
console.log(data); Fields you can pull.
React SPA handling
Full browser rendering for streaming content and dynamic React UI.
Structured parsing
Extract code blocks, documentation, repository data, and metadata.
Load completion
Smart network idle detection waits for dynamically loaded content to finish.
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Start scraping llamaindex.ai.
Grab an API key and call the endpoint above. The first request resolves the config; every request after hits cache.