Complete Research Workflow
Use this workflow to research a topic efficiently. It searches the web for relevant pages and then extracts key findings using structured or text-based prompts. You can choose between structured JSON results or quick text summaries.// Searches the web and extracts structured key findings
const apiKey = process.env.WEBLINQ_API_KEY;
const base = 'https://api.weblinq.dev/v1';
export async function researchTopic(query) {
// 1️⃣ search
const {
data: { results },
} = await post('/web/search', { query, limit: 5 });
// 2️⃣ minimal schema
const schema = {
type: 'object',
properties: {
title: { type: 'string' },
keyFindings: { type: 'array', items: { type: 'string' } },
conclusions: { type: 'array', items: { type: 'string' } },
},
required: ['title', 'keyFindings'],
};
// 3️⃣ structured extract
const extracted = await Promise.all(
results.map((r) =>
post('/web/ai-extract', {
url: r.url,
response_format: { type: 'json_schema', json_schema: schema },
prompt: `Key findings on "${query}"`,
}),
),
);
return results.map((r, i) => ({
url: r.url,
...extracted[i].data.extracted,
}));
}
function post(p, body) {
return fetch(`${base}${p}`, {
method: 'POST',
headers: {
Authorization: `Bearer ${apiKey}`,
'Content-Type': 'application/json',
},
body: JSON.stringify(body),
}).then((r) => r.json());
}
// Quick text summaries of search results for a given query
const apiKey = process.env.WEBLINQ_API_KEY;
const base = 'https://api.weblinq.dev/v1';
export async function researchInsights(query) {
const {
data: { results },
} = await post('/web/search', { query, limit: 4 });
const insights = await Promise.all(
results.map((r) =>
post('/web/ai-extract', {
url: r.url,
responseType: 'text',
prompt: `Summarise important insights about "${query}".`,
}).then((res) => ({ url: r.url, analysis: res.data.text })),
),
);
return { query, insights };
}
function post(p, body) {
// same helper as above
}
# Python version of the structured research workflow
import requests, os
API = 'https://api.weblinq.dev/v1'
KEY = os.environ['WEBLINQ_API_KEY']
def quick_research(q):
search = req('/web/search', {'query': q, 'limit': 3})['data']['results']
schema = {
"type":"object",
"properties":{
"title":{"type":"string"},
"keyFindings":{"type":"array","items":{"type":"string"}}
},
"required":["title","keyFindings"]
}
return [ req('/web/ai-extract',{
"url": r['url'],
"response_format":{ "type":"json_schema", "json_schema":schema },
"prompt":f'Key findings on "{q}"'
})['data']['extracted']
for r in search ]
def req(path, payload):
return requests.post(
f'{API}{path}',
headers={'Authorization':f'Bearer {KEY}','Content-Type':'application/json'},
json=payload
).json()
E-commerce Monitoring Workflow
Track product listings on e-commerce sites. This workflow captures a screenshot of the page, extracts visible product links, and parses out product names and prices using a custom schema.// Tracks products, captures screenshot, extracts data
const apiKey = process.env.WEBLINQ_API_KEY;
const base = 'https://api.weblinq.dev/v1';
export async function monitor(url) {
const shot = await post('/web/screenshot', {
url,
screenshotOptions: { fullPage: true },
});
const {
data: { links },
} = await post('/web/links', { url });
const productLinks = links
.filter((l) => /\/product\//.test(l.url))
.slice(0, 10);
const schema = {
type: 'object',
properties: {
products: {
type: 'array',
items: {
type: 'object',
properties: { name: { type: 'string' }, price: { type: 'number' } },
},
},
},
};
const products = await post('/web/ai-extract', {
url,
response_format: { type: 'json_schema', json_schema: schema },
prompt: 'List products and prices you can see.',
});
return {
url,
screenshot: shot.data.permanentUrl,
productLinks,
products: products.data.extracted,
};
}
function post(p, b) {
// helper
}
Content Creation Pipeline
Turn webpages into content assets like summaries, PDFs, and Markdown. This is useful for archiving pages or preparing content for blog posts and reports.// Converts a list of URLs into PDFs, Markdown, and structured summaries
const apiKey = process.env.WEBLINQ_API_KEY;
const base = 'https://api.weblinq.dev/v1';
export async function generateReport(urls) {
const schema = {
type: 'object',
properties: { title: { type: 'string' }, summary: { type: 'string' } },
};
return Promise.all(
urls.map(async (url) => {
const [md, pdf, info] = await Promise.all([
post('/web/markdown', { url }),
post('/web/pdf', { url }),
post('/web/ai-extract', {
url,
response_format: { type: 'json_schema', json_schema: schema },
prompt: 'Short summary',
}),
]);
return {
url,
title: info.data.extracted.title,
summary: info.data.extracted.summary,
pdf: pdf.data.permanentUrl,
};
}),
);
}
function post(p, b) {
// helper
}
Hybrid Extraction (CSS + Schema)
Combine raw scraping (CSS selectors) with structured schema-based extraction for more flexible data collection.// Scrapes with CSS selectors and adds structured schema extraction
const apiKey = process.env.WEBLINQ_API_KEY;
const base = 'https://api.weblinq.dev/v1';
export async function extractBusiness(url) {
const scraped = await post('/web/scrape', {
url,
elements: [
{ selector: '.contact-info', attributes: ['text'] },
{ selector: '.business-hours', attributes: ['text'] },
],
});
const schema = {
type: 'object',
properties: { businessName: { type: 'string' }, phone: { type: 'string' } },
};
const {
data: { extracted },
} = await post('/web/ai-extract', {
url,
response_format: { type: 'json_schema', json_schema: schema },
});
return { url, scraped: scraped.data.elements, structured: extracted };
}
function post(p, b) {
// helper
}
Content Intelligence
Design a content strategy based on what your competitors are doing. This automation searches for guides, analyzes them, and generates a content plan.// Builds a content plan based on competitor analysis
const apiKey = process.env.WEBLINQ_API_KEY;
const base = 'https://api.weblinq.dev/v1';
export async function buildStrategy(topic) {
// 1️⃣ search
const {
data: { results },
} = await post('/web/search', { query: `${topic} guide`, limit: 5 });
// 2️⃣ analyse competitors
const schema = {
type: 'object',
properties: {
wordCount: { type: 'number' },
keyTakeaways: { type: 'array', items: { type: 'string' } },
},
};
const analysis = await Promise.all(
results.map((r) =>
post('/web/ai-extract', {
url: r.url,
response_format: { type: 'json_schema', json_schema: schema },
prompt: `Analyse strengths for "${topic}".`,
}).then((a) => ({ url: r.url, ...a.data.extracted })),
),
);
// 3️⃣ content plan (text mode)
const {
data: { text: strategy },
} = await post('/web/ai-extract', {
url:
'data:text/plain;base64,' +
Buffer.from(JSON.stringify(analysis)).toString('base64'),
responseType: 'text',
prompt: `Create a content plan for "${topic}" using this JSON.`,
});
return { topic, analysis, strategy };
}
function post(p, b) {
// helper
}