WeChat Official Accounts & Articles Insights Harvester avatar

WeChat Official Accounts & Articles Insights Harvester

Pricing

from $1.40 / 1,000 extracted wechat official accounts & articles insights harvesters

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WeChat Official Accounts & Articles Insights Harvester

WeChat Official Accounts & Articles Insights Harvester

Scrapes WeChat Official Account (公众号) articles, brand news, publication dates, author metadata, read counters, and article body text without requiring a Chinese phone number or login.

Pricing

from $1.40 / 1,000 extracted wechat official accounts & articles insights harvesters

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Developer

David Sandor

David Sandor

Maintained by Community

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1

Monthly active users

3 days ago

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WeChat Official Accounts & Articles Insights Harvester 🚀

Scrapes WeChat Official Account (公众号) articles, brand news, publication dates, author metadata, read counters, and article body text without requiring a Chinese phone number or login.

🌟 20+ Enterprise Enhancements (v2.0)

  • RAG & LLM Ready: Pre-computed OpenAI token counts and chunked embeddings.
  • Smart Keyword Filters: Include or exclude records by targeted keyword lists.
  • Sentiment Scoring: Built-in lexical sentiment rating on text contents.
  • Noise & Tracking Scrubber: Removes tracking query parameters and boilerplate banners.
  • Pay-Per-Event (PPE): Ultra-cost-effective pricing per extracted item.
  • Zero Cold Start: Sub-second execution with automated fallback guarantees.

💻 Integration Examples

Node.js (Apify Client)

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('fast-wechat-articles-scraper').call({
// Pass customized inputs here
enableRagEnrichment: true
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log('Extracted Items:', items);

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_APIFY_TOKEN')
run = client.actor('fast-wechat-articles-scraper').call(run_input={ 'enableRagEnrichment': True })
for item in client.dataset(run['defaultDatasetId']).iterate_items():
print(item)

📄 Output Schema

Returns structured JSON, token counts, and RAG vector chunks.