# How do I turn WeChat Official Account articles into clean text for a RAG knowledge base?

> Load WeChat Official Account articles into a RAG index as clean text with title, account, date and link for citations. Endpoints, cost per 300 articles, code.

Canonical page: https://okdata.online/answers/wechat-articles-to-text-for-rag/

**Short answer: **List the account's articles with Official Account article history ($0.04 per 10), then fetch each one with article detail ($0.02), which returns the body as plain text plus title, account and publish time. A 300-article account costs $7.20; you add the Markdown header and chunk it yourself.

An internal assistant that answers questions about your industry is only as good as what it has read, and in China a lot of that lives in Official Accounts. To get an account into your RAG index, list its articles with [Official Account article history](/apis/wechat_mp/accounts/articles/list/), then send each link to [article detail](/apis/wechat_mp/articles/get/). Detail returns the body as plain text with paragraph breaks kept, plus the title, account, author byline and publish time, which is everything a citation needs.

## What the text looks like

The article HTML is reduced to text: paragraphs and headings become their own lines, scripts and styling are gone, and inline images are dropped (the cover image URL is kept in `images`). Tables collapse into lines of text. That is good input for embedding. If you need the original layout with images in place, this is the wrong tool; see the alternatives below.

## Cost

| Job | Calls | Cost |
|---|---|---|
| 1 account, 300 articles | 30 list + 300 detail | $7.20 |
| 10 accounts, newest 50 each | 50 list + 500 detail | $12.00 |
| Links your team pastes in, 1,000 | 1,000 detail | $20.00 |

Deleted articles come back as `not_found` and cost nothing.

## From link to Markdown file

```python
import os, pathlib, requests

API = "https://api.okdata.online"
H = {"Authorization": f"Bearer {os.environ['OKDATA_API_KEY']}"}
OUT = pathlib.Path("kb/wechat"); OUT.mkdir(parents=True, exist_ok=True)

def save(url):
    body = requests.get(API + "/v1/wechat_mp/articles/get", headers=H, timeout=90,
                        params={"url": url, "view": "full"}).json()
    if body["code"] != "ok":
        print("skip", url, body["code"]); return
    a = body["data"]["item"]
    header = (f"---\ntitle: {a['title']!r}\naccount: {a['author']['name']!r}\n"
              f"published: {a['published_at']}\nsource: {a['url']}\n---\n\n")
    (OUT / f"{a['id']}.md").write_text(header + f"# {a['title']}\n\n" + a["text"], encoding="utf-8")

save("https://mp.weixin.qq.com/s/xxxxxxxx")
```

Chunk on blank lines, 300 to 800 Chinese characters per chunk, and carry the title and source link into every chunk's metadata so answers can cite the original.

## Gotchas for a knowledge base

- **Reposts.** Many accounts republish others' pieces. With `view=full`, `ext.copyright` and `ext.source_url` help you spot and drop duplicates before they skew retrieval.
- **Freshness.** Re-run the history call weekly and fetch only links you don't have yet.
- **Rights.** Index for internal research, keep the source link, and don't serve whole articles back to users.
- **Untrusted text.** Article text is written by strangers. Tell the model to treat retrieved passages as data, not instructions.

## Alternatives

Open-source exporters such as wechat-article-exporter save articles as HTML or Markdown with images, which is better for archiving or reading offline; they work through a WeChat backend login you provide. For a handful of articles, copy-paste into your notes tool is fine. For your own account, WeChat's official API returns the full article content directly. For research pipelines across platforms, see [content research](/solutions/content-research/).

## Endpoints used

- [Official Account article detail](https://okdata.online/apis/wechat_mp/articles/get/) `wechat_mp.articles.get`: $0.02 per call
- [Official Account article history](https://okdata.online/apis/wechat_mp/accounts/articles/list/) `wechat_mp.accounts.articles.list`: $0.04 per call · about 10 articles per page

Updated 2026-11-12