Can GLM-4.6 scrape e-commerce websites by itself?

No. GLM-4.6 can call a web tool and interpret its results, but a crawler, browser, API, or other retrieval service must acquire the page. The model should receive explicit content and provenance.

Should I send raw HTML to GLM-4.6?

Usually not. Parse deterministic fields in code and send compact records plus short evidence excerpts. HTML remains useful for debugging, while cleaned Markdown is generally better for model context.

How often should a product intelligence app crawl pages?

It depends on the business decision and source volatility. Set a schedule that meets the alert requirement, then measure change yield and accepted-page rate before increasing frequency.

What is the most important production metric?

Accepted-page rate is a strong starting point because it detects challenge pages, soft errors, and wrong locales that HTTP success metrics miss. Pair it with field completeness and alert precision.

构建一个使用 GLM-4.6 的电子商务智能应用程序

字段	目的	验证规则
`source_url`	证据和重新爬取目标	绝对的、标准化的 URL
`retrieved_at`	新鲜度	您的服务生成的 UTC 时间戳
`sku`	稳定的身份	商户 ID 或受控指纹
`title`	人类可读的身份	非空字符串
`price_text`	作为显示的证据	保留货币和限定条件
`price_value`	比较	代码解析的十进制数
`currency`	可比较的分组	可确定的 ISO 货币
`availability`	库存信号	受控枚举加原始文本
`promotion`	商业背景	可空文本与来源摘录
`evidence`	审计追踪	短引用或选择器绑定的值

import os
import requests

CRAWL_URL = "https://api.nstproxy.com/api/v1/crawl/scrape/submit-sync"

def crawl_product(url: str) -> dict:
    response = requests.post(
        CRAWL_URL,
        headers={
            "x-api-key": os.environ["NSTPROXY_API_KEY"],
            "Content-Type": "application/json",
        },
        json={"url": url, "formats": ["markdown", "html"], "onlyMainContent": True},
        timeout=90,
    )
    response.raise_for_status()
    payload = response.json()["data"]
    if not payload.get("success") or payload.get("status") != "completed":
        raise RuntimeError(f"Crawl failed: {payload}")
    return payload["data"]

import json
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["ZAI_API_KEY"],
    base_url="https://api.z.ai/api/paas/v4/",
)

def analyze_change(before: dict, after: dict) -> str:
    prompt = {
        "task": "Classify the commercial significance of this product-page change.",
        "rules": [
            "Use only supplied facts.",
            "Cite source_url for every factual conclusion.",
            "Return unknown when evidence is insufficient.",
        ],
        "before": before,
        "after": after,
    }
    result = client.chat.completions.create(
        model="glm-4.6",
        messages=[{"role": "user", "content": json.dumps(prompt)}],
        thinking={"type": "enabled"},
        temperature=0.2,
    )
    return result.choices[0].message.content

TL;DR

GLM-4.6 的贡献

定义数据合同

方法 1：构建受控集合层

步骤 1：从允许列表开始

步骤 2：获取渲染内容

第 3 步：在模型分析前解析事实

方法 2：添加 GLM-4.6 作为分析师

第 1 步：发送标准化记录，而不是整个页面

第 2 步：要求证据和不确定性

第 3 步：添加人工审核通道

调度、存储和变更检测

设计的失败模式

最终裁决

常见问题