通义千问办公 - 智能长文档速读提炼 · 10倍速吸收行业报告 - 千问AI

curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \
  -H "Authorization: Bearer $DASHSCOPE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen-long",
    "messages": [
      {"role": "system", "content": "你是通义千问办公研报速读专家，擅长提炼万字行业报告核心要点。"},
      {"role": "user", "content": "请速读以下行业报告并输出核心量化指标与瓶颈对策。"}
    ],
    "stream": true
  }'

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="qwen-long",
    messages=[
        {"role": "system", "content": "你是通义千问办公长文档速读助手。"},
        {"role": "user", "content": "请将百页研报内容按期刊连载结构提炼核心观点。"}
    ],
    stream=True
)

for chunk in completion:
    print(chunk.choices[0].delta.content or "", end="")

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.DASHSCOPE_API_KEY,
  baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1"
});

async function main() {
  const stream = await openai.chat.completions.create({
    model: "qwen-long",
    messages: [
      { role: "system", content: "你是通义千问办公智能长文档速读提炼引擎。" },
      { role: "user", content: "提炼全球算力供应链报告核心财务指标。" }
    ],
    stream: true,
  });
  for await (const chunk of stream) {
    process.stdout.write(chunk.choices[0]?.delta?.content || "");
  }
}
main();