81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
#!/usr/bin/env python3
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"""
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QwenPaw + MetaGPT + ECC + AnySearch 全套流水线
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使用方式：
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1. 确保 metaGPT 和 ECC 已安装配置
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2. 运行: python3 pipeline_demo.py "你的需求"
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"""
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import subprocess
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import sys
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import json
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def search_reference(query: str):
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"""Step 1: AnySearch 搜索参考资料"""
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print(f"\n{'='*60}")
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print(f"🔍 [1/4] AnySearch 搜索: {query}")
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print(f"{'='*60}")
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# 通过 curl 调 AnySearch MCP
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cmd = f'''curl -s 'https://api.anysearch.com/mcp' -X POST \
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-H 'Content-Type: application/json' \
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-H 'Authorization: Bearer as_sk_f92eddbbd0360ccd793ac7aa5a391f77' \
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-d '{{"jsonrpc":"2.0","method":"tools/call","id":1,"params":{{"name":"search","arguments":{{"query":"{query}","max_results":3}}}}}}' '''
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result = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=15)
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print(f"✅ 搜索完成")
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return result.stdout
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def generate_code(requirement: str):
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"""Step 2: MetaGPT 生成代码"""
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print(f"\n{'='*60}")
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print(f"🏢 [2/4] MetaGPT 生成项目: {requirement}")
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print(f"{'='*60}")
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result = subprocess.run(
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f'metagpt "{requirement}" --n-round 1',
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shell=True, capture_output=True, text=True, timeout=120
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)
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print(f"✅ MetaGPT 生成完成")
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return result
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def review_code(project_path: str):
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"""Step 3: ECC 代码审查"""
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print(f"\n{'='*60}")
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print(f"🔧 [3/4] ECC 代码审查")
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print(f"{'='*60}")
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# ECC 审查通过 MCP 工具调用（在 QwenPaw 会话中自动可用）
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print(f"✅ ECC 准备就绪")
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def deploy():
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"""Step 4: ECC 部署"""
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print(f"\n{'='*60}")
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print(f"🚀 [4/4] ECC 部署上线")
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print(f"{'='*60}")
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print(f"✅ 部署准备就绪")
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if __name__ == "__main__":
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requirement = " ".join(sys.argv[1:]) or "创建一个Python命令行计算器"
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print(f"🔥 QwenPaw + MetaGPT + ECC + AnySearch 全套流水线")
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print(f"📝 需求: {requirement}")
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# 1. 搜资料
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search_reference(f"{requirement} 技术方案")
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# 2. 生成代码
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result = generate_code(requirement)
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# 3. 审查
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review_code("./workspace")
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# 4. 部署
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deploy()
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print(f"\n{'='*60}")
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print(f"🎉 流水线执行完成!")
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print(f"{'='*60}")
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