Files
2026-06-19 06:38:31 +00:00

81 lines
2.3 KiB
Python

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