273 lines
9.5 KiB
Python
273 lines
9.5 KiB
Python
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"""
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新工具自动发现与集成 — 扫描内外工具源，自动评估和生成集成方案
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"""
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from __future__ import annotations
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import logging
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import os
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import json
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from typing import Any, Dict, List, Optional
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from app.services.llm_service import llm_service
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logger = logging.getLogger(__name__)
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TOOL_DISCOVERY_PROMPT = """你是一个工具集成分析专家。分析以下新工具是否适合集成到 AI Agent 平台。
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当前平台能力:
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- Agent 工作流编排 (DAG)
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- LLM 调用 (多模型支持)
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- API 调用节点
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- 代码执行节点 (Python/JS)
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- 数据库查询节点
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- 条件分支节点
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- 通知节点 (飞书/Email)
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新工具信息:
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{tool_info}
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请分析:
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1. 匹配度 (0-1): 该工具与平台现有能力的互补程度
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2. 适用场景: 什么情况下 Agent 需要使用此工具
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3. 集成复杂度: low/medium/high
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4. 建议的 adapter 类型: api_wrapper / code_executor / plugin
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5. 集成方案: 简要描述如何接入
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6. 潜在风险: 使用此工具需要注意的问题
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返回 JSON 格式:
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{{"match_score": 0.8, "scenarios": ["场景1"], "complexity": "medium", "adapter_type": "api_wrapper", "integration_plan": "方案描述", "risks": ["风险1"]}}
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"""
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class ToolDiscovery:
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"""新工具自动发现与集成"""
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def __init__(self):
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self._external_sources = [
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{"name": "github_trending", "url": "https://github.com/trending/python?since=weekly"},
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{"name": "mcp_marketplace", "url": "https://github.com/modelcontextprotocol/servers"},
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]
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def scan_internal_tools(self, tools_dir: str = "") -> List[Dict[str, Any]]:
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"""扫描平台内部 tools 目录，发现未注册的工具。"""
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if not tools_dir:
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base = os.path.dirname(os.path.dirname(__file__))
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tools_dir = os.path.join(base, "tools")
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if not os.path.isdir(tools_dir):
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tools_dir = os.path.join(os.path.dirname(base), "tools")
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discovered = []
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if not os.path.isdir(tools_dir):
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return discovered
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from app.services.tool_registry import tool_registry
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for filename in os.listdir(tools_dir):
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if filename.startswith("_") or filename.startswith("__"):
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continue
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if filename.endswith(".py") and filename != "__init__.py":
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tool_name = filename[:-3]
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registered = tool_registry.get(tool_name) if hasattr(tool_registry, 'get') else None
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discovered.append({
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"tool_name": tool_name,
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"source": "internal",
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"registered": registered is not None,
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"file_path": os.path.join(tools_dir, filename),
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})
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return discovered
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def scan_external_sources(self) -> List[Dict[str, Any]]:
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"""扫描外部工具源（GitHub trending, MCP marketplace）。"""
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discovered = []
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for source in self._external_sources:
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try:
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import urllib.request
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req = urllib.request.Request(source["url"], headers={"User-Agent": "AI-Agent-Platform"})
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# 只记录源信息，实际爬取在 evaluate_and_rank 中按需进行
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discovered.append({
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"source_name": source["name"],
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"url": source["url"],
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"status": "available",
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})
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except Exception as e:
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logger.warning("外部源 %s 不可用: %s", source["name"], e)
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discovered.append({
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"source_name": source["name"],
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"url": source["url"],
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"status": "unavailable",
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"error": str(e),
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})
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return discovered
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def evaluate_tool(self, tool_name: str, tool_description: str = "",
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tool_source: str = "", tool_docs: str = "") -> Dict[str, Any]:
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"""评估单个工具的集成价值（使用 LLM）。"""
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tool_info = f"""
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工具名称: {tool_name}
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来源: {tool_source}
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描述: {tool_description}
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文档/代码摘要: {tool_docs[:3000]}
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"""
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prompt = TOOL_DISCOVERY_PROMPT.format(tool_info=tool_info)
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result = {
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"tool_name": tool_name,
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"match_score": 0.0,
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"scenarios": [],
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"complexity": "unknown",
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"adapter_type": "unknown",
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"integration_plan": "",
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"risks": [],
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}
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try:
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response = llm_service.chat_sync(
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messages=[{"role": "user", "content": prompt}],
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temperature=0.3,
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max_tokens=1000,
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)
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content = response.get("content", "") if isinstance(response, dict) else str(response)
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# 提取 JSON
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json_match = self._extract_json(content)
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if json_match:
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evaluation = json.loads(json_match)
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result.update(evaluation)
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result["llm_raw"] = content[:500]
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except Exception as e:
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logger.error("LLM 评估工具 %s 失败: %s", tool_name, e)
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result["error"] = str(e)
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return result
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def _extract_json(self, text: str) -> Optional[str]:
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"""从文本中提取 JSON 块。"""
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import re
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# 尝试提取 ```json ... ``` 块
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match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', text, re.DOTALL)
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if match:
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return match.group(1)
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# 尝试直接找 {...}
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match = re.search(r'\{[^{}]*"match_score"[^{}]*\}', text, re.DOTALL)
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if match:
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return match.group(0)
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return None
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def discover_and_rank(self, limit: int = 10) -> Dict[str, Any]:
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"""完整的发现+评估+排序流程。"""
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internal = self.scan_internal_tools()
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# 聚焦未注册的工具
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unregistered = [t for t in internal if not t["registered"]]
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evaluations = []
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for tool in unregistered[:limit]:
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eval_result = self.evaluate_tool(
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tool_name=tool["tool_name"],
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tool_source="internal",
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)
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evaluations.append(eval_result)
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evaluations.sort(key=lambda x: x.get("match_score", 0), reverse=True)
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high_match = [e for e in evaluations if e.get("match_score", 0) >= 0.8]
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medium_match = [e for e in evaluations if 0.5 <= e.get("match_score", 0) < 0.8]
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return {
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"total_discovered": len(internal),
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"unregistered": len(unregistered),
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"evaluated": len(evaluations),
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"high_match": high_match,
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"medium_match": medium_match,
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"all_ranked": evaluations,
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"recommendation": (
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f"发现 {len(high_match)} 个高匹配工具建议立即集成, "
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f"{len(medium_match)} 个中匹配工具可进一步评估"
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),
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}
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def generate_adapter(self, tool_name: str, evaluation: Dict[str, Any]) -> str:
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"""根据评估结果生成 tool adapter 代码框架。"""
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adapter_type = evaluation.get("adapter_type", "api_wrapper")
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if adapter_type == "api_wrapper":
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return self._generate_api_adapter(tool_name, evaluation)
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elif adapter_type == "code_executor":
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return self._generate_code_adapter(tool_name, evaluation)
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else:
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return self._generate_plugin_adapter(tool_name, evaluation)
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def _generate_api_adapter(self, tool_name: str, eval_result: Dict[str, Any]) -> str:
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"""生成 API 包装器 adapter。"""
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return f'''"""
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{tool_name} — 自动发现的工具适配器
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匹配度: {eval_result.get("match_score", "N/A")}
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场景: {", ".join(eval_result.get("scenarios", []))}
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"""
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from typing import Any, Dict, Optional
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async def {tool_name}(params: Dict[str, Any]) -> Dict[str, Any]:
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"""Auto-generated adapter for {tool_name}"""
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try:
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# TODO: 根据 API 文档实现具体调用逻辑
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result = {{
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"success": True,
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"data": None,
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"message": "Adapter stub — 请根据 API 文档完善",
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}}
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return result
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except Exception as e:
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return {{"success": False, "error": str(e)}}
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'''
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def _generate_code_adapter(self, tool_name: str, eval_result: Dict[str, Any]) -> str:
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return f'''"""
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{tool_name} — 代码执行器适配器
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匹配度: {eval_result.get("match_score", "N/A")}
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"""
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import subprocess
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from typing import Any, Dict
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async def {tool_name}(code: str, language: str = "python") -> Dict[str, Any]:
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"""Auto-generated code executor adapter for {tool_name}"""
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try:
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executor = "python" if language == "python" else "node"
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result = subprocess.run(
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[executor, "-c", code],
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capture_output=True, text=True, timeout=30
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)
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return {{
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"success": result.returncode == 0,
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"stdout": result.stdout,
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"stderr": result.stderr,
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}}
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except Exception as e:
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return {{"success": False, "error": str(e)}}
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'''
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def _generate_plugin_adapter(self, tool_name: str, eval_result: Dict[str, Any]) -> str:
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return f'''"""
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{tool_name} — 插件适配器
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匹配度: {eval_result.get("match_score", "N/A")}
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"""
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from typing import Any, Dict
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class {tool_name.title().replace("_", "")}Plugin:
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"""Auto-generated plugin adapter for {tool_name}"""
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def __init__(self):
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self.name = "{tool_name}"
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async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
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"""执行插件逻辑"""
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# TODO: 根据文档实现
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return {{"success": True, "data": None}}
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'''
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tool_discovery = ToolDiscovery()
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