e4d41e3ae5
- Added skills/_env_loader.py - shared env loader for all scripts - Updated 17 scripts to use load_unified_env() - Updated install-skills.sh to copy .env into skills/ - Updated README with simpler OpenClaw install instructions - .env in skills/ is gitignored (credentials stay private)
86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
import os
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from typing import Optional, List, Dict, Any
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from agno.agent import Agent
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from agno.models.base import Model
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from loguru import logger
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from ..llm.factory import get_model
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def test_tool_call_support(model: Model) -> bool:
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"""
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测试模型是否支持原生的 Tool Call (Function Calling)。
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通过尝试执行一个简单的加法工具来验证。
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"""
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def get_current_weather(location: str):
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"""获取指定地点的天气"""
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return f"{location} 的天气是晴天，25度。"
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test_agent = Agent(
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model=model,
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tools=[get_current_weather],
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instructions="请调用工具查询北京的天气，并直接返回工具的输出结果。",
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)
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try:
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# 运行一个简单的任务，观察是否触发了 tool_call
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response = test_agent.run("北京天气怎么样？")
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# 检查 response 中是否包含 tool_calls
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# Agno 的 RunResponse 对象通常包含 messages，我们可以检查最后几条消息
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has_tool_call = False
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for msg in response.messages:
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if hasattr(msg, "tool_calls") and msg.tool_calls:
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has_tool_call = True
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break
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if has_tool_call:
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logger.info(f"✅ Model {model.id} supports native tool calling.")
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return True
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else:
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# 如果没有 tool_calls 但返回了正确答案，可能是模型通过纯文本模拟了工具调用（ReAct）
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# 或者根本没用工具。对于原生支持的判断，我们坚持要求有 tool_calls 结构。
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logger.warning(
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f"⚠️ Model {model.id} did NOT use native tool calling structure."
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)
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return False
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except Exception as e:
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logger.error(f"❌ Error testing tool call for {model.id}: {e}")
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return False
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class ModelCapabilityRegistry:
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"""
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模型能力注册表，用于缓存和管理不同模型的能力测试结果。
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"""
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_cache = {}
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@classmethod
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def get_capabilities(
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cls, provider: str, model_id: str, **kwargs
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) -> Dict[str, bool]:
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key = f"{provider}:{model_id}"
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if key not in cls._cache:
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logger.info(f"🔍 Testing capabilities for {key}...")
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model = get_model(provider, model_id, **kwargs)
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supports_tool_call = test_tool_call_support(model)
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cls._cache[key] = {"supports_tool_call": supports_tool_call}
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return cls._cache[key]
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if __name__ == "__main__":
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import os
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from skills._env_loader import load_unified_env
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load_unified_env()
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# 测试当前配置的模型
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p = os.getenv("LLM_PROVIDER", "minimax")
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m = os.getenv("LLM_MODEL", "Qwen")
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print(f"Testing {p}/{m}...")
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res = ModelCapabilityRegistry.get_capabilities(p, m)
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print(f"Result: {res}")
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