Import 9 alphaear finance skills
- alphaear-deepear-lite: DeepEar Lite API integration - alphaear-logic-visualizer: Draw.io XML finance diagrams - alphaear-news: Real-time finance news (10+ sources) - alphaear-predictor: Kronos time-series forecasting - alphaear-reporter: Professional financial reports - alphaear-search: Web search + local RAG - alphaear-sentiment: FinBERT/LLM sentiment analysis - alphaear-signal-tracker: Signal evolution tracking - alphaear-stock: A-Share/HK/US stock data Updates: - All scripts updated to use universal .env path - Added JINA_API_KEY, LLM_*, DEEPSEEK_API_KEY to .env.example - Updated load_dotenv() to use ~/.config/opencode/.env
This commit is contained in:
Kunthawat Greethong
85
skills/alphaear-predictor/scripts/utils/llm/capability.py
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85
skills/alphaear-predictor/scripts/utils/llm/capability.py
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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 dotenv import load_dotenv
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load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
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# 测试当前配置的模型
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p = os.getenv("LLM_PROVIDER", "ust")
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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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114
skills/alphaear-predictor/scripts/utils/llm/factory.py
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skills/alphaear-predictor/scripts/utils/llm/factory.py
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import os
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from agno.models.openai import OpenAIChat
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from agno.models.ollama import Ollama
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from agno.models.dashscope import DashScope
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from agno.models.deepseek import DeepSeek
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from agno.models.openrouter import OpenRouter
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def get_model(model_provider: str, model_id: str, **kwargs):
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"""
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Factory to get the appropriate LLM model.
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Args:
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model_provider: "openai", "ollama", "deepseek"
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model_id: The specific model ID (e.g., "gpt-4o", "llama3", "deepseek-chat")
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**kwargs: Additional arguments for the model constructor
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"""
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if model_provider == "openai":
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return OpenAIChat(id=model_id, **kwargs)
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elif model_provider == "ollama":
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return Ollama(id=model_id, **kwargs)
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elif model_provider == "deepseek":
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# DeepSeek is OpenAI compatible
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api_key = os.getenv("DEEPSEEK_API_KEY")
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if not api_key:
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print("Warning: DEEPSEEK_API_KEY not set.")
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return DeepSeek(
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id=model_id,
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api_key=api_key,
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**kwargs
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)
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elif model_provider == "dashscope":
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api_key = os.getenv("DASHSCOPE_API_KEY")
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if not api_key:
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print("Warning: DASHSCOPE_API_KEY not set.")
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return DashScope(
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id=model_id,
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
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api_key=api_key,
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**kwargs
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)
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elif model_provider == 'openrouter':
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api_key = os.getenv("OPENROUTER_API_KEY")
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if not api_key:
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print('Warning: OPENROUTER_API_KEY not set.')
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return OpenRouter(
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id=model_id,
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api_key=api_key,
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**kwargs
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)
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elif model_provider == 'zai':
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api_key = os.getenv("ZAI_KEY_API")
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if not api_key:
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print('Warning: ZAI_KEY_API not set.')
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# role_map to ensure compatibility.
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default_role_map = {
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"system": "system",
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"user": "user",
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"assistant": "assistant",
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"tool": "tool",
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"model": "assistant",
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}
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# Allow callers to override role_map via kwargs, otherwise use default
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role_map = kwargs.pop("role_map", default_role_map)
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return OpenAIChat(
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id=model_id,
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base_url="https://api.z.ai/api/paas/v4",
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api_key=api_key,
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timeout=60,
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role_map=role_map,
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extra_body={"enable_thinking": False}, # TODO: one more setting for thinking
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**kwargs
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)
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elif model_provider == 'ust':
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api_key = os.getenv("UST_KEY_API")
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if not api_key:
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print('Warning: UST_KEY_API not set.')
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# Some UST-compatible endpoints expect the standard OpenAI role names
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# (e.g. "system", "user", "assistant") rather than Agno's default
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# mapping which maps "system" -> "developer". Provide an explicit
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# role_map to ensure compatibility.
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default_role_map = {
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"system": "system",
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"user": "user",
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"assistant": "assistant",
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"tool": "tool",
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"model": "assistant",
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}
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# Allow callers to override role_map via kwargs, otherwise use default
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role_map = kwargs.pop("role_map", default_role_map)
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return OpenAIChat(
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id=model_id,
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api_key=api_key,
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base_url=os.getenv("UST_URL"),
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role_map=role_map,
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extra_body={"enable_thinking": False}, # TODO: one more setting for thinking
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**kwargs
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)
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else:
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raise ValueError(f"Unknown model provider: {model_provider}")
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81
skills/alphaear-predictor/scripts/utils/llm/router.py
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81
skills/alphaear-predictor/scripts/utils/llm/router.py
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import os
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from typing import Optional, List, Dict, Any, Union
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from agno.models.base import Model
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from loguru import logger
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from dotenv import load_dotenv
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from ..llm.factory import get_model
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from ..llm.capability import ModelCapabilityRegistry
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# Load environment variables from universal .env
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load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
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class ModelRouter:
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"""
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模型路由管理器
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功能：
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1. 管理“推理/写作模型” (Reasoning Model) 和“工具调用模型” (Tool Model)。
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2. 根据任务需求自动选择合适的模型。
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"""
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def __init__(self):
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# 默认从环境变量读取
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self.reasoning_provider = os.getenv(
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"REASONING_MODEL_PROVIDER", os.getenv("LLM_PROVIDER", "openai")
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)
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self.reasoning_id = os.getenv(
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"REASONING_MODEL_ID", os.getenv("LLM_MODEL", "gpt-4o")
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)
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self.reasoning_host = os.getenv("REASONING_MODEL_HOST", os.getenv("LLM_HOST"))
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self.tool_provider = os.getenv("TOOL_MODEL_PROVIDER", self.reasoning_provider)
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self.tool_id = os.getenv("TOOL_MODEL_ID", self.reasoning_id)
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self.tool_host = os.getenv("TOOL_MODEL_HOST", self.reasoning_host)
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self._reasoning_model = None
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self._tool_model = None
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logger.info(
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f"🤖 ModelRouter initialized: Reasoning={self.reasoning_id} ({self.reasoning_host or 'default'}), Tool={self.tool_id} ({self.tool_host or 'default'})"
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)
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def get_reasoning_model(self, **kwargs) -> Model:
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if not self._reasoning_model:
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# 优先使用路由配置的 host
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if self.reasoning_host and "host" not in kwargs:
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kwargs["host"] = self.reasoning_host
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self._reasoning_model = get_model(
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self.reasoning_provider, self.reasoning_id, **kwargs
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)
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return self._reasoning_model
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def get_tool_model(self, **kwargs) -> Model:
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if not self._tool_model:
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# 优先使用路由配置的 host
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if self.tool_host and "host" not in kwargs:
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kwargs["host"] = self.tool_host
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# 检查 tool_model 是否真的支持 tool call
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caps = ModelCapabilityRegistry.get_capabilities(
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self.tool_provider, self.tool_id, **kwargs
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)
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if not caps["supports_tool_call"]:
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logger.warning(
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f"⚠️ Configured tool model {self.tool_id} might not support native tool calls! Consider using ReAct mode or a different model."
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)
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self._tool_model = get_model(self.tool_provider, self.tool_id, **kwargs)
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return self._tool_model
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def get_model_for_agent(self, has_tools: bool = False, **kwargs) -> Model:
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"""
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根据 Agent 是否包含工具来返回合适的模型。
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"""
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if has_tools:
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return self.get_tool_model(**kwargs)
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return self.get_reasoning_model(**kwargs)
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# 全局单例
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router = ModelRouter()
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