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)
81 lines
2.9 KiB
Python
81 lines
2.9 KiB
Python
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 ..llm.factory import get_model
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from ..llm.capability import ModelCapabilityRegistry
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from skills._env_loader import load_unified_env
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load_unified_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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