221 lines
8.5 KiB
Python
221 lines
8.5 KiB
Python
# -*- coding: utf-8 -*-
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# TradingAgentsX/graph/setup.py
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from typing import Dict, Any
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from langchain_openai import ChatOpenAI
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from langgraph.graph import END, StateGraph, START
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from langgraph.prebuilt import ToolNode
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from tradingagents.agents import *
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from tradingagents.agents.utils.agent_states import AgentState
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from .conditional_logic import ConditionalLogic
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class GraphSetup:
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"""
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處理代理圖的設定和組態。
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這個類別負責根據所選的分析師和設定來建立和連接圖中的所有節點。
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"""
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def __init__(
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self,
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quick_thinking_llm: ChatOpenAI,
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deep_thinking_llm: ChatOpenAI,
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tool_nodes: Dict[str, ToolNode],
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bull_memory,
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bear_memory,
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trader_memory,
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invest_judge_memory,
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risk_manager_memory,
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conditional_logic: ConditionalLogic,
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language: str = "zh-TW",
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):
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"""
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使用必要的組件進行初始化。
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Args:
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quick_thinking_llm (ChatOpenAI): 用於快速任務的 LLM。
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deep_thinking_llm (ChatOpenAI): 用於深度分析的 LLM。
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tool_nodes (Dict[str, ToolNode]): 包含工具節點的字典。
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bull_memory: 看漲研究員的記憶體。
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bear_memory: 看跌研究員的記憶體。
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trader_memory: 交易員的記憶體。
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invest_judge_memory: 投資裁判的記憶體。
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risk_manager_memory: 風險管理者的記憶體。
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conditional_logic (ConditionalLogic): 處理圖中條件分支的邏輯。
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language (str): 報告語言 ('en' 或 'zh-TW')。
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"""
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self.quick_thinking_llm = quick_thinking_llm
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self.deep_thinking_llm = deep_thinking_llm
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self.tool_nodes = tool_nodes
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self.bull_memory = bull_memory
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self.bear_memory = bear_memory
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self.trader_memory = trader_memory
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self.invest_judge_memory = invest_judge_memory
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self.risk_manager_memory = risk_manager_memory
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self.conditional_logic = conditional_logic
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self.language = language
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def setup_graph(
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self, selected_analysts=["market", "social", "news", "fundamentals"]
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):
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"""
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設定並編譯代理工作流程圖。
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Args:
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selected_analysts (list): 要包含的分析師類型列表。選項包括：
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- "market": 市場分析師
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- "social": 社群媒體分析師
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- "news": 新聞分析師
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- "fundamentals": 基本面分析師
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Returns:
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CompiledGraph: 編譯完成的 langgraph 圖。
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"""
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if len(selected_analysts) == 0:
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raise ValueError("交易代理圖設定錯誤：未選擇任何分析師！")
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# 建立分析師節點
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analyst_nodes = {}
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delete_nodes = {}
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tool_nodes = {}
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if "market" in selected_analysts:
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analyst_nodes["market"] = create_market_analyst(
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self.quick_thinking_llm, self.language
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)
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delete_nodes["market"] = create_msg_delete()
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tool_nodes["market"] = self.tool_nodes["market"]
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if "social" in selected_analysts:
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analyst_nodes["social"] = create_social_media_analyst(
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self.quick_thinking_llm, self.language
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)
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delete_nodes["social"] = create_msg_delete()
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tool_nodes["social"] = self.tool_nodes["social"]
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if "news" in selected_analysts:
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analyst_nodes["news"] = create_news_analyst(
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self.quick_thinking_llm, self.language
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)
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delete_nodes["news"] = create_msg_delete()
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tool_nodes["news"] = self.tool_nodes["news"]
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if "fundamentals" in selected_analysts:
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analyst_nodes["fundamentals"] = create_fundamentals_analyst(
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self.quick_thinking_llm, self.language
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)
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delete_nodes["fundamentals"] = create_msg_delete()
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tool_nodes["fundamentals"] = self.tool_nodes["fundamentals"]
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# 建立研究員和管理者節點
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bull_researcher_node = create_bull_researcher(
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self.quick_thinking_llm, self.bull_memory, self.language
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)
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bear_researcher_node = create_bear_researcher(
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self.quick_thinking_llm, self.bear_memory, self.language
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)
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research_manager_node = create_research_manager(
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self.deep_thinking_llm, self.invest_judge_memory, self.language
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)
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trader_node = create_trader(self.quick_thinking_llm, self.trader_memory, self.language)
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# 建立風險分析節點
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risky_analyst = create_risky_debator(self.quick_thinking_llm, self.language)
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neutral_analyst = create_neutral_debator(self.quick_thinking_llm, self.language)
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safe_analyst = create_safe_debator(self.quick_thinking_llm, self.language)
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risk_manager_node = create_risk_manager(
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self.deep_thinking_llm, self.risk_manager_memory, self.language
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)
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# 建立工作流程
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workflow = StateGraph(AgentState)
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# 將分析師節點新增到圖中
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for analyst_type, node in analyst_nodes.items():
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workflow.add_node(f"{analyst_type.capitalize()} Analyst", node)
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workflow.add_node(
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f"Msg Clear {analyst_type.capitalize()}", delete_nodes[analyst_type]
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)
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workflow.add_node(f"tools_{analyst_type}", tool_nodes[analyst_type])
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# 新增其他節點
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workflow.add_node("Bull Researcher", bull_researcher_node)
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workflow.add_node("Bear Researcher", bear_researcher_node)
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workflow.add_node("Research Manager", research_manager_node)
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workflow.add_node("Trader", trader_node)
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workflow.add_node("Risky Analyst", risky_analyst)
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workflow.add_node("Neutral Analyst", neutral_analyst)
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workflow.add_node("Safe Analyst", safe_analyst)
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workflow.add_node("Risk Judge", risk_manager_node)
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# 定義邊
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# 平行啟動所有分析師（而不是順序連接）
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for analyst_type in selected_analysts:
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workflow.add_edge(START, f"{analyst_type.capitalize()} Analyst")
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# 連接所有分析師到其工具和清除節點
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for analyst_type in selected_analysts:
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current_analyst = f"{analyst_type.capitalize()} Analyst"
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current_tools = f"tools_{analyst_type}"
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current_clear = f"Msg Clear {analyst_type.capitalize()}"
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# 為當前分析師新增條件邊
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workflow.add_conditional_edges(
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current_analyst,
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getattr(self.conditional_logic, f"should_continue_{analyst_type}"),
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[current_tools, current_clear],
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)
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workflow.add_edge(current_tools, current_analyst)
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# 所有分析師完成後都直接連接到看漲研究員（平行匯聚）
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workflow.add_edge(current_clear, "Bull Researcher")
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# 新增剩餘的邊
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workflow.add_conditional_edges(
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"Bull Researcher",
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self.conditional_logic.should_continue_debate,
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{
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"Bear Researcher": "Bear Researcher",
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"Research Manager": "Research Manager",
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},
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)
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workflow.add_conditional_edges(
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"Bear Researcher",
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self.conditional_logic.should_continue_debate,
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{
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"Bull Researcher": "Bull Researcher",
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"Research Manager": "Research Manager",
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},
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)
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workflow.add_edge("Research Manager", "Trader")
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workflow.add_edge("Trader", "Risky Analyst")
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workflow.add_conditional_edges(
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"Risky Analyst",
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self.conditional_logic.should_continue_risk_analysis,
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{
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"Safe Analyst": "Safe Analyst",
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"Risk Judge": "Risk Judge",
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},
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)
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workflow.add_conditional_edges(
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"Safe Analyst",
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self.conditional_logic.should_continue_risk_analysis,
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{
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"Neutral Analyst": "Neutral Analyst",
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"Risk Judge": "Risk Judge",
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},
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)
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workflow.add_conditional_edges(
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"Neutral Analyst",
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self.conditional_logic.should_continue_risk_analysis,
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{
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"Risky Analyst": "Risky Analyst",
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"Risk Judge": "Risk Judge",
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},
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)
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workflow.add_edge("Risk Judge", END)
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# 編譯並返回
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return workflow.compile() |