83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
# -*- coding: utf-8 -*-
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import time
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import json
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from tradingagents.agents.utils.output_filter import fix_common_llm_errors, validate_and_warn
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from tradingagents.agents.utils.prompts import get_neutral_debator_prompt
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def create_neutral_debator(llm, language: str = "zh-TW"):
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"""
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建立一個中立的風險辯論員節點。
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Args:
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llm: 用於生成回應的語言模型。
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language: 報告語言 ('en' 或 'zh-TW')
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Returns:
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function: 一個代表中立辯論員節點的函式。
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"""
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def neutral_node(state) -> dict:
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"""中立辯論員節點的執行函式。"""
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risk_debate_state = state["risk_debate_state"]
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history = risk_debate_state.get("history", "")
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neutral_history = risk_debate_state.get("neutral_history", "")
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current_risky_response = risk_debate_state.get("current_risky_response", "")
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current_safe_response = risk_debate_state.get("current_safe_response", "")
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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news_report = state["news_report"]
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fundamentals_report = state["fundamentals_report"]
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trader_decision = state["trader_investment_plan"]
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# Get language-specific prompt
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base_prompt = get_neutral_debator_prompt(language)
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if language == "en":
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prompt = f"""{base_prompt}
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【Available Information】
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- Trader Plan: {trader_decision}
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- Reports: {market_research_report}, {sentiment_report}, {news_report}, {fundamentals_report}
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- Debate History: {history}
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- Opponent Views: {current_risky_response}, {current_safe_response}
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Please provide your neutral risk analysis."""
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else:
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prompt = f"""{base_prompt}
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【可用資訊】
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- 交易員計畫：{trader_decision}
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- 各類報告：{market_research_report}, {sentiment_report}, {news_report}, {fundamentals_report}
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- 辯論歷史：{history}
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- 對手觀點：{current_risky_response}, {current_safe_response}
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請提供您的中立風險分析。"""
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response = llm.invoke(prompt)
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response.content = fix_common_llm_errors(response.content)
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validate_and_warn(response.content, "Neutral_Debator")
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if language == "en":
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argument = f"Neutral Analyst: {response.content}"
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else:
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argument = f"中立分析師：{response.content}"
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new_risk_debate_state = {
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"history": history + "\n" + argument,
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"neutral_history": neutral_history + "\n" + argument,
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"risky_history": risk_debate_state.get("risky_history", ""),
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"safe_history": risk_debate_state.get("safe_history", ""),
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"latest_speaker": "Neutral",
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"current_neutral_response": argument,
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"current_risky_response": risk_debate_state.get("current_risky_response", ""),
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"current_safe_response": risk_debate_state.get("current_safe_response", ""),
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"count": risk_debate_state["count"] + 1,
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}
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return {"risk_debate_state": new_risk_debate_state}
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return neutral_node |