105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
def create_safe_debator(llm, config):
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"""Create the safe debator node with language support."""
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language = config["output_language"]
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language = config["output_language"]
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language_prompts = {
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"en": "",
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"zh-tw": "Use Traditional Chinese as the output.",
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"zh-cn": "Use Simplified Chinese as the output.",
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}
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language_prompt = language_prompts.get(language, "")
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def safe_node(state) -> dict:
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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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safe_history = risk_debate_state.get("safe_history", "")
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current_risky_response = risk_debate_state.get("current_risky_response", "")
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current_neutral_response = risk_debate_state.get("current_neutral_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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prompt = f"""
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As the Safe/Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth.
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You prioritize stability, security, and risk mitigation, carefully assessing potential losses, economic downturns, and market volatility.
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When evaluating the trader's decision or plan, critically examine high-risk elements, pointing out where the decision may expose the firm to undue risk and where more cautious alternatives could secure long-term gains.
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Here is the trader's decision:
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{trader_decision}
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Your task is to actively counter the arguments of the Risky and Neutral Analysts, highlighting where their views may overlook potential threats or fail to prioritize sustainability.
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Respond directly to their points, drawing from the following data sources to build a convincing case for a low-risk approach adjustment to the trader's decision:
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Market Research Report:
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{market_research_report}
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--------------------------------------
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Social Media Sentiment Report:
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{sentiment_report}
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--------------------------------------
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Latest World Affairs Report:
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{news_report}
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--------------------------------------
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Company Fundamentals Report:
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{fundamentals_report}
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--------------------------------------
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Here is the current conversation history:
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{history}
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--------------------------------------
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Here is the last response from the risky analyst:
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{current_risky_response}
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--------------------------------------
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Here is the last response from the neutral analyst:
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{current_neutral_response}
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--------------------------------------
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If there are no responses from the other viewpoints, do not halluncinate and just present your point.
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Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked.
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Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets.
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Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches.
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Output conversationally as if you are speaking without any special formatting.
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Output language: ***{language_prompt}***
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"""
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response = llm.invoke(prompt)
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argument = f"Safe Analyst: {response.content}"
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new_risk_debate_state = {
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"history": history + "\n" + argument,
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"risky_history": risk_debate_state.get("risky_history", ""),
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"safe_history": safe_history + "\n" + argument,
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"neutral_history": risk_debate_state.get("neutral_history", ""),
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"latest_speaker": "Safe",
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"current_risky_response": risk_debate_state.get(
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"current_risky_response", ""
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),
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"current_safe_response": argument,
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"current_neutral_response": risk_debate_state.get(
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"current_neutral_response", ""
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),
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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 safe_node
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