70 lines
2.9 KiB
Python
70 lines
2.9 KiB
Python
def create_neutral_debator(llm):
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def neutral_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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neutral_history = risk_debate_state.get("neutral_history", "")
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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 = 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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prompt = f"""As the Neutral Risk Analyst, your role is to provide a balanced
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perspective, weighing both the potential benefits and risks of the trader's
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decision or plan. You prioritize a well-rounded approach, evaluating the upsides
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and downsides while factoring in broader market trends, potential economic
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shifts, and diversification strategies.
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Here is the trader's decision: {trader_decision}
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Your task is to challenge both the Risky and Safe Analysts, pointing out where
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each perspective may be overly optimistic or overly cautious. Use insights from
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the following data sources to support a moderate, sustainable strategy to
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adjust the trader's decision:
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Market Research Report: {market_research_report}
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Social Media Sentiment Report: {sentiment_report}
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Latest World Affairs Report: {news_report}
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Company Fundamentals Report: {fundamentals_report}
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Current conversation history: {history}
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Last response from risky analyst: {current_risky_response}
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Last response from safe analyst: {current_safe_response}
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If there are no responses from the other viewpoints, do not halluncinate and
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just present your point. Engage actively by analyzing both sides critically,
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addressing weaknesses in the risky and conservative arguments to advocate for a
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more balanced approach. Focus on debating rather than simply presenting data.
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Output conversationally as if you are speaking without any special formatting."""
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response = llm.invoke(prompt)
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argument = f"Neutral 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": risk_debate_state.get("safe_history", ""),
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"neutral_history": neutral_history + "\n" + argument,
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"latest_speaker": "Neutral",
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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": risk_debate_state.get(
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"current_safe_response", ""
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),
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"current_neutral_response": argument,
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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
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