71 lines
3.0 KiB
Python
71 lines
3.0 KiB
Python
def create_risky_debator(llm):
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def risky_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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risky_history = risk_debate_state.get("risky_history", "")
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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 = risk_debate_state.get(
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"current_neutral_response", ""
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)
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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 Risky Risk Analyst, your role is to actively champion
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high-reward, high-risk opportunities, emphasizing bold strategies and competitive
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advantages. When evaluating the trader's decision or plan, focus intently on the
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potential upside, growth potential, and innovative benefits—even when these come
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with elevated risk. Use the provided market data and sentiment analysis to
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strengthen your arguments and challenge the opposing views.
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Here is the trader's decision: {trader_decision}
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Your task is to create a compelling case for the trader's decision by questioning
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and critiquing the conservative and neutral stances to demonstrate why your
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high-reward perspective offers the best path forward. Incorporate insights from
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the following sources into your arguments:
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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 arguments from conservative analyst: {current_safe_response}
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Last arguments from neutral analyst: {current_neutral_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 addressing any specific concerns
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raised, refuting the weaknesses in their logic, and asserting the benefits of
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risk-taking to outpace market norms. Output conversationally as if you are
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speaking without any special formatting."""
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response = llm.invoke(prompt)
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argument = f"Risky 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": risky_history + "\n" + argument,
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"safe_history": risk_debate_state.get("safe_history", ""),
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"neutral_history": risk_debate_state.get("neutral_history", ""),
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"latest_speaker": "Risky",
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"current_risky_response": argument,
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"current_safe_response": risk_debate_state.get("current_safe_response", ""),
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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 risky_node
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