104 lines
4.1 KiB
Python
104 lines
4.1 KiB
Python
def create_risky_debator(llm, config):
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"""Create the risky debator node with language support."""
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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 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("current_safe_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 Risky Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages.
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When evaluating the trader's decision or plan, focus intently on the potential upside, growth potential, and innovative benefits—even when these come with elevated risk.
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Use the provided market data and sentiment analysis to strengthen your arguments and challenge the opposing views.
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Specifically, respond directly to each point made by the conservative and neutral analysts, countering with data-driven rebuttals and persuasive reasoning.
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Highlight where their caution might miss critical opportunities or where their assumptions may be overly conservative.
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Here is the trader's decision:
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{trader_decision}
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Your task is to create a compelling case for the trader's decision by questioning and critiquing the conservative and neutral stances to demonstrate why your high-reward perspective offers the best path forward.
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Incorporate insights from the following sources into your arguments:
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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 are the last arguments from the conservative analyst:
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{current_safe_response}
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--------------------------------------
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Here are the last arguments 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 actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms.
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Maintain a focus on debating and persuading, not just presenting data.
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Challenge each counterpoint to underscore why a high-risk approach is optimal. 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"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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