65 lines
2.9 KiB
Python
65 lines
2.9 KiB
Python
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def create_yes_advocate(llm, memory):
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def yes_node(state) -> dict:
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investment_debate_state = state["investment_debate_state"]
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history = investment_debate_state.get("history", "")
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yes_history = investment_debate_state.get("yes_history", "")
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event_question = state["event_question"]
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current_no_response = investment_debate_state.get("current_no_response", "")
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current_timing_response = investment_debate_state.get("current_timing_response", "")
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odds_report = state["odds_report"]
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sentiment_report = state["sentiment_report"]
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news_report = state["news_report"]
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event_report = state["event_report"]
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curr_situation = f"{odds_report}\n\n{sentiment_report}\n\n{news_report}\n\n{event_report}"
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past_memories = memory.get_memories(curr_situation, n_matches=2)
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past_memory_str = ""
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for rec in past_memories:
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past_memory_str += rec["recommendation"] + "\n\n"
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prompt = f"""You are the YES Advocate for the prediction market question: "{event_question}"
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Your task is to build a strong, evidence-based case that this event WILL occur (YES outcome). Leverage the provided research and data to support your position and counter opposing arguments.
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Key points to focus on:
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- Evidence Supporting YES: Highlight data, news, and trends that increase the probability of YES.
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- Counterarguments: Directly address and rebut the NO Advocate's concerns and the Timing Advocate's hesitations.
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- Market Mispricing: If the current odds undervalue YES, explain why using specific evidence.
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- Engagement: Present your argument conversationally, engaging directly with opposing points rather than just listing facts.
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Resources:
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Odds & Market Analysis: {odds_report}
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Social Sentiment: {sentiment_report}
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News Analysis: {news_report}
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Event Analysis: {event_report}
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Debate History: {history}
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Last NO argument: {current_no_response}
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Last Timing argument: {current_timing_response}
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Lessons from past predictions: {past_memory_str}
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Build a compelling YES case. Address past mistakes and learn from them."""
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response = llm.invoke(prompt)
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argument = f"YES Advocate: {response.content}"
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new_state = {
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"history": history + "\n" + argument,
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"yes_history": yes_history + "\n" + argument,
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"no_history": investment_debate_state.get("no_history", ""),
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"timing_history": investment_debate_state.get("timing_history", ""),
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"current_yes_response": argument,
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"current_no_response": investment_debate_state.get("current_no_response", ""),
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"current_timing_response": investment_debate_state.get("current_timing_response", ""),
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"latest_speaker": "YES Advocate",
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"judge_decision": investment_debate_state.get("judge_decision", ""),
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"count": investment_debate_state["count"] + 1,
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}
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return {"investment_debate_state": new_state}
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return yes_node
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