94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
# -*- coding: utf-8 -*-
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from langchain_core.messages import AIMessage
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import time
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import json
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from tradingagents.agents.utils.output_filter import fix_common_llm_errors, validate_and_warn
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from tradingagents.agents.utils.prompts import get_bear_researcher_prompt
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def create_bear_researcher(llm, memory, language: str = "zh-TW"):
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"""
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建立一個看跌研究員節點。
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Args:
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llm: 用於生成回應的語言模型。
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memory: 儲存過去情況和反思的記憶體物件。
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language: 報告語言 ('en' 或 'zh-TW')
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Returns:
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function: 一個代表看跌研究員節點的函式。
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"""
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def bear_node(state) -> dict:
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"""看跌研究員節點的執行函式。"""
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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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bear_history = investment_debate_state.get("bear_history", "")
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current_response = investment_debate_state.get("current_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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curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_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 i, rec in enumerate(past_memories, 1):
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recommendation = rec["recommendation"]
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past_memory_str += recommendation + "\n\n"
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# Get language-specific prompt template
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base_prompt = get_bear_researcher_prompt(language)
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# Build the full prompt with context
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if language == "en":
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prompt = f"""{base_prompt}
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【Available Data】
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- Market Analysis: {market_research_report}
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- Sentiment Report: {sentiment_report}
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- News Report: {news_report}
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- Fundamentals Report: {fundamentals_report}
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- Debate History: {history}
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- Bullish Arguments: {current_response}
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- Past Experience: {past_memory_str}
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Please provide your bearish analysis now."""
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else:
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prompt = f"""{base_prompt}
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【可用資源】
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- 市場分析：{market_research_report}
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- 社群情緒：{sentiment_report}
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- 新聞：{news_report}
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- 基本面：{fundamentals_report}
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- 辯論歷史：{history}
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- 看漲論點：{current_response}
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- 過往經驗：{past_memory_str}
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請提供您的看跌分析。"""
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response = llm.invoke(prompt)
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response.content = fix_common_llm_errors(response.content)
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validate_and_warn(response.content, "Bear_Researcher")
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# Format argument based on language
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if language == "en":
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argument = f"Bear Analyst: {response.content}"
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else:
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argument = f"看跌分析師：{response.content}"
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new_investment_debate_state = {
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"history": history + "\n" + argument,
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"bear_history": bear_history + "\n" + argument,
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"bull_history": investment_debate_state.get("bull_history", ""),
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"current_response": argument,
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"count": investment_debate_state["count"] + 1,
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}
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return {"investment_debate_state": new_investment_debate_state}
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return bear_node |