76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_insider_sentiment, get_insider_transactions
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from tradingagents.agents.utils.prompts import get_fundamentals_analyst_prompt, get_agent_role_instruction, get_context_message
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from tradingagents.dataflows.config import get_config
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def create_fundamentals_analyst(llm, language: str = "zh-TW"):
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"""
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建立一個基本面分析師節點。
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Args:
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llm: 用於分析的語言模型。
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language: 報告語言 ('en' 或 'zh-TW')
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Returns:
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一個處理基本面分析的節點函式。
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"""
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def fundamentals_analyst_node(state):
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"""
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分析公司的基本面資訊。
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Args:
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state: 當前的代理狀態。
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Returns:
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更新後的代理狀態，包含分析報告和訊息。
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"""
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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company_name = state.get("company_name", ticker)
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tools = [
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get_fundamentals,
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get_balance_sheet,
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get_cashflow,
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get_income_statement,
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]
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# Get language-specific prompts
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system_message = get_fundamentals_analyst_prompt(language)
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role_instruction = get_agent_role_instruction(language)
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context_msg = get_context_message(language, current_date, company_name, ticker)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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f"{role_instruction}"
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" 您可以使用以下工具：{tool_names}。\n{system_message}"
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f" {context_msg}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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chain = prompt | llm.bind_tools(tools)
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result = chain.invoke(state["messages"])
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# Report logic: only save report when LLM gives final response
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report = state.get("fundamentals_report", "")
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if len(result.tool_calls) == 0:
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report = result.content
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return {
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"messages": [result],
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"fundamentals_report": report,
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}
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return fundamentals_analyst_node |