import os from langchain_classic.agents import AgentExecutor, create_tool_calling_agent from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser from langchain_openai import ChatOpenAI from dotenv import load_dotenv from adata import stock from langchain_core.tools import tool from telegram import Update from telegram.ext import ApplicationBuilder, ContextTypes, CommandHandler, MessageHandler, filters load_dotenv() # ================= 你的原有逻辑(完全保留) ================= @tool def get_fina_info(stock_code: str) -> str: """获取指定股票代码的财务数据。当需要获取财务数据的时使用此工具""" df = stock.finance.get_core_index(stock_code=stock_code) data = df[['report_date', 'report_type', 'total_rev', 'total_rev_yoy_gr', 'net_profit_attr_sh', 'net_profit_yoy_gr', 'non_gaap_net_profit', 'non_gaap_net_profit_yoy_gr', 'roe_wtd', 'gross_margin', 'net_margin', 'quick_ratio', 'inv_turn_rate', 'acct_recv_turn_rate']] last_type = data['report_type'].to_list()[0] recent_df = data[data.report_type == last_type].iloc[:2, :].reset_index(drop=True) recent_df.rename(columns={"report_date": "报告日期", "report_type": "报告类型", "total_rev": "营业总收入", "total_rev_yoy_gr": "营业总收入同比增长率", "net_profit_attr_sh": "归母净利润", "net_profit_yoy_gr": "归母净利润同比增长率", "non_gaap_net_profit": "扣非净利润", "non_gaap_net_profit_yoy_gr": "扣非净利润同比增长率", "roe_wtd": "加权净资产收益率", "gross_margin": "销售毛利率", "net_margin": "销售净利率", "quick_ratio": "速动比率", "inv_turn_rate": "存货周转率", "acct_recv_turn_rate": "应收账款周转率" }, inplace=True) formatted_lines = [] for _, row in recent_df.iterrows(): line_items = [] for col, val in zip(recent_df.columns, row): if isinstance(val, (int, float)): if "率" in col: formatted_val = f"{val:.2f}%" else: formatted_val = f"{val:,.2f}" else: formatted_val = str(val) line_items.append(f"{col}:{formatted_val}") formatted_lines.append("\n".join(line_items)) final_report = f"近两期{last_type}主要财务数据如下:\n" + "\n\n".join(formatted_lines) return final_report class MyOutputParser(StrOutputParser): def parse(self, text) -> str: return text.strip() llm = ChatOpenAI(model="deepseek-v4-flash") tools = [get_fina_info] template = """ 你是一个专业的股票分析助手。用户会输入一个股票代码,你可以使用工具来获取其财务数据,之后根据获取到的财务数据来分析这家公司的基本面情况。 """ chat_prompt = ChatPromptTemplate.from_messages([ ("system", template), ("human", "{input}"), ("placeholder", "{agent_scratchpad}"), ]) agent = create_tool_calling_agent(llm, tools, chat_prompt) agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) # ================= 新增:Telegram 机器人交互逻辑 ================= async def start(update: Update, context: ContextTypes.DEFAULT_TYPE): """处理 /start 命令""" await update.message.reply_text("你好!我是你的股票分析助手,请直接发送类似“分析一下600699的基本面”的消息。") async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE): """处理普通文本消息,调用你的 Agent""" user_input = update.message.text # 提示用户正在处理,因为 Agent 调用工具可能需要几秒钟 await update.message.reply_text("🤔 正在分析,请稍候...") try: # 调用你原有的 Agent 逻辑 res = agent_executor.invoke({"input": user_input}) output = res["output"] except Exception as e: output = f"分析出错:{str(e)}" await update.message.reply_text(output) if __name__ == "__main__": # 从环境变量读取 Token token = os.getenv("TELEGRAM_BOT_TOKEN") if not token: raise ValueError("未在 .env 文件中找到 TELEGRAM_BOT_TOKEN") proxy_url = "http://127.0.0.1:8889" # 启动机器人 application = ApplicationBuilder().token(token).proxy(proxy_url).get_updates_proxy(proxy_url).build() application.add_handler(CommandHandler("start", start)) application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message)) print("Telegram Bot 正在运行...") application.run_polling()