330 lines
13 KiB
Python
330 lines
13 KiB
Python
import questionary
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from typing import List, Optional, Tuple, Dict
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from rich.console import Console
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from cli.models import AnalystType
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console = Console()
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ANALYST_ORDER = [
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("Market Analyst", AnalystType.MARKET),
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("Social Media Analyst", AnalystType.SOCIAL),
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("News Analyst", AnalystType.NEWS),
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("Fundamentals Analyst", AnalystType.FUNDAMENTALS),
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]
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def get_ticker() -> str:
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"""Prompt the user to enter a ticker symbol."""
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ticker = questionary.text(
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"📈 请输入要分析的股票代码:",
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validate=lambda x: len(x.strip()) > 0 or "请输入有效的股票代码。",
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style=questionary.Style(
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[
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("text", "fg:green"),
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("highlighted", "noinherit"),
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]
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),
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).ask()
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if not ticker:
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console.print("\n[red]❌ 未提供股票代码，退出...[/red]")
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exit(1)
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return ticker.strip().upper()
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def get_analysis_date() -> str:
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"""Prompt the user to enter a date in YYYY-MM-DD format."""
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import re
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from datetime import datetime
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def validate_date(date_str: str) -> bool:
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if not re.match(r"^\d{4}-\d{2}-\d{2}$", date_str):
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return False
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try:
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datetime.strptime(date_str, "%Y-%m-%d")
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return True
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except ValueError:
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return False
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date = questionary.text(
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"📅 请输入分析日期 (YYYY-MM-DD):",
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validate=lambda x: validate_date(x.strip())
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or "请输入有效的日期格式 (YYYY-MM-DD)。",
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style=questionary.Style(
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[
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("text", "fg:green"),
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("highlighted", "noinherit"),
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]
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),
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).ask()
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if not date:
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console.print("\n[red]❌ 未提供分析日期，退出...[/red]")
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exit(1)
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return date.strip()
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def select_analysts() -> List[AnalystType]:
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"""Select analysts using an interactive checkbox."""
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choices = questionary.checkbox(
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"👥 选择分析师团队:",
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choices=[
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questionary.Choice(display, value=value) for display, value in ANALYST_ORDER
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],
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instruction="\n- 按空格键选择/取消选择分析师\n- 按'a'键全选/取消全选\n- 按Enter确认",
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validate=lambda x: len(x) > 0 or "必须至少选择一个分析师。",
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style=questionary.Style(
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[
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("checkbox-selected", "fg:green"),
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("selected", "fg:green noinherit"),
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("highlighted", "noinherit"),
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("pointer", "noinherit"),
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]
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),
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).ask()
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if not choices:
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console.print("\n[red]❌ 未选择任何分析师，退出...[/red]")
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exit(1)
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return choices
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def select_research_depth() -> int:
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"""Select research depth using an interactive selection."""
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# Define research depth options with their corresponding values
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DEPTH_OPTIONS = [
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("🔍 浅层 - 快速研究，少量辩论和策略讨论", 1),
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("⚖️ 中等 - 平衡研究，适度辩论和策略讨论", 3),
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("🔬 深度 - 全面研究，深入辩论和策略讨论", 5),
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]
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choice = questionary.select(
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"📊 选择研究深度:",
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choices=[
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questionary.Choice(display, value=value) for display, value in DEPTH_OPTIONS
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],
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instruction="\n- 使用方向键导航\n- 按Enter选择\n- 深度越高，分析越全面但耗时越长",
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style=questionary.Style(
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[
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("selected", "fg:yellow noinherit"),
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("highlighted", "fg:yellow noinherit"),
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("pointer", "fg:yellow noinherit"),
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]
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),
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).ask()
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if choice is None:
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console.print("\n[red]❌ 未选择研究深度，退出...[/red]")
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exit(1)
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return choice
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def select_shallow_thinking_agent(provider) -> str:
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"""Select shallow thinking llm engine using an interactive selection."""
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# Define shallow thinking llm engine options with their corresponding model names
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SHALLOW_AGENT_OPTIONS = {
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# 国内免费大模型
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"qwen": [
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("Qwen-Turbo - 快速响应，适合简单任务", "qwen-turbo"),
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("Qwen-Plus - 平衡性能和速度", "qwen-plus"),
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("Qwen-Max - 最强性能，适合复杂任务", "qwen-max"),
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],
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"ernie": [
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("ERNIE-3.5-8K - 快速响应版本", "ernie-3.5-8k"),
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("ERNIE-4.0-8K - 最新版本，性能更强", "ernie-4.0-8k"),
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("ERNIE-4.0-128K - 长文本处理版本", "ernie-4.0-128k"),
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],
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"glm": [
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("GLM-4 - 智谱AI最新模型", "glm-4"),
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("GLM-4-Flash - 快速响应版本", "glm-4-flash"),
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("GLM-4V - 多模态版本", "glm-4v"),
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],
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"kimi": [
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("Moonshot-v1-8K - 标准版本", "moonshot-v1-8k"),
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("Moonshot-v1-32K - 长文本版本", "moonshot-v1-32k"),
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("Moonshot-v1-128K - 超长文本版本", "moonshot-v1-128k"),
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],
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# 国外模型
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"openai": [
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("GPT-4o-mini - Fast and efficient for quick tasks", "gpt-4o-mini"),
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("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
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("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
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("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
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],
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"anthropic": [
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("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
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("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
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("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
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("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
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],
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"google": [
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("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
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("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
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("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
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],
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"openrouter": [
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("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
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("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"),
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("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"),
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],
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"ollama": [
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("llama3.1 local", "llama3.1"),
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("llama3.2 local", "llama3.2"),
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]
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}
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choice = questionary.select(
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"🚀 选择快速思考模型:",
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choices=[
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questionary.Choice(display, value=value)
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for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
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],
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instruction="\n- 使用方向键导航\n- 按Enter选择\n- 快速模型用于简单任务",
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style=questionary.Style(
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[
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("selected", "fg:cyan noinherit"),
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("highlighted", "fg:cyan noinherit"),
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("pointer", "fg:cyan noinherit"),
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]
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),
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).ask()
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if choice is None:
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console.print(
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"\n[red]❌ 未选择快速思考模型，退出...[/red]"
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)
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exit(1)
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return choice
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def select_deep_thinking_agent(provider) -> str:
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"""Select deep thinking llm engine using an interactive selection."""
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# Define deep thinking llm engine options with their corresponding model names
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DEEP_AGENT_OPTIONS = {
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# 国内免费大模型
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"qwen": [
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("Qwen-Plus - 平衡性能，适合复杂分析", "qwen-plus"),
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("Qwen-Max - 最强性能，适合深度思考", "qwen-max"),
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("Qwen-Turbo - 快速版本，适合一般任务", "qwen-turbo"),
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],
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"ernie": [
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("ERNIE-4.0-8K - 最新版本，性能最强", "ernie-4.0-8k"),
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("ERNIE-4.0-128K - 长文本处理版本", "ernie-4.0-128k"),
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("ERNIE-3.5-8K - 稳定版本", "ernie-3.5-8k"),
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],
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"glm": [
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("GLM-4 - 智谱AI最新模型，性能最强", "glm-4"),
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("GLM-4-Flash - 快速响应版本", "glm-4-flash"),
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("GLM-4V - 多模态版本", "glm-4v"),
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],
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"kimi": [
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("Moonshot-v1-32K - 长文本版本，适合深度分析", "moonshot-v1-32k"),
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("Moonshot-v1-128K - 超长文本版本", "moonshot-v1-128k"),
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("Moonshot-v1-8K - 标准版本", "moonshot-v1-8k"),
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],
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# 国外模型
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"openai": [
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("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
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("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
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("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
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("o4-mini - Specialized reasoning model (compact)", "o4-mini"),
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("o3-mini - Advanced reasoning model (lightweight)", "o3-mini"),
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("o3 - Full advanced reasoning model", "o3"),
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("o1 - Premier reasoning and problem-solving model", "o1"),
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],
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"anthropic": [
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("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
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("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
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("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
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("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
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("Claude Opus 4 - Most powerful Anthropic model", " claude-opus-4-0"),
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],
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"google": [
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("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
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("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
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("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
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("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"),
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],
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"openrouter": [
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("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),
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("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"),
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],
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"ollama": [
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("llama3.1 local", "llama3.1"),
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("qwen3", "qwen3"),
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]
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}
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choice = questionary.select(
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"🧠 选择深度思考模型:",
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choices=[
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questionary.Choice(display, value=value)
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for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
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],
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instruction="\n- 使用方向键导航\n- 按Enter选择\n- 深度模型用于复杂分析",
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style=questionary.Style(
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[
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("selected", "fg:yellow noinherit"),
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("highlighted", "fg:yellow noinherit"),
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("pointer", "fg:yellow noinherit"),
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]
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),
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).ask()
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if choice is None:
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console.print("\n[red]❌ 未选择深度思考模型，退出...[/red]")
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exit(1)
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return choice
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def select_llm_provider() -> tuple[str, str]:
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"""Select the LLM provider using interactive selection."""
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# Define LLM provider options with their corresponding endpoints
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BASE_URLS = [
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# 国内免费大模型（推荐）
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("🇨🇳 通义千问 (Qwen) - 金融领域表现优秀", "qwen", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
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("🇨🇳 文心一言 (ERNIE) - 免费额度最高", "ernie", "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat"),
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("🇨🇳 智谱AI (GLM) - 清华大学出品", "glm", "https://open.bigmodel.cn/api/paas/v4"),
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("🇨🇳 月之暗面Kimi - 长文本处理强", "kimi", "https://api.moonshot.cn/v1"),
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# 国外模型
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("🌍 OpenAI - GPT系列", "openai", "https://api.openai.com/v1"),
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("🌍 Anthropic - Claude系列", "anthropic", "https://api.anthropic.com/"),
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("🌍 Google - Gemini系列", "google", "https://generativelanguage.googleapis.com/v1"),
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("🌍 OpenRouter - 多模型聚合", "openrouter", "https://openrouter.ai/api/v1"),
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("🌍 Ollama - 本地部署", "ollama", "http://localhost:11434/v1"),
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]
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choice = questionary.select(
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"🤖 选择AI模型提供商:",
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choices=[
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questionary.Choice(display, value=(provider, url))
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for display, provider, url in BASE_URLS
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],
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instruction="\n- 使用方向键导航\n- 按Enter选择\n- 国内模型推荐用于金融分析",
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style=questionary.Style(
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[
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("selected", "fg:green noinherit"),
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("highlighted", "fg:green noinherit"),
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("pointer", "fg:green noinherit"),
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]
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),
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).ask()
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if choice is None:
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console.print("\n[red]❌ 未选择AI模型提供商，退出...[/red]")
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exit(1)
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provider, url = choice
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print(f"✅ 已选择: {provider}\tURL: {url}")
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return provider, url
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