This commit is contained in:
MarkLo
parent
22e525778a
commit
357aa45390
144
cli/utils.py
144
cli/utils.py
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@ -168,20 +168,20 @@ def select_research_depth() -> int:
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return choice
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def select_shallow_thinking_agent(provider) -> str:
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def select_shallow_thinking_agent(provider=None) -> str:
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"""
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使用互動式選單選擇淺層思維的 LLM 引擎。
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參數:
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provider (str): LLM 供應商的名稱。
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provider (str, optional): LLM 供應商的名稱（已廢棄，不再使用）。
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返回:
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str: 選擇的 LLM 模型的名稱。
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"""
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# 定義不同供應商的淺層思維 LLM 引擎選項
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SHALLOW_AGENT_OPTIONS = {
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"openai": [
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"OpenAI": [
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("GPT-5.1", "gpt-5.1-2025-11-13"),
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("GPT-5-mini","gpt-5-mini-2025-08-07"),
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("GPT-5-nano","gpt-5-nano-2025-08-07"),
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@ -189,14 +189,14 @@ def select_shallow_thinking_agent(provider) -> str:
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("GPT-4.1-nano", "gpt-4.1-nano"),
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("o4-mini", "o4-mini-2025-04-16")
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],
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"anthropic": [
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"Anthropic": [
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("Claude Haiku 4.5", "claude-haiku-4-5-20251001"),
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("Claude Sonnet 4.5", "claude-sonnet-4-5-20250929"),
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("Claude Sonnet 4", "claude-sonnet-4-0"),
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("Claude Haiku 3.5", "claude-3-5-haiku-20241022"),
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("Claude Haiku 3", "claude-3-haiku-20240307")
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],
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"google": [
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"Google": [
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("Gemini 2.5 Pro", "gemini-2.5-pro"),
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("Gemini 2.5 Flash", "gemini-2.5-flash"),
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("Gemini 2.5 Flash Lite", "gemini-2.5-flash-lite"),
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@ -219,19 +219,39 @@ def select_shallow_thinking_agent(provider) -> str:
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"Qwen":[
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("Qwen 3 Max", "qwen3-max"),
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("Qwen Plus", "qwen-plus")
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]
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],
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"自訂": [("手動輸入模型名稱", "custom")]
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}
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choice = questionary.select(
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"選擇您的 [快速思維 LLM 引擎]：",
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# 根據供應商顯示選項
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# 第一步：選擇供應商
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provider_choice = questionary.select(
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"選擇 [快速思維] 模型供應商：",
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choices=[
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questionary.Choice(provider_name, value=provider_name)
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for provider_name in SHALLOW_AGENT_OPTIONS.keys()
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],
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instruction="\n- 使用方向鍵導覽\n- 按下 Enter 鍵選擇",
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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 provider_choice is None:
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console.print("\n[red]未選擇供應商。正在結束程式...[/red]")
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exit(1)
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# 第二步：根據選擇的供應商顯示模型列表
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model_choice = questionary.select(
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f"選擇 [{provider_choice}] 的快速思維模型：",
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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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for display, value in SHALLOW_AGENT_OPTIONS[provider_choice]
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],
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# 提供操作說明
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instruction="\n- 使用方向鍵導覽\n- 按下 Enter 鍵選擇",
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# 設定提示的樣式
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style=questionary.Style(
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[
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("selected", "fg:magenta noinherit"),
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@ -241,23 +261,43 @@ def select_shallow_thinking_agent(provider) -> str:
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),
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).ask()
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# 如果使用者沒有選擇，則退出程式
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if choice is None:
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if model_choice is None:
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console.print(
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"\n[red]未選擇快速思維 LLM 引擎。正在結束程式...[/red]"
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)
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exit(1)
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# 如果選擇自訂，提示輸入模型名稱
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if model_choice == "custom":
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model_name = questionary.text(
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"請輸入快速思維 LLM 模型名稱：",
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validate=lambda x: len(x.strip()) > 0 or "請輸入有效的模型名稱。",
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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 model_name:
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console.print(
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"\n[red]未提供模型名稱。正在結束程式...[/red]"
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)
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exit(1)
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return model_name.strip()
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# 返回選擇的 LLM 模型
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return choice
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return model_choice
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def select_deep_thinking_agent(provider) -> str:
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def select_deep_thinking_agent(provider=None) -> str:
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"""
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使用互動式選單選擇深層思維的 LLM 引擎。
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參數:
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provider (str): LLM 供應商的名稱。
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provider (str, optional): LLM 供應商的名稱（已廢棄，不再使用）。
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返回:
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str: 選擇的 LLM 模型的名稱。
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@ -265,7 +305,7 @@ def select_deep_thinking_agent(provider) -> str:
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# 定義不同供應商的深層思維 LLM 引擎選項
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DEEP_AGENT_OPTIONS = {
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"openai": [
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"OpenAI": [
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("GPT-5.1", "gpt-5.1-2025-11-13"),
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("GPT-5-mini","gpt-5-mini-2025-08-07"),
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("GPT-5-nano","gpt-5-nano-2025-08-07"),
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@ -273,14 +313,14 @@ def select_deep_thinking_agent(provider) -> str:
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("GPT-4.1-nano", "gpt-4.1-nano"),
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("o4-mini", "o4-mini-2025-04-16")
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],
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"anthropic": [
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"Anthropic": [
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("Claude Haiku 4.5", "claude-haiku-4-5-20251001"),
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("Claude Sonnet 4.5", "claude-sonnet-4-5-20250929"),
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("Claude Sonnet 4", "claude-sonnet-4-0"),
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("Claude Haiku 3.5", "claude-3-5-haiku-20241022"),
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("Claude Haiku 3", "claude-3-haiku-20240307")
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],
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"google": [
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"Google": [
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("Gemini 2.5 Pro", "gemini-2.5-pro"),
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("Gemini 2.5 Flash", "gemini-2.5-flash"),
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("Gemini 2.5 Flash Lite", "gemini-2.5-flash-lite"),
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@ -303,19 +343,39 @@ def select_deep_thinking_agent(provider) -> str:
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"Qwen":[
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("Qwen 3 Max", "qwen3-max"),
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("Qwen Plus", "qwen-plus")
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]
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],
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"自訂": [("手動輸入模型名稱", "custom")]
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}
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choice = questionary.select(
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"選擇您的 [深度思維 LLM 引擎]：",
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# 根據供應商顯示選項
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# 第一步：選擇供應商
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provider_choice = questionary.select(
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"選擇 [深度思維] 模型供應商：",
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choices=[
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questionary.Choice(provider_name, value=provider_name)
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for provider_name in DEEP_AGENT_OPTIONS.keys()
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],
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instruction="\n- 使用方向鍵導覽\n- 按下 Enter 鍵選擇",
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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 provider_choice is None:
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console.print("\n[red]未選擇供應商。正在結束程式...[/red]")
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exit(1)
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# 第二步：根據選擇的供應商顯示模型列表
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model_choice = questionary.select(
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f"選擇 [{provider_choice}] 的深度思維模型：",
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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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for display, value in DEEP_AGENT_OPTIONS[provider_choice]
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],
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# 提供操作說明
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instruction="\n- 使用方向鍵導覽\n- 按下 Enter 鍵選擇",
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# 設定提示的樣式
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style=questionary.Style(
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[
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("selected", "fg:magenta noinherit"),
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@ -325,13 +385,33 @@ def select_deep_thinking_agent(provider) -> str:
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),
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).ask()
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# 如果使用者沒有選擇，則退出程式
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if choice is None:
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if model_choice is None:
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console.print("\n[red]未選擇深度思維 LLM 引擎。正在結束程式...[/red]")
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exit(1)
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# 如果選擇自訂，提示輸入模型名稱
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if model_choice == "custom":
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model_name = questionary.text(
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"請輸入深度思維 LLM 模型名稱：",
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validate=lambda x: len(x.strip()) > 0 or "請輸入有效的模型名稱。",
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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 model_name:
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console.print(
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"\n[red]未提供模型名稱。正在結束程式...[/red]"
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)
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exit(1)
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return model_name.strip()
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# 返回選擇的 LLM 模型
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return choice
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return model_choice
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def select_llm_provider() -> tuple[str, str]:
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"""
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@ -2,7 +2,7 @@ import os
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DEFAULT_CONFIG = {
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"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
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"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
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"results_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "results")),
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"data_dir": os.path.join(os.path.expanduser("~"), "Documents/Code/ScAI/FR1-data"),
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"data_cache_dir": os.getenv("TRADINGAGENTS_DATA_CACHE_DIR", os.path.join(
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os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
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