From cdba5bf7802691bb1ad72aa5bf7ebf0e308d1421 Mon Sep 17 00:00:00 2001 From: liuping Date: Sun, 6 Jul 2025 01:15:35 +0800 Subject: [PATCH] feat: add DashScope (Alibaba Cloud) LLM provider support - Add DashScope to CLI LLM provider selection (first option) - Add DashScope support in TradingAgentsGraph with proper error handling - Import ChatDashScope adapter with fallback for missing dependencies - Update default_config.py with DashScope configuration comments - Create comprehensive DashScope configuration example Features added: - DashScope provider selection in CLI (cli/utils.py) - LLM initialization for DashScope models (trading_graph.py) - Configuration validation and testing (examples/dashscope_config_example.py) - Support for qwen-turbo, qwen-plus, qwen-max models - Proper error handling for missing dashscope package Usage: 1. Install: pip install dashscope 2. Configure: DASHSCOPE_API_KEY in .env file 3. Select 'DashScope (Alibaba Cloud)' in CLI 4. Use models: qwen-turbo (fast), qwen-plus (balanced), qwen-max (best) This enables Chinese users to use Alibaba Cloud's Qwen models for financial analysis with optimized Chinese language support. --- cli/utils.py | 8 +- examples/dashscope_config_example.py | 202 +++++++++++++++++ test_merged_features.py | 321 --------------------------- tradingagents/default_config.py | 2 + tradingagents/graph/trading_graph.py | 39 +++- 5 files changed, 246 insertions(+), 326 deletions(-) create mode 100644 examples/dashscope_config_example.py delete mode 100644 test_merged_features.py diff --git a/cli/utils.py b/cli/utils.py index 7b9682a6..01a7bddc 100644 --- a/cli/utils.py +++ b/cli/utils.py @@ -240,14 +240,16 @@ def select_deep_thinking_agent(provider) -> str: return choice def select_llm_provider() -> tuple[str, str]: - """Select the OpenAI api url using interactive selection.""" - # Define OpenAI api options with their corresponding endpoints + """Select the LLM provider using interactive selection.""" + # Define LLM provider options with their corresponding endpoints + # DashScope (Alibaba Cloud) is recommended for Chinese users BASE_URLS = [ + ("DashScope (Alibaba Cloud)", "https://dashscope.aliyuncs.com/api/v1"), ("OpenAI", "https://api.openai.com/v1"), ("Anthropic", "https://api.anthropic.com/"), ("Google", "https://generativelanguage.googleapis.com/v1"), ("Openrouter", "https://openrouter.ai/api/v1"), - ("Ollama", "http://localhost:11434/v1"), + ("Ollama", "http://localhost:11434/v1"), ] choice = questionary.select( diff --git a/examples/dashscope_config_example.py b/examples/dashscope_config_example.py new file mode 100644 index 00000000..bf486541 --- /dev/null +++ b/examples/dashscope_config_example.py @@ -0,0 +1,202 @@ +#!/usr/bin/env python3 +""" +DashScope (Alibaba Cloud) Configuration Example +阿里云百炼模型配置示例 + +This example shows how to configure TradingAgents to use DashScope models. +这个示例展示如何配置TradingAgents使用阿里云百炼模型。 +""" + +import os +import sys +from pathlib import Path + +# Add project root to path +project_root = Path(__file__).parent.parent +sys.path.insert(0, str(project_root)) + +from tradingagents.default_config import DEFAULT_CONFIG + +def create_dashscope_config(): + """ + Create configuration for DashScope models + 创建百炼模型配置 + """ + + # Copy default config + config = DEFAULT_CONFIG.copy() + + # Configure for DashScope + config.update({ + # LLM Provider Settings + "llm_provider": "dashscope", + "backend_url": "https://dashscope.aliyuncs.com/api/v1", + + # Model Selection + # 模型选择 - 根据需要调整 + "deep_think_llm": "qwen-plus", # For complex analysis 复杂分析 + "quick_think_llm": "qwen-turbo", # For quick tasks 快速任务 + + # Optional: Reduce rounds for faster execution + # 可选：减少轮次以加快执行速度 + "max_debate_rounds": 1, + "max_risk_discuss_rounds": 1, + + # Enable online tools + "online_tools": True, + }) + + return config + +def check_dashscope_setup(): + """ + Check if DashScope is properly configured + 检查百炼配置是否正确 + """ + + print("🔍 Checking DashScope Configuration") + print("🔍 检查百炼配置") + print("=" * 50) + + # Check API key + api_key = os.getenv('DASHSCOPE_API_KEY') + if api_key: + print(f"✅ DASHSCOPE_API_KEY: {api_key[:10]}...") + else: + print("❌ DASHSCOPE_API_KEY not found in environment variables") + print("❌ 环境变量中未找到 DASHSCOPE_API_KEY") + print("\n💡 To fix this:") + print("💡 解决方法:") + print("1. Get API key from: https://dashscope.aliyun.com/") + print("1. 从以下网址获取API密钥: https://dashscope.aliyun.com/") + print("2. Add to .env file: DASHSCOPE_API_KEY=your_key_here") + print("2. 添加到.env文件: DASHSCOPE_API_KEY=your_key_here") + return False + + # Check DashScope package + try: + import dashscope + print("✅ dashscope package installed") + print("✅ dashscope包已安装") + except ImportError: + print("❌ dashscope package not installed") + print("❌ dashscope包未安装") + print("\n💡 To install:") + print("💡 安装方法:") + print("pip install dashscope") + return False + + # Check adapter + try: + from tradingagents.llm_adapters.dashscope_adapter import ChatDashScope + print("✅ DashScope adapter available") + print("✅ 百炼适配器可用") + except ImportError: + print("❌ DashScope adapter not available") + print("❌ 百炼适配器不可用") + return False + + print("\n🎉 DashScope configuration is ready!") + print("🎉 百炼配置已就绪!") + return True + +def test_dashscope_connection(): + """ + Test connection to DashScope + 测试百炼连接 + """ + + print("\n🧪 Testing DashScope Connection") + print("🧪 测试百炼连接") + print("=" * 50) + + try: + from tradingagents.llm_adapters.dashscope_adapter import ChatDashScope + from langchain_core.messages import HumanMessage + + # Create model instance + llm = ChatDashScope( + model="qwen-turbo", + temperature=0.1, + max_tokens=100 + ) + + # Test simple query + test_message = HumanMessage(content="Hello, please respond with 'DashScope connection successful!'") + response = llm.invoke([test_message]) + + print(f"✅ Connection successful!") + print(f"✅ 连接成功!") + print(f"📝 Response: {response.content}") + print(f"📝 响应: {response.content}") + + return True + + except Exception as e: + print(f"❌ Connection failed: {str(e)}") + print(f"❌ 连接失败: {str(e)}") + return False + +def main(): + """ + Main function to demonstrate DashScope configuration + 主函数演示百炼配置 + """ + + print("🚀 DashScope Configuration Example") + print("🚀 百炼配置示例") + print("=" * 50) + + # Check setup + if not check_dashscope_setup(): + print("\n❌ Please fix the configuration issues above") + print("❌ 请修复上述配置问题") + return + + # Test connection + if not test_dashscope_connection(): + print("\n❌ Connection test failed") + print("❌ 连接测试失败") + return + + # Show configuration + config = create_dashscope_config() + + print(f"\n📋 DashScope Configuration:") + print(f"📋 百炼配置:") + print(f" Provider: {config['llm_provider']}") + print(f" Deep Think Model: {config['deep_think_llm']}") + print(f" Quick Think Model: {config['quick_think_llm']}") + print(f" Backend URL: {config['backend_url']}") + + print(f"\n💡 Usage Example:") + print(f"💡 使用示例:") + print(f""" +from tradingagents.graph.trading_graph import TradingAgentsGraph + +# Create config +config = create_dashscope_config() + +# Initialize trading graph +ta = TradingAgentsGraph(config) + +# Run analysis +result, decision = ta.propagate("AAPL", "2024-01-15") +print(result) +""") + + print(f"\n🎯 Available DashScope Models:") + print(f"🎯 可用的百炼模型:") + + models = { + "qwen-turbo": "Fast response, suitable for daily conversations", + "qwen-plus": "Balanced performance and cost", + "qwen-max": "Best performance", + "qwen-max-longcontext": "Supports ultra-long context" + } + + for model, description in models.items(): + print(f" • {model}: {description}") + +if __name__ == "__main__": + main() diff --git a/test_merged_features.py b/test_merged_features.py deleted file mode 100644 index 2b73f86d..00000000 --- a/test_merged_features.py +++ /dev/null @@ -1,321 +0,0 @@ -#!/usr/bin/env python3 -""" -合并后功能测试验证脚本 -测试所有新增功能和原有功能的兼容性 -""" - -import sys -import os -import traceback -import tempfile -import shutil -from pathlib import Path - -# 添加项目路径 -sys.path.insert(0, os.path.abspath('.')) - -class MergedFeaturesTest: - """合并功能测试类""" - - def __init__(self): - self.test_results = { - "passed": [], - "failed": [], - "warnings": [] - } - self.temp_dir = None - - def setup(self): - """测试环境设置""" - print("🔧 设置测试环境...") - self.temp_dir = tempfile.mkdtemp() - print(f" 临时目录: {self.temp_dir}") - - def cleanup(self): - """清理测试环境""" - if self.temp_dir and os.path.exists(self.temp_dir): - shutil.rmtree(self.temp_dir) - print(f"🧹 清理临时目录: {self.temp_dir}") - - def test_basic_imports(self): - """测试基础模块导入""" - print("\n📦 测试基础模块导入...") - - basic_modules = [ - "tradingagents.default_config", - "tradingagents.dataflows.interface", - "tradingagents.dataflows.config", - ] - - for module in basic_modules: - try: - __import__(module) - self.test_results["passed"].append(f"基础导入: {module}") - print(f" ✅ {module}") - except Exception as e: - self.test_results["failed"].append(f"基础导入: {module} - {e}") - print(f" ❌ {module}: {e}") - - def test_cache_system(self): - """测试缓存系统""" - print("\n💾 测试缓存系统...") - - try: - # 测试原有缓存管理器 - from tradingagents.dataflows.cache_manager import StockDataCache, get_cache - - # 创建缓存实例 - cache = StockDataCache(cache_dir=self.temp_dir) - - # 测试基本功能 - test_data = "Test stock data for AAPL" - cache_key = cache.save_stock_data("AAPL", test_data, "2024-01-01", "2024-01-31", "test") - - if cache_key: - loaded_data = cache.load_stock_data(cache_key) - if loaded_data == test_data: - self.test_results["passed"].append("缓存系统: 基本功能正常") - print(" ✅ 基本缓存功能正常") - else: - self.test_results["failed"].append("缓存系统: 数据不匹配") - print(" ❌ 缓存数据不匹配") - else: - self.test_results["failed"].append("缓存系统: 保存失败") - print(" ❌ 缓存保存失败") - - # 测试市场类型检测 - us_market = cache._determine_market_type("AAPL") - china_market = cache._determine_market_type("000001") - - if us_market == "us" and china_market == "china": - self.test_results["passed"].append("缓存系统: 市场类型检测正常") - print(" ✅ 市场类型检测正常") - else: - self.test_results["failed"].append("缓存系统: 市场类型检测异常") - print(" ❌ 市场类型检测异常") - - except Exception as e: - self.test_results["failed"].append(f"缓存系统: {e}") - print(f" ❌ 缓存系统测试失败: {e}") - - def test_new_features_import(self): - """测试新功能模块导入""" - print("\n🆕 测试新功能模块导入...") - - new_modules = [ - # 中国市场数据 - ("tradingagents.dataflows.chinese_finance_utils", "中国财经数据工具"), - ("tradingagents.dataflows.tdx_utils", "通达信API工具"), - ("tradingagents.dataflows.optimized_china_data", "优化A股数据提供器"), - - # 高级缓存 - ("tradingagents.dataflows.adaptive_cache", "自适应缓存"), - ("tradingagents.dataflows.integrated_cache", "集成缓存"), - ("tradingagents.dataflows.db_cache_manager", "数据库缓存管理"), - - # 配置管理 - ("tradingagents.config.database_config", "数据库配置"), - ("tradingagents.config.database_manager", "数据库管理器"), - ("tradingagents.config.mongodb_storage", "MongoDB存储"), - - # LLM适配器 - ("tradingagents.llm_adapters.dashscope_adapter", "DashScope适配器"), - - # API服务 - ("tradingagents.api.stock_api", "股票API"), - ("tradingagents.dataflows.stock_data_service", "股票数据服务"), - ("tradingagents.dataflows.realtime_news_utils", "实时新闻工具"), - ] - - for module_name, description in new_modules: - try: - __import__(module_name) - self.test_results["passed"].append(f"新功能导入: {description}") - print(f" ✅ {description}") - except ImportError as e: - if "No module named" in str(e): - self.test_results["warnings"].append(f"新功能导入: {description} - 可能缺少依赖") - print(f" ⚠️ {description}: 可能缺少依赖 ({e})") - else: - self.test_results["failed"].append(f"新功能导入: {description} - {e}") - print(f" ❌ {description}: {e}") - except Exception as e: - self.test_results["failed"].append(f"新功能导入: {description} - {e}") - print(f" ❌ {description}: {e}") - - def test_optimized_data_providers(self): - """测试优化的数据提供器""" - print("\n📊 测试优化数据提供器...") - - try: - # 测试美股数据提供器 - from tradingagents.dataflows.optimized_us_data import OptimizedUSDataProvider - - provider = OptimizedUSDataProvider() - self.test_results["passed"].append("数据提供器: 美股提供器初始化成功") - print(" ✅ 美股数据提供器初始化成功") - - # 测试基本方法存在 - required_methods = ['get_stock_data', '_wait_for_rate_limit', '_format_stock_data'] - for method in required_methods: - if hasattr(provider, method): - self.test_results["passed"].append(f"数据提供器: {method} 方法存在") - print(f" ✅ {method} 方法存在") - else: - self.test_results["failed"].append(f"数据提供器: {method} 方法缺失") - print(f" ❌ {method} 方法缺失") - - except Exception as e: - self.test_results["failed"].append(f"数据提供器: {e}") - print(f" ❌ 数据提供器测试失败: {e}") - - def test_config_system(self): - """测试配置系统""" - print("\n⚙️ 测试配置系统...") - - try: - # 测试默认配置 - from tradingagents.default_config import DEFAULT_CONFIG - - # 检查基本配置项 - required_configs = [ - "project_dir", "results_dir", "data_dir", - "llm_provider", "deep_think_llm", "quick_think_llm" - ] - - for config_key in required_configs: - if config_key in DEFAULT_CONFIG: - self.test_results["passed"].append(f"配置系统: {config_key} 存在") - print(f" ✅ {config_key} 配置存在") - else: - self.test_results["failed"].append(f"配置系统: {config_key} 缺失") - print(f" ❌ {config_key} 配置缺失") - - # 测试动态配置 - from tradingagents.dataflows.config import get_config, set_config - - current_config = get_config() - if current_config: - self.test_results["passed"].append("配置系统: 动态配置获取正常") - print(" ✅ 动态配置获取正常") - else: - self.test_results["failed"].append("配置系统: 动态配置获取失败") - print(" ❌ 动态配置获取失败") - - except Exception as e: - self.test_results["failed"].append(f"配置系统: {e}") - print(f" ❌ 配置系统测试失败: {e}") - - def test_main_functionality(self): - """测试主要功能""" - print("\n🚀 测试主要功能...") - - try: - # 测试主程序导入 - import main - self.test_results["passed"].append("主功能: main.py 导入成功") - print(" ✅ main.py 导入成功") - - # 测试交易图形导入 - from tradingagents.graph.trading_graph import TradingAgentsGraph - self.test_results["passed"].append("主功能: TradingAgentsGraph 导入成功") - print(" ✅ TradingAgentsGraph 导入成功") - - except Exception as e: - self.test_results["failed"].append(f"主功能: {e}") - print(f" ❌ 主功能测试失败: {e}") - - def test_documentation(self): - """测试文档完整性""" - print("\n📚 测试文档完整性...") - - doc_files = [ - "docs/README.md", - "docs/en-US/configuration_guide.md", - "docs/en-US/quick_reference.md", - "docs/en-US/prompt_templates.md", - "MERGE_SUMMARY.md" - ] - - for doc_file in doc_files: - if os.path.exists(doc_file): - self.test_results["passed"].append(f"文档: {doc_file} 存在") - print(f" ✅ {doc_file}") - else: - self.test_results["failed"].append(f"文档: {doc_file} 缺失") - print(f" ❌ {doc_file}") - - def run_all_tests(self): - """运行所有测试""" - print("🧪 开始合并后功能测试验证") - print("=" * 50) - - self.setup() - - try: - self.test_basic_imports() - self.test_cache_system() - self.test_new_features_import() - self.test_optimized_data_providers() - self.test_config_system() - self.test_main_functionality() - self.test_documentation() - - finally: - self.cleanup() - - self.print_summary() - - def print_summary(self): - """打印测试摘要""" - print("\n" + "=" * 50) - print("📋 测试结果摘要") - print("=" * 50) - - total_passed = len(self.test_results["passed"]) - total_failed = len(self.test_results["failed"]) - total_warnings = len(self.test_results["warnings"]) - total_tests = total_passed + total_failed + total_warnings - - print(f"\n📊 统计:") - print(f" 总测试项: {total_tests}") - print(f" ✅ 通过: {total_passed}") - print(f" ❌ 失败: {total_failed}") - print(f" ⚠️ 警告: {total_warnings}") - - if total_failed == 0: - print(f"\n🎉 所有核心功能测试通过！") - if total_warnings > 0: - print(f"⚠️ 有 {total_warnings} 个警告，主要是可选依赖缺失") - else: - print(f"\n❌ 有 {total_failed} 个测试失败，需要修复") - - # 详细结果 - if self.test_results["failed"]: - print(f"\n❌ 失败的测试:") - for failure in self.test_results["failed"]: - print(f" - {failure}") - - if self.test_results["warnings"]: - print(f"\n⚠️ 警告:") - for warning in self.test_results["warnings"]: - print(f" - {warning}") - - # 建议 - print(f"\n💡 建议:") - if total_failed == 0: - print(" 1. 核心功能正常，可以进行更深入的集成测试") - print(" 2. 考虑安装可选依赖以启用完整功能") - print(" 3. 运行实际的股票数据获取测试") - else: - print(" 1. 修复失败的测试项") - print(" 2. 检查依赖项安装") - print(" 3. 验证文件路径和导入") - -def main(): - """主函数""" - tester = MergedFeaturesTest() - tester.run_all_tests() - -if __name__ == "__main__": - main() diff --git a/tradingagents/default_config.py b/tradingagents/default_config.py index ebacc6f2..6c707d85 100644 --- a/tradingagents/default_config.py +++ b/tradingagents/default_config.py @@ -9,6 +9,8 @@ DEFAULT_CONFIG = { "dataflows/data_cache", ), # LLM settings + # Supported providers: "openai", "anthropic", "google", "dashscope", "ollama", "openrouter" + # For DashScope: set llm_provider="dashscope", deep_think_llm="qwen-plus", quick_think_llm="qwen-turbo" "llm_provider": "openai", "deep_think_llm": "o4-mini", "quick_think_llm": "gpt-4o-mini", diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py index 80a29e53..7e8e26f3 100644 --- a/tradingagents/graph/trading_graph.py +++ b/tradingagents/graph/trading_graph.py @@ -10,6 +10,14 @@ from langchain_openai import ChatOpenAI from langchain_anthropic import ChatAnthropic from langchain_google_genai import ChatGoogleGenerativeAI +# Import DashScope adapter if available +try: + from ..llm_adapters.dashscope_adapter import ChatDashScope + DASHSCOPE_AVAILABLE = True +except ImportError: + DASHSCOPE_AVAILABLE = False + ChatDashScope = None + from langgraph.prebuilt import ToolNode from tradingagents.agents import * @@ -65,8 +73,35 @@ class TradingAgentsGraph: self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"]) self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"]) elif self.config["llm_provider"].lower() == "google": - self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"]) - self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"]) + google_api_key = os.getenv('GOOGLE_API_KEY') + self.deep_thinking_llm = ChatGoogleGenerativeAI( + model=self.config["deep_think_llm"], + google_api_key=google_api_key, + temperature=0.1, + max_tokens=2000 + ) + self.quick_thinking_llm = ChatGoogleGenerativeAI( + model=self.config["quick_think_llm"], + google_api_key=google_api_key, + temperature=0.1, + max_tokens=2000 + ) + elif (self.config["llm_provider"].lower() == "dashscope" or + "dashscope" in self.config["llm_provider"].lower() or + "alibaba" in self.config["llm_provider"].lower()): + if not DASHSCOPE_AVAILABLE: + raise ValueError("DashScope adapter not available. Please install dashscope package: pip install dashscope") + + self.deep_thinking_llm = ChatDashScope( + model=self.config["deep_think_llm"], + temperature=0.1, + max_tokens=2000 + ) + self.quick_thinking_llm = ChatDashScope( + model=self.config["quick_think_llm"], + temperature=0.1, + max_tokens=2000 + ) else: raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")