添加阿里千问模型支持
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parent
7abff0f354
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@ -1,6 +1,8 @@
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env/
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__pycache__/
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.DS_Store
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.vscode/
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tradingagents-env/
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*.csv
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src/
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eval_results/
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10
cli/main.py
10
cli/main.py
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@ -741,6 +741,16 @@ def run_analysis():
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config["deep_think_llm"] = selections["deep_thinker"]
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config["backend_url"] = selections["backend_url"]
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config["llm_provider"] = selections["llm_provider"].lower()
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# Add API key based on provider
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import os
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if config["llm_provider"] == "alibaba":
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config["api_key"] = os.getenv("DASHSCOPE_API_KEY", "")
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if not config["api_key"]:
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console.print("\n[red]Error: DASHSCOPE_API_KEY environment variable not set for Alibaba provider![/red]")
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console.print("[yellow]Please set your Alibaba API key:[/yellow]")
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console.print("[cyan]export DASHSCOPE_API_KEY='your-api-key-here'[/cyan]")
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exit(1)
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# Initialize the graph
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graph = TradingAgentsGraph(
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15
cli/utils.py
15
cli/utils.py
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@ -151,6 +151,11 @@ def select_shallow_thinking_agent(provider) -> str:
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],
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"ollama": [
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("llama3.2 local", "llama3.2"),
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],
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"alibaba": [
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("Qwen-Plus (阿里云) - 平衡性能和成本", "qwen-plus"),
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("Qwen-Max (阿里云) - 最强推理能力", "qwen-max"),
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("Qwen-Turbo (阿里云) - 快速低成本", "qwen-turbo"),
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]
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}
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@ -211,7 +216,12 @@ def select_deep_thinking_agent(provider) -> str:
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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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("qwen3", "qwen3"),
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("llama3.2 local", "llama3.2"),
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],
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"alibaba": [
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("Qwen-Plus (阿里云) - 平衡性能和成本", "qwen-plus"),
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("Qwen-Max (阿里云) - 最强推理能力", "qwen-max"),
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("Qwen-Turbo (阿里云) - 快速低成本", "qwen-turbo"),
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]
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}
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@ -245,7 +255,8 @@ def select_llm_provider() -> tuple[str, str]:
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("Anthropic", "https://api.anthropic.com/"),
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("Google", "https://generativelanguage.googleapis.com/v1"),
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("Openrouter", "https://openrouter.ai/api/v1"),
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("Ollama", "http://localhost:11434/v1"),
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("Ollama", "http://localhost:11434/v1"),
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("Alibaba", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
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]
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choice = questionary.select(
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@ -5,16 +5,24 @@ from openai import OpenAI
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class FinancialSituationMemory:
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def __init__(self, name, config):
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self.config = config
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if config["backend_url"] == "http://localhost:11434/v1":
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self.embedding = "nomic-embed-text"
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self.client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama") # Ollama client
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elif config.get("llm_provider", "").lower() == "alibaba":
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self.embedding = "text-embedding-v3" # 阿里云embedding模型
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self.client = OpenAI(
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base_url=config["backend_url"],
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api_key=config.get("api_key", "")
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) # 阿里云客户端
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else:
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self.embedding = "text-embedding-3-small"
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self.client = OpenAI()
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self.client = OpenAI() # Standard OpenAI client
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self.chroma_client = chromadb.Client(Settings(allow_reset=True))
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self.situation_collection = self.chroma_client.create_collection(name=name)
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def get_embedding(self, text):
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"""Get OpenAI embedding for a text"""
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"""Get embedding for a text using the configured client"""
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response = self.client.embeddings.create(
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model=self.embedding, input=text
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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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"data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
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"data_dir": "/Users/luosibao/Documents/Code/ScAI/FR1-data",
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"data_cache_dir": os.path.join(
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os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
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"dataflows/data_cache",
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@ -61,6 +61,18 @@ class TradingAgentsGraph:
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if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "openrouter":
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self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
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self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
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elif self.config["llm_provider"].lower() == "alibaba":
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# 阿里云需要特殊的API key配置
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self.deep_thinking_llm = ChatOpenAI(
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model=self.config["deep_think_llm"],
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base_url=self.config["backend_url"],
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api_key=self.config.get("api_key", "")
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)
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self.quick_thinking_llm = ChatOpenAI(
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model=self.config["quick_think_llm"],
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base_url=self.config["backend_url"],
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api_key=self.config.get("api_key", "")
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)
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elif self.config["llm_provider"].lower() == "anthropic":
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self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
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self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
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