feat: 为三个美股 AI Agent 添加 /chat 端点，支持 LLM 智能对话

- stock_quote_agent: 添加 chat 功能，可分析行情数据并给出投资建议
- stock_news_agent: 添加 chat 功能，可解读新闻并分析市场影响
- stock_analysis_agent: 添加 chat 功能，可进行技术分析并提供交易信号

Chat 端点使用方法:
POST /chat
{
  "message": "请帮我分析 AAPL 行情",
  "api_key": "your-llm-api-key"
}

LLM 配置支持环境变量:
- LLM_BASE_URL: LLM 服务地址
- LLM_API_KEY: API 密钥
- LLM_MODEL: 模型名称

agent_templates/agents/stock_analysis_agent/stock_analysis_agent.py

@@ -36,6 +36,11 @@ SERVICE_PORT = int(os.getenv("SERVICE_PORT", "8080"))
 POD_NAME = os.getenv("POD_NAME", "stock-analysis-agent")
 USER_ID = os.getenv("USER_ID", "")
 
+# LLM 配置
+LLM_BASE_URL = os.getenv("LLM_BASE_URL", "https://litellm.graystone-fb459c5d.southeastasia.azurecontainerapps.io/v1")
+LLM_API_KEY = os.getenv("LLM_API_KEY", "")
+LLM_MODEL = os.getenv("LLM_MODEL", "taiji/gpt-4o-mini")
+
 # FastAPI 应用
 app = FastAPI(
     title="Stock Analysis Agent",
@@ -123,6 +128,20 @@ class HealthResponse(BaseModel):
     timestamp: str
 
 
+class ChatRequest(BaseModel):
+    """Chat 请求"""
+    message: str = Field(..., description="用户消息")
+    api_key: Optional[str] = Field(None, description="LLM API Key")
+    user_id: Optional[str] = Field(None, description="用户ID")
+
+
+class ChatResponse(BaseModel):
+    """Chat 响应"""
+    response: str
+    data: Optional[Dict[str, Any]] = None
+    timestamp: str
+
+
 # ==================== 生命周期 ====================
 
 @app.on_event("startup")
@@ -563,6 +582,100 @@ async def stock_screener(
     }
 
 
+# ==================== Chat 功能 ====================
+
+async def chat_with_llm(message: str, context: str, api_key: str) -> str:
+    """调用 LLM 生成响应"""
+    try:
+        async with aiohttp.ClientSession() as session:
+            payload = {
+                "model": LLM_MODEL,
+                "messages": [
+                    {
+                        "role": "system", 
+                        "content": """你是一个专业的美股技术分析师。你可以：
+1. 分析股票技术指标（SMA, RSI, MACD, 布林带等）
+2. 判断股票趋势（看涨/看跌/中性）
+3. 提供买卖信号和投资建议
+4. 评估风险等级
+
+请根据提供的技术分析数据，用简洁专业的语言回答用户问题。注意：投资有风险，建议仅供参考。"""
+                    },
+                    {
+                        "role": "user", 
+                        "content": f"技术分析数据:\n{context}\n\n用户问题: {message}"
+                    }
+                ],
+                "max_tokens": 600,
+                "temperature": 0.7
+            }
+            headers = {
+                "Authorization": f"Bearer {api_key}",
+                "Content-Type": "application/json"
+            }
+            
+            async with session.post(
+                f"{LLM_BASE_URL}/chat/completions",
+                json=payload,
+                headers=headers,
+                timeout=aiohttp.ClientTimeout(total=30)
+            ) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    return data.get("choices", [{}])[0].get("message", {}).get("content", "抱歉，无法生成回复")
+                else:
+                    error = await response.text()
+                    logger.error(f"LLM 请求失败: {response.status} - {error}")
+                    return f"LLM 服务错误: {response.status}"
+    except Exception as e:
+        logger.error(f"LLM 调用失败: {e}")
+        return f"调用失败: {str(e)}"
+
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    """智能对话 - 获取技术分析并提供投资建议"""
+    api_key = request.api_key or LLM_API_KEY
+    if not api_key:
+        raise HTTPException(status_code=400, detail="请提供 api_key 参数或设置 LLM_API_KEY 环境变量")
+    
+    # 从消息中提取股票代码
+    import re
+    symbols = re.findall(r'\b([A-Z]{1,5})\b', request.message.upper())
+    common_words = {"I", "A", "THE", "IS", "IT", "TO", "OF", "AND", "FOR", "IN", "ON", "AT", "BY", "BUY", "SELL"}
+    symbols = [s for s in symbols if s not in common_words][:3]
+    
+    if not symbols:
+        symbols = ["AAPL"]  # 默认分析苹果
+    
+    # 获取技术分析数据
+    analysis_data = []
+    for symbol in symbols:
+        analysis = await analyze_stock(symbol)
+        if analysis:
+            analysis_data.append(analysis)
+    
+    # 构建上下文
+    if analysis_data:
+        context = "\n".join([
+            f"{a['symbol']}: 价格${a['current_price']:.2f}, 趋势:{a['trend']}, "
+            f"信号:{a['signal']}, RSI:{a['indicators']['rsi']:.1f}, "
+            f"风险:{a['risk_level']}"
+            for a in analysis_data
+        ])
+    else:
+        context = "暂无技术分析数据"
+    
+    # 调用 LLM 生成回复
+    llm_response = await chat_with_llm(request.message, context, api_key)
+    
+    return ChatResponse(
+        response=llm_response,
+        data={"analysis": analysis_data, "symbols": symbols},
+        timestamp=datetime.utcnow().isoformat()
+    )
+
+
 # ==================== 主入口 ====================
 
 def main():

agent_templates/agents/stock_news_agent/stock_news_agent.py

@@ -39,6 +39,11 @@ USER_ID = os.getenv("USER_ID", "")
 # News API (可选)
 NEWS_API_KEY = os.getenv("NEWS_API_KEY", "")
 
+# LLM 配置
+LLM_BASE_URL = os.getenv("LLM_BASE_URL", "https://litellm.graystone-fb459c5d.southeastasia.azurecontainerapps.io/v1")
+LLM_API_KEY = os.getenv("LLM_API_KEY", "")
+LLM_MODEL = os.getenv("LLM_MODEL", "taiji/gpt-4o-mini")
+
 # FastAPI 应用
 app = FastAPI(
     title="Stock News Agent",
@@ -105,6 +110,20 @@ class HealthResponse(BaseModel):
     timestamp: str
 
 
+class ChatRequest(BaseModel):
+    """Chat 请求"""
+    message: str = Field(..., description="用户消息")
+    api_key: Optional[str] = Field(None, description="LLM API Key")
+    user_id: Optional[str] = Field(None, description="用户ID")
+
+
+class ChatResponse(BaseModel):
+    """Chat 响应"""
+    response: str
+    data: Optional[Dict[str, Any]] = None
+    timestamp: str
+
+
 # ==================== 生命周期 ====================
 
 @app.on_event("startup")
@@ -390,6 +409,96 @@ async def get_trending_news():
     )
 
 
+# ==================== Chat 功能 ====================
+
+async def chat_with_llm(message: str, context: str, api_key: str) -> str:
+    """调用 LLM 生成响应"""
+    try:
+        async with aiohttp.ClientSession() as session:
+            payload = {
+                "model": LLM_MODEL,
+                "messages": [
+                    {
+                        "role": "system", 
+                        "content": """你是一个专业的美股新闻分析师。你可以：
+1. 获取和分析股票相关新闻
+2. 解读市场动态和公司公告
+3. 提供新闻情感分析和市场影响评估
+
+请根据提供的新闻数据，用简洁专业的语言回答用户问题。"""
+                    },
+                    {
+                        "role": "user", 
+                        "content": f"最新新闻:\n{context}\n\n用户问题: {message}"
+                    }
+                ],
+                "max_tokens": 500,
+                "temperature": 0.7
+            }
+            headers = {
+                "Authorization": f"Bearer {api_key}",
+                "Content-Type": "application/json"
+            }
+            
+            async with session.post(
+                f"{LLM_BASE_URL}/chat/completions",
+                json=payload,
+                headers=headers,
+                timeout=aiohttp.ClientTimeout(total=30)
+            ) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    return data.get("choices", [{}])[0].get("message", {}).get("content", "抱歉，无法生成回复")
+                else:
+                    error = await response.text()
+                    logger.error(f"LLM 请求失败: {response.status} - {error}")
+                    return f"LLM 服务错误: {response.status}"
+    except Exception as e:
+        logger.error(f"LLM 调用失败: {e}")
+        return f"调用失败: {str(e)}"
+
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    """智能对话 - 获取新闻并提供分析"""
+    api_key = request.api_key or LLM_API_KEY
+    if not api_key:
+        raise HTTPException(status_code=400, detail="请提供 api_key 参数或设置 LLM_API_KEY 环境变量")
+    
+    # 从消息中提取关键词
+    import re
+    symbols = re.findall(r'\b([A-Z]{1,5})\b', request.message.upper())
+    common_words = {"I", "A", "THE", "IS", "IT", "TO", "OF", "AND", "FOR", "IN", "ON", "AT", "BY", "NEWS", "WHAT"}
+    symbols = [s for s in symbols if s not in common_words][:3]
+    
+    # 获取相关新闻
+    news_data = []
+    if symbols:
+        for symbol in symbols:
+            data = await fetch_yahoo_news(symbol=symbol, limit=3)
+            news_data.extend(data)
+    else:
+        news_data = await fetch_yahoo_news(query="stock market", limit=5)
+    
+    # 构建上下文
+    if news_data:
+        context = "\n".join([
+            f"- {item['title']} ({item['source']}, {item['published_at'][:10]})"
+            for item in news_data[:5]
+        ])
+    else:
+        context = "暂无相关新闻"
+    
+    # 调用 LLM 生成回复
+    llm_response = await chat_with_llm(request.message, context, api_key)
+    
+    return ChatResponse(
+        response=llm_response,
+        data={"news_count": len(news_data), "symbols": symbols},
+        timestamp=datetime.utcnow().isoformat()
+    )
+
+
 # ==================== 主入口 ====================
 
 def main():

agent_templates/agents/stock_quote_agent/stock_quote_agent.py

@@ -40,6 +40,11 @@ USER_ID = os.getenv("USER_ID", "")
 RAPIDAPI_KEY = os.getenv("RAPIDAPI_KEY", "")
 YAHOO_FINANCE_HOST = "yahoo-finance15.p.rapidapi.com"
 
+# LLM 配置
+LLM_BASE_URL = os.getenv("LLM_BASE_URL", "https://litellm.graystone-fb459c5d.southeastasia.azurecontainerapps.io/v1")
+LLM_API_KEY = os.getenv("LLM_API_KEY", "")
+LLM_MODEL = os.getenv("LLM_MODEL", "taiji/gpt-4o-mini")
+
 # FastAPI 应用
 app = FastAPI(
     title="Stock Quote Agent",
@@ -105,6 +110,20 @@ class HealthResponse(BaseModel):
     timestamp: str
 
 
+class ChatRequest(BaseModel):
+    """Chat 请求"""
+    message: str = Field(..., description="用户消息")
+    api_key: Optional[str] = Field(None, description="LLM API Key")
+    user_id: Optional[str] = Field(None, description="用户ID")
+
+
+class ChatResponse(BaseModel):
+    """Chat 响应"""
+    response: str
+    data: Optional[Dict[str, Any]] = None
+    timestamp: str
+
+
 # ==================== 生命周期 ====================
 
 @app.on_event("startup")
@@ -336,6 +355,104 @@ async def get_popular_stocks():
     return await batch_get_quotes(request)
 
 
+async def chat_with_llm(message: str, context: str, api_key: str) -> str:
+    """调用 LLM 生成响应"""
+    try:
+        async with aiohttp.ClientSession() as session:
+            payload = {
+                "model": LLM_MODEL,
+                "messages": [
+                    {
+                        "role": "system", 
+                        "content": """你是一个专业的美股分析助手。你可以：
+1. 查询股票实时行情（价格、涨跌幅、成交量）
+2. 分析股票数据并给出建议
+3. 解答关于美股市场的问题
+
+请根据提供的股票数据，用简洁专业的语言回答用户问题。"""
+                    },
+                    {
+                        "role": "user", 
+                        "content": f"当前股票数据:\n{context}\n\n用户问题: {message}"
+                    }
+                ],
+                "max_tokens": 500,
+                "temperature": 0.7
+            }
+            headers = {
+                "Authorization": f"Bearer {api_key}",
+                "Content-Type": "application/json"
+            }
+            
+            async with session.post(
+                f"{LLM_BASE_URL}/chat/completions",
+                json=payload,
+                headers=headers,
+                timeout=aiohttp.ClientTimeout(total=30)
+            ) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    return data.get("choices", [{}])[0].get("message", {}).get("content", "抱歉，无法生成回复")
+                else:
+                    error = await response.text()
+                    logger.error(f"LLM 请求失败: {response.status} - {error}")
+                    return f"LLM 服务错误: {response.status}"
+    except Exception as e:
+        logger.error(f"LLM 调用失败: {e}")
+        return f"调用失败: {str(e)}"
+
+
+def extract_symbols_from_message(message: str) -> List[str]:
+    """从消息中提取股票代码"""
+    import re
+    # 匹配常见美股代码格式（1-5个大写字母）
+    symbols = re.findall(r'\b([A-Z]{1,5})\b', message.upper())
+    # 过滤常见词汇
+    common_words = {"I", "A", "THE", "IS", "IT", "TO", "OF", "AND", "FOR", "IN", "ON", "AT", "BY"}
+    return [s for s in symbols if s not in common_words][:5]  # 最多5个
+
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    """智能对话 - 支持自然语言查询股票信息"""
+    api_key = request.api_key or LLM_API_KEY
+    if not api_key:
+        raise HTTPException(status_code=400, detail="请提供 api_key 参数或设置 LLM_API_KEY 环境变量")
+    
+    # 从消息中提取股票代码
+    symbols = extract_symbols_from_message(request.message)
+    
+    # 如果没有提取到，默认查询热门股票
+    if not symbols:
+        symbols = ["AAPL", "TSLA", "NVDA"]
+    
+    # 获取股票数据
+    stock_data = []
+    for symbol in symbols:
+        result = await fetch_stock_quote(symbol)
+        if result.get("success"):
+            stock_data.append(result)
+    
+    # 构建上下文
+    if stock_data:
+        context = "\n".join([
+            f"{d['symbol']} ({d['name']}): ${d['price']:.2f}, 涨跌: {d['change_percent']:+.2f}%, "
+            f"成交量: {d['volume']:,}, 52周范围: ${d.get('low_52week', 0):.2f}-${d.get('high_52week', 0):.2f}"
+            for d in stock_data
+        ])
+    else:
+        context = "暂无股票数据"
+    
+    # 调用 LLM 生成回复
+    llm_response = await chat_with_llm(request.message, context, api_key)
+    
+    return ChatResponse(
+        response=llm_response,
+        data={"stocks": stock_data, "symbols_detected": symbols},
+        timestamp=datetime.utcnow().isoformat()
+    )
+
+
 # ==================== 主入口 ====================
 
 def main():

app.py

@@ -389,8 +389,8 @@ async def create_agent(request: CreateAgentRequest, db: Session = Depends(get_db
             config_data=config_data
         )
         
-        # 步骤3: 获取服务端口
+        # 步骤3: 获取服务端口（从数据库动态获取）
-        service_port = k8s_manager.TEMPLATE_PORTS.get(request.template)
+        service_port = template_manager.get_port(request.template)
         
         # 步骤4: 创建 LoadBalancer Service（AKS 会自动分配外网 IP）
         service_info = None

k8s_manager.py

@@ -331,10 +331,6 @@ class K8sManager:
         "microsoft_learn_agent": 8000,
         "aws_docs_mcp": 8000,
         "google_mcp": 8000,
-        # 美股 Agent
-        "stock_quote_agent": 8080,
-        "stock_news_agent": 8080,
-        "stock_analysis_agent": 8080,
     }
     
     # 模板所需环境变量说明