agent功能开发增加MCP后端

mcp_database.py

@@ -544,3 +544,240 @@ async def get_stock_comparison(
                 "comparison_type": metric,
                 "stocks": [convert_row(row) for row in results]
             }
+
+
+async def get_user_favorite_stocks(user_id: str, limit: int = 100) -> List[Dict[str, Any]]:
+    """
+    获取用户自选股列表
+
+    Args:
+        user_id: 用户ID
+        limit: 返回条数
+
+    Returns:
+        自选股列表（包含最新行情数据）
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            # 查询用户自选股（假设有 user_favorites 表）
+            # 如果没有此表，可以根据实际情况调整
+            query = """
+                SELECT
+                    f.user_id,
+                    f.stock_code,
+                    b.SECNAME as stock_name,
+                    b.F030V as industry,
+                    t.F007N as current_price,
+                    t.F010N as change_pct,
+                    t.F012N as turnover_rate,
+                    t.F026N as pe_ratio,
+                    t.TRADEDATE as latest_trade_date,
+                    f.created_at as favorite_time
+                FROM user_favorites f
+                INNER JOIN ea_baseinfo b ON f.stock_code = b.SECCODE
+                LEFT JOIN (
+                    SELECT SECCODE, MAX(TRADEDATE) as max_date
+                    FROM ea_trade
+                    GROUP BY SECCODE
+                ) latest ON b.SECCODE = latest.SECCODE
+                LEFT JOIN ea_trade t ON b.SECCODE = t.SECCODE
+                    AND t.TRADEDATE = latest.max_date
+                WHERE f.user_id = %s AND f.is_deleted = 0
+                ORDER BY f.created_at DESC
+                LIMIT %s
+            """
+
+            await cursor.execute(query, [user_id, limit])
+            results = await cursor.fetchall()
+
+            return [convert_row(row) for row in results]
+
+
+async def get_user_favorite_events(user_id: str, limit: int = 100) -> List[Dict[str, Any]]:
+    """
+    获取用户自选事件列表
+
+    Args:
+        user_id: 用户ID
+        limit: 返回条数
+
+    Returns:
+        自选事件列表
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            # 查询用户自选事件（假设有 user_event_favorites 表）
+            query = """
+                SELECT
+                    f.user_id,
+                    f.event_id,
+                    e.title,
+                    e.description,
+                    e.event_date,
+                    e.importance,
+                    e.related_stocks,
+                    e.category,
+                    f.created_at as favorite_time
+                FROM user_event_favorites f
+                INNER JOIN events e ON f.event_id = e.id
+                WHERE f.user_id = %s AND f.is_deleted = 0
+                ORDER BY e.event_date DESC
+                LIMIT %s
+            """
+
+            await cursor.execute(query, [user_id, limit])
+            results = await cursor.fetchall()
+
+            return [convert_row(row) for row in results]
+
+
+async def add_favorite_stock(user_id: str, stock_code: str) -> Dict[str, Any]:
+    """
+    添加自选股
+
+    Args:
+        user_id: 用户ID
+        stock_code: 股票代码
+
+    Returns:
+        操作结果
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            # 检查是否已存在
+            check_query = """
+                SELECT id, is_deleted
+                FROM user_favorites
+                WHERE user_id = %s AND stock_code = %s
+            """
+            await cursor.execute(check_query, [user_id, stock_code])
+            existing = await cursor.fetchone()
+
+            if existing:
+                if existing['is_deleted'] == 1:
+                    # 恢复已删除的记录
+                    update_query = """
+                        UPDATE user_favorites
+                        SET is_deleted = 0, updated_at = NOW()
+                        WHERE id = %s
+                    """
+                    await cursor.execute(update_query, [existing['id']])
+                    return {"success": True, "message": "已恢复自选股"}
+                else:
+                    return {"success": False, "message": "该股票已在自选中"}
+
+            # 插入新记录
+            insert_query = """
+                INSERT INTO user_favorites (user_id, stock_code, created_at, updated_at, is_deleted)
+                VALUES (%s, %s, NOW(), NOW(), 0)
+            """
+            await cursor.execute(insert_query, [user_id, stock_code])
+            return {"success": True, "message": "添加自选股成功"}
+
+
+async def remove_favorite_stock(user_id: str, stock_code: str) -> Dict[str, Any]:
+    """
+    删除自选股
+
+    Args:
+        user_id: 用户ID
+        stock_code: 股票代码
+
+    Returns:
+        操作结果
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            query = """
+                UPDATE user_favorites
+                SET is_deleted = 1, updated_at = NOW()
+                WHERE user_id = %s AND stock_code = %s AND is_deleted = 0
+            """
+            result = await cursor.execute(query, [user_id, stock_code])
+
+            if result > 0:
+                return {"success": True, "message": "删除自选股成功"}
+            else:
+                return {"success": False, "message": "未找到该自选股"}
+
+
+async def add_favorite_event(user_id: str, event_id: int) -> Dict[str, Any]:
+    """
+    添加自选事件
+
+    Args:
+        user_id: 用户ID
+        event_id: 事件ID
+
+    Returns:
+        操作结果
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            # 检查是否已存在
+            check_query = """
+                SELECT id, is_deleted
+                FROM user_event_favorites
+                WHERE user_id = %s AND event_id = %s
+            """
+            await cursor.execute(check_query, [user_id, event_id])
+            existing = await cursor.fetchone()
+
+            if existing:
+                if existing['is_deleted'] == 1:
+                    # 恢复已删除的记录
+                    update_query = """
+                        UPDATE user_event_favorites
+                        SET is_deleted = 0, updated_at = NOW()
+                        WHERE id = %s
+                    """
+                    await cursor.execute(update_query, [existing['id']])
+                    return {"success": True, "message": "已恢复自选事件"}
+                else:
+                    return {"success": False, "message": "该事件已在自选中"}
+
+            # 插入新记录
+            insert_query = """
+                INSERT INTO user_event_favorites (user_id, event_id, created_at, updated_at, is_deleted)
+                VALUES (%s, %s, NOW(), NOW(), 0)
+            """
+            await cursor.execute(insert_query, [user_id, event_id])
+            return {"success": True, "message": "添加自选事件成功"}
+
+
+async def remove_favorite_event(user_id: str, event_id: int) -> Dict[str, Any]:
+    """
+    删除自选事件
+
+    Args:
+        user_id: 用户ID
+        event_id: 事件ID
+
+    Returns:
+        操作结果
+    """
+    pool = await get_pool()
+
+    async with pool.acquire() as conn:
+        async with conn.cursor(aiomysql.DictCursor) as cursor:
+            query = """
+                UPDATE user_event_favorites
+                SET is_deleted = 1, updated_at = NOW()
+                WHERE user_id = %s AND event_id = %s AND is_deleted = 0
+            """
+            result = await cursor.execute(query, [user_id, event_id])
+
+            if result > 0:
+                return {"success": True, "message": "删除自选事件成功"}
+            else:
+                return {"success": False, "message": "未找到该自选事件"}

mcp_elasticsearch.py

@@ -0,0 +1,320 @@
+"""
+Elasticsearch 连接和工具模块
+用于聊天记录存储和向量搜索
+"""
+
+from elasticsearch import Elasticsearch, helpers
+from datetime import datetime
+from typing import List, Dict, Any, Optional
+import logging
+import json
+import openai
+
+logger = logging.getLogger(__name__)
+
+# ==================== 配置 ====================
+
+# ES 配置
+ES_CONFIG = {
+    "host": "http://222.128.1.157:19200",
+    "index_chat_history": "agent_chat_history",  # 聊天记录索引
+}
+
+# Embedding 配置
+EMBEDDING_CONFIG = {
+    "api_key": "dummy",
+    "base_url": "http://222.128.1.157:18008/v1",
+    "model": "qwen3-embedding-8b",
+    "dims": 4096,  # 向量维度
+}
+
+# ==================== ES 客户端 ====================
+
+class ESClient:
+    """Elasticsearch 客户端封装"""
+
+    def __init__(self):
+        self.es = Elasticsearch([ES_CONFIG["host"]], request_timeout=60)
+        self.chat_index = ES_CONFIG["index_chat_history"]
+
+        # 初始化 OpenAI 客户端用于 embedding
+        self.embedding_client = openai.OpenAI(
+            api_key=EMBEDDING_CONFIG["api_key"],
+            base_url=EMBEDDING_CONFIG["base_url"],
+        )
+        self.embedding_model = EMBEDDING_CONFIG["model"]
+
+        # 初始化索引
+        self.create_chat_history_index()
+
+    def create_chat_history_index(self):
+        """创建聊天记录索引"""
+        if self.es.indices.exists(index=self.chat_index):
+            logger.info(f"索引 {self.chat_index} 已存在")
+            return
+
+        mappings = {
+            "properties": {
+                "session_id": {"type": "keyword"},  # 会话ID
+                "user_id": {"type": "keyword"},  # 用户ID
+                "user_nickname": {"type": "text"},  # 用户昵称
+                "user_avatar": {"type": "keyword"},  # 用户头像URL
+                "message_type": {"type": "keyword"},  # user / assistant
+                "message": {"type": "text"},  # 消息内容
+                "message_embedding": {  # 消息向量
+                    "type": "dense_vector",
+                    "dims": EMBEDDING_CONFIG["dims"],
+                    "index": True,
+                    "similarity": "cosine"
+                },
+                "plan": {"type": "text"},  # 执行计划（仅 assistant）
+                "steps": {"type": "text"},  # 执行步骤（仅 assistant）
+                "timestamp": {"type": "date"},  # 时间戳
+                "created_at": {"type": "date"},  # 创建时间
+            }
+        }
+
+        self.es.indices.create(index=self.chat_index, body={"mappings": mappings})
+        logger.info(f"创建索引: {self.chat_index}")
+
+    def generate_embedding(self, text: str) -> List[float]:
+        """生成文本向量"""
+        try:
+            if not text or len(text.strip()) == 0:
+                return []
+
+            # 截断过长文本
+            text = text[:16000] if len(text) > 16000 else text
+
+            response = self.embedding_client.embeddings.create(
+                model=self.embedding_model,
+                input=[text]
+            )
+            return response.data[0].embedding
+        except Exception as e:
+            logger.error(f"Embedding 生成失败: {e}")
+            return []
+
+    def save_chat_message(
+        self,
+        session_id: str,
+        user_id: str,
+        user_nickname: str,
+        user_avatar: str,
+        message_type: str,  # "user" or "assistant"
+        message: str,
+        plan: Optional[str] = None,
+        steps: Optional[str] = None,
+    ) -> str:
+        """
+        保存聊天消息
+
+        Args:
+            session_id: 会话ID
+            user_id: 用户ID
+            user_nickname: 用户昵称
+            user_avatar: 用户头像URL
+            message_type: 消息类型 (user/assistant)
+            message: 消息内容
+            plan: 执行计划（可选）
+            steps: 执行步骤（可选）
+
+        Returns:
+            文档ID
+        """
+        try:
+            # 生成向量
+            embedding = self.generate_embedding(message)
+
+            doc = {
+                "session_id": session_id,
+                "user_id": user_id,
+                "user_nickname": user_nickname,
+                "user_avatar": user_avatar,
+                "message_type": message_type,
+                "message": message,
+                "message_embedding": embedding if embedding else None,
+                "plan": plan,
+                "steps": steps,
+                "timestamp": datetime.now(),
+                "created_at": datetime.now(),
+            }
+
+            result = self.es.index(index=self.chat_index, body=doc)
+            logger.info(f"保存聊天记录: {result['_id']}")
+            return result["_id"]
+
+        except Exception as e:
+            logger.error(f"保存聊天记录失败: {e}")
+            raise
+
+    def get_chat_sessions(self, user_id: str, limit: int = 50) -> List[Dict[str, Any]]:
+        """
+        获取用户的聊天会话列表
+
+        Args:
+            user_id: 用户ID
+            limit: 返回数量
+
+        Returns:
+            会话列表，每个会话包含：session_id, last_message, last_timestamp
+        """
+        try:
+            # 聚合查询：按 session_id 分组，获取每个会话的最后一条消息
+            query = {
+                "query": {
+                    "term": {"user_id": user_id}
+                },
+                "aggs": {
+                    "sessions": {
+                        "terms": {
+                            "field": "session_id",
+                            "size": limit,
+                            "order": {"last_message": "desc"}
+                        },
+                        "aggs": {
+                            "last_message": {
+                                "max": {"field": "timestamp"}
+                            },
+                            "last_message_content": {
+                                "top_hits": {
+                                    "size": 1,
+                                    "sort": [{"timestamp": {"order": "desc"}}],
+                                    "_source": ["message", "timestamp", "message_type"]
+                                }
+                            }
+                        }
+                    }
+                },
+                "size": 0
+            }
+
+            result = self.es.search(index=self.chat_index, body=query)
+
+            sessions = []
+            for bucket in result["aggregations"]["sessions"]["buckets"]:
+                session_data = bucket["last_message_content"]["hits"]["hits"][0]["_source"]
+                sessions.append({
+                    "session_id": bucket["key"],
+                    "last_message": session_data["message"],
+                    "last_timestamp": session_data["timestamp"],
+                    "message_count": bucket["doc_count"],
+                })
+
+            return sessions
+
+        except Exception as e:
+            logger.error(f"获取会话列表失败: {e}")
+            return []
+
+    def get_chat_history(
+        self,
+        session_id: str,
+        limit: int = 100
+    ) -> List[Dict[str, Any]]:
+        """
+        获取指定会话的聊天历史
+
+        Args:
+            session_id: 会话ID
+            limit: 返回数量
+
+        Returns:
+            聊天记录列表
+        """
+        try:
+            query = {
+                "query": {
+                    "term": {"session_id": session_id}
+                },
+                "sort": [{"timestamp": {"order": "asc"}}],
+                "size": limit
+            }
+
+            result = self.es.search(index=self.chat_index, body=query)
+
+            messages = []
+            for hit in result["hits"]["hits"]:
+                doc = hit["_source"]
+                messages.append({
+                    "message_type": doc["message_type"],
+                    "message": doc["message"],
+                    "plan": doc.get("plan"),
+                    "steps": doc.get("steps"),
+                    "timestamp": doc["timestamp"],
+                })
+
+            return messages
+
+        except Exception as e:
+            logger.error(f"获取聊天历史失败: {e}")
+            return []
+
+    def search_chat_history(
+        self,
+        user_id: str,
+        query_text: str,
+        top_k: int = 10
+    ) -> List[Dict[str, Any]]:
+        """
+        向量搜索聊天历史
+
+        Args:
+            user_id: 用户ID
+            query_text: 查询文本
+            top_k: 返回数量
+
+        Returns:
+            相关聊天记录列表
+        """
+        try:
+            # 生成查询向量
+            query_embedding = self.generate_embedding(query_text)
+            if not query_embedding:
+                return []
+
+            # 向量搜索
+            query = {
+                "query": {
+                    "bool": {
+                        "must": [
+                            {"term": {"user_id": user_id}},
+                            {
+                                "script_score": {
+                                    "query": {"match_all": {}},
+                                    "script": {
+                                        "source": "cosineSimilarity(params.query_vector, 'message_embedding') + 1.0",
+                                        "params": {"query_vector": query_embedding}
+                                    }
+                                }
+                            }
+                        ]
+                    }
+                },
+                "size": top_k
+            }
+
+            result = self.es.search(index=self.chat_index, body=query)
+
+            messages = []
+            for hit in result["hits"]["hits"]:
+                doc = hit["_source"]
+                messages.append({
+                    "session_id": doc["session_id"],
+                    "message_type": doc["message_type"],
+                    "message": doc["message"],
+                    "timestamp": doc["timestamp"],
+                    "score": hit["_score"],
+                })
+
+            return messages
+
+        except Exception as e:
+            logger.error(f"向量搜索失败: {e}")
+            return []
+
+
+# ==================== 全局实例 ====================
+
+# 创建全局 ES 客户端
+es_client = ESClient()

mcp_server.py

@@ -17,6 +17,8 @@ import mcp_database as db
 from openai import OpenAI
 import json
 import asyncio
+import uuid
+from mcp_elasticsearch import es_client
 
 # 配置日志
 logging.basicConfig(level=logging.INFO)
@@ -135,6 +137,10 @@ class AgentChatRequest(BaseModel):
     """聊天请求"""
     message: str
     conversation_history: List[ConversationMessage] = []
+    user_id: Optional[str] = None  # 用户ID
+    user_nickname: Optional[str] = None  # 用户昵称
+    user_avatar: Optional[str] = None  # 用户头像URL
+    session_id: Optional[str] = None  # 会话ID（如果为空则创建新会话）
 
 # ==================== MCP工具定义 ====================
 
@@ -1023,7 +1029,14 @@ class MCPAgentIntegrated:
             for tool in tools
         ])
 
-        return f"""你是一个专业的金融研究助手。根据用户问题，制定详细的执行计划。
+        return f"""你是"价小前"，北京价值前沿科技公司的AI投研聊天助手。
+
+## 你的人格特征
+- **名字**: 价小前
+- **身份**: 北京价值前沿科技公司的专业AI投研助手
+- **专业领域**: 股票投资研究、市场分析、新闻解读、财务分析
+- **性格**: 专业、严谨、友好，擅长用简洁的语言解释复杂的金融概念
+- **服务宗旨**: 帮助投资者做出更明智的投资决策，提供数据驱动的研究支持
 
 ## 可用工具
 
@@ -1040,7 +1053,7 @@ class MCPAgentIntegrated:
 - 概念板块: 相同题材股票分类
 
 ## 任务
-分析用户问题，制定执行计划。返回 JSON：
+分析用户问题，制定详细的执行计划。返回 JSON：
 
 ```json
 {{
@@ -1533,7 +1546,32 @@ async def chat(request: ChatRequest):
 @app.post("/agent/chat", response_model=AgentResponse)
 async def agent_chat(request: AgentChatRequest):
     """智能代理对话端点（非流式）"""
-    logger.info(f"Agent chat: {request.message}")
+    logger.info(f"Agent chat: {request.message} (user: {request.user_id})")
+
+    # ==================== 权限检查 ====================
+    # 仅允许 max 用户使用
+    if request.user_id != "max":
+        raise HTTPException(
+            status_code=403,
+            detail="很抱歉，「价小前投研」功能目前仅对特定用户开放。如需使用，请联系管理员。"
+        )
+
+    # ==================== 会话管理 ====================
+    # 如果没有提供 session_id，创建新会话
+    session_id = request.session_id or str(uuid.uuid4())
+
+    # 保存用户消息到 ES
+    try:
+        es_client.save_chat_message(
+            session_id=session_id,
+            user_id=request.user_id or "anonymous",
+            user_nickname=request.user_nickname or "匿名用户",
+            user_avatar=request.user_avatar or "",
+            message_type="user",
+            message=request.message,
+        )
+    except Exception as e:
+        logger.error(f"保存用户消息失败: {e}")
 
     # 获取工具列表
     tools = [tool.dict() for tool in TOOLS]
@@ -1565,7 +1603,31 @@ async def agent_chat(request: AgentChatRequest):
         tool_handlers=TOOL_HANDLERS,
     )
 
-    return response
+    # 保存 Agent 回复到 ES
+    try:
+        # 将执行步骤转换为JSON字符串
+        steps_json = json.dumps(
+            [{"tool": step.tool, "result": step.result} for step in response.steps],
+            ensure_ascii=False
+        )
+
+        es_client.save_chat_message(
+            session_id=session_id,
+            user_id=request.user_id or "anonymous",
+            user_nickname=request.user_nickname or "匿名用户",
+            user_avatar=request.user_avatar or "",
+            message_type="assistant",
+            message=response.final_answer,
+            plan=response.plan,
+            steps=steps_json,
+        )
+    except Exception as e:
+        logger.error(f"保存 Agent 回复失败: {e}")
+
+    # 在响应中返回 session_id
+    response_dict = response.dict()
+    response_dict["session_id"] = session_id
+    return response_dict
 
 @app.post("/agent/chat/stream")
 async def agent_chat_stream(request: AgentChatRequest):
@@ -1610,6 +1672,81 @@ async def agent_chat_stream(request: AgentChatRequest):
         },
     )
 
+# ==================== 聊天记录管理 API ====================
+
+@app.get("/agent/sessions")
+async def get_chat_sessions(user_id: str, limit: int = 50):
+    """
+    获取用户的聊天会话列表
+
+    Args:
+        user_id: 用户ID
+        limit: 返回数量（默认50）
+
+    Returns:
+        会话列表
+    """
+    try:
+        sessions = es_client.get_chat_sessions(user_id, limit)
+        return {
+            "success": True,
+            "data": sessions,
+            "count": len(sessions)
+        }
+    except Exception as e:
+        logger.error(f"获取会话列表失败: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/agent/history/{session_id}")
+async def get_chat_history(session_id: str, limit: int = 100):
+    """
+    获取指定会话的聊天历史
+
+    Args:
+        session_id: 会话ID
+        limit: 返回数量（默认100）
+
+    Returns:
+        聊天记录列表
+    """
+    try:
+        messages = es_client.get_chat_history(session_id, limit)
+        return {
+            "success": True,
+            "data": messages,
+            "count": len(messages)
+        }
+    except Exception as e:
+        logger.error(f"获取聊天历史失败: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/agent/search")
+async def search_chat_history(user_id: str, query: str, top_k: int = 10):
+    """
+    向量搜索聊天历史
+
+    Args:
+        user_id: 用户ID
+        query: 查询文本
+        top_k: 返回数量（默认10）
+
+    Returns:
+        相关聊天记录列表
+    """
+    try:
+        results = es_client.search_chat_history(user_id, query, top_k)
+        return {
+            "success": True,
+            "data": results,
+            "count": len(results)
+        }
+    except Exception as e:
+        logger.error(f"向量搜索失败: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 # ==================== 健康检查 ====================
 
 @app.get("/health")

src/routes/routeConfig.js

@@ -157,8 +157,8 @@ export const routeConfig = [
         protection: PROTECTION_MODES.MODAL,
         layout: 'main',
         meta: {
-            title: 'AI投资助手',
+            title: '价小前投研',
-            description: '基于MCP的智能投资顾问'
+            description: '北京价值前沿科技公司的AI投研聊天助手'
         }
     },
 ];