Merge remote-tracking branch 'github/main'

Author: wenfeng.wf

component/mydba/README.md

@@ -77,7 +77,15 @@ uv sync --inexact
  data_dir = /usr/local/mydba/vector_store
  ```
 
-2. **Initialize Agent**
+2. **Create Log Directory**
+ - The log directory can be found in the configuration: [log].dir
+ - Default path: /usr/local/mydba/logs
+
+ ```shell
+ mkdir /usr/local/mydba/logs
+ ```
+
+3. **Initialize Agent**
  - Execute the following command to initialize the Agent. Ensure you have correctly configured the **`config_app.ini`** file and replace `xxxxxx` with your Alibaba Cloud account AK/SK.
 
  ```shell
@@ -90,7 +98,7 @@ uv sync --inexact
  --rds_access_key xxxxxx # Replace with your Alicloud account secret
  ```
 
-3. **Add Self-Built Database**
+4. **Add Self-Built Database**
  - Execute the following command to add a self-built database. Ensure you have correctly configured the **`config_app.ini`** file and replace `--db_info` parameters with actual database connection details.
 
  ```shell
@@ -101,7 +109,7 @@ uv sync --inexact
  --db_info 'mysql####127.0.0.1##3306##root##123456##utf8mb4##mybase' # Database connection info, pay attention to the escape of special characters
  ```
 
-4. **Initialize RAG Tool**
+5. **Initialize RAG Tool**
  - Execute the following command to initialize the RAG tool. Ensure you have correctly configured the **`config_app.ini`** file and added the **self-built database**.
 
  ```shell
@@ -138,4 +146,4 @@ uv sync --inexact
 
 ## Contact Us
 
-- Welcome joining the DingTalk group for feedback, refer to the README.md of RDS MCP for details.
+- Welcome joining the DingTalk group for feedback, refer to the <a href="../../README.md">README.md</a> of RDS MCP for details.

component/mydba/README_CN.md

@@ -76,7 +76,15 @@ uv sync --inexact
  data_dir = /usr/local/mydba/vector_store
  ```
 
-2. **初始化 Agent**
+2. **创建日志目录**
+ - 日志目录可以查看配置: [log].dir
+ - 默认路径：`/usr/local/mydba/logs`
+
+ ```shell
+ mkdir /usr/local/mydba/logs
+ ```
+
+3. **初始化 Agent**
  - 执行以下命令以初始化 Agent。请确保您已经正确配置了 **`config_app.ini`** 文件，并用您的阿里云账号替换 `xxxxxx`。
 
  ```shell
@@ -89,7 +97,7 @@ uv sync --inexact
  --rds_access_key xxxxxx # 替换为您的阿里云账号密钥
  ```
 
-3. **添加自建数据库**
+4. **添加自建数据库**
  - 执行以下命令以添加自建数据库。请确保您已正确配置 **`config_app.ini`** 文件，并根据实际情况替换 `--db_info` 参数中的数据库连接信息。
 
  ```shell
@@ -100,7 +108,7 @@ uv sync --inexact
  --db_info 'mysql####127.0.0.1##3306##root##123456##utf8mb4##mybase' # 数据库连接信息，注意特殊字符的转义
  ```
 
-4. **初始化 RAG 工具**
+5. **初始化 RAG 工具**
  - 执行以下命令以初始化 RAG 工具。请确保您已经正确配置了 **`config_app.ini`** 文件，并添加了**自建数据库**。
 
  ```shell
@@ -137,4 +145,4 @@ uv sync --inexact
 
 ## 联系我们
 
-- 向上查看 RDS MCP 的 README.md，加入钉钉群。
+- 向上查看 RDS MCP 的 <a href="../../README_CN.md">README_CN.md</a>，加入钉钉群。

component/mydba/init_config.py

@@ -14,36 +14,52 @@
 from mydba.app.config.mcp_tool import Transport
 from mydba.app.config.settings import settings as app_settings
 from mydba.app.database.base_database import BaseDatabases
-from mydba.app.prompt import ask_table, reflection, router, using_tool
+from mydba.app.prompt import ask_table, chat, rds_agent, reflection, router
 from mydba.common import encryption
 from mydba.common.global_settings import global_settings
 
 def get_agent_config() -> List[Dict]:
- """获取 agent 的配置信息，**编辑此部分内容，定制 agent**"""
+ """
+ 获取 agent 的配置信息，**编辑此部分内容，定制 agent**<br/>
+ 配置项中，prompts 为提示词模版，包含字段:
+ 1) system: 系统提示词模版，
+ 2) user: 用户提示词模版，在需要改写用户提示词的场景下使用，例如：REFLECTION 模式、ROUTER 模式，
+ 3) reflection_system: 反思提示词模版，仅在 REFLECTION 模式下使用，
+ 4) reflection_user: 反思提示词模版，仅在 REFLECTION 模式下使用，
+ 5) condition: 条件提示词(List)，在意图识别时，描述意图的约束条件，
+ 6) shot: 样本提示词(List)，在意图识别时，提供意图样本
+ Returns:
+ List[Dict]: Agent 的配置信息列表
+ """
  main_agent = {
  "name": "main_agent",
  "mode": AgentMode.ROUTER,
  "intent": "识别意图",
  "intent_description": "识别用户的意图，并路由请求到相关 Agent",
  "prompts": {
+ # 系统提示词模版
  "system": router.SYSTEM_PROMPT,
- "act": router.ACT_PROMPT
+ # 用户提示词模版，意图识别时会改写用户指令
+ "user": router.USER_PROMPT
  },
  "is_main": True,
  "is_default": False
  }
- rds_agent = {
+ rds_agent_ = {
  "name": "rds_agent",
  "mode": AgentMode.USING_TOOL,
  "intent": "阿里云RDS管理",
  "intent_description": "进行阿里云 RDS 数据库的管理运维，或者对阿里云 RDS 数据库进行问题诊断",
  "prompts": {
- "system": using_tool.SYSTEM_PROMPT,
+ # 系统提示词模版
+ "system": rds_agent.SYSTEM_PROMPT,
+ # 相关的意图约束条件，集成进意图识别提示词里，用于提升意图识别的准确度
  "condition": 
  [
  "用户明确希望进行阿里云 RDS 数据库相关的操作时，才能归类到阿里云RDS管理",
  "用户希望对阿里云 RDS 数据库进行问题诊断时，才能归类到阿里云RDS管理"
  ],
+ # 相关的意图样例，集成进意图识别提示词里，用于提升意图识别的准确度
  "shot": 
  [
  "查下张北有多少RDS实例",
@@ -63,11 +79,14 @@ def get_agent_config() -> List[Dict]:
  "intent": "数据查询",
  "intent_description": "帮助生成查询计划，执行数据库查询，最后完成数据的统计和分析",
  "prompts": {
+ # 系统提示词模版
  "system": ask_table.SYSTEM_PROMPT,
+ # 相关的意图约束条件，集成进意图识别提示词里，用于提升意图识别的准确度
  "condition": 
  [
  "用户希望进行数据计算和统计时，要归类到数据查询"
  ],
+ # 相关的意图样例，集成进意图识别提示词里，用于提升意图识别的准确度
  "shot": 
  [
  "查询集群信息"
@@ -83,19 +102,20 @@ def get_agent_config() -> List[Dict]:
  }
  default_agent = {
  "name": "default_agent",
- "mode": AgentMode.REFLECTION,
+ "mode": AgentMode.CHAT,
  "intent": "默认",
  "intent_description": "无法匹配用户意图，使用此默认项",
- "prompts": {"system": reflection.SYSTEM_PROMPT, "act": reflection.ACT_PROMPT, "reflection": reflection.REFLECTION_PROMPT},
+ "prompts": {"system": chat.SYSTEM_PROMPT},
  "is_main": False,
  "is_default": True
  }
- agents = [main_agent, rds_agent, ask_table_agent, default_agent]
+ agents = [main_agent, rds_agent_, ask_table_agent, default_agent]
  return agents
 
 def get_mcp_config() -> List[Dict]:
  """获取 mcp 服务的配置信息，**编辑此部分内容，添加工具**"""
- base_dir = os.path.dirname(os.path.abspath(__file__)) + os.path.sep + 'mydba' + os.path.sep + 'mcp'
+ # 目前 RDS MCP 采用代码的相对路径进行配置，如果不符合请手动调整
+ base_dir = os.path.dirname(os.path.abspath(__file__)) + os.path.sep + '..' + os.path.sep + '..' + os.path.sep + '..'
  aliyun_rds_dir = os.path.join(base_dir, 'alibabacloud-rds-openapi-mcp-server', 'src', 'alibabacloud_rds_openapi_mcp_server')
  mcp_aliyun_rds = {
  "name": "rds-openapi-mcp-server",
@@ -463,10 +483,10 @@ def print_args(args: Namespace) -> None:
  for key, value in args.__dict__.items():
  # 过滤敏感信息
  if key.startswith('rds_access'):
- value = '******' if value else value
+ value = mask_info(value)
  if key == 'db_info':
  v = value.split('##')
- v[5] = '******' if v[5] else v[5]
+ v[5] = mask_info(v[5])
  value = '##'.join(v)
  # 打印参数
  if isinstance(value, str):
@@ -478,6 +498,13 @@ def print_args(args: Namespace) -> None:
  else:
  print(f" {key}: {value}")
 
+def mask_info(info: Optional[str]) -> Optional[str]:
+ if not info:
+ return info
+ if len(info) > 4:
+ return info[:2] + '*' * (len(info) - 4) + info[-2:]
+ return info[:1]+ "**"
+
 if __name__ == "__main__":
  args = parse_arguments()
  print_args(args)

component/mydba/mydba/app/agent/base.py

@@ -9,7 +9,7 @@
 from mydba.app.message import memory_history
 from mydba.app.message.message import Message, ToolCall
 from mydba.app.message.memory_history import MemoryInfo
-from mydba.common.session import request_context
+from mydba.common.session import get_context
 
 def cleanup_decorator(func):
  def wrapper(self, *args, **kwargs):
@@ -30,7 +30,7 @@ class BaseAgent(ABC, BaseModel):
  intent_description: Optional[str] = Field(None, description="意图描述")
  is_main: bool = Field(..., description="是否主 Agent")
  memory: List[Message] = Field(default_factory=list, description="本次处理过程中的短期记忆")
- prompt_patterns: Dict[str, str] = Field(default_factory=dict)
+ prompt_patterns: Dict[str, str] = Field(default_factory=dict, description="提示词模版，不同类型的 Agent 依赖的提示词模版会有不同")
  llm: LLM = Field(..., description="LLM 实例")
 
  @abstractmethod
@@ -51,7 +51,7 @@ async def get_history_memory(self) -> List[MemoryInfo]:
  Returns:
  list: Agent 的历史 memory。
  """
- context = request_context.get()
+ context = get_context()
  start_time = datetime.now() - timedelta(minutes=30)
  context_memory = await memory_history.get_memory(user_name=context.user_name, session=context.session,
  agent_name=self.name, start_time=start_time)
@@ -72,7 +72,7 @@ async def save_memory_history(self,
  assistant_tool_calls (List[ToolCall]): 大模型返回的工具调用。
  tool_contents (List[Dict[str, str]): 工具调用的结果，结果信息的格式为 {"tool_call_id": "xxx", "content": "xxx"}。
  """
- context = request_context.get()
+ context = get_context()
  await memory_history.save_memory(
  MemoryInfo(
  time=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
@@ -120,10 +120,12 @@ def create_agent(agent_info: AgentInfo, llm: LLM) -> "BaseAgent":
  Returns:
  BaseAgent: Agent 实例
  """
+ from mydba.app.agent.chat import ChatAgent
  from mydba.app.agent.router import RouterAgent
  from mydba.app.agent.reflection import ReflectionAgent
  from mydba.app.agent.using_tool import UsingToolAgent
  agent_factory = {
+ AgentMode.CHAT.value: ChatAgent,
  AgentMode.ROUTER.value: RouterAgent,
  AgentMode.REFLECTION.value: ReflectionAgent,
  AgentMode.USING_TOOL.value: UsingToolAgent,

component/mydba/mydba/app/agent/chat.py

@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+from pydantic import BaseModel, Field
+from typing import List, Optional
+from mydba.app.agent.base import BaseAgent, cleanup_decorator
+from mydba.app.message.memory_history import MemoryInfo
+from mydba.app.message.message import Message
+from mydba.common.logger import logger
+from mydba.common.session import get_context
+
+class ChatAgent(BaseAgent, BaseModel):
+ """
+ 普通对话型 Agent
+ """
+ system_message: Optional[Message] = Field(None, description="对话 Agent 的系统提示信息")
+ 
+ def __init__(self, **data):
+ super().__init__(**data)
+ prompts = data.get("prompts", {})
+ self.prompt_patterns["system"] = prompts.get("system")
+ self.system_message = Message.system_message(self.prompt_patterns["system"])
+
+ @cleanup_decorator
+ async def run(self, query: str, context_memory: Optional[List[MemoryInfo]] = None) -> str:
+ logger.info(f"[{self.name}] start to query: {query}")
+ content = await self._execute_model(query, context_memory)
+ if content is None:
+ logger.error(f"[{self.name}] query model failed, query: {query}")
+ raise Exception(f"Agent({self.name}) query model failed, query: {query}")
+ #logger.info(f"[{self.name}] query over, result: {content}")
+ await self.save_memory_history(system_content=self.system_message.content,
+ user_content=query, assistant_content=content)
+ return content
+
+ async def _execute_model(self, query: str, context_memory: Optional[List[MemoryInfo]]) -> str:
+ context = get_context()
+ messages = self.format_memory(context_memory) if context_memory else []
+ messages.append(Message.user_message(query))
+ result = await self.llm.ask(
+ messages=messages,
+ system_msgs=[self.system_message],
+ stream=context.detail_info,
+ )
+ return result