♻️ refactor(代码): 重构数据库优化器逻辑

- 移除不必要的DatabaseOptimizer类及其配置
- 添加WebsiteKnowledgeBase以支持网页知识库
- 优化MCPTools的使用，合并多个工具的初始化

数据库优化工程师.py

@@ -24,55 +24,9 @@ from agno.knowledge.combined import CombinedKnowledgeBase
 from agno.tools.mcp import MCPTools
 from mcp.client.stdio import stdio_client
 from mcp import ClientSession, StdioServerParameters
+from agno.knowledge.website import WebsiteKnowledgeBase
 
-
-@dataclass
-class DatabaseOptimizerConfig:
-    """数据库优化器配置"""
-
-    pdf_path: str = "D:\\Sources\\DONGJAK-TOOLS\\pdfs\\Database Fundamentals.pdf"
-    db_connection: str = "postgresql://postgres:postgres@192.168.1.7:5432/postgres"
-    model_id: str = "deepseek-chat"
-    vector_db_path: str = "tmp/lancedb"
-
-
-class DatabaseOptimizer:
-    """PostgreSQL 数据库优化引擎"""
-
-    def __init__(self, config: Optional[DatabaseOptimizerConfig] = None):
-        self.config = config or DatabaseOptimizerConfig()
-        self._load_environment()
-        self.knowledge_base = self._setup_knowledge_base()
-        self.postgres_tools = self._setup_postgres_tools()
-        self.agent = self._create_agent()
-
-    def _setup_postgres_tools(self) -> MCPTools:
-        """设置 PostgreSQL 工具"""
-        server_params = StdioServerParameters(
-            command="cmd",
-            args=[
-                "/c",
-                "npx",
-                "-y",
-                "@modelcontextprotocol/server-postgres",
-                self.config.db_connection,
-            ],
-            env={},
-        )
-        # Create a client session to connect to the MCP server
-        with stdio_client(server_params) as (read, write):
-            with ClientSession(read, write) as session:
-                agent = create_filesystem_agent(session)
-
-                # Run the agent
-                agent.print_response(message, stream=True)
-        return MCPTools(server_params=server_params)
-
-    def analyze_database(self, query: str, stream: bool = True):
-        """分析数据库"""
-        with self.postgres_tools:
-            self.agent.print_response(query, stream=stream)
-
+ 
 
 async def main():
     load_dotenv()
@@ -86,9 +40,20 @@ async def main():
             embedder=OpenAIEmbedder(id="text-embedding-3-small"),
         ),
     )
+    # Create Website knowledge base
+    website_kb = WebsiteKnowledgeBase(
+        urls=["https://www.lucidchart.com/blog/database-design-best-practices"],
+        max_links=10,
+        vector_db=LanceDb(
+            table_name="website_documents",
+            uri="tmp/lancedb",
+            search_type=SearchType.vector,
+            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
+        ),
+    )
 
     knowledge_base = CombinedKnowledgeBase(
-        sources=[local_pdf_kb],
+        sources=[local_pdf_kb, website_kb],
         vector_db=LanceDb(
             table_name="combined_documents",
             uri="tmp/lancedb",
@@ -98,31 +63,45 @@ async def main():
     )
     knowledge_base.load()
 
-    server_params = StdioServerParameters(
+    postgres_server_params = StdioServerParameters(
         command="cmd",
         args=[
             "/c",
             "npx",
             "-y",
             "@modelcontextprotocol/server-postgres",
-            "postgresql://postgres:postgres@192.168.1.7:5432/postgres",
+            "postgresql://postgres:postgres@192.168.1.7:5432/aq",
         ],
         env={},
     )
+
+    searxng_server_params = StdioServerParameters(
+        command="cmd",
+        args=[
+            "/c",
+            "npx",
+            "-y",
+            "https://github.com/ihor-sokoliuk/mcp-searxng.git",
+        ],
+        env={
+            "SEARXNG_URL": "https://searchxng.ailoveworld.cn",
+        },
+    )
     # Create a client session to connect to the MCP server
-    async with stdio_client(server_params) as (read, write):
-        async with ClientSession(read, write) as session:
-            postgres_tools = MCPTools(session=session)
-            await postgres_tools.initialize()
-            agent = Agent(
-                model=DeepSeek(id="deepseek-chat"),
-                markdown=True,
-                knowledge=knowledge_base,
-                search_knowledge=True,
-                show_tool_calls=True,
-                tools=[postgres_tools],
-            )
-            await agent.aprint_response("看下aq这个数据库", stream=True)
+    async with (
+        MCPTools(server_params=postgres_server_params) as postgres_tools,
+        MCPTools(server_params=searxng_server_params) as searxng_tools,
+    ):
+        agent = Agent(
+            model=DeepSeek(id="deepseek-chat"),
+            markdown=True,
+            knowledge=knowledge_base,
+            search_knowledge=True,
+            show_tool_calls=True,
+            tools=[postgres_tools, searxng_tools],
+        )
+        await agent.aprint_response("帮我分析一下aq.public数据库,并给出优化建议", stream=True)
+        # await agent.aprint_response("阅读下 https://www.lucidchart.com/blog/database-design-best-practices 这篇文章", stream=True)
 
 
 if __name__ == "__main__":