✨ feat(main): 增加PDF知识库和MCP工具集成

- 创建PDF知识库以从本地文件加载数据并支持向量搜索
- 集成MCP工具以与PostgreSQL数据库进行交互
- 通过组合知识库实现多源知识集成

数据库优化工程师.py

@@ -20,12 +20,77 @@ if __name__ == "__main__":
     load_dotenv()
     from agno.agent import Agent, RunResponse  # noqa
     from agno.models.deepseek import DeepSeek
+    from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
+    from agno.knowledge.pdf import PDFKnowledgeBase
+    from agno.vectordb.lancedb import LanceDb, SearchType
+    from agno.embedder.openai import OpenAIEmbedder
+    from agno.knowledge.combined import CombinedKnowledgeBase
+    from agno.tools.mcp import MCPTools
+    from mcp import StdioServerParameters
 
-    agent = Agent(model=DeepSeek(id="deepseek-chat"), markdown=True)
+    # Create a knowledge base of PDFs from URLs
+    # pdf_url_kb = PDFUrlKnowledgeBase(
+    #     urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    #     # Use LanceDB as the vector database and store embeddings in the `recipes` table
+    #     vector_db=LanceDb(
+    #         table_name="recipes",
+    #         uri="tmp/lancedb",
+    #         search_type=SearchType.vector,
+    #         embedder=OpenAIEmbedder(id="text-embedding-3-small"),
+    #     ),
+    # )
+    # Create Local PDF knowledge base
+    local_pdf_kb = PDFKnowledgeBase(
+        path="D:\\Sources\\DONGJAK-TOOLS\\pdfs\\Database Fundamentals.pdf",
+        vector_db=LanceDb(
+            table_name="database_fundamentals",
+            uri="tmp/lancedb",
+            search_type=SearchType.vector,
+            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
+        ),
+    )
 
-    # Get the response in a variable
-    # run: RunResponse = agent.run("Share a 2 sentence horror story")
-    # print(run.content)
+    # Combine knowledge bases
+    knowledge_base = CombinedKnowledgeBase(
+        sources=[
+            local_pdf_kb,
+        ],
+        vector_db=LanceDb(
+            table_name="combined_documents",
+            uri="tmp/lancedb",
+            search_type=SearchType.vector,
+            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
+        ),
+    )
+    # Load the knowledge base: Comment after first run as the knowledge base is already loaded
+    knowledge_base.load()
 
-    # Print the response in the terminal
-    agent.print_response("你好")
+    server_params = StdioServerParameters(
+        command="cmd",  # 或 "uvx"，取决于你的安装方式
+        args=[
+            "/c",
+            "npx",
+            "-y",
+            "@modelcontextprotocol/server-postgres",
+            "postgresql://postgres:postgres@192.168.1.7:5432/postgres",
+        ],
+        env={},  # 可选的环境变量
+    )
+    with MCPTools(server_params=server_params) as postgres_server:
+        # 使用mcp_tools
+
+        agent = Agent(
+            model=DeepSeek(id="deepseek-chat"),
+            markdown=True,
+            knowledge=knowledge_base,
+            search_knowledge=True,
+            show_tool_calls=True,
+            tools=[postgres_server],
+        )
+
+        # Get the response in a variable
+        # run: RunResponse = agent.run("Share a 2 sentence horror story")
+        # print(run.content)
+
+        # Print the response in the terminal
+        agent.print_response("看下aq这个数据库", stream=True)