129 lines
4.4 KiB
Python
129 lines
4.4 KiB
Python
#!/usr/bin/env python3
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"""
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PostgreSQL 数据库优化工程师智能体
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功能:
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- 使用 DeepSeek 模型进行智能分析
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- 集成 PostgreSQL MCP 服务器工具
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- 加载数据库知识库
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- 提供数据库优化建议
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"""
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import asyncio
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import os
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from typing import Optional
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from dataclasses import dataclass
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from dotenv import load_dotenv
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from agno.agent import Agent, AgentMemory
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from agno.models.deepseek import DeepSeek
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from agno.knowledge.pdf import PDFKnowledgeBase
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from agno.vectordb.lancedb import LanceDb, SearchType
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from agno.embedder.openai import OpenAIEmbedder
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from agno.knowledge.combined import CombinedKnowledgeBase
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from agno.tools.mcp import MCPTools
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from mcp.client.stdio import stdio_client
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from mcp import ClientSession, StdioServerParameters
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from agno.knowledge.website import WebsiteKnowledgeBase
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from agno.storage.agent.sqlite import SqliteAgentStorage
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from agno.memory.db.sqlite import SqliteMemoryDb
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async def main():
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load_dotenv()
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"""设置知识库"""
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local_pdf_kb = PDFKnowledgeBase(
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path="D:\\Sources\\DONGJAK-TOOLS\\pdfs\\Database Fundamentals.pdf",
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vector_db=LanceDb(
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table_name="database_fundamentals",
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uri="tmp/lancedb",
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search_type=SearchType.vector,
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embedder=OpenAIEmbedder(id="text-embedding-3-small"),
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),
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)
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# Create Website knowledge base
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website_kb = WebsiteKnowledgeBase(
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urls=["https://www.lucidchart.com/blog/database-design-best-practices"],
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max_links=10,
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vector_db=LanceDb(
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table_name="website_documents",
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uri="tmp/lancedb",
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search_type=SearchType.vector,
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embedder=OpenAIEmbedder(id="text-embedding-3-small"),
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),
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)
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knowledge_base = CombinedKnowledgeBase(
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sources=[local_pdf_kb, website_kb],
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vector_db=LanceDb(
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table_name="combined_documents",
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uri="tmp/lancedb",
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search_type=SearchType.vector,
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embedder=OpenAIEmbedder(id="text-embedding-3-small"),
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),
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)
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knowledge_base.load()
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postgres_server_params = StdioServerParameters(
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command="cmd",
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args=[
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"/c",
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"npx",
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"-y",
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"@modelcontextprotocol/server-postgres",
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"postgresql://postgres:postgres@192.168.1.7:5432/aq",
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],
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env={},
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)
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searxng_server_params = StdioServerParameters(
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command="cmd",
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args=[
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"/c",
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"npx",
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"-y",
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"https://github.com/ihor-sokoliuk/mcp-searxng.git",
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],
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env={
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"SEARXNG_URL": "https://searchxng.ailoveworld.cn",
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},
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)
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# Create a client session to connect to the MCP server
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async with (
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MCPTools(server_params=postgres_server_params) as postgres_tools,
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MCPTools(server_params=searxng_server_params) as searxng_tools,
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):
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agent = Agent(
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model=DeepSeek(id="deepseek-chat"),
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storage=SqliteAgentStorage(
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table_name="agent_sessions", db_file="tmp/agent_storage.db"
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),
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memory=AgentMemory(
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db=SqliteMemoryDb(
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table_name="agent_memory", db_file="tmp/agent_storage.db"
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),
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create_user_memories=True,
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create_session_summary=True,
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),
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# Set add_history_to_messages=true to add the previous chat history to the messages sent to the Model.
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add_history_to_messages=True,
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# Number of historical responses to add to the messages.
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num_history_responses=3,
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markdown=True,
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knowledge=knowledge_base,
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search_knowledge=True,
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show_tool_calls=True,
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tools=[postgres_tools, searxng_tools],
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session_id="1",
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# Enable the agent to read the chat history
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read_chat_history=True,
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)
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# await agent.aprint_response("我现在想要记录用户对智能体和课程的使用权限及使用情况，需要如何设计表结构", stream=True)
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await agent.aprint_response("调用get_chat_history", stream=True)
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# await agent.aprint_response("帮我分析一下aq.public数据库,并给出优化建议", stream=True)
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# await agent.aprint_response("阅读下 https://www.lucidchart.com/blog/database-design-best-practices 这篇文章", stream=True)
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if __name__ == "__main__":
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asyncio.run(main())
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