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测试mcp基座
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@@ -0,0 +1,163 @@
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import socket
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import json
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import time
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from openai import OpenAI
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# 初始化 DeepSeek 客户端
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client = OpenAI(
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api_key="sk-420190f448fe41158c4e2ccff90e35ce",
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base_url="https://api.deepseek.com"
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)
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MODEL_NAME = "deepseek-chat"
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def fetch_and_translate_tools():
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"""向 CAD 基座发送 tools/list，并转换为大模型认识的格式"""
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try:
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cad_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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cad_socket.connect(("127.0.0.1", 8080))
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# 1. 向 MCP Server 发送标准的 tools/list 请求
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req = {
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"jsonrpc": "2.0",
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"id": 100,
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"method": "tools/list"
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}
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cad_socket.sendall(json.dumps(req).encode('utf-8'))
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response_data = cad_socket.recv(8192)
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cad_socket.close()
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mcp_response = json.loads(response_data.decode('utf-8'))
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if "result" not in mcp_response or "tools" not in mcp_response["result"]:
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print("[Error] 无法从 CAD 获取工具列表")
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return []
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llm_tools = []
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# 2. 遍历 CAD 返回的工具，将其翻译为 DeepSeek/OpenAI 格式
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for mcp_tool in mcp_response["result"]["tools"]:
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llm_tools.append({
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"type": "function",
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"function": {
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"name": mcp_tool["name"],
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"description": mcp_tool["description"],
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# 核心转换：MCP 的 inputSchema 等价于 LLM 的 parameters
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"parameters": mcp_tool["inputSchema"]
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}
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})
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print(f"[Init] 成功从 CAD 动态加载了 {len(llm_tools)} 个工具！")
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return llm_tools
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except Exception as e:
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print(f"[Error] 获取工具列表失败: {e}")
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return []
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def call_cad_mcp_server(tool_name, arguments):
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"""底层 TCP 通信保持不变"""
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try:
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cad_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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cad_socket.connect(("127.0.0.1", 8080))
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req = {
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"jsonrpc": "2.0", "id": 1, "method": "tools/call",
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"params": { "name": tool_name, "arguments": arguments }
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}
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cad_socket.sendall(json.dumps(req).encode('utf-8'))
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response_data = b""
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while True:
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chunk = cad_socket.recv(8192)
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response_data += chunk
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if len(chunk) < 8192: break
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cad_socket.close()
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return json.loads(response_data.decode('utf-8'))
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except Exception as e:
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print(f"[Error] 连接 CAD 基座失败: {e}")
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return None
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def run_agent(user_prompt):
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print("=== 🚀 CAD 具身智能 Agent 启动 ===")
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# 动态向 CAD 请求可用工具，彻底解耦！
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tools = fetch_and_translate_tools()
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if not tools:
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print("没有可用的工具，Agent 退出。")
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return
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# 核心心智设定：教它怎么形成视觉闭环
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system_prompt = """你是一个高阶 AutoCAD 视觉检查专家。
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请严格遵循以下工作流：
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1. 第一步永远是调用 `get_viewport_screenshot` 观察当前画面。
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2. 仔细评估图像。如果目标物体（如图元、文字）太小导致无法确信细节，请调用 `zoom_window_normalized` 放大该局部区域。
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3. 【关键指令】：每次执行缩放操作（zoom）后，你必须在下一回合再次调用 `get_viewport_screenshot` 获取放大后的新截图！
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4. 反复观察和放大，直到你 100% 看清细节，再用自然语言向用户输出最终结论。"""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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]
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MAX_TURNS = 8 # 熔断机制：最多允许思考 8 个回合，防止无限套娃
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for turn in range(MAX_TURNS):
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print(f"\n--- [第 {turn + 1} 回合] 思考中 ---")
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response = client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages,
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tools=tools
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)
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assistant_message = response.choices[0].message
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messages.append(assistant_message) # 记录思维路径
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# 1. 检查是否需要执行物理动作
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if assistant_message.tool_calls:
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for tool_call in assistant_message.tool_calls:
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tool_name = tool_call.function.name
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args = json.loads(tool_call.function.arguments)
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print(f"[动作决定] ⚡ 调用工具: {tool_name}")
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if args: print(f" 参数: {args}")
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# 执行 CAD 通信
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cad_res = call_cad_mcp_server(tool_name, args)
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time.sleep(0.5) # 给予 CAD 渲染刷新窗口的时间缓冲
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# 处理执行结果，并准备发回给 LLM 的观测报告
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tool_result_content = []
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if cad_res and "result" in cad_res:
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for item in cad_res["result"]["content"]:
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if item["type"] == "text":
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tool_result_content.append({"type": "text", "text": item["text"]})
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elif item["type"] == "image":
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base64_img = item["data"]
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mime = item.get("mimeType", "image/png")
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tool_result_content.append({
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"type": "image_url",
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"image_url": {"url": f"data:{mime};base64,{base64_img}"}
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})
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print("[观测反馈] 📸 已将最新 CAD 屏幕画面传回视觉中枢。")
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else:
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tool_result_content.append({"type": "text", "text": "CAD 工具执行失败或无响应。"})
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# 必须向大模型提交 Tool 返回结果
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"content": tool_result_content
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})
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# 2. 如果不调工具，说明任务已完成，输出最终自然语言结论
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else:
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print("\n==================================")
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print(f"🎯 [最终结论]:\n{assistant_message.content}")
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print("==================================")
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break
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else:
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print("\n[警告] 达到最大思考回合数限制，Agent 强行中止。")
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if __name__ == "__main__":
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run_agent("请仔细检查当前图纸，告诉我图纸中心那些小圆的内部，是否还包含了更小的同心圆？如果看不清，请务必放大确认。")
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@@ -0,0 +1,127 @@
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import socket
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import json
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from openai import OpenAI
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# 1. 初始化 DeepSeek 客户端
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# 注意：务必确保已经执行过 pip install openai
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client = OpenAI(
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api_key="sk-420190f448fe41158c4e2ccff90e35ce",
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base_url="https://api.deepseek.com" # 核心修改：将网关指向 DeepSeek 服务器
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)
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# 核心修改：使用 DeepSeek 的主模型。
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# (注: DeepSeek-V4/V4.1 的视觉支持已集成，具体模型名请以你 DeepSeek 后台显示的可用模型为准)
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MODEL_NAME = "deepseek-flash"
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def call_cad_mcp_server(tool_name, arguments):
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"""封装与 AutoCAD MCP 基座的 TCP 通信"""
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try:
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cad_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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cad_socket.connect(("127.0.0.1", 8080))
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req = {
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"jsonrpc": "2.0",
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"id": 1,
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"method": "tools/call",
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"params": {
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"name": tool_name,
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"arguments": arguments
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}
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}
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cad_socket.sendall(json.dumps(req).encode('utf-8'))
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response_data = b""
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while True:
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chunk = cad_socket.recv(8192)
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response_data += chunk
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if len(chunk) < 8192:
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break
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cad_socket.close()
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return json.loads(response_data.decode('utf-8'))
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except Exception as e:
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print(f"[Error] 连接 CAD 基座失败: {e}")
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return None
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def main():
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print("=== DeepSeek CAD Vision Agent 启动 ===")
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# 2. 定义 MCP 工具 (Tool Calling)
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_viewport_screenshot",
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"description": "获取当前 AutoCAD 视口的实时截图。当用户询问图纸上的视觉特征、数量或位置时，必须先调用此工具获取画面。"
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}
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}
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]
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# 3. 初始化历史
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messages = [
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{"role": "system", "content": "你是一个专业的 AutoCAD 视觉审查助手。必须先使用截图工具观察图纸，再回答用户问题。"},
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{"role": "user", "content": "请看看当前 CAD 屏幕上，我一共画了几个圆？"}
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]
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print("\n[DeepSeek] 正在思考如何完成任务...")
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# === 第一回合：DeepSeek 思考并决定调用工具 ===
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response = client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages,
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tools=tools
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)
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assistant_message = response.choices[0].message
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messages.append(assistant_message)
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# 检查 DeepSeek 是否发起了 Tool Call
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if assistant_message.tool_calls:
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tool_call = assistant_message.tool_calls[0]
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tool_name = tool_call.function.name
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print(f"[DeepSeek] 决定调用 CAD 工具: {tool_name}")
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print(f"[CAD] 正在执行截图，请稍候...")
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# === 与 CAD 基座通信 ===
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cad_res = call_cad_mcp_server(tool_name, {})
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if cad_res and "result" in cad_res:
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content_array = cad_res["result"]["content"]
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base64_img = ""
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mime_type = "image/png"
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for item in content_array:
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if item["type"] == "image":
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base64_img = item["data"]
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mime_type = item.get("mimeType", "image/png")
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print("[CAD] 截图成功！正在将视觉数据传回大模型神经中枢...")
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# 4. 将 Base64 图片按标准多模态格式塞回历史
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"content": [
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{"type": "text", "text": "这是 AutoCAD 当前界面的实时截图。请根据图片回答用户的问题。"},
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{"type": "image_url", "image_url": {"url": f"data:{mime_type};base64,{base64_img}"}}
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]
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})
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# === 第二回合：DeepSeek “看”图并回答 ===
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print("[DeepSeek] 正在进行视觉分析...")
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final_response = client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages
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)
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print("\n==================================")
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print(f"[DeepSeek 最终回答]:\n{final_response.choices[0].message.content}")
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print("==================================")
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else:
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print("[Error] CAD 工具执行失败。")
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else:
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print(f"[DeepSeek 盲猜]: {assistant_message.content}")
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if __name__ == "__main__":
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main()
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@@ -3,74 +3,157 @@
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#include <Windows.h>
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#include "tchar.h"
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#include "rxregsvc.h"
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#include <gdiplus.h>
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#pragma comment(lib, "gdiplus.lib")
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#pragma comment(lib, "rxapi.lib")
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#pragma comment(linker, "/export:acrxGetApiVersion,PRIVATE")
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class mcp_Tool_DrawCircleCpp : public mcp_Tool
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namespace
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{
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// 辅助函数：Base64 编码
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std::string Base64Encode(const unsigned char *data, size_t length)
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{
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static const char encoding_table[] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
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size_t out_len = 4 * ((length + 2) / 3);
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std::string ret(out_len, '\0');
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size_t i;
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char *p = const_cast<char *>(ret.c_str());
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for (i = 0; i < length - 2; i += 3)
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{
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*p++ = encoding_table[(data[i] >> 2) & 0x3F];
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*p++ = encoding_table[((data[i] & 0x3) << 4) | ((int)(data[i + 1] & 0xF0) >> 4)];
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*p++ = encoding_table[((data[i + 1] & 0xF) << 2) | ((int)(data[i + 2] & 0xC0) >> 6)];
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*p++ = encoding_table[data[i + 2] & 0x3F];
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}
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if (i < length)
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{
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*p++ = encoding_table[(data[i] >> 2) & 0x3F];
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if (i == (length - 1))
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{
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*p++ = encoding_table[((data[i] & 0x3) << 4)];
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*p++ = '=';
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}
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else
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{
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*p++ = encoding_table[((data[i] & 0x3) << 4) | ((int)(data[i + 1] & 0xF0) >> 4)];
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*p++ = encoding_table[((data[i + 1] & 0xF) << 2)];
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}
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*p++ = '=';
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}
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return ret;
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}
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// 辅助函数：获取 GDI+ 图像编码器的 Clsid (用于转 PNG)
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int GetEncoderClsid(const WCHAR *format, CLSID *pClsid)
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{
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UINT num = 0, size = 0;
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Gdiplus::GetImageEncodersSize(&num, &size);
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if (size == 0) return -1;
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Gdiplus::ImageCodecInfo *pImageCodecInfo = (Gdiplus::ImageCodecInfo *)(malloc(size));
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if (pImageCodecInfo == NULL) return -1;
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Gdiplus::GetImageEncoders(num, size, pImageCodecInfo);
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for (UINT j = 0; j < num; ++j)
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{
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if (_tcscmp(pImageCodecInfo[j].MimeType, format) == 0)
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{
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*pClsid = pImageCodecInfo[j].Clsid;
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free(pImageCodecInfo);
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return j;
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}
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}
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free(pImageCodecInfo);
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return -1;
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}
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}
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class mcp_Tool_ViewportScreenshot: public mcp_Tool
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{
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public:
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virtual nlohmann::json Execute(const nlohmann::json &args) override
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{
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// 1. 从大模型传入的参数中提取坐标和半径
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double x = args.value("cx", 0.0);
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double y = args.value("cy", 0.0);
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double r = args.value("r", 100.0);
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// 1. 获取 AutoCAD 当前文档的绘图区句柄
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HWND hWnd = adsw_acadDocWnd();
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if (!hWnd)
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return mcp_Tool_Utility::make_error(-32000, L"无法获取CAD绘图区窗口句柄");
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// 2. 获取当前活动文档
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AcApDocument* pDoc = acDocManager->curDocument();
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if (pDoc == nullptr)
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// 2. 初始化 GDI+
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Gdiplus::GdiplusStartupInput gdiplusStartupInput;
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ULONG_PTR gdiplusToken;
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Gdiplus::GdiplusStartup(&gdiplusToken, &gdiplusStartupInput, NULL);
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std::string base64Result;
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bool success = false;
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// 3. 开始 Windows GDI 抓屏逻辑 (使用花括号限定生命周期)
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{
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return { { "error", { { "code", -32000 }, { "message", "当前没有打开的 CAD 文档" } } } };
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RECT rc;
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GetClientRect(hWnd, &rc);
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int width = rc.right - rc.left;
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int height = rc.bottom - rc.top;
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HDC hdcScreen = GetDC(hWnd);
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HDC hdcMem = CreateCompatibleDC(hdcScreen);
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HBITMAP hBitmap = CreateCompatibleBitmap(hdcScreen, width, height);
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HBITMAP hOldBitmap = (HBITMAP)SelectObject(hdcMem, hBitmap);
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// 将屏幕内容块拷贝到内存位图中
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BitBlt(hdcMem, 0, 0, width, height, hdcScreen, 0, 0, SRCCOPY);
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// 将 GDI 位图转换为 GDI+ 位图
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Gdiplus::Bitmap bmp(hBitmap, NULL);
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|
||||
// 创建内存流，将位图以 PNG 格式保存到内存，而非硬盘
|
||||
IStream *pStream = nullptr;
|
||||
if (CreateStreamOnHGlobal(NULL, TRUE, &pStream) == S_OK)
|
||||
{
|
||||
CLSID pngClsid;
|
||||
GetEncoderClsid(L"image/png", &pngClsid);
|
||||
|
||||
if (bmp.Save(pStream, &pngClsid, NULL) == Gdiplus::Ok)
|
||||
{
|
||||
// 从内存流中读取二进制数据
|
||||
STATSTG stat;
|
||||
pStream->Stat(&stat, STATFLAG_NONAME);
|
||||
ULONG size = stat.cbSize.LowPart;
|
||||
|
||||
std::vector<BYTE> buffer(size);
|
||||
LARGE_INTEGER liZero = {};
|
||||
pStream->Seek(liZero, STREAM_SEEK_SET, NULL);
|
||||
|
||||
ULONG bytesRead;
|
||||
pStream->Read(buffer.data(), size, &bytesRead);
|
||||
|
||||
// 转换为 Base64
|
||||
base64Result = Base64Encode(buffer.data(), size);
|
||||
success = true;
|
||||
}
|
||||
pStream->Release();
|
||||
}
|
||||
|
||||
// 3. 极其重要：因为调用发起者是隐式窗口消息，并非标准 CAD 命令，必须显式锁文档！
|
||||
acDocManager->lockDocument(pDoc);
|
||||
AcDbDatabase* pDb = pDoc->database();
|
||||
|
||||
AcDbBlockTable* pBlockTable = nullptr;
|
||||
Acad::ErrorStatus es = pDb->getBlockTable(pBlockTable, AcDb::kForRead);
|
||||
|
||||
if (es == Acad::eOk)
|
||||
{
|
||||
AcDbBlockTableRecord* pModelSpace = nullptr;
|
||||
es = pBlockTable->getAt(ACDB_MODEL_SPACE, pModelSpace, AcDb::kForWrite);
|
||||
|
||||
if (es == Acad::eOk)
|
||||
{
|
||||
// 在底层 C++ 层面构造实体对象
|
||||
AcGePoint3d center(x, y, 0.0);
|
||||
AcGeVector3d normal(0.0, 0.0, 1.0); // Z轴法线
|
||||
AcDbCircle* pCircle = new AcDbCircle(center, normal, r);
|
||||
|
||||
// 追加到模型空间
|
||||
AcDbObjectId circleId;
|
||||
pModelSpace->appendAcDbEntity(circleId, pCircle);
|
||||
|
||||
// 释放对象
|
||||
pCircle->close();
|
||||
pModelSpace->close();
|
||||
}
|
||||
pBlockTable->close();
|
||||
// 清理 GDI 资源
|
||||
SelectObject(hdcMem, hOldBitmap);
|
||||
DeleteObject(hBitmap);
|
||||
DeleteDC(hdcMem);
|
||||
ReleaseDC(hWnd, hdcScreen);
|
||||
}
|
||||
|
||||
// 4. 解锁文档并刷新显示
|
||||
acDocManager->unlockDocument(pDoc);
|
||||
acedUpdateDisplay();
|
||||
// 4. 关闭 GDI+
|
||||
Gdiplus::GdiplusShutdown(gdiplusToken);
|
||||
|
||||
// 5. 返回标准 MCP 结果，如果失败可以根据 es 的值返回 make_error
|
||||
if (es == Acad::eOk)
|
||||
// 5. 按照 MCP 多模态协议返回数据
|
||||
if (success)
|
||||
{
|
||||
return mcp_Tool_Utility::make_text_result(L"C++底层接口已成功在模型空间生成圆形！");
|
||||
return mcp_Tool_Utility::make_mixed_result(L"已成功截取 AutoCAD 当前视口画面。", base64Result, "image/png");
|
||||
}
|
||||
else
|
||||
{
|
||||
return mcp_Tool_Utility::make_error((int)es, L"ObjectARX 内部错误");
|
||||
return mcp_Tool_Utility::make_error(-32001, L"截图或图像编码失败");
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
// 使用宏导出 C 风格工厂函数，供基座的 LoadLibrary 解析
|
||||
EXPORT_MCP_TOOL(mcp_Tool_DrawCircleCpp, CreateDrawCircleToolCpp)
|
||||
EXPORT_MCP_TOOL(mcp_Tool_ViewportScreenshot, CreateViewportScreenshotTool)
|
||||
|
||||
|
||||
@@ -0,0 +1,41 @@
|
||||
import socket
|
||||
import json
|
||||
|
||||
def test_fake_llm_zoom():
|
||||
print("=== 模拟 LLM 发起缩放指令测试 ===")
|
||||
|
||||
try:
|
||||
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
||||
client.connect(("127.0.0.1", 8080))
|
||||
|
||||
# 伪造大模型生成的 tool_call 报文
|
||||
req = {
|
||||
"jsonrpc": "2.0",
|
||||
"id": 999,
|
||||
"method": "tools/call",
|
||||
"params": {
|
||||
"name": "zoom_window_normalized",
|
||||
"arguments": {
|
||||
"x1": 0.4,
|
||||
"y1": 0.4,
|
||||
"x2": 0.6,
|
||||
"y2": 0.6
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
print(f"\n[发送] 请求参数:\n{json.dumps(req['params'], indent=2)}")
|
||||
client.sendall(json.dumps(req).encode('utf-8'))
|
||||
|
||||
response_data = client.recv(4096)
|
||||
|
||||
print(f"\n[接收] CAD 基座返回:")
|
||||
print(json.dumps(json.loads(response_data.decode('utf-8')), indent=2, ensure_ascii=False))
|
||||
|
||||
client.close()
|
||||
|
||||
except Exception as e:
|
||||
print(f"测试失败: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_fake_llm_zoom()
|
||||
+30
-51
@@ -1,64 +1,43 @@
|
||||
import socket
|
||||
import json
|
||||
import time
|
||||
import base64
|
||||
|
||||
def send_request(req_data, silent=False):
|
||||
try:
|
||||
def test_screenshot():
|
||||
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
||||
client.connect(("127.0.0.1", 8080))
|
||||
|
||||
# 发送 UTF-8 编码的 JSON 请求
|
||||
client.sendall(json.dumps(req_data).encode('utf-8'))
|
||||
|
||||
# 接收并解码结果
|
||||
response = client.recv(4096)
|
||||
|
||||
if not silent:
|
||||
print(">>> 收到回复:")
|
||||
print(json.dumps(json.loads(response.decode('utf-8')), indent=2, ensure_ascii=False))
|
||||
print("-" * 50)
|
||||
|
||||
client.close()
|
||||
except Exception as e:
|
||||
print(f"连接失败: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("=== 测试 1: 验证 C++ DLL 工具调用 ===")
|
||||
request_call = {
|
||||
"jsonrpc": "2.0",
|
||||
"id": 4,
|
||||
"method": "tools/call",
|
||||
"params": {
|
||||
"name": "draw_circle_cpp",
|
||||
"arguments": {
|
||||
"cx": 2000.0,
|
||||
"cy": 2000.0,
|
||||
"r": 500.0
|
||||
}
|
||||
}
|
||||
}
|
||||
send_request(request_call)
|
||||
|
||||
# 稍作停顿，方便观察 CAD 屏幕
|
||||
time.sleep(1)
|
||||
|
||||
print("\n=== 测试 2: C++ 并发写入压力测试 (生成10个同心圆) ===")
|
||||
for i in range(10):
|
||||
radius = 100.0 + i * 50.0
|
||||
req = {
|
||||
"jsonrpc": "2.0",
|
||||
"id": 100 + i,
|
||||
"id": 5,
|
||||
"method": "tools/call",
|
||||
"params": {
|
||||
"name": "draw_circle_cpp",
|
||||
"arguments": {
|
||||
"cx": 4000.0,
|
||||
"cy": 2000.0,
|
||||
"r": radius
|
||||
"name": "get_viewport_screenshot",
|
||||
"arguments": {}
|
||||
}
|
||||
}
|
||||
}
|
||||
# 连续快速发送，不打印详细返回以模拟极限并发
|
||||
send_request(req, silent=True)
|
||||
client.sendall(json.dumps(req).encode('utf-8'))
|
||||
|
||||
print(">>> 并发指令发送完毕，请检查 CAD 屏幕。")
|
||||
# 图片 base64 报文可能很大，需要循环接收直到拿完
|
||||
response_data = b""
|
||||
while True:
|
||||
chunk = client.recv(8192)
|
||||
response_data += chunk
|
||||
if len(chunk) < 8192:
|
||||
break
|
||||
|
||||
client.close()
|
||||
|
||||
resp_json = json.loads(response_data.decode('utf-8'))
|
||||
|
||||
# 解析并保存图片
|
||||
content_list = resp_json["result"]["content"]
|
||||
for item in content_list:
|
||||
if item["type"] == "image":
|
||||
base64_data = item["data"]
|
||||
image_bytes = base64.b64decode(base64_data)
|
||||
with open("test_screenshot.png", "wb") as f:
|
||||
f.write(image_bytes)
|
||||
print("截图已保存为 test_screenshot.png，请在当前目录查看！")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_screenshot()
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 243 KiB |
Reference in new issue
Block a user