多模态Agent开发

一、架构设计：三层核心组件

graph TB
A[输入层] --> B[多模态感知]
B --> C[认知决策层]
C --> D[行动执行层]
D --> E[输出层]

subgraph 多模态感知
B1[图像OCR] --> B2[视觉描述生成]
B3[语音转文本] --> B4[语义增强]
end

subgraph 认知决策层
C1[记忆检索] --> C2[状态跟踪]
C3[任务拆解] --> C4[工具选择]
end

subgraph 行动执行层
D1[API调用] --> D2[浏览器操作]
D3[代码执行] --> D4[硬件控制]
end

二、关键技术实现

1. 视觉理解分级处理流水线

from transformers import pipeline
from PIL import Image

# 第一层：本地快速检测
object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
objects = object_detector(image)

# 第二层：关键区域细粒度识别
if "product_label" in [obj['label'] for obj in objects]:
    crop_area = calculate_crop(objects["product_label"])
    text_recognizer = pipeline("image-to-text", model="microsoft/trocr-large-printed")
    label_text = text_recognizer(image.crop(crop_area))[0]['generated_text']
    
# 第三层：复杂场景调用多模态大模型
if need_deep_analysis:
    vision_result = openai.chat.completions.create(
        model="gpt-4-vision-preview", 
        messages=[{"role": "user", "content": f"已识别标签：{label_text}，请分析..."}]
    )

2. 浏览器操作的人类行为模拟

from playwright.sync_api import sync_playwright
import numpy as np

def human_like_action(page, selector, action_type):
    element = page.query_selector(selector)
    
    # 贝塞尔曲线模拟移动轨迹
    mouse_path = generate_bezier_curve(
        start_pos=(np.random.randint(0, 100), np.random.randint(0, 100)),
        end_pos=element.bounding_box()
    )
    for point in mouse_path:
        page.mouse.move(point[0], point[1])
        time.sleep(np.random.uniform(0.01, 0.05))
    
    # 随机操作延迟
    time.sleep(np.random.uniform(0.2, 1.0))  
    
    if action_type == "click":
        page.mouse.click()
    elif action_type == "type":
        for char in text:
            element.type(char, delay=np.random.uniform(50, 150))

三、认知决策层实现

1. 动态工具选择算法

tools = [
    {"name": "product_search_api", "desc": "官方API查询商品数据"},
    {"name": "web_scraping", "desc": "网页抓取实时价格"},
    {"name": "database_query", "desc": "访问内部库存数据库"}
]

def tool_selection_agent(query):
    prompt = f"""
    任务：{query}
    可用工具：
    {json.dumps(tools, indent=2)}
    
    决策逻辑：
    1. 需实时数据 → web_scraping
    2. 涉及内部数据 → database_query
    3. 常规查询 → product_search_api
    """
    return llm.generate(prompt).strip()

2. 记忆压缩技术

from langchain.memory import ConversationSummaryBufferMemory

memory = ConversationSummaryBufferMemory(
    llm=local_llm, 
    max_token_limit=1000,
    memory_key="chat_history"
)

def update_memory(new_message):
    if memory.llm.get_num_tokens(memory.buffer) > 900:
        memory.prune()  # 摘要压缩旧对话
    memory.add_message(new_message)

四、性能优化方案

压力测试数据对比（8核32GB）

优化策略：

1. 视觉任务卸载到Edge AI芯片（Jetson Orin）
2. 决策LLM替换为DeepSeek-Coder 7B
3. 浏览器实例池化实现70%复用率

五、安全防护体系

三阶防护墙

graph LR
A[输入] --> B[预过滤层]
B --> C[执行隔离层]
C --> D[输出审计层]

subgraph 预过滤层
B1[图像NSFW检测] --> B2[敏感词过滤]
B3[操作权限校验] 
end

subgraph 执行隔离层
C1[Docker沙箱] --> C2[资源配额限制]
end

subgraph 输出审计层
D1[结果毒性检测] --> D2[操作录像回放]
end

关键代码实现：

# 沙箱执行封装
def safe_execute(action):
    with DockerSandbox(
        cpu_quota=0.5,  # 限制50%CPU
        mem_limit="512m"
    ) as sandbox:
        return sandbox.run(action)

# 实时操作录制
recorder = ActionRecorder(
    storage_path="/ops/logs",
    retention_days=30
)
recorder.start_recording()