feat: 四层架构数据分析 Agent
- Layer 1 Planner: 意图规划，将问题转为结构化分析计划 - Layer 2 Explorer: 自适应探索循环，多轮迭代动态生成 SQL - Layer 3 InsightEngine: 异常检测 + 主动洞察 - Layer 4 ContextManager: 多轮对话上下文记忆 安全设计：AI 只看 Schema + 聚合结果，不接触原始数据。 支持任意 OpenAI 兼容 API（OpenAI / Ollama / DeepSeek / vLLM）
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OpenClaw Agent
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demo.py
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demo.py
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"""
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演示脚本 —— 创建示例数据，运行四层架构分析
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"""
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import os
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import sys
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import sqlite3
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import random
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from datetime import datetime, timedelta
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sys.path.insert(0, os.path.dirname(__file__))
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from config import DB_PATH, LLM_CONFIG
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from agent import DataAnalysisAgent
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def create_demo_data(db_path: str):
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"""创建示例数据库：电商订单 + 用户 + 商品"""
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if os.path.exists(db_path):
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os.remove(db_path)
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conn = sqlite3.connect(db_path)
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cur = conn.cursor()
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cur.execute("""
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CREATE TABLE users (
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id INTEGER PRIMARY KEY,
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name TEXT NOT NULL,
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region TEXT NOT NULL,
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tier TEXT NOT NULL,
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created_at TEXT NOT NULL
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)
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""")
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cur.execute("""
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CREATE TABLE products (
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id INTEGER PRIMARY KEY,
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name TEXT NOT NULL,
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category TEXT NOT NULL,
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price REAL NOT NULL
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)
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""")
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cur.execute("""
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CREATE TABLE orders (
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id INTEGER PRIMARY KEY,
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user_id INTEGER NOT NULL,
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product_id INTEGER NOT NULL,
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amount REAL NOT NULL,
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quantity INTEGER NOT NULL,
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status TEXT NOT NULL,
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region TEXT NOT NULL,
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order_date TEXT NOT NULL,
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FOREIGN KEY (user_id) REFERENCES users(id),
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FOREIGN KEY (product_id) REFERENCES products(id)
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)
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""")
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regions = ["华东", "华南", "华北", "西南", "华中", "东北"]
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tiers = ["普通", "银卡", "金卡", "钻石"]
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categories = ["电子产品", "服饰", "食品", "家居", "图书", "美妆"]
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statuses = ["已完成", "已发货", "待发货", "已取消", "退款中"]
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users = []
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for i in range(1, 2001):
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reg = random.choice(regions)
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tier = random.choices(tiers, weights=[50, 25, 15, 10])[0]
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created = datetime(2024, 1, 1) + timedelta(days=random.randint(0, 400))
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users.append((i, f"用户{i}", reg, tier, created.strftime("%Y-%m-%d")))
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cur.executemany("INSERT INTO users VALUES (?, ?, ?, ?, ?)", users)
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products = []
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for i in range(1, 201):
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cat = random.choice(categories)
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price = round(random.uniform(9.9, 9999.99), 2)
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products.append((i, f"商品{i}", cat, price))
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cur.executemany("INSERT INTO products VALUES (?, ?, ?, ?)", products)
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orders = []
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for i in range(1, 50001):
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uid = random.randint(1, 2000)
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pid = random.randint(1, 200)
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qty = random.choices([1, 2, 3, 4, 5], weights=[50, 25, 15, 7, 3])[0]
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status = random.choices(statuses, weights=[60, 15, 10, 10, 5])[0]
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region = random.choices(regions, weights=[35, 25, 15, 10, 10, 5])[0]
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order_date = datetime(2025, 1, 1) + timedelta(days=random.randint(0, 440))
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cur.execute("SELECT price FROM products WHERE id = ?", (pid,))
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price = cur.fetchone()[0]
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amount = round(price * qty, 2)
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orders.append((i, uid, pid, amount, qty, status, region, order_date.strftime("%Y-%m-%d")))
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cur.executemany("INSERT INTO orders VALUES (?, ?, ?, ?, ?, ?, ?, ?)", orders)
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conn.commit()
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conn.close()
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print(f"✅ 示例数据库已创建: {db_path}")
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print(f" - users: 2,000 条")
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print(f" - products: 200 条")
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print(f" - orders: 50,000 条")
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def main():
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print("=" * 60)
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print(" 🤖 数据分析 Agent —— 四层架构自适应分析")
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print("=" * 60)
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if not LLM_CONFIG["api_key"]:
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print("\n⚠️ 未配置 LLM_API_KEY，请设置环境变量：")
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print()
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print(" # OpenAI")
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print(" export LLM_API_KEY=sk-xxx")
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print(" export LLM_BASE_URL=https://api.openai.com/v1")
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print(" export LLM_MODEL=gpt-4o")
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print()
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print(" # Ollama (本地)")
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print(" export LLM_API_KEY=ollama")
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print(" export LLM_BASE_URL=http://localhost:11434/v1")
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print(" export LLM_MODEL=qwen2.5-coder:7b")
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print()
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sys.exit(1)
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print(f"\n🔗 LLM: {LLM_CONFIG['model']} @ {LLM_CONFIG['base_url']}")
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if not os.path.exists(DB_PATH):
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create_demo_data(DB_PATH)
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else:
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print(f"\n📂 使用已有数据库: {DB_PATH}")
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# 初始化 Agent（自动加载 Schema）
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agent = DataAnalysisAgent(DB_PATH)
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print("\n" + "=" * 60)
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print(" ⬆️ AI 只看到 Schema：表结构 + 数据画像")
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print(" ⬇️ 四层架构分析：规划 → 探索 → 洞察 → 报告")
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print("=" * 60)
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questions = [
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"各地区的销售表现如何？帮我全面分析一下",
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"不同商品类别的销售情况和利润贡献",
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"整体订单的完成率怎么样？有没有什么异常需要关注？",
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]
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for q in questions:
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try:
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report = agent.analyze(q)
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print("\n" + report)
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print("\n" + "~" * 60)
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except Exception as e:
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print(f"\n❌ 分析出错: {e}")
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import traceback
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traceback.print_exc()
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# 最终审计
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print("\n📋 会话审计:")
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print(agent.get_audit())
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print("\n📋 分析历史:")
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print(agent.get_history())
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print("\n✅ 演示完成！")
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if __name__ == "__main__":
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main()
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