删除 smart_agent.py
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smart_agent.py
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smart_agent.py
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import pandas as pd
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import joblib
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import json
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import re
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# ==========================================
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# 0. 基础工具类定义
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# ==========================================
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class BaseTool:
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def __init__(self, name, description):
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self.name = name
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self.description = description
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def run(self, *args, **kwargs):
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raise NotImplementedError
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# ==========================================
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# 1. 工具实现
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# ==========================================
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class MLPredictionTool(BaseTool):
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"""
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工具 1: 机器学习预测工具
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功能: 加载预训练模型，预测客户转化概率，并提供特征归因。
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"""
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def __init__(self, artifact_path='model_artifacts.pkl'):
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super().__init__("ML_Predictor", "输入客户特征，输出购买概率和关键影响因素")
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print(f"[{self.name}] 正在加载模型资产...")
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self.artifacts = joblib.load(artifact_path)
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self.model = self.artifacts['model']
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self.encoders = self.artifacts['encoders']
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self.feature_meta = self.artifacts['feature_meta']
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# 获取特征重要性，用于简单的归因解释
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self.feature_importances = pd.Series(
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self.model.feature_importances_,
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index=self.feature_meta['all_cols']
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).sort_values(ascending=False)
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def preprocess(self, customer_data):
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df = pd.DataFrame([customer_data])
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if 'duration' in df.columns: df = df.drop('duration', axis=1)
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for col, le in self.encoders.items():
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if col in df.columns:
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try:
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df[col] = le.transform(df[col])
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except:
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df[col] = 0 # 简单处理未知值
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# 补齐可能缺失的列（全0填充）并保持顺序
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for col in self.feature_meta['all_cols']:
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if col not in df.columns:
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df[col] = 0
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return df[self.feature_meta['all_cols']]
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def run(self, customer_data):
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# 1. 预处理
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X = self.preprocess(customer_data)
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# 2. 预测
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prob = float(self.model.predict_proba(X)[0][1]) # 强制转换为 python float
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# 3. 归因 (Attribution)
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# 简单逻辑：找出该客户数据中，属于 Top 5 重要特征的字段及其值
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top_features = self.feature_importances.head(5).index.tolist()
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attribution = {feat: customer_data.get(feat, 'N/A') for feat in top_features}
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return {
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"probability": round(prob, 4),
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"risk_level": "High" if prob < 0.3 else ("Medium" if prob < 0.7 else "Low"),
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"key_factors": attribution
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}
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class StrategyRetrievalTool(BaseTool):
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"""
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工具 2: 策略检索工具
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功能: 根据客户分群或意向等级，检索对应的营销话术和产品包。
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"""
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def __init__(self):
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super().__init__("Strategy_Retriever", "根据意向分检索营销策略")
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# 模拟向量数据库或规则库
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self.knowledge_base = {
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"High_Intent": {
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"segment": "VIP_Growth",
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"channel": "Personal_Call",
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"product": "大额存单/结构性存款",
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"script_template": "尊贵的{name}，鉴于您良好的{key_factor}，我们要为您推荐专属..."
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},
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"Medium_Intent": {
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"segment": "Potential_Saver",
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"channel": "SMS_Web",
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"product": "灵活理财/定投",
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"script_template": "你好，发现您对{key_factor}感兴趣，这里有一份理财攻略..."
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},
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"Low_Intent": {
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"segment": "General_Mass",
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"channel": "Email",
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"product": "货币基金/新人礼包",
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"script_template": "本月财经快讯：如何打理您的零钱..."
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}
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}
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def run(self, probability):
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if probability > 0.7:
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key = "High_Intent"
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elif probability > 0.4:
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key = "Medium_Intent"
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else:
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key = "Low_Intent"
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return self.knowledge_base[key]
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# ==========================================
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# 2. Agent 定义 (Orchestrator)
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# ==========================================
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class SalesAgent:
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def __init__(self):
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self.tools = {
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"predictor": MLPredictionTool(),
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"retriever": StrategyRetrievalTool()
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}
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def mock_llm_inference(self, prompt):
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"""
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模拟 LLM 的生成能力。
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在真实场景中，这里调用 openai.ChatCompletion.create(model="gpt-4", messages=...)
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"""
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# 从 Prompt 中解析 Context
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# 这是一个 Mock，所以我们用正则或简单的逻辑把 Prompt 里的信息“反刍”出来
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# 实际上 LLM 会进行语义理解和润色
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# 提取关键信息用于 Mock 输出
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try:
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context_str = re.search(r"【Context】(.*?)【Instruction】", prompt, re.S).group(1)
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context = json.loads(context_str)
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pred_result = context['prediction']
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strategy_result = context['strategy']
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customer_info = context['customer_raw']
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# 模拟 LLM 生成话术
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script = strategy_result['script_template'].format(
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name="客户",
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key_factor=list(pred_result['key_factors'].keys())[0]
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)
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response = {
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"thought_process": f"模型预测概率为 {pred_result['probability']}，属于 {strategy_result['segment']} 客群。已检索到对应策略，建议通过 {strategy_result['channel']} 触达。",
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"final_decision": {
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"action": strategy_result['channel'],
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"product_recommendation": strategy_result['product'],
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"personalized_script": script,
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"attribution_explanation": f"预测模型显示该客户成交概率为 {pred_result['probability']*100}%，主要受 {json.dumps(pred_result['key_factors'], ensure_ascii=False)} 等因素影响。"
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}
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}
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return json.dumps(response, ensure_ascii=False, indent=2)
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except Exception as e:
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return json.dumps({"error": f"LLM Mock Failed: {str(e)}"})
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def process_request(self, customer_data):
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print(f"\n[Agent] 收到新请求: {customer_data.get('job', 'Unknown')} | {customer_data.get('age')}岁")
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# --- Step 1: 调用 ML 工具进行预测 ---
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print(f"[Agent] 调用工具: {self.tools['predictor'].name} ...")
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pred_result = self.tools['predictor'].run(customer_data)
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print(f" >>> 预测结果: 概率={pred_result['probability']}, 关键因素={list(pred_result['key_factors'].keys())}")
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# --- Step 2: 调用 检索工具获取策略 ---
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print(f"[Agent] 调用工具: {self.tools['retriever'].name} ...")
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strategy_result = self.tools['retriever'].run(pred_result['probability'])
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print(f" >>> 检索结果: 渠道={strategy_result['channel']}, 产品={strategy_result['product']}")
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# --- Step 3: LLM 整合信息 ---
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print(f"[Agent] 请求 LLM 进行最终决策与生成...")
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# 构建 Context
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context = {
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"customer_raw": customer_data,
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"prediction": pred_result,
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"strategy": strategy_result
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}
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prompt = f"""
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你是一个智能营销助手。请根据以下上下文信息，生成结构化的营销建议。
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【Context】
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{json.dumps(context, ensure_ascii=False)}
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【Instruction】
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1. 解释模型预测结果。
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2. 结合策略库，生成具体的话术。
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3. 输出 JSON 格式。
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"""
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final_output = self.mock_llm_inference(prompt)
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return final_output
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# ==========================================
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# 3. 主程序入口
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# ==========================================
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if __name__ == "__main__":
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# 1. 准备数据
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df_raw = pd.read_csv('bank.csv')
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# 2. 初始化 Agent
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agent = SalesAgent()
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# 3. 模拟场景
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print("\n" + "="*60)
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print("场景演示: Agent 协调多个工具完成决策")
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print("="*60)
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# 场景 A: 低意向客户
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customer_a = df_raw.iloc[1].to_dict() # 假设这是低概率
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if 'deposit' in customer_a: del customer_a['deposit']
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result_a = agent.process_request(customer_a)
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print("\n[Agent Final Output]")
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print(result_a)
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print("-" * 60)
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# 场景 B: 高意向客户 (人工构造)
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customer_b = customer_a.copy()
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customer_b.update({
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'poutcome': 'success',
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'duration': 1000, # 注意工具内部会移除 duration，这里只是模拟输入
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'contact': 'cellular',
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'month': 'oct'
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})
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result_b = agent.process_request(customer_b)
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print("\n[Agent Final Output]")
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print(result_b)
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