利用过程奖励模型，实现企业级AI智能体的DeepSeek Moment

第一步，确定上周的日期范围；第二步，确定华东大区的系统ID；第三步，确定销售额字段为Sales，且位于具体的某个宽表；第四步，组装成完整的SQL语句

SELECT     sale_date, SUM(quantity) AS total_salesFROM     salesWHERE     -- 筛选出上周的记录    YEAR(sale_date) = YEAR(CURDATE() - INTERVAL 1 WEEK)     AND WEEK(sale_date) = WEEK(CURDATE() - INTERVAL 1 WEEK)GROUP BY     sale_dateORDER BY     sale_date;

import torchfrom transformers import BertTokenizer, BertModel
class DialogueRewardModel(torch.nn.Module):    def __init__(self):        super().__init__()        self.bert = BertModel.from_pretrained("bert-base-uncased")        self.intent_head = torch.nn.Linear(768, 3)  # 3种意图        self.retrieval_score = torch.nn.Linear(768, 1)        self.fluency_head = torch.nn.Linear(768, 1)
    def forward(self, user_query, bot_response, retrieved_doc):        # 编码用户输入        query_embed = self.bert(**user_query).last_hidden_state.mean(dim=1)
        # 阶段1：意图理解奖励        intent_logits = self.intent_head(query_embed)        intent_reward = torch.nn.functional.cross_entropy(intent_logits, true_intent_label)
        # 阶段2：检索奖励        doc_embed = self.bert(**retrieved_doc).last_hidden_state.mean(dim=1)        retrieval_sim = torch.cosine_similarity(query_embed, doc_embed, dim=1)
        # 阶段3：生成奖励        response_embed = self.bert(**bot_response).last_hidden_state.mean(dim=1)        fluency_score = self.fluency_head(response_embed)
        # 综合奖励        total_reward = 0.3 * (intent_reward) + 0.5 * retrieval_sim + 0.2 * fluency_score        return total_reward

# 基于 Hugging Face Transformers 的对话策略from transformers import GPT2LMHeadModel, GPT2Tokenizerimport torch
class DialoguePolicy:    def __init__(self):        self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2-medium")        self.model = GPT2LMHeadModel.from_pretrained("gpt2-medium")
    def generate_response(self, state):        input_text = f"User: {state['user_query']}\nBot:"        inputs = self.tokenizer(input_text, return_tensors="pt")        outputs = self.model.generate(**inputs, max_length=100)        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)        return response.split("Bot:")[-1].strip()
# 训练循环（PPO 框架）def train_ppo(policy, reward_model, epochs=10):    optimizer = torch.optim.Adam(policy.parameters(), lr=1e-5)    for epoch in range(epochs):        state = env.reset()  # 假设 env 为模拟对话环境        for t in range(max_steps):            action = policy.generate_response(state)            next_state, done = env.step(action)            reward = get_reward(state, action)            # PPO 更新逻辑（简化）            advantage = calculate_advantage(reward, policy, next_state)            loss = -torch.mean(advantage)            optimizer.zero_grad()            loss.backward()            optimizer.step()

第一步，查询1楼的供水管温度传感器；第二步，查询该供水管温度传感器的数值；第三步，查询1楼的回水管温度传感器；第四步，查询该回水管温度传感器的数值；第五步，计算两个数值的差

MATCH(r:Sensor) WHERE r.Name =~ ‘.*1F.*' AND r.type =~ ‘.*供水管温度传感器.*' RETURN r