腾讯云智能体开发平台与DeepSeek集成实践，构建新零售企业的智能知识中枢

class KnowledgeEngineer:
    def __init__(self, secret_id, secret_key):
         # 创建一个 TencentCloudClient 实例，并将其赋值给实例属性 self.client
        self.client = TencentCloudClient(
        # 指定腾讯云客户端的区域为 "ap-guangzhou"，即广州区域
        region="ap-guangzhou",
        # 使用传入的 secret_id 参数初始化客户端的 secret_id
        secret_id=secret_id,
         # 使用传入的 secret_key 参数初始化客户端的 secret_key
        secret_key=secret_key
      )

    def create_knowledge_base(self, corp_data):
        """创建企业专属知识库"""
        params = {
            "EngineType": "DeepSeek-R1",
            "KnowledgeName": f"{corp_data['name']}_KB",
            "EmbeddingConfig": {
                "ChunkSize": 512,
                "Overlap": 0.2
            },
            "DataSources": [
                {
                    "Type": "COS",  # 支持COS/本地文件/数据库
                    "Path": corp_data['cos_path'],
                    "RefreshPolicy": "Realtime"  # 实时更新策略
                }
            ]
        }
        return self.client.call("CreateKnowledgeBase", params)

# 初始化示例
engineer = KnowledgeEngineer("AKIDxxxx", "xxxxxx") 
corp_data = {"name": "SmartRetail", "cos_path": "cos://retail-data-125000000"}
kb_id = engineer.create_knowledge_base(corp_data)

class AIAssistant:
    def __init__(self, kb_id):
        self.session = create_session(kb_id)
        
    def query(self, question, context=None):
        """多轮对话处理"""
        params = {
            "Query": question,
            "SessionId": self.session.id,
            "AdvancedConfig": {
                "Temperature": 0.3,  # 严谨模式
                "EnableFollowup": True,
                "SafetyCheckLevel": "Strict"  # 内容安全过滤
            }
        }
        if context:
            params["Context"] = json.dumps(context)
            
        response = self.session.client.call("ChatCompletion", params)
        return self._process_response(response)
    
    def _process_response(self, response):
        """结果增强处理"""
        return {
            "answer": response['Answer'],
            "confidence": response['Score'],
            "sources": self._format_sources(response['References']),
            "suggestions": response['FollowupQuestions'][:3]
        }

# 使用示例    
assistant = AIAssistant(kb_id)
response = assistant.query("朝阳区望京店还有吹风机库存吗？")
print(f"应答准确率：{response['confidence']:.2%}")

> 问：吹风机什么时候补货？
> 答：根据2025-03-13最新数据，吹风机预计4月5日到货，建议您登记手机号到货后第一时间通知（置信度92%）
> 来源：《2025Q1~Q2季度补货计划表》第15条
> 推荐追问：
> 1. 吹风机有哪些款式？
> 2. 现在预订有优惠吗？

# 渐进式流量切换示例
def canary_release(user_id):
    # 老用户优先使用新系统
    if get_user_history(user_id).order_count > 5:
        return "deepseek_v2"
    else:
        return "legacy_system"

class DataSanitizer:
    def remove_duplicates(self, text):
        return list(dict.fromkeys(text.split('\n')))
    
    def mask_sensitive(self, text):
        return re.sub(r'\d{11}', '<PHONE>', text)