Appearance
第73天：智能客服系统-需求分析与架构设计
学习目标
- 掌握需求分析方法
- 学习系统架构设计
- 理解功能模块划分
- 掌握技术选型
- 学习数据库设计
需求分析
用户需求
python
class UserRequirements:
def __init__(self):
self.requirements = {
"customer": {
"description": "客户需求",
"needs": [
"快速响应",
"准确回答",
"多轮对话",
"个性化服务",
"多渠道接入"
]
},
"agent": {
"description": "客服人员需求",
"needs": [
"智能辅助",
"工单管理",
"知识库查询",
"客户画像",
"效率提升"
]
},
"manager": {
"description": "管理人员需求",
"needs": [
"监控分析",
"报表统计",
"质量评估",
"知识管理",
"系统配置"
]
}
}
def analyze_requirements(self) -> Dict:
analysis = {}
for role, role_info in self.requirements.items():
analysis[role] = {
"description": role_info["description"],
"needs": role_info["needs"],
"priority": self._calculate_priority(role_info["needs"])
}
return analysis
def _calculate_priority(self, needs: List[str]) -> str:
high_priority_keywords = [
"快速",
"准确",
"多轮",
"智能",
"效率"
]
high_count = sum(
1 for need in needs
if any(keyword in need for keyword in high_priority_keywords)
)
if high_count >= 3:
return "高"
elif high_count >= 2:
return "中"
else:
return "低"功能需求
python
class FunctionalRequirements:
def __init__(self):
self.modules = {
"chat": {
"description": "对话模块",
"features": [
"多轮对话",
"上下文管理",
"意图识别",
"情感分析",
"对话路由"
]
},
"knowledge": {
"description": "知识库模块",
"features": [
"文档上传",
"智能切片",
"向量化存储",
"语义检索",
"答案生成"
]
},
"ticket": {
"description": "工单模块",
"features": [
"工单创建",
"工单分配",
"工单跟踪",
"工单统计",
"工单评价"
]
},
"analytics": {
"description": "分析模块",
"features": [
"对话分析",
"客户画像",
"服务质量",
"效率统计",
"趋势分析"
]
},
"integration": {
"description": "集成模块",
"features": [
"渠道集成",
"系统集成",
"API接口",
"Webhook",
"数据同步"
]
}
}
def get_functional_spec(self) -> Dict:
spec = {}
for module_name, module_info in self.modules.items():
spec[module_name] = {
"description": module_info["description"],
"features": [
{
"name": feature,
"description": self._describe_feature(feature),
"priority": self._determine_priority(feature)
}
for feature in module_info["features"]
]
}
return spec
def _describe_feature(self, feature: str) -> str:
descriptions = {
"多轮对话": "支持多轮对话，保持上下文连贯",
"上下文管理": "管理对话上下文，支持历史记录",
"意图识别": "识别用户意图，进行分类",
"情感分析": "分析用户情感，调整回复策略",
"对话路由": "根据意图路由到合适的处理模块",
"文档上传": "支持上传知识库文档",
"智能切片": "智能切分文档，保持语义完整",
"向量化存储": "将文档向量化存储到向量数据库",
"语义检索": "基于语义相似度检索相关文档",
"答案生成": "基于检索结果生成准确答案",
"工单创建": "自动或手动创建工单",
"工单分配": "智能分配工单给客服人员",
"工单跟踪": "跟踪工单处理进度",
"工单统计": "统计工单数据，生成报表",
"工单评价": "客户对工单服务进行评价",
"对话分析": "分析对话数据，提取洞察",
"客户画像": "构建客户画像，了解客户特征",
"服务质量": "评估服务质量，发现问题",
"效率统计": "统计客服效率，优化流程",
"趋势分析": "分析对话趋势，预测需求",
"渠道集成": "集成多种渠道（网页、微信、APP等）",
"系统集成": "与现有系统集成",
"API接口": "提供API接口供外部调用",
"Webhook": "支持Webhook回调",
"数据同步": "与外部系统同步数据"
}
return descriptions.get(feature, feature)
def _determine_priority(self, feature: str) -> str:
high_priority_features = [
"多轮对话",
"上下文管理",
"意图识别",
"语义检索",
"答案生成"
]
if feature in high_priority_features:
return "高"
else:
return "中"非功能需求
python
class NonFunctionalRequirements:
def __init__(self):
self.categories = {
"performance": {
"description": "性能要求",
"requirements": [
{
"metric": "响应时间",
"target": "< 2秒",
"priority": "高"
},
{
"metric": "并发用户",
"target": "> 1000",
"priority": "高"
},
{
"metric": "吞吐量",
"target": "> 100 QPS",
"priority": "中"
}
]
},
"reliability": {
"description": "可靠性要求",
"requirements": [
{
"metric": "可用性",
"target": "> 99.9%",
"priority": "高"
},
{
"metric": "故障恢复",
"target": "< 5分钟",
"priority": "高"
},
{
"metric": "数据备份",
"target": "每日备份",
"priority": "中"
}
]
},
"security": {
"description": "安全性要求",
"requirements": [
{
"metric": "数据加密",
"target": "传输加密+存储加密",
"priority": "高"
},
{
"metric": "访问控制",
"target": "RBAC",
"priority": "高"
},
{
"metric": "审计日志",
"target": "完整审计",
"priority": "中"
}
]
},
"scalability": {
"description": "可扩展性要求",
"requirements": [
{
"metric": "水平扩展",
"target": "支持",
"priority": "高"
},
{
"metric": "垂直扩展",
"target": "支持",
"priority": "中"
},
{
"metric": "模块化",
"target": "模块化设计",
"priority": "中"
}
]
}
}
def get_non_functional_spec(self) -> Dict:
return self.categories系统架构设计
整体架构
python
class SystemArchitecture:
def __init__(self):
self.layers = {
"presentation": {
"name": "展示层",
"components": [
"Web前端",
"移动端APP",
"微信小程序",
"管理后台"
]
},
"api_gateway": {
"name": "API网关层",
"components": [
"API网关",
"负载均衡",
"限流熔断",
"认证授权"
]
},
"application": {
"name": "应用层",
"components": [
"对话服务",
"知识库服务",
"工单服务",
"分析服务"
]
},
"domain": {
"name": "领域层",
"components": [
"对话引擎",
"意图识别",
"情感分析",
"检索引擎",
"答案生成"
]
},
"infrastructure": {
"name": "基础设施层",
"components": [
"数据库",
"向量数据库",
"缓存",
"消息队列",
"对象存储"
]
}
}
def get_architecture(self) -> Dict:
return self.layers
def get_component_dependencies(self) -> Dict:
dependencies = {
"presentation": ["api_gateway"],
"api_gateway": ["application"],
"application": ["domain", "infrastructure"],
"domain": ["infrastructure"],
"infrastructure": []
}
return dependencies微服务架构
python
class MicroservicesArchitecture:
def __init__(self):
self.services = {
"chat_service": {
"name": "对话服务",
"responsibilities": [
"处理对话请求",
"管理对话上下文",
"调用LLM生成回复"
],
"ports": [
{"name": "chat", "port": 8001},
{"name": "health", "port": 8002}
],
"dependencies": [
"llm_service",
"context_service",
"cache"
]
},
"intent_service": {
"name": "意图服务",
"responsibilities": [
"识别用户意图",
"意图分类",
"意图置信度计算"
],
"ports": [
{"name": "intent", "port": 8003},
{"name": "health", "port": 8004}
],
"dependencies": [
"llm_service",
"cache"
]
},
"knowledge_service": {
"name": "知识库服务",
"responsibilities": [
"文档处理",
"向量化存储",
"语义检索"
],
"ports": [
{"name": "knowledge", "port": 8005},
{"name": "health", "port": 8006}
],
"dependencies": [
"vector_db",
"object_storage"
]
},
"ticket_service": {
"name": "工单服务",
"responsibilities": [
"工单创建",
"工单分配",
"工单跟踪"
],
"ports": [
{"name": "ticket", "port": 8007},
{"name": "health", "port": 8008}
],
"dependencies": [
"database",
"notification_service"
]
},
"analytics_service": {
"name": "分析服务",
"responsibilities": [
"对话分析",
"客户画像",
"报表生成"
],
"ports": [
{"name": "analytics", "port": 8009},
{"name": "health", "port": 8010}
],
"dependencies": [
"database",
"data_warehouse"
]
}
}
def get_services(self) -> Dict:
return self.services
def get_service_mesh(self) -> Dict:
mesh = {
"gateway": {
"service": "api_gateway",
"routes": [
{"path": "/api/chat", "service": "chat_service"},
{"path": "/api/intent", "service": "intent_service"},
{"path": "/api/knowledge", "service": "knowledge_service"},
{"path": "/api/ticket", "service": "ticket_service"},
{"path": "/api/analytics", "service": "analytics_service"}
]
},
"services": self.services
}
return mesh数据库设计
关系数据库设计
python
class DatabaseSchema:
def __init__(self):
self.tables = {
"users": {
"description": "用户表",
"columns": [
{"name": "id", "type": "BIGINT", "primary_key": True},
{"name": "username", "type": "VARCHAR(50)", "unique": True},
{"name": "email", "type": "VARCHAR(100)", "unique": True},
{"name": "password_hash", "type": "VARCHAR(255)"},
{"name": "role", "type": "VARCHAR(20)"},
{"name": "created_at", "type": "TIMESTAMP"},
{"name": "updated_at", "type": "TIMESTAMP"}
],
"indexes": [
{"name": "idx_username", "columns": ["username"]},
{"name": "idx_email", "columns": ["email"]}
]
},
"conversations": {
"description": "对话表",
"columns": [
{"name": "id", "type": "BIGINT", "primary_key": True},
{"name": "user_id", "type": "BIGINT", "foreign_key": "users.id"},
{"name": "session_id", "type": "VARCHAR(100)", "unique": True},
{"name": "status", "type": "VARCHAR(20)"},
{"name": "created_at", "type": "TIMESTAMP"},
{"name": "updated_at", "type": "TIMESTAMP"}
],
"indexes": [
{"name": "idx_user_id", "columns": ["user_id"]},
{"name": "idx_session_id", "columns": ["session_id"]},
{"name": "idx_status", "columns": ["status"]}
]
},
"messages": {
"description": "消息表",
"columns": [
{"name": "id", "type": "BIGINT", "primary_key": True},
{"name": "conversation_id", "type": "BIGINT", "foreign_key": "conversations.id"},
{"name": "role", "type": "VARCHAR(20)"},
{"name": "content", "type": "TEXT"},
{"name": "intent", "type": "VARCHAR(100)"},
{"name": "sentiment", "type": "VARCHAR(20)"},
{"name": "created_at", "type": "TIMESTAMP"}
],
"indexes": [
{"name": "idx_conversation_id", "columns": ["conversation_id"]},
{"name": "idx_created_at", "columns": ["created_at"]}
]
},
"tickets": {
"description": "工单表",
"columns": [
{"name": "id", "type": "BIGINT", "primary_key": True},
{"name": "conversation_id", "type": "BIGINT", "foreign_key": "conversations.id"},
{"name": "user_id", "type": "BIGINT", "foreign_key": "users.id"},
{"name": "agent_id", "type": "BIGINT", "foreign_key": "users.id"},
{"name": "title", "type": "VARCHAR(200)"},
{"name": "description", "type": "TEXT"},
{"name": "priority", "type": "VARCHAR(20)"},
{"name": "status", "type": "VARCHAR(20)"},
{"name": "created_at", "type": "TIMESTAMP"},
{"name": "updated_at", "type": "TIMESTAMP"}
],
"indexes": [
{"name": "idx_conversation_id", "columns": ["conversation_id"]},
{"name": "idx_user_id", "columns": ["user_id"]},
{"name": "idx_agent_id", "columns": ["agent_id"]},
{"name": "idx_status", "columns": ["status"]}
]
},
"knowledge_documents": {
"description": "知识库文档表",
"columns": [
{"name": "id", "type": "BIGINT", "primary_key": True},
{"name": "title", "type": "VARCHAR(200)"},
{"name": "content", "type": "TEXT"},
{"name": "category", "type": "VARCHAR(100)"},
{"name": "tags", "type": "TEXT"},
{"name": "file_path", "type": "VARCHAR(500)"},
{"name": "status", "type": "VARCHAR(20)"},
{"name": "created_at", "type": "TIMESTAMP"},
{"name": "updated_at", "type": "TIMESTAMP"}
],
"indexes": [
{"name": "idx_category", "columns": ["category"]},
{"name": "idx_status", "columns": ["status"]}
]
}
}
def get_schema(self) -> Dict:
return self.tables向量数据库设计
python
class VectorDatabaseSchema:
def __init__(self):
self.collections = {
"knowledge_chunks": {
"description": "知识库切片集合",
"dimension": 1536,
"metric": "cosine",
"fields": [
{"name": "id", "type": "VARCHAR", "primary_key": True},
{"name": "document_id", "type": "VARCHAR"},
{"name": "chunk_index", "type": "INT"},
{"name": "content", "type": "TEXT"},
{"name": "embedding", "type": "VECTOR"},
{"name": "metadata", "type": "JSON"}
],
"indexes": [
{"name": "idx_document_id", "fields": ["document_id"]},
{"name": "idx_embedding", "fields": ["embedding"], "type": "IVF_FLAT"}
]
},
"conversation_embeddings": {
"description": "对话嵌入集合",
"dimension": 1536,
"metric": "cosine",
"fields": [
{"name": "id", "type": "VARCHAR", "primary_key": True},
{"name": "conversation_id", "type": "VARCHAR"},
{"name": "message_id", "type": "VARCHAR"},
{"name": "content", "type": "TEXT"},
{"name": "embedding", "type": "VECTOR"},
{"name": "metadata", "type": "JSON"}
],
"indexes": [
{"name": "idx_conversation_id", "fields": ["conversation_id"]},
{"name": "idx_embedding", "fields": ["embedding"], "type": "IVF_FLAT"}
]
}
}
def get_schema(self) -> Dict:
return self.collections技术选型
技术栈选择
python
class TechnologyStack:
def __init__(self):
self.stack = {
"frontend": {
"framework": "React",
"ui_library": "Ant Design",
"state_management": "Redux Toolkit",
"http_client": "Axios",
"build_tool": "Vite"
},
"backend": {
"language": "Python",
"framework": "FastAPI",
"async_runtime": "asyncio",
"orm": "SQLAlchemy",
"validation": "Pydantic"
},
"database": {
"relational": "PostgreSQL",
"vector": "ChromaDB",
"cache": "Redis",
"message_queue": "RabbitMQ"
},
"ai": {
"llm": "GPT-4o",
"embedding": "text-embedding-3-small",
"framework": "LangChain",
"vector_store": "Chroma"
},
"infrastructure": {
"container": "Docker",
"orchestration": "Kubernetes",
"ci_cd": "GitHub Actions",
"monitoring": "Prometheus + Grafana",
"logging": "ELK Stack"
}
}
def get_stack(self) -> Dict:
return self.stack
def get_rationale(self) -> Dict:
rationale = {
"frontend": {
"React": "生态成熟，组件丰富",
"Ant Design": "企业级UI组件库",
"Redux Toolkit": "状态管理最佳实践"
},
"backend": {
"FastAPI": "高性能，异步支持，自动文档",
"SQLAlchemy": "功能强大的ORM",
"Pydantic": "数据验证，类型安全"
},
"database": {
"PostgreSQL": "功能强大，可靠性高",
"ChromaDB": "开源向量数据库，易用",
"Redis": "高性能缓存",
"RabbitMQ": "可靠的消息队列"
},
"ai": {
"GPT-4o": "性能最强，多模态支持",
"LangChain": "LLM应用开发框架",
"Chroma": "向量数据库集成"
},
"infrastructure": {
"Docker": "容器化部署",
"Kubernetes": "容器编排",
"GitHub Actions": "CI/CD自动化",
"Prometheus": "监控告警",
"ELK Stack": "日志分析"
}
}
return rationale实践练习
练习1：分析需求
python
def analyze_requirements():
user_req = UserRequirements()
func_req = FunctionalRequirements()
non_func_req = NonFunctionalRequirements()
user_analysis = user_req.analyze_requirements()
func_spec = func_req.get_functional_spec()
non_func_spec = non_func_req.get_non_functional_spec()
return user_analysis, func_spec, non_func_spec练习2：设计架构
python
def design_architecture():
arch = SystemArchitecture()
microservices = MicroservicesArchitecture()
architecture = arch.get_architecture()
services = microservices.get_services()
mesh = microservices.get_service_mesh()
return architecture, services, mesh练习3：设计数据库
python
def design_database():
db_schema = DatabaseSchema()
vector_schema = VectorDatabaseSchema()
tables = db_schema.get_schema()
collections = vector_schema.get_schema()
return tables, collections总结
本节我们学习了智能客服系统的需求分析与架构设计：
- 需求分析
- 系统架构设计
- 数据库设计
- 技术选型
需求分析和架构设计是项目成功的基础。