0b5d0a8086
主要改动： - 订单列表：使用 order_list 格式，展示 5 个订单（全部状态） - 订单详情：使用 order_detail 格式，优化价格和时间显示 - 物流信息：使用 logistics 格式，根据 track id 动态生成步骤 - 商品图片：从 orderProduct.imageUrl 字段获取 - 时间格式：统一为 YYYY-MM-DD HH:MM:SS - 多语言支持：amountLabel、orderTime 支持中英文 - 配置管理：新增 FRONTEND_URL 环境变量 - API 集成：改进 Mall API tracks 数据解析 - 认证优化：account_id 从 webhook 动态获取 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
307 lines
8.9 KiB
Python
307 lines
8.9 KiB
Python
"""
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Agent state definitions for LangGraph workflow
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"""
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from typing import Any, Optional, Literal
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from typing_extensions import TypedDict, Annotated
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from dataclasses import dataclass, field
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from enum import Enum
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class Intent(str, Enum):
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"""User intent categories"""
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CUSTOMER_SERVICE = "customer_service" # FAQ, general inquiries
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ORDER = "order" # Order query, tracking, modify, cancel
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AFTERSALE = "aftersale" # Return, exchange, complaint
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PRODUCT = "product" # Search, recommend, quote
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HUMAN_HANDOFF = "human_handoff" # Transfer to human agent
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UNKNOWN = "unknown" # Cannot determine intent
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class ConversationState(str, Enum):
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"""Conversation state machine"""
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INITIAL = "initial"
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CLASSIFYING = "classifying"
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PROCESSING = "processing"
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AWAITING_INFO = "awaiting_info"
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TOOL_CALLING = "tool_calling"
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GENERATING = "generating"
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HUMAN_REVIEW = "human_review"
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COMPLETED = "completed"
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ERROR = "error"
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@dataclass
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class Entity:
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"""Extracted entity from user message"""
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type: str # Entity type (order_id, product_id, date, etc.)
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value: Any # Entity value
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confidence: float # Extraction confidence (0-1)
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@dataclass
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class ToolCall:
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"""MCP tool call record"""
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tool_name: str
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arguments: dict[str, Any]
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server: str # MCP server name (strapi, order, aftersale, product)
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@dataclass
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class ToolResult:
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"""MCP tool execution result"""
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tool_name: str
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success: bool
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data: Any
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error: Optional[str] = None
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class AgentState(TypedDict):
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"""Main agent state for LangGraph workflow
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This state is passed through all nodes in the workflow graph.
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"""
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# ============ Session Information ============
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conversation_id: str # Chatwoot conversation ID
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user_id: str # User identifier
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account_id: str # B2B account identifier
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user_token: Optional[str] # User JWT token for API calls
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mall_token: Optional[str] # Mall API token (if different from user_token)
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# ============ Message Content ============
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messages: list[dict[str, Any]] # Conversation history [{role, content}]
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current_message: str # Current user message being processed
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# ============ Intent Recognition ============
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intent: Optional[str] # Recognized intent (Intent enum value)
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intent_confidence: float # Intent confidence score (0-1)
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sub_intent: Optional[str] # Sub-intent for more specific routing
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# ============ Language Detection ============
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detected_language: Optional[str] # Detected user language (en, nl, de, etc.)
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language_confidence: float # Language detection confidence (0-1)
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# ============ Entity Extraction ============
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entities: dict[str, Any] # Extracted entities {type: value}
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# ============ Agent Routing ============
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current_agent: Optional[str] # Current processing agent name
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agent_history: list[str] # History of agents involved
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# ============ Tool Calling ============
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tool_calls: list[dict[str, Any]] # Pending tool calls
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tool_results: list[dict[str, Any]] # Tool execution results
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# ============ Response Generation ============
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response: Optional[str] # Generated response text
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response_type: str # Response type (text, rich, action)
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# ============ Human Handoff ============
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requires_human: bool # Whether human intervention is needed
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handoff_reason: Optional[str] # Reason for human handoff
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# ============ Conversation Context ============
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context: dict[str, Any] # Accumulated context (order details, etc.)
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# ============ State Control ============
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state: str # Current conversation state
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step_count: int # Number of steps taken
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max_steps: int # Maximum allowed steps
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error: Optional[str] # Error message if any
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finished: bool # Whether processing is complete
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def create_initial_state(
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conversation_id: str,
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user_id: str,
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account_id: str,
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current_message: str,
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messages: Optional[list[dict[str, Any]]] = None,
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context: Optional[dict[str, Any]] = None,
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user_token: Optional[str] = None,
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mall_token: Optional[str] = None
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) -> AgentState:
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"""Create initial agent state for a new message
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Args:
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conversation_id: Chatwoot conversation ID
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user_id: User identifier
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account_id: B2B account identifier
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current_message: User's message to process
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messages: Previous conversation history
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context: Existing conversation context
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user_token: User JWT token for API calls
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mall_token: Mall API token (if different from user_token)
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Returns:
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Initialized AgentState
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"""
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return AgentState(
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# Session
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conversation_id=conversation_id,
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user_id=user_id,
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account_id=account_id,
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user_token=user_token,
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mall_token=mall_token,
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# Messages
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messages=messages or [],
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current_message=current_message,
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# Intent
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intent=None,
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intent_confidence=0.0,
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sub_intent=None,
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# Language
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detected_language=None,
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language_confidence=0.0,
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# Entities
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entities={},
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# Routing
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current_agent=None,
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agent_history=[],
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# Tools
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tool_calls=[],
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tool_results=[],
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# Response
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response=None,
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response_type="text",
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# Human handoff
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requires_human=False,
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handoff_reason=None,
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# Context
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context=context or {},
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# Control
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state=ConversationState.INITIAL.value,
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step_count=0,
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max_steps=10,
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error=None,
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finished=False
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)
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# ============ State Update Helpers ============
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def add_message(state: AgentState, role: str, content: str) -> AgentState:
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"""Add a message to the conversation history"""
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state["messages"].append({"role": role, "content": content})
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return state
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def set_intent(
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state: AgentState,
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intent: Intent,
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confidence: float,
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sub_intent: Optional[str] = None
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) -> AgentState:
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"""Set the recognized intent"""
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state["intent"] = intent.value
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state["intent_confidence"] = confidence
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state["sub_intent"] = sub_intent
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return state
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def add_entity(state: AgentState, entity_type: str, value: Any) -> AgentState:
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"""Add an extracted entity"""
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state["entities"][entity_type] = value
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return state
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def add_tool_call(
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state: AgentState,
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tool_name: str,
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arguments: dict[str, Any],
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server: str
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) -> AgentState:
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"""Add a tool call to pending calls"""
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state["tool_calls"].append({
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"tool_name": tool_name,
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"arguments": arguments,
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"server": server
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})
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return state
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def add_tool_result(
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state: AgentState,
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tool_name: str,
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success: bool,
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data: Any,
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error: Optional[str] = None
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) -> AgentState:
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"""Add a tool execution result"""
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state["tool_results"].append({
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"tool_name": tool_name,
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"success": success,
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"data": data,
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"error": error
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})
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return state
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def set_response(
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state: AgentState,
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response: str,
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response_type: str = "text"
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) -> AgentState:
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"""Set the generated response"""
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state["response"] = response
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state["response_type"] = response_type
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return state
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def request_human_handoff(
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state: AgentState,
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reason: str
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) -> AgentState:
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"""Request transfer to human agent"""
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state["requires_human"] = True
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state["handoff_reason"] = reason
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return state
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def update_context(state: AgentState, updates: dict[str, Any]) -> AgentState:
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"""Update conversation context"""
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state["context"].update(updates)
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return state
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def set_error(state: AgentState, error: str) -> AgentState:
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"""Set error state"""
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state["error"] = error
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state["state"] = ConversationState.ERROR.value
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return state
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def mark_finished(state: AgentState) -> AgentState:
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"""Mark processing as complete"""
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state["finished"] = True
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state["state"] = ConversationState.COMPLETED.value
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return state
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def set_language(state: AgentState, language: str, confidence: float) -> AgentState:
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"""Set the detected language in state
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Args:
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state: Agent state
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language: Detected locale code (en, nl, de, etc.)
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confidence: Detection confidence (0-1)
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Returns:
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Updated state
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"""
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state["detected_language"] = language
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state["language_confidence"] = confidence
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# Also cache in context for future reference
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state["context"]["language"] = language
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return state
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