feat: 初始化 B2B AI Shopping Assistant 项目
- 配置 Docker Compose 多服务编排 - 实现 Chatwoot + Agent 集成 - 配置 Strapi MCP 知识库 - 支持 7 种语言的 FAQ 系统 - 实现 LangGraph AI 工作流 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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agent/core/graph.py
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agent/core/graph.py
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"""
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LangGraph workflow definition for B2B Shopping AI Assistant
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"""
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from typing import Literal
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import httpx
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from langgraph.graph import StateGraph, END
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from .state import AgentState, ConversationState, mark_finished, add_tool_result, set_response
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from agents.router import classify_intent, route_by_intent
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from agents.customer_service import customer_service_agent
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from agents.order import order_agent
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from agents.aftersale import aftersale_agent
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from agents.product import product_agent
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from config import settings
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from utils.logger import get_logger
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logger = get_logger(__name__)
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# ============ Node Functions ============
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async def receive_message(state: AgentState) -> AgentState:
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"""Receive and preprocess incoming message
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This is the entry point of the workflow.
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"""
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logger.info(
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"Receiving message",
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conversation_id=state["conversation_id"],
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message_length=len(state["current_message"])
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)
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# Add user message to history
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state["messages"].append({
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"role": "user",
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"content": state["current_message"]
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})
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state["state"] = ConversationState.INITIAL.value
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return state
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async def call_mcp_tools(state: AgentState) -> AgentState:
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"""Execute pending MCP tool calls
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Calls the appropriate MCP server based on the tool_calls in state.
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"""
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if not state["tool_calls"]:
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logger.debug("No tool calls to execute")
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return state
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logger.info(
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"Executing MCP tools",
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tool_count=len(state["tool_calls"])
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)
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# MCP server URL mapping
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mcp_servers = {
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"strapi": settings.strapi_mcp_url,
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"order": settings.order_mcp_url,
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"aftersale": settings.aftersale_mcp_url,
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"product": settings.product_mcp_url
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}
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async with httpx.AsyncClient(timeout=30.0) as client:
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for tool_call in state["tool_calls"]:
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server = tool_call["server"]
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tool_name = tool_call["tool_name"]
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arguments = tool_call["arguments"]
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server_url = mcp_servers.get(server)
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if not server_url:
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state = add_tool_result(
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state,
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tool_name=tool_name,
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success=False,
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data=None,
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error=f"Unknown MCP server: {server}"
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)
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continue
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try:
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# Call MCP tool endpoint
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response = await client.post(
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f"{server_url}/tools/{tool_name}",
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json=arguments
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)
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response.raise_for_status()
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result = response.json()
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state = add_tool_result(
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state,
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tool_name=tool_name,
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success=True,
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data=result
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)
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logger.debug(
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"Tool executed successfully",
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tool=tool_name,
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server=server
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)
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except httpx.HTTPStatusError as e:
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logger.error(
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"Tool HTTP error",
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tool=tool_name,
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status=e.response.status_code
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)
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state = add_tool_result(
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state,
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tool_name=tool_name,
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success=False,
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data=None,
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error=f"HTTP {e.response.status_code}"
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)
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except Exception as e:
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logger.error("Tool execution failed", tool=tool_name, error=str(e))
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state = add_tool_result(
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state,
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tool_name=tool_name,
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success=False,
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data=None,
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error=str(e)
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)
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# Clear pending tool calls
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state["tool_calls"] = []
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return state
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async def human_handoff(state: AgentState) -> AgentState:
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"""Handle transfer to human agent
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Sets up the state for human intervention.
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"""
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logger.info(
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"Human handoff requested",
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conversation_id=state["conversation_id"],
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reason=state.get("handoff_reason")
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)
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state["state"] = ConversationState.HUMAN_REVIEW.value
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# Generate handoff message
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reason = state.get("handoff_reason", "您的问题需要人工客服协助")
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state = set_response(
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state,
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f"正在为您转接人工客服，请稍候。\n转接原因：{reason}\n\n人工客服将尽快为您服务。"
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)
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return state
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async def send_response(state: AgentState) -> AgentState:
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"""Finalize and send response
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This is the final node that marks processing as complete.
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"""
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logger.info(
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"Sending response",
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conversation_id=state["conversation_id"],
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response_length=len(state.get("response", ""))
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)
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# Add assistant response to history
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if state.get("response"):
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state["messages"].append({
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"role": "assistant",
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"content": state["response"]
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})
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state = mark_finished(state)
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return state
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async def handle_error(state: AgentState) -> AgentState:
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"""Handle errors in the workflow"""
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logger.error(
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"Workflow error",
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conversation_id=state["conversation_id"],
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error=state.get("error")
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)
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state = set_response(
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state,
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"抱歉，处理您的请求时遇到了问题。请稍后重试，或联系人工客服获取帮助。"
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)
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state = mark_finished(state)
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return state
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# ============ Routing Functions ============
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def should_call_tools(state: AgentState) -> Literal["call_tools", "send_response", "back_to_agent"]:
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"""Determine if tools need to be called"""
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# If there are pending tool calls, execute them
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if state.get("tool_calls"):
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return "call_tools"
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# If we have a response ready, send it
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if state.get("response"):
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return "send_response"
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# If we're waiting for info, send the question
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if state.get("state") == ConversationState.AWAITING_INFO.value:
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return "send_response"
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# Otherwise, something went wrong
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return "send_response"
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def after_tools(state: AgentState) -> str:
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"""Route after tool execution
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Returns the agent that should process the tool results.
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"""
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current_agent = state.get("current_agent")
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# Route back to the agent that made the tool call
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agent_mapping = {
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"customer_service": "customer_service_agent",
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"order": "order_agent",
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"aftersale": "aftersale_agent",
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"product": "product_agent"
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}
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return agent_mapping.get(current_agent, "customer_service_agent")
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def check_completion(state: AgentState) -> Literal["continue", "end", "error"]:
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"""Check if workflow should continue or end"""
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# Check for errors
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if state.get("error"):
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return "error"
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# Check if finished
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if state.get("finished"):
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return "end"
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# Check step limit
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if state.get("step_count", 0) >= state.get("max_steps", 10):
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logger.warning("Max steps reached", conversation_id=state["conversation_id"])
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return "end"
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return "continue"
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# ============ Graph Construction ============
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def create_agent_graph() -> StateGraph:
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"""Create the main agent workflow graph
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Returns:
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Compiled LangGraph workflow
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"""
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# Create graph with AgentState
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graph = StateGraph(AgentState)
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# Add nodes
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graph.add_node("receive", receive_message)
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graph.add_node("classify", classify_intent)
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graph.add_node("customer_service_agent", customer_service_agent)
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graph.add_node("order_agent", order_agent)
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graph.add_node("aftersale_agent", aftersale_agent)
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graph.add_node("product_agent", product_agent)
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graph.add_node("call_tools", call_mcp_tools)
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graph.add_node("human_handoff", human_handoff)
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graph.add_node("send_response", send_response)
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graph.add_node("handle_error", handle_error)
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# Set entry point
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graph.set_entry_point("receive")
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# Add edges
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graph.add_edge("receive", "classify")
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# Conditional routing based on intent
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graph.add_conditional_edges(
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"classify",
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route_by_intent,
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{
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"customer_service_agent": "customer_service_agent",
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"order_agent": "order_agent",
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"aftersale_agent": "aftersale_agent",
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"product_agent": "product_agent",
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"human_handoff": "human_handoff"
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}
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)
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# After each agent, check if tools need to be called
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for agent_node in ["customer_service_agent", "order_agent", "aftersale_agent", "product_agent"]:
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graph.add_conditional_edges(
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agent_node,
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should_call_tools,
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{
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"call_tools": "call_tools",
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"send_response": "send_response",
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"back_to_agent": agent_node
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}
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)
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# After tool execution, route back to appropriate agent
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graph.add_conditional_edges(
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"call_tools",
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after_tools,
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{
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"customer_service_agent": "customer_service_agent",
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"order_agent": "order_agent",
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"aftersale_agent": "aftersale_agent",
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"product_agent": "product_agent"
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}
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)
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# Human handoff leads to send response
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graph.add_edge("human_handoff", "send_response")
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# Error handling
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graph.add_edge("handle_error", END)
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# Final node
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graph.add_edge("send_response", END)
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return graph.compile()
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# Global compiled graph
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_compiled_graph = None
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def get_agent_graph():
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"""Get or create the compiled agent graph"""
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global _compiled_graph
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if _compiled_graph is None:
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_compiled_graph = create_agent_graph()
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return _compiled_graph
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async def process_message(
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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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message: str,
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history: list[dict] = None,
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context: dict = None
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) -> AgentState:
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"""Process a user message through the agent workflow
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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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message: User's message
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history: Previous conversation history
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context: Existing conversation context
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Returns:
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Final agent state with response
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"""
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from .state import create_initial_state
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# Create initial state
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initial_state = create_initial_state(
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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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current_message=message,
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messages=history,
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context=context
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)
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# Get compiled graph
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graph = get_agent_graph()
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# Run the workflow
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logger.info(
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"Starting workflow",
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conversation_id=conversation_id,
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message=message[:100]
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)
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try:
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final_state = await graph.ainvoke(initial_state)
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logger.info(
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"Workflow completed",
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conversation_id=conversation_id,
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intent=final_state.get("intent"),
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steps=final_state.get("step_count")
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)
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return final_state
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except Exception as e:
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logger.error("Workflow failed", error=str(e))
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initial_state["error"] = str(e)
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initial_state["response"] = "抱歉，处理您的请求时遇到了问题。请稍后重试。"
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initial_state["finished"] = True
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return initial_state
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