After-Sales Assistant

LangGraph-powered after-sales agent with summary-based knowledge routing and on-demand retrieval. Supports order lookup, refund and exchange workflows with a built-in knowledge management panel. Deployed on EdgeOne Makers.

Framework: LangGraph · Category: Chat · Language: TypeScript

Overview

This template implements a state-machine-driven customer service agent that recognizes user intent, routes to specialized handlers, and retrieves knowledge on demand. No vector database is required — FAQs and product knowledge are matched via summary-based routing and loaded only when needed.

Intent Recognition — Classifies incoming messages into FAQ, order lookup, refund, exchange, or general chat.
Knowledge Routing — Matches user queries to the most relevant knowledge base entry via summary similarity, then loads the full content on demand.
Order Workflows — Dedicated handlers for order lookup, refund requests, and exchange requests with structured data validation.
Knowledge Management Panel — A separate management endpoint for adding, updating, and organizing FAQ entries and product documents.
State Persistence — Workflow state is persisted via langgraphStore so multi-turn interactions survive across requests.
Bilingual UI — Full Chinese / English interface with locale-aware AI output.

Environment Variables

Variable	Required	Description
`AI_GATEWAY_API_KEY`	Yes	Model gateway API key. Use your Makers Models API Key, or any OpenAI-compatible provider key.
`AI_GATEWAY_BASE_URL`	Yes	Gateway base URL. For Makers Models, use `https://ai-gateway.edgeone.link/v1`.
`AI_GATEWAY_MODEL`	No	Model ID. Defaults to `@makers/deepseek-v4-flash`.

This template follows the OpenAI-compatible standard — point these at Makers Models or any compatible provider.

How to get AI_GATEWAY_API_KEY

Open the Makers Console (https://edgeone.ai/makers/new?s_url=https://console.tencentcloud.com/edgeone/makers)
Sign in and enable Makers
Go to Makers → Models → API Key and create a key
Copy it into AI_GATEWAY_API_KEY

Built-in models are free within quota and great for validation. For production, bind your own paid provider key (BYOK).

Local Development

Prerequisites

Node.js 18+
EdgeOne CLI (npm i -g edgeone)

npm install
cp .env.example .env
# Edit .env with your AI_GATEWAY_API_KEY and AI_GATEWAY_BASE_URL
edgeone makers dev

Open the local observability dashboard at http://localhost:8088/agent-metrics.

Project Structure

after-sales-assistant/
├── agents/
│   ├── chat/
│   │   └── index.ts        # POST /chat — main chat with intent routing
│   ├── manage/
│   │   └── index.ts        # POST /manage — knowledge base management
│   ├── upload/
│   │   └── index.ts        # POST /upload — document upload
│   ├── stop/
│   │   └── index.ts        # POST /stop — abort active run
│   ├── seed-demo/
│   │   └── index.ts        # POST /seed-demo — initialize demo data
│   ├── _graph/
│   │   ├── builder.ts      # LangGraph state machine builder
│   │   ├── state.ts        # State schema definition
│   │   ├── nodes.ts        # Intent handler nodes
│   │   └── edges.ts        # Conditional routing edges
│   ├── _data/              # Demo knowledge base data
│   ├── _i18n.ts            # Chinese / English translations
│   └── _shared.ts          # Model init, SSE helpers, logger
├── cloud-functions/
│   └── health/             # GET /health
├── app/                    # Next.js App Router frontend
└── edgeone.json            # EdgeOne deployment config

Files prefixed with _ are private modules — not exposed as public routes.

How It Works

Runtime Mode

Files under agents/ run in session mode: requests with the same conversation_id are sticky-routed to the same agent instance. This ensures LangGraph state and conversation context persist across follow-up messages.

End-to-End Workflow

Message intake — The frontend POSTs /chat with the user message and locale via the makers-conversation-id header.
State load — The handler loads any previously saved workflow state from langgraphStore for this conversation.
Intent recognition — The LangGraph intent_recognition node classifies the message into one of: faq_search, lookup_order, request_refund, request_exchange, or general_chat.
Conditional routing — The routeByIntent edge dispatches to the appropriate handler node based on the classified intent.
Node execution:
- faq_search — Matches the query against knowledge base summaries, loads the best-matching full entry, and returns the answer.
- lookup_order — Queries order data and returns status, items, and tracking info.
- request_refund / request_exchange — Guides the user through the return/exchange flow with structured field collection.
- general_chat — Handles open-ended questions with the LLM directly.
State save — The updated workflow state is persisted back to langgraphStore.
SSE output — The response streams back as SSE events including text_delta, tool_called, and UI card events for the frontend to render.

Key Routes & Parameters

/chat — Main customer service endpoint. Header: makers-conversation-id: <uuid>; Body: { message, locale? }.
/manage — Knowledge base management (add/update/delete FAQ entries). Body: { action, data }.
/upload — Document upload for knowledge base ingestion. Body: { files[] }.
/seed-demo — Initializes demo FAQ and order data for first-time setup.
/stop — Aborts the active run. Body: { conversation_id }.
/health — Liveness probe (lives in cloud-functions/, not AI-related).
conversation_id is generated client-side and forwarded via the makers-conversation-id header; the runtime auto-binds it to context.conversation_id.