意图路由

%%{init: {"fontFamily": "Inter, sans-serif", "flowchart": {"rankSpacing": 35, "wrappingWidth": 300, "subGraphTitleMargin": {"top": 12, "bottom": 36}}}}%%
flowchart LR
    message["customer message"]

    subgraph req["TypeSafe evaluates questions<br/>in parallel"]
        direction TB
        intent["<b>Choice:</b> intent"]
        complexity["<b>Score:</b> complexity"]
        %% Invisible links stack the questions; they are answered in parallel.
        intent ~~~ complexity
    end

    message -- "one request<br/>message + 2 questions" --> req
    req -- "one response<br/>2 answers with<br/>confidence" --> confidence{"<b>intent confidence<br/>≥ 0.5?</b><br/>your code"}
    confidence blue_low@-- "no" --> human["human agent"]
    confidence -- "yes" --> route{"<b>which intent?</b><br/>"}
    route green_order@-- "order_status" --> order["order lookup<br/>deterministic code"]
    route green_product@-- "product_question" --> product["product specialist LLM"]
    route green_returns@-- "return_exchange" --> returns["returns specialist LLM"]
    route -- "complaint" --> escalate{"<b>complexity > 1<br/>or its confidence < 0.5?</b><br/>"}
    escalate blue_complex@-- "yes" --> human
    escalate green_complaint@-- "no" --> complaint["complaint resolution LLM"]
    %% Branch colours (styles.css): node classes and green_/blue_/purple_/orange_ edge ids.
    class order,product,returns,complaint neonGreen
    class human neonBlue

{
  "model": "jev-latest",
  "questions": {
    "intent": {
      "type": "choice",
      "instructions": "The primary intent of this customer message",
      "criteria": {
        "order_status": "Asking about an existing order",
        "product_question": "Asking about a product before buying",
        "return_exchange": "Wants to return or exchange something",
        "complaint": "Unhappy with experience, wants resolution"
      }
    },
    "complexity": {
      "type": "score",
      "instructions": "How complex is this request to resolve",
      "criteria": [
        "Simple lookup or standard procedure",
        "Requires some judgment or multi-step process",
        "Unusual situation, edge case, or escalation needed"
      ]
    }
  }
}

def route_ticket(ticket_id, response):
    intent = response.answers["intent"]
    complexity = response.answers["complexity"]

    if intent.confidence < 0.5:
        # If we don't have enough confidence to classify, route to a human agent
        return route_to_human_agent(ticket_id)

    if intent.choice == "order_status":
        handle_order_status(ticket_id)

    elif intent.choice == "product_question":
        handle_with_llm(ticket_id, PRODUCT_SPECIALIST)

    elif intent.choice == "return_exchange":
        handle_with_llm(ticket_id, RETURNS_SPECIALIST)

    elif intent.choice == "complaint":
        low_confidence = complexity.confidence < 0.5
        # A higher complexity.score leans toward the "escalation needed" end of the scale.
        if complexity.score > 1 or low_confidence:
            # Too complex for safe automation, or we're not sure about the complexity; route to a human.
            route_to_human_agent(ticket_id)
        else:
            handle_with_llm(ticket_id, COMPLAINT_RESOLUTION)