意圖路由

對進來的請求分類，並把每個請求路由到最合適的處理器：確定性邏輯、專業 LLM，或人工。

並非每個使用者請求都需要同一種處理器。有些查一次資料庫就能回答；有些需要帶領域上下文的 LLM；有些需要人工。TypeSafe 可以坐在這些處理器前面，做一個快速、便宜的分類器，決定該呼叫哪個。

示例：客服路由

設想你在做一個客服系統。訊息進來後要路由到正確的處理器。與其把每條訊息都送進昂貴的 LLM 去判斷它屬於哪類請求，不如先分類，再據此路由。

%%{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 -- "no" --> human["human agent"]
    confidence -- "yes" --> route{"<b>which intent?</b><br/>"}
    route -- "order_status" --> order["order lookup<br/>deterministic code"]
    route -- "product_question" --> product["product specialist LLM"]
    route -- "return_exchange" --> returns["returns specialist LLM"]
    route -- "complaint" --> escalate{"<b>complexity > 1<br/>or its confidence < 0.5?</b><br/>"}
    escalate -- "yes" --> human
    escalate -- "no" --> complaint["complaint resolution LLM"]

第 1 步：分類意圖和複雜度

{
  "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"
    ]
  }
}

第 2 步：路由到最合適的處理器

routing.py

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)

有一個意圖路由到不涉及 LLM 的確定性程式碼；有兩個路由到不同的專業 LLM，各自載入不同的上下文；還有一個用複雜度評分在 LLM 和人工之間做決定。TypeSafe 用一次快速呼叫就完成全部分類；昂貴的資源只會在確實需要它們的請求上被呼叫。

注意這裡對複雜度評分多做了一次置信度檢查。正如置信度中討論的，一個低置信度分數意味著什麼，永遠要放到系統上下文和決策風險裡去看。