意图路由

意图路由

对进来的请求分类，并把每个请求路由到最合适的处理器：确定性逻辑、专业 LLM，或人工。

并非每个用户请求都需要同一种处理器。有些查一次数据库就能回答；有些需要带领域上下文的 LLM；有些需要人工。TypeSafe 可以坐在这些处理器前面，做一个快速、便宜的分类器，决定该调用哪个。

示例：客服路由

设想你在做一个客服系统。消息进来后要路由到正确的处理器。与其把每条消息都送进昂贵的 LLM 去判断它属于哪类请求，不如先分类，再据此路由。

%%{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：分类意图和复杂度

{
  "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：路由到最合适的处理器

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)

有一个意图路由到不涉及 LLM 的确定性代码；有两个路由到不同的专业 LLM，各自加载不同的上下文；还有一个用复杂度评分在 LLM 和人工之间做决定。TypeSafe 用一次快速调用就完成全部分类；昂贵的资源只会在确实需要它们的请求上被调用。

注意这里对复杂度评分多做了一次置信度检查。正如置信度中讨论的，一个低置信度分数意味着什么，永远要放到系统上下文和决策风险里去看。