复合评分

把一个复杂判断拆分为原子化的评分，再与你在代码中掌控的权重组合。

我们常常想同时基于多项标准对一组条目排序。复合评分是思考这个问题的一种简单方式：把判断拆分成独立的维度，分别对每个维度评分，再用你在代码中掌控的权重组合它们。

示例：简历筛选

假设你正在处理工程岗位的简历。你想基于多项标准对候选人排序，并最终选出前 X 名候选人进入进一步审核。

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

    subgraph req["TypeSafe evaluates questions<br/>in parallel"]
        direction TB
        py["<b>Score:</b> Python depth"]
        lead["<b>Score:</b> team leadership"]
        arch["<b>Score:</b> system design"]
        general["<b>Score:</b> generalist"]
        %% Invisible links stack the questions; they are answered in parallel.
        py ~~~ lead ~~~ arch ~~~ general
    end

    resume -- "one request<br/>resume + 4 questions" --> req
    req -- "one response<br/>4 score answers" --> normalize["<b>normalize scores to 0–1</b><br/>divide each by 4 in your code"]
    normalize --> ic["<b>senior IC weights</b><br/>40% Python + 10% leadership<br/>40% design + 10% generalist"]
    normalize --> em["<b>engineering manager weights</b><br/>15% Python + 40% leadership<br/>20% design + 25% generalist"]
    ic --> rank["rank candidates<br/>for each role"]
    em --> rank

%%{init: {"fontFamily": "Inter, sans-serif", "flowchart": {"rankSpacing": 35, "wrappingWidth": 300, "subGraphTitleMargin": {"top": 12, "bottom": 36}}}}%% flowchart LR resume["candidate resume"] subgraph req["TypeSafe evaluates questions in parallel"] direction TB py["Score: Python depth"] lead["Score: team leadership"] arch["Score: system design"] general["Score: generalist"] %% Invisible links stack the questions; they are answered in parallel. py ~~~ lead ~~~ arch ~~~ general end resume -- "one request resume + 4 questions" --> req req -- "one response 4 score answers" --> normalize["normalize scores to 0–1 divide each by 4 in your code"] normalize --> ic["senior IC weights 40% Python + 10% leadership 40% design + 10% generalist"] normalize --> em["engineering manager weights 15% Python + 40% leadership 20% design + 25% generalist"] ic --> rank["rank candidates for each role"] em --> rank

第 1 步：独立地对每个维度评分

questions

{
  "questions": {
    "python_depth": {
      "type": "score",
      "instructions": "How much depth of python experience does this candidate have, based on the supplied resume?",
      "criteria": [
        "No Python experience mentioned",
        "Mentioned but no detail",
        "Used in projects, some specifics",
        "Primary language, multiple projects",
        "Deep expertise: architecture, performance, libraries"
      ]
    },
    "team_leadership": {
      "type": "score",
      "instructions": "How much experience does this candidate have managing or leading engineering teams?",
      "criteria": [
        "No management experience mentioned",
        "Informal mentorship or tech lead role",
        "Led a small team or project",
        "Managed a team with direct reports",
        "Managed multiple teams or an engineering org"
      ]
    },
    "system_design": {
      "type": "score",
      "instructions": "How much experience does this candidate have designing large-scale or distributed systems?",
      "criteria": [
        "No architecture work mentioned",
        "Contributed to design discussions",
        "Designed components of a larger system",
        "Owned architecture of a significant system",
        "Designed systems at scale across multiple domains"
      ]
    },
    "generalist": {
      "type": "score",
      "instructions": "How much evidence is there that this candidate picks up unfamiliar tools, roles, or domains outside their core specialty?",
      "criteria": [
        "Only one domain or role mentioned",
        "Some variety but within a narrow field",
        "Worked across a few different areas or tech stacks",
        "Regularly moved between domains, wore many hats",
        "Track record of ramping up in unfamiliar areas and delivering"
      ]
    }
  }
}

在 Playground 中打开 →

第 2 步：用权重组合

每个维度被归一化到 0–1 并加权。权重让你能够轻松调整每个维度的相对重要性，而不会丢失单个评分的任何细节。

scoring.py

py      = response.answers["python_depth"].score / 4
lead    = response.answers["team_leadership"].score / 4
arch    = response.answers["system_design"].score / 4
general = response.answers["generalist"].score / 4

# Senior IC
ic_score = (0.40 * py) + (0.10 * lead) + (0.40 * arch) + (0.10 * general)

# Engineering Manager
em_score = (0.15 * py) + (0.40 * lead) + (0.20 * arch) + (0.25 * general)

这让你能够基于综合得分对候选人排序。但更重要的是，它让你清楚地看到最终得分究竟是如何计算出来的。如果排名最高的候选人不符合你的预期，你可以调整权重来找到恰当的平衡。

🔬 交互实验室

改一改 state 或问题再运行，观察概率与置信度如何变化。默认运行本地演示（离线可用、非真实模型输出，仅演示答案形状）；填入 API Key 可尝试直连真实 API（官方暂未开放跨域时会给出一键复制的本地调用代码）；也可一键到官方 Playground 在线试跑。配套讲解见本页上方内容与「深入学习」素材。

state · 要判断的内容（可以是普通文本，或 JSON 字符串）

Key 仅保存在本浏览器（localStorage），不会上传到本站。留空 = 本地演示。

📓 内嵌教程 · 第 3 章 · 架构模式实验笔记本（四种模式全套可运行实验）

像运行 notebook 一样逐步学习：点「下一步」逐格展开——代码格会先出现再"运行"出结果。内容来自教程仓库配套笔记本。

0 / 73

📖 想看带真实 API Key 的完整运行与源文件：第 3 章 · 架构模式实验笔记本（四种模式全套可运行实验）（GitHub）↗