文档索引
在以下地址获取完整的文档索引：https://docs.typesafe.ai/llms.txt 使用此文件可以在进一步探索之前发现所有可用页面。

双重检查引用

通过对照源文档检查来捕获错误或幻觉引用。一个 TypeSafe Choice 问题可判定引用的上下文是否支持该主张，其置信度还可标记该引用以供人工审查。

LLM 回答一个问题并附上引用：对于每个声明，对应源文档的一个部分及其所依据的引用。其中一些引用是错误的或幻觉：引用可能完全不存在于文档中，或者虽然逐字出现在文档中，但其上下文与声明的意思相反。

手动逐个检查很慢：找到文档，找到其中的引用，然后阅读足够的上下文以判断其是否支持声明。

为了自动化该检查，我们首先通过普通字符串匹配查找缺失的引用，然后使用 Choice 问题读取每个幸存引用的上下文并决定其是否支持声明。

  %%{init: {"flowchart": {"wrappingWidth": 330}}}%%
flowchart LR
    cite["source document + citation"]

    match{"is the quote<br/>in the source?"}
    fab["mark <b>fabricated</b>"]

    subgraph request[" "]
        q["Choice &mdash; how does the<br/>section relate to the claim?<br/>supports &rarr; mark <b>verified</b><br/>contradicts &rarr; mark <b>contradicted</b><br/>says nothing &rarr; mark <b>unsupported</b>"]
    end

    gate{"confidence<br/>&ge; 0.8?"}
    stand["let the verdict stand"]
    review["a human confirms it"]

    cite --> match
    %% the two edges that reach the call come first, so they stay adjacent; the
    %% string match's own verdict is declared last and lands below them
    match -- "found" --> request
    match -- "no quote" --> request
    match -- "not found" --> fab
    request --> gate
    gate --> stand
    gate --> review

以下，八个来自 LLM 关于 RFC 7519 (JSON Web Token) 的回答引用经过检查。四个准确的引用以 0.93 或更高的置信度返回了 verified。所有四个植入的失败案例都被捕获：一个伪造的引用、一个被反驳的声明，以及两个发送给人工审核的未支持引用。

check_citation() 是你在这里构建的函数，它接受一个源文档和一个引用，并返回四个判决之一：verified、unsupported、contradicted 或 fabricated。它还返回一个置信度，用于标记需要人工查看的项目。

Setup

pip install ipython "typesafe-sdk>=0.5.7" cooksafe --extra-index-url https://pypi.typesafe.ai/

然后设置 TYPESAFE_API_KEY。每次 API 调用都缓存在 json_cache.json 中，该文件随 cookbook 一起提供，因此重新运行会重现已发布的数字而不是调用 API。删除该文件以实时运行所有内容。

以下数字来自 2026-08-16 的 jev-1.12。

import json
import os
import re
from pathlib import Path
from time import perf_counter

from cooksafe import JsonCache, make_playground_link
from IPython.display import Markdown, display
from typesafe_sdk import Choice, TypeSafeClient

TYPESAFE_MODEL = "jev-1.12"
AUTO_ACCEPT = 0.8  # start high for more human review as you build trust in the model

client = TypeSafeClient(
    api_key=os.environ.get("TYPESAFE_API_KEY", "cache-only"),
    base_url=os.environ.get("TYPESAFE_ENDPOINT"),
    timeout=120.0,
)
json_cache = JsonCache(Path("json_cache.json"))

加载源和引用

来源是 RFC 7519 (JSON Web Token)，从 rfc-editor.org 获取并作为 rfc7519.txt 提交到本 cookbook 旁边。下面的代码剥离页面头部和尾部，然后将文本拆分为编号的各部分。

citations.json 中的八个引用是由 LLM 针对 RFC 编写的。四个是准确的；我们编辑了另外四个以使其无法通过检查。

def load_source() -> str:
    """RFC 7519 verbatim, minus the page headers and footers that interrupt its paragraphs."""
    lines = []
    for line in Path("rfc7519.txt").read_text().splitlines():
        bare = line.lstrip("\f")
        if re.match(r"Jones, et al\.\s.*\[Page \d+\]$", bare):
            continue
        if re.match(r"RFC 7519\s+JSON Web Token \(JWT\)\s+May 2015$", bare):
            continue
        lines.append(bare)
    return re.sub(r"\n{3,}", "\n\n", "\n".join(lines))


def split_sections(source: str) -> dict[str, str]:
    """Map each numbered section ("4.1.3") to its text, split on the RFC's header lines."""
    boundary = re.compile(r"(?m)^(?:(\d+(?:\.\d+)*)\.  .+|Appendix [A-Z]\..*)$")
    marks = list(boundary.finditer(source))
    sections = {}
    for mark, nxt in zip(marks, marks[1:] + [None]):
        if mark.group(1) is None:  # an appendix header only terminates the section before it
            continue
        sections[mark.group(1)] = source[mark.start() : nxt.start() if nxt else len(source)].strip()
    return sections


SOURCE = load_source()
SECTIONS = split_sections(SOURCE)
CITATIONS = json.loads(Path("citations.json").read_text())

print(f"{len(SOURCE):,} characters, {len(SECTIONS)} numbered sections, {len(CITATIONS)} citations")
print("\nA citation with a quote:")
print(json.dumps(CITATIONS[1], indent=2))
print("\nA claim-only citation:")
print(json.dumps(next(c for c in CITATIONS if c["quote"] is None), indent=2))

58,365 characters, 45 numbered sections, 8 citations

A citation with a quote:
{
  "id": "aud_reject",
  "claim": "If a validator does not find itself in a token's audience list, it has to reject the token.",
  "quote": "If the principal processing the claim does not identify itself with a value in the \"aud\" claim when this claim is present, then the JWT MUST be rejected.",
  "section": "4.1.3"
}

A claim-only citation:
{
  "id": "iat_future",
  "claim": "The \"iat\" claim requires validators to reject tokens whose issue time is in the future.",
  "quote": null,
  "section": "4.1.6"
}

在源中找到每个引用

不在源中的引用是伪造的，不需要模型即可找出这一点。标准化空格和弯引号，以便引用在 RFC 的行换行中仍然匹配，然后将其作为子字符串查找。匹配还会说明引用来自哪个部分，该部分是模型在下一步读取的文本。

一个引用可以命名一个部分而不对其中任何内容进行引用。在这种情况下没有可匹配的内容，因此采用引用命名的部分并直接进入模型。

def normalize(text: str) -> str:
    """Collapse whitespace and fold curly quotes, so a quote matches across line wraps."""
    table = str.maketrans({"“": '"', "”": '"', "‘": "'", "’": "'"})
    return re.sub(r"\s+", " ", text.translate(table)).strip()


def find_quote(sections: dict[str, str], quote: str) -> str | None:
    """The number of the section that contains the quote verbatim, or None."""
    needle = normalize(quote)
    for number in sorted(sections, key=lambda n: [int(p) for p in n.split(".")]):
        if needle in normalize(sections[number]):
            return number
    return None


def locate(sections: dict[str, str], citation: dict) -> tuple[str, str | None]:
    """Step 1 for one citation: a status, plus the section step 2 will read."""
    if citation["quote"] is None:
        return "section-only", sections[citation["section"]]
    number = find_quote(sections, citation["quote"])
    if number is None:
        return "missing", None
    return "found", sections[number]


for citation in CITATIONS:
    status, section = locate(SECTIONS, citation)
    where = f"section of {len(section):,} chars" if section else "not in the source"
    print(f"{citation['id']:<18}{status:<14}{where}")

epoch_seconds     found         section of 3,122 chars
aud_reject        found         section of 761 chars
sig_reporting     missing       not in the source
clock_skew        found         section of 529 chars
exp_required      found         section of 529 chars
pii_encryption    found         section of 1,653 chars
iat_future        section-only  section of 270 chars
duplicate_names   found         section of 918 chars

验证来源是否支持声明

此时仍带有引文的引用与源逐字匹配。这还不够：引文可能准确，但基于它构建的声明仍然可能是错误的。做出这个决定需要引文的上下文，即步骤 1 找到的部分。

每个幸存的引用都有一个 Choice 问题，涵盖部分与声明相关的三种方式。概率最高的选项是判决，AUTO_ACCEPT（上面代码中的 0.8）决定接下来会发生什么：

置信度 ≥ 0.8：裁决独立成立；
置信度 < 0.8：在执行任何操作前，需人工确认裁决。

从高位开始，并随着你观察模型在你自己文档上的表现而降低阈值。

QUESTIONS = {
    "relation": Choice(
        instructions="How does the section relate to the claim?",
        criteria={
            "supports": "The section states the claim or directly implies that it is true",
            "contradicts": "The section states the opposite of the claim or implies it is false",
            "says_nothing": "The section does not address what the claim asserts, either way",
        },
    ),
}

RELATION_TO_VERDICT = {
    "supports": "verified",
    "contradicts": "contradicted",
    "says_nothing": "unsupported",
}


@json_cache
def ask(claim: str, section: str) -> dict:
    started = perf_counter()
    response = client.system_one(
        state={"claim": claim, "section": section},
        questions=QUESTIONS,
        model=TYPESAFE_MODEL,
    )
    answer = response.answers["relation"]
    return {
        "choice": answer.choice,
        "probabilities": answer.probabilities,
        "confidence": answer.confidence,
        "seconds": round(perf_counter() - started, 2),
        "input_tokens": response.usage.input_tokens or 0,
        "output_tokens": response.usage.output_tokens or 0,
    }


def verdict(status: str, answer: dict | None) -> dict:
    """Fold step 1 and step 2 into one of the four labels, plus an auto-or-review flag."""
    if status == "missing":
        # confidence None: no model was called, so there is no model confidence to report
        return {"verdict": "fabricated", "confidence": None, "auto": True}
    return {
        "verdict": RELATION_TO_VERDICT[answer["choice"]],
        "confidence": answer["confidence"],
        "auto": answer["confidence"] >= AUTO_ACCEPT,
    }


def check_citation(sections: dict[str, str], citation: dict) -> dict:
    status, section = locate(sections, citation)
    answer = ask(citation["claim"], section) if section is not None else None
    return {"id": citation["id"], "status": status, "answer": answer, **verdict(status, answer)}

检查每条引用

所有八条引用都经过同一检查:

print(f"{'citation':<18}{'quote':<14}{'relation':<14}{'conf':>6}  {'verdict':<13}{'action':>7}")
for citation in CITATIONS:
    result = check_citation(SECTIONS, citation)
    answer = result["answer"]
    relation = answer["choice"] if answer else "-"
    conf = f"{answer['confidence']:.2f}" if answer else "-"
    action = "auto" if result["auto"] else "review"
    print(
        f"{result['id']:<18}{result['status']:<14}{relation:<14}{conf:>6}"
        f"  {result['verdict']:<13}{action:>7}"
    )

citation          quote         relation        conf  verdict       action
epoch_seconds     found         supports        0.93  verified        auto
aud_reject        found         supports        0.95  verified        auto
sig_reporting     missing       -                  -  fabricated      auto
clock_skew        found         supports        0.99  verified        auto
exp_required      found         contradicts     0.99  contradicted    auto
pii_encryption    found         says_nothing    0.27  unsupported   review
iat_future        section-only  says_nothing    0.56  unsupported   review
duplicate_names   found         supports        0.99  verified        auto

四个引用返回 verified，一个 fabricated，一个 contradicted，两个 unsupported。

epoch_seconds、aud_reject、clock_skew 和 duplicate_names 是四个准确的引用。所有这些都以 0.93 或更高的置信度返回 verified，远高于 AUTO_ACCEPT。
sig_reporting 从未到达模型。它的引用不在 RFC 中，因此仅字符串匹配就将其标记为 fabricated。
exp_required 逐字引用第 4.1.4 节，并且同一节指出“使用此声明是 OPTIONAL 的”，因此它是 contradicted，置信度为 0.99。
pii_encryption 和 iat_future 以 0.27 和 0.56 返回 unsupported，两者均低于阈值，因此都发送给了人工。pii_encryption 显示了为什么仅字符串匹配是不够的：它的引用逐字出现在源中，并且其来源部分并未提及该声明。

要将其指向你自己的数据，请替换 rfc7519.txt 和 citations.json。load_source() 和 split_sections() 是为 RFC 的布局编写的，因此其他形状的文件需要自己的解析。

字符串匹配在标准化后是精确的：被截断或轻微改写的引用将返回 fabricated。容忍粗略引用的生产系统需要模糊匹配。

Open it in the playground

该链接包含一个引用的声明和部分，以及问题。打开它以在浏览器中实时运行相同的调用。

example = next(c for c in CITATIONS if c["id"] == "exp_required")
_, example_section = locate(SECTIONS, example)
playground_link = make_playground_link(
    {"claim": example["claim"], "section": example_section}, QUESTIONS, models=[TYPESAFE_MODEL]
)
display(Markdown(f"🔗 [Open one citation's claim + section in the TypeSafe playground]({playground_link})"))

在 TypeSafe playground 中打开某个引用的声明 + 部分 →