ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter10/book-translation/consistency.py

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+"""
+术语一致性检查工具。
+
+思路：对每个受关注的英文术语，预先列出它在中文里“几种常见但不同”的译法。
+扫描全书各章译文，统计每个术语实际出现了几种不同译法：
+  - 只出现 1 种  → 全书一致；
+  - 出现 >= 2 种 → 术语漂移（不一致）。
+
+这不是给模型评分，而是用确定性的字符串匹配，客观度量“同一术语是否全书统一”。
+"""
+
+# 每个术语：canonical 为推荐/术语表规定译法；variants 为若干“互不相同”的常见译法。
+# 注意：variants 之间尽量不互为子串，避免重复计数（如“嵌入向量”归入“嵌入”一族）。
+TRACKED_TERMS = [
+    {"en": "token",       "canonical": "词元",  "variants": ["词元", "令牌", "标记", "token"]},
+    {"en": "embedding",   "canonical": "嵌入",  "variants": ["嵌入", "词向量", "向量表示"]},
+    {"en": "prompt",      "canonical": "提示词", "variants": ["提示词", "提示语", "提示"]},
+    {"en": "inference",   "canonical": "推理",  "variants": ["推理", "推断"]},
+    {"en": "latency",     "canonical": "时延",  "variants": ["延迟", "时延", "延时"]},
+    {"en": "attention",   "canonical": "注意力", "variants": ["注意力", "关注度"]},
+    {"en": "transformer", "canonical": "Transformer", "variants": ["Transformer", "变换器", "转换器"]},
+    {"en": "throughput",  "canonical": "吞吐量", "variants": ["吞吐量", "吞吐率", "通量"]},
+    {"en": "fine-tuning", "canonical": "微调",  "variants": ["微调", "精调"]},
+]
+
+
+import re
+
+
+def _strip_code(text):
+    """去掉围栏代码块与行内代码：代码按翻译指南原样保留英文，不应计入术语一致性统计。"""
+    text = re.sub(r"```.*?```", " ", text, flags=re.DOTALL)
+    text = re.sub(r"`[^`]*`", " ", text)
+    return text
+
+
+# 编辑部“指定术语”（house style）：为几个术语规定一个明确的、区别于模型默认译法的译名。
+# 这些译法都是合法且更精确的选择，用来考察“共享术语表能否把指定译法贯彻到全书”。
+#   mandated：术语表规定的译法；default：模型自由翻译时常用的默认译法。
+MANDATED_TERMS = [
+    {"en": "token",     "mandated": "词元",   "default": "标记"},
+    {"en": "prompt",    "mandated": "提示词", "default": "提示"},
+    {"en": "latency",   "mandated": "时延",   "default": "延迟"},
+    {"en": "embedding", "mandated": "嵌入向量", "default": "嵌入"},
+]
+
+
+def check_adherence(translations):
+    """
+    术语表遵从率：对每个“指定术语”，统计在出现该概念的章节里，
+    有多少章使用了术语表规定的译法（而非默认译法）。
+
+    这是管理者模式的核心价值：共享术语表能把指定译法贯彻到每一章；
+    单 Agent 看不到术语表，只能用自己的默认译法。
+    """
+    rows = []
+    hit_total = 0
+    concept_total = 0
+    for t in MANDATED_TERMS:
+        m, d = t["mandated"], t["default"]
+        chapters_with_concept = 0
+        chapters_adhered = 0
+        for name, raw in translations.items():
+            text = _strip_code(raw)
+            has_m = m in text
+            # default 若是 mandated 的子串（如“嵌入”是“嵌入向量”子串），需去掉 mandated 再判断
+            has_d = (d in text.replace(m, "")) if d in m else (d in text)
+            if has_m or has_d:
+                chapters_with_concept += 1
+                if has_m:
+                    chapters_adhered += 1
+        if chapters_with_concept:
+            concept_total += chapters_with_concept
+            hit_total += chapters_adhered
+            rows.append({
+                "en": t["en"], "mandated": m, "default": d,
+                "adhered": chapters_adhered, "total": chapters_with_concept,
+            })
+    rate = hit_total / concept_total if concept_total else 1.0
+    return {"rows": rows, "rate": rate}
+
+
+def _variant_in_chapter(text, variant, other_variants):
+    """
+    判断某个 variant 是否在 text 中“独立”出现。
+    对“提示”这种会成为“提示词/提示语”子串的情况：仅当去掉更长 variant 后仍出现才算。
+    """
+    longer = [v for v in other_variants if variant in v and v != variant]
+    if not longer:
+        return variant in text
+    tmp = text
+    for v in longer:
+        tmp = tmp.replace(v, "")
+    return variant in tmp
+
+
+def analyze(translations):
+    """
+    translations：{chapter_name: 译文文本}
+    返回：
+      results：每个术语的分析（用到哪些译法、是否一致、各章用法）
+      consistent_terms / total_terms / rate
+    """
+    results = []
+    consistent = 0
+    total = 0
+    for term in TRACKED_TERMS:
+        variants = term["variants"]
+        used = {}  # variant -> [出现该译法的章节]
+        for name, raw in translations.items():
+            text = _strip_code(raw)
+            for v in variants:
+                others = [x for x in variants if x != v]
+                if _variant_in_chapter(text, v, others):
+                    used.setdefault(v, []).append(name)
+        if not used:
+            # 全书都没出现该术语，跳过统计
+            continue
+        total += 1
+        distinct = list(used.keys())
+        is_consistent = len(distinct) == 1
+        if is_consistent:
+            consistent += 1
+        results.append(
+            {
+                "en": term["en"],
+                "canonical": term["canonical"],
+                "distinct_used": distinct,
+                "consistent": is_consistent,
+                "by_variant": used,
+            }
+        )
+    rate = consistent / total if total else 1.0
+    return {
+        "results": results,
+        "consistent_terms": consistent,
+        "total_terms": total,
+        "rate": rate,
+    }