evalution 标注标准优化

Made-with: Cursor

evalution 标注标准优化
Made-with: Cursor
tangwang
1 parent c81b0fc1
Showing 2 changed files with 246 additions and 64 deletions Show diff stats
.gitignore
scripts/evaluation/eval_framework/prompts.py
@@ -77,3 +77,8 @@ logs_*/
  
 models/
 model_cache/
+
+# Search evaluation: local SQLite DB and generated outputs (large / regenerable)
+artifacts/search_evaluation/*.sqlite3
+artifacts/search_evaluation/batch_reports/
+artifacts/search_evaluation/tuning_runs/
@@ -5,62 +5,251 @@ from __future__ import annotations
 import json
 from typing import Any, Dict, Sequence
  
+_CLASSIFY_BATCH_SIMPLE_TEMPLATE = """You are a relevance evaluation assistant for an apparel e-commerce search system. 
+Given the user query and each product's information, assign one relevance label to each product.
+
+## Relevance Labels
+
+### Exact
+The product fully satisfies the user's search intent.
+
+Use Exact when:
+- The product matches the core product type named in the query.
+- The key requirements explicitly stated in the query are satisfied.
+- There is no clear conflict with any explicit user requirement.
+
+Typical cases:
+- The query is only a product type, and the product is exactly that product type.
+- The query includes product type + attributes, and the product matches the type and those attributes.
+
+### Partial
+The product satisfies the user's primary intent, but does not fully satisfy all specified details.
+
+Use Partial when:
+- The core product type matches, but some requested attributes cannot be confirmed.
+- The core product type matches, but only some secondary attributes are satisfied.
+- The core product type matches, and there are minor or non-critical deviations from the query.
+- The product does not clearly contradict the user's explicit requirements, but it also cannot be considered a full match.
+
+Typical cases:
+- Query: "red fitted t-shirt", product: "Women's T-Shirt" → color/fit cannot be confirmed.
+- Query: "red fitted t-shirt", product: "Blue Fitted T-Shirt" → product type and fit match, but color differs.
+- Query: "cotton long sleeve blouse", product: "Long Sleeve Blouse" → material not confirmed.
+
+Important:
+Partial should mainly be used when the core product type is correct, but the detailed requirements are incomplete, uncertain, or only partially matched.
+
+### Irrelevant
+The product does not satisfy the user's main shopping intent.
+
+Use Irrelevant when:
+- The core product type does not match the query.
+- The product matches the general category but is a different product type that shoppers would not consider interchangeable.
+- The core product type matches, but the product clearly contradicts an explicit and important requirement in the query.
+
+Typical cases:
+- Query: "pants", product: "shoes" → wrong product type.
+- Query: "dress", product: "skirt" → different product type.
+- Query: "fitted pants", product: "loose wide-leg pants" → explicit contradiction on fit.
+- Query: "sleeveless dress", product: "long sleeve dress" → explicit contradiction on sleeve style.
+
+## Decision Principles
+
+1. Product type is the highest-priority factor.
+   If the query clearly specifies a concrete product type, the result must match that product type to be Exact or Partial.
+   A different product type is usually Irrelevant, not Partial.
+
+2. Similar or related product types are not interchangeable when the query is specific.
+   For example:
+   - dress vs skirt vs jumpsuit
+   - jeans vs pants
+   - t-shirt vs blouse
+   - cardigan vs sweater
+   - boots vs shoes
+   - bra vs top
+   - backpack vs bag
+   If the user explicitly searched for one of these, the others should usually be judged Irrelevant.
+
+3. If the core product type matches, then evaluate attributes.
+   - If attributes fully match → Exact
+   - If attributes are missing, uncertain, or only partially matched → Partial
+   - If attributes clearly contradict an explicit important requirement → Irrelevant
+
+4. Distinguish carefully between "not mentioned" and "contradicted".
+   - If an attribute is not mentioned or cannot be verified, prefer Partial.
+   - If an attribute is explicitly opposite to the query, use Irrelevant.
+
+5. Do not overuse Exact.
+   Exact requires strong evidence that the product satisfies the user's stated intent, not just the general category.
+
+Query: {query}
+
+Products:
+{lines}
+
+## Output Format
+Strictly output {n} lines, each line containing exactly one of:
+Exact
+Partial
+Irrelevant
+
+The lines must correspond sequentially to the products above.
+Do not output any other information.
+"""
+
+_CLASSIFY_BATCH_SIMPLE_TEMPLATE__zh = """你是一个服装电商搜索系统的相关性评估助手。
+给定用户查询和每个产品的信息，为每个产品分配一个相关性标签。
+
+## 相关性标签
+
+### 完全相关
+该产品完全满足用户的搜索意图。
+
+在以下情况使用完全相关：
+- 产品与查询中指定的核心产品类型相匹配。
+- 满足了查询中明确说明的关键要求。
+- 与用户明确的任何要求没有明显冲突。
+
+典型情况：
+- 查询仅包含产品类型，而产品恰好是该产品类型。
+- 查询包含产品类型 + 属性，而产品与该类型及这些属性相匹配。
+
+### 部分相关
+该产品满足了用户的主要意图，但并未完全满足所有指定的细节。
+
+在以下情况使用部分相关：
+- 核心产品类型匹配，但部分请求的属性无法确认。
+- 核心产品类型匹配，但仅满足了部分次要属性。
+- 核心产品类型匹配，但与查询存在微小或非关键的偏差。
+- 产品未明显违背用户的明确要求，但也不能视为完全匹配。
+
+典型情况：
+- 查询："红色修身T恤"，产品："女士T恤" → 颜色/版型无法确认。
+- 查询："红色修身T恤"，产品："蓝色修身T恤" → 产品类型和版型匹配，但颜色不同。
+- 查询："棉质长袖衬衫"，产品："长袖衬衫" → 材质未确认。
+
+重要提示：
+部分相关主要应在核心产品类型正确，但详细要求不完整、不确定或仅部分匹配时使用。
+
+### 不相关
+该产品不满足用户的主要购物意图。
+
+在以下情况使用不相关：
+- 核心产品类型与查询不匹配。
+- 产品匹配了大致类别，但属于购物者不会认为可互换的不同产品类型。
+- 核心产品类型匹配，但产品明显违背了查询中一个明确且重要的要求。
+
+典型情况：
+- 查询："裤子"，产品："鞋子" → 错误的产品类型。
+- 查询："连衣裙"，产品："半身裙" → 不同的产品类型。
+- 查询："修身裤"，产品："宽松阔腿裤" → 版型上明显矛盾。
+- 查询："无袖连衣裙"，产品："长袖连衣裙" → 袖型上明显矛盾。
+
+## 决策原则
+
+1. 产品类型是最高优先级的因素。
+   如果查询明确指定了具体产品类型，结果必须匹配该产品类型才能被评为完全相关或部分相关。
+   不同的产品类型通常是不相关，而非部分相关。
+
+2. 当查询明确时，相似或相关的产品类型不可互换。
+   例如：
+   - 连衣裙 vs 半身裙 vs 连体裤
+   - 牛仔裤 vs 裤子
+   - T恤 vs 衬衫
+   - 开衫 vs 毛衣
+   - 靴子 vs 鞋子
+   - 文胸 vs 上衣
+   - 双肩包 vs 包
+   如果用户明确搜索了其中一种，其他的通常应判断为不相关。
+
+3. 如果核心产品类型匹配，则评估属性。
+   - 如果属性完全匹配 → 完全相关
+   - 如果属性缺失、不确定或仅部分匹配 → 部分相关
+   - 如果属性明显违背明确的重点要求 → 不相关
+
+4. 仔细区分“未提及”和“矛盾”。
+   - 如果属性未提及或无法验证，倾向于部分相关。
+   - 如果属性与查询明确相反，使用不相关。
+
+5. 不要过度使用完全相关。
+   完全相关需要强有力的证据表明产品满足了用户声明的意图，而不仅仅是通用类别。
+
+查询: {query}
+
+产品:
+{lines}
+
+## 输出格式
+严格输出 {n} 行，每行包含以下之一：
+Exact
+Partial
+Irrelevant
+
+这些行必须按顺序对应上面的产品。
+不要输出任何其他信息。
+"""
+
+
  
 def classify_batch_simple_prompt(query: str, numbered_doc_lines: Sequence[str]) -> str:
     lines = "\n".join(numbered_doc_lines)
     n = len(numbered_doc_lines)
-    return (
-        "You are an e-commerce search result relevance evaluation assistant. "
-        "Based on the user query and each product's information, output the relevance level for each product.\n\n"
-        "## Relevance Level Criteria\n"
-        "Exact — Fully matches the user's search intent.\n"
-        "Partial — Primary intent satisfied (same category or similar use, basically aligns with search intent), "
-        "but secondary attributes such as color, style, size, fit, length, or material deviate from or cannot be confirmed.\n"
-        "Irrelevant — Category or use case mismatched, primary intent not satisfied.\n\n"
-        "Additional judging guidance:\n"
-        "- If the query clearly names a product type, product type matching has the highest priority. "
-        "Dress vs skirt vs jumpsuit, jeans vs pants, T-shirt vs blouse, cardigan vs sweater, boots vs shoes, "
-        "bra vs top, backpack vs bag are not interchangeable.\n"
-        "- When the query clearly specifies a concrete product type, a different product type should usually be Irrelevant, not Partial.\n"
-        "- If an attribute looks missing or uncertain, prefer Partial instead of Exact.\n"
-        "- Do not guess missing attributes.\n"
-        "- Graphic, slogan, holiday, memorial, or message tees are not Exact for a plain tee query unless that graphic/theme is requested.\n"
-        "- Be conservative with Exact.\n\n"
-        f"Query: {query}\n\n"
-        "Products:\n"
-        f"{lines}\n\n"
-        "## Output Format\n"
-        f"Strictly output {n} lines, each line containing exactly one of Exact / Partial / Irrelevant. "
-        "They must correspond sequentially to the products above. Do not output any other information.\n"
-    )
+    return _CLASSIFY_BATCH_SIMPLE_TEMPLATE.format(query=query, lines=lines, n=n)
+
+
+_EXTRACT_QUERY_PROFILE_TEMPLATE = """You are building a structured intent profile for e-commerce relevance judging.
+Use the original user query as the source of truth. Parser hints may help, but if a hint conflicts with the original query, trust the original query.
+Be conservative: only mark an attribute as required if the user explicitly asked for it.
+
+Return JSON with this schema:
+{{
+  "normalized_query_en": string,
+  "primary_category": string,
+  "allowed_categories": [string],
+  "required_attributes": [
+    {{"name": string, "required_terms": [string], "conflicting_terms": [string], "match_mode": "explicit"}}
+  ],
+  "notes": [string]
+}}
+
+Guidelines:
+- Exact later will require explicit evidence for all required attributes.
+- allowed_categories should contain only near-synonyms of the same product type, not substitutes. For example dress can allow midi dress/cocktail dress, but not skirt, top, jumpsuit, or outfit unless the query explicitly asks for them.
+- If the query asks for dress/skirt/jeans/t-shirt, near but different product types are not Exact.
+- If the query includes color, fit, silhouette, or length, include them as required_attributes.
+- For fit words, include conflicting terms when obvious, e.g. fitted conflicts with oversized/loose; oversized conflicts with fitted/tight.
+- For color, include conflicting colors only when clear from the query.
+
+Original query: {query}
+Parser hints JSON: {hints_json}
+"""
  
  
 def extract_query_profile_prompt(query: str, parser_hints: Dict[str, Any]) -> str:
     hints_json = json.dumps(parser_hints, ensure_ascii=False)
-    return (
-        "You are building a structured intent profile for e-commerce relevance judging.\n"
-        "Use the original user query as the source of truth. Parser hints may help, but if a hint conflicts with the original query, trust the original query.\n"
-        "Be conservative: only mark an attribute as required if the user explicitly asked for it.\n\n"
-        "Return JSON with this schema:\n"
-        "{\n"
-        '  "normalized_query_en": string,\n'
-        '  "primary_category": string,\n'
-        '  "allowed_categories": [string],\n'
-        '  "required_attributes": [\n'
-        '    {"name": string, "required_terms": [string], "conflicting_terms": [string], "match_mode": "explicit"}\n'
-        "  ],\n"
-        '  "notes": [string]\n'
-        "}\n\n"
-        "Guidelines:\n"
-        "- Exact later will require explicit evidence for all required attributes.\n"
-        "- allowed_categories should contain only near-synonyms of the same product type, not substitutes. For example dress can allow midi dress/cocktail dress, but not skirt, top, jumpsuit, or outfit unless the query explicitly asks for them.\n"
-        "- If the query asks for dress/skirt/jeans/t-shirt, near but different product types are not Exact.\n"
-        "- If the query includes color, fit, silhouette, or length, include them as required_attributes.\n"
-        "- For fit words, include conflicting terms when obvious, e.g. fitted conflicts with oversized/loose; oversized conflicts with fitted/tight.\n"
-        "- For color, include conflicting colors only when clear from the query.\n\n"
-        f"Original query: {query}\n"
-        f"Parser hints JSON: {hints_json}\n"
-    )
+    return _EXTRACT_QUERY_PROFILE_TEMPLATE.format(query=query, hints_json=hints_json)
+
+
+_CLASSIFY_BATCH_COMPLEX_TEMPLATE = """You are an e-commerce search relevance judge.
+Judge each product against the structured query profile below.
+
+Relevance rules:
+- Exact: product type matches the target intent, and every explicit required attribute is positively supported by the title/options/tags/category. If an attribute is missing or only guessed, it is NOT Exact.
+- Partial: main product type/use case matches, but some required attribute is missing, weaker, uncertain, or only approximately matched.
+- Irrelevant: product type/use case mismatched, or an explicit required attribute clearly conflicts.
+- Be conservative with Exact.
+- Graphic/holiday/message tees are not Exact for a plain color/style tee query unless that graphic/theme was requested.
+- Jumpsuit/romper/set is not Exact for dress/skirt/jeans queries.
+
+Original query: {query}
+Structured query profile JSON: {profile_json}
+
+Products:
+{lines}
+
+Return JSON only, with schema:
+{{"labels":[{{"index":1,"label":"Exact","reason":"short phrase"}}]}}
+"""
  
  
 def classify_batch_complex_prompt(
@@ -70,20 +259,8 @@ def classify_batch_complex_prompt(
 ) -> str:
     lines = "\n".join(numbered_doc_lines)
     profile_json = json.dumps(query_profile, ensure_ascii=False)
-    return (
-        "You are an e-commerce search relevance judge.\n"
-        "Judge each product against the structured query profile below.\n\n"
-        "Relevance rules:\n"
-        "- Exact: product type matches the target intent, and every explicit required attribute is positively supported by the title/options/tags/category. If an attribute is missing or only guessed, it is NOT Exact.\n"
-        "- Partial: main product type/use case matches, but some required attribute is missing, weaker, uncertain, or only approximately matched.\n"
-        "- Irrelevant: product type/use case mismatched, or an explicit required attribute clearly conflicts.\n"
-        "- Be conservative with Exact.\n"
-        "- Graphic/holiday/message tees are not Exact for a plain color/style tee query unless that graphic/theme was requested.\n"
-        "- Jumpsuit/romper/set is not Exact for dress/skirt/jeans queries.\n\n"
-        f"Original query: {query}\n"
-        f"Structured query profile JSON: {profile_json}\n\n"
-        "Products:\n"
-        f"{lines}\n\n"
-        "Return JSON only, with schema:\n"
-        '{"labels":[{"index":1,"label":"Exact","reason":"short phrase"}]}\n'
+    return _CLASSIFY_BATCH_COMPLEX_TEMPLATE.format(
+        query=query,
+        profile_json=profile_json,
+        lines=lines,
     )