wehub-resource-sync
424 lines
19 KiB
Python
424 lines
19 KiB
Python
"""
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eBay app task definitions.
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"""
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# -- Task Index (auto-generated, do not edit) --
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# 8 tasks | L1×1 L2×2 L3×4 L4×1
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#
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# [L1] SwitchTheme 把 eBay 的主题切换成{theme}。
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# [L2] SortSearchResults 在 eBay 搜索「{query}」，按{sort}排序。
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# [L2] SearchFirstResult 在 eBay 搜索「{query}」，告诉我第一个商品{metric}。
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# [L3] CountSonyHeadphonesEurope 帮我看看 eBay 上{location}发货的{condition}{brand}{query}，有多少个。
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# [L3] CountNikeSneakersInRange eBay 上{location}发货的{brand}{query}，要{condition}的，{price_min} 到 {price_max} 块之间的有多少个？
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# [L4] FindCheapestProduct 我想买个{location}发货的{brand}{query}，要{condition}的，最便宜的是哪一个，算上运费多少钱？
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# [L3] CompareTwoProductPrices 帮我在 eBay 上分别搜亚洲发货的{item1}和{item2}，要全新的，看看各自{extreme}的算上运费多少钱，哪个{comparison}？
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# [L3] CompareTwoGroupCounts 帮我比较两组筛选结果：{location1}发货的{condition1} {brand1} {query1}里，{price_min1} 到 {price_max1} 块的；以及{location2}发货的{condition2} {brand2} {query2}里，{price_min2} 到 {price_max2} 块的。哪个选择更多，各有多少个？
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# -- End Task Index --
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from __future__ import annotations
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from typing import Any
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from bench_env.task.base import BaseTask
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from bench_env.task.common_tasks import AnswerTask, CriteriaTask, build_answer_checks
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from bench_env.task.ebay.app import (
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EBAY_CATEGORY_VALUES,
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EBAY_SEARCH_QUERY_PARAM,
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EBAY_SORT_PARAM,
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EBAY_THEME_PARAM,
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Ebay,
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build_compare_counts_checks,
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build_compare_two_totals_checks,
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expect_count,
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expect_top,
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)
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from bench_env.task.judge import JudgeInput
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# =============================================================================
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# L1 — Atomic navigation & simple settings
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# =============================================================================
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class SwitchTheme(CriteriaTask):
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templates = [
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"把 eBay 的主题切换成{theme}。",
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"帮我把 eBay 设成{theme}主题。",
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]
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apps = ["ebay"]
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scope = "S1"
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objective = "operate"
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composition = "atomic"
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difficulty = "L1"
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capabilities = ["settings"]
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parameters = {"theme": EBAY_THEME_PARAM}
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criteria = {"settings.themeId": "{theme}"}
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optimal_paths = [["tab.me", "me.settings.open"]]
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async def _post_sample(self, env):
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await self._invert_criteria(env)
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class SortSearchResults(CriteriaTask):
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templates = [
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"在 eBay 搜索「{query}」，按{sort}排序。",
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"帮我搜一下 eBay 上的「{query}」，结果按{sort}排列。",
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]
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apps = ["ebay"]
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scope = "S1"
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objective = "operate"
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composition = "sequential"
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difficulty = "L2"
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capabilities = ["search"]
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parameters = {"query": EBAY_SEARCH_QUERY_PARAM, "sort": EBAY_SORT_PARAM}
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criteria = {
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"search.current.query": "{query}",
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"search.current.sortOption": "{sort}",
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}
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optimal_paths = [["tab.search"]]
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expected_changes = ["search.current", "search.history", "search.lastCompare", "recentSearches"]
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class SearchFirstResult(CriteriaTask):
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templates = [
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"在 eBay 搜索「{query}」，告诉我第一个商品{metric}。",
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"帮我看一下 eBay 搜「{query}」时排在最前面的商品{metric}。",
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]
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apps = ["ebay"]
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scope = "S1"
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objective = "hybrid"
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composition = "sequential"
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difficulty = "L2"
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capabilities = ["search", "extract"]
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parameters = {
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"query": EBAY_SEARCH_QUERY_PARAM,
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"metric": {
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"type": "enum",
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"values": {"叫什么": "title", "算上运费一共多少钱": "total_cost"},
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"default": "title",
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"description": "查询指标",
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},
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}
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criteria = {"search.current.query": "{query}"}
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optimal_paths = [["tab.search"]]
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expected_changes = ["search.current", "search.history", "search.lastCompare", "recentSearches"]
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@property
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def answer_fields(self): # type: ignore[override]
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metric = getattr(self.p, "metric", None)
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label = next(
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(k for k, v in self.parameters["metric"]["values"].items() if v == metric),
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"商品信息",
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)
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t = "number" if metric == "total_cost" else "text"
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field: dict = {"type": t, "label": label}
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if metric == "title":
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field["hint"] = "请填写商品完整标题"
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elif metric == "total_cost":
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field["hint"] = "填写¥金额数字"
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return [field]
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def get_answer(self, input: JudgeInput) -> Any:
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product = expect_top(query=self.p.query, sort_id="bestMatch", n=1)[0]
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if self.p.metric == "title":
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return product.title
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return round(product.total_cost, 2)
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# =============================================================================
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# L3 — Multi-filter search tasks
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# =============================================================================
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class CountSonyHeadphonesEurope(BaseTask):
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templates = [
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"帮我看看 eBay 上{location}发货的{condition}{brand}{query}，有多少个。",
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"eBay 里{location}发货的{condition}{brand}{query}有几个？",
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]
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apps = ["ebay"]
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scope = "S1"
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objective = "hybrid"
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composition = "sequential"
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difficulty = "L3"
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capabilities = ["search", "extract"]
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parameters = {
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"query": {"type": "string", "default": "耳机", "description": "搜索词"},
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"brand": {"type": "string", "default": "Sony", "description": "品牌"},
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"location": {"type": "string", "default": "欧洲", "description": "发货地"},
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"condition": {"type": "string", "default": "全新", "description": "成色"},
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"_case": {
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"sampler": Ebay.sample_brand_location_case,
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"fields": {"query": "query", "brand": "brand", "location": "location", "condition": "condition"},
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},
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}
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expected_changes = ["search.current", "search.history", "search.lastCompare", "recentSearches"]
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optimal_paths = [["tab.search"]]
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answer_fields = [{"type": "number", "label": "商品数量"}]
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def get_answer(self, input: JudgeInput) -> Any:
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return expect_count(
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query=self.p.query, brand=self.p.brand,
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condition=self.p.condition, location=self.p.location,
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)
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def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]:
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ebay = Ebay(input.apps["ebay"])
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checks: list[dict[str, Any]] = []
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checks.append(ebay.check_has_snapshot(
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query=self.p.query, brand=self.p.brand,
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condition=self.p.condition, location=self.p.location,
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))
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checks.extend(build_answer_checks(self.get_answer(input), input.answer))
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return checks
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class CountNikeSneakersInRange(BaseTask):
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templates = [
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"eBay 上{location}发货的{brand}{query}，要{condition}的，{price_min} 到 {price_max} 块之间的有多少个？",
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"帮我看看{location}发货的{condition}的{brand}{query}里，{price_min} 到 {price_max} 这个价位有多少个。",
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]
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apps = ["ebay"]
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scope = "S1"
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objective = "hybrid"
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composition = "sequential"
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difficulty = "L3"
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capabilities = ["search", "extract"]
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parameters = {
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"query": {"type": "string", "default": "运动鞋", "description": "搜索词"},
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"brand": {"type": "string", "default": "Nike", "description": "品牌"},
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"location": {"type": "string", "default": "欧洲", "description": "发货地"},
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"condition": {"type": "string", "default": "全新", "description": "成色"},
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"price_min": {"type": "string", "default": "510", "description": "总价下限"},
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"price_max": {"type": "string", "default": "540", "description": "总价上限"},
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"_case": {
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"sampler": Ebay.sample_range_case,
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"fields": {
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"query": "query", "brand": "brand", "location": "location",
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"condition": "condition", "price_min": "price_min", "price_max": "price_max",
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},
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},
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}
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expected_changes = ["search.current", "search.history", "search.lastCompare", "recentSearches"]
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optimal_paths = [["tab.search"]]
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answer_fields = [{"type": "number", "label": "商品数量"}]
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def get_answer(self, input: JudgeInput) -> Any:
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return expect_count(
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query=self.p.query, brand=self.p.brand,
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condition=self.p.condition, location=self.p.location,
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min_total=float(self.p.price_min), max_total=float(self.p.price_max),
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)
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def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]:
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ebay = Ebay(input.apps["ebay"])
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checks: list[dict[str, Any]] = []
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checks.append(ebay.check_has_snapshot(
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query=self.p.query, brand=self.p.brand,
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condition=self.p.condition, location=self.p.location,
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price_min=self.p.price_min, price_max=self.p.price_max,
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))
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checks.extend(build_answer_checks(self.get_answer(input), input.answer))
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return checks
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class FindCheapestProduct(AnswerTask):
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templates = [
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"我想买个{location}发货的{brand}{query}，要{condition}的，最便宜的是哪一个，算上运费多少钱？",
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"帮我找一下 eBay 上{location}发货、{condition}的{brand}{query}里最便宜的那个，告诉我算上运费多少钱。",
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]
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apps = ["ebay"]
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scope = "S1"
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objective = "query"
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composition = "sequential"
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difficulty = "L4"
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max_steps = 45
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capabilities = ["search", "extract"]
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parameters = {
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"query": {"type": "string", "default": "吸尘器", "description": "搜索词"},
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"brand": {"type": "string", "default": "Dyson", "description": "品牌"},
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"location": {"type": "string", "default": "亚洲", "description": "发货地"},
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"condition": {"type": "string", "default": "全新", "description": "成色"},
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"_case": {
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"sampler": Ebay.sample_brand_location_case,
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"fields": {"query": "query", "brand": "brand", "location": "location", "condition": "condition"},
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},
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}
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expected_changes = ["search.current", "search.history", "search.lastCompare", "recentSearches"]
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optimal_paths = [["tab.search"]]
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answer_fields = [
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{"type": "text", "label": "最便宜商品的标题", "hint": "如：Dyson V15 Detect"},
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{"type": "number", "label": "总价(¥)"},
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]
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def get_answer(self, input: JudgeInput) -> Any:
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ebay = Ebay(input.apps["ebay"])
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return ebay.cheapest_product_answer(
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query=self.p.query, brand=self.p.brand,
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condition=self.p.condition, location=self.p.location,
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)
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def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]:
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ebay = Ebay(input.apps["ebay"])
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checks = [ebay.check_has_snapshot(
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query=self.p.query, brand=self.p.brand,
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condition=self.p.condition, location=self.p.location,
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)]
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checks.extend(build_answer_checks(self.get_answer(input), input.answer))
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return checks
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# =============================================================================
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# L4 — Deep-dive comparisons
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# =============================================================================
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class CompareTwoProductPrices(BaseTask):
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templates = [
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"帮我在 eBay 上分别搜亚洲发货的{item1}和{item2}，要全新的，看看各自{extreme}的算上运费多少钱，哪个{comparison}？",
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"帮我比较一下 eBay 上亚洲发货的全新的{item1}和{item2}，各自{extreme}的算上运费各是多少？哪个{comparison}？",
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]
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apps = ["ebay"]
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scope = "S1"
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objective = "hybrid"
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composition = "deep_dive"
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difficulty = "L3"
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max_steps = 60
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capabilities = ["search", "extract", "reasoning"]
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parameters = {
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"item1": {"type": "string", "default": "电脑", "description": "第一个商品"},
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"item2": {"type": "string", "default": "电视", "description": "第二个商品"},
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"sort_id": {"type": "string", "default": "priceLow", "description": "排序方式"},
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"extreme": {"type": "string", "default": "最便宜", "description": "极值描述"},
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"comparison": {"type": "string", "default": "更便宜", "description": "比较词"},
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"_pair": {
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"sampler": Ebay.sample_compare_pair,
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"fields": {
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"item1": "item1", "item2": "item2",
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"sort_id": "sort_id",
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"extreme": "extreme", "comparison": "comparison",
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},
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},
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}
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optimal_paths = [["tab.search"]]
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expected_changes = ["search.current", "search.history", "search.lastCompare", "recentSearches"]
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answer_fields = [
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{"type": "choice", "label": "价格{comparison}的", "options": ["{item1}{comparison}", "{item2}{comparison}", "相同"]},
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{"type": "number", "label": "{item1}总价(¥)"},
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{"type": "number", "label": "{item2}总价(¥)"},
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]
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def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]:
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ebay = Ebay(input.apps["ebay"])
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t1, t2 = Ebay.compare_top_totals(
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self.p.item1, self.p.item2,
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condition="全新", location="亚洲", sort_id=self.p.sort_id,
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)
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if self.p.sort_id == "priceLow":
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winner = self.p.item1 if t1 < t2 else (self.p.item2 if t2 < t1 else "相同")
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else:
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winner = self.p.item1 if t1 > t2 else (self.p.item2 if t2 > t1 else "相同")
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checks = [
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ebay.check_has_snapshot(
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query=self.p.item1, condition="全新",
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location="亚洲", field=f"history.{self.p.item1}_search",
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),
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ebay.check_has_snapshot(
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query=self.p.item2, condition="全新",
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location="亚洲", field=f"history.{self.p.item2}_search",
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),
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]
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checks.extend(build_compare_two_totals_checks(
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label_expected=winner, label_key="winner",
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first_total=t1, first_key=f"{self.p.item1}Total",
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second_total=t2, second_key=f"{self.p.item2}Total",
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winner_marker_words=[self.p.comparison], answer=input.answer,
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))
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return checks
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class CompareTwoGroupCounts(BaseTask):
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templates = [
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"帮我比较两组筛选结果：{location1}发货的{condition1} {brand1} {query1}里，{price_min1} 到 {price_max1} 块的；以及{location2}发货的{condition2} {brand2} {query2}里，{price_min2} 到 {price_max2} 块的。哪个选择更多，各有多少个？",
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"我想对比两个范围：{location1}发货的{condition1} {brand1} {query1}（{price_min1} 到 {price_max1}）和{location2}发货的{condition2} {brand2} {query2}（{price_min2} 到 {price_max2}）。哪个结果更多，把两个数量都告诉我。",
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]
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apps = ["ebay"]
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scope = "S1"
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objective = "hybrid"
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composition = "deep_dive"
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difficulty = "L3"
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max_steps = 60
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capabilities = ["search", "extract", "reasoning"]
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parameters = {
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"query1": {"type": "string", "default": "耳机"},
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"brand1": {"type": "string", "default": "Sony"},
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"location1": {"type": "string", "default": "欧洲"},
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"condition1": {"type": "string", "default": "全新"},
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"price_min1": {"type": "string", "default": "620"},
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"price_max1": {"type": "string", "default": "690"},
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"query2": {"type": "string", "default": "运动鞋"},
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"brand2": {"type": "string", "default": "Nike"},
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"location2": {"type": "string", "default": "欧洲"},
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"condition2": {"type": "string", "default": "全新"},
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"price_min2": {"type": "string", "default": "510"},
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"price_max2": {"type": "string", "default": "540"},
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"_groups": {
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"sampler": Ebay.sample_compare_counts_groups,
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"fields": {
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"query1": "query1", "brand1": "brand1", "location1": "location1",
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"condition1": "condition1", "price_min1": "price_min1", "price_max1": "price_max1",
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"query2": "query2", "brand2": "brand2", "location2": "location2",
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"condition2": "condition2", "price_min2": "price_min2", "price_max2": "price_max2",
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},
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},
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}
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optimal_paths = [["tab.search"]]
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expected_changes = ["search.current", "search.history", "search.lastCompare", "recentSearches"]
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answer_fields = [
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{"type": "choice", "label": "选择更多的", "options": ["{query1}更多", "{query2}更多", "数量相同"]},
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{"type": "number", "label": "{query1}数量"},
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{"type": "number", "label": "{query2}数量"},
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]
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def check_goals(self, input: JudgeInput) -> list[dict[str, Any]]:
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ebay = Ebay(input.apps["ebay"])
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snap1 = ebay.find_latest_snapshot(
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query=self.p.query1, brand=self.p.brand1, condition=self.p.condition1,
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location=self.p.location1, price_min=self.p.price_min1, price_max=self.p.price_max1,
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)
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snap2 = ebay.find_latest_snapshot(
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query=self.p.query2, brand=self.p.brand2, condition=self.p.condition2,
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location=self.p.location2, price_min=self.p.price_min2, price_max=self.p.price_max2,
|
||
)
|
||
c1 = expect_count(
|
||
query=self.p.query1, brand=self.p.brand1, condition=self.p.condition1,
|
||
location=self.p.location1, min_total=int(self.p.price_min1), max_total=int(self.p.price_max1),
|
||
)
|
||
c2 = expect_count(
|
||
query=self.p.query2, brand=self.p.brand2, condition=self.p.condition2,
|
||
location=self.p.location2, min_total=int(self.p.price_min2), max_total=int(self.p.price_max2),
|
||
)
|
||
if snap1 and isinstance(snap1.get("resultsCount"), (int, float)):
|
||
c1 = int(snap1["resultsCount"])
|
||
if snap2 and isinstance(snap2.get("resultsCount"), (int, float)):
|
||
c2 = int(snap2["resultsCount"])
|
||
more = self.p.query1 if c1 > c2 else (self.p.query2 if c2 > c1 else "相同")
|
||
checks = [
|
||
ebay.check_has_snapshot(
|
||
query=self.p.query1, brand=self.p.brand1, condition=self.p.condition1,
|
||
location=self.p.location1, price_min=self.p.price_min1, price_max=self.p.price_max1,
|
||
field=f"history.{self.p.query1}_search",
|
||
),
|
||
ebay.check_has_snapshot(
|
||
query=self.p.query2, brand=self.p.brand2, condition=self.p.condition2,
|
||
location=self.p.location2, price_min=self.p.price_min2, price_max=self.p.price_max2,
|
||
field=f"history.{self.p.query2}_search",
|
||
),
|
||
]
|
||
checks.extend(build_compare_counts_checks(
|
||
more_expected=more,
|
||
label1=self.p.query1, count1=c1,
|
||
label2=self.p.query2, count2=c2,
|
||
answer=input.answer,
|
||
))
|
||
return checks
|