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Test: compaction summary via KV cache reuse.
Simulates appending a summary instruction to an existing conversation
(with tools defined) and verifies each model:
1. Generates a structured summary (not a tool call)
2. Follows the 8-section format
3. Does NOT call any tools despite tools being present
Usage:
python tests/test_compaction_kvcache.py
Nzhttp://macstudio.localz#https://api.gaterouter.ai/openai/v1�namezdeepseek/deepseek-chat�basezhttps://api.deepseek.com/v1�key_env�DEEPSEEK_API_KEYz!doubao/doubao-seed-2-0-pro-260215z(https://ark.cn-beijing.volces.com/api/v3�ARK_API_KEYzgoogle/gemini-2.5-flash�GATE_ROUTER_KEYzgoogle/gemini-3-flash-previewzminimax/minimax-m2.5zgoogle/gemini-3.1-pro-previewzz-ai/glm-5-turbozmoonshotai/kimi-k2.5zanthropic/claude-sonnet-4.6zllama/llamazhttp://218.22.75.183:8000/v1�LLAMA_REMOTE_KEYzollama/qwen3.5:9bzollama/qwen3.5:27bzollama/gemma4:26b�role�user�contentu? 帮我写一个Python爬虫,抓取豆瓣电影Top250的数据� assistantuZ 好的,我来帮你写一个豆瓣电影Top250的爬虫。让我先创建项目结构。�
tool_calls�id�call_1�type�function�execute_command� argumentsz8{"command":"mkdir -p douban_spider && cd douban_spider"}�tool�tool_call_idu 目录已创建�call_2�
write_fileaY {"path":"douban_spider/spider.py","content":"import requests\nfrom bs4 import BeautifulSoup\n\ndef crawl_top250():\n results = []\n for page in range(0, 250, 25):\n url = f'https://movie.douban.com/top250?start={page}'\n resp = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'})\n soup = BeautifulSoup(resp.text, 'html.parser')\n for item in soup.select('.item'):\n title = item.select_one('.title').text\n rating = item.select_one('.rating_num').text\n results.append({'title': title, 'rating': rating})\n return results\n"}u( 文件已写入: douban_spider/spider.pyu9 爬虫脚本已创建。现在让我运行测试一下。�call_3z2{"command":"cd douban_spider && python spider.py"}uk 抓取完成,共250部电影。前3部:
1. 肖申克的救赎 9.7
2. 霸王别姬 9.6
3. 阿甘正传 9.5u� 爬虫运行成功!已抓取250部电影数据。前三名是肖申克的救赎(9.7)、霸王别姬(9.6)、阿甘正传(9.5)。需要我把数据导出为CSV吗?u9 好的,导出CSV,然后帮我分析一下评分分布�call_4uA {"path":"douban_spider/export.py","content":"import csv\nfrom spider import crawl_top250\n\ndata = crawl_top250()\nwith open('top250.csv', 'w', newline='') as f:\n writer = csv.DictWriter(f, fieldnames=['title', 'rating'])\n writer.writeheader()\n writer.writerows(data)\nprint(f'导出完成: {len(data)}条')"}u( 文件已写入: douban_spider/export.py�call_5z2{"command":"cd douban_spider && python export.py"}u 导出完成: 250条u* CSV已导出。让我分析评分分布。�call_6uR {"command":"cd douban_spider && python -c \"import csv; data=[float(r['rating']) for r in csv.DictReader(open('top250.csv'))]; print(f'平均:{sum(data)/len(data):.1f}'); print(f'最高:{max(data)}'); print(f'最低:{min(data)}'); bins={f'{i/10:.0f}分段':sum(1 for d in data if i/10<=d<(i+1)/10) for i in range(80,100)}; print(bins)\""}u@ 平均:8.9
最高:9.7
最低:8.2
{'8分段': 45, '9分段': 205}u� 分析完成!豆瓣Top250评分分布:
- 平均分: 8.9
- 最高: 9.7 (肖申克的救赎)
- 最低: 8.2
- 9分以上: 205部 (82%)
- 8分段: 45部 (18%)
大部分影片都在9分以上,说明Top250的门槛很高。uN 不错!再帮我加一个功能:抓取每部电影的导演和年份信息�descriptionzRun a shell command�
parameters�object�
properties�command�string�requiredzWrite content to a file�path� read_filezRead a file�memoryz Search or store long-term memory�action�query�
web_searchzSearch the webu 请将以上对话压缩为结构化摘要。不要调用任何工具,只输出文本摘要。
按以下格式输出:
## Active Task
[当前正在做什么]
## Goal
[最终目标]
## Completed
[已完成的操作和结果]
## In Progress
[正在进行、未完成的]
## Key Data
[必须原样保留的值: 路径/文件名/数字/关键输出]
## Decisions
[已做的技术决策]
## Pending
[等用户确认的、被阻塞的]
## Files
[涉及的文件和修改]
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