2026-06-25 03:00:15 [scrapy.utils.log] INFO: Scrapy 2.12.0 started (bot: SwiggyReviewData)
2026-06-25 03:00:15 [scrapy.utils.log] INFO: Versions: lxml 6.0.2.0, libxml2 2.14.6, cssselect 1.3.0, parsel 1.10.0, w3lib 2.3.1, Twisted 25.5.0, Python 3.12.3 (main, Mar 23 2026, 19:04:32) [GCC 13.3.0], pyOpenSSL 25.1.0 (OpenSSL 3.5.2 5 Aug 2025), cryptography 45.0.6, Platform Linux-6.17.0-1013-aws-x86_64-with-glibc2.39
2026-06-25 03:00:15 [scrapy.addons] INFO: Enabled addons:
[]
2026-06-25 03:00:15 [asyncio] DEBUG: Using selector: EpollSelector
2026-06-25 03:00:15 [scrapy.utils.log] DEBUG: Using reactor: twisted.internet.asyncioreactor.AsyncioSelectorReactor
2026-06-25 03:00:15 [scrapy.utils.log] DEBUG: Using asyncio event loop: asyncio.unix_events._UnixSelectorEventLoop
2026-06-25 03:00:15 [scrapy.utils.log] DEBUG: Using reactor: twisted.internet.asyncioreactor.AsyncioSelectorReactor
2026-06-25 03:00:15 [scrapy.utils.log] DEBUG: Using asyncio event loop: asyncio.unix_events._UnixSelectorEventLoop
2026-06-25 03:00:15 [scrapy.extensions.telnet] INFO: Telnet Password: 6b9f41cd82cde740
2026-06-25 03:00:15 [scrapy.middleware] INFO: Enabled extensions:
['scrapy.extensions.corestats.CoreStats',
 'scrapy.extensions.telnet.TelnetConsole',
 'scrapy.extensions.memusage.MemoryUsage',
 'scrapy.extensions.logstats.LogStats']
2026-06-25 03:00:15 [scrapy.crawler] INFO: Overridden settings:
{'BOT_NAME': 'SwiggyReviewData',
 'FEED_EXPORT_ENCODING': 'utf-8',
 'LOG_FILE': '/home/ubuntu/logs/SwiggyReviewData/swiggy_review_data/f98cdbbf704111f1a8fb0aab37b1cebd.log',
 'NEWSPIDER_MODULE': 'SwiggyReviewData.spiders',
 'SPIDER_MODULES': ['SwiggyReviewData.spiders'],
 'TWISTED_REACTOR': 'twisted.internet.asyncioreactor.AsyncioSelectorReactor'}
2026-06-25 03:00:15 [scrapy.middleware] INFO: Enabled downloader middlewares:
['scrapy.downloadermiddlewares.offsite.OffsiteMiddleware',
 'scrapy.downloadermiddlewares.httpauth.HttpAuthMiddleware',
 'scrapy.downloadermiddlewares.downloadtimeout.DownloadTimeoutMiddleware',
 'scrapy.downloadermiddlewares.defaultheaders.DefaultHeadersMiddleware',
 'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware',
 'scrapy.downloadermiddlewares.retry.RetryMiddleware',
 'scrapy.downloadermiddlewares.redirect.MetaRefreshMiddleware',
 'scrapy.downloadermiddlewares.httpcompression.HttpCompressionMiddleware',
 'scrapy.downloadermiddlewares.redirect.RedirectMiddleware',
 'scrapy.downloadermiddlewares.cookies.CookiesMiddleware',
 'scrapy.downloadermiddlewares.httpproxy.HttpProxyMiddleware',
 'scrapy.downloadermiddlewares.stats.DownloaderStats']
2026-06-25 03:00:15 [scrapy.middleware] INFO: Enabled spider middlewares:
['scrapy.spidermiddlewares.httperror.HttpErrorMiddleware',
 'scrapy.spidermiddlewares.referer.RefererMiddleware',
 'scrapy.spidermiddlewares.urllength.UrlLengthMiddleware',
 'scrapy.spidermiddlewares.depth.DepthMiddleware']
2026-06-25 03:00:16 [scrapy.middleware] INFO: Enabled item pipelines:
['SwiggyReviewData.pipelines.SwiggyreviewdataPipeline']
2026-06-25 03:00:16 [scrapy.core.engine] INFO: Spider opened
2026-06-25 03:00:16 [scrapy.extensions.logstats] INFO: Crawled 0 pages (at 0 pages/min), scraped 0 items (at 0 items/min)
2026-06-25 03:00:16 [scrapy.extensions.telnet] INFO: Telnet console listening on 127.0.0.1:6024
2026-06-25 03:00:17 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?mutation=loginMutation> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:17 [py.warnings] WARNING: /home/ubuntu/eggs/SwiggyReviewData/1777095842.egg/SwiggyReviewData/spiders/SwiggyReviewData.py:183: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.
  comp_response = pd.read_sql_query(comp_query, self.conn)

2026-06-25 03:00:17 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:17 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:17 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:18 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:18 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:18 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:19 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:20 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-25 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-25 03:00:22 [scrapy.core.engine] INFO: Closing spider (finished)
2026-06-25 03:00:23 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-724b7f92-8101-4ee3-b4e5-f8d732216ac5', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            No taste waste of money \n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:23 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:23 [httpcore.connection] DEBUG: connect_tcp.started host='bedrock-mantle.ap-south-1.api.aws' port=443 local_address=None timeout=5.0 socket_options=None
2026-06-25 03:00:23 [httpcore.connection] DEBUG: connect_tcp.complete return_value=<httpcore._backends.sync.SyncStream object at 0x75637d21fec0>
2026-06-25 03:00:23 [httpcore.connection] DEBUG: start_tls.started ssl_context=<ssl.SSLContext object at 0x75637e3e5550> server_hostname='bedrock-mantle.ap-south-1.api.aws' timeout=5.0
2026-06-25 03:00:23 [httpcore.connection] DEBUG: start_tls.complete return_value=<httpcore._backends.sync.SyncStream object at 0x75637d4a5160>
2026-06-25 03:00:23 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:23 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:23 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:23 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:23 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:25 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:25 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2918'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_m5hckhqd6oqaw7lzpk4jwak2osqe65unyzqk4gqaprq44ustpf7a'), (b'x-request-id', b'req_m5hckhqd6oqaw7lzpk4jwak2osqe65unyzqk4gqaprq44ustpf7a'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:25 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:25 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:25 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:25 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:25 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:25 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:25 GMT', 'content-type': 'application/json', 'content-length': '2918', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_m5hckhqd6oqaw7lzpk4jwak2osqe65unyzqk4gqaprq44ustpf7a', 'x-request-id': 'req_m5hckhqd6oqaw7lzpk4jwak2osqe65unyzqk4gqaprq44ustpf7a', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:25 [openai._base_client] DEBUG: request_id: req_m5hckhqd6oqaw7lzpk4jwak2osqe65unyzqk4gqaprq44ustpf7a
2026-06-25 03:00:25 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-79116a72-6e1e-4d9c-98d0-26e6329044bf', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Although vthe filling of the kathi roll was good, the wrap outer cover which was a roomali roti was raw. U should make sure to cook or fry things well before delivering to customers \n                            Customer Rating:\n                            3 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:25 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:25 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:25 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:25 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:25 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:25 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:29 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:29 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'4346'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_ehmaf6ey467nh77xmnjw4gn2mzr7virlu3stlr6jabo6ewavi7na'), (b'x-request-id', b'req_ehmaf6ey467nh77xmnjw4gn2mzr7virlu3stlr6jabo6ewavi7na'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:29 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:29 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:29 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:29 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:29 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:29 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:29 GMT', 'content-type': 'application/json', 'content-length': '4346', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_ehmaf6ey467nh77xmnjw4gn2mzr7virlu3stlr6jabo6ewavi7na', 'x-request-id': 'req_ehmaf6ey467nh77xmnjw4gn2mzr7virlu3stlr6jabo6ewavi7na', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:29 [openai._base_client] DEBUG: request_id: req_ehmaf6ey467nh77xmnjw4gn2mzr7virlu3stlr6jabo6ewavi7na
2026-06-25 03:00:29 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-78e83b53-33c1-4ebb-890b-2d00a67fcd26', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Sabudana khichdi did not have salt in it and trust me i eat very less salt \n                            Customer Rating:\n                            3 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:29 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:29 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:29 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:29 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:29 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:29 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:31 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:31 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2986'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_oq6zt7rc2j5ib7437hou44e4y45r7dfs3k545sf5wtss7ecokfwq'), (b'x-request-id', b'req_oq6zt7rc2j5ib7437hou44e4y45r7dfs3k545sf5wtss7ecokfwq'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:31 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:31 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:31 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:31 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:31 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:31 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:31 GMT', 'content-type': 'application/json', 'content-length': '2986', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_oq6zt7rc2j5ib7437hou44e4y45r7dfs3k545sf5wtss7ecokfwq', 'x-request-id': 'req_oq6zt7rc2j5ib7437hou44e4y45r7dfs3k545sf5wtss7ecokfwq', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:31 [openai._base_client] DEBUG: request_id: req_oq6zt7rc2j5ib7437hou44e4y45r7dfs3k545sf5wtss7ecokfwq
2026-06-25 03:00:31 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-02b5d91b-62ae-4ddb-8536-ed0bcb84e9bc', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            The taste was amazing,  I ordered it first time and found it different from other biryani.\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:31 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:31 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:31 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:31 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:31 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:31 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:32 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:32 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2391'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_odi4qb34gm53gaok4jytfhrwrnts4sogpxw72j2med3amttimqja'), (b'x-request-id', b'req_odi4qb34gm53gaok4jytfhrwrnts4sogpxw72j2med3amttimqja'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:32 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:32 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:32 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:32 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:32 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:32 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:32 GMT', 'content-type': 'application/json', 'content-length': '2391', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_odi4qb34gm53gaok4jytfhrwrnts4sogpxw72j2med3amttimqja', 'x-request-id': 'req_odi4qb34gm53gaok4jytfhrwrnts4sogpxw72j2med3amttimqja', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:32 [openai._base_client] DEBUG: request_id: req_odi4qb34gm53gaok4jytfhrwrnts4sogpxw72j2med3amttimqja
2026-06-25 03:00:32 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-95678a95-fccd-4406-89e9-078a53676220', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Found a piece of mirror in biryani\n                            Customer Rating:\n                            4 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:32 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:32 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:32 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:32 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:32 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:32 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:34 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:34 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3070'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_swcagiw5th6otvpnl6tptljyjsxyz6fseypg22sag4lqqtuotbla'), (b'x-request-id', b'req_swcagiw5th6otvpnl6tptljyjsxyz6fseypg22sag4lqqtuotbla'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:34 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:34 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:34 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:34 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:34 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:34 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:34 GMT', 'content-type': 'application/json', 'content-length': '3070', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_swcagiw5th6otvpnl6tptljyjsxyz6fseypg22sag4lqqtuotbla', 'x-request-id': 'req_swcagiw5th6otvpnl6tptljyjsxyz6fseypg22sag4lqqtuotbla', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:34 [openai._base_client] DEBUG: request_id: req_swcagiw5th6otvpnl6tptljyjsxyz6fseypg22sag4lqqtuotbla
2026-06-25 03:00:34 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-29f127f6-f441-4d52-a9be-87f678486160', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Worst biryani ever... I throw it all..waste of money \n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:34 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:34 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:34 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:34 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:34 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:34 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:36 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:36 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2683'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_no62siytvij375quoo3srt2idib6lnsy4ai6mp77pfcrytmv5hjq'), (b'x-request-id', b'req_no62siytvij375quoo3srt2idib6lnsy4ai6mp77pfcrytmv5hjq'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:36 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:36 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:36 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:36 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:36 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:36 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:36 GMT', 'content-type': 'application/json', 'content-length': '2683', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_no62siytvij375quoo3srt2idib6lnsy4ai6mp77pfcrytmv5hjq', 'x-request-id': 'req_no62siytvij375quoo3srt2idib6lnsy4ai6mp77pfcrytmv5hjq', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:36 [openai._base_client] DEBUG: request_id: req_no62siytvij375quoo3srt2idib6lnsy4ai6mp77pfcrytmv5hjq
2026-06-25 03:00:36 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-24f5c115-06ec-42a3-9efe-e74c0e30cc1c', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Never eat such a great biryani in pune , thankyou soo much you made my day \n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:36 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:36 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:36 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:36 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:36 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:36 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:39 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:39 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2979'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_a445oqrh6b2tb6cndxuvaqhn5s6rm72amigdlrzfvfbh2znbgv4q'), (b'x-request-id', b'req_a445oqrh6b2tb6cndxuvaqhn5s6rm72amigdlrzfvfbh2znbgv4q'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:39 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:39 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:39 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:39 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:39 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:39 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:39 GMT', 'content-type': 'application/json', 'content-length': '2979', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_a445oqrh6b2tb6cndxuvaqhn5s6rm72amigdlrzfvfbh2znbgv4q', 'x-request-id': 'req_a445oqrh6b2tb6cndxuvaqhn5s6rm72amigdlrzfvfbh2znbgv4q', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:39 [openai._base_client] DEBUG: request_id: req_a445oqrh6b2tb6cndxuvaqhn5s6rm72amigdlrzfvfbh2znbgv4q
2026-06-25 03:00:39 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-59cf7867-68f1-4ed5-b197-bbdb0d9646eb', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            The biryani was soo soo yummm , thankyou chef disha ♥️\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:39 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:39 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:39 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:39 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:39 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:39 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:40 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:40 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2334'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_tr7ogsk43nhrhvqxjhz6wronmejgczpteree6blgcbfm6spzg2ha'), (b'x-request-id', b'req_tr7ogsk43nhrhvqxjhz6wronmejgczpteree6blgcbfm6spzg2ha'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:40 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:40 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:40 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:40 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:40 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:40 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:40 GMT', 'content-type': 'application/json', 'content-length': '2334', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_tr7ogsk43nhrhvqxjhz6wronmejgczpteree6blgcbfm6spzg2ha', 'x-request-id': 'req_tr7ogsk43nhrhvqxjhz6wronmejgczpteree6blgcbfm6spzg2ha', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:40 [openai._base_client] DEBUG: request_id: req_tr7ogsk43nhrhvqxjhz6wronmejgczpteree6blgcbfm6spzg2ha
2026-06-25 03:00:40 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-78dd7d49-ecdb-4eca-bf58-405c868f2123', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Good \n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:40 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:40 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:40 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:40 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:40 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:40 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:41 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:41 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'1586'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_wbqkwcbrj2cp6atmibc2d2g3adb7vudgcdttgppzbmydbsnllnca'), (b'x-request-id', b'req_wbqkwcbrj2cp6atmibc2d2g3adb7vudgcdttgppzbmydbsnllnca'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:41 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:41 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:41 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:41 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:41 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:41 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:41 GMT', 'content-type': 'application/json', 'content-length': '1586', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_wbqkwcbrj2cp6atmibc2d2g3adb7vudgcdttgppzbmydbsnllnca', 'x-request-id': 'req_wbqkwcbrj2cp6atmibc2d2g3adb7vudgcdttgppzbmydbsnllnca', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:41 [openai._base_client] DEBUG: request_id: req_wbqkwcbrj2cp6atmibc2d2g3adb7vudgcdttgppzbmydbsnllnca
2026-06-25 03:00:41 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-fdb0da16-5605-4046-8e9e-abfb1673e5b4', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            The rice was very very less quantity the platter was covered with 2 big pieces of chicken with very heavy bones barely one person can eat \n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:41 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:41 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:41 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:41 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:41 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:41 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:44 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:44 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3573'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_euyhgdictqlmv7gnqtt43wanqqf46wu3y3mwegoklvpdtturatqq'), (b'x-request-id', b'req_euyhgdictqlmv7gnqtt43wanqqf46wu3y3mwegoklvpdtturatqq'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:44 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:44 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:44 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:44 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:44 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:44 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:44 GMT', 'content-type': 'application/json', 'content-length': '3573', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_euyhgdictqlmv7gnqtt43wanqqf46wu3y3mwegoklvpdtturatqq', 'x-request-id': 'req_euyhgdictqlmv7gnqtt43wanqqf46wu3y3mwegoklvpdtturatqq', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:44 [openai._base_client] DEBUG: request_id: req_euyhgdictqlmv7gnqtt43wanqqf46wu3y3mwegoklvpdtturatqq
2026-06-25 03:00:44 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-95582af8-8b47-44d6-89e7-8e36b90ffa36', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Sufficient quantity with perfect taste of biryani 😊\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:44 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:44 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:44 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:44 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:44 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:44 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:46 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:46 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2351'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_imzstjrzjkyez2empjgjwvc7yz5tebguubuzozbcjmzmop2qufka'), (b'x-request-id', b'req_imzstjrzjkyez2empjgjwvc7yz5tebguubuzozbcjmzmop2qufka'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:46 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:46 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:46 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:46 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:46 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:46 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:46 GMT', 'content-type': 'application/json', 'content-length': '2351', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_imzstjrzjkyez2empjgjwvc7yz5tebguubuzozbcjmzmop2qufka', 'x-request-id': 'req_imzstjrzjkyez2empjgjwvc7yz5tebguubuzozbcjmzmop2qufka', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:46 [openai._base_client] DEBUG: request_id: req_imzstjrzjkyez2empjgjwvc7yz5tebguubuzozbcjmzmop2qufka
2026-06-25 03:00:46 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-fc236b71-3fa8-451f-b2b0-42915d15b1e6', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Chicken amount was very less compared to other minibowls\n                            Customer Rating:\n                            2 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:46 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:46 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:46 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:46 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:46 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:46 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:48 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:48 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2521'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_dbpi4qp3uduivxq6656nipdoomorpt22u7ofgwmreqn3oyrbn6na'), (b'x-request-id', b'req_dbpi4qp3uduivxq6656nipdoomorpt22u7ofgwmreqn3oyrbn6na'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:48 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:48 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:48 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:48 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:48 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:48 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:48 GMT', 'content-type': 'application/json', 'content-length': '2521', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_dbpi4qp3uduivxq6656nipdoomorpt22u7ofgwmreqn3oyrbn6na', 'x-request-id': 'req_dbpi4qp3uduivxq6656nipdoomorpt22u7ofgwmreqn3oyrbn6na', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:48 [openai._base_client] DEBUG: request_id: req_dbpi4qp3uduivxq6656nipdoomorpt22u7ofgwmreqn3oyrbn6na
2026-06-25 03:00:48 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-c5606671-bb5c-48af-ae65-09d463db8395', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            They have delivered us a steal and stinking food and customer care is so arrogant that they are not giving full refund for their garbage food\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:48 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:48 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:48 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:48 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:48 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:48 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:52 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:52 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'5670'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_ue25mow2fvd2ey656uysqwwl3mjg2ssib6hpvxrp45rd7krf5ruq'), (b'x-request-id', b'req_ue25mow2fvd2ey656uysqwwl3mjg2ssib6hpvxrp45rd7krf5ruq'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:52 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:52 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:52 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:52 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:52 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:52 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:52 GMT', 'content-type': 'application/json', 'content-length': '5670', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_ue25mow2fvd2ey656uysqwwl3mjg2ssib6hpvxrp45rd7krf5ruq', 'x-request-id': 'req_ue25mow2fvd2ey656uysqwwl3mjg2ssib6hpvxrp45rd7krf5ruq', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:52 [openai._base_client] DEBUG: request_id: req_ue25mow2fvd2ey656uysqwwl3mjg2ssib6hpvxrp45rd7krf5ruq
2026-06-25 03:00:52 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-cc234c95-330d-427b-94ec-a13822e37407', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            No taste with full of oil in it\n\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:52 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:52 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:52 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:52 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:52 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:52 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:54 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:54 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2535'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_w2sfqk73cx6qccdmvkwu5idaqtzdooa6dxacjlxw6ozcxde7byna'), (b'x-request-id', b'req_w2sfqk73cx6qccdmvkwu5idaqtzdooa6dxacjlxw6ozcxde7byna'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:54 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:54 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:54 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:54 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:54 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:54 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:54 GMT', 'content-type': 'application/json', 'content-length': '2535', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_w2sfqk73cx6qccdmvkwu5idaqtzdooa6dxacjlxw6ozcxde7byna', 'x-request-id': 'req_w2sfqk73cx6qccdmvkwu5idaqtzdooa6dxacjlxw6ozcxde7byna', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:54 [openai._base_client] DEBUG: request_id: req_w2sfqk73cx6qccdmvkwu5idaqtzdooa6dxacjlxw6ozcxde7byna
2026-06-25 03:00:54 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-5b9dfb27-cda9-4bb8-893a-26b1a08668f7', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Never ate such a tasty biryani and in this price... Ordered this for the 2nd time and recommended it to a friend now it has been her 5th time ordering biryani from here. Thank you.\n\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:54 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:54 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:54 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:54 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:54 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:54 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:00:58 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:00:58 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'4626'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_m7o6oxwyacfqs3ktacon7e4jo7ghfp4xf5u6mnmbzqugvr2gy7lq'), (b'x-request-id', b'req_m7o6oxwyacfqs3ktacon7e4jo7ghfp4xf5u6mnmbzqugvr2gy7lq'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:00:58 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:00:58 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:00:58 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:00:58 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:00:58 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:00:58 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:00:58 GMT', 'content-type': 'application/json', 'content-length': '4626', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_m7o6oxwyacfqs3ktacon7e4jo7ghfp4xf5u6mnmbzqugvr2gy7lq', 'x-request-id': 'req_m7o6oxwyacfqs3ktacon7e4jo7ghfp4xf5u6mnmbzqugvr2gy7lq', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:00:58 [openai._base_client] DEBUG: request_id: req_m7o6oxwyacfqs3ktacon7e4jo7ghfp4xf5u6mnmbzqugvr2gy7lq
2026-06-25 03:00:58 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-cba38247-8bb9-4349-a1fe-829a7caf3ded', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Continue bohot kaam hai ek insan ka bhi pet nhi bharega taste wise aacha hai but agr hotel wale vegetable to or continue bada sagte hai \n\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:00:58 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:00:58 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:00:58 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:00:58 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:00:58 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:00:58 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:01:03 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:01:03 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'4849'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_kwb32q2hyheb3dgryzumv2gkoeezkhwg33od5hqh4hadflsqgxmq'), (b'x-request-id', b'req_kwb32q2hyheb3dgryzumv2gkoeezkhwg33od5hqh4hadflsqgxmq'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:01:03 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:01:03 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:01:03 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:01:03 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:01:03 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:01:03 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:01:03 GMT', 'content-type': 'application/json', 'content-length': '4849', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_kwb32q2hyheb3dgryzumv2gkoeezkhwg33od5hqh4hadflsqgxmq', 'x-request-id': 'req_kwb32q2hyheb3dgryzumv2gkoeezkhwg33od5hqh4hadflsqgxmq', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:01:03 [openai._base_client] DEBUG: request_id: req_kwb32q2hyheb3dgryzumv2gkoeezkhwg33od5hqh4hadflsqgxmq
2026-06-25 03:01:03 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-27f3cbf0-0eec-4e6c-a744-f670995d5aa3', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Taste was good but less quantity \n                            Customer Rating:\n                            3 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:01:03 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:01:03 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:01:03 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:01:03 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:01:03 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:01:03 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:01:04 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:01:04 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2778'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_3bh45ndb327su3c4u6q5kipcxsr23msd5cgccshjbzpryu5eoxxa'), (b'x-request-id', b'req_3bh45ndb327su3c4u6q5kipcxsr23msd5cgccshjbzpryu5eoxxa'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:01:04 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:01:04 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:01:04 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:01:04 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:01:04 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:01:04 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:01:04 GMT', 'content-type': 'application/json', 'content-length': '2778', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_3bh45ndb327su3c4u6q5kipcxsr23msd5cgccshjbzpryu5eoxxa', 'x-request-id': 'req_3bh45ndb327su3c4u6q5kipcxsr23msd5cgccshjbzpryu5eoxxa', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:01:04 [openai._base_client] DEBUG: request_id: req_3bh45ndb327su3c4u6q5kipcxsr23msd5cgccshjbzpryu5eoxxa
2026-06-25 03:01:04 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-4394f5a3-bfe7-4a71-925f-10c7fccfa81e', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Cutlery nhi bheji \n                            Customer Rating:\n                            2 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:01:04 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:01:04 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:01:04 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:01:04 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:01:04 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:01:04 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:01:06 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:01:06 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2741'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_2koxz6sni3hct4fpibhaxy2uykpywoy42ytoyr7qhxsgsqdzd5pq'), (b'x-request-id', b'req_2koxz6sni3hct4fpibhaxy2uykpywoy42ytoyr7qhxsgsqdzd5pq'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:01:06 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:01:06 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:01:06 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:01:06 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:01:06 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:01:06 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:01:06 GMT', 'content-type': 'application/json', 'content-length': '2741', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_2koxz6sni3hct4fpibhaxy2uykpywoy42ytoyr7qhxsgsqdzd5pq', 'x-request-id': 'req_2koxz6sni3hct4fpibhaxy2uykpywoy42ytoyr7qhxsgsqdzd5pq', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:01:06 [openai._base_client] DEBUG: request_id: req_2koxz6sni3hct4fpibhaxy2uykpywoy42ytoyr7qhxsgsqdzd5pq
2026-06-25 03:01:06 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-512db068-5a05-4875-8c5a-72f66a8cb511', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Very delicious \n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:01:06 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:01:06 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:01:06 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:01:06 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:01:06 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:01:06 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:01:08 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:01:08 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2387'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_7y33az5uoxio4r6xf3xyfnoosfequcpys5w7ucqbakpk7offaoda'), (b'x-request-id', b'req_7y33az5uoxio4r6xf3xyfnoosfequcpys5w7ucqbakpk7offaoda'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:01:08 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:01:08 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:01:08 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:01:08 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:01:08 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:01:08 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:01:08 GMT', 'content-type': 'application/json', 'content-length': '2387', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_7y33az5uoxio4r6xf3xyfnoosfequcpys5w7ucqbakpk7offaoda', 'x-request-id': 'req_7y33az5uoxio4r6xf3xyfnoosfequcpys5w7ucqbakpk7offaoda', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:01:08 [openai._base_client] DEBUG: request_id: req_7y33az5uoxio4r6xf3xyfnoosfequcpys5w7ucqbakpk7offaoda
2026-06-25 03:01:08 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-59b8190b-e262-4b3c-a2f4-c72e1f331d79', 'content': None, 'json_data': {'input': ' You are an expert restaurant review analyst.\n                            Analyze the customer review using Aspect-Based Sentiment Analysis (ABSA).\n                            Your job is to identify every distinct aspect mentioned in the review and classify each one.\n                            Use this taxonomy of aspects and corresponding subcategories/root:\n                            Food:\n                            - Taste\n                            - Food Quality\n                            - Portion Size / Quantity\n                            - Freshness\n                            - Presentation\n                            - Food Temperature\n                            - Consistency\n                            - Menu Variety\n                            Service:\n                            - Staff Behavior\n                            - Friendliness\n                            - Attentiveness\n                            - Speed of Service\n                            - Order Accuracy\n                            - Problem Resolution\n                            Delivery:\n                            - Delivery Time\n                            - Packaging Quality\n                            - Food Spillage\n                            - Order Completeness\n                            - Food Condition on Arrival\n                            Value:\n                            - Pricing\n                            - Value for Money\n                            Hygiene:\n                            - Cleanliness\n                            - Food Safety\n                            Ambience:\n                            - Seating\n                            - Noise\n                            - Lighting\n                            - Comfort\n                            For each aspect detected, return:\n                            1. aspect\n                            2. subcategory/root cause\n                            3. sentiment (Positive, Negative, Neutral, Mixed)\n                            4. severity (Low, Medium, High)\n                            5. intent (Praise, Complaint, Suggestion, Refund Request, Churn Risk, Loyalty Signal)\n                            6. evidence (exact phrase from the review supporting the classification)\n                            Rules:\n                            - A single review may contain multiple aspects.\n                            - Extract ALL aspects mentioned. \n                            - Do not infer aspects not mentioned.\n                            - Do not infer subcategories not mentioned.\n                            - If one sentence contains both positive and negative signals for the same aspect, mark sentiment as Mixed.\n                            - Be strict and operationally useful.\n                            - Focus on actionable business insights.\n                            - Also provide an overall review sentiment (Positive, Negative, Neutral, Mixed).\n                            Return output ONLY in valid JSON using this format:\n                            {\n                            "overall_sentiment": "",\n                            "aspects": \n                                {\n                                "aspect": "",\n                                "subcategory": "",\n                                "sentiment": "",\n                                "severity": "",\n                                "intent": "",\n                                "evidence": ""\n                                }\n                            }\n\n                            Customer Review:\n                            Wrong order was received. Dal khichdi was sent instead. Extremely oily pungent and unhealthy \n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-25 03:01:08 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-25 03:01:08 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-25 03:01:08 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-25 03:01:08 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-25 03:01:08 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-25 03:01:08 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-25 03:01:12 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Thu, 25 Jun 2026 03:01:12 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'4096'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_3o3aae3gyt4uxqtc2jtxk56tsjycntlkwzh2vktojiopyfqihrqq'), (b'x-request-id', b'req_3o3aae3gyt4uxqtc2jtxk56tsjycntlkwzh2vktojiopyfqihrqq'), (b'vary', b'origin, access-control-request-method, access-control-request-headers'), (b'access-control-allow-origin', b'*'), (b'access-control-expose-headers', b'x-amzn-requestid,x-request-id,date')])
2026-06-25 03:01:12 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-25 03:01:12 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-25 03:01:12 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-25 03:01:12 [httpcore.http11] DEBUG: response_closed.started
2026-06-25 03:01:12 [httpcore.http11] DEBUG: response_closed.complete
2026-06-25 03:01:12 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Thu, 25 Jun 2026 03:01:12 GMT', 'content-type': 'application/json', 'content-length': '4096', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_3o3aae3gyt4uxqtc2jtxk56tsjycntlkwzh2vktojiopyfqihrqq', 'x-request-id': 'req_3o3aae3gyt4uxqtc2jtxk56tsjycntlkwzh2vktojiopyfqihrqq', 'vary': 'origin, access-control-request-method, access-control-request-headers', 'access-control-allow-origin': '*', 'access-control-expose-headers': 'x-amzn-requestid,x-request-id,date'})
2026-06-25 03:01:12 [openai._base_client] DEBUG: request_id: req_3o3aae3gyt4uxqtc2jtxk56tsjycntlkwzh2vktojiopyfqihrqq
2026-06-25 03:01:12 [scrapy.statscollectors] INFO: Dumping Scrapy stats:
{'downloader/request_bytes': 528787,
 'downloader/request_count': 148,
 'downloader/request_method_count/POST': 148,
 'downloader/response_bytes': 253998,
 'downloader/response_count': 148,
 'downloader/response_status_count/200': 148,
 'elapsed_time_seconds': 55.515104,
 'finish_reason': 'finished',
 'finish_time': datetime.datetime(2026, 6, 25, 3, 1, 12, 229389, tzinfo=datetime.timezone.utc),
 'httpcompression/response_bytes': 327101,
 'httpcompression/response_count': 148,
 'item_scraped_count': 140,
 'items_per_minute': None,
 'log_count/DEBUG': 577,
 'log_count/INFO': 30,
 'log_count/WARNING': 1,
 'memusage/max': 145670144,
 'memusage/startup': 145670144,
 'request_depth_max': 8,
 'response_received_count': 148,
 'responses_per_minute': None,
 'scheduler/dequeued': 148,
 'scheduler/dequeued/memory': 148,
 'scheduler/enqueued': 148,
 'scheduler/enqueued/memory': 148,
 'start_time': datetime.datetime(2026, 6, 25, 3, 0, 16, 714285, tzinfo=datetime.timezone.utc)}
2026-06-25 03:01:12 [scrapy.core.engine] INFO: Spider closed (finished)
2026-06-25 03:01:12 [httpcore.connection] DEBUG: close.started
2026-06-25 03:01:12 [httpcore.connection] DEBUG: close.complete