2026-06-21 03:00:15 [scrapy.utils.log] INFO: Scrapy 2.12.0 started (bot: SwiggyReviewData)
2026-06-21 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
2026-06-21 03:00:15 [scrapy.addons] INFO: Enabled addons:
[]
2026-06-21 03:00:15 [asyncio] DEBUG: Using selector: EpollSelector
2026-06-21 03:00:15 [scrapy.utils.log] DEBUG: Using reactor: twisted.internet.asyncioreactor.AsyncioSelectorReactor
2026-06-21 03:00:15 [scrapy.utils.log] DEBUG: Using asyncio event loop: asyncio.unix_events._UnixSelectorEventLoop
2026-06-21 03:00:15 [scrapy.utils.log] DEBUG: Using reactor: twisted.internet.asyncioreactor.AsyncioSelectorReactor
2026-06-21 03:00:15 [scrapy.utils.log] DEBUG: Using asyncio event loop: asyncio.unix_events._UnixSelectorEventLoop
2026-06-21 03:00:15 [scrapy.extensions.telnet] INFO: Telnet Password: 84fa2bb4c38ef5b2
2026-06-21 03:00:15 [scrapy.middleware] INFO: Enabled extensions:
['scrapy.extensions.corestats.CoreStats',
 'scrapy.extensions.telnet.TelnetConsole',
 'scrapy.extensions.memusage.MemoryUsage',
 'scrapy.extensions.logstats.LogStats']
2026-06-21 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/501eac306d1d11f1a8d80aab37b1cebd.log',
 'NEWSPIDER_MODULE': 'SwiggyReviewData.spiders',
 'SPIDER_MODULES': ['SwiggyReviewData.spiders'],
 'TWISTED_REACTOR': 'twisted.internet.asyncioreactor.AsyncioSelectorReactor'}
2026-06-21 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']
2026-06-21 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']
2026-06-21 03:00:16 [scrapy.middleware] INFO: Enabled item pipelines:
['SwiggyReviewData.pipelines.SwiggyreviewdataPipeline']
2026-06-21 03:00:16 [scrapy.core.engine] INFO: Spider opened
2026-06-21 03:00:16 [scrapy.extensions.logstats] INFO: Crawled 0 pages (at 0 pages/min), scraped 0 items (at 0 items/min)
2026-06-21 03:00:16 [scrapy.extensions.telnet] INFO: Telnet console listening on 127.0.0.1:6026
2026-06-21 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/)
2026-06-21 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)

2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:20 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:21 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:22 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getRestaurantRatingsAndReviews> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 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/)
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 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/)
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 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/)
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 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/)
2026-06-21 03:00:22 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 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/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:23 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:24 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:25 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:25 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:26 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:27 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:27 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:28 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:29 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:29 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:29 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:29 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:29 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:29 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:29 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:29 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:29 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:29 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:29 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:29 [scrapy.core.engine] DEBUG: Crawled (200) <POST https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails> (referer: https://partner-self-client.swiggy.com/)
2026-06-21 03:00:29 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:29 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:29 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:29 [scrapy.core.scraper] DEBUG: Scraped from <200 https://vhc-composer.swiggy.com/query?query=getOrderRatingDetails>
None
2026-06-21 03:00:29 [scrapy.core.engine] INFO: Closing spider (finished)
2026-06-21 03:00:29 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-f3fe6b44-cbfb-4312-9253-60de4c5c4f9b', '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                            "Amazing Paneer Biryani! The paneer was incredibly soft and the rice was perfectly cooked and fragrant. Great portion size and absolutely delicious. \nHighly recommended!"\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:29 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:29 [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
2026-06-21 03:00:29 [httpcore.connection] DEBUG: connect_tcp.complete return_value=<httpcore._backends.sync.SyncStream object at 0x7213753fc110>
2026-06-21 03:00:29 [httpcore.connection] DEBUG: start_tls.started ssl_context=<ssl.SSLContext object at 0x721376484350> server_hostname='bedrock-mantle.ap-south-1.api.aws' timeout=5.0
2026-06-21 03:00:29 [httpcore.connection] DEBUG: start_tls.complete return_value=<httpcore._backends.sync.SyncStream object at 0x7213753d0500>
2026-06-21 03:00:29 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:29 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:29 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:29 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:29 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:32 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:32 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3945'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_ucffzdblmjgbceyoh3rn7myq3r53en5dqbrm5gbigimzc3psa3pa'), (b'x-request-id', b'req_ucffzdblmjgbceyoh3rn7myq3r53en5dqbrm5gbigimzc3psa3pa'), (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')])
2026-06-21 03:00:32 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:32 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:32 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:32 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:32 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:32 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:32 GMT', 'content-type': 'application/json', 'content-length': '3945', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_ucffzdblmjgbceyoh3rn7myq3r53en5dqbrm5gbigimzc3psa3pa', 'x-request-id': 'req_ucffzdblmjgbceyoh3rn7myq3r53en5dqbrm5gbigimzc3psa3pa', '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'})
2026-06-21 03:00:32 [openai._base_client] DEBUG: request_id: req_ucffzdblmjgbceyoh3rn7myq3r53en5dqbrm5gbigimzc3psa3pa
2026-06-21 03:00:32 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-12abfae1-fe4d-408e-8c8e-efeb059c95e0', '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                            Itvwas frsh an tasty\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:32 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:32 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:32 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:32 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:32 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:32 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:34 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:34 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3259'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_yw7nb3cm7phsfkq4qmfjbxwbfhs7taqwke3lcdtqlihpnacse3ca'), (b'x-request-id', b'req_yw7nb3cm7phsfkq4qmfjbxwbfhs7taqwke3lcdtqlihpnacse3ca'), (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')])
2026-06-21 03:00:34 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:34 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:34 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:34 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:34 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:34 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:34 GMT', 'content-type': 'application/json', 'content-length': '3259', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_yw7nb3cm7phsfkq4qmfjbxwbfhs7taqwke3lcdtqlihpnacse3ca', 'x-request-id': 'req_yw7nb3cm7phsfkq4qmfjbxwbfhs7taqwke3lcdtqlihpnacse3ca', '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'})
2026-06-21 03:00:34 [openai._base_client] DEBUG: request_id: req_yw7nb3cm7phsfkq4qmfjbxwbfhs7taqwke3lcdtqlihpnacse3ca
2026-06-21 03:00:34 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-d6335934-3477-4860-8901-806223e51148', '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                            Really unique and smoky flavour.\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:34 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:34 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:34 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:34 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:34 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:34 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:35 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:35 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2189'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_unky5dfdhkfe2hb4vttc23qcdaexgrl65bgweluqi3gsngtszyhq'), (b'x-request-id', b'req_unky5dfdhkfe2hb4vttc23qcdaexgrl65bgweluqi3gsngtszyhq'), (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')])
2026-06-21 03:00:35 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:35 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:35 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:35 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:35 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:35 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:35 GMT', 'content-type': 'application/json', 'content-length': '2189', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_unky5dfdhkfe2hb4vttc23qcdaexgrl65bgweluqi3gsngtszyhq', 'x-request-id': 'req_unky5dfdhkfe2hb4vttc23qcdaexgrl65bgweluqi3gsngtszyhq', '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'})
2026-06-21 03:00:35 [openai._base_client] DEBUG: request_id: req_unky5dfdhkfe2hb4vttc23qcdaexgrl65bgweluqi3gsngtszyhq
2026-06-21 03:00:35 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-5897440e-d3ee-4737-a50f-5e7a1699ad14', '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                            Delivered exactly what i ordered.  Even didn\'t add chilli as per my request.  Great service \n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:35 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:35 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:35 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:35 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:35 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:35 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:38 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:38 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3975'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_4bgs6p5vg36wfg3hgzybn4ss3wyfo2khs76xla7lebuy4s3q2yuq'), (b'x-request-id', b'req_4bgs6p5vg36wfg3hgzybn4ss3wyfo2khs76xla7lebuy4s3q2yuq'), (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')])
2026-06-21 03:00:38 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:38 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:38 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:38 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:38 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:38 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:38 GMT', 'content-type': 'application/json', 'content-length': '3975', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_4bgs6p5vg36wfg3hgzybn4ss3wyfo2khs76xla7lebuy4s3q2yuq', 'x-request-id': 'req_4bgs6p5vg36wfg3hgzybn4ss3wyfo2khs76xla7lebuy4s3q2yuq', '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'})
2026-06-21 03:00:38 [openai._base_client] DEBUG: request_id: req_4bgs6p5vg36wfg3hgzybn4ss3wyfo2khs76xla7lebuy4s3q2yuq
2026-06-21 03:00:38 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-5793a81d-9973-4f2f-965f-f7b174a25a92', '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                            Noodels were raw and smelled stale\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:38 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:38 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:38 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:38 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:38 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:38 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:39 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:39 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2820'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_z4eqrqqbaug54eohkn56bogf7c33cqwy5e54fbai3hwwitbf265a'), (b'x-request-id', b'req_z4eqrqqbaug54eohkn56bogf7c33cqwy5e54fbai3hwwitbf265a'), (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')])
2026-06-21 03:00:39 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:39 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:39 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:39 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:39 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:39 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:39 GMT', 'content-type': 'application/json', 'content-length': '2820', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_z4eqrqqbaug54eohkn56bogf7c33cqwy5e54fbai3hwwitbf265a', 'x-request-id': 'req_z4eqrqqbaug54eohkn56bogf7c33cqwy5e54fbai3hwwitbf265a', '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'})
2026-06-21 03:00:39 [openai._base_client] DEBUG: request_id: req_z4eqrqqbaug54eohkn56bogf7c33cqwy5e54fbai3hwwitbf265a
2026-06-21 03:00:39 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-7a2f0c2a-a012-48a0-961c-30a8fd942dd6', '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 food experience ever!\nI ordered food from this restaurant for my parents\' anniversary, and I was extremely disappointed. I honestly don\'t understand how the food could taste so bad.\nThe biryani had absolutely no flavor, and even the masala onions served with it were completely tasteless. The overall quality and taste were far below expectations.\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:39 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:39 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:39 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:39 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:39 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:39 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:42 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:42 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3850'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_unerhfgqelx2jjkfmvpdvu4zc4p7kv24zh5givuvxymmwje7ubzq'), (b'x-request-id', b'req_unerhfgqelx2jjkfmvpdvu4zc4p7kv24zh5givuvxymmwje7ubzq'), (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')])
2026-06-21 03:00:42 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:42 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:42 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:42 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:42 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:42 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:42 GMT', 'content-type': 'application/json', 'content-length': '3850', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_unerhfgqelx2jjkfmvpdvu4zc4p7kv24zh5givuvxymmwje7ubzq', 'x-request-id': 'req_unerhfgqelx2jjkfmvpdvu4zc4p7kv24zh5givuvxymmwje7ubzq', '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'})
2026-06-21 03:00:42 [openai._base_client] DEBUG: request_id: req_unerhfgqelx2jjkfmvpdvu4zc4p7kv24zh5givuvxymmwje7ubzq
2026-06-21 03:00:42 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-26478089-143f-44ef-9bab-4e4b36c1f8b2', '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                            super delicious \n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:42 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:42 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:42 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:42 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:42 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:42 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:44 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:44 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2257'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_psiqcracms4rnu4of4pfnopekcz4c5v6qhykzxb3fk2bt53cfk2q'), (b'x-request-id', b'req_psiqcracms4rnu4of4pfnopekcz4c5v6qhykzxb3fk2bt53cfk2q'), (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')])
2026-06-21 03:00:44 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:44 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:44 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:44 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:44 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:44 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:44 GMT', 'content-type': 'application/json', 'content-length': '2257', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_psiqcracms4rnu4of4pfnopekcz4c5v6qhykzxb3fk2bt53cfk2q', 'x-request-id': 'req_psiqcracms4rnu4of4pfnopekcz4c5v6qhykzxb3fk2bt53cfk2q', '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'})
2026-06-21 03:00:44 [openai._base_client] DEBUG: request_id: req_psiqcracms4rnu4of4pfnopekcz4c5v6qhykzxb3fk2bt53cfk2q
2026-06-21 03:00:44 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-1878c11b-8af0-4d2d-ba4a-99d73a9fcde5', '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                            Quantity jhat bhar Paisa gand bhar\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:44 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:44 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:44 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:44 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:44 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:44 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:46 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:46 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3605'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_lkaj3eo43n35ybnm4ldgn2zhg3r6mddfsidlpnbkentvxws3uwia'), (b'x-request-id', b'req_lkaj3eo43n35ybnm4ldgn2zhg3r6mddfsidlpnbkentvxws3uwia'), (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')])
2026-06-21 03:00:46 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:46 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:46 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:46 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:46 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:46 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:46 GMT', 'content-type': 'application/json', 'content-length': '3605', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_lkaj3eo43n35ybnm4ldgn2zhg3r6mddfsidlpnbkentvxws3uwia', 'x-request-id': 'req_lkaj3eo43n35ybnm4ldgn2zhg3r6mddfsidlpnbkentvxws3uwia', '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'})
2026-06-21 03:00:46 [openai._base_client] DEBUG: request_id: req_lkaj3eo43n35ybnm4ldgn2zhg3r6mddfsidlpnbkentvxws3uwia
2026-06-21 03:00:46 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-a23ddf74-e722-44cb-8af6-27a99f5dc537', '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                            Loved this heavenly bowl of biryani \n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:46 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:46 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:46 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:46 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:46 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:46 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:48 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:48 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3617'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_jzeqcxhaqz7dk44sn5c4eq4u3imeof2ufjieo6oxnxb6rm4gccza'), (b'x-request-id', b'req_jzeqcxhaqz7dk44sn5c4eq4u3imeof2ufjieo6oxnxb6rm4gccza'), (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')])
2026-06-21 03:00:48 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:48 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:48 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:48 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:48 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:48 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:48 GMT', 'content-type': 'application/json', 'content-length': '3617', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_jzeqcxhaqz7dk44sn5c4eq4u3imeof2ufjieo6oxnxb6rm4gccza', 'x-request-id': 'req_jzeqcxhaqz7dk44sn5c4eq4u3imeof2ufjieo6oxnxb6rm4gccza', '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'})
2026-06-21 03:00:48 [openai._base_client] DEBUG: request_id: req_jzeqcxhaqz7dk44sn5c4eq4u3imeof2ufjieo6oxnxb6rm4gccza
2026-06-21 03:00:48 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-39891302-7ef2-4522-8de4-48b9f7a87edf', '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                            Biryani tastes good but quantity make me disappointed \n                            Customer Rating:\n                            2 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:48 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:48 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:48 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:48 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:48 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:48 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:50 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:50 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3407'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_bz7vj6w5b4wynshpaectkojtmx6fklwhaoelga73u3sxb5mtxj2a'), (b'x-request-id', b'req_bz7vj6w5b4wynshpaectkojtmx6fklwhaoelga73u3sxb5mtxj2a'), (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')])
2026-06-21 03:00:50 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:50 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:50 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:50 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:50 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:50 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:50 GMT', 'content-type': 'application/json', 'content-length': '3407', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_bz7vj6w5b4wynshpaectkojtmx6fklwhaoelga73u3sxb5mtxj2a', 'x-request-id': 'req_bz7vj6w5b4wynshpaectkojtmx6fklwhaoelga73u3sxb5mtxj2a', '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'})
2026-06-21 03:00:50 [openai._base_client] DEBUG: request_id: req_bz7vj6w5b4wynshpaectkojtmx6fklwhaoelga73u3sxb5mtxj2a
2026-06-21 03:00:50 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-0188567a-11e5-4aae-a601-001035d86b9c', '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'}}
2026-06-21 03:00:50 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:50 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:50 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:50 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:50 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:50 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:51 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:51 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'1950'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_xtnntuym643zcjqq3hgf5gon7ogy5onyywmsdhrb4leub6rsfjva'), (b'x-request-id', b'req_xtnntuym643zcjqq3hgf5gon7ogy5onyywmsdhrb4leub6rsfjva'), (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')])
2026-06-21 03:00:51 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:51 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:51 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:51 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:51 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:51 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:51 GMT', 'content-type': 'application/json', 'content-length': '1950', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_xtnntuym643zcjqq3hgf5gon7ogy5onyywmsdhrb4leub6rsfjva', 'x-request-id': 'req_xtnntuym643zcjqq3hgf5gon7ogy5onyywmsdhrb4leub6rsfjva', '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'})
2026-06-21 03:00:51 [openai._base_client] DEBUG: request_id: req_xtnntuym643zcjqq3hgf5gon7ogy5onyywmsdhrb4leub6rsfjva
2026-06-21 03:00:51 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-7296a55c-3180-4a81-b6f5-75ba4d74f807', '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                            Ordered mutton briyani and instead received chicken briyani \n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:51 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:51 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:51 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:51 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:51 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:51 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:53 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:53 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2802'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_x3sdexl4qipws26dlucnokjndqahdoseoxkd3fplqcg7sn4mhfdq'), (b'x-request-id', b'req_x3sdexl4qipws26dlucnokjndqahdoseoxkd3fplqcg7sn4mhfdq'), (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')])
2026-06-21 03:00:53 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:53 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:53 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:53 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:53 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:53 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:53 GMT', 'content-type': 'application/json', 'content-length': '2802', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_x3sdexl4qipws26dlucnokjndqahdoseoxkd3fplqcg7sn4mhfdq', 'x-request-id': 'req_x3sdexl4qipws26dlucnokjndqahdoseoxkd3fplqcg7sn4mhfdq', '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'})
2026-06-21 03:00:53 [openai._base_client] DEBUG: request_id: req_x3sdexl4qipws26dlucnokjndqahdoseoxkd3fplqcg7sn4mhfdq
2026-06-21 03:00:53 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-3886a222-7bc5-4834-8ae0-1096f6eacbc6', '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                            Awesome taste\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:53 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:53 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:53 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:53 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:53 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:53 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:54 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:54 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2168'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_d364nvqfykhm7oxjt2fueuk7lpli2rf4lo376fnkcmhapmrcxjuq'), (b'x-request-id', b'req_d364nvqfykhm7oxjt2fueuk7lpli2rf4lo376fnkcmhapmrcxjuq'), (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')])
2026-06-21 03:00:54 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:54 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:54 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:54 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:54 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:54 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:54 GMT', 'content-type': 'application/json', 'content-length': '2168', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_d364nvqfykhm7oxjt2fueuk7lpli2rf4lo376fnkcmhapmrcxjuq', 'x-request-id': 'req_d364nvqfykhm7oxjt2fueuk7lpli2rf4lo376fnkcmhapmrcxjuq', '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'})
2026-06-21 03:00:54 [openai._base_client] DEBUG: request_id: req_d364nvqfykhm7oxjt2fueuk7lpli2rf4lo376fnkcmhapmrcxjuq
2026-06-21 03:00:54 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-7e0cb00b-fa8a-4fe7-b627-d2eec8d53474', '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                            Actually even that 1 star is bcuz there was no rating lower to it..pathetic u charge so much for a veg roll and that too forget the roll size,ordered soya cheap tilka roll..trust me there were hardy 2 chunks and rest was all onions n chutney..like seriusly the whole roll got over and I was waiting to taste soya chaap..really bad.feeling like a fool.looks like a paid to hve roti rolled with onions inside..I need compensation for this experience.\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:54 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:54 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:54 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:54 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:54 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:54 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:00:58 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:00:58 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'5662'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_s6hazfv5tvg7gafcdee7d66i3dbptdmdclmyxbh6pjyttvgi67oa'), (b'x-request-id', b'req_s6hazfv5tvg7gafcdee7d66i3dbptdmdclmyxbh6pjyttvgi67oa'), (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')])
2026-06-21 03:00:58 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:00:58 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:00:58 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:00:58 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:00:58 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:00:58 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:00:58 GMT', 'content-type': 'application/json', 'content-length': '5662', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_s6hazfv5tvg7gafcdee7d66i3dbptdmdclmyxbh6pjyttvgi67oa', 'x-request-id': 'req_s6hazfv5tvg7gafcdee7d66i3dbptdmdclmyxbh6pjyttvgi67oa', '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'})
2026-06-21 03:00:58 [openai._base_client] DEBUG: request_id: req_s6hazfv5tvg7gafcdee7d66i3dbptdmdclmyxbh6pjyttvgi67oa
2026-06-21 03:00:58 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-3b48c89f-05ca-474c-a97e-4b5e919318a1', '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                            Juicy and well flavoured biryani packed with love over them\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:00:58 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:00:58 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:00:58 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:00:58 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:00:58 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:00:58 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:00 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:00 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2825'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_uzyqllxczjzsn7ulg4buewrouz4ymwyuehincdortgn7wanr2jaa'), (b'x-request-id', b'req_uzyqllxczjzsn7ulg4buewrouz4ymwyuehincdortgn7wanr2jaa'), (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')])
2026-06-21 03:01:00 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:00 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:00 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:00 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:00 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:00 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:00 GMT', 'content-type': 'application/json', 'content-length': '2825', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_uzyqllxczjzsn7ulg4buewrouz4ymwyuehincdortgn7wanr2jaa', 'x-request-id': 'req_uzyqllxczjzsn7ulg4buewrouz4ymwyuehincdortgn7wanr2jaa', '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'})
2026-06-21 03:01:00 [openai._base_client] DEBUG: request_id: req_uzyqllxczjzsn7ulg4buewrouz4ymwyuehincdortgn7wanr2jaa
2026-06-21 03:01:00 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-bb453c76-69e3-4a83-9695-dddbc3a3164c', '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                            low quantity, cheap quality of chicken \n                            Customer Rating:\n                            2 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:00 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:00 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:00 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:00 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:00 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:00 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:01 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:01 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2671'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_5b7mch7cb4tebd7fhe42wpyitlnzam6bs5hzx27u44i2inan3via'), (b'x-request-id', b'req_5b7mch7cb4tebd7fhe42wpyitlnzam6bs5hzx27u44i2inan3via'), (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')])
2026-06-21 03:01:01 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:01 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:01 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:01 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:01 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:01 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:01 GMT', 'content-type': 'application/json', 'content-length': '2671', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_5b7mch7cb4tebd7fhe42wpyitlnzam6bs5hzx27u44i2inan3via', 'x-request-id': 'req_5b7mch7cb4tebd7fhe42wpyitlnzam6bs5hzx27u44i2inan3via', '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'})
2026-06-21 03:01:01 [openai._base_client] DEBUG: request_id: req_5b7mch7cb4tebd7fhe42wpyitlnzam6bs5hzx27u44i2inan3via
2026-06-21 03:01:01 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-c50c99a0-f275-47c8-a0f5-5a7130f853be', '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                            I\n                            Customer Rating:\n                            2 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:01 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:01 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:01 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:01 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:01 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:01 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:02 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:02 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2145'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_ljhok3sv75tt6bxw3qc5xnwl56boie7wp3oszbwockz5cllqwnrq'), (b'x-request-id', b'req_ljhok3sv75tt6bxw3qc5xnwl56boie7wp3oszbwockz5cllqwnrq'), (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')])
2026-06-21 03:01:02 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:02 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:02 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:02 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:02 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:02 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:02 GMT', 'content-type': 'application/json', 'content-length': '2145', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_ljhok3sv75tt6bxw3qc5xnwl56boie7wp3oszbwockz5cllqwnrq', 'x-request-id': 'req_ljhok3sv75tt6bxw3qc5xnwl56boie7wp3oszbwockz5cllqwnrq', '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'})
2026-06-21 03:01:02 [openai._base_client] DEBUG: request_id: req_ljhok3sv75tt6bxw3qc5xnwl56boie7wp3oszbwockz5cllqwnrq
2026-06-21 03:01:02 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-d1c8f71f-c5a1-4a48-a9cd-bc03e0c6fa9e', '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                            Awesome birya mam sarita \n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:02 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:02 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:02 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:02 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:02 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:02 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:03 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:03 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2458'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_rdmojpvejhgcrdgufzfbwzo3xehfrgmusfdnhvzou47xkgtdmikq'), (b'x-request-id', b'req_rdmojpvejhgcrdgufzfbwzo3xehfrgmusfdnhvzou47xkgtdmikq'), (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')])
2026-06-21 03:01:03 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:03 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:03 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:03 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:03 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:03 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:03 GMT', 'content-type': 'application/json', 'content-length': '2458', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_rdmojpvejhgcrdgufzfbwzo3xehfrgmusfdnhvzou47xkgtdmikq', 'x-request-id': 'req_rdmojpvejhgcrdgufzfbwzo3xehfrgmusfdnhvzou47xkgtdmikq', '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'})
2026-06-21 03:01:03 [openai._base_client] DEBUG: request_id: req_rdmojpvejhgcrdgufzfbwzo3xehfrgmusfdnhvzou47xkgtdmikq
2026-06-21 03:01:03 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-32a787bf-2a90-477b-b15c-3b6154aa73ff', '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                            Not good taste\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:03 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:03 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:03 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:03 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:03 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:03 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:05 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:05 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2376'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_vtmrfp7odcg3ucmndknfcwcuz2snv6fafqz63akeaur5medzqaba'), (b'x-request-id', b'req_vtmrfp7odcg3ucmndknfcwcuz2snv6fafqz63akeaur5medzqaba'), (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')])
2026-06-21 03:01:05 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:05 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:05 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:05 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:05 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:05 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:05 GMT', 'content-type': 'application/json', 'content-length': '2376', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_vtmrfp7odcg3ucmndknfcwcuz2snv6fafqz63akeaur5medzqaba', 'x-request-id': 'req_vtmrfp7odcg3ucmndknfcwcuz2snv6fafqz63akeaur5medzqaba', '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'})
2026-06-21 03:01:05 [openai._base_client] DEBUG: request_id: req_vtmrfp7odcg3ucmndknfcwcuz2snv6fafqz63akeaur5medzqaba
2026-06-21 03:01:05 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-eb2288a2-e506-4e4b-8150-9c3ed2cca22a', '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                            Vada was not upto the mark. They have not given chatani\n\n                            Customer Rating:\n                            2 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:05 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:05 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:05 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:05 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:05 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:05 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:07 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:07 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3414'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_p7mvbjktbym7n6eiypypgbg2kdypiroodcyw27kiw62syt3wpb7a'), (b'x-request-id', b'req_p7mvbjktbym7n6eiypypgbg2kdypiroodcyw27kiw62syt3wpb7a'), (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')])
2026-06-21 03:01:07 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:07 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:07 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:07 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:07 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:07 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:07 GMT', 'content-type': 'application/json', 'content-length': '3414', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_p7mvbjktbym7n6eiypypgbg2kdypiroodcyw27kiw62syt3wpb7a', 'x-request-id': 'req_p7mvbjktbym7n6eiypypgbg2kdypiroodcyw27kiw62syt3wpb7a', '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'})
2026-06-21 03:01:07 [openai._base_client] DEBUG: request_id: req_p7mvbjktbym7n6eiypypgbg2kdypiroodcyw27kiw62syt3wpb7a
2026-06-21 03:01:07 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-a81051f4-66dd-4be6-b483-c35efebbae93', '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 peices were very bad in taste. Chemical smell was coming. Full waste of my money\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:07 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:07 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:07 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:07 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:07 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:07 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:08 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:08 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3201'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_ogxvvy33lyn35cxymqoommjw23yds5fft5gmgrn2elqrozwfuhqq'), (b'x-request-id', b'req_ogxvvy33lyn35cxymqoommjw23yds5fft5gmgrn2elqrozwfuhqq'), (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')])
2026-06-21 03:01:08 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:08 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:08 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:08 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:08 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:08 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:08 GMT', 'content-type': 'application/json', 'content-length': '3201', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_ogxvvy33lyn35cxymqoommjw23yds5fft5gmgrn2elqrozwfuhqq', 'x-request-id': 'req_ogxvvy33lyn35cxymqoommjw23yds5fft5gmgrn2elqrozwfuhqq', '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'})
2026-06-21 03:01:08 [openai._base_client] DEBUG: request_id: req_ogxvvy33lyn35cxymqoommjw23yds5fft5gmgrn2elqrozwfuhqq
2026-06-21 03:01:08 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-6ee94a2a-a443-4ed7-ab6f-34a06f86e3df', '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                            Too salted. Was not able to eat. Very bad experience. Chicken leg piece was too and salted and bheja had bad taste. Never ordering again\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:08 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:08 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:08 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:08 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:08 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:08 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:10 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:10 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3040'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_qigm6i2kxftn74jdqwv27nyfq2dsll4lohzlxkhkqhlmkopugtga'), (b'x-request-id', b'req_qigm6i2kxftn74jdqwv27nyfq2dsll4lohzlxkhkqhlmkopugtga'), (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')])
2026-06-21 03:01:10 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:10 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:10 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:10 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:10 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:10 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:10 GMT', 'content-type': 'application/json', 'content-length': '3040', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_qigm6i2kxftn74jdqwv27nyfq2dsll4lohzlxkhkqhlmkopugtga', 'x-request-id': 'req_qigm6i2kxftn74jdqwv27nyfq2dsll4lohzlxkhkqhlmkopugtga', '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'})
2026-06-21 03:01:10 [openai._base_client] DEBUG: request_id: req_qigm6i2kxftn74jdqwv27nyfq2dsll4lohzlxkhkqhlmkopugtga
2026-06-21 03:01:10 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-de0055b0-c5a5-4ef6-a541-57a3480d848c', '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                            हमेशा food बहोत टेस्टी होता है, पर इस बार सब गडबड थी. पुरा खाना फिका और बिना स्वाद का था.\n\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:10 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:10 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:10 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:10 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:10 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:10 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:12 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:12 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3605'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_ocie5zbudx3x7piwessuuzdscjo7nnz4m4qs53b2adfpven6jd6q'), (b'x-request-id', b'req_ocie5zbudx3x7piwessuuzdscjo7nnz4m4qs53b2adfpven6jd6q'), (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')])
2026-06-21 03:01:12 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:12 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:12 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:12 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:12 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:12 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:12 GMT', 'content-type': 'application/json', 'content-length': '3605', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_ocie5zbudx3x7piwessuuzdscjo7nnz4m4qs53b2adfpven6jd6q', 'x-request-id': 'req_ocie5zbudx3x7piwessuuzdscjo7nnz4m4qs53b2adfpven6jd6q', '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'})
2026-06-21 03:01:12 [openai._base_client] DEBUG: request_id: req_ocie5zbudx3x7piwessuuzdscjo7nnz4m4qs53b2adfpven6jd6q
2026-06-21 03:01:12 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-0c0f5f68-9eee-4fbf-ba3c-9271b73c2975', '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                            Awesome biryani\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:12 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:12 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:12 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:12 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:12 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:12 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:14 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:14 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2339'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_5kcg3rrxop36hnjb6rtqggzaaxqkitj5mlxluttofre6x5bq4hfa'), (b'x-request-id', b'req_5kcg3rrxop36hnjb6rtqggzaaxqkitj5mlxluttofre6x5bq4hfa'), (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')])
2026-06-21 03:01:14 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:14 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:14 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:14 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:14 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:14 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:14 GMT', 'content-type': 'application/json', 'content-length': '2339', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_5kcg3rrxop36hnjb6rtqggzaaxqkitj5mlxluttofre6x5bq4hfa', 'x-request-id': 'req_5kcg3rrxop36hnjb6rtqggzaaxqkitj5mlxluttofre6x5bq4hfa', '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'})
2026-06-21 03:01:14 [openai._base_client] DEBUG: request_id: req_5kcg3rrxop36hnjb6rtqggzaaxqkitj5mlxluttofre6x5bq4hfa
2026-06-21 03:01:14 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-fa568b5f-bfdf-4b1f-9f4f-79a6e038de2a', '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                            Rice was very low. \n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:14 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:14 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:14 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:14 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:14 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:14 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:15 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:15 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2346'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_mvg643i6mgdjl6caevknay7u5hakqpr2besjyalm242ulonciwuq'), (b'x-request-id', b'req_mvg643i6mgdjl6caevknay7u5hakqpr2besjyalm242ulonciwuq'), (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')])
2026-06-21 03:01:15 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:15 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:15 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:15 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:15 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:15 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:15 GMT', 'content-type': 'application/json', 'content-length': '2346', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_mvg643i6mgdjl6caevknay7u5hakqpr2besjyalm242ulonciwuq', 'x-request-id': 'req_mvg643i6mgdjl6caevknay7u5hakqpr2besjyalm242ulonciwuq', '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'})
2026-06-21 03:01:15 [openai._base_client] DEBUG: request_id: req_mvg643i6mgdjl6caevknay7u5hakqpr2besjyalm242ulonciwuq
2026-06-21 03:01:15 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-d1da5aec-2191-42c6-b36e-5fb815e39ae1', '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 Tandoori became soggy since it was a 35 min delivery but still the rice was hot but the tandoori was not even warm so a bit disappointed\n                            Customer Rating:\n                            2 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:15 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:15 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:15 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:15 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:15 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:15 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:18 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:18 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'4707'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_ggtl7uevazooan5kawv7zb4nd65tbswtc6mtqpn4kpzb34mc473a'), (b'x-request-id', b'req_ggtl7uevazooan5kawv7zb4nd65tbswtc6mtqpn4kpzb34mc473a'), (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')])
2026-06-21 03:01:18 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:18 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:18 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:18 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:18 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:18 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:18 GMT', 'content-type': 'application/json', 'content-length': '4707', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_ggtl7uevazooan5kawv7zb4nd65tbswtc6mtqpn4kpzb34mc473a', 'x-request-id': 'req_ggtl7uevazooan5kawv7zb4nd65tbswtc6mtqpn4kpzb34mc473a', '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'})
2026-06-21 03:01:18 [openai._base_client] DEBUG: request_id: req_ggtl7uevazooan5kawv7zb4nd65tbswtc6mtqpn4kpzb34mc473a
2026-06-21 03:01:18 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-929b0ace-8a3e-47ec-ae2e-52220e57b7f6', '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                            Stale chicken and uncooked chicken was served…due to which i was hospitalised for severe bacterial stomach infection…bekal house food was stale and uncooked…\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:18 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:18 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:18 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:18 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:18 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:18 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:20 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:20 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'4489'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_u3t35tomkbuumlg5tveycw37z5ejlxh6i4vhzqnr3fdib67a7ftq'), (b'x-request-id', b'req_u3t35tomkbuumlg5tveycw37z5ejlxh6i4vhzqnr3fdib67a7ftq'), (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')])
2026-06-21 03:01:20 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:20 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:20 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:20 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:20 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:20 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:20 GMT', 'content-type': 'application/json', 'content-length': '4489', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_u3t35tomkbuumlg5tveycw37z5ejlxh6i4vhzqnr3fdib67a7ftq', 'x-request-id': 'req_u3t35tomkbuumlg5tveycw37z5ejlxh6i4vhzqnr3fdib67a7ftq', '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'})
2026-06-21 03:01:20 [openai._base_client] DEBUG: request_id: req_u3t35tomkbuumlg5tveycw37z5ejlxh6i4vhzqnr3fdib67a7ftq
2026-06-21 03:01:20 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-335fb67b-77d5-4d59-910a-f9ac9ff0accb', '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                            According to price its not ok\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:20 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:20 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:20 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:20 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:20 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:20 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:21 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:21 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2201'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_xly3qy3evnipp53ech5j4pv54n37kowg4jk3jk56boqb6jjdkxqa'), (b'x-request-id', b'req_xly3qy3evnipp53ech5j4pv54n37kowg4jk3jk56boqb6jjdkxqa'), (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')])
2026-06-21 03:01:21 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:21 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:21 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:21 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:21 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:21 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:21 GMT', 'content-type': 'application/json', 'content-length': '2201', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_xly3qy3evnipp53ech5j4pv54n37kowg4jk3jk56boqb6jjdkxqa', 'x-request-id': 'req_xly3qy3evnipp53ech5j4pv54n37kowg4jk3jk56boqb6jjdkxqa', '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'})
2026-06-21 03:01:21 [openai._base_client] DEBUG: request_id: req_xly3qy3evnipp53ech5j4pv54n37kowg4jk3jk56boqb6jjdkxqa
2026-06-21 03:01:21 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-f8826cc9-9adc-4a13-8e5a-ac09a72b5990', '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                            Only half egg in the entire bowl \n                            Customer Rating:\n                            3 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:21 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:21 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:21 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:21 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:21 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:21 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:23 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:23 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3107'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_r6ljzjxiey4gducf7cpjyvlbscbehvgciqwmenpufarq3csna6va'), (b'x-request-id', b'req_r6ljzjxiey4gducf7cpjyvlbscbehvgciqwmenpufarq3csna6va'), (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')])
2026-06-21 03:01:23 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:23 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:23 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:23 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:23 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:23 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:23 GMT', 'content-type': 'application/json', 'content-length': '3107', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_r6ljzjxiey4gducf7cpjyvlbscbehvgciqwmenpufarq3csna6va', 'x-request-id': 'req_r6ljzjxiey4gducf7cpjyvlbscbehvgciqwmenpufarq3csna6va', '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'})
2026-06-21 03:01:23 [openai._base_client] DEBUG: request_id: req_r6ljzjxiey4gducf7cpjyvlbscbehvgciqwmenpufarq3csna6va
2026-06-21 03:01:23 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-8f6c4106-feab-49ea-8507-11fd056aafd4', '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                            Biryani is damn good\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:23 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:23 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:23 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:23 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:23 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:23 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:24 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:24 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2147'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_ibamarx4vtmxby2ypxgntkytmmgss6e2zqjtd4nj5x6i6mcjaz4q'), (b'x-request-id', b'req_ibamarx4vtmxby2ypxgntkytmmgss6e2zqjtd4nj5x6i6mcjaz4q'), (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')])
2026-06-21 03:01:24 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:24 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:24 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:24 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:24 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:24 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:24 GMT', 'content-type': 'application/json', 'content-length': '2147', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_ibamarx4vtmxby2ypxgntkytmmgss6e2zqjtd4nj5x6i6mcjaz4q', 'x-request-id': 'req_ibamarx4vtmxby2ypxgntkytmmgss6e2zqjtd4nj5x6i6mcjaz4q', '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'})
2026-06-21 03:01:24 [openai._base_client] DEBUG: request_id: req_ibamarx4vtmxby2ypxgntkytmmgss6e2zqjtd4nj5x6i6mcjaz4q
2026-06-21 03:01:24 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-86f5df6a-8e90-47de-bc90-738d90b4da46', '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                            Biryani was delicious.\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:24 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:24 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:24 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:24 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:24 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:24 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:26 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:26 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2446'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_nyzluxx4o2oar5t3yij5ooxrnluwopw2ngmj57lg3gjbm7g2iysa'), (b'x-request-id', b'req_nyzluxx4o2oar5t3yij5ooxrnluwopw2ngmj57lg3gjbm7g2iysa'), (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')])
2026-06-21 03:01:26 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:26 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:26 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:26 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:26 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:26 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:26 GMT', 'content-type': 'application/json', 'content-length': '2446', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_nyzluxx4o2oar5t3yij5ooxrnluwopw2ngmj57lg3gjbm7g2iysa', 'x-request-id': 'req_nyzluxx4o2oar5t3yij5ooxrnluwopw2ngmj57lg3gjbm7g2iysa', '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'})
2026-06-21 03:01:26 [openai._base_client] DEBUG: request_id: req_nyzluxx4o2oar5t3yij5ooxrnluwopw2ngmj57lg3gjbm7g2iysa
2026-06-21 03:01:26 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-df7413ef-65ec-4cc8-83d2-6032c9ed2a11', '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                            Just okay. But packing was not okay. That chutny was opened and was not taped. So I’m giving 2 star \n                            Customer Rating:\n                            2 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:26 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:26 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:26 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:26 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:26 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:26 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:30 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:30 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'4391'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_j2ugx3atalegdn2eozvnf2uecnwnhmya2pnzekoxyxp2s5fxtseq'), (b'x-request-id', b'req_j2ugx3atalegdn2eozvnf2uecnwnhmya2pnzekoxyxp2s5fxtseq'), (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')])
2026-06-21 03:01:30 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:30 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:30 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:30 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:30 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:30 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:30 GMT', 'content-type': 'application/json', 'content-length': '4391', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_j2ugx3atalegdn2eozvnf2uecnwnhmya2pnzekoxyxp2s5fxtseq', 'x-request-id': 'req_j2ugx3atalegdn2eozvnf2uecnwnhmya2pnzekoxyxp2s5fxtseq', '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'})
2026-06-21 03:01:30 [openai._base_client] DEBUG: request_id: req_j2ugx3atalegdn2eozvnf2uecnwnhmya2pnzekoxyxp2s5fxtseq
2026-06-21 03:01:30 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-e02d9995-3b3a-4acc-984e-2d8560bcd3b1', '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                            4 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:30 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:30 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:30 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:30 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:30 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:30 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:31 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:31 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'1755'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_hekjx5oxaoxp7neytgvlm2qjrsyuvjx7kto4trpl7locgtf5jslq'), (b'x-request-id', b'req_hekjx5oxaoxp7neytgvlm2qjrsyuvjx7kto4trpl7locgtf5jslq'), (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')])
2026-06-21 03:01:31 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:31 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:31 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:31 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:31 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:31 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:31 GMT', 'content-type': 'application/json', 'content-length': '1755', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_hekjx5oxaoxp7neytgvlm2qjrsyuvjx7kto4trpl7locgtf5jslq', 'x-request-id': 'req_hekjx5oxaoxp7neytgvlm2qjrsyuvjx7kto4trpl7locgtf5jslq', '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'})
2026-06-21 03:01:31 [openai._base_client] DEBUG: request_id: req_hekjx5oxaoxp7neytgvlm2qjrsyuvjx7kto4trpl7locgtf5jslq
2026-06-21 03:01:31 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-d3acc34e-b014-4a35-a0a7-979c3212d063', '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 speed with which food was out for delivery was amazing. Medu wada was very well made. Crispy on the outside and fluffy on the inside. However both the masala dosa were EXTREMELY salty. Borderline inedible.\n                            Customer Rating:\n                            3 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:31 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:31 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:31 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:31 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:31 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:31 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:33 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:33 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3814'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_qida4ng62bb75gwzn5h2klk3cbhrsvuooxclk4umraughofcq2lq'), (b'x-request-id', b'req_qida4ng62bb75gwzn5h2klk3cbhrsvuooxclk4umraughofcq2lq'), (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')])
2026-06-21 03:01:33 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:33 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:33 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:33 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:33 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:33 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:33 GMT', 'content-type': 'application/json', 'content-length': '3814', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_qida4ng62bb75gwzn5h2klk3cbhrsvuooxclk4umraughofcq2lq', 'x-request-id': 'req_qida4ng62bb75gwzn5h2klk3cbhrsvuooxclk4umraughofcq2lq', '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'})
2026-06-21 03:01:33 [openai._base_client] DEBUG: request_id: req_qida4ng62bb75gwzn5h2klk3cbhrsvuooxclk4umraughofcq2lq
2026-06-21 03:01:33 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-ef0e7ac3-cd30-4de3-8d56-ce01ef39b1e1', '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                            There is just a single piece of tikka that is served in the biryani. \n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:33 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:33 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:33 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:33 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:33 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:33 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:35 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:35 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2558'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_s7myzj5uxj5bvktj6dpkm4qlbjx7xujc2kulg7l33fuzixofn4eq'), (b'x-request-id', b'req_s7myzj5uxj5bvktj6dpkm4qlbjx7xujc2kulg7l33fuzixofn4eq'), (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')])
2026-06-21 03:01:35 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:35 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:35 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:35 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:35 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:35 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:35 GMT', 'content-type': 'application/json', 'content-length': '2558', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_s7myzj5uxj5bvktj6dpkm4qlbjx7xujc2kulg7l33fuzixofn4eq', 'x-request-id': 'req_s7myzj5uxj5bvktj6dpkm4qlbjx7xujc2kulg7l33fuzixofn4eq', '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'})
2026-06-21 03:01:35 [openai._base_client] DEBUG: request_id: req_s7myzj5uxj5bvktj6dpkm4qlbjx7xujc2kulg7l33fuzixofn4eq
2026-06-21 03:01:35 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-6bb8a88a-b1e5-42bb-bd57-b00a8a5f5496', '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                            As per prize quantity is less\n\n                            Customer Rating:\n                            5 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:35 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:35 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:35 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:35 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:35 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:35 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:36 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:36 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'3155'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_dtd4zxdvkgbd3laapugwsdkyhblayts47ybxnplu2lxusiwk6lxa'), (b'x-request-id', b'req_dtd4zxdvkgbd3laapugwsdkyhblayts47ybxnplu2lxusiwk6lxa'), (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')])
2026-06-21 03:01:36 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:36 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:36 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:36 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:36 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:36 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:36 GMT', 'content-type': 'application/json', 'content-length': '3155', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_dtd4zxdvkgbd3laapugwsdkyhblayts47ybxnplu2lxusiwk6lxa', 'x-request-id': 'req_dtd4zxdvkgbd3laapugwsdkyhblayts47ybxnplu2lxusiwk6lxa', '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'})
2026-06-21 03:01:36 [openai._base_client] DEBUG: request_id: req_dtd4zxdvkgbd3laapugwsdkyhblayts47ybxnplu2lxusiwk6lxa
2026-06-21 03:01:36 [openai._base_client] DEBUG: Request options: {'method': 'post', 'url': '/responses', 'files': None, 'idempotency_key': 'stainless-python-retry-8dfca39e-1e02-4501-a38f-3a88e338cfbd', '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                            Quantity is very less. Compare to others.\n                            Customer Rating:\n                            1 ', 'model': 'openai.gpt-oss-120b'}}
2026-06-21 03:01:36 [openai._base_client] DEBUG: Sending HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses
2026-06-21 03:01:36 [httpcore.http11] DEBUG: send_request_headers.started request=<Request [b'POST']>
2026-06-21 03:01:36 [httpcore.http11] DEBUG: send_request_headers.complete
2026-06-21 03:01:36 [httpcore.http11] DEBUG: send_request_body.started request=<Request [b'POST']>
2026-06-21 03:01:36 [httpcore.http11] DEBUG: send_request_body.complete
2026-06-21 03:01:36 [httpcore.http11] DEBUG: receive_response_headers.started request=<Request [b'POST']>
2026-06-21 03:01:38 [httpcore.http11] DEBUG: receive_response_headers.complete return_value=(b'HTTP/1.1', 200, b'OK', [(b'Date', b'Sun, 21 Jun 2026 03:01:38 GMT'), (b'Content-Type', b'application/json'), (b'Content-Length', b'2691'), (b'Connection', b'keep-alive'), (b'x-amzn-requestid', b'req_hzw2uciql4tto7s5khnr6sz5y2saljvd5ymbkrbujoj5gukjgf7a'), (b'x-request-id', b'req_hzw2uciql4tto7s5khnr6sz5y2saljvd5ymbkrbujoj5gukjgf7a'), (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')])
2026-06-21 03:01:38 [httpx] INFO: HTTP Request: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "HTTP/1.1 200 OK"
2026-06-21 03:01:38 [httpcore.http11] DEBUG: receive_response_body.started request=<Request [b'POST']>
2026-06-21 03:01:38 [httpcore.http11] DEBUG: receive_response_body.complete
2026-06-21 03:01:38 [httpcore.http11] DEBUG: response_closed.started
2026-06-21 03:01:38 [httpcore.http11] DEBUG: response_closed.complete
2026-06-21 03:01:38 [openai._base_client] DEBUG: HTTP Response: POST https://bedrock-mantle.ap-south-1.api.aws/v1/responses "200 OK" Headers({'date': 'Sun, 21 Jun 2026 03:01:38 GMT', 'content-type': 'application/json', 'content-length': '2691', 'connection': 'keep-alive', 'x-amzn-requestid': 'req_hzw2uciql4tto7s5khnr6sz5y2saljvd5ymbkrbujoj5gukjgf7a', 'x-request-id': 'req_hzw2uciql4tto7s5khnr6sz5y2saljvd5ymbkrbujoj5gukjgf7a', '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'})
2026-06-21 03:01:38 [openai._base_client] DEBUG: request_id: req_hzw2uciql4tto7s5khnr6sz5y2saljvd5ymbkrbujoj5gukjgf7a
2026-06-21 03:01:38 [scrapy.statscollectors] INFO: Dumping Scrapy stats:
{'downloader/request_bytes': 1199286,
 'downloader/request_count': 335,
 'downloader/request_method_count/POST': 335,
 'downloader/response_bytes': 525192,
 'downloader/response_count': 335,
 'downloader/response_status_count/200': 335,
 'elapsed_time_seconds': 81.650538,
 'finish_reason': 'finished',
 'finish_time': datetime.datetime(2026, 6, 21, 3, 1, 38, 298166, tzinfo=datetime.timezone.utc),
 'httpcompression/response_bytes': 561177,
 'httpcompression/response_count': 335,
 'item_scraped_count': 318,
 'items_per_minute': None,
 'log_count/DEBUG': 1180,
 'log_count/INFO': 47,
 'log_count/WARNING': 1,
 'memusage/max': 145395712,
 'memusage/startup': 145395712,
 'request_depth_max': 17,
 'response_received_count': 335,
 'responses_per_minute': None,
 'scheduler/dequeued': 335,
 'scheduler/dequeued/memory': 335,
 'scheduler/enqueued': 335,
 'scheduler/enqueued/memory': 335,
 'start_time': datetime.datetime(2026, 6, 21, 3, 0, 16, 647628, tzinfo=datetime.timezone.utc)}
2026-06-21 03:01:38 [scrapy.core.engine] INFO: Spider closed (finished)
2026-06-21 03:01:38 [httpcore.connection] DEBUG: close.started
2026-06-21 03:01:38 [httpcore.connection] DEBUG: close.complete