{"id":96227,"date":"2026-09-08T09:11:59","date_gmt":"2026-09-08T06:11:59","guid":{"rendered":"https:\/\/twodots.gr\/?p=96227"},"modified":"2026-09-08T09:12:01","modified_gmt":"2026-09-08T06:12:01","slug":"sage-ai-functions-sql-kostos","status":"publish","type":"post","link":"https:\/\/twodots.gr\/bg\/sage-ai-functions-sql-kostos\/","title":{"rendered":"SAGE: \u03c0\u03ce\u03c2 \u03b7 AI \u03bc\u03c0\u03b1\u03af\u03bd\u03b5\u03b9 \u03c3\u03c4\u03b7 SQL \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b5\u03ba\u03c4\u03bf\u03be\u03b5\u03cd\u03b5\u03b9 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>\u03a4\u03bf SAGE \u03bf\u03c1\u03b3\u03b1\u03bd\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b9\u03c2 AI functions \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03b7 SQL \u03c3\u03b5 \u03c4\u03c1\u03b5\u03b9\u03c2 \u03c3\u03c7\u03b5\u03c3\u03b9\u03b1\u03ba\u03ad\u03c2 primitives \u2014 AI_SCALAR, AI_AGG \u03ba\u03b1\u03b9 AI_JOIN \u2014 \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03c6\u03c5\u03c3\u03b9\u03ba\u03ae \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7 \u03b1\u03bd\u03ac\u03bb\u03bf\u03b3\u03b1 \u03bc\u03b5 \u03c4\u03bf \u03b1\u03bd \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b1\u03c6\u03bf\u03c1\u03ac \u03b3\u03c1\u03b1\u03bc\u03bc\u03ad\u03c2, \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 \u03ae \u03b6\u03b5\u03cd\u03b3\u03b7.<\/strong> \u03a3\u03c4\u03bf representative factorable join \u03c4\u03bf\u03c5 paper, \u03b1\u03c5\u03c4\u03ae \u03b7 \u03b4\u03bf\u03bc\u03ae \u03bc\u03b5\u03af\u03c9\u03c3\u03b5 \u03c4\u03b9\u03c2 model calls \u03b1\u03c0\u03cc 16.256 \u03c3\u03b5 128 \u03ba\u03b1\u03b9 \u03c4\u03bf \u03bc\u03b5\u03c4\u03c1\u03b7\u03bc\u03ad\u03bd\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03c4\u03ac 358\u00d7.<\/p>\n<p>\u03a4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 query \u03b8\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 358\u00d7 \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf. \u0395\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03b7\u03c2 AI \u03c3\u03c4\u03b7 \u03b2\u03ac\u03c3\u03b7 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf whole plan: deterministic filtering \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc model calls, \u03b5\u03c0\u03b1\u03bd\u03b1\u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 virtual columns, \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 evidence loss \u03c3\u03c4\u03b1 aggregates \u03ba\u03b1\u03b9 escalation \u03bc\u03cc\u03bd\u03bf \u03cc\u03c0\u03bf\u03c5 \u03b7 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf \u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">\u0421\u044a\u0434\u044a\u0440\u0436\u0430\u043d\u0438\u0435<\/div>\n<ul>\n<li><a href=\"#giati-ai-functions-aplopoieitai\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03ad\u03ba\u03c1\u03b7\u03be\u03b7 \u03c4\u03c9\u03bd AI functions \u03ba\u03c1\u03cd\u03b2\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03b1\u03c0\u03bb\u03bf\u03cd\u03c3\u03c4\u03b5\u03c1\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1<\/a><\/li>\n<li><a href=\"#ai-scalar\">AI_SCALAR: \u03bc\u03af\u03b1 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ae \u03bc\u03c0\u03b1\u03af\u03bd\u03b5\u03b9, \u03bc\u03af\u03b1 \u03c4\u03b9\u03bc\u03ae \u03b2\u03b3\u03b1\u03af\u03bd\u03b5\u03b9<\/a><\/li>\n<li><a href=\"#ai-agg\">AI_AGG: \u03b7 \u03c3\u03cd\u03bd\u03bf\u03c8\u03b7 \u03bf\u03bc\u03ac\u03b4\u03b1\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03ac\u03bb\u03bb\u03bf \u03ad\u03bd\u03b1 scalar<\/a><\/li>\n<li><a href=\"#ai-join\">AI_JOIN: \u03c4\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03cc\u03c0\u03bf\u03c5 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 \u03c4\u03b5\u03c4\u03c1\u03b1\u03b3\u03c9\u03bd\u03b9\u03ba\u03cc<\/a><\/li>\n<li><a href=\"#routes-semantic-predicates\">\u03a4\u03c1\u03b5\u03b9\u03c2 \u03c6\u03c5\u03c3\u03b9\u03ba\u03ad\u03c2 \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ad\u03c2 \u03b3\u03b9\u03b1 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac semantic predicates<\/a><\/li>\n<li><a href=\"#confidence-gated-execution\">\u039a\u03bf\u03b9\u03bd\u03cc\u03c2 confidence-gated \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc\u03c2, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03b9\u03bc\u03b1 \u00ab\u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u00bb<\/a><\/li>\n<li><a href=\"#probe-and-race\">Probe-and-race: \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae configuration \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03c4\u03c1\u03ad\u03c7\u03bf\u03bd query<\/a><\/li>\n<li><a href=\"#apotelesmata-axiologisis\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03bf\u03b9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af<\/a><\/li>\n<li><a href=\"#business-workflows\">\u03a0\u03ce\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03c1\u03b1\u03c3\u03c4\u03b5\u03af \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac business workflows<\/a><\/li>\n<li><a href=\"#oria-sage\">\u03a4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03bf\u03c5 SAGE \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03b3\u03bd\u03bf\u03b7\u03b8\u03bf\u03cd\u03bd<\/a><\/li>\n<li><a href=\"#symperasma-sage\">\u03a4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b3\u03b9\u03b1 data \u03ba\u03b1\u03b9 marketing leaders<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"giati-ai-functions-aplopoieitai\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03ad\u03ba\u03c1\u03b7\u03be\u03b7 \u03c4\u03c9\u03bd AI functions \u03ba\u03c1\u03cd\u03b2\u03b5\u03b9 \u03ad\u03bd\u03b1 \u03b1\u03c0\u03bb\u03bf\u03cd\u03c3\u03c4\u03b5\u03c1\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1<\/h2>\n<p>\u03a3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03ad\u03c1\u03b5\u03c5\u03bd\u03b1\u03c2 \u03ba\u03b1\u03b9 data warehouses \u03b5\u03ba\u03b8\u03ad\u03c4\u03bf\u03c5\u03bd \u03bf\u03bd\u03cc\u03bc\u03b1\u03c4\u03b1 \u03cc\u03c0\u03c9\u03c2 AI_CLASSIFY, AI_EXTRACT, AI_FILTER, AI_SIMILARITY, AI_JOIN \u03ba\u03b1\u03b9 AI_AGG. \u0397 \u03bf\u03bd\u03bf\u03bc\u03b1\u03c3\u03af\u03b1 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03b1\u03c5\u03c4\u03cc \u03c0\u03bf\u03c5 \u03b2\u03bb\u03ad\u03c0\u03b5\u03b9 \u03bf \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7\u03c2, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03b1 \u03b1\u03c5\u03c4\u03cc \u03c0\u03bf\u03c5 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9 \u03bf query planner. \u03a4\u03bf SAGE \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae \u03b1\u03c0\u03cc \u03c4\u03bf API name \u03c3\u03c4\u03b7\u03bd cardinality \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03c4\u03c9\u03bd \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd: \u03b7 \u03ba\u03bb\u03ae\u03c3\u03b7 \u03ba\u03bf\u03b9\u03c4\u03ac \u03bc\u03af\u03b1 \u03ae\u03b4\u03b7 \u03b4\u03b5\u03c3\u03bc\u03b5\u03c5\u03bc\u03ad\u03bd\u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ae, \u03ad\u03bd\u03b1 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf group \u03ae \u03ad\u03bd\u03b1 \u03c5\u03c0\u03bf\u03c8\u03ae\u03c6\u03b9\u03bf \u03b6\u03b5\u03cd\u03b3\u03bf\u03c2;<\/p>\n<p>\u0391\u03c5\u03c4\u03ae \u03b7 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ae \u03b3\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 data, e-commerce \u03ba\u03b1\u03b9 marketing \u03c0\u03bf\u03c5 \u03b8\u03ad\u03bb\u03bf\u03c5\u03bd \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c8\u03bf\u03c5\u03bd \u03c4\u03b1 <a href=\"https:\/\/twodots.gr\/analytics-platforms-data-decisions\/\">analytics \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03c3\u03b5 \u03b5\u03bb\u03ad\u03b3\u03be\u03b9\u03bc\u03b5\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2<\/a>, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03c3\u03c7\u03ae\u03bc\u03b1 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03cc \u03c1\u03af\u03c3\u03ba\u03bf. \u0397 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 \u03b5\u03bd\u03cc\u03c2 review \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c4\u03cd\u03c7\u03b5\u03b9 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ac \u03c3\u03b5 \u03ad\u03bd\u03b1 item. \u039c\u03b9\u03b1 \u03c3\u03cd\u03bd\u03bf\u03c8\u03b7 \u03bf\u03bc\u03ac\u03b4\u03b1\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c7\u03ac\u03c3\u03b5\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf evidence. \u0388\u03bd\u03b1 semantic join \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03ae\u03c3\u03b5\u03b9 \u03c4\u03b5\u03c4\u03c1\u03b1\u03b3\u03c9\u03bd\u03b9\u03ba\u03cc \u03c7\u03ce\u03c1\u03bf \u03c5\u03c0\u03bf\u03c8\u03b7\u03c6\u03af\u03c9\u03bd \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03b1\u03c3\u03b9\u03ac\u03c3\u03b5\u03b9 \u03c4\u03b9\u03c2 \u03ba\u03bb\u03ae\u03c3\u03b5\u03b9\u03c2. \u0394\u03b5\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03bb\u03bf\u03b9\u03c0\u03cc\u03bd \u03bc\u03af\u03b1 \u03b5\u03bd\u03b9\u03b1\u03af\u03b1 \u03c6\u03c5\u03c3\u03b9\u03ba\u03ae \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae \u03c0\u03bf\u03c5 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae\u03c2 \u03ba\u03b1\u03b9 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03ae \u03c0\u03b1\u03bd\u03c4\u03bf\u03cd.<\/p>\n<h2 id=\"ai-scalar\">AI_SCALAR: \u03bc\u03af\u03b1 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ae \u03bc\u03c0\u03b1\u03af\u03bd\u03b5\u03b9, \u03bc\u03af\u03b1 \u03c4\u03b9\u03bc\u03ae \u03b2\u03b3\u03b1\u03af\u03bd\u03b5\u03b9<\/h2>\n<p>\u0397 \u03c0\u03c1\u03ce\u03c4\u03b7 primitive \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03b5\u03af \u03ba\u03ac\u03b8\u03b5 row \u03c3\u03b5 \u03bc\u03af\u03b1 \u03c4\u03b9\u03bc\u03ae \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03c4\u03bf \u03c0\u03bb\u03ae\u03b8\u03bf\u03c2 \u03c4\u03c9\u03bd \u03b3\u03c1\u03b1\u03bc\u03bc\u03ce\u03bd. Classification, extraction, rewriting, scoring, \u03b5\u03c1\u03ce\u03c4\u03b7\u03c3\u03b7 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf context \u03c4\u03b7\u03c2 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ae\u03c2 \u03ba\u03b1\u03b9 Boolean filtering \u03b1\u03bd\u03ae\u03ba\u03bf\u03c5\u03bd \u03b5\u03b4\u03ce. \u0388\u03bd\u03b1 sentiment label \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 review \u03ae \u03b7 \u03b5\u03be\u03b1\u03b3\u03c9\u03b3\u03ae brand name \u03b1\u03c0\u03cc \u03ba\u03ac\u03b8\u03b5 ticket \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1. \u03a4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 virtual column \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03b5\u03af \u03b1\u03c1\u03b3\u03cc\u03c4\u03b5\u03c1\u03b1 \u03c3\u03b5 SELECT, WHERE, ORDER BY \u03ae \u03ac\u03bb\u03bb\u03b7 AI primitive.<\/p>\n<p>\u0395\u03c0\u03b5\u03b9\u03b4\u03ae \u03b7 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03bc\u03b9\u03b1\u03c2 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ae\u03c2 \u03b4\u03b5\u03bd \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 \u03ac\u03bb\u03bb\u03b5\u03c2, \u03b7 \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 \u03bc\u03b5 batching, parallel inference \u03ba\u03b1\u03b9 caching \u03c7\u03c9\u03c1\u03af\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c4\u03b7\u03c2 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae\u03c2 \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1\u03c2. \u0391\u03bd \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c0\u03b1\u03c1\u03b1\u03b3\u03cc\u03bc\u03b5\u03bd\u03bf \u03c0\u03b5\u03b4\u03af\u03bf \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03c0\u03bf\u03bb\u03bb\u03ac downstream \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1, \u03c4\u03bf SAGE \u03c4\u03bf \u03c5\u03bb\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03bc\u03af\u03b1 \u03c6\u03bf\u03c1\u03ac. \u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03bc\u03c0\u03bf\u03c1\u03b9\u03ba\u03ae \u03bf\u03bc\u03ac\u03b4\u03b1 \u03b1\u03c5\u03c4\u03cc \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 \u03b1\u03ba\u03c1\u03b9\u03b2\u03cc enrichment \u03b4\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7, \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 \u03ad\u03bd\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b1 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ac \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1 \u03c0\u03bf\u03c5 \u03c3\u03c5\u03bd\u03b8\u03ad\u03c4\u03bf\u03c5\u03bd <a href=\"https:\/\/twodots.gr\/to-kostos-tis-ai-ti-prepei-na-gnorizoun-oi-epicheiriseis\/\">\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03b7\u03c2 AI \u03b3\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7<\/a>.<\/p>\n<h2 id=\"ai-agg\">AI_AGG: \u03b7 \u03c3\u03cd\u03bd\u03bf\u03c8\u03b7 \u03bf\u03bc\u03ac\u03b4\u03b1\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03ac\u03bb\u03bb\u03bf \u03ad\u03bd\u03b1 scalar<\/h2>\n<p>\u0397 AI_AGG \u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1 bag \u03b3\u03c1\u03b1\u03bc\u03bc\u03ce\u03bd \u03bc\u03b5 \u03ba\u03bf\u03b9\u03bd\u03cc group key \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c3\u03c4\u03c1\u03ad\u03c6\u03b5\u03b9 \u03bc\u03af\u03b1 \u03c4\u03b9\u03bc\u03ae \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03bf\u03bc\u03ac\u03b4\u03b1. \u03a3\u03cd\u03bd\u03bf\u03c8\u03b7 reviews \u03b1\u03bd\u03ac \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd, \u03b5\u03be\u03b1\u03b3\u03c9\u03b3\u03ae consensus \u03b1\u03c0\u03cc tickets \u03ae structured theme list \u03b1\u03bd\u03ac \u03ba\u03b1\u03bc\u03c0\u03ac\u03bd\u03b9\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1. \u0395\u03b4\u03ce \u03b7 cardinality \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc N \u03b3\u03c1\u03b1\u03bc\u03bc\u03ad\u03c2 \u03c3\u03b5 G outputs, \u03b1\u03bb\u03bb\u03ac \u03b7 \u03b4\u03c5\u03c3\u03ba\u03bf\u03bb\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc evidence \u03c0\u03bf\u03c5 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac documents.<\/p>\n<p>\u03a4\u03bf SAGE \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c0\u03c1\u03bf\u03b1\u03b9\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc compaction \u03cc\u03c4\u03b1\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03c5\u03bd \u03c3\u03c7\u03b5\u03b4\u03cc\u03bd \u03b4\u03b9\u03c0\u03bb\u03cc\u03c4\u03c5\u03c0\u03b5\u03c2 \u03b5\u03b3\u03b3\u03c1\u03b1\u03c6\u03ad\u03c2: embeddings, clusters \u03c0\u03ac\u03bd\u03c9 \u03b1\u03c0\u03cc threshold \u03ba\u03b1\u03b9 \u03b5\u03ba\u03c0\u03c1\u03bf\u03c3\u03ce\u03c0\u03b7\u03c3\u03b7 \u03ba\u03ac\u03b8\u03b5 cluster \u03bc\u03b5 exemplar \u03ae \u03b2\u03ac\u03c1\u03bf\u03c2. \u038c\u03bc\u03c9\u03c2 \u03c4\u03bf paper \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf compaction \u03b1\u03c0\u03cc \u03c4\u03bf chunking. \u03a4\u03bf chunking \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ad\u03c2 \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf aggregate \u03b5\u03af\u03bd\u03b1\u03b9 modular \u03ae algebraic \u03ba\u03b1\u03b9 \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 mergeable partial state. \u0388\u03bd\u03b1 count \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03ae\u03b4\u03b7 \u03b5\u03be\u03b1\u03c7\u03b8\u03b5\u03af\u03c3\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b9\u03ba\u03b1\u03bd\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b7\u00b7 \u03bc\u03b9\u03b1 \u03bf\u03bb\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03c3\u03cd\u03bd\u03bf\u03c8\u03b7 \u03c0\u03bf\u03c5 \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc cross-document evidence \u03c3\u03c5\u03bd\u03ae\u03b8\u03c9\u03c2 \u03cc\u03c7\u03b9, \u03cc\u03c0\u03c9\u03c2 \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b1 <a href=\"https:\/\/twodots.gr\/private-chatbot-rag-knowledge-base-business\/\">RAG workflows \u03c0\u03bf\u03c5 \u03b1\u03c0\u03b1\u03bd\u03c4\u03bf\u03cd\u03bd \u03b1\u03c0\u03cc \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03ae \u03b3\u03bd\u03ce\u03c3\u03b7<\/a>.<\/p>\n<p>\u0397 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03c0\u03c1\u03bf\u03b5\u03b9\u03b4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ae: \u03c4\u03bf \u03bd\u03b1 \u03ba\u03cc\u03b2\u03b5\u03b9\u03c2 \u03ad\u03bd\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b5\u03b3\u03b3\u03c1\u03ac\u03c6\u03c9\u03bd \u03c3\u03b5 chunks \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03bd\u03ce\u03bd\u03b5\u03b9\u03c2 \u03b1\u03c5\u03b8\u03b1\u03af\u03c1\u03b5\u03c4\u03b1 \u03c4\u03b9\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bb\u03ae\u03c8\u03b5\u03b9\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1 \u03b9\u03c3\u03bf\u03b4\u03cd\u03bd\u03b1\u03bc\u03bf \u03bc\u03b5 \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03cc\u03bb\u03bf\u03c5 \u03c4\u03bf\u03c5 group. \u0391\u03bd \u03c4\u03bf insight \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 brand strategy, customer experience \u03ae compliance, \u03b7 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03b1\u03bd \u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03b7\u03bc\u03ad\u03bd\u03b7 \u03c0\u03c1\u03bf\u03c3\u03ad\u03b3\u03b3\u03b9\u03c3\u03b7 \u03ae \u03c3\u03b7\u03bc\u03b1\u03c3\u03b9\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ac \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae\u03c2 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03bf\u03c0\u03ae.<\/p>\n<h2 id=\"ai-join\">AI_JOIN: \u03c4\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03cc\u03c0\u03bf\u03c5 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 \u03c4\u03b5\u03c4\u03c1\u03b1\u03b3\u03c9\u03bd\u03b9\u03ba\u03cc<\/h2>\n<p>\u0397 \u03c4\u03c1\u03af\u03c4\u03b7 primitive \u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03b1\u03bd \u03ad\u03bd\u03b1 \u03b6\u03b5\u03cd\u03b3\u03bf\u03c2 \u03b1\u03c0\u03cc \u03b4\u03cd\u03bf relations \u03b9\u03ba\u03b1\u03bd\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03bc\u03b9\u03b1 semantic condition. Entity matching, query-document relevance, <a href=\"https:\/\/twodots.gr\/hybrid-ai-search-papers-with-code\/\">hybrid semantic search<\/a> \u03ba\u03b1\u03b9 natural-language joins \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b9\u03b4\u03c9\u03b8\u03bf\u03cd\u03bd \u03c9\u03c2 Boolean judgments \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 candidate pairs. \u03a0\u03c1\u03b9\u03bd \u03c4\u03bf selection, \u03bf \u03c7\u03ce\u03c1\u03bf\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 |A|\u00d7|B|. \u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf: \u03b4\u03cd\u03bf \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2 \u03bc\u03ad\u03c4\u03c1\u03b9\u03bf\u03c5 \u03bc\u03b5\u03b3\u03ad\u03b8\u03bf\u03c5\u03c2 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03ae\u03c3\u03bf\u03c5\u03bd \u03c4\u03b5\u03c1\u03ac\u03c3\u03c4\u03b9\u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc model evaluations.<\/p>\n<p>\u03a4\u03bf SAGE \u03b4\u03b5\u03bd \u03b8\u03b5\u03c9\u03c1\u03b5\u03af \u03cc\u03c4\u03b9 \u03cc\u03bb\u03b1 \u03c4\u03b1 joins \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03bb\u03ae\u03c1\u03b7 generative reasoning. \u0388\u03bd\u03b1\u03c2 compiler \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf predicate \u03ba\u03b1\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc sample \u03ad\u03c9\u03c2 \u03c0\u03ad\u03bd\u03c4\u03b5 candidate pairs, \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03ce\u03bd\u03c4\u03b1\u03c2 \u03bc\u03b9\u03b1 cached recipe card. \u0397 card \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 route, featurizer, labels, negative check \u03ba\u03b1\u03b9 configuration space. \u03a3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1 \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 membership, relational \u03ae reasoning route \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03b7 \u03b4\u03bf\u03bc\u03ae \u03c4\u03bf\u03c5 predicate.<\/p>\n<div class=\"td-comparison td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid td-comparison-grid--three\">\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">AI_SCALAR<\/p>\n<p>\u0395\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03b4\u03b5\u03c3\u03bc\u03b5\u03c5\u03bc\u03ad\u03bd\u03b7 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ae \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03c4\u03b7\u03bd cardinality. \u03a4\u03bf \u03b2\u03b1\u03c3\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03ac row, \u03b5\u03bd\u03ce caching \u03ba\u03b1\u03b9 batching \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">N \u2192 N<\/span><span class=\"td-badge\">Per-item error<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">AI_AGG<\/p>\n<p>\u039c\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1 group \u03c3\u03b5 \u03bc\u03af\u03b1 \u03c4\u03b9\u03bc\u03ae. \u03a4\u03bf \u03c1\u03af\u03c3\u03ba\u03bf \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2: compaction \u03ae \u03b1\u03c5\u03b8\u03b1\u03af\u03c1\u03b5\u03c4\u03bf chunking \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c6\u03b1\u03b9\u03c1\u03ad\u03c3\u03b5\u03b9 cross-document evidence.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">N \u2192 G<\/span><span class=\"td-badge\">Lost evidence<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<p class=\"td-comparison-title\">AI_JOIN<\/p>\n<p>\u0391\u03c0\u03bf\u03c6\u03b1\u03c3\u03af\u03b6\u03b5\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03b6\u03b5\u03cd\u03b3\u03b7 \u03b5\u03c0\u03b9\u03b2\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd. \u03a7\u03c9\u03c1\u03af\u03c2 \u03c0\u03b1\u03c1\u03b1\u03b3\u03bf\u03bd\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03ae \u03c3\u03c5\u03bd\u03c4\u03b7\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc pruning, \u03bf candidate space \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c6\u03c4\u03ac\u03c3\u03b5\u03b9 \u03c4\u03bf N\u00d7M.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">N\u00d7M \u2192 K<\/span><span class=\"td-badge\">Pair explosion<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"routes-semantic-predicates\">\u03a4\u03c1\u03b5\u03b9\u03c2 \u03c6\u03c5\u03c3\u03b9\u03ba\u03ad\u03c2 \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ad\u03c2 \u03b3\u03b9\u03b1 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac semantic predicates<\/h2>\n<p>\u03a3\u03c4\u03b7 membership route, \u03c4\u03bf predicate \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b3\u03bf\u03bd\u03c4\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03b5\u03af \u03c3\u03b5 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b5\u03c2 virtual columns \u03b1\u03bd\u03ac \u03c0\u03bb\u03b5\u03c5\u03c1\u03ac \u03ba\u03b1\u03b9 deterministic comparison. \u0393\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03c4\u03bf \u00ab\u03af\u03b4\u03b9\u03bf sentiment\u00bb \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c4\u03b1\u03be\u03b9\u03bd\u03bf\u03bc\u03ae\u03c3\u03b5\u03b9 \u03ba\u03ac\u03b8\u03b5 row \u03c3\u03b5 \u03ba\u03bb\u03b5\u03b9\u03c3\u03c4\u03cc label set \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03ac \u03bd\u03b1 \u03ba\u03ac\u03bd\u03b5\u03b9 ordinary hash join. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c0\u03ad\u03c6\u03c4\u03b5\u03b9 \u03b1\u03c0\u03cc N\u00d7M pair evaluations \u03c3\u03b5 N+M row evaluations, \u03b5\u03c6\u03cc\u03c3\u03bf\u03bd \u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03bf\u03bd\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03b3\u03ba\u03c5\u03c1\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03b1 predicted values \u03c3\u03c9\u03c3\u03c4\u03ac.<\/p>\n<p>\u03a3\u03c4\u03b7 relational route, \u03b7 \u03c3\u03c7\u03ad\u03c3\u03b7 \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03b6\u03b5\u03cd\u03b3\u03bf\u03c2, \u03cc\u03c0\u03c9\u03c2 \u00ab\u03c4\u03bf \u03c0\u03c1\u03cc\u03c3\u03c9\u03c0\u03bf \u0391 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03cd\u03b6\u03c5\u03b3\u03bf\u03c2 \u03c4\u03bf\u03c5 \u0392\u00bb. \u0388\u03bd\u03b1 lightweight relation model \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c1\u03c1\u03af\u03c8\u03b5\u03b9 \u03b5\u03cd\u03ba\u03bf\u03bb\u03b1 negatives, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf pair grid \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ac \u03c0\u03b1\u03c1\u03cc\u03bd. \u03a3\u03c4\u03b7 reasoning route, \u03cc\u03c0\u03c9\u03c2 \u00ab\u03c4\u03bf document \u03c0\u03b1\u03c1\u03ad\u03c7\u03b5\u03b9 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1 \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03b1\u03c0\u03b1\u03bd\u03c4\u03b7\u03b8\u03b5\u03af \u03b7 \u03b5\u03c1\u03ce\u03c4\u03b7\u03c3\u03b7\u00bb, \u03ad\u03bd\u03b1 \u03b1\u03c0\u03bb\u03cc embedding \u03ae membership rewrite \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b5\u03c4\u03ac\u03be\u03b5\u03b9 valid matches. \u0393\u03b9\u2019 \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf front end \u03ba\u03ac\u03bd\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c3\u03c5\u03bd\u03c4\u03b7\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc negative pruning \u03ba\u03b1\u03b9 \u03c4\u03b1 \u03b1\u03bc\u03c6\u03af\u03b2\u03bf\u03bb\u03b1 \u03b6\u03b5\u03cd\u03b3\u03b7 \u03c0\u03b5\u03c1\u03bd\u03bf\u03cd\u03bd \u03b1\u03c0\u03cc generative reasoning.<\/p>\n<p>\u0393\u03b9\u03b1 compound predicates \u03c4\u03bf SAGE \u03c6\u03c4\u03b9\u03ac\u03c7\u03bd\u03b5\u03b9 \u03c3\u03c7\u03ad\u03b4\u03b9\u03bf \u03b1\u03bd\u03ac atom \u03ba\u03b1\u03b9 \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03b9 predicate pushdown. \u0395\u03ba\u03c4\u03b9\u03bc\u03ac \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac candidate \u03ba\u03b1\u03b9 \u03c4\u03bf pass fraction, \u03ce\u03c3\u03c4\u03b5 \u03ad\u03bd\u03b1 \u03c6\u03b8\u03b7\u03bd\u03cc \u03ba\u03b1\u03b9 selective atom \u03bd\u03b1 \u03ba\u03cc\u03c8\u03b5\u03b9 \u03c0\u03c1\u03ce\u03c4\u03b1 \u03c4\u03bf\u03bd \u03c7\u03ce\u03c1\u03bf \u03c0\u03c1\u03b9\u03bd \u03b5\u03ba\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03b5\u03af \u03c4\u03bf \u03b1\u03ba\u03c1\u03b9\u03b2\u03cc\u03c4\u03b5\u03c1\u03bf reasoning. \u0391\u03c5\u03c4\u03ae \u03b7 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b8\u03c5\u03bc\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf\u03bd \u03ba\u03bb\u03b1\u03c3\u03b9\u03ba\u03cc query optimizer, \u03b1\u03bb\u03bb\u03ac \u03c4\u03ce\u03c1\u03b1 \u03b7 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b1\u03c6\u03bf\u03c1\u03ac \u03ba\u03b1\u03b9 model behavior.<\/p>\n<h2 id=\"confidence-gated-execution\">\u039a\u03bf\u03b9\u03bd\u03cc\u03c2 confidence-gated \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc\u03c2, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03b9\u03bc\u03b1 \u00ab\u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u00bb<\/h2>\n<p>\u039a\u03b1\u03b9 \u03bf\u03b9 \u03c4\u03c1\u03b5\u03b9\u03c2 primitives \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 adaptive skeleton. \u0388\u03bd\u03b1 optional cheap front end \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03b9\u03b5\u03c5\u03b8\u03b5\u03c4\u03ae\u03c3\u03b5\u03b9 \u03ae \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c1\u03c1\u03af\u03c8\u03b5\u03b9 \u03b5\u03cd\u03ba\u03bf\u03bb\u03b1 inputs. \u0388\u03bd\u03b1 \u03c0\u03c1\u03ce\u03c4\u03bf generative model \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 confidence. \u039c\u03cc\u03bd\u03bf \u03bf\u03b9 \u03c7\u03b1\u03bc\u03b7\u03bb\u03ae\u03c2 \u03b5\u03bc\u03c0\u03b9\u03c3\u03c4\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03ba\u03bb\u03b9\u03bc\u03b1\u03ba\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf model, \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf routing \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 <a href=\"https:\/\/twodots.gr\/ai-agent-harnesses-elegchos-kostos-self-hosting\/\">\u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03c4\u03bf\u03c5 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2 \u03c4\u03bf\u03c5 AI harness<\/a>. \u039f\u03b9 \u03c1\u03cc\u03bb\u03bf\u03b9 \u00absmall\u00bb \u03ba\u03b1\u03b9 \u00ablarge\u00bb \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03bf\u03c5\u03bd \u03c4\u03b7 \u03b8\u03ad\u03c3\u03b7 \u03c3\u03c4\u03bf routing \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf parameter count \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf.<\/p>\n<p>\u0391\u03bd\u03c4\u03af \u03b3\u03b9\u03b1 \u03b1\u03c0\u03bb\u03cc \u03bc\u03ad\u03c3\u03bf token log-probability, \u03c4\u03bf SAGE \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af TokenSAR, \u03c0\u03bf\u03c5 \u03b4\u03af\u03bd\u03b5\u03b9 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b2\u03ac\u03c1\u03bf\u03c2 \u03c3\u03c4\u03b1 tokens \u03c4\u03b1 \u03bf\u03c0\u03bf\u03af\u03b1 \u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03c4\u03bf \u03bd\u03cc\u03b7\u03bc\u03b1 \u03c4\u03b7\u03c2 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7\u03c2. \u03a3\u03c4\u03bf diagnostic sample \u03c4\u03bf\u03c5 paper, \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf signal \u03b4\u03b9\u03b1\u03c7\u03ce\u03c1\u03b9\u03c3\u03b5 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03c3\u03c9\u03c3\u03c4\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2. \u03a3\u03c4\u03bf evaluated opposite-sentiment join, \u03b7 confidence-ordered escalation \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 \u03c4\u03b7\u03bd \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03bf\u03c5 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03b1\u03c6\u03bf\u03cd \u03ba\u03bb\u03b9\u03bc\u03ac\u03ba\u03c9\u03c3\u03b5 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u03c4\u03bf 40% \u03c4\u03c9\u03bd pairs. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c4\u03bf\u03bd\u03af\u03b6\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03c4\u03bf saturation point \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03b1\u03bd\u03ac predicate, \u03ac\u03c1\u03b1 \u03b4\u03b5\u03bd \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03ad\u03bd\u03b1 \u03c0\u03b1\u03b3\u03ba\u03cc\u03c3\u03bc\u03b9\u03bf threshold.<\/p>\n<aside class=\"td-article-note\">\n<p><strong>\u03a4\u03bf SAGE \u03b5\u03af\u03bd\u03b1\u03b9 arXiv v1 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 production benchmark.<\/strong> \u0397 \u03c4\u03c1\u03ad\u03c7\u03bf\u03c5\u03c3\u03b1 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03bb\u03bf\u03c5\u03c3\u03b9\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c3\u03c4\u03bf AI_JOIN. \u039f\u03b9 \u03af\u03b4\u03b9\u03bf\u03b9 \u03bf\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b6\u03b7\u03c4\u03bf\u03cd\u03bd \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03b1 scalar \u03ba\u03b1\u03b9 aggregate ablations, \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 production concurrency. \u0386\u03c1\u03b1 \u03c4\u03bf confidence gate \u03ba\u03b1\u03b9 \u03c4\u03bf routing \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 workload.<\/p>\n<\/aside>\n<h2 id=\"probe-and-race\">Probe-and-race: \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae configuration \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03c4\u03c1\u03ad\u03c7\u03bf\u03bd query<\/h2>\n<p>\u03a4\u03bf product space \u03cc\u03bb\u03c9\u03bd \u03c4\u03c9\u03bd \u03c0\u03b9\u03b8\u03b1\u03bd\u03ce\u03bd models, thresholds, temperatures, batching, compaction \u03ba\u03b1\u03b9 join routes \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03bf\u03bb\u03cd \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf \u03b3\u03b9\u03b1 \u03c0\u03bb\u03ae\u03c1\u03b7 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae. \u038c\u03c0\u03c9\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd <a href=\"https:\/\/twodots.gr\/ai-skills-parousiasi-epilogi-ergaleiou\/\">\u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03c4\u03bf\u03c5 \u03c3\u03c9\u03c3\u03c4\u03bf\u03cd AI skill<\/a>, \u03c4\u03bf \u03cc\u03bd\u03bf\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af. \u039a\u03ac\u03b8\u03b5 primitive \u03ad\u03c7\u03b5\u03b9 lightweight proposer \u03c0\u03bf\u03c5 \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03bc\u03b9\u03ba\u03c1\u03cc slate \u03bc\u03b5 cheap anchor, quality anchor \u03ba\u03b1\u03b9 history-guided exploratory configurations. \u0393\u03b9\u03b1 multi-node query, \u03c4\u03bf slate \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03ac whole-plan configurations.<\/p>\n<p>\u03a4\u03bf SAGE \u03c0\u03b1\u03af\u03c1\u03bd\u03b5\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc stratified probe \u03b1\u03c0\u03cc \u03c4\u03b1 \u03c4\u03c1\u03ad\u03c7\u03bf\u03bd\u03c4\u03b1 rows, groups \u03ae pairs \u03ba\u03b1\u03b9 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af \u03cc\u03bb\u03bf\u03c5\u03c2 \u03c4\u03bf\u03c5\u03c2 candidates \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf sample. \u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc token usage, cost \u03ba\u03b1\u03b9 latency, \u03b5\u03bd\u03ce fixed judge model \u03b4\u03af\u03bd\u03b5\u03b9 label-free quality estimate. \u039c\u03b9\u03b1 racing \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03b1\u03c6\u03b1\u03b9\u03c1\u03b5\u03af \u03c3\u03c4\u03b1\u03c4\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03ba\u03b1\u03c4\u03ce\u03c4\u03b5\u03c1\u03b1 \u03ae budget-infeasible plans \u03ba\u03b1\u03b9 \u03c0\u03b1\u03b3\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03bf configuration \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03c0\u03bb\u03ae\u03c1\u03b7 \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7, \u03b7 \u03b9\u03b4\u03ad\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03b1\u03be\u03af\u03b1 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf prototype: \u03b7 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03b4\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 static setting \u03c3\u03b5 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf \u03c4\u03bf data platform. \u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 query-level \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03bc\u03b5 \u03c1\u03b7\u03c4\u03ac budgets. \u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03bd \u03b5\u03be\u03b1\u03bb\u03b5\u03af\u03c6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03b3\u03ba\u03b7 \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5, \u03b1\u03bb\u03bb\u03ac \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c4\u03bf \u00ab\u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bc\u03b5 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03c4\u03bf premium model\u00bb \u03c3\u03b5 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf optimization problem.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-title\">\u0397 \u03bc\u03b5\u03c4\u03c1\u03b7\u03bc\u03ad\u03bd\u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03c3\u03b5 \u03ad\u03bd\u03b1 factorable AI_JOIN<\/p>\n<p class=\"td-chart-subtitle\">\u03a0\u03bb\u03ae\u03c1\u03b5\u03b9\u03c2 serial \u03b5\u03ba\u03c4\u03b5\u03bb\u03ad\u03c3\u03b5\u03b9\u03c2 \u03c3\u03c4\u03bf representative join \u03c4\u03bf\u03c5 paper\u00b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 SQL workload.<\/p>\n<\/div>\n<div class=\"td-chart-body\">\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">128<\/span><span class=\"td-metric-label\">LLM calls \u03bc\u03b5 SAGE<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">127\u00d7<\/span><span class=\"td-metric-label\">\u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03ba\u03bb\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c0\u03cc all-pairs<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">89\u00d7<\/span><span class=\"td-metric-label\">\u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c2 wall-clock \u03c7\u03c1\u03cc\u03bd\u03bf\u03c2<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">358\u00d7<\/span><span class=\"td-metric-label\">\u03c7\u03b1\u03bc\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf \u03bc\u03b5\u03c4\u03c1\u03b7\u03bc\u03ad\u03bd\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"apotelesmata-axiologisis\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03bf\u03b9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af<\/h2>\n<p>\u0393\u03b9\u03b1 logical coverage, \u03bf\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 \u03b5\u03be\u03ad\u03c4\u03b1\u03c3\u03b1\u03bd 78 operators \u03b1\u03c0\u03cc 11 \u03b4\u03b7\u03bc\u03cc\u03c3\u03b9\u03b1 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1. \u0391\u03c0\u03cc \u03b1\u03c5\u03c4\u03bf\u03cd\u03c2, 63 model-invoking semantic operators \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd \u03b1\u03c0\u03b5\u03c5\u03b8\u03b5\u03af\u03b1\u03c2 \u03c3\u03c4\u03b9\u03c2 \u03c4\u03c1\u03b5\u03b9\u03c2 primitives\u00b7 \u03bf\u03b9 \u03c5\u03c0\u03cc\u03bb\u03bf\u03b9\u03c0\u03bf\u03b9 \u03b1\u03c6\u03bf\u03c1\u03bf\u03cd\u03c3\u03b1\u03bd multimodal conversions \u03ae non-semantic indexing, control \u03ba\u03b1\u03b9 loop constructs \u03b5\u03ba\u03c4\u03cc\u03c2 scope. \u0395\u03c0\u03af\u03c3\u03b7\u03c2 \u03b1\u03c0\u03bf\u03c3\u03c5\u03bd\u03c4\u03ad\u03b8\u03b7\u03ba\u03b1\u03bd 37 semantic-query intents: \u03ba\u03b1\u03bd\u03ad\u03bd\u03b1 \u03b4\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 \u03c0\u03ac\u03bd\u03c9 \u03b1\u03c0\u03cc \u03c4\u03c1\u03b5\u03b9\u03c2 AI primitives \u03ba\u03b1\u03b9 \u03c4\u03bf 94,6% \u03c7\u03c1\u03b5\u03b9\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 \u03c4\u03bf \u03c0\u03bf\u03bb\u03cd \u03b4\u03cd\u03bf.<\/p>\n<p>\u03a3\u03c4\u03bf representative factorable join, \u03b7 predicate-aware \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 128 LLM calls \u03b1\u03bd\u03c4\u03af 16.256 \u03c3\u03c4\u03bf exact all-pairs plan. \u039f measured wall-clock \u03c7\u03c1\u03cc\u03bd\u03bf\u03c2 \u03ae\u03c4\u03b1\u03bd 53,4 \u03b1\u03bd\u03c4\u03af 4.770 \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03bf\u03bb\u03ad\u03c0\u03c4\u03c9\u03bd \u03ba\u03b1\u03b9 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 0,00166 \u03b1\u03bd\u03c4\u03af 0,595 \u03b4\u03bf\u03bb\u03b1\u03c1\u03af\u03c9\u03bd: \u03bc\u03b5\u03b9\u03ce\u03c3\u03b5\u03b9\u03c2 127\u00d7 \u03c3\u03c4\u03b9\u03c2 calls, 89\u00d7 \u03c3\u03c4\u03bf\u03bd \u03c7\u03c1\u03cc\u03bd\u03bf \u03ba\u03b1\u03b9 358\u00d7 \u03c3\u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2. \u03a0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf evaluated join, \u03cc\u03c7\u03b9 \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c3\u03b7 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 workload \u03b8\u03b1 \u03c0\u03b5\u03c4\u03cd\u03c7\u03b5\u03b9 \u03c4\u03bf\u03c5\u03c2 \u03af\u03b4\u03b9\u03bf\u03c5\u03c2 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03b1\u03c3\u03b9\u03b1\u03c3\u03c4\u03ad\u03c2.<\/p>\n<p>\u03a3\u03c4\u03b9\u03c2 ablations, \u03b7 \u03b1\u03c6\u03b1\u03af\u03c1\u03b5\u03c3\u03b7 predicate-aware routing \u03ae\u03c4\u03b1\u03bd \u03b7 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c3\u03c4\u03bf\u03c7\u03af\u03b1. \u03a4\u03bf membership workload \u03c0\u03ae\u03b3\u03b5 \u03b1\u03c0\u03cc F1 0,850 \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 0,003 \u03b4\u03bf\u03bb\u03ac\u03c1\u03b9\u03b1 \u03c3\u03b5 F1 0,720 \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 0,563. \u0397 \u03c7\u03c1\u03ae\u03c3\u03b7 mean log-probability \u03b1\u03bd\u03c4\u03af TokenSAR \u03bc\u03b5\u03af\u03c9\u03c3\u03b5 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c3\u03c4\u03b1 membership \u03ba\u03b1\u03b9 reasoning cases. \u03a4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03b3\u03ba\u03b7 \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03bf\u03cd route, front end, confidence \u03ba\u03b1\u03b9 racing, \u03cc\u03c7\u03b9 \u03bc\u03af\u03b1 \u03bc\u03cc\u03bd\u03bf \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae.<\/p>\n<div class=\"td-decision-band\">\n<div class=\"td-decision-band-content\">\n<p class=\"td-decision-band-kicker\">\u0391\u03c1\u03c7\u03ae \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2<\/p>\n<p class=\"td-decision-band-title\">\u0392\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03c4\u03b7 \u03c3\u03c7\u03b5\u03c3\u03b9\u03b1\u03ba\u03ae \u03bc\u03bf\u03c1\u03c6\u03ae \u03c0\u03c1\u03b9\u03bd \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03c4\u03b5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf<\/p>\n<p>\u039e\u03b5\u03ba\u03b9\u03bd\u03ae\u03c3\u03c4\u03b5 \u03b1\u03c0\u03cc \u03c4\u03bf \u03b1\u03bd \u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 row, group \u03ae pair operation. \u039c\u03b5\u03c4\u03ac \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03bf\u03bd candidate space, \u03b5\u03c0\u03b1\u03bd\u03b1\u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 virtual columns \u03ba\u03b1\u03b9 \u03ba\u03bb\u03b9\u03bc\u03b1\u03ba\u03ce\u03c3\u03c4\u03b5 \u03bc\u03cc\u03bd\u03bf \u03c4\u03b1 \u03b1\u03b2\u03ad\u03b2\u03b1\u03b9\u03b1 inputs. \u0397 \u03b1\u03b3\u03bf\u03c1\u03ac \u03c6\u03b8\u03b7\u03bd\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b4\u03b9\u03bf\u03c1\u03b8\u03ce\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1 N\u00d7M \u03c3\u03c7\u03ad\u03b4\u03b9\u03bf \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03ad\u03c0\u03c1\u03b5\u03c0\u03b5 \u03bd\u03b1 \u03b5\u03ba\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03b5\u03af.<\/p>\n<\/div>\n<\/div>\n<h2 id=\"business-workflows\">\u03a0\u03ce\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03c1\u03b1\u03c3\u03c4\u03b5\u03af \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac business workflows<\/h2>\n<p>\u03a3\u03b5 e-commerce \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd, \u03bf\u03b9 <a href=\"https:\/\/twodots.gr\/toffee-data-agents-axiopisti-ai-analysi-dedomenon\/\">\u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ad\u03c2 \u03c4\u03c9\u03bd data agents<\/a> \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03be\u03b5\u03ba\u03b9\u03bd\u03bf\u03cd\u03bd \u03b1\u03c0\u03cc scalar tasks \u03cc\u03c0\u03c9\u03c2 sentiment \u03ae product-attribute extraction \u03b1\u03bd\u03ac review. \u0388\u03bd\u03b1 aggregate \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c5\u03bd\u03bf\u03c8\u03af\u03b6\u03b5\u03b9 themes \u03b1\u03bd\u03ac SKU \u03ae \u03b1\u03b3\u03bf\u03c1\u03ac, \u03bc\u03b5 \u03c0\u03c1\u03bf\u03c3\u03bf\u03c7\u03ae \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf compaction \u03bd\u03b1 \u03bc\u03b7 \u03b4\u03b9\u03b1\u03b3\u03c1\u03ac\u03c8\u03b5\u03b9 \u03c3\u03c0\u03ac\u03bd\u03b9\u03b1 \u03b1\u03bb\u03bb\u03ac \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ac \u03c0\u03b1\u03c1\u03ac\u03c0\u03bf\u03bd\u03b1. \u0388\u03bd\u03b1 join \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 tickets \u03bc\u03b5 knowledge-base documents \u03ae \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03b1\u03c6\u03ad\u03c2 \u03c0\u03c1\u03bf\u03bc\u03b7\u03b8\u03b5\u03c5\u03c4\u03ce\u03bd \u03bc\u03b5 catalog records, \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03bf\u03bd\u03c4\u03b1\u03c2 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc route \u03b1\u03bd\u03ac predicate.<\/p>\n<p>\u0397 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae\u03c2 \u03c5\u03b9\u03bf\u03b8\u03ad\u03c4\u03b7\u03c3\u03b7 \u03be\u03b5\u03ba\u03b9\u03bd\u03ac \u03bc\u03b5 inventory \u03c4\u03c9\u03bd AI functions \u03ba\u03b1\u03b9 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03ae \u03c4\u03bf\u03c5\u03c2 \u03c9\u03c2 row, group \u03ae pair operations. \u039c\u03b5\u03c4\u03ac \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 quality metrics \u03b1\u03bd\u03ac task, cost \u03ba\u03b1\u03b9 latency budgets, \u03bc\u03b9\u03ba\u03c1\u03cc representative probe \u03ba\u03b1\u03b9 logging \u03c4\u03c9\u03bd escalations. \u0397 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ac \u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac \u03c4\u03b7\u03bd \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03c4\u03bf\u03c5 logical transformation \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 model output, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03bc\u03b9\u03b1 \u03ad\u03b3\u03ba\u03c5\u03c1\u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03bf\u03bd\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03b1\u03ba\u03cc\u03bc\u03b7 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03bb\u03ac\u03b8\u03bf\u03c2 virtual columns.<\/p>\n<p>\u0395\u03be\u03af\u03c3\u03bf\u03c5 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf downstream impact. \u0388\u03bd\u03b1 scalar extraction \u03c0\u03bf\u03c5 \u03c5\u03bb\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03bd\u03c9\u03c1\u03af\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03b5\u03b9 \u03b4\u03c1\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf join cost. \u0391\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1, \u03ad\u03bd\u03b1 lossy aggregate \u03ae join \u03bd\u03c9\u03c1\u03af\u03c2 \u03c3\u03c4\u03b7 \u03c1\u03bf\u03ae \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c6\u03b1\u03b9\u03c1\u03ad\u03c3\u03b5\u03b9 evidence \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b1\u03bd\u03b1\u03ba\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c1\u03b3\u03cc\u03c4\u03b5\u03c1\u03b1. \u0395\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2, \u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf whole plan \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c3\u03b5 isolated prompt accuracy.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">Pilot \u03c3\u03b5 7 \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5\u03c2 \u03c0\u03c1\u03b9\u03bd \u03c4\u03bf AI \u03bc\u03c0\u03b5\u03b9 \u03c3\u03c4\u03b7 SQL \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">\u0388\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 1<\/span><strong>\u03a7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03ae\u03c3\u03c4\u03b5 \u03ba\u03ac\u03b8\u03b5 function \u03c3\u03c4\u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae primitive<\/strong>\n<p>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03b1\u03bd \u03b7 \u03ba\u03bb\u03ae\u03c3\u03b7 \u03b4\u03b9\u03b1\u03b2\u03ac\u03b6\u03b5\u03b9 \u03bc\u03af\u03b1 row, \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf group \u03ae candidate pair. \u0391\u03bd \u03b7 \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ae\u03c2, \u03bc\u03b7\u03bd \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c8\u03b5\u03c4\u03b5 \u03b1\u03ba\u03cc\u03bc\u03b7 rewrite \u03c4\u03bf\u03c5 query plan.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0388\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 2<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac task<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 task-specific metric, \u03b1\u03bd\u03ce\u03c4\u03b1\u03c4\u03bf token cost \u03ba\u03b1\u03b9 latency budget. \u03a4\u03bf \u03af\u03b4\u03b9\u03bf F1 \u03ae ROUGE threshold \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03bf \u03b3\u03b9\u03b1 extraction, aggregate summary \u03ba\u03b1\u03b9 semantic join.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0388\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 3<\/span><strong>\u039a\u03cc\u03c8\u03c4\u03b5 \u03c0\u03c1\u03ce\u03c4\u03b1 \u03c4\u03bf\u03bd deterministic \u03c7\u03ce\u03c1\u03bf<\/strong>\n<p>\u0395\u03c6\u03b1\u03c1\u03bc\u03cc\u03c3\u03c4\u03b5 ordinary SQL \u03c6\u03af\u03bb\u03c4\u03c1\u03b1, indexes \u03ba\u03b1\u03b9 lossless predicate pushdown \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc model calls. \u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c0\u03cc\u03c3\u03b1 rows, groups \u03ba\u03b1\u03b9 pairs \u03c6\u03c4\u03ac\u03bd\u03bf\u03c5\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c3\u03c4\u03bf AI layer.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0388\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 4<\/span><strong>\u03a7\u03c4\u03af\u03c3\u03c4\u03b5 representative probe<\/strong>\n<p>\u03a4\u03bf probe \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03b5\u03cd\u03ba\u03bf\u03bb\u03b1, \u03bf\u03c1\u03b9\u03b1\u03ba\u03ac \u03ba\u03b1\u03b9 \u03c3\u03c0\u03ac\u03bd\u03b9\u03b1 cases \u03c4\u03bf\u03c5 \u03c4\u03c1\u03ad\u03c7\u03bf\u03bd\u03c4\u03bf\u03c2 query. \u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac recipe parse failure, route error \u03ba\u03b1\u03b9 proxy-quality \u03b1\u03c0\u03cc\u03ba\u03bb\u03b9\u03c3\u03b7.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0388\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 5<\/span><strong>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 escalation \u03ba\u03b1\u03b9 evidence loss<\/strong>\n<p>\u0393\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 configuration \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 token usage, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2, latency, confidence, escalation rate \u03ba\u03b1\u03b9 rejected candidates. \u03a3\u03c4\u03b1 aggregates \u03b5\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03b1\u03bd compaction \u03ae chunking \u03b5\u03be\u03b1\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03b9 \u03c3\u03c0\u03ac\u03bd\u03b9\u03b1 \u03b1\u03bb\u03bb\u03ac \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b1 \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0388\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 6<\/span><strong>\u03a0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03c3\u03c4\u03b5 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03ba\u03b1\u03b9\u03ce\u03bc\u03b1\u03c4\u03b1<\/strong>\n<p>\u0395\u03bb\u03b1\u03c7\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03c4\u03b1 \u03c0\u03b5\u03b4\u03af\u03b1 \u03c0\u03bf\u03c5 \u03c6\u03b5\u03cd\u03b3\u03bf\u03c5\u03bd \u03c0\u03c1\u03bf\u03c2 \u03c4\u03bf model, \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03c3\u03c4\u03b5 access control, retention policy \u03ba\u03b1\u03b9 human approval \u03cc\u03c0\u03bf\u03c5 \u03c4\u03bf output \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2, \u03c0\u03b5\u03bb\u03ac\u03c4\u03b5\u03c2 \u03ae compliance \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u0388\u03bb\u03b5\u03b3\u03c7\u03bf\u03c2 7<\/span><strong>\u0395\u03c0\u03b9\u03ba\u03c5\u03c1\u03ce\u03c3\u03c4\u03b5 whole-plan \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03ba\u03b1\u03b9 rollback<\/strong>\n<p>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 \u03c4\u03b7\u03bd \u03c0\u03bb\u03ae\u03c1\u03b7 downstream \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03bc\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7 baseline, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf isolated prompt accuracy. \u039f\u03c1\u03af\u03c3\u03c4\u03b5 stop condition \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c3\u03c4\u03c1\u03bf\u03c6\u03ae \u03c3\u03b5 deterministic plan \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf budget \u03ae \u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03be\u03b5\u03c6\u03b5\u03cd\u03b3\u03b5\u03b9.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<aside class=\"td-article-note\">\n<p><strong>\u0397 recipe card \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bb\u03ac\u03bd\u03b8\u03b1\u03c3\u03c4\u03bf\u03c2 compiler.<\/strong> \u03a3\u03c4\u03bf appendix \u03c4\u03bf\u03c5 paper \u03c4\u03bf 88,3% \u03c4\u03c9\u03bd \u03ba\u03b1\u03c1\u03c4\u03ce\u03bd \u03ae\u03c4\u03b1\u03bd JSON-parseable, \u03b5\u03bd\u03ce \u03b7 proxy \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 \u03ba\u03b1\u03c4\u03ac \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf 0,843 \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 0,892 \u03b3\u03b9\u03b1 gold selection. \u0395\u03c0\u03b5\u03b9\u03b4\u03ae \u03bc\u03af\u03b1 \u03ba\u03ac\u03c1\u03c4\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03bb\u03ad\u03b3\u03be\u03b5\u03b9 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf join, parse failure, \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03bf route \u03ba\u03b1\u03b9 sample shift \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac production metrics.<\/p>\n<\/aside>\n<h2 id=\"oria-sage\">\u03a4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03bf\u03c5 SAGE \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03b3\u03bd\u03bf\u03b7\u03b8\u03bf\u03cd\u03bd<\/h2>\n<p>\u03a4\u03bf scope \u03b1\u03c6\u03bf\u03c1\u03ac single-pass, query-time semantic functions \u03c0\u03bf\u03c5 \u03ba\u03b1\u03c4\u03b1\u03bd\u03b1\u03bb\u03ce\u03bd\u03bf\u03c5\u03bd relational data \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c3\u03c4\u03c1\u03ad\u03c6\u03bf\u03c5\u03bd value \u03ae decision. Recursive agents, training, multimodal conversion, indexing \u03ba\u03b1\u03b9 side-effecting actions \u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03b5\u03ba\u03c4\u03cc\u03c2. \u0386\u03c1\u03b1 \u03c4\u03bf \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc\u03c2 orchestrator \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 AI workflow \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03b1\u03bd\u03c4\u03ac \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03c5 \u03c3\u03b5 \u03b8\u03ad\u03bc\u03b1\u03c4\u03b1 governance, privacy \u03ae human approval, \u03c4\u03b1 \u03bf\u03c0\u03bf\u03af\u03b1 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03cc <a href=\"https:\/\/twodots.gr\/enterprise-ai-harnesses-diakyvernisi-architektoniki\/\">enterprise AI harness \u03bc\u03b5 \u03b4\u03b9\u03b1\u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7 \u03c3\u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae<\/a>.<\/p>\n<p>\u0397 label-free \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae configuration \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc proxy quality estimates \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03ba\u03b1\u03c4\u03ac \u03c0\u03cc\u03c3\u03bf \u03c4\u03bf probe \u03b1\u03bd\u03c4\u03b9\u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b5\u03cd\u03b5\u03b9 \u03c4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 query. \u0397 recipe card \u03b5\u03bb\u03ad\u03b3\u03c7\u03b5\u03b9 route \u03b3\u03b9\u03b1 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf join, \u03bf\u03c0\u03cc\u03c4\u03b5 parse failure \u03ae \u03bb\u03b1\u03bd\u03b8\u03b1\u03c3\u03bc\u03ad\u03bd\u03b7 semantic routing \u03ad\u03c7\u03b5\u03b9 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7. \u03a4\u03bf \u03af\u03b4\u03b9\u03bf \u03c4\u03bf paper \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af structural validity, sample-shift robustness \u03ba\u03b1\u03b9 sensitivity, \u03b1\u03bb\u03bb\u03ac \u03b1\u03c5\u03c4\u03ac \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03c3\u03b7\u03bc\u03b5\u03af\u03b1 \u03c0\u03bf\u03c5 \u03bc\u03b9\u03b1 production \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af.<\/p>\n<p>\u03a4\u03ad\u03bb\u03bf\u03c2, \u03bf\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 benchmarks, models, predicates \u03ba\u03b1\u03b9 execution settings. \u039f\u03b9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03b5\u03af\u03bd\u03b1\u03b9 \u03b9\u03c3\u03c7\u03c5\u03c1\u03ae \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03b4\u03cd\u03bd\u03b1\u03bc\u03b7 \u03c4\u03b7\u03c2 \u03b4\u03bf\u03bc\u03b9\u03ba\u03ae\u03c2 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2, \u03cc\u03c7\u03b9 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b7 \u03c0\u03c1\u03cc\u03b2\u03bb\u03b5\u03c8\u03b7 ROI \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03cc dataset. \u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03c0\u03c1\u03bf\u03c3\u03ad\u03b3\u03b3\u03b9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 controlled pilot \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03b5\u03af\u03c2 failure criteria.<\/p>\n<h2 id=\"symperasma-sage\">\u03a4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b3\u03b9\u03b1 data \u03ba\u03b1\u03b9 marketing leaders<\/h2>\n<p>\u03a4\u03bf \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03bf \u03bc\u03ae\u03bd\u03c5\u03bc\u03b1 \u03c4\u03bf\u03c5 SAGE \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03cc\u03c4\u03b9 \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03ad\u03bd\u03b1\u03c2 \u03b1\u03ba\u03cc\u03bc\u03b7 optimizer. \u0395\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03bf\u03b9 AI functions \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b3\u03af\u03bd\u03bf\u03c5\u03bd \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03b7\u03c4\u03ad\u03c2 \u03bc\u03b5 \u03c4\u03b7 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1 \u03c4\u03b7\u03c2 \u03c3\u03c7\u03b5\u03c3\u03b9\u03b1\u03ba\u03ae\u03c2 \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7\u03c2. \u038c\u03c4\u03b1\u03bd \u03be\u03ad\u03c1\u03b5\u03b9\u03c2 \u03b1\u03bd \u03ad\u03bd\u03b1 task \u03b5\u03af\u03bd\u03b1\u03b9 row-preserving, group-reducing \u03ae pair-generating, \u03bc\u03c0\u03bf\u03c1\u03b5\u03af\u03c2 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03bb\u03ad\u03be\u03b5\u03b9\u03c2 \u03c0\u03b9\u03bf \u03c3\u03c9\u03c3\u03c4\u03ac caching, compaction, candidate pruning \u03ba\u03b1\u03b9 escalation.<\/p>\n<p>\u0393\u03b9\u03b1 \u03c4\u03bf\u03c5\u03c2 decision makers, \u03b1\u03c5\u03c4\u03cc \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03ad\u03bd\u03b1\u03bd \u03c0\u03b9\u03bf \u03ce\u03c1\u03b9\u03bc\u03bf \u03c4\u03c1\u03cc\u03c0\u03bf \u03c3\u03c5\u03b6\u03ae\u03c4\u03b7\u03c3\u03b7\u03c2: \u03cc\u03c7\u03b9 \u00ab\u03c0\u03bf\u03b9\u03bf LLM \u03b8\u03b1 \u03b1\u03b3\u03bf\u03c1\u03ac\u03c3\u03bf\u03c5\u03bc\u03b5;\u00bb, \u03b1\u03bb\u03bb\u03ac \u00ab\u03c0\u03bf\u03b9\u03b1 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b5\u03ba\u03c4\u03b5\u03bb\u03b5\u03af\u03c4\u03b1\u03b9, \u03c0\u03bf\u03b9\u03bf evidence \u03b4\u03b5\u03bd \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03c7\u03b1\u03b8\u03b5\u03af, \u03c0\u03bf\u03cd \u03b5\u03ba\u03c1\u03ae\u03b3\u03bd\u03c5\u03c4\u03b1\u03b9 \u03bf candidate space \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03bf budget \u03b9\u03c3\u03c7\u03cd\u03b5\u03b9;\u00bb. \u0397 AI \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03b7 SQL \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ad\u03c4\u03c3\u03b9 engineering discipline \u03bc\u03b5 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03b1 trade-offs, \u03cc\u03c7\u03b9 \u03b1\u03b4\u03b9\u03b1\u03c6\u03b1\u03bd\u03ae\u03c2 \u03c3\u03b5\u03b9\u03c1\u03ac \u03b1\u03c0\u03cc prompt calls.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-kicker\">AI automation \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1<\/p>\n<p class=\"td-service-cta-title\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 \u03c4\u03bf SQL pilot \u03c0\u03c1\u03b9\u03bd \u03c0\u03bb\u03b7\u03c1\u03ce\u03c3\u03b5\u03c4\u03b5 \u03b3\u03b9\u03b1 scale<\/p>\n<p>\u0397 TWO DOTS \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03b5\u03af row, group \u03ba\u03b1\u03b9 pair workloads, \u03bf\u03c1\u03af\u03b6\u03b5\u03b9 quality \u03ba\u03b1\u03b9 cost budgets, \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03b9 model calls \u03bc\u03b5 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 filtering \u03ba\u03b1\u03b9 \u03c3\u03c4\u03ae\u03bd\u03b5\u03b9 observability, human approval \u03ba\u03b1\u03b9 rollback \u03b3\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03bf\u03cd\u03c2 AI \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 \u03c4\u03bf\u03bd AI \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03cc \u03c3\u03b1\u03c2<\/a><\/div>\n<\/div>\n<\/section>\n<section id=\"sychnes-erotiseis\" class=\"td-faq-section\">\n<div class=\"td-faq\">\n<p class=\"td-faq-heading\">\u0427\u0435\u0441\u0442\u043e \u0437\u0430\u0434\u0430\u0432\u0430\u043d\u0438 \u0432\u044a\u043f\u0440\u043e\u0441\u0438<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf SAGE;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf SAGE, \u03b1\u03c0\u03cc \u03c4\u03bf Self-Adaptive Generative Execution, \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc logical \u03ba\u03b1\u03b9 physical framework \u03c0\u03bf\u03c5 \u03bf\u03c1\u03b3\u03b1\u03bd\u03ce\u03bd\u03b5\u03b9 query-time AI functions \u03c3\u03b5 AI_SCALAR, AI_AGG \u03ba\u03b1\u03b9 AI_JOIN \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 configuration \u03b1\u03bd\u03ac query.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03b5\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03b9 \u03c4\u03c1\u03b5\u03b9\u03c2 primitives \u03c4\u03bf\u03c5 SAGE;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0397 AI_SCALAR \u03bc\u03b5\u03c4\u03b1\u03c3\u03c7\u03b7\u03bc\u03b1\u03c4\u03af\u03b6\u03b5\u03b9 \u03bc\u03af\u03b1 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ae \u03c3\u03b5 \u03c4\u03b9\u03bc\u03ae, \u03b7 AI_AGG \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1 group \u03c3\u03b5 \u03c4\u03b9\u03bc\u03ae \u03ba\u03b1\u03b9 \u03b7 AI_JOIN \u03b1\u03c0\u03bf\u03c6\u03b1\u03c3\u03af\u03b6\u03b5\u03b9 \u03b1\u03bd \u03ad\u03bd\u03b1 \u03b6\u03b5\u03cd\u03b3\u03bf\u03c2 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ce\u03bd \u03b9\u03ba\u03b1\u03bd\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03ad\u03bd\u03b1 semantic predicate.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf AI_JOIN \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 \u03c4\u03cc\u03c3\u03bf \u03b1\u03ba\u03c1\u03b9\u03b2\u03cc;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03c0\u03b5\u03b9\u03b4\u03ae \u03b4\u03cd\u03bf relations \u03bc\u03b5\u03b3\u03ad\u03b8\u03bf\u03c5\u03c2 N \u03ba\u03b1\u03b9 M \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd\u03bd \u03ad\u03c9\u03c2 N\u00d7M candidate pairs. \u0391\u03bd \u03ba\u03ac\u03b8\u03b5 \u03b6\u03b5\u03cd\u03b3\u03bf\u03c2 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af generative call, \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03bf latency \u03b1\u03c5\u03be\u03ac\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c4\u03b5\u03c4\u03c1\u03b1\u03b3\u03c9\u03bd\u03b9\u03ba\u03ac.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03b7 recipe card;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039f compiler \u03b4\u03b9\u03b1\u03b2\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf predicate \u03ba\u03b1\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc sample \u03ad\u03c9\u03c2 \u03c0\u03ad\u03bd\u03c4\u03b5 candidate pairs \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03cd\u03b5\u03b9 route, featurizer, labels, negative check \u03ba\u03b1\u03b9 configuration space \u03b3\u03b9\u03b1 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf \u03c4\u03bf join.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03bf SAGE \u03c0\u03ac\u03bd\u03c4\u03b1 \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0388\u03bd\u03b1 \u03c0\u03c1\u03ce\u03c4\u03bf model \u03c7\u03b5\u03b9\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c4\u03b9\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03b1 \u03c7\u03b1\u03bc\u03b7\u03bb\u03ae\u03c2 \u03b5\u03bc\u03c0\u03b9\u03c3\u03c4\u03bf\u03c3\u03cd\u03bd\u03b7\u03c2 inputs \u03ba\u03bb\u03b9\u03bc\u03b1\u03ba\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf model. \u039f\u03b9 \u03c1\u03cc\u03bb\u03bf\u03b9 small \u03ba\u03b1\u03b9 large \u03b4\u03b5\u03bd \u03b5\u03b3\u03b3\u03c5\u03ce\u03bd\u03c4\u03b1\u03b9 \u03bc\u03bf\u03bd\u03bf\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0395\u03af\u03bd\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 \u03bd\u03b1 \u03ba\u03ac\u03bd\u03bf\u03c5\u03bc\u03b5 chunk \u03ba\u03ac\u03b8\u03b5 AI aggregate;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0391\u03ba\u03c1\u03b9\u03b2\u03ad\u03c2 chunking \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af mergeable partial state, merge operator \u03ba\u03b1\u03b9 finalizer. \u0397 \u03bf\u03bb\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03c3\u03cd\u03bd\u03bf\u03c8\u03b7 \u03bc\u03b5 cross-document evidence \u03c3\u03c5\u03bd\u03ae\u03b8\u03c9\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf \u03c4\u03bf group.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0399\u03c3\u03c7\u03cd\u03b5\u03b9 \u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2 358\u00d7 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 workload;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0395\u03af\u03bd\u03b1\u03b9 measured \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c3\u03b5 \u03ad\u03bd\u03b1 representative factorable join \u03c0\u03bf\u03c5 \u03b5\u03ba\u03c4\u03b5\u03bb\u03ad\u03c3\u03c4\u03b7\u03ba\u03b5 \u03c0\u03bb\u03ae\u03c1\u03c9\u03c2 \u03ba\u03b1\u03b9 serially. \u039a\u03ac\u03b8\u03b5 dataset, predicate, model stack \u03ba\u03b1\u03b9 budget \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b4\u03b9\u03ba\u03cc \u03c4\u03bf\u03c5 probe.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03b2\u03ae\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039d\u03b1 \u03b1\u03c0\u03bf\u03b3\u03c1\u03ac\u03c8\u03b5\u03b9 \u03c4\u03b9\u03c2 AI functions, \u03bd\u03b1 \u03c4\u03b9\u03c2 \u03c4\u03b1\u03be\u03b9\u03bd\u03bf\u03bc\u03ae\u03c3\u03b5\u03b9 \u03c9\u03c2 row, group \u03ae pair operations \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03bf\u03c1\u03af\u03c3\u03b5\u03b9 task-specific quality, cost, latency \u03ba\u03b1\u03b9 failure criteria \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc production pilot.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">\u0418\u0437\u0442\u043e\u0447\u043d\u0438\u0446\u0438<\/p>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2608.20630\" target=\"_blank\" rel=\"noopener\">arXiv \u2014 SAGE: A Unified Algebra and Self-Adaptive Execution for AI Functions in SQL<\/a><\/li>\n<li><a href=\"https:\/\/docs.databricks.com\/aws\/en\/large-language-models\/ai-functions\" target=\"_blank\" rel=\"noopener\">Databricks \u2014 Enrich data using AI Functions<\/a><\/li>\n<li><a href=\"https:\/\/docs.snowflake.com\/en\/user-guide\/snowflake-cortex\/aisql\" target=\"_blank\" rel=\"noopener\">Snowflake \u2014 Cortex AI Functions<\/a><\/li>\n<li><a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/generative-ai-overview\" target=\"_blank\" rel=\"noopener\">Google Cloud \u2014 Generative AI in BigQuery<\/a><\/li>\n<li><a href=\"https:\/\/www.nist.gov\/itl\/ai-risk-management-framework\" target=\"_blank\" rel=\"noopener\">NIST \u2014 AI Risk Management Framework<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u03a4\u03bf SAGE \u03bf\u03c1\u03b3\u03b1\u03bd\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b9\u03c2 AI functions \u03c3\u03c4\u03b7 SQL \u03c3\u03b5 \u03c4\u03c1\u03b5\u03b9\u03c2 primitives \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03b9 execution plan \u03b1\u03bd\u03ac query, \u03ce\u03c3\u03c4\u03b5 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2, latency \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bd\u03b1 \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b1\u03b6\u03af.<\/p>","protected":false},"author":1,"featured_media":96243,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[199],"tags":[20184,20188,20186,20187,20185],"class_list":["post-96227","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-ai-functions","tag-ai-optimization","tag-data-engineering","tag-semantic-join","tag-sql"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"_links":{"self":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/96227","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/comments?post=96227"}],"version-history":[{"count":1,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/96227\/revisions"}],"predecessor-version":[{"id":96244,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/posts\/96227\/revisions\/96244"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media\/96243"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/media?parent=96227"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/categories?post=96227"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/bg\/wp-json\/wp\/v2\/tags?post=96227"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}