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id=\"heading-prerequisites-and-environment-setup\">\u0634\u0631\u0627\u0626\u0637 \u0627\u0648\u0631 \u062a\u0631\u062c\u06cc\u062d\u0627\u062a<\/h2>\n<p>\u0627\u0633 \u0639\u0645\u0644\u06cc \u06af\u0627\u0626\u06cc\u0688 \u06a9\u06cc \u067e\u06cc\u0631\u0648\u06cc \u06a9\u0631\u0646\u06d2 \u0627\u0648\u0631 \u0646\u0641\u0627\u0630 \u06a9\u0648\u0688 \u06a9\u0648 \u0645\u0642\u0627\u0645\u06cc \u0637\u0648\u0631 \u067e\u0631 \u0686\u0644\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u0622\u067e \u06a9\u0648 \u062f\u0631\u062c \u0630\u06cc\u0644 \u0628\u0646\u06cc\u0627\u062f\u06cc \u0636\u0631\u0648\u0631\u06cc\u0627\u062a \u06a9\u0648 \u067e\u0648\u0631\u0627 \u06a9\u0631\u0646\u0627 \u06c1\u0648\u06af\u0627:<\/p>\n<ul>\n<li>\n<p>\u0635\u0627\u0641\u060c \u0633\u0627\u062e\u062a\u06c1 \u0627\u0632\u06af\u0631 \u06a9\u0648\u0688 \u0644\u06a9\u06be\u0646\u06d2 \u06a9\u06cc \u0635\u0644\u0627\u062d\u06cc\u062a<\/p>\n<\/li>\n<li>\n<p>\u0633\u0631\u0686 \u0627\u06af\u0645\u06cc\u0646\u0679\u0688 \u062c\u0646\u0631\u06cc\u0634\u0646 (RAG) \u06a9\u06d2 \u062a\u0635\u0648\u0631\u0627\u062a \u0627\u0648\u0631 \u0648\u06cc\u06a9\u0679\u0631 \u0627\u06cc\u0645\u0628\u06cc\u0688\u0646\u06af\u0632 \u06a9\u06cc \u0628\u0646\u06cc\u0627\u062f\u06cc \u062a\u0641\u06c1\u06cc\u0645\u06d4<\/p>\n<\/li>\n<li>\n<p>\u0622\u067e \u06a9\u06d2 \u06a9\u0645\u067e\u06cc\u0648\u0679\u0631 \u067e\u0631 Python 3.9 \u06cc\u0627 \u0627\u0633 \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u0627\u0646\u0633\u0679\u0627\u0644 \u06c1\u06d2\u06d4<\/p>\n<\/li>\n<li>\n<p>\u0627\u06cc\u06a9 \u0645\u0631\u0628\u0648\u0637 \u062a\u0631\u0642\u06cc\u0627\u062a\u06cc \u0645\u0627\u062d\u0648\u0644 \u062c\u06cc\u0633\u06d2 \u0628\u0635\u0631\u06cc \u0627\u0633\u0679\u0648\u0688\u06cc\u0648 \u06a9\u0648\u0688\u06d4<\/p>\n<\/li>\n<\/ul>\n<h3 id=\"heading-package-installation\">\u067e\u06cc\u06a9\u06cc\u062c \u0627\u0646\u0633\u0679\u0627\u0644 \u06a9\u0631\u06cc\u06ba<\/h3>\n<p>\u0627\u06cc\u06a9 \u0679\u0631\u0645\u06cc\u0646\u0644 \u06a9\u06be\u0648\u0644\u06cc\u06ba \u0627\u0648\u0631 \u0645\u0637\u0644\u0648\u0628\u06c1 \u0628\u06cc\u0631\u0648\u0646\u06cc \u0627\u0646\u062d\u0635\u0627\u0631 \u06a9\u0648 \u0627\u0646\u0633\u0679\u0627\u0644 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u062f\u0631\u062c \u0630\u06cc\u0644 \u06a9\u0645\u0627\u0646\u0688\u0632 \u0686\u0644\u0627\u0626\u06cc\u06ba\u06d4<\/p>\n<pre><code class=\"language-python\">pip install openai sentence-transformers numpy python-dotenv\n<\/code><\/pre>\n<h3 id=\"heading-local-directory-structure\">\u0645\u0642\u0627\u0645\u06cc \u0688\u0627\u0626\u0631\u06cc\u06a9\u0679\u0631\u06cc \u0688\u06be\u0627\u0646\u0686\u06c1<\/h3>\n<p>\u0639\u0645\u0644 \u0622\u0648\u0631\u06cc \u06a9\u06cc \u062a\u0648\u0644\u06cc\u062f\u06cc \u0635\u0644\u0627\u062d\u06cc\u062a \u06a9\u0648 \u0628\u0631\u0642\u0631\u0627\u0631 \u0631\u06a9\u06be\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u0627\u067e\u0646\u06d2 \u06a9\u0627\u0645 \u06a9\u06cc \u062c\u06af\u06c1 \u06a9\u0648 \u0645\u0646\u0638\u0645 \u0627\u0648\u0631 \u0635\u0627\u0641 \u0631\u06a9\u06be\u06cc\u06ba\u06d4<\/p>\n<pre><code class=\"language-python\">uncertainty-engine\/\n\n\u2502\n\n\u251c\u2500\u2500 .env\n\n\u251c\u2500\u2500 README.md\n\n\u2514\u2500\u2500 app.py\n<\/code><\/pre>\n<h3 id=\"heading-environment-configuration\">\u0645\u0627\u062d\u0648\u0644\u06cc\u0627\u062a \u06a9\u06cc \u062a\u0631\u062a\u06cc\u0628<\/h3>\n<p>\u0631\u0633\u0627\u0626\u06cc \u06a9\u06cc \u0627\u0633\u0646\u0627\u062f \u0627\u0648\u0631 \u062d\u062f \u06a9\u06cc \u062a\u0631\u062a\u06cc\u0628 \u06a9\u0648 \u0630\u062e\u06cc\u0631\u06c1 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u0627\u067e\u0646\u06d2 \u067e\u0631\u0648\u062c\u06cc\u06a9\u0679 \u06a9\u06cc \u0631\u0648\u0679 \u0688\u0627\u0626\u0631\u06a9\u0679\u0631\u06cc \u0645\u06cc\u06ba \u0627\u06cc\u06a9 .env \u0641\u0627\u0626\u0644 \u0628\u0646\u0627\u0626\u06cc\u06ba\u06d4<\/p>\n<p>\u06a9\u0648\u0688 \u06a9\u0627 \u0679\u06a9\u0691\u0627<\/p>\n<pre><code class=\"language-python\">OPENAI_API_KEY=your_actual_api_key_here\nENVIRONMENT=development\nCONFIDENCE_THRESHOLD=0.75\n<\/code><\/pre>\n<h2 id=\"heading-the-challenge-addressing-the-overconfidence-vulnerability\">\u0686\u06cc\u0644\u0646\u062c: \u062d\u062f \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u0627\u0639\u062a\u0645\u0627\u062f \u06a9\u06cc \u06a9\u0645\u0632\u0648\u0631\u06cc\u0648\u06ba \u06a9\u0648 \u062f\u0648\u0631 \u06a9\u0631\u0646\u0627<\/h2>\n<p>\u0627\u06cc\u06a9 \u0645\u0639\u06cc\u0627\u0631\u06cc LLM \u0645\u06cc\u06ba &quot;\u0645\u062c\u06be\u06d2 \u0646\u06c1\u06cc\u06ba \u0645\u0639\u0644\u0648\u0645&#8221; \u06a9\u0627 \u0627\u0639\u0644\u0627\u0646 \u06a9\u0631\u0646\u06d2 \u06a9\u0627 \u06a9\u0648\u0626\u06cc \u0627\u0646\u062f\u0631\u0648\u0646\u06cc \u0637\u0631\u06cc\u0642\u06c1 \u06a9\u0627\u0631 \u0646\u06c1\u06cc\u06ba \u06c1\u06d2\u06d4 \u062c\u0628 \u06a9\u0633\u06cc RAG \u0627\u06cc\u067e\u0644\u06cc\u06a9\u06cc\u0634\u0646 \u06a9\u0648 \u06a9\u0633\u06cc \u06af\u0645\u0634\u062f\u06c1 \u062f\u0633\u062a\u0627\u0648\u06cc\u0632 \u06a9\u0627 \u0633\u0627\u0645\u0646\u0627 \u06c1\u0648\u062a\u0627 \u06c1\u06d2 \u06cc\u0627 \u06a9\u0648\u0626\u06cc \u062d\u062f \u0633\u06d2 \u0628\u0627\u06c1\u0631 \u06a9\u0648\u0626\u06cc \u0633\u0648\u0627\u0644 \u0645\u0648\u0635\u0648\u0644 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u060c \u062a\u0648 \u0628\u0646\u06cc\u0627\u062f\u06cc \u0645\u0627\u0688\u0644 \u06af\u0645\u0634\u062f\u06c1 \u0688\u06cc\u0679\u0627 \u06a9\u0648 \u0679\u06cc\u06a9\u0633\u0679 \u0645\u06a9\u0645\u0644 \u06a9\u0631\u0646\u06d2 \u0648\u0627\u0644\u06cc \u067e\u06c1\u06cc\u0644\u06cc \u06a9\u06d2 \u0637\u0648\u0631 \u067e\u0631 \u067e\u06cc\u0634 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u062c\u0633\u06d2 \u06c1\u0631 \u0642\u06cc\u0645\u062a \u067e\u0631 \u062d\u0644 \u06a9\u0631\u0646\u0627 \u0636\u0631\u0648\u0631\u06cc \u06c1\u06d2\u06d4<\/p>\n<p>\u0634\u06a9\u0644 1: \u0627\u06cc\u06a9 \u0645\u0639\u06cc\u0627\u0631\u06cc LLM \u067e\u0627\u0626\u067e \u0644\u0627\u0626\u0646 \u06a9\u06cc \u06a9\u0645\u0632\u0648\u0631\u06cc \u06a9\u0627 \u0641\u0646 \u062a\u0639\u0645\u06cc\u0631 \u062c\u0633 \u0645\u06cc\u06ba \u0645\u0628\u06c1\u0645\/ \u062d\u062f \u0633\u06d2 \u0628\u0627\u06c1\u0631 \u06a9\u06cc \u062f\u0631\u062e\u0648\u0627\u0633\u062a\u06cc\u06ba\u060c \u0628\u06d2 \u06c1\u0648\u062f\u06c1 \u0641\u0648\u0631\u06cc \u0639\u0645\u0644 \u062f\u0631\u0622\u0645\u062f\u060c \u0627\u0648\u0631 \u067e\u0631\u0627\u0639\u062a\u0645\u0627\u062f \u0648\u06c1\u0645 \u062f\u06a9\u06be\u0627\u06cc\u0627 \u06af\u06cc\u0627 \u06c1\u06d2\u06d4<\/p>\n<p>\u0633\u0633\u0679\u0645 \u067e\u0631\u0627\u0645\u067e\u0679\u0633 \u067e\u0631 \u0628\u06be\u0631\u0648\u0633\u06c1 \u06a9\u0631\u06cc\u06ba \u062c\u06cc\u0633\u06d2: <em>&quot;\u062c\u0648\u0627\u0628 \u0635\u0631\u0641 \u0627\u0633 \u0635\u0648\u0631\u062a \u0645\u06cc\u06ba \u062f\u06cc\u06ba \u062c\u0628 \u0622\u067e \u06a9\u0648 100% \u06cc\u0642\u06cc\u0646 \u06c1\u0648&#8221;<\/em> \u0679\u0648\u06a9\u0646 \u06a9\u06cc \u062a\u0631\u062a\u06cc\u0628 \u06a9\u06cc \u067e\u06cc\u0634\u0646 \u06af\u0648\u0626\u06cc \u06a9\u0631\u062a\u06d2 \u0648\u0642\u062a \u06cc\u06c1 \u0645\u0624\u062b\u0631 \u0646\u06c1\u06cc\u06ba \u06c1\u0648\u062a\u0627 \u06c1\u06d2 \u06a9\u06cc\u0648\u0646\u06a9\u06c1 \u0645\u0627\u0688\u0644 \u0622\u0633\u0627\u0646\u06cc \u0633\u06d2 \u0633\u0633\u0679\u0645 \u067e\u0631\u0627\u0645\u067e\u0679 \u0631\u06a9\u0627\u0648\u0679\u0648\u06ba \u06a9\u0648 \u0646\u0638\u0631\u0627\u0646\u062f\u0627\u0632 \u06a9\u0631 \u062f\u06cc\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u0646\u0679\u0631\u067e\u0631\u0627\u0626\u0632 \u0633\u0633\u0679\u0645\u0632 \u06a9\u0648 \u0688\u06cc\u0679\u0631\u0645\u0646\u0633\u0679\u06a9 \u06a9\u0648\u0688 \u0628\u0627\u0624\u0646\u0688\u0631\u06cc\u0632 \u06a9\u06cc \u0636\u0631\u0648\u0631\u062a \u06c1\u0648\u062a\u06cc \u06c1\u06d2 \u062c\u0648 LLM \u06a9\u06d2 \u062e\u0627\u0645 \u0622\u0624\u0679 \u067e\u0679 \u0633\u06d2 \u0622\u0632\u0627\u062f \u0633\u06cc\u0645\u0646\u0679\u06a9 \u0645\u0637\u0627\u0628\u0642\u062a\u060c \u062f\u0633\u062a\u0627\u0648\u06cc\u0632 \u06a9\u06cc \u062f\u0648\u0631\u06cc\u060c \u0627\u0648\u0631 \u0679\u0648\u06a9\u0646 \u0627\u0645\u06a9\u0627\u0646 \u06a9\u0627 \u062c\u0627\u0626\u0632\u06c1 \u0644\u06cc\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n<h2 id=\"heading-understanding-the-enterprise-request-lifecycle-for-uncertainty-evaluation\">\u063a\u06cc\u0631 \u06cc\u0642\u06cc\u0646\u06cc \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0627 \u0627\u0646\u062f\u0627\u0632\u06c1 \u0644\u06af\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u0646\u0679\u0631\u067e\u0631\u0627\u0626\u0632 \u0631\u06cc\u06a9\u0648\u0626\u0633\u0679 \u0644\u0627\u0626\u0641 \u0633\u0627\u0626\u06cc\u06a9\u0644 \u06a9\u0648 \u0633\u0645\u062c\u06be\u0646\u0627<\/h2>\n<p>\u063a\u06cc\u0631 \u062a\u0635\u062d\u06cc\u062d \u0634\u062f\u06c1 \u0622\u0624\u0679 \u067e\u0679 \u06a9\u0648 \u0631\u0648\u06a9\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u06c1\u0645 \u062f\u0631\u062e\u0648\u0627\u0633\u062a\u0648\u06ba \u06a9\u0648 \u0645\u0633\u062f\u0648\u062f \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0688\u06cc\u0679\u0631\u0645\u0646\u0633\u0679\u06a9 \u0631\u06cc\u06a9\u0648\u0626\u0633\u0679 \u0644\u0627\u0626\u0641 \u0633\u0627\u0626\u06cc\u06a9\u0644 \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u062d\u062a\u0645\u06cc \u0622\u0624\u0679 \u067e\u0679 \u0622\u062e\u0631\u06cc \u0635\u0627\u0631\u0641 \u06a9\u0648 \u0628\u06be\u06cc\u062c\u06d2 \u062c\u0627\u0646\u06d2 \u0633\u06d2 \u067e\u06c1\u0644\u06d2 \u06c1\u0631 \u0679\u0631\u0627\u0646\u0632\u06cc\u06a9\u0634\u0646 \u062a\u0635\u062f\u06cc\u0642 \u06a9\u06cc \u062a\u06cc\u0646 \u067e\u0631\u062a\u0648\u06ba \u0633\u06d2 \u06af\u0632\u0631\u062a\u06cc \u06c1\u06d2\u06d4<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/umang.pk\/wp-content\/uploads\/2026\/09\/1788485024_888_\u0627\u06d2-\u0622\u0626\u06cc-\u0633\u0633\u0679\u0645\u0632-\u06a9\u06cc\u0633\u06d2-\u0628\u0646\u0627\u0626\u06cc\u06ba-\u062c\u0648-\u062c\u0627\u0646\u062a\u06d2-\u06c1\u0648\u06ba-\u06a9\u06c1-\u062c\u0628.png\" alt=\"\u0634\u06a9\u0644 2: \u0645\u062a\u0628\u0627\u062f\u0644 \u0627\u0636\u0627\u0641\u06c1 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0627\u0646\u0679\u0631\u067e\u0631\u0627\u0626\u0632 \u0627\u06cc\u0644 \u0627\u06cc\u0644 \u0627\u06cc\u0645 \u0641\u0646 \u062a\u0639\u0645\u06cc\u0631 \u06a9\u0648 \u0645\u062d\u0641\u0648\u0638 \u0628\u0646\u0627\u0626\u06cc\u06ba\u06d4\" style=\"display:block;margin:0 auto\" width=\"600\" height=\"400\" loading=\"lazy\" title=\"\"><\/p>\n<p>\u062a\u0635\u0648\u06cc\u0631 2: \u0641\u0627\u0644 \u0628\u06cc\u06a9 \u0627\u06cc\u0633\u06a9\u0644\u06cc\u0634\u0646 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0627\u0646\u0679\u0631\u067e\u0631\u0627\u0626\u0632 \u0627\u06cc\u0644 \u0627\u06cc\u0644 \u0627\u06cc\u0645 \u0622\u0631\u06a9\u06cc\u0679\u06cc\u06a9\u0686\u0631 \u06a9\u0648 \u0645\u062d\u0641\u0648\u0638 \u06a9\u0631\u06cc\u06ba\u060c \u062c\u0633 \u0645\u06cc\u06ba \u0635\u0627\u0631\u0641 \u06a9\u06cc \u062f\u0631\u062e\u0648\u0627\u0633\u062a\u0648\u06ba \u06a9\u0648 \u0627\u0646 \u067e\u0679 \u0628\u0627\u0624\u0646\u0688\u0631\u06cc \u06a9\u06cc \u062a\u0648\u062b\u06cc\u0642\u060c \u062a\u0644\u0627\u0634 \u06a9\u06d2 \u0645\u0639\u06cc\u0627\u0631 \u06a9\u06cc \u062a\u0634\u062e\u06cc\u0635\u060c \u0627\u0648\u0631 \u0622\u0624\u0679 \u067e\u0679 \u06a9\u06cc \u063a\u06cc\u0631 \u06cc\u0642\u06cc\u0646\u06cc \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u06cc \u062c\u0627\u0646\u0686 \u067e\u0691\u062a\u0627\u0644 \u062f\u06a9\u06be\u0627 \u0631\u06c1\u0627 \u06c1\u0648<\/p>\n<p>LLM \u0633\u06d2 \u062d\u0641\u0627\u0638\u062a\u06cc \u0641\u06cc\u0635\u0644\u0648\u06ba \u06a9\u0648 \u0627\u0644\u06af \u06a9\u0631 \u06a9\u06d2\u060c \u06a9\u0648\u0688 \u0641\u06cc\u0635\u0644\u06c1 \u06a9\u06cc \u062d\u062f \u06a9\u06d2 \u0637\u0648\u0631 \u067e\u0631 \u06a9\u0627\u0645 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u0627\u0648\u0631 \u0632\u0628\u0627\u0646 \u06a9\u0627 \u0645\u0627\u0688\u0644 \u0633\u062e\u062a\u06cc \u0633\u06d2 \u062a\u062c\u0632\u06cc\u06c1 \u06a9\u0631\u0646\u06d2 \u0648\u0627\u0644\u06d2 \u0627\u0646\u062c\u0646 \u06a9\u06d2 \u0637\u0648\u0631 \u067e\u0631 \u06a9\u0627\u0645 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<h3 id=\"heading-step-1-implementing-layer-1-input-intent-amp-boundary-detection\">\u0645\u0631\u062d\u0644\u06c1 1: \u067e\u0631\u062a 1 \u06a9\u0648 \u0644\u0627\u06af\u0648 \u06a9\u0631\u06cc\u06ba &#8211; \u0627\u0646 \u067e\u0679 \u06a9\u06d2 \u0627\u0631\u0627\u062f\u06d2 \u0627\u0648\u0631 \u062d\u062f\u0648\u062f \u06a9\u0627 \u067e\u062a\u06c1 \u0644\u06af\u0627\u0626\u06cc\u06ba\u06d4<\/h3>\n<p>\u062f\u0641\u0627\u0639 \u06a9\u06cc \u067e\u06c1\u0644\u06cc \u067e\u0631\u062a \u0627\u0633 \u0628\u0627\u062a \u06a9\u0627 \u062a\u0639\u06cc\u0646 \u06a9\u0631\u062a\u06cc \u06c1\u06d2 \u06a9\u06c1 \u0622\u06cc\u0627 \u0633\u0631\u0686 \u067e\u0627\u0626\u067e \u0644\u0627\u0626\u0646 \u06cc\u0627 \u0645\u0627\u0688\u0644 API \u06a9\u0648 \u06a9\u0627\u0644 \u06a9\u0631\u0646\u06d2 \u0633\u06d2 \u067e\u06c1\u0644\u06d2 \u0622\u0646\u06d2 \u0648\u0627\u0644\u0627 \u0633\u0648\u0627\u0644 \u0633\u0633\u0679\u0645 \u06a9\u06d2 \u062f\u0631\u0633\u062a \u0688\u0648\u0645\u06cc\u0646 \u067e\u06cc\u0631\u0627\u0645\u06cc\u0679\u0631\u0632 \u06a9\u06d2 \u0627\u0646\u062f\u0631 \u0622\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<pre><code class=\"language-python\">import numpy as np\nfrom sentence_transformers import SentenceTransformer\n\nclass BoundaryDetector:\n    def __init__(self, target_domains: list, similarity_threshold: float = 0.45):\n        self.encoder = SentenceTransformer(\"all-MiniLM-L6-v2\")\n        self.domain_embeddings = self.encoder.encode(target_domains)\n        self.threshold = similarity_threshold\n\n    def verify_domain_relevance(self, query: str) -> dict:\n        query_vector = self.encoder.encode([query])\n        \n        # Calculate cosine similarity against domain boundaries\n        similarities = np.dot(self.domain_embeddings, query_vector.T) \/ (\n            np.linalg.norm(self.domain_embeddings, axis=1, keepdims=True) * np.linalg.norm(query_vector)\n        )\n        max_similarity = float(np.max(similarities))\n        \n        if max_similarity < self.threshold:\n            return {\n                \"is_valid\": False,\n                \"score\": round(max_similarity, 4),\n                \"reason\": \"Query falls outside operational domain boundaries.\"\n            }\n            \n        return {\n            \"is_valid\": True,\n            \"score\": round(max_similarity, 4),\n            \"reason\": \"Query verified within target operational scope.\"\n        }\n\nif __name__ == \"__main__\":\n    approved_topics = [\n        \"company VPN configuration\",\n        \"employee payroll schedules\",\n        \"internal IT software deployment\"\n    ]\n    detector = BoundaryDetector(target_domains=approved_topics)\n    out_of_scope_query = \"What is the optimal baking temperature for sourdough bread?\"\n    result = detector.verify_domain_relevance(out_of_scope_query)\n    print(f\"Domain Validation Result: {result}\")\n<\/code><\/pre>\n<p>\u06cc\u06c1 \u0645\u0627\u0688\u06cc\u0648\u0644 \u0645\u0646\u0638\u0648\u0631 \u0634\u062f\u06c1 \u0622\u067e\u0631\u06cc\u0634\u0646\u0644 \u0645\u0648\u0636\u0648\u0639\u0627\u062a \u06a9\u0648 \u0633\u06cc\u0645\u0646\u0679\u06a9 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\u0645\u0639\u06cc\u0627\u0631 \u06a9\u0627 \u062a\u0639\u06cc\u0646 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u06c1\u0645 \u0627\u0633\u062a\u0641\u0633\u0627\u0631 \u0627\u0648\u0631 \u0628\u0627\u0632\u06cc\u0627\u0641\u062a \u0634\u062f\u06c1 \u0633\u06cc\u0627\u0642 \u0648 \u0633\u0628\u0627\u0642 \u06a9\u06d2 \u0679\u06a9\u0691\u0648\u06ba \u06a9\u06d2 \u062f\u0631\u0645\u06cc\u0627\u0646 \u0645\u0639\u0646\u0648\u06cc \u0641\u0627\u0635\u0644\u06d2 \u06a9\u06cc \u067e\u06cc\u0645\u0627\u0626\u0634 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n<pre><code class=\"language-python\">class RetrievalQualityScorer:\n    def __init__(self, minimum_relevance: float = 0.60):\n        self.encoder = SentenceTransformer(\"all-MiniLM-L6-v2\")\n        self.min_relevance = minimum_relevance\n\n    def evaluate_retrieved_context(self, user_query: str, retrieved_chunks: list) -> tuple:\n        if not retrieved_chunks:\n            return False, 0.0\n\n        query_vec = self.encoder.encode(user_query)\n        chunk_vecs = self.encoder.encode(retrieved_chunks)\n\n        # Compute cosine similarity across retrieved chunks\n        scores = np.dot(chunk_vecs, query_vec) \/ (\n            np.linalg.norm(chunk_vecs, axis=1) * np.linalg.norm(query_vec)\n        )\n        top_score = float(np.max(scores))\n\n        is_sufficient = top_score >= self.min_relevance\n        return is_sufficient, round(top_score, 4)\n\nif __name__ == \"__main__\":\n    scorer = RetrievalQualityScorer()\n    sample_query = \"How do I configure mutual TLS for gRPC services?\"\n    sample_context = [\n        \"Standard deployment uses isolated network clusters with automated releases.\"\n    ]\n    has_context, score = scorer.evaluate_retrieved_context(sample_query, sample_context)\n    print(f\"Context Sufficient: {has_context} | Top Match Score: {score}\")\n<\/code><\/pre>\n<p>\u0627\u0633 \u0645\u0631\u062d\u0644\u06d2 \u0645\u06cc\u06ba\u060c 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\u062a\u062c\u0627\u0648\u0632 \u0646\u06c1\u06cc\u06ba \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u060c \u062a\u0648 \u0645\u0627\u0688\u06cc\u0648\u0644 \u0633\u06cc\u0627\u0642 \u0648 \u0633\u0628\u0627\u0642 \u06a9\u0648 \u0646\u0627\u06a9\u0627\u0641\u06cc \u06a9\u06d2 \u0637\u0648\u0631 \u067e\u0631 \u062c\u06be\u0646\u0688\u0627 \u0644\u06af\u0627\u062a\u0627 \u06c1\u06d2\u060c \u062c\u0633 \u0633\u06d2 \u0633\u0633\u0679\u0645 \u06a9\u0648 \u0645\u0627\u0688\u0644 \u06a9\u0648 \u063a\u06cc\u0631 \u0645\u062a\u0639\u0644\u0642\u06c1 \u0645\u062a\u0646 \u0628\u06be\u06cc\u062c\u0646\u06d2 \u0633\u06d2 \u0631\u0648\u06a9\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<h3 id=\"heading-step-3-implementing-layer-3-probabilistic-logit-analysis-amp-output-validation\">\u0645\u0631\u062d\u0644\u06c1 3: \u067e\u0631\u062a 3 \u06a9\u0627 \u0646\u0641\u0627\u0630 - \u0627\u0645\u06a9\u0627\u0646\u06cc \u0644\u0627\u06af\u0679 \u062a\u062c\u0632\u06cc\u06c1 \u0627\u0648\u0631 \u0622\u0624\u0679 \u067e\u0679 \u062a\u0635\u062f\u06cc\u0642<\/h3>\n<p>\u062d\u062a\u0645\u06cc \u067e\u0631\u062a \u0645\u0627\u0688\u0644 API \u0633\u06d2 \u0648\u0627\u067e\u0633 \u0622\u0646\u06d2 \u0648\u0627\u0644\u06d2 \u0679\u0648\u06a9\u0646 \u062c\u0646\u0631\u06cc\u0634\u0646 \u0627\u0645\u06a9\u0627\u0646 (\u0644\u0627\u06af \u0627\u0645\u06a9\u0627\u0646) \u06a9\u06cc \u062c\u0627\u0646\u0686 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4 \u0627\u06af\u0631 LLM \u06a9\u0648 \u062c\u0648\u0627\u0628 \u06a9\u0627 \u06cc\u0642\u06cc\u0646 \u0646\u06c1\u06cc\u06ba \u06c1\u06d2\u060c \u062a\u0648 \u0679\u0648\u06a9\u0646 \u0688\u0633\u0679\u0631\u06cc \u0628\u06cc\u0648\u0634\u0646 \u0627\u06cc\u0646\u0679\u0631\u0648\u067e\u06cc \u0628\u0691\u06be \u062c\u0627\u062a\u06cc \u06c1\u06d2\u060c \u062c\u0648 \u0628\u0631\u0627\u06c1 \u0631\u0627\u0633\u062a API \u067e\u06d2 \u0644\u0648\u0688 \u0645\u06cc\u06ba \u063a\u06cc\u0631 \u06cc\u0642\u06cc\u0646\u06cc \u0635\u0648\u0631\u062a\u062d\u0627\u0644 \u06a9\u0648 \u0638\u0627\u06c1\u0631 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4<\/p>\n<pre><code class=\"language-python\">import math\n\nclass OutputLogprobValidator:\n    def __init__(self, logprob_threshold: float = -0.35):\n        self.threshold = logprob_threshold\n\n    def evaluate_token_certainty(self, token_logprobs: list) -> dict:\n        if not token_logprobs:\n            return {\"is_confident\": False, \"avg_logprob\": -1.0, \"perplexity\": 999.0}\n\n        avg_logprob = sum(token_logprobs) \/ len(token_logprobs)\n        perplexity = math.exp(-avg_logprob)\n        is_confident = avg_logprob >= self.threshold\n\n        return {\n            \"is_confident\": is_confident,\n            \"avg_logprob\": round(avg_logprob, 4),\n            \"perplexity\": round(perplexity, 4)\n        }\n\nif __name__ == \"__main__\":\n    validator = OutputLogprobValidator()\n    # Simulated logprob arrays from an API output\n    unconfident_logprobs = [-0.12, -0.85, -1.20, -0.45, -0.95]\n    result = validator.evaluate_token_certainty(unconfident_logprobs)\n    print(f\"Generation Certainty Assessment: {result}\")\n<\/code><\/pre>\n<p>\u06cc\u06c1 \u06a9\u0644\u0627\u0633 \u0627\u0648\u0633\u0637 \u0644\u0627\u06af \u067e\u0631\u0648\u0628 \u0645\u06cc\u0679\u0631\u06a9 \u06a9\u06cc \u06af\u0646\u062a\u06cc \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u0679\u06cc\u06a9\u0633\u0679 \u06a9\u0646\u0641\u06cc\u0648\u0698\u0646 \u06a9\u06d2 \u0633\u0627\u062a\u06be\u060c \u067e\u06cc\u062f\u0627 \u06a9\u0631\u062f\u06c1 \u0679\u0648\u06a9\u0646\u0632 \u06a9\u06d2 \u062e\u0627\u0645 \u0644\u0627\u06af \u0627\u0645\u06a9\u0627\u0646\u0627\u062a \u067e\u0631 \u06a9\u0627\u0631\u0631\u0648\u0627\u0626\u06cc \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4 \u0627\u0633 \u0642\u062f\u0631 \u06a9\u0627 \u0627\u06cc\u06a9 \u06a9\u06cc\u0644\u06cc\u0628\u0631\u06cc\u0679\u0688 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OutputLogprobValidator()\n\n    def process_request(self, user_query: str, retrieved_docs: list) -> str:\n        print(f\"n--- Processing Query: '{user_query}' ---\")\n\n        # Check 1: Input Boundary Evaluation\n        boundary_result = self.boundary_layer.verify_domain_relevance(user_query)\n        if not boundary_result[\"is_valid\"]:\n            return f\"Request Rejected: {boundary_result['reason']}\"\n        print(\"[Pass] Input verified within operational domain.\")\n\n        # Check 2: Retrieval Context Quality\n        valid_context, ret_score = self.retrieval_layer.evaluate_retrieved_context(user_query, retrieved_docs)\n        if not valid_context:\n            return f\"Escalated: Insufficient ground-truth data retrieved (Score: {ret_score}). Routing to support team.\"\n        print(f\"[Pass] Context quality validated (Score: {ret_score}).\")\n\n        # Step 3: Simulated LLM Generation & Logprob Verification\n        # In production, replace dummy logprobs with actual API responses\n        simulated_logprobs = [-0.08, -0.05, -0.12, -0.04]\n        certainty = self.output_layer.evaluate_token_certainty(simulated_logprobs)\n\n        if not certainty[\"is_confident\"]:\n            return \"Fallback Active: Generated output exhibited low token certainty.\"\n        print(f\"[Pass] Output probability verified (Avg Logprob: {certainty['avg_logprob']}).\")\n\n        return \"Response Generated: Navigate to portal.company.internal to reset your VPN credentials.\"\n\nif __name__ == \"__main__\":\n    domains = [\"VPN credentials\", \"software provisioning\", \"network settings\"]\n    engine = EnterpriseUncertaintyEngine(approved_domains=domains)\n\n    # Test Case: Query with valid retrieved context\n    context_data = [\"To update VPN credentials, access portal.company.internal.\"]\n    final_output = engine.process_request(\"How do I update my VPN password?\", context_data)\n    print(f\"System Output: {final_output}\")\n<\/code><\/pre>\n<p>\u06cc\u06c1 \u0622\u0631\u06a9\u06cc\u0633\u0679\u0631\u06cc\u0634\u0646 \u06a9\u0644\u0627\u0633 \u0627\u0646 \u067e\u0679 \u06a9\u06cc \u062a\u0648\u062b\u06cc\u0642\u060c \u062a\u0644\u0627\u0634 \u06a9\u06d2 \u0627\u0633\u06a9\u0648\u0631\u0646\u06af\u060c \u0627\u0648\u0631 \u0644\u0627\u06af \u067e\u0631\u0648\u0628 \u06a9\u0648 \u0627\u06cc\u06a9 \u0648\u0627\u062d\u062f \u0639\u0645\u0644 \u06a9\u0627\u0631\u06cc \u06a9\u06d2 \u06a9\u0627\u0645 \u06a9\u06d2 \u0641\u0644\u0648 \u0645\u06cc\u06ba \u06cc\u06a9\u062c\u0627 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4 \u06cc\u06c1 \u062a\u0631\u062a\u06cc\u0628 \u0648\u0627\u0631 \u06c1\u0631 \u0627\u06cc\u06a9 \u062a\u0635\u062f\u06cc\u0642\u06cc \u0686\u0648\u06a9\u06cc \u06a9\u06d2 \u0630\u0631\u06cc\u0639\u06d2 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