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self.style_adapter = StyleAdapter()\n        \n        # 评估层\n        self.quality_assessor = QualityAssessor()\n        self.coherence_checker = CoherenceChecker()\n    \n    def process_user_input(self, raw_text, context=None):\n        \"\"\"完整的NLP处理流程\"\"\"\n        # 1. 预处理阶段\n        cleaned_text = self.normalizer.normalize(raw_text)\n        tokens = self.tokenizer.tokenize(cleaned_text)\n        \n        # 2. 语法分析\n        syntax_tree = self.syntactic_parser.parse(tokens)\n        \n        # 3. 语义理解\n        semantic_representation = self.semantic_analyzer.analyze(\n            syntax_tree, context\n        )\n        \n        # 4. 语用分析\n        pragmatic_intent = self.pragmatic_processor.infer_intent(\n            semantic_representation, context\n        )\n        \n        # 5. 内容生成\n        generated_content = self.content_generator.generate(\n            pragmatic_intent, semantic_representation\n        )\n        \n        # 6. 质量评估\n        quality_score = self.quality_assessor.evaluate(generated_content)\n        \n        return {\n            'semantic_understanding': semantic_representation,\n            'inferred_intent': pragmatic_intent,\n            'generated_content': generated_content,\n            'quality_metrics': quality_score\n        }\n","python","language-python shiki shiki-themes github-dark 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understand_requirements(self, user_text):\n        \"\"\"使用BERT进行需求理解\"\"\"\n        # 生成上下文嵌入\n        embeddings = self.understanding_model.encode(user_text)\n        \n        # 多维度分析\n        analysis = {\n            'business_intent': self.classify_business_intent(embeddings),\n            'technical_requirements': self.extract_tech_specs(embeddings),\n            'design_preferences': self.infer_design_style(embeddings),\n            'content_strategy': self.plan_content_approach(embeddings)\n        }\n        \n        return analysis\n    \n    def generate_contextual_content(self, requirements, style_guide):\n        \"\"\"基于理解生成上下文相关内容\"\"\"\n        prompt = self.construct_generation_prompt(requirements, style_guide)\n        \n        content = self.generation_model.generate(\n            prompt=prompt,\n            max_tokens=2048,\n            temperature=0.7,\n            top_p=0.9\n        )\n        \n        return 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