RAG-DD：企业私有知识库与代码生成的实时融合

# 标准JWT实现
def authenticate(username, password):
    user = db.query(f"SELECT * FROM users WHERE username='{username}'")
    if user and user.password == password:
        return generate_jwt(user.id)
    return None

class KnowledgeBaseIndexer:
    def index_knowledge(self, knowledge_items):
        """
        将知识库内容索引为向量
        """
        for item in knowledge_items:
            # 1. 文本分块
            chunks = self.chunk_document(item.content)
            
            for chunk in chunks:
                # 2. 生成嵌入向量
                embedding = self.embedding_model.encode(chunk)
                
                # 3. 存储到向量数据库
                self.vector_store.add(
                    id=generate_id(),
                    embedding=embedding,
                    content=chunk,
                    metadata={
                        'source': item.source,
                        'type': item.type,
                        'tags': item.tags
                    }
                )

class IntentParser:
    def parse(self, user_intent):
        """
        解析开发者意图，提取关键信息
        """
        return {
            'domain': self.extract_domain(user_intent),      # 领域：支付、用户管理
            'operation': self.extract_operation(user_intent), # 操作：创建、查询
            'entities': self.extract_entities(user_intent),   # 实体：订单、用户
            'constraints': self.extract_constraints(user_intent), # 约束：性能、安全
            'context': self.extract_context(user_intent)      # 上下文：微服务、单体
        }

class KnowledgeRetrievalEngine:
    def retrieve(self, parsed_intent, top_k=5):
        """
        根据意图检索相关知识
        """
        # 1. 生成查询向量
        query_vector = self.encode_intent(parsed_intent)
        
        # 2. 多维度检索
        results = []
        
        # 检索编码规范
        coding_standards = self.vector_store.search(
            query_vector,
            filter={'type': 'coding_standard'},
            top_k=top_k
        )
        
        # 检索架构模式
        arch_patterns = self.vector_store.search(
            query_vector,
            filter={'type': 'architecture_pattern'},
            top_k=top_k
        )
        
        # 检索业务规则
        business_rules = self.vector_store.search(
            query_vector,
            filter={'type': 'business_rule', 'domain': parsed_intent['domain']},
            top_k=top_k
        )
        
        # 检索历史代码
        similar_code = self.vector_store.search(
            query_vector,
            filter={'type': 'code_example'},
            top_k=top_k
        )
        
        return {
            'coding_standards': coding_standards,
            'architecture_patterns': arch_patterns,
            'business_rules': business_rules,
            'similar_code': similar_code
        }

class ContextFusionEngine:
    def fuse(self, user_intent, retrieved_knowledge):
        """
        融合用户意图和检索到的知识
        """
        prompt = f"""
生成代码要求：
{user_intent}

必须遵循的企业规范：
{self.format_coding_standards(retrieved_knowledge['coding_standards'])}

必须使用的架构模式：
{self.format_architecture_patterns(retrieved_knowledge['architecture_patterns'])}

必须实现的业务规则：
{self.format_business_rules(retrieved_knowledge['business_rules'])}

参考实现：
{self.format_similar_code(retrieved_knowledge['similar_code'])}

请生成符合以上所有要求的代码。
"""
        return prompt

class IntentAugmentedQuery:
    def enhance(self, user_query, intent_context):
        """
        增强查询以提高检索相关性
        """
        # 扩展同义词
        expanded_terms = self.expand_synonyms(user_query)
        
        # 添加上下文关键词
        context_keywords = self.extract_keywords(intent_context)
        
        # 组合增强查询
        enhanced_query = f"{user_query} {' '.join(expanded_terms)} {' '.join(context_keywords)}"
        
        return enhanced_query

class Reranker:
    def rerank(self, retrieved_results, user_intent):
        """
        基于意图对检索结果重排序
        """
        scores = []
        for result in retrieved_results:
            score = 0
            
            # 领域匹配度
            score += self.domain_match(result, user_intent) * 0.3
            
            # 时效性（优先使用最新知识）
            score += self.recency_score(result) * 0.2
            
            # 使用频率（优先使用常用知识）
            score += self.usage_score(result) * 0.2
            
            # 语义相似度
            score += self.semantic_similarity(result, user_intent) * 0.3
            
            scores.append((result, score))
        
        # 按分数排序
        return sorted(scores, key=lambda x: x[1], reverse=True)

class IterativeRefinement:
    def refine(self, initial_code, knowledge_base, max_iterations=3):
        """
        多轮精化生成的代码
        """
        code = initial_code
        
        for i in range(max_iterations):
            # 检查是否符合规范
            violations = self.check_compliance(code, knowledge_base)
            
            if not violations:
                break
            
            # 基于违规点精化
            code = self.refine_code(code, violations)
        
        return code