长文本处理

def fixed_size_chunking(text, chunk_size=512, overlap=50):
    """最常用的基础策略"""
    chunks = []
    for i in range(0, len(text), chunk_size - overlap):
        chunk = text[i:i + chunk_size]
        chunks.append({
            'content': chunk,
            'position': i,
            'metadata': extract_metadata(chunk)
        })
    return chunks

def content_aware_chunking(document):
    """基于文档结构的智能分块"""
    strategies = {
        'sentence': split_by_sentences,
        'paragraph': split_by_paragraphs,
        'section': split_by_headers,
        'semantic': split_by_semantic_similarity
    }
    
    # 动态选择最佳策略
    best_strategy = analyze_document_structure(document)
    return strategies[best_strategy](document)

class ContextRouter:
    def __init__(self, llm, rag_system):
        self.llm = llm
        self.rag = rag_system
    
    def route_query(self, query, context):
        # 模型自评估能力
        confidence = self.llm.assess_capability(query, context)
        
        if confidence > 0.8:
            # 直接使用长上下文
            return self.llm.generate(query, full_context=context)
        else:
            # 使用RAG检索相关片段
            chunks = self.rag.retrieve(query, top_k=5)
            return self.llm.generate(query, context=chunks)

# YaRN策略实现
def yarn_position_encoding(seq_length, target_length=128000):
    """
    保持短距离细节，扩展长序列能力
    """
    scaling_factors = []
    for dim in range(embedding_dim):
        if dim < embedding_dim // 3:
            # 低维度：保持不变（短距离细节）
            scale = 1.0
        elif dim > 2 * embedding_dim // 3:
            # 高维度：完全缩放（长序列）
            scale = target_length / seq_length
        else:
            # 中间维度：混合策略
            scale = interpolate(1.0, target_length/seq_length, dim)
        scaling_factors.append(scale)
    return scaling_factors

class ContextCache:
    def __init__(self, max_size=100000):
        self.static_cache = {}  # 静态内容缓存
        self.dynamic_buffer = deque(maxlen=max_size)
        
    def process_context(self, query, document):
        # 识别静态部分（用户配置、系统提示等）
        static_parts = identify_static_content(document)
        
        # 缓存静态内容
        if static_parts not in self.static_cache:
            self.static_cache[hash(static_parts)] = encode(static_parts)
        
        # 只处理动态部分
        dynamic_parts = extract_dynamic_content(document)
        return self.combine_contexts(static_parts, dynamic_parts)

class OrderPreserveRAG:
    """保持文档原始顺序的检索增强生成"""
    
    def retrieve_and_reorder(self, query, document):
        # 1. 标准检索
        relevant_chunks = self.retrieve(query)
        
        # 2. 恢复原始顺序
        ordered_chunks = sorted(relevant_chunks, 
                              key=lambda x: x['original_position'])
        
        # 3. 添加过渡上下文
        enhanced_chunks = []
        for i, chunk in enumerate(ordered_chunks):
            if i > 0:
                # 添加chunk间的过渡文本
                gap = chunk['position'] - ordered_chunks[i-1]['end_position']
                if gap < 100:  # 小间隙，添加连接文本
                    transition = extract_transition(document, 
                                                   ordered_chunks[i-1]['end_position'],
                                                   chunk['position'])
                    enhanced_chunks.append(transition)
            enhanced_chunks.append(chunk['content'])
        
        return enhanced_chunks

# 混合策略实现
def process_large_document(doc, query):
    # 第一步：文档分析
    doc_type = classify_document(doc)
    
    if doc_type == 'structured':
        # 结构化文档：使用章节分块
        chunks = chunk_by_sections(doc)
        strategy = 'rag'
    elif len(doc) < 50000:
        # 中等长度：直接使用长上下文
        strategy = 'long_context'
    else:
        # 超长文档：混合策略
        strategy = 'hybrid'
    
    return execute_strategy(strategy, doc, query)

class CodebaseAnalyzer:
    def analyze_repository(self, repo_path):
        # 1. 构建代码图谱
        code_graph = build_dependency_graph(repo_path)
        
        # 2. 智能分块（保持函数/类完整性）
        chunks = []
        for file in iterate_files(repo_path):
            ast_tree = parse_code(file)
            chunks.extend(extract_semantic_units(ast_tree))
        
        # 3. 建立索引
        index = create_vector_index(chunks)
        
        # 4. 支持复杂查询
        return QueryInterface(index, code_graph)

class ConversationManager:
    def __init__(self, window_size=100000):
        self.context_window = window_size
        self.conversation_history = []
        self.summary_cache = {}
    
    def manage_context(self, new_message):
        # 动态压缩历史对话
        if self.get_total_tokens() > self.context_window * 0.8:
            # 保留最近10轮完整对话
            recent = self.conversation_history[-10:]
            
            # 早期对话压缩为摘要
            early = self.conversation_history[:-10]
            summary = self.summarize_conversation(early)
            
            # 重构上下文
            self.conversation_history = [summary] + recent
        
        self.conversation_history.append(new_message)