作者：北辰alk

引言

随着大语言模型在处理长文档、多轮对话等复杂任务中的广泛应用，上下文长度限制已成为制约模型性能的关键瓶颈。无论是GPT-4的128K上下文还是Claude的200K上下文，在面对真正的长文本处理时仍然显得捉襟见肘。本文将深入探讨长上下文处理的完整解决方案，从理论原理到工程实践，提供全面的技术指南。

第一章：理解上下文长度问题

1.1 为什么上下文长度如此重要？

# 上下文长度对模型性能的影响分析
context_length_impact = {
    "信息完整性": "长上下文确保关键信息不丢失",
    "对话连贯性": "在多轮对话中维持话题一致性", 
    "文档理解": "处理长文档时需要全局视角",
    "推理能力": "复杂推理需要更多上下文支持",
    "知识检索": "从大量信息中定位相关知识"
}

class ContextLengthAnalyzer:
    """上下文长度分析器"""
    
    def __init__(self):
        self.typical_limits = {
            "GPT-3.5": 4096,
            "GPT-4": 128000,
            "Claude-2": 200000,
            "LLaMA-2": 4096,
            "ChatGLM": 4096
        }
    
    def calculate_usage_efficiency(self, text: str, model_limit: int) -> dict:
        """计算上下文使用效率"""
        token_count = len(text.split())  # 简化估计
        usage_ratio = token_count / model_limit
        
        efficiency_metrics = {
            "token_count": token_count,
            "model_limit": model_limit,
            "usage_ratio": usage_ratio,
            "efficiency_level": self._get_efficiency_level(usage_ratio),
            "remaining_tokens": model_limit - token_count
        }
        
        return efficiency_metrics
    
    def _get_efficiency_level(self, ratio: float) -> str:
        """获取效率等级"""
        if ratio < 0.3:
            return "低效"
        elif ratio < 0.7:
            return "适中"
        elif ratio < 0.9:
            return "高效"
        else:
            return "危险"
    
    def analyze_context_patterns(self, conversations: list) -> dict:
        """分析上下文使用模式"""
        pattern_analysis = {
            "avg_turn_length": 0,
            "max_context_used": 0,
            "truncation_frequency": 0,
            "common_bottlenecks": []
        }
        
        total_turns = 0
        total_length = 0
        
        for conv in conversations:
            for turn in conv.get('turns', []):
                turn_length = len(turn.get('content', '').split())
                total_length += turn_length
                total_turns += 1
                
                pattern_analysis['max_context_used'] = max(
                    pattern_analysis['max_context_used'], turn_length
                )
        
        if total_turns > 0:
            pattern_analysis['avg_turn_length'] = total_length / total_turns
        
        return pattern_analysis

# 使用示例
analyzer = ContextLengthAnalyzer()
sample_text = "这是一段测试文本 " * 1000
efficiency = analyzer.calculate_usage_efficiency(sample_text, 4096)
print("上下文使用效率分析:", efficiency)

1.2 上下文长度的技术挑战

# 上下文长度的技术挑战
context_challenges = {
    "计算复杂度": {
        "问题": "注意力机制的复杂度是O(n²)",
        "影响": "长上下文导致计算量指数级增长",
        "示例": "4096 tokens → 1600万计算, 8192 tokens → 6700万计算"
    },
    "内存限制": {
        "问题": "KV缓存占用大量GPU内存",
        "影响": "限制批处理大小和序列长度", 
        "示例": "32K上下文在A100上约占用20GB内存"
    },
    "信息稀释": {
        "问题": "关键信息在长上下文中被稀释",
        "影响": "模型难以关注重要信息",
        "示例": "在10万字文档中定位关键段落"
    },
    "位置编码": {
        "问题": "传统位置编码在长序列中效果下降",
        "影响": "模型理解长距离依赖关系困难",
        "示例": "RoPE等新型位置编码的局限性"
    }
}

class TechnicalChallengeDemonstrator:
    """技术挑战演示器"""
    
    def demonstrate_complexity_growth(self):
        """演示计算复杂度增长"""
        import matplotlib.pyplot as plt
        import numpy as np
        
        sequence_lengths = np.array([1024, 2048, 4096, 8192, 16384])
        complexity = sequence_lengths ** 2
        
        plt.figure(figsize=(10, 6))
        plt.plot(sequence_lengths, complexity, 'b-', linewidth=2, marker='o')
        plt.xlabel('序列长度')
        plt.ylabel('计算复杂度')
        plt.title('注意力机制计算复杂度增长 (O(n²))')
        plt.grid(True)
        plt.yscale('log')
        plt.show()
        
        return {
            "lengths": sequence_lengths.tolist(),
            "complexity": complexity.tolist()
        }
    
    def estimate_memory_usage(self, seq_length: int, hidden_size: int = 4096, 
                            num_layers: int = 32, num_heads: int = 32):
        """估计内存使用量"""
        # KV缓存内存估算
        kv_cache_per_token = 2 * hidden_size * num_layers * 2  # 2 bytes per float16
        total_kv_cache = seq_length * kv_cache_per_token / (1024**3)  # GB
        
        # 注意力矩阵内存
        attention_matrix = (seq_length ** 2) * 2 / (1024**3)  # GB
        
        return {
            "sequence_length": seq_length,
            "kv_cache_gb": round(total_kv_cache, 2),
            "attention_matrix_gb": round(attention_matrix, 2),
            "total_estimated_gb": round(total_kv_cache + attention_matrix, 2)
        }

# 演示技术挑战
demonstrator = TechnicalChallengeDemonstrator()
complexity_data = demonstrator.demonstrate_complexity_growth()

# 内存使用估算
memory_usage = demonstrator.estimate_memory_usage(8192)
print("内存使用估算:", memory_usage)

第二章：核心解决方案架构

2.1 分层处理架构

from typing import List, Dict, Any, Optional
import hashlib
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import re

class HierarchicalContextProcessor:
    """分层上下文处理器"""
    
    def __init__(self, max_tokens: int = 4000, chunk_size: int = 1000):
        self.max_tokens = max_tokens
        self.chunk_size = chunk_size
        self.vectorizer = TfidfVectorizer(max_features=1000, stop_words=None)
        
    def process_long_context(self, context: str, query: str = None) -> Dict[str, Any]:
        """处理长上下文"""
        # 1. 文本分块
        chunks = self._split_into_chunks(context)
        print(f"将文本分成 {len(chunks)} 个块")
        
        # 2. 块重要性评分
        scored_chunks = self._score_chunks(chunks, query)
        
        # 3. 选择最重要的块
        selected_chunks = self._select_chunks(scored_chunks)
        
        # 4. 重构上下文
        compressed_context = self._reconstruct_context(selected_chunks)
        
        return {
            "original_length": len(context),
            "compressed_length": len(compressed_context),
            "compression_ratio": len(compressed_context) / len(context),
            "selected_chunks": len(selected_chunks),
            "compressed_context": compressed_context,
            "chunk_scores": scored_chunks
        }
    
    def _split_into_chunks(self, text: str, overlap: int = 100) -> List[str]:
        """将文本分割成重叠的块"""
        words = text.split()
        chunks = []
        
        for i in range(0, len(words), self.chunk_size - overlap):
            chunk = ' '.join(words[i:i + self.chunk_size])
            chunks.append(chunk)
            
            if i + self.chunk_size >= len(words):
                break
                
        return chunks
    
    def _score_chunks(self, chunks: List[str], query: str = None) -> List[Dict]:
        """对块进行重要性评分"""
        scored_chunks = []
        
        # 基于TF-IDF的评分
        if len(chunks) > 1:
            try:
                tfidf_matrix = self.vectorizer.fit_transform(chunks)
                chunk_scores = np.array(tfidf_matrix.sum(axis=1)).flatten()
            except:
                chunk_scores = np.ones(len(chunks))
        else:
            chunk_scores = np.ones(len(chunks))
        
        # 如果有查询，计算相关性
        if query:
            query_vec = self.vectorizer.transform([query])
            similarities = cosine_similarity(query_vec, tfidf_matrix).flatten()
            chunk_scores = chunk_scores * 0.5 + similarities * 0.5
        
        for i, (chunk, score) in enumerate(zip(chunks, chunk_scores)):
            # 额外特征评分
            additional_score = self._calculate_additional_features(chunk)
            final_score = score * 0.7 + additional_score * 0.3
            
            scored_chunks.append({
                "chunk_id": i,
                "content": chunk,
                "base_score": float(score),
                "additional_score": additional_score,
                "final_score": final_score,
                "token_count": len(chunk.split())
            })
        
        return scored_chunks
    
    def _calculate_additional_features(self, chunk: str) -> float:
        """计算额外特征分数"""
        score = 0.0
        
        # 1. 密度评分（信息密度）
        sentences = re.split(r'[。！？!?]', chunk)
        avg_sentence_length = sum(len(s.split()) for s in sentences) / max(len(sentences), 1)
        density_score = min(avg_sentence_length / 20, 1.0)  # 假设20词/句为理想密度
        
        # 2. 关键短语评分
        key_phrases = ['总结', '重要', '关键', '结论', '因此', '所以']
        key_phrase_count = sum(1 for phrase in key_phrases if phrase in chunk)
        phrase_score = min(key_phrase_count / 5, 1.0)
        
        # 3. 数字和事实密度
        number_count = len(re.findall(r'\d+', chunk))
        number_score = min(number_count / 10, 1.0)
        
        # 综合评分
        score = (density_score * 0.4 + phrase_score * 0.3 + number_score * 0.3)
        return score
    
    def _select_chunks(self, scored_chunks: List[Dict]) -> List[Dict]:
        """选择最重要的块"""
        # 按分数排序
        sorted_chunks = sorted(scored_chunks, key=lambda x: x['final_score'], reverse=True)
        
        selected_chunks = []
        total_tokens = 0
        
        for chunk in sorted_chunks:
            if total_tokens + chunk['token_count'] <= self.max_tokens:
                selected_chunks.append(chunk)
                total_tokens += chunk['token_count']
            else:
                break
        
        # 确保至少选择一些内容
        if not selected_chunks and scored_chunks:
            selected_chunks = [scored_chunks[0]]
        
        return selected_chunks
    
    def _reconstruct_context(self, selected_chunks: List[Dict]) -> str:
        """重构压缩后的上下文"""
        # 按原始顺序重新排列选中的块
        selected_chunks.sort(key=lambda x: x['chunk_id'])
        
        reconstructed = ' '.join(chunk['content'] for chunk in selected_chunks)
        return reconstructed

# 使用示例
processor = HierarchicalContextProcessor(max_tokens=2000, chunk_size=800)

# 模拟长文本
long_text = "这是一段很长的文档。" * 500 + "这是关键信息。" + "这是更多内容。" * 300
query = "关键信息"

result = processor.process_long_context(long_text, query)
print(f"压缩比: {result['compression_ratio']:.2%}")
print(f"选中块数: {result['selected_chunks']}")
print(f"压缩后长度: {result['compressed_length']} 字符")

2.2 智能摘要与提取

class SmartSummarizer:
    """智能摘要器"""
    
    def __init__(self, model=None):
        self.model = model
        self.summary_cache = {}
    
    def extractive_summarize(self, text: str, ratio: float = 0.3) -> str:
        """抽取式摘要"""
        from collections import defaultdict
        import networkx as nx
        
        # 分句
        sentences = self._split_sentences(text)
        if len(sentences) <= 1:
            return text
        
        # 构建句子相似度图
        similarity_matrix = self._build_similarity_matrix(sentences)
        
        # 使用TextRank算法
        graph = nx.from_numpy_array(similarity_matrix)
        scores = nx.pagerank(graph)
        
        # 选择重要句子
        ranked_sentences = sorted(
            ((scores[i], i, sent) for i, sent in enumerate(sentences)),
            reverse=True
        )
        
        # 按比例选择句子
        num_selected = max(1, int(len(sentences) * ratio))
        selected_sentences = sorted(
            [item[2] for item in ranked_sentences[:num_selected]],
            key=lambda x: sentences.index(x)
        )
        
        return '。'.join(selected_sentences) + '。'
    
    def _split_sentences(self, text: str) -> List[str]:
        """分句处理"""
        # 简单的中英文分句
        sentences = re.split(r'[。！？!?\.\n]', text)
        sentences = [s.strip() for s in sentences if s.strip()]
        return sentences
    
    def _build_similarity_matrix(self, sentences: List[str]) -> np.ndarray:
        """构建句子相似度矩阵"""
        n = len(sentences)
        matrix = np.zeros((n, n))
        
        for i in range(n):
            for j in range(n):
                if i != j:
                    matrix[i][j] = self._sentence_similarity(sentences[i], sentences[j])
        
        return matrix
    
    def _sentence_similarity(self, sent1: str, sent2: str) -> float:
        """计算句子相似度"""
        words1 = set(sent1.split())
        words2 = set(sent2.split())
        
        if not words1 or not words2:
            return 0.0
        
        intersection = len(words1.intersection(words2))
        union = len(words1.union(words2))
        
        return intersection / union
    
    def abstractive_summarize(self, text: str, max_length: int = 500) -> str:
        """生成式摘要（需要模型支持）"""
        if self.model is None:
            # 如果没有模型，回退到抽取式摘要
            return self.extractive_summarize(text, ratio=0.3)
        
        # 这里可以集成Hugging Face的摘要模型
        # 例如: from transformers import pipeline
        # summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
        # return summarizer(text, max_length=max_length, min_length=30, do_sample=False)[0]['summary_text']
        
        return self.extractive_summarize(text, ratio=0.3)
    
    def hierarchical_summarize(self, long_text: str, levels: int = 2) -> Dict[str, str]:
        """分层摘要"""
        summaries = {}
        current_text = long_text
        
        for level in range(levels):
            ratio = 0.5 ** (level + 1)  # 每层压缩一半
            summary = self.extractive_summarize(current_text, ratio)
            summaries[f'level_{level}'] = {
                'summary': summary,
                'length': len(summary),
                'compression_ratio': len(summary) / len(current_text)
            }
            current_text = summary
        
        return summaries

class ContextCompressor:
    """上下文压缩器"""
    
    def __init__(self):
        self.summarizer = SmartSummarizer()
    
    def compress_conversation(self, conversation: List[Dict], 
                           max_tokens: int = 4000) -> List[Dict]:
        """压缩对话历史"""
        if self._estimate_tokens(conversation) <= max_tokens:
            return conversation
        
        compressed = []
        current_tokens = 0
        
        # 保留最近的对话
        recent_conversation = conversation[-5:]  # 最后5轮
        
        # 压缩早期对话
        early_conversation = conversation[:-5]
        if early_conversation:
            early_text = self._conversation_to_text(early_conversation)
            early_summary = self.summarizer.extractive_summarize(early_text, ratio=0.2)
            
            # 添加摘要作为系统消息
            summary_turn = {
                'role': 'system',
                'content': f'之前对话的摘要: {early_summary}'
            }
            compressed.append(summary_turn)
            current_tokens += self._estimate_tokens([summary_turn])
        
        # 添加最近的对话
        for turn in recent_conversation:
            turn_tokens = self._estimate_tokens([turn])
            if current_tokens + turn_tokens <= max_tokens:
                compressed.append(turn)
                current_tokens += turn_tokens
            else:
                break
        
        return compressed
    
    def _conversation_to_text(self, conversation: List[Dict]) -> str:
        """对话转换为文本"""
        texts = []
        for turn in conversation:
            role = "用户" if turn['role'] == 'user' else "助手"
            texts.append(f"{role}: {turn['content']}")
        return '\n'.join(texts)
    
    def _estimate_tokens(self, conversation: List[Dict]) -> int:
        """估计token数量"""
        total_text = ' '.join(turn['content'] for turn in conversation)
        return len(total_text.split())  # 简化估计

# 使用示例
compressor = ContextCompressor()

# 模拟长对话
long_conversation = [
    {'role': 'user', 'content': '你好'},
    {'role': 'assistant', 'content': '你好！有什么可以帮助你的？'},
    # ... 更多对话轮次
] * 20  # 模拟40轮对话

compressed = compressor.compress_conversation(long_conversation, max_tokens=2000)
print(f"原始对话轮次: {len(long_conversation)}")
print(f"压缩后轮次: {len(compressed)}")

第三章：高级处理技术

3.1 检索增强与记忆网络

import faiss
import pickle
from typing import List, Tuple

class KnowledgeRetriever:
    """知识检索器"""
    
    def __init__(self, embedding_model=None):
        self.embedding_model = embedding_model
        self.index = None
        self.texts = []
        self.metadata = []
        
    def build_index(self, documents: List[str], metadatas: List[Dict] = None):
        """构建检索索引"""
        if not documents:
            return
        
        self.texts = documents
        self.metadata = metadatas or [{}] * len(documents)
        
        # 生成嵌入向量
        if self.embedding_model is None:
            # 使用TF-IDF作为回退方案
            embeddings = self._tfidf_embeddings(documents)
        else:
            embeddings = self.embedding_model.encode(documents)
        
        # 创建FAISS索引
        dimension = embeddings.shape[1]
        self.index = faiss.IndexFlatIP(dimension)  # 内积相似度
        
        # 归一化向量（用于余弦相似度）
        faiss.normalize_L2(embeddings)
        self.index.add(embeddings)
        
        print(f"索引构建完成，包含 {len(documents)} 个文档")
    
    def _tfidf_embeddings(self, documents: List[str]) -> np.ndarray:
        """TF-IDF嵌入向量"""
        from sklearn.feature_extraction.text import TfidfVectorizer
        
        vectorizer = TfidfVectorizer(max_features=1000)
        tfidf_matrix = vectorizer.fit_transform(documents)
        return tfidf_matrix.toarray()
    
    def retrieve(self, query: str, k: int = 5, threshold: float = 0.5) -> List[Tuple[str, float, Dict]]:
        """检索相关文档"""
        if self.index is None or not self.texts:
            return []
        
        # 生成查询向量
        if self.embedding_model:
            query_embedding = self.embedding_model.encode([query])
        else:
            query_embedding = self._tfidf_embeddings([query])
        
        # 归一化
        faiss.normalize_L2(query_embedding)
        
        # 搜索
        scores, indices = self.index.search(query_embedding, k)
        
        results = []
        for score, idx in zip(scores[0], indices[0]):
            if score >= threshold and idx < len(self.texts):
                results.append((
                    self.texts[idx],
                    float(score),
                    self.metadata[idx]
                ))
        
        return results
    
    def save_index(self, filepath: str):
        """保存索引"""
        if self.index is None:
            return
        
        faiss.write_index(self.index, f"{filepath}.index")
        
        with open(f"{filepath}.metadata", 'wb') as f:
            pickle.dump({
                'texts': self.texts,
                'metadata': self.metadata
            }, f)
    
    def load_index(self, filepath: str):
        """加载索引"""
        self.index = faiss.read_index(f"{filepath}.index")
        
        with open(f"{filepath}.metadata", 'rb') as f:
            data = pickle.load(f)
            self.texts = data['texts']
            self.metadata = data['metadata']

class MemoryAugmentedProcessor:
    """记忆增强处理器"""
    
    def __init__(self, retriever: KnowledgeRetriever):
        self.retriever = retriever
        self.conversation_memory = []
        self.important_facts = set()
    
    def process_with_memory(self, query: str, conversation_history: List[Dict], 
                          max_context_tokens: int = 4000) -> Dict[str, Any]:
        """使用记忆增强处理"""
        # 1. 从记忆中检索相关信息
        relevant_memories = self.retrieve_relevant_memories(query)
        
        # 2. 压缩对话历史
        compressed_history = self.compress_conversation(
            conversation_history, max_context_tokens - 1000
        )
        
        # 3. 构建增强上下文
        augmented_context = self.build_augmented_context(
            compressed_history, relevant_memories, query
        )
        
        # 4. 更新记忆
        self.update_memory(query, compressed_history)
        
        return {
            "augmented_context": augmented_context,
            "retrieved_memories": relevant_memories,
            "context_length": len(augmented_context),
            "memory_usage": len(self.conversation_memory)
        }
    
    def retrieve_relevant_memories(self, query: str, top_k: int = 3) -> List[Tuple]:
        """检索相关记忆"""
        # 从外部知识库检索
        external_memories = self.retriever.retrieve(query, k=top_k)
        
        # 从对话记忆中检索
        conversation_texts = [self._turn_to_text(turn) for turn in self.conversation_memory]
        if conversation_texts:
            # 临时构建对话记忆索引
            temp_retriever = KnowledgeRetriever()
            temp_retriever.build_index(conversation_texts)
            conversation_memories = temp_retriever.retrieve(query, k=top_k)
        else:
            conversation_memories = []
        
        return external_memories + conversation_memories
    
    def compress_conversation(self, conversation: List[Dict], max_tokens: int) -> List[Dict]:
        """压缩对话历史"""
        compressor = ContextCompressor()
        return compressor.compress_conversation(conversation, max_tokens)
    
    def build_augmented_context(self, conversation: List[Dict], 
                              memories: List[Tuple], query: str) -> str:
        """构建增强上下文"""
        context_parts = []
        
        # 添加相关记忆
        if memories:
            memory_text = "相关背景信息:\n"
            for memory, score, metadata in memories:
                memory_text += f"- {memory} (相关性: {score:.2f})\n"
            context_parts.append(memory_text)
        
        # 添加压缩后的对话历史
        conversation_text = "对话历史:\n"
        for turn in conversation:
            role = "用户" if turn['role'] == 'user' else "助手"
            conversation_text += f"{role}: {turn['content']}\n"
        context_parts.append(conversation_text)
        
        # 添加当前查询
        context_parts.append(f"当前问题: {query}")
        
        return "\n\n".join(context_parts)
    
    def update_memory(self, current_query: str, conversation: List[Dict]):
        """更新记忆"""
        # 提取重要信息添加到长期记忆
        important_info = self.extract_important_info(conversation)
        self.important_facts.update(important_info)
        
        # 更新对话记忆（限制大小）
        self.conversation_memory.extend(conversation[-3:])  # 保留最近3轮
        if len(self.conversation_memory) > 20:  # 限制总大小
            self.conversation_memory = self.conversation_memory[-20:]
    
    def extract_important_info(self, conversation: List[Dict]) -> List[str]:
        """提取重要信息"""
        important_info = []
        
        for turn in conversation:
            content = turn['content']
            # 简单的重要信息提取规则
            if any(keyword in content for keyword in ['重要', '关键', '记住', '需要知道']):
                important_info.append(content)
            
            # 提取数字事实
            numbers = re.findall(r'\b\d+\b', content)
            if numbers and len(content) < 100:  # 短文本中的数字更可能是重要事实
                important_info.append(content)
        
        return important_info
    
    def _turn_to_text(self, turn: Dict) -> str:
        """对话轮次转文本"""
        return f"{turn['role']}: {turn['content']}"

# 使用示例
# 初始化检索器
retriever = KnowledgeRetriever()
documents = [
    "Python是一种高级编程语言",
    "机器学习是人工智能的一个分支", 
    "深度学习使用神经网络",
    "自然语言处理涉及文本分析"
]
retriever.build_index(documents)

# 初始化记忆增强处理器
memory_processor = MemoryAugmentedProcessor(retriever)

# 处理长对话
conversation_history = [
    {'role': 'user', 'content': '我想了解人工智能'},
    {'role': 'assistant', 'content': '人工智能包括多个领域如机器学习和自然语言处理'},
    # ... 更多历史
] * 10

result = memory_processor.process_with_memory(
    "深度学习是什么？", 
    conversation_history,
    max_context_tokens=3000
)

print(f"增强上下文长度: {result['context_length']}")
print(f"检索到的记忆数量: {len(result['retrieved_memories'])}")

3.2 滑动窗口与动态上下文

class DynamicContextManager:
    """动态上下文管理器"""
    
    def __init__(self, max_context_tokens: int = 4000, 
                 min_keep_tokens: int = 500,
                 strategy: str = "sliding_window"):
        self.max_context_tokens = max_context_tokens
        self.min_keep_tokens = min_keep_tokens
        self.strategy = strategy
        self.conversation_buffer = []
    
    def add_to_buffer(self, role: str, content: str):
        """添加到对话缓冲区"""
        self.conversation_buffer.append({
            'role': role,
            'content': content,
            'tokens': self.estimate_tokens(content),
            'timestamp': time.time()
        })
    
    def get_optimized_context(self, query: str = None) -> List[Dict]:
        """获取优化后的上下文"""
        if self.strategy == "sliding_window":
            return self._sliding_window_strategy()
        elif self.strategy == "importance_based":
            return self._importance_based_strategy(query)
        elif self.strategy == "hybrid":
            return self._hybrid_strategy(query)
        else:
            return self._sliding_window_strategy()
    
    def _sliding_window_strategy(self) -> List[Dict]:
        """滑动窗口策略"""
        current_tokens = 0
        selected_turns = []
        
        # 从最新开始选择
        for turn in reversed(self.conversation_buffer):
            if current_tokens + turn['tokens'] <= self.max_context_tokens:
                selected_turns.append(turn)
                current_tokens += turn['tokens']
            else:
                break
        
        # 恢复原始顺序
        selected_turns.reverse()
        return selected_turns
    
    def _importance_based_strategy(self, query: str) -> List[Dict]:
        """基于重要性的策略"""
        if not self.conversation_buffer:
            return []
        
        # 计算每个轮次的重要性分数
        scored_turns = []
        for i, turn in enumerate(self.conversation_buffer):
            score = self._calculate_turn_importance(turn, query, i)
            scored_turns.append((score, turn))
        
        # 按分数排序
        scored_turns.sort(key=lambda x: x[0], reverse=True)
        
        # 选择最重要的轮次
        selected_turns = []
        current_tokens = 0
        
        for score, turn in scored_turns:
            if current_tokens + turn['tokens'] <= self.max_context_tokens:
                selected_turns.append(turn)
                current_tokens += turn['tokens']
        
        # 按时间顺序重新排列
        selected_turns.sort(key=lambda x: x['timestamp'])
        return selected_turns
    
    def _hybrid_strategy(self, query: str) -> List[Dict]:
        """混合策略"""
        # 保留最近的几个轮次
        recent_turns = self.conversation_buffer[-3:] if len(self.conversation_buffer) >= 3 else self.conversation_buffer.copy()
        recent_tokens = sum(turn['tokens'] for turn in recent_turns)
        
        # 从剩余轮次中选择重要的
        remaining_turns = self.conversation_buffer[:-3] if len(self.conversation_buffer) >= 3 else []
        remaining_budget = self.max_context_tokens - recent_tokens
        
        if remaining_budget > 0 and remaining_turns:
            # 计算重要性
            scored_remaining = []
            for turn in remaining_turns:
                score = self._calculate_turn_importance(turn, query, 0)  # 位置不重要了
                scored_remaining.append((score, turn))
            
            scored_remaining.sort(key=lambda x: x[0], reverse=True)
            
            for score, turn in scored_remaining:
                if remaining_budget >= turn['tokens']:
                    recent_turns.append(turn)
                    remaining_budget -= turn['tokens']
                else:
                    break
        
        # 按时间排序
        recent_turns.sort(key=lambda x: x['timestamp'])
        return recent_turns
    
    def _calculate_turn_importance(self, turn: Dict, query: str, position: int) -> float:
        """计算轮次重要性"""
        score = 0.0
        
        # 1. 与查询的相关性
        if query and turn['role'] == 'user':
            similarity = self._text_similarity(turn['content'], query)
            score += similarity * 0.4
        
        # 2. 位置权重（越近越重要）
        recency_weight = (position + 1) / len(self.conversation_buffer)
        score += recency_weight * 0.3
        
        # 3. 内容特征
        content = turn['content']
        if any(keyword in content for keyword in ['总结', '重要', '关键']):
            score += 0.2
        
        # 4. 轮次类型权重
        if turn['role'] == 'user':
            score += 0.1
        
        return score
    
    def _text_similarity(self, text1: str, text2: str) -> float:
        """文本相似度"""
        words1 = set(text1.split())
        words2 = set(text2.split())
        
        if not words1 or not words2:
            return 0.0
        
        intersection = len(words1.intersection(words2))
        union = len(words1.union(words2))
        
        return intersection / union
    
    def estimate_tokens(self, text: str) -> int:
        """估计token数量"""
        return len(text.split())  # 简化估计
    
    def clear_buffer(self):
        """清空缓冲区"""
        self.conversation_buffer = []

# 使用示例
context_manager = DynamicContextManager(
    max_context_tokens=3000,
    strategy="hybrid"
)

# 模拟添加对话
for i in range(20):
    role = 'user' if i % 2 == 0 else 'assistant'
    content = f"这是第{i+1}轮对话的内容，包含一些重要的信息。" * (i % 3 + 1)
    context_manager.add_to_buffer(role, content)

# 获取优化后的上下文
optimized_context = context_manager.get_optimized_context("重要信息")
print(f"优化后上下文轮次: {len(optimized_context)}")
print(f"缓冲区总轮次: {len(context_manager.conversation_buffer)}")

第四章：工程实现与优化

4.1 高效注意力机制

import torch
import torch.nn as nn
import math

class EfficientAttentionMechanism:
    """高效注意力机制实现"""
    
    def __init__(self, segment_size: int = 512, overlap: int = 50):
        self.segment_size = segment_size
        self.overlap = overlap
    
    def segmented_attention(self, query: torch.Tensor, key: torch.Tensor, 
                          value: torch.Tensor, mask: torch.Tensor = None) -> torch.Tensor:
        """分段注意力计算"""
        batch_size, seq_len, dim = query.shape
        
        if seq_len <= self.segment_size:
            # 序列不长，直接计算
            return self._standard_attention(query, key, value, mask)
        
        # 分段处理
        num_segments = math.ceil((seq_len - self.overlap) / (self.segment_size - self.overlap))
        outputs = []
        
        for i in range(num_segments):
            start = i * (self.segment_size - self.overlap)
            end = min(start + self.segment_size, seq_len)
            
            # 提取段
            seg_query = query[:, start:end, :]
            seg_key = key[:, max(0, start - self.overlap):end, :]
            seg_value = value[:, max(0, start - self.overlap):end, :]
            
            # 计算段注意力
            seg_mask = None
            if mask is not None:
                seg_mask = mask[:, max(0, start - self.overlap):end]
            
            seg_output = self._standard_attention(seg_query, seg_key, seg_value, seg_mask)
            outputs.append(seg_output)
        
        # 合并输出（处理重叠部分）
        final_output = self._merge_segments(outputs, seq_len, self.overlap)
        return final_output
    
    def _standard_attention(self, query: torch.Tensor, key: torch.Tensor,
                          value: torch.Tensor, mask: torch.Tensor = None) -> torch.Tensor:
        """标准注意力计算"""
        scores = torch.bmm(query, key.transpose(1, 2)) / math.sqrt(query.size(-1))
        
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)
        
        attention_weights = torch.softmax(scores, dim=-1)
        output = torch.bmm(attention_weights, value)
        
        return output
    
    def _merge_segments(self, segments: List[torch.Tensor], total_len: int, 
                       overlap: int) -> torch.Tensor:
        """合并分段结果"""
        if not segments:
            return torch.tensor([])
        
        batch_size, seg_len, dim = segments[0].shape
        output = torch.zeros(batch_size, total_len, dim)
        
        for i, seg in enumerate(segments):
            start = i * (seg_len - overlap)
            end = start + seg_len
            
            if i == 0:
                # 第一段直接复制
                output[:, start:end] = seg
            else:
                # 处理重叠部分：加权平均
                overlap_start = start
                overlap_end = start + overlap
                
                # 重叠部分权重
                weights = torch.linspace(0, 1, overlap).unsqueeze(0).unsqueeze(-1)
                weights = weights.expand(batch_size, overlap, dim)
                
                # 加权合并重叠部分
                output[:, overlap_start:overlap_end] = (
                    weights * seg[:, :overlap] + 
                    (1 - weights) * output[:, overlap_start:overlap_end]
                )
                
                # 非重叠部分直接复制
                output[:, overlap_end:end] = seg[:, overlap:]
        
        return output

class LongContextTransformer:
    """长上下文Transformer"""
    
    def __init__(self, d_model: int = 512, nhead: int = 8, 
                 num_layers: int = 6, segment_size: int = 512):
        self.d_model = d_model
        self.nhead = nhead
        self.num_layers = num_layers
        self.segment_size = segment_size
        
        # 注意力层
        self.self_attentions = nn.ModuleList([
            EfficientAttentionMechanism(segment_size) 
            for _ in range(num_layers)
        ])
        
        # 前馈网络
        self.feed_forward = nn.Sequential(
            nn.Linear(d_model, d_model * 4),
            nn.ReLU(),
            nn.Linear(d_model * 4, d_model)
        )
        
        # 层归一化
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
    
    def forward(self, x: torch.Tensor, mask: torch.Tensor = None) -> torch.Tensor:
        """前向传播"""
        for i in range(self.num_layers):
            # 自注意力
            attn_output = self.self_attentions[i](
                self.norm1(x), self.norm1(x), self.norm1(x), mask
            )
            x = x + attn_output
            
            # 前馈网络
            ff_output = self.feed_forward(self.norm2(x))
            x = x + ff_output
        
        return x

# 内存优化的KV缓存
class MemoryEfficientKVCache:
    """内存高效的KV缓存"""
    
    def __init__(self, max_size: int = 10000, compression_ratio: float = 0.5):
        self.max_size = max_size
        self.compression_ratio = compression_ratio
        self.cache = {}
        self.access_count = {}
    
    def get(self, key: str) -> Optional[torch.Tensor]:
        """获取缓存值"""
        if key in self.cache:
            self.access_count[key] += 1
            return self.cache[key]
        return None
    
    def set(self, key: str, value: torch.Tensor):
        """设置缓存值"""
        if len(self.cache) >= self.max_size:
            self._evict_least_used()
        
        self.cache[key] = value
        self.access_count[key] = 1
    
    def _evict_least_used(self):
        """淘汰最少使用的项"""
        if not self.cache:
            return
        
        # 找到访问次数最少的key
        min_key = min(self.access_count.items(), key=lambda x: x[1])[0]
        
        # 压缩而不是完全删除
        if self.compression_ratio < 1.0:
            self._compress_value(min_key)
        else:
            del self.cache[min_key]
            del self.access_count[min_key]
    
    def _compress_value(self, key: str):
        """压缩缓存值"""
        original_value = self.cache[key]
        
        # 简单的压缩：降采样或量化
        if len(original_value.shape) > 1:
            # 对序列维度进行降采样
            seq_len = original_value.shape[1]
            compressed_len = max(1, int(seq_len * self.compression_ratio))
            
            if compressed_len < seq_len:
                # 均匀采样
                indices = torch.linspace(0, seq_len-1, compressed_len).long()
                compressed_value = original_value[:, indices, :]
                self.cache[key] = compressed_value
    
    def clear(self):
        """清空缓存"""
        self.cache.clear()
        self.access_count.clear()

4.2 流式处理与增量更新

class StreamingContextProcessor:
    """流式上下文处理器"""
    
    def __init__(self, chunk_size: int = 1000, overlap: int = 100):
        self.chunk_size = chunk_size
        self.overlap = overlap
        self.processed_chunks = []
        self.current_chunk = ""
        self.summary_state = ""
    
    def process_stream(self, text_stream, query: str = None):
        """处理文本流"""
        for text_chunk in text_stream:
            self.current_chunk += text_chunk
            
            # 当当前块达到大小时处理
            if len(self.current_chunk.split()) >= self.chunk_size:
                processed = self._process_chunk(self.current_chunk, query)
                self.processed_chunks.append(processed)
                
                # 更新摘要状态
                self._update_summary_state(processed)
                
                # 保留重叠部分用于下一个块
                words = self.current_chunk.split()
                overlap_words = words[-self.overlap:] if len(words) > self.overlap else words
                self.current_chunk = ' '.join(overlap_words)
        
        # 处理剩余部分
        if self.current_chunk.strip():
            processed = self._process_chunk(self.current_chunk, query)
            self.processed_chunks.append(processed)
            self._update_summary_state(processed)
    
    def _process_chunk(self, chunk: str, query: str = None) -> Dict:
        """处理单个块"""
        # 提取关键信息
        key_sentences = self._extract_key_sentences(chunk, query)
        
        # 生成块摘要
        chunk_summary = self._generate_chunk_summary(chunk)
        
        return {
            'content': chunk,
            'key_sentences': key_sentences,
            'summary': chunk_summary,
            'token_count': len(chunk.split()),
            'timestamp': time.time()
        }
    
    def _extract_key_sentences(self, chunk: str, query: str = None) -> List[str]:
        """提取关键句子"""
        sentences = re.split(r'[。！？!?]', chunk)
        sentences = [s.strip() for s in sentences if s.strip()]
        
        if not sentences:
            return []
        
        # 简单的关键句提取
        key_sentences = []
        for sentence in sentences:
            # 基于查询相关性
            if query and self._sentence_relevance(sentence, query) > 0.3:
                key_sentences.append(sentence)
            # 基于内容特征
            elif any(keyword in sentence for keyword in ['重要', '关键', '总结', '因此']):
                key_sentences.append(sentence)
            # 基于长度（避免太短或太长的句子）
            elif 10 <= len(sentence.split()) <= 50:
                key_sentences.append(sentence)
        
        return key_sentences[:3]  # 最多返回3个关键句
    
    def _sentence_relevance(self, sentence: str, query: str) -> float:
        """句子相关性"""
        if not query:
            return 0.0
        
        sentence_words = set(sentence.split())
        query_words = set(query.split())
        
        if not sentence_words or not query_words:
            return 0.0
        
        intersection = len(sentence_words.intersection(query_words))
        return intersection / len(query_words)
    
    def _generate_chunk_summary(self, chunk: str) -> str:
        """生成块摘要"""
        sentences = re.split(r'[。！？!?]', chunk)
        sentences = [s.strip() for s in sentences if s.strip()]
        
        if len(sentences) <= 2:
            return chunk
        
        # 选择首句、尾句和中间的重要句子
        summary_sentences = []
        if sentences:
            summary_sentences.append(sentences[0])  # 首句
            summary_sentences.append(sentences[-1])  # 尾句
            
            # 中间的重要句子
            if len(sentences) > 3:
                middle_idx = len(sentences) // 2
                summary_sentences.append(sentences[middle_idx])
        
        return '。'.join(summary_sentences) + '。'
    
    def _update_summary_state(self, processed_chunk: Dict):
        """更新摘要状态"""
        # 将新摘要与现有状态合并
        new_info = processed_chunk['summary']
        
        if not self.summary_state:
            self.summary_state = new_info
        else:
            # 简单的摘要合并
            current_sentences = self.summary_state.split('。')
            new_sentences = new_info.split('。')
            
            # 保留最重要的句子（避免太长）
            all_sentences = current_sentences + new_sentences
            all_sentences = [s for s in all_sentences if s.strip()]
            
            # 去重和选择
            unique_sentences = list(dict.fromkeys(all_sentences))  # 保持顺序的去重
            self.summary_state = '。'.join(unique_sentences[:5]) + '。'  # 最多5句
    
    def get_current_context(self, max_tokens: int = 3000) -> str:
        """获取当前上下文"""
        context_parts = []
        
        # 添加全局摘要
        if self.summary_state:
            context_parts.append(f"全局摘要: {self.summary_state}")
        
        # 添加最近的处理块
        recent_chunks = self.processed_chunks[-3:]  # 最近3个块
        for chunk in recent_chunks:
            context_parts.append(f"最近内容: {chunk['content']}")
        
        # 合并并截断
        full_context = '\n\n'.join(context_parts)
        words = full_context.split()
        
        if len(words) > max_tokens:
            words = words[:max_tokens]
            full_context = ' '.join(words)
        
        return full_context

# 使用示例
def simulate_text_stream():
    """模拟文本流"""
    paragraphs = [
        "第一段内容。这是关于人工智能的介绍。机器学习是重要组成部分。",
        "第二段讨论深度学习。神经网络有很多层。训练需要大量数据。",
        "第三段涉及自然语言处理。Transformer架构很关键。注意力机制很重要。",
        "第四段讲应用场景。包括聊天机器人、翻译系统等。未来很有前景。"
    ] * 5  # 重复5次模拟长文本
    
    for para in paragraphs:
        yield para

# 流式处理
stream_processor = StreamingContextProcessor(chunk_size=50, overlap=10)
text_stream = simulate_text_stream()

stream_processor.process_stream(text_stream, query="机器学习")
current_context = stream_processor.get_current_context()

print(f"处理块数量: {len(stream_processor.processed_chunks)}")
print(f"当前上下文长度: {len(current_context)} 字符")
print(f"全局摘要: {stream_processor.summary_state}")

第五章：实战应用与性能评估

5.1 完整解决方案集成

class LongContextSolution:
    """长上下文完整解决方案"""
    
    def __init__(self, model=None, embedding_model=None):
        self.model = model
        self.embedding_model = embedding_model
        
        # 初始化各个组件
        self.hierarchical_processor = HierarchicalContextProcessor()
        self.context_compressor = ContextCompressor()
        self.dynamic_manager = DynamicContextManager()
        self.memory_processor = MemoryAugmentedProcessor(
            KnowledgeRetriever(embedding_model)
        )
        self.stream_processor = StreamingContextProcessor()
        
        # 配置
        self.config = {
            'max_context_tokens': 4000,
            'compression_enabled': True,
            'memory_augmentation': True,
            'streaming_processing': False
        }
    
    def process_long_text(self, text: str, query: str = None, 
                         conversation_history: List[Dict] = None) -> Dict[str, Any]:
        """处理长文本"""
        start_time = time.time()
        
        # 1. 根据配置选择处理策略
        if self.config['streaming_processing'] and len(text.split()) > 10000:
            processed_context = self._streaming_process(text, query)
        else:
            processed_context = self._batch_process(text, query)
        
        # 2. 如果有对话历史，进行集成
        if conversation_history and self.config['memory_augmentation']:
            final_context = self._integrate_conversation_history(
                processed_context, conversation_history, query
            )
        else:
            final_context = processed_context
        
        processing_time = time.time() - start_time
        
        return {
            'processed_context': final_context,
            'original_length': len(text),
            'processed_length': len(final_context),
            'compression_ratio': len(final_context) / len(text),
            'processing_time': processing_time,
            'strategy_used': 'streaming' if self.config['streaming_processing'] else 'batch'
        }
    
    def _streaming_process(self, text: str, query: str) -> str:
        """流式处理"""
        # 模拟文本流
        words = text.split()
        chunk_size = 1000
        
        def text_stream():
            for i in range(0, len(words), chunk_size):
                yield ' '.join(words[i:i + chunk_size])
        
        self.stream_processor.process_stream(text_stream(), query)
        return self.stream_processor.get_current_context()
    
    def _batch_process(self, text: str, query: str) -> str:
        """批处理"""
        # 分层处理
        hierarchical_result = self.hierarchical_processor.process_long_context(text, query)
        
        if hierarchical_result['compression_ratio'] < 0.8:
            # 压缩效果较好，使用压缩结果
            return hierarchical_result['compressed_context']
        else:
            # 压缩效果不佳，使用智能摘要
            summarizer = SmartSummarizer(self.model)
            return summarizer.extractive_summarize(text, ratio=0.3)
    
    def _integrate_conversation_history(self, processed_context: str,
                                      conversation_history: List[Dict],
                                      query: str) -> str:
        """集成对话历史"""
        memory_result = self.memory_processor.process_with_memory(
            query, conversation_history, 
            self.config['max_context_tokens'] - 1000
        )
        
        # 合并处理后的文本和记忆增强的上下文
        integrated_context = f"""
处理后的文档内容:
{processed_context}

{memory_result['augmented_context']}
"""
        
        # 确保不超过长度限制
        words = integrated_context.split()
        if len(words) > self.config['max_context_tokens']:
            words = words[:self.config['max_context_tokens']]
            integrated_context = ' '.join(words)
        
        return integrated_context
    
    def evaluate_solution(self, test_cases: List[Dict]) -> Dict[str, Any]:
        """评估解决方案"""
        evaluation_results = {
            'compression_ratios': [],
            'processing_times': [],
            'quality_scores': [],
            'success_rates': []
        }
        
        for test_case in test_cases:
            try:
                result = self.process_long_text(
                    test_case['text'],
                    test_case.get('query'),
                    test_case.get('conversation_history')
                )
                
                evaluation_results['compression_ratios'].append(result['compression_ratio'])
                evaluation_results['processing_times'].append(result['processing_time'])
                
                # 质量评估（简化版）
                quality_score = self._evaluate_quality(
                    test_case['text'], result['processed_context'], test_case.get('query')
                )
                evaluation_results['quality_scores'].append(quality_score)
                evaluation_results['success_rates'].append(1.0)
                
            except Exception as e:
                print(f"测试用例处理失败: {e}")
                evaluation_results['success_rates'].append(0.0)
        
        # 计算统计指标
        stats = {
            'avg_compression_ratio': np.mean(evaluation_results['compression_ratios']),
            'avg_processing_time': np.mean(evaluation_results['processing_times']),
            'avg_quality_score': np.mean(evaluation_results['quality_scores']),
            'success_rate': np.mean(evaluation_results['success_rates']),
            'total_test_cases': len(test_cases)
        }
        
        return stats
    
    def _evaluate_quality(self, original: str, processed: str, query: str = None) -> float:
        """评估处理质量"""
        quality_metrics = {}
        
        # 1. 信息保留度
        original_words = set(original.split())
        processed_words = set(processed.split())
        overlap = len(original_words.intersection(processed_words))
        information_retention = overlap / len(original_words) if original_words else 0.0
        quality_metrics['information_retention'] = information_retention
        
        # 2. 连贯性（简单评估）
        coherence_score = self._evaluate_coherence(processed)
        quality_metrics['coherence'] = coherence_score
        
        # 3. 相关性（如果有查询）
        if query:
            relevance_score = self._evaluate_relevance(processed, query)
            quality_metrics['relevance'] = relevance_score
        else:
            quality_metrics['relevance'] = 0.5  # 默认值
        
        # 综合质量分数
        final_score = (
            quality_metrics['information_retention'] * 0.4 +
            quality_metrics['coherence'] * 0.3 +
            quality_metrics['relevance'] * 0.3
        )
        
        return final_score
    
    def _evaluate_coherence(self, text: str) -> float:
        """评估连贯性"""
        sentences = re.split(r'[。！？!?]', text)
        if len(sentences) <= 1:
            return 1.0
        
        # 简单的连贯性评估：检查相邻句子的词汇重叠
        overlap_scores = []
        for i in range(len(sentences) - 1):
            words1 = set(sentences[i].split())
            words2 = set(sentences[i+1].split())
            
            if words1 and words2:
                overlap = len(words1.intersection(words2)) / len(words1.union(words2))
                overlap_scores.append(overlap)
        
        return np.mean(overlap_scores) if overlap_scores else 0.5
    
    def _evaluate_relevance(self, text: str, query: str) -> float:
        """评估相关性"""
        text_words = set(text.split())
        query_words = set(query.split())
        
        if not text_words or not query_words:
            return 0.0
        
        intersection = len(text_words.intersection(query_words))
        return intersection / len(query_words)

# 使用示例
solution = LongContextSolution()

# 测试用例
test_cases = [
    {
        'text': "这是一篇长文档。" * 1000 + "关键信息在这里。" + "更多内容。" * 500,
        'query': "关键信息",
        'conversation_history': [
            {'role': 'user', 'content': '之前讨论过相关话题'},
            {'role': 'assistant', 'content': '是的，我们讨论过人工智能'}
        ]
    }
]

# 处理长文本
result = solution.process_long_text(
    test_cases[0]['text'],
    test_cases[0]['query'],
    test_cases[0]['conversation_history']
)

print(f"处理结果: {result}")

# 评估解决方案
evaluation = solution.evaluate_solution(test_cases)
print(f"解决方案评估: {evaluation}")

5.2 性能优化建议

class PerformanceOptimizer:
    """性能优化器"""
    
    def __init__(self):
        self.optimization_strategies = {
            '内存优化': [
                '使用梯度检查点',
                '激活值量化',
                'KV缓存压缩',
                '模型分片'
            ],
            '计算优化': [
                '注意力优化',
                '算子融合', 
                '混合精度训练',
                '流水线并行'
            ],
            '算法优化': [
                '早期退出',
                '自适应计算',
                '模型蒸馏',
                '动态批处理'
            ]
        }
    
    def analyze_bottlenecks(self, processing_stats: Dict) -> List[str]:
        """分析性能瓶颈"""
        bottlenecks = []
        
        # 内存瓶颈分析
        if processing_stats.get('memory_usage', 0) > 0.8:  # 假设80%为阈值
            bottlenecks.append("内存使用过高")
        
        # 计算瓶颈分析  
        if processing_stats.get('computation_time', 0) > processing_stats.get('total_time', 1) * 0.7:
            bottlenecks.append("计算时间过长")
        
        # I/O瓶颈分析
        if processing_stats.get('io_time', 0) > processing_stats.get('total_time', 1) * 0.3:
            bottlenecks.append("I/O操作频繁")
        
        return bottlenecks
    
    def suggest_optimizations(self, bottlenecks: List[str], context_length: int) -> Dict[str, List[str]]:
        """建议优化方案"""
        optimizations = {}
        
        for bottleneck in bottlenecks:
            if bottleneck == "内存使用过高":
                optimizations[bottleneck] = [
                    "启用KV缓存压缩",
                    "使用梯度检查点",
                    "降低批处理大小",
                    "使用模型量化"
                ]
            elif bottleneck == "计算时间过长":
                optimizations[bottleneck] = [
                    "使用分段注意力",
                    "启用混合精度",
                    "优化注意力计算",
                    "使用早期退出策略"
                ]
            elif bottleneck == "I/O操作频繁":
                optimizations[bottleneck] = [
                    "增加缓存大小",
                    "使用内存映射文件",
                    "批量数据加载",
                    "预加载常用数据"
                ]
        
        # 基于上下文长度的额外建议
        if context_length > 8000:
            optimizations['长上下文处理'] = [
                "启用流式处理",
                "使用分层摘要",
                "实现滑动窗口",
                "优化位置编码"
            ]
        
        return optimizations
    
    def generate_optimization_plan(self, current_config: Dict, 
                                 target_metrics: Dict) -> Dict[str, Any]:
        """生成优化计划"""
        optimization_plan = {
            '短期优化': [],
            '中期优化': [],
            '长期优化': []
        }
        
        # 短期优化（配置调整）
        if current_config.get('max_context_tokens', 0) < target_metrics.get('desired_context_length', 0):
            optimization_plan['短期优化'].append(
                "调整max_context_tokens配置参数"
            )
        
        # 中期优化（算法改进）
        if target_metrics.get('require_low_latency', False):
            optimization_plan['中期优化'].append(
                "实现动态上下文管理算法"
            )
        
        # 长期优化（架构升级）
        if target_metrics.get('scale_requirement', 'medium') == 'high':
            optimization_plan['长期优化'].append(
                "升级到支持更长上下文的模型架构"
            )
        
        return optimization_plan

# 性能监控器
class PerformanceMonitor:
    """性能监控器"""
    
    def __init__(self):
        self.metrics_history = []
        self.alert_thresholds = {
            'memory_usage': 0.8,
            'processing_time': 10.0,  # 秒
            'compression_ratio': 0.1,
            'error_rate': 0.05
        }
    
    def record_metrics(self, metrics: Dict):
        """记录性能指标"""
        self.metrics_history.append({
            'timestamp': time.time(),
            'metrics': metrics
        })
        
        # 检查警报条件
        alerts = self._check_alerts(metrics)
        if alerts:
            self._trigger_alerts(alerts)
    
    def _check_alerts(self, metrics: Dict) -> List[str]:
        """检查警报条件"""
        alerts = []
        
        if metrics.get('memory_usage', 0) > self.alert_thresholds['memory_usage']:
            alerts.append("内存使用超过阈值")
        
        if metrics.get('processing_time', 0) > self.alert_thresholds['processing_time']:
            alerts.append("处理时间过长")
        
        if metrics.get('compression_ratio', 1.0) < self.alert_thresholds['compression_ratio']:
            alerts.append("压缩率过低")
        
        if metrics.get('error_rate', 0) > self.alert_thresholds['error_rate']:
            alerts.append("错误率过高")
        
        return alerts
    
    def _trigger_alerts(self, alerts: List[str]):
        """触发警报"""
        for alert in alerts:
            print(f"🚨 性能警报: {alert}")
    
    def generate_performance_report(self, time_window: int = 3600) -> Dict:
        """生成性能报告"""
        current_time = time.time()
        recent_metrics = [
            m for m in self.metrics_history 
            if current_time - m['timestamp'] <= time_window
        ]
        
        if not recent_metrics:
            return {}
        
        report = {
            'time_window': time_window,
            'total_operations': len(recent_metrics),
            'avg_processing_time': np.mean([m['metrics'].get('processing_time', 0) for m in recent_metrics]),
            'avg_memory_usage': np.mean([m['metrics'].get('memory_usage', 0) for m in recent_metrics]),
            'avg_compression_ratio': np.mean([m['metrics'].get('compression_ratio', 0) for m in recent_metrics]),
            'alert_count': sum(len(self._check_alerts(m['metrics'])) for m in recent_metrics)
        }
        
        return report

总结

本文全面探讨了大模型上下文长度限制的解决方案，从基础理论到高级技术，提供了完整的处理框架：

核心解决方案

1. 分层处理架构：将长文本分解为可管理的块
2. 智能摘要与压缩：保留关键信息，去除冗余内容
3. 检索增强记忆：使用外部知识库补充上下文
4. 动态上下文管理：根据重要性动态选择内容
5. 流式处理：实时处理超长文本流

关键技术亮点

• 分段注意力机制：突破O(n²)计算复杂度限制
• 记忆增强架构：解决长期依赖问题
• 自适应压缩算法：平衡信息保留与长度限制
• 多策略融合：根据场景选择最优处理方案

实践建议

1. 评估需求：首先确定实际需要的上下文长度
2. 渐进优化：从简单方案开始，逐步应用高级技术
3. 监控性能：持续监控并优化处理效果
4. 权衡取舍：在压缩率、信息保留和计算成本间找到平衡

未来展望

随着模型技术的不断发展，长上下文处理将继续演进：

• 更高效的位置编码方案
• 改进的注意力机制
• 自适应计算技术
• 多模态长上下文处理

通过实施本文介绍的技术方案，可以显著提升大模型在处理长文本和多轮对话中的能力，突破上下文长度限制，为复杂应用场景提供有力支持。