1. 面试题目 #

在构建基于大型语言模型（LLM）的对话系统时，如何有效处理过长的对话上下文（Long Context）问题？请详细阐述至少五种主流的处理策略，并对每种策略的核心原理、实现方法、优缺点以及典型适用场景进行深入分析。同时，请结合实际项目经验，说明如何根据具体需求选择和组合这些策略。

2. 参考答案 #

2.1 引言 #

在大型语言模型（LLM）驱动的对话系统中，对话上下文的长度是一个关键挑战。随着对话轮次的增加，上下文可能会超出模型的输入限制，导致信息丢失或推理能力下降。有效处理过长的对话上下文是构建高效、连贯对话系统的基础。本文将详细阐述几种主流的处理策略。

2.2 主流的对话上下文处理策略 #

2.2.1 截断 (Truncation) #

核心原理： 直接保留对话中最近的一部分内容，而丢弃较早的内容。这种方法基于假设，即最近的对话内容与当前回复的相关性最高。

实现方法： 设置一个固定的上下文长度限制（例如，最大Token数或消息条数）。当对话超出此限制时，从对话的开头部分开始删除旧消息，直到满足长度要求。

def truncate_context(dialogue_history, max_tokens=4000):
    """截断对话上下文"""

    # 计算当前上下文长度
    current_tokens = sum(len(turn['content'].split()) for turn in dialogue_history)

    if current_tokens <= max_tokens:
        return dialogue_history

    # 从最新内容开始保留
    truncated_history = []
    token_count = 0

    for turn in reversed(dialogue_history):
        turn_tokens = len(turn['content'].split())
        if token_count + turn_tokens <= max_tokens:
            truncated_history.insert(0, turn)
            token_count += turn_tokens
        else:
            break

    return truncated_history

def smart_truncation(dialogue_history, max_tokens=4000, keep_important=True):
    """智能截断：保留重要信息"""

    if not keep_important:
        return truncate_context(dialogue_history, max_tokens)

    # 识别重要对话轮次
    important_turns = identify_important_turns(dialogue_history)

    # 优先保留重要轮次
    truncated_history = []
    token_count = 0

    # 先添加重要轮次
    for turn in important_turns:
        turn_tokens = len(turn['content'].split())
        if token_count + turn_tokens <= max_tokens:
            truncated_history.append(turn)
            token_count += turn_tokens

    # 再添加最近的轮次
    for turn in reversed(dialogue_history):
        if turn not in truncated_history:
            turn_tokens = len(turn['content'].split())
            if token_count + turn_tokens <= max_tokens:
                truncated_history.append(turn)
                token_count += turn_tokens
            else:
                break

    return truncated_history

def identify_important_turns(dialogue_history):
    """识别重要对话轮次"""

    important_turns = []

    for turn in dialogue_history:
        # 基于关键词识别重要内容
        important_keywords = ['问题', '需求', '要求', '重要', '关键', '紧急']
        if any(keyword in turn['content'] for keyword in important_keywords):
            important_turns.append(turn)

        # 基于长度识别重要内容
        if len(turn['content']) > 100:
            important_turns.append(turn)

        # 基于用户标记识别重要内容
        if turn.get('important', False):
            important_turns.append(turn)

    return important_turns

优点： 实现简单，计算开销小，易于部署。

缺点： 可能会丢失早期但重要的上下文信息，导致对话连贯性受损，尤其是在需要回顾早期细节的场景中。

适用场景： 实时聊天机器人、在线客服系统等对实时性要求高，且近期对话相关性更强的场景。

2.2.2 摘要 (Summarization) #

核心原理： 通过生成对话摘要的方式，将冗长的上下文压缩成精炼的关键信息。这有助于在减少上下文长度的同时，尽可能保留对话的核心要点。

实现方法：

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

class DialogueSummarizer:
    def __init__(self, model_name="facebook/bart-large-cnn"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

    def summarize_dialogue(self, dialogue_history, max_length=200):
        """生成对话摘要"""

        # 将对话转换为文本
        dialogue_text = self.format_dialogue_for_summarization(dialogue_history)

        # 分词
        inputs = self.tokenizer(
            dialogue_text,
            max_length=1024,
            truncation=True,
            return_tensors="pt"
        )

        # 生成摘要
        with torch.no_grad():
            summary_ids = self.model.generate(
                inputs.input_ids,
                max_length=max_length,
                min_length=50,
                length_penalty=2.0,
                num_beams=4,
                early_stopping=True
            )

        # 解码摘要
        summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)

        return summary

    def format_dialogue_for_summarization(self, dialogue_history):
        """格式化对话用于摘要"""

        formatted_text = ""
        for turn in dialogue_history:
            speaker = "用户" if turn['speaker'] == 'user' else "助手"
            formatted_text += f"{speaker}: {turn['content']}\n"

        return formatted_text

def hierarchical_summarization(dialogue_history, chunk_size=10):
    """分层摘要：先对对话块进行摘要，再对摘要进行摘要"""

    summarizer = DialogueSummarizer()

    # 将对话分成块
    chunks = [dialogue_history[i:i+chunk_size] 
              for i in range(0, len(dialogue_history), chunk_size)]

    # 对每个块进行摘要
    chunk_summaries = []
    for chunk in chunks:
        summary = summarizer.summarize_dialogue(chunk)
        chunk_summaries.append(summary)

    # 对摘要进行再次摘要
    final_summary = summarizer.summarize_dialogue([
        {'speaker': 'system', 'content': summary} 
        for summary in chunk_summaries
    ])

    return final_summary

def extractive_summarization(dialogue_history, top_k=5):
    """抽取式摘要：提取关键句子"""

    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.metrics.pairwise import cosine_similarity
    import numpy as np

    # 提取所有句子
    sentences = []
    for turn in dialogue_history:
        sentences.extend(turn['content'].split('。'))

    # 计算TF-IDF
    vectorizer = TfidfVectorizer()
    tfidf_matrix = vectorizer.fit_transform(sentences)

    # 计算句子重要性
    sentence_scores = []
    for i, sentence in enumerate(sentences):
        # 计算与所有句子的相似度
        similarities = cosine_similarity(tfidf_matrix[i:i+1], tfidf_matrix).flatten()
        # 重要性 = 与所有句子的平均相似度
        importance = np.mean(similarities)
        sentence_scores.append((i, importance, sentence))

    # 选择最重要的句子
    sentence_scores.sort(key=lambda x: x[1], reverse=True)
    top_sentences = [item[2] for item in sentence_scores[:top_k]]

    return '。'.join(top_sentences)

优点： 保留了对话的核心信息，有效减少上下文长度，同时维持了较高的信息密度。

缺点： 摘要过程本身需要额外的计算资源和时间；摘要质量依赖于模型能力，可能丢失细微信息或引入偏差。

适用场景： 文档生成、长文本分析、需要保留较多背景信息的对话系统，如会议纪要生成、长篇邮件总结。

2.2.3 窗口滑动 (Sliding Window) #

核心原理： 将整个对话上下文分割成多个固定大小的"窗口"，每次只处理一个窗口的内容，并通过滑动窗口来维护上下文的连续性。窗口之间可以有重叠部分，以确保上下文的平滑过渡。

实现方法：

class SlidingWindowProcessor:
    def __init__(self, window_size=10, overlap=2):
        self.window_size = window_size
        self.overlap = overlap

    def process_dialogue(self, dialogue_history):
        """使用滑动窗口处理对话"""

        if len(dialogue_history) <= self.window_size:
            return dialogue_history

        # 创建滑动窗口
        windows = self.create_sliding_windows(dialogue_history)

        # 处理每个窗口
        processed_windows = []
        for i, window in enumerate(windows):
            processed_window = self.process_window(window, i)
            processed_windows.append(processed_window)

        # 合并处理结果
        return self.merge_windows(processed_windows)

    def create_sliding_windows(self, dialogue_history):
        """创建滑动窗口"""

        windows = []
        start = 0

        while start < len(dialogue_history):
            end = min(start + self.window_size, len(dialogue_history))
            window = dialogue_history[start:end]
            windows.append(window)

            # 滑动窗口，考虑重叠
            start += self.window_size - self.overlap

            if end == len(dialogue_history):
                break

        return windows

    def process_window(self, window, window_index):
        """处理单个窗口"""

        # 这里可以应用各种处理策略
        # 例如：摘要、嵌入、记忆存储等

        processed_window = {
            'window_index': window_index,
            'turns': window,
            'summary': self.summarize_window(window),
            'embeddings': self.embed_window(window),
            'key_info': self.extract_key_info(window)
        }

        return processed_window

    def summarize_window(self, window):
        """对窗口进行摘要"""
        summarizer = DialogueSummarizer()
        return summarizer.summarize_dialogue(window)

    def embed_window(self, window):
        """对窗口进行嵌入"""
        from sentence_transformers import SentenceTransformer

        model = SentenceTransformer('all-MiniLM-L6-v2')
        texts = [turn['content'] for turn in window]
        embeddings = model.encode(texts)

        return embeddings

    def extract_key_info(self, window):
        """提取窗口关键信息"""
        key_info = {
            'topics': self.extract_topics(window),
            'entities': self.extract_entities(window),
            'sentiment': self.analyze_sentiment(window),
            'intent': self.classify_intent(window)
        }

        return key_info

    def merge_windows(self, processed_windows):
        """合并窗口处理结果"""

        # 合并摘要
        summaries = [window['summary'] for window in processed_windows]
        merged_summary = ' '.join(summaries)

        # 合并关键信息
        merged_key_info = self.merge_key_info(processed_windows)

        # 返回合并结果
        return {
            'summary': merged_summary,
            'key_info': merged_key_info,
            'window_count': len(processed_windows)
        }

    def merge_key_info(self, processed_windows):
        """合并关键信息"""

        merged_info = {
            'topics': set(),
            'entities': set(),
            'sentiments': [],
            'intents': []
        }

        for window in processed_windows:
            key_info = window['key_info']
            merged_info['topics'].update(key_info['topics'])
            merged_info['entities'].update(key_info['entities'])
            merged_info['sentiments'].extend(key_info['sentiment'])
            merged_info['intents'].extend(key_info['intent'])

        return merged_info

优点： 能够处理非常长的上下文，同时在一定程度上保证了上下文的连续性和完整性。

缺点： 窗口大小的选择是关键，过小可能丢失上下文，过大则增加计算负担；跨窗口的长期依赖可能难以捕捉。

适用场景： 长时间对话处理、会议记录分析等需要处理超长上下文，且对局部连贯性要求较高的场景。

2.2.4 嵌入向量 (Embedding Vectors) #

核心原理： 利用嵌入向量技术，将整个对话上下文转化为固定长度的数值向量表示，从而规避文本长度限制。这些向量能够捕捉文本的语义信息。

实现方法：

from sentence_transformers import SentenceTransformer
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

class EmbeddingBasedContextProcessor:
    def __init__(self, model_name='all-MiniLM-L6-v2'):
        self.model = SentenceTransformer(model_name)
        self.context_embeddings = []
        self.context_memory = []

    def process_dialogue_context(self, dialogue_history):
        """使用嵌入向量处理对话上下文"""

        # 为每个对话轮次生成嵌入
        turn_embeddings = []
        for turn in dialogue_history:
            embedding = self.model.encode(turn['content'])
            turn_embeddings.append({
                'turn': turn,
                'embedding': embedding,
                'timestamp': turn.get('timestamp', '')
            })

        # 生成上下文嵌入
        context_embedding = self.generate_context_embedding(turn_embeddings)

        # 存储到记忆中
        self.store_in_memory(turn_embeddings, context_embedding)

        return {
            'context_embedding': context_embedding,
            'turn_embeddings': turn_embeddings,
            'memory_size': len(self.context_memory)
        }

    def generate_context_embedding(self, turn_embeddings):
        """生成上下文嵌入"""

        # 方法1：平均池化
        avg_embedding = np.mean([te['embedding'] for te in turn_embeddings], axis=0)

        # 方法2：加权平均（基于时间衰减）
        weighted_embedding = self.weighted_average_embedding(turn_embeddings)

        # 方法3：注意力机制
        attention_embedding = self.attention_based_embedding(turn_embeddings)

        return {
            'average': avg_embedding,
            'weighted': weighted_embedding,
            'attention': attention_embedding
        }

    def weighted_average_embedding(self, turn_embeddings):
        """基于时间衰减的加权平均嵌入"""

        weights = []
        embeddings = []

        for i, te in enumerate(turn_embeddings):
            # 时间衰减权重：越近的对话权重越大
            weight = np.exp(-0.1 * (len(turn_embeddings) - i - 1))
            weights.append(weight)
            embeddings.append(te['embedding'])

        weights = np.array(weights)
        weights = weights / np.sum(weights)  # 归一化

        weighted_embedding = np.average(embeddings, axis=0, weights=weights)
        return weighted_embedding

    def attention_based_embedding(self, turn_embeddings):
        """基于注意力机制的嵌入"""

        embeddings = np.array([te['embedding'] for te in turn_embeddings])

        # 计算注意力权重
        attention_weights = self.compute_attention_weights(embeddings)

        # 加权求和
        attention_embedding = np.sum(embeddings * attention_weights.reshape(-1, 1), axis=0)

        return attention_embedding

    def compute_attention_weights(self, embeddings):
        """计算注意力权重"""

        # 使用自注意力机制
        similarity_matrix = cosine_similarity(embeddings)

        # 计算每个嵌入的重要性
        importance_scores = np.sum(similarity_matrix, axis=1)

        # 归一化为注意力权重
        attention_weights = importance_scores / np.sum(importance_scores)

        return attention_weights

    def store_in_memory(self, turn_embeddings, context_embedding):
        """存储到记忆中"""

        memory_entry = {
            'turn_embeddings': turn_embeddings,
            'context_embedding': context_embedding,
            'timestamp': turn_embeddings[-1]['timestamp'] if turn_embeddings else ''
        }

        self.context_memory.append(memory_entry)

        # 限制记忆大小
        if len(self.context_memory) > 100:
            self.context_memory.pop(0)

    def retrieve_relevant_context(self, query, top_k=5):
        """检索相关上下文"""

        if not self.context_memory:
            return []

        # 对查询进行嵌入
        query_embedding = self.model.encode(query)

        # 计算与所有上下文的相似度
        similarities = []
        for memory in self.context_memory:
            similarity = cosine_similarity(
                query_embedding.reshape(1, -1),
                memory['context_embedding']['average'].reshape(1, -1)
            )[0][0]
            similarities.append((memory, similarity))

        # 排序并返回最相关的上下文
        similarities.sort(key=lambda x: x[1], reverse=True)
        return similarities[:top_k]

    def semantic_search(self, query, dialogue_history):
        """语义搜索"""

        # 为查询生成嵌入
        query_embedding = self.model.encode(query)

        # 为对话历史生成嵌入
        dialogue_embeddings = []
        for turn in dialogue_history:
            embedding = self.model.encode(turn['content'])
            dialogue_embeddings.append({
                'turn': turn,
                'embedding': embedding
            })

        # 计算相似度
        similarities = []
        for de in dialogue_embeddings:
            similarity = cosine_similarity(
                query_embedding.reshape(1, -1),
                de['embedding'].reshape(1, -1)
            )[0][0]
            similarities.append((de['turn'], similarity))

        # 返回最相似的结果
        similarities.sort(key=lambda x: x[1], reverse=True)
        return similarities

优点： 将变长文本转化为固定长度表示，便于模型处理；能够捕捉文本的语义信息，实现高效的语义匹配。

缺点： 向量表示可能丢失文本的细粒度信息和语序结构；聚合方式会影响信息保留程度，可能无法完全保留所有细节。

适用场景： 意图识别、情感分析、主题分类等需要对上下文进行全局理解，而非细致文本分析的任务。

2.2.5 记忆网络 (Memory Networks) #

核心原理： 通过专门的记忆机制存储和管理重要的上下文信息，并根据需要适时地提取和使用这些信息。这是一种更智能、更动态的上下文管理方式。

实现方法：

import torch
import torch.nn as nn
import torch.nn.functional as F

class MemoryNetwork(nn.Module):
    def __init__(self, input_dim, memory_dim, num_memories=100):
        super(MemoryNetwork, self).__init__()
        self.input_dim = input_dim
        self.memory_dim = memory_dim
        self.num_memories = num_memories

        # 输入编码器
        self.input_encoder = nn.Linear(input_dim, memory_dim)

        # 记忆存储
        self.memory = nn.Parameter(torch.randn(num_memories, memory_dim))

        # 注意力机制
        self.attention = nn.MultiheadAttention(memory_dim, num_heads=8)

        # 输出解码器
        self.output_decoder = nn.Linear(memory_dim, input_dim)

    def forward(self, input_sequence):
        """前向传播"""

        # 编码输入序列
        encoded_input = self.input_encoder(input_sequence)

        # 计算注意力权重
        attention_output, attention_weights = self.attention(
            encoded_input, self.memory.unsqueeze(0), self.memory.unsqueeze(0)
        )

        # 生成输出
        output = self.output_decoder(attention_output)

        return output, attention_weights

    def update_memory(self, new_information, importance_scores):
        """更新记忆"""

        # 计算更新权重
        update_weights = F.softmax(importance_scores, dim=-1)

        # 更新记忆
        memory_update = torch.matmul(update_weights, new_information)
        self.memory.data = 0.9 * self.memory.data + 0.1 * memory_update

class DialogueMemoryManager:
    def __init__(self, model_name='all-MiniLM-L6-v2'):
        self.embedding_model = SentenceTransformer(model_name)
        self.memory_network = MemoryNetwork(
            input_dim=384,  # all-MiniLM-L6-v2的维度
            memory_dim=256,
            num_memories=100
        )
        self.memory_store = []
        self.importance_threshold = 0.5

    def process_dialogue_turn(self, turn):
        """处理对话轮次"""

        # 生成嵌入
        embedding = self.embedding_model.encode(turn['content'])

        # 计算重要性
        importance = self.calculate_importance(turn, embedding)

        # 存储到记忆
        if importance > self.importance_threshold:
            self.store_in_memory(turn, embedding, importance)

        # 更新记忆网络
        self.update_memory_network(embedding, importance)

        return {
            'embedding': embedding,
            'importance': importance,
            'stored': importance > self.importance_threshold
        }

    def calculate_importance(self, turn, embedding):
        """计算对话轮次的重要性"""

        # 基于内容特征
        content_features = self.extract_content_features(turn)

        # 基于语义相似度
        semantic_importance = self.calculate_semantic_importance(embedding)

        # 基于时间衰减
        temporal_importance = self.calculate_temporal_importance(turn)

        # 综合重要性
        importance = (
            0.4 * content_features +
            0.4 * semantic_importance +
            0.2 * temporal_importance
        )

        return importance

    def extract_content_features(self, turn):
        """提取内容特征"""

        content = turn['content']

        # 长度特征
        length_score = min(len(content) / 100, 1.0)

        # 关键词特征
        keyword_score = self.calculate_keyword_score(content)

        # 情感特征
        sentiment_score = self.calculate_sentiment_score(content)

        # 综合特征
        content_score = (length_score + keyword_score + sentiment_score) / 3

        return content_score

    def calculate_keyword_score(self, content):
        """计算关键词分数"""

        important_keywords = [
            '问题', '需求', '要求', '重要', '关键', '紧急',
            '问题', '需求', '要求', '重要', '关键', '紧急'
        ]

        keyword_count = sum(1 for keyword in important_keywords if keyword in content)
        return min(keyword_count / 5, 1.0)

    def calculate_sentiment_score(self, content):
        """计算情感分数"""

        # 这里可以使用情感分析模型
        # 简化实现：基于情感词汇
        positive_words = ['好', '棒', '优秀', '满意', '喜欢']
        negative_words = ['坏', '差', '糟糕', '不满', '讨厌']

        pos_count = sum(1 for word in positive_words if word in content)
        neg_count = sum(1 for word in negative_words if word in content)

        if pos_count + neg_count == 0:
            return 0.5

        return pos_count / (pos_count + neg_count)

    def calculate_semantic_importance(self, embedding):
        """计算语义重要性"""

        if not self.memory_store:
            return 0.5

        # 计算与现有记忆的相似度
        similarities = []
        for memory in self.memory_store:
            similarity = cosine_similarity(
                embedding.reshape(1, -1),
                memory['embedding'].reshape(1, -1)
            )[0][0]
            similarities.append(similarity)

        # 重要性 = 1 - 平均相似度（越不相似越重要）
        avg_similarity = np.mean(similarities)
        importance = 1 - avg_similarity

        return importance

    def calculate_temporal_importance(self, turn):
        """计算时间重要性"""

        # 基于时间戳计算重要性
        timestamp = turn.get('timestamp', '')
        if not timestamp:
            return 0.5

        # 简化实现：基于对话顺序
        turn_index = turn.get('index', 0)
        total_turns = turn.get('total_turns', 1)

        # 越靠后的对话越重要
        temporal_importance = turn_index / total_turns

        return temporal_importance

    def store_in_memory(self, turn, embedding, importance):
        """存储到记忆中"""

        memory_entry = {
            'turn': turn,
            'embedding': embedding,
            'importance': importance,
            'timestamp': turn.get('timestamp', ''),
            'index': len(self.memory_store)
        }

        self.memory_store.append(memory_entry)

        # 限制记忆大小
        if len(self.memory_store) > 100:
            # 移除最不重要的记忆
            self.memory_store.sort(key=lambda x: x['importance'])
            self.memory_store.pop(0)

    def update_memory_network(self, embedding, importance):
        """更新记忆网络"""

        if not self.memory_store:
            return

        # 准备输入
        embeddings = np.array([memory['embedding'] for memory in self.memory_store])
        importances = np.array([memory['importance'] for memory in self.memory_store])

        # 转换为张量
        embeddings_tensor = torch.FloatTensor(embeddings)
        importances_tensor = torch.FloatTensor(importances)

        # 更新记忆网络
        self.memory_network.update_memory(embeddings_tensor, importances_tensor)

    def retrieve_relevant_memories(self, query, top_k=5):
        """检索相关记忆"""

        if not self.memory_store:
            return []

        # 对查询进行嵌入
        query_embedding = self.embedding_model.encode(query)

        # 计算相似度
        similarities = []
        for memory in self.memory_store:
            similarity = cosine_similarity(
                query_embedding.reshape(1, -1),
                memory['embedding'].reshape(1, -1)
            )[0][0]
            similarities.append((memory, similarity))

        # 排序并返回最相关的记忆
        similarities.sort(key=lambda x: x[1], reverse=True)
        return similarities[:top_k]

    def get_context_summary(self):
        """获取上下文摘要"""

        if not self.memory_store:
            return "无记忆内容"

        # 按重要性排序
        sorted_memories = sorted(
            self.memory_store, 
            key=lambda x: x['importance'], 
            reverse=True
        )

        # 生成摘要
        summary_parts = []
        for memory in sorted_memories[:10]:  # 取前10个最重要的记忆
            turn = memory['turn']
            summary_parts.append(f"{turn['speaker']}: {turn['content']}")

        return "\n".join(summary_parts)

优点： 能够处理长时依赖和复杂上下文，更智能地管理和利用历史信息，理论上能达到最佳的上下文理解效果。

缺点： 实现复杂，计算资源消耗大；设计和训练记忆网络需要专业的知识和大量数据。

适用场景： 需要反复引用之前对话内容的复杂对话系统、问答系统等需要长时记忆和复杂上下文理解的场景，如多轮推理、知识图谱问答。

2.3 策略组合与优化 #

2.3.1 混合策略 #

class HybridContextProcessor:
    def __init__(self):
        self.truncation_processor = TruncationProcessor()
        self.summarizer = DialogueSummarizer()
        self.embedding_processor = EmbeddingBasedContextProcessor()
        self.memory_manager = DialogueMemoryManager()

    def process_context(self, dialogue_history, strategy='hybrid'):
        """混合策略处理上下文"""

        if strategy == 'hybrid':
            return self.hybrid_processing(dialogue_history)
        elif strategy == 'truncation':
            return self.truncation_processor.process(dialogue_history)
        elif strategy == 'summarization':
            return self.summarizer.summarize_dialogue(dialogue_history)
        elif strategy == 'embedding':
            return self.embedding_processor.process_dialogue_context(dialogue_history)
        elif strategy == 'memory':
            return self.memory_manager.process_dialogue_turn(dialogue_history[-1])
        else:
            raise ValueError(f"Unknown strategy: {strategy}")

    def hybrid_processing(self, dialogue_history):
        """混合处理策略"""

        # 1. 首先使用截断减少长度
        truncated_history = self.truncation_processor.process(dialogue_history)

        # 2. 对截断后的内容进行摘要
        summary = self.summarizer.summarize_dialogue(truncated_history)

        # 3. 生成嵌入向量
        embedding_result = self.embedding_processor.process_dialogue_context(truncated_history)

        # 4. 存储到记忆中
        memory_result = self.memory_manager.process_dialogue_turn(truncated_history[-1])

        return {
            'truncated_history': truncated_history,
            'summary': summary,
            'embedding': embedding_result,
            'memory': memory_result,
            'strategy': 'hybrid'
        }

2.3.2 自适应策略选择 #

class AdaptiveContextProcessor:
    def __init__(self):
        self.processors = {
            'truncation': TruncationProcessor(),
            'summarization': DialogueSummarizer(),
            'embedding': EmbeddingBasedContextProcessor(),
            'memory': DialogueMemoryManager()
        }

    def select_strategy(self, dialogue_history, requirements):
        """自适应选择策略"""

        # 基于对话长度
        if len(dialogue_history) < 10:
            return 'truncation'
        elif len(dialogue_history) < 50:
            return 'summarization'
        elif len(dialogue_history) < 100:
            return 'embedding'
        else:
            return 'memory'

        # 基于计算资源
        if requirements.get('low_compute', False):
            return 'truncation'

        # 基于质量要求
        if requirements.get('high_quality', False):
            return 'memory'

        # 基于实时性要求
        if requirements.get('real_time', False):
            return 'truncation'

        return 'hybrid'

    def process_with_adaptive_strategy(self, dialogue_history, requirements):
        """使用自适应策略处理"""

        strategy = self.select_strategy(dialogue_history, requirements)
        processor = self.processors[strategy]

        return processor.process(dialogue_history)

2.4 实际应用案例 #

2.4.1 客服系统 #

class CustomerServiceContextProcessor:
    def __init__(self):
        self.processor = HybridContextProcessor()
        self.requirements = {
            'real_time': True,
            'high_quality': True,
            'low_compute': False
        }

    def handle_customer_query(self, dialogue_history, new_query):
        """处理客户查询"""

        # 处理上下文
        context_result = self.processor.process_context(
            dialogue_history, 
            strategy='hybrid'
        )

        # 生成回复
        response = self.generate_response(
            new_query, 
            context_result
        )

        return response

    def generate_response(self, query, context_result):
        """生成回复"""

        # 使用上下文信息生成回复
        # 这里可以调用LLM API

        prompt = f"""
        上下文摘要: {context_result['summary']}
        用户查询: {query}

        请基于上下文信息生成回复。
        """

        # 调用LLM生成回复
        response = self.call_llm(prompt)

        return response

2.4.2 教育对话系统 #

class EducationalDialogueProcessor:
    def __init__(self):
        self.processor = HybridContextProcessor()
        self.knowledge_base = {}

    def process_educational_dialogue(self, dialogue_history, student_query):
        """处理教育对话"""

        # 提取学习主题
        topics = self.extract_learning_topics(dialogue_history)

        # 处理上下文
        context_result = self.processor.process_context(
            dialogue_history,
            strategy='memory'  # 教育对话需要长期记忆
        )

        # 生成教育回复
        response = self.generate_educational_response(
            student_query,
            context_result,
            topics
        )

        return response

    def extract_learning_topics(self, dialogue_history):
        """提取学习主题"""

        topics = set()
        for turn in dialogue_history:
            # 使用NLP技术提取主题
            turn_topics = self.extract_topics_from_text(turn['content'])
            topics.update(turn_topics)

        return list(topics)

    def generate_educational_response(self, query, context_result, topics):
        """生成教育回复"""

        # 基于学习主题和上下文生成回复
        prompt = f"""
        学习主题: {', '.join(topics)}
        上下文: {context_result['summary']}
        学生问题: {query}

        请生成教育性的回复，帮助学生理解概念。
        """

        response = self.call_llm(prompt)
        return response

2.5 总结 #

选择哪种上下文处理策略取决于具体的应用需求、可用的计算资源以及对对话连贯性和信息保留程度的要求。在实际应用中，通常会结合多种策略，例如先进行摘要，再对摘要后的内容进行截断，或者利用记忆网络辅助滑动窗口，以达到最佳效果。

策略选择指南：

1. 简单场景： 使用截断策略
2. 中等复杂度： 使用摘要或嵌入策略
3. 复杂场景： 使用记忆网络策略
4. 混合场景： 结合多种策略

最佳实践：

1. 根据对话长度动态选择策略
2. 结合业务需求优化策略参数
3. 持续监控和优化策略效果
4. 建立完善的评估体系

通过合理选择和组合这些策略，可以构建出高效、智能的对话上下文处理系统，为用户提供更好的对话体验。