上下文管理
引言
上下文管理是RAG系统中的关键技术，直接影响生成质量和系统性能。有效的上下文管理能够在有限的输入长度内最大化信息价值，确保模型获得最相关的信息来生成准确回答。本文将深入探讨上下文管理的策略、技术和最佳实践。
上下文管理概述
什么是上下文管理
上下文管理是指在RAG系统中，对检索到的文档信息进行组织、筛选、排序和优化的过程。它确保传递给大语言模型的上下文信息既相关又完整，同时不超过模型的输入长度限制。
上下文管理的挑战
上下文管理的目标
- 最大化相关性：确保上下文与查询高度相关
- 优化信息密度：在有限空间内提供最大价值
- 保持逻辑完整性：维护信息的逻辑结构
- 控制成本：在质量和成本之间找到平衡
基础上下文管理
1. 简单截断策略
实现示例
python
class SimpleTruncationManager:
def __init__(self, max_length: int = 4000):
self.max_length = max_length
def manage_context(self, documents: List[str], query: str) -> str:
"""简单的上下文管理"""
# 合并所有文档
combined_context = "\n\n".join(documents)
# 如果超过长度限制，进行截断
if len(combined_context) > self.max_length:
combined_context = combined_context[:self.max_length]
# 尝试在句子边界截断
combined_context = self._truncate_at_sentence_boundary(combined_context)
return combined_context
def _truncate_at_sentence_boundary(self, text: str) -> str:
"""在句子边界截断"""
sentence_endings = ['。', '！', '？', '.', '!', '?']
for i in range(len(text) - 1, -1, -1):
if text[i] in sentence_endings:
return text[:i + 1]
return text2. 基于长度的上下文管理
实现示例
python
class LengthBasedContextManager:
def __init__(self, max_length: int = 4000, overlap_ratio: float = 0.1):
self.max_length = max_length
self.overlap_ratio = overlap_ratio
def manage_context(self, documents: List[str], query: str) -> str:
"""基于长度的上下文管理"""
# 计算每个文档的重要性分数
doc_scores = self._calculate_document_scores(documents, query)
# 按重要性排序
sorted_docs = sorted(zip(documents, doc_scores), key=lambda x: x[1], reverse=True)
# 选择文档直到达到长度限制
selected_docs = []
current_length = 0
for doc, score in sorted_docs:
doc_length = len(doc)
if current_length + doc_length <= self.max_length:
selected_docs.append(doc)
current_length += doc_length
else:
# 如果添加完整文档会超出限制，尝试截断
remaining_length = self.max_length - current_length
if remaining_length > 100: # 至少保留100个字符
truncated_doc = self._smart_truncate(doc, remaining_length)
selected_docs.append(truncated_doc)
break
return "\n\n".join(selected_docs)
def _calculate_document_scores(self, documents: List[str], query: str) -> List[float]:
"""计算文档重要性分数"""
query_words = set(query.lower().split())
scores = []
for doc in documents:
doc_words = set(doc.lower().split())
# 计算词汇重叠
overlap = len(query_words.intersection(doc_words))
total_words = len(query_words.union(doc_words))
if total_words > 0:
jaccard_score = overlap / total_words
else:
jaccard_score = 0
# 考虑文档长度
length_score = min(len(doc.split()) / 200, 1.0)
# 综合分数
combined_score = 0.7 * jaccard_score + 0.3 * length_score
scores.append(combined_score)
return scores
def _smart_truncate(self, document: str, max_length: int) -> str:
"""智能截断文档"""
if len(document) <= max_length:
return document
# 尝试在段落边界截断
paragraphs = document.split('\n\n')
truncated_paragraphs = []
current_length = 0
for para in paragraphs:
if current_length + len(para) <= max_length:
truncated_paragraphs.append(para)
current_length += len(para)
else:
# 如果段落太长，在句子边界截断
remaining_length = max_length - current_length
if remaining_length > 50:
truncated_para = self._truncate_at_sentence_boundary(para, remaining_length)
truncated_paragraphs.append(truncated_para)
break
return '\n\n'.join(truncated_paragraphs)
def _truncate_at_sentence_boundary(self, text: str, max_length: int) -> str:
"""在句子边界截断"""
if len(text) <= max_length:
return text
sentence_endings = ['。', '！', '？', '.', '!', '?']
for i in range(max_length - 1, -1, -1):
if text[i] in sentence_endings:
return text[:i + 1]
return text[:max_length]高级上下文管理
1. 语义感知的上下文管理
实现示例
python
from sentence_transformers import SentenceTransformer
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
class SemanticContextManager:
def __init__(self, model_name: str = 'all-MiniLM-L6-v2', max_length: int = 4000):
self.model = SentenceTransformer(model_name)
self.max_length = max_length
def manage_context(self, documents: List[str], query: str) -> str:
"""语义感知的上下文管理"""
# 计算查询和文档的语义相似度
similarities = self._calculate_semantic_similarities(documents, query)
# 按相似度排序
sorted_docs = sorted(zip(documents, similarities), key=lambda x: x[1], reverse=True)
# 选择最相关的文档片段
selected_chunks = self._select_relevant_chunks(sorted_docs, query)
# 组织上下文
organized_context = self._organize_context(selected_chunks, query)
return organized_context
def _calculate_semantic_similarities(self, documents: List[str], query: str) -> List[float]:
"""计算语义相似度"""
query_embedding = self.model.encode([query])
doc_embeddings = self.model.encode(documents)
similarities = cosine_similarity(query_embedding, doc_embeddings)[0]
return similarities.tolist()
def _select_relevant_chunks(self, sorted_docs: List[Tuple[str, float]],
query: str) -> List[Tuple[str, float]]:
"""选择相关文档片段"""
selected_chunks = []
current_length = 0
for doc, similarity in sorted_docs:
if similarity < 0.3: # 相似度阈值
break
# 将长文档分块
chunks = self._split_document_into_chunks(doc)
# 计算每个块与查询的相似度
chunk_similarities = self._calculate_chunk_similarities(chunks, query)
# 选择最相关的块
for chunk, chunk_sim in zip(chunks, chunk_similarities):
if current_length + len(chunk) <= self.max_length:
selected_chunks.append((chunk, chunk_sim))
current_length += len(chunk)
else:
break
if current_length >= self.max_length:
break
return selected_chunks
def _split_document_into_chunks(self, document: str, chunk_size: int = 500) -> List[str]:
"""将文档分割成块"""
words = document.split()
chunks = []
for i in range(0, len(words), chunk_size):
chunk = ' '.join(words[i:i + chunk_size])
chunks.append(chunk)
return chunks
def _calculate_chunk_similarities(self, chunks: List[str], query: str) -> List[float]:
"""计算块相似度"""
if not chunks:
return []
query_embedding = self.model.encode([query])
chunk_embeddings = self.model.encode(chunks)
similarities = cosine_similarity(query_embedding, chunk_embeddings)[0]
return similarities.tolist()
def _organize_context(self, chunks: List[Tuple[str, float]], query: str) -> str:
"""组织上下文"""
# 按相似度排序
sorted_chunks = sorted(chunks, key=lambda x: x[1], reverse=True)
# 添加查询相关的上下文
context_parts = [f"查询: {query}\n"]
# 添加相关文档片段
for i, (chunk, similarity) in enumerate(sorted_chunks):
context_parts.append(f"相关文档 {i+1} (相似度: {similarity:.3f}):\n{chunk}")
return "\n\n".join(context_parts)2. 动态上下文管理
实现示例
python
class DynamicContextManager:
def __init__(self, max_length: int = 4000):
self.max_length = max_length
self.context_history = []
def manage_context(self, documents: List[str], query: str,
conversation_history: List[Dict] = None) -> str:
"""动态上下文管理"""
# 分析查询类型
query_type = self._analyze_query_type(query)
# 根据查询类型调整策略
if query_type == 'follow_up':
context = self._handle_follow_up_query(documents, query, conversation_history)
elif query_type == 'clarification':
context = self._handle_clarification_query(documents, query)
elif query_type == 'comparison':
context = self._handle_comparison_query(documents, query)
else:
context = self._handle_general_query(documents, query)
# 记录上下文历史
self._record_context_history(query, context)
return context
def _analyze_query_type(self, query: str) -> str:
"""分析查询类型"""
query_lower = query.lower()
# 后续问题
if any(word in query_lower for word in ['还有', '另外', '其他', '继续']):
return 'follow_up'
# 澄清问题
elif any(word in query_lower for word in ['具体', '详细', '解释', '说明']):
return 'clarification'
# 比较问题
elif any(word in query_lower for word in ['比较', '对比', '区别', '差异']):
return 'comparison'
else:
return 'general'
def _handle_follow_up_query(self, documents: List[str], query: str,
conversation_history: List[Dict]) -> str:
"""处理后续问题"""
# 结合历史上下文
historical_context = self._extract_historical_context(conversation_history)
# 选择与历史相关的文档
relevant_docs = self._select_historically_relevant_docs(documents, historical_context)
# 组织上下文
context_parts = []
if historical_context:
context_parts.append(f"之前的讨论: {historical_context}")
context_parts.append(f"当前问题: {query}")
context_parts.append(f"相关信息: {' '.join(relevant_docs)}")
return "\n\n".join(context_parts)
def _handle_clarification_query(self, documents: List[str], query: str) -> str:
"""处理澄清问题"""
# 选择最相关的文档片段
relevant_chunks = self._select_most_relevant_chunks(documents, query)
# 组织上下文，突出关键信息
context_parts = [f"问题: {query}"]
for i, chunk in enumerate(relevant_chunks):
context_parts.append(f"关键信息 {i+1}: {chunk}")
return "\n\n".join(context_parts)
def _handle_comparison_query(self, documents: List[str], query: str) -> str:
"""处理比较问题"""
# 识别比较对象
comparison_objects = self._identify_comparison_objects(query)
# 为每个比较对象选择相关文档
object_docs = {}
for obj in comparison_objects:
object_docs[obj] = self._select_object_related_docs(documents, obj)
# 组织比较上下文
context_parts = [f"比较问题: {query}"]
for obj, docs in object_docs.items():
context_parts.append(f"{obj}相关信息: {' '.join(docs)}")
return "\n\n".join(context_parts)
def _handle_general_query(self, documents: List[str], query: str) -> str:
"""处理一般问题"""
# 使用标准上下文管理
return "\n\n".join(documents)
def _extract_historical_context(self, conversation_history: List[Dict]) -> str:
"""提取历史上下文"""
if not conversation_history:
return ""
# 提取最近的几个对话
recent_history = conversation_history[-3:] # 最近3轮对话
context_parts = []
for turn in recent_history:
if 'query' in turn:
context_parts.append(f"问题: {turn['query']}")
if 'response' in turn:
context_parts.append(f"回答: {turn['response'][:200]}...") # 截断回答
return "\n".join(context_parts)
def _select_historically_relevant_docs(self, documents: List[str],
historical_context: str) -> List[str]:
"""选择与历史相关的文档"""
if not historical_context:
return documents
# 计算文档与历史上下文的相似度
historical_words = set(historical_context.lower().split())
doc_scores = []
for doc in documents:
doc_words = set(doc.lower().split())
overlap = len(historical_words.intersection(doc_words))
score = overlap / len(historical_words.union(doc_words)) if historical_words.union(doc_words) else 0
doc_scores.append(score)
# 选择最相关的文档
sorted_docs = sorted(zip(documents, doc_scores), key=lambda x: x[1], reverse=True)
return [doc for doc, score in sorted_docs[:3]] # 返回前3个最相关的文档
def _select_most_relevant_chunks(self, documents: List[str], query: str) -> List[str]:
"""选择最相关的文档片段"""
query_words = set(query.lower().split())
chunk_scores = []
for doc in documents:
# 将文档分块
chunks = self._split_document_into_chunks(doc)
for chunk in chunks:
chunk_words = set(chunk.lower().split())
overlap = len(query_words.intersection(chunk_words))
score = overlap / len(query_words.union(chunk_words)) if query_words.union(chunk_words) else 0
chunk_scores.append((chunk, score))
# 选择得分最高的块
sorted_chunks = sorted(chunk_scores, key=lambda x: x[1], reverse=True)
return [chunk for chunk, score in sorted_chunks[:5]] # 返回前5个最相关的块
def _identify_comparison_objects(self, query: str) -> List[str]:
"""识别比较对象"""
# 简单的比较对象识别
comparison_keywords = ['和', '与', 'vs', '对比', '比较']
objects = []
for keyword in comparison_keywords:
if keyword in query:
parts = query.split(keyword)
objects.extend([part.strip() for part in parts])
return [obj for obj in objects if len(obj) > 1]
def _select_object_related_docs(self, documents: List[str], obj: str) -> List[str]:
"""选择与对象相关的文档"""
obj_words = set(obj.lower().split())
doc_scores = []
for doc in documents:
doc_words = set(doc.lower().split())
overlap = len(obj_words.intersection(doc_words))
score = overlap / len(obj_words.union(doc_words)) if obj_words.union(doc_words) else 0
doc_scores.append((doc, score))
# 选择最相关的文档
sorted_docs = sorted(doc_scores, key=lambda x: x[1], reverse=True)
return [doc for doc, score in sorted_docs[:2]] # 返回前2个最相关的文档
def _split_document_into_chunks(self, document: str, chunk_size: int = 300) -> List[str]:
"""将文档分割成块"""
sentences = document.split('。')
chunks = []
current_chunk = ""
for sentence in sentences:
if len(current_chunk) + len(sentence) <= chunk_size:
current_chunk += sentence + "。"
else:
if current_chunk:
chunks.append(current_chunk)
current_chunk = sentence + "。"
if current_chunk:
chunks.append(current_chunk)
return chunks
def _record_context_history(self, query: str, context: str):
"""记录上下文历史"""
self.context_history.append({
'query': query,
'context': context,
'timestamp': time.time()
})
# 只保留最近的历史
if len(self.context_history) > 10:
self.context_history = self.context_history[-10:]上下文优化策略
1. 信息去重
实现示例
python
class ContextDeduplicator:
def __init__(self, similarity_threshold: float = 0.8):
self.similarity_threshold = similarity_threshold
def deduplicate_context(self, documents: List[str]) -> List[str]:
"""去重上下文"""
if not documents:
return []
# 计算文档间的相似度
similarity_matrix = self._calculate_similarity_matrix(documents)
# 找到重复的文档
duplicates = self._find_duplicates(similarity_matrix)
# 移除重复文档
unique_documents = self._remove_duplicates(documents, duplicates)
return unique_documents
def _calculate_similarity_matrix(self, documents: List[str]) -> np.ndarray:
"""计算相似度矩阵"""
n = len(documents)
similarity_matrix = np.zeros((n, n))
for i in range(n):
for j in range(i + 1, n):
similarity = self._calculate_similarity(documents[i], documents[j])
similarity_matrix[i][j] = similarity
similarity_matrix[j][i] = similarity
return similarity_matrix
def _calculate_similarity(self, doc1: str, doc2: str) -> float:
"""计算文档相似度"""
words1 = set(doc1.lower().split())
words2 = set(doc2.lower().split())
intersection = words1.intersection(words2)
union = words1.union(words2)
if len(union) == 0:
return 0.0
return len(intersection) / len(union)
def _find_duplicates(self, similarity_matrix: np.ndarray) -> List[Tuple[int, int]]:
"""找到重复文档"""
duplicates = []
n = similarity_matrix.shape[0]
for i in range(n):
for j in range(i + 1, n):
if similarity_matrix[i][j] >= self.similarity_threshold:
duplicates.append((i, j))
return duplicates
def _remove_duplicates(self, documents: List[str],
duplicates: List[Tuple[int, int]]) -> List[str]:
"""移除重复文档"""
to_remove = set()
for i, j in duplicates:
# 保留较长的文档
if len(documents[i]) >= len(documents[j]):
to_remove.add(j)
else:
to_remove.add(i)
# 返回未标记为删除的文档
unique_documents = [doc for i, doc in enumerate(documents) if i not in to_remove]
return unique_documents2. 信息压缩
实现示例
python
class ContextCompressor:
def __init__(self, compression_ratio: float = 0.7):
self.compression_ratio = compression_ratio
def compress_context(self, documents: List[str], query: str) -> str:
"""压缩上下文"""
# 提取关键信息
key_information = self._extract_key_information(documents, query)
# 压缩冗余信息
compressed_info = self._compress_redundant_information(key_information)
# 组织压缩后的上下文
organized_context = self._organize_compressed_context(compressed_info, query)
return organized_context
def _extract_key_information(self, documents: List[str], query: str) -> List[str]:
"""提取关键信息"""
query_words = set(query.lower().split())
key_sentences = []
for doc in documents:
sentences = doc.split('。')
for sentence in sentences:
if sentence.strip():
sentence_words = set(sentence.lower().split())
overlap = len(query_words.intersection(sentence_words))
if overlap > 0:
relevance_score = overlap / len(query_words.union(sentence_words))
key_sentences.append((sentence, relevance_score))
# 按相关性排序
key_sentences.sort(key=lambda x: x[1], reverse=True)
return [sentence for sentence, score in key_sentences]
def _compress_redundant_information(self, key_sentences: List[str]) -> List[str]:
"""压缩冗余信息"""
compressed_sentences = []
seen_concepts = set()
for sentence in key_sentences:
# 提取句子中的关键概念
concepts = self._extract_concepts(sentence)
# 检查是否有新概念
new_concepts = concepts - seen_concepts
if new_concepts:
compressed_sentences.append(sentence)
seen_concepts.update(concepts)
return compressed_sentences
def _extract_concepts(self, sentence: str) -> set:
"""提取句子中的关键概念"""
# 简单的概念提取
words = sentence.split()
concepts = set()
for word in words:
if len(word) > 3: # 过滤短词
concepts.add(word.lower())
return concepts
def _organize_compressed_context(self, compressed_info: List[str], query: str) -> str:
"""组织压缩后的上下文"""
context_parts = [f"查询: {query}"]
# 添加关键信息
for i, info in enumerate(compressed_info[:10]): # 最多10条信息
context_parts.append(f"关键信息 {i+1}: {info}")
return "\n\n".join(context_parts)上下文质量评估
1. 上下文相关性评估
实现示例
python
class ContextRelevanceEvaluator:
def __init__(self):
self.evaluation_metrics = {}
def evaluate_context_relevance(self, context: str, query: str) -> Dict[str, float]:
"""评估上下文相关性"""
metrics = {}
# 词汇重叠度
metrics['lexical_overlap'] = self._calculate_lexical_overlap(context, query)
# 语义相关性
metrics['semantic_relevance'] = self._calculate_semantic_relevance(context, query)
# 信息密度
metrics['information_density'] = self._calculate_information_density(context)
# 结构完整性
metrics['structural_completeness'] = self._calculate_structural_completeness(context)
return metrics
def _calculate_lexical_overlap(self, context: str, query: str) -> float:
"""计算词汇重叠度"""
query_words = set(query.lower().split())
context_words = set(context.lower().split())
intersection = query_words.intersection(context_words)
union = query_words.union(context_words)
if len(union) == 0:
return 0.0
return len(intersection) / len(union)
def _calculate_semantic_relevance(self, context: str, query: str) -> float:
"""计算语义相关性"""
# 使用简单的语义相关性计算
# 实际应用中可以使用更复杂的语义模型
query_concepts = self._extract_concepts(query)
context_concepts = self._extract_concepts(context)
intersection = query_concepts.intersection(context_concepts)
union = query_concepts.union(context_concepts)
if len(union) == 0:
return 0.0
return len(intersection) / len(union)
def _extract_concepts(self, text: str) -> set:
"""提取概念"""
# 简单的概念提取
words = text.lower().split()
concepts = set()
for word in words:
if len(word) > 3: # 过滤短词
concepts.add(word)
return concepts
def _calculate_information_density(self, context: str) -> float:
"""计算信息密度"""
# 基于词汇多样性和句子结构计算信息密度
words = context.split()
unique_words = set(words)
if len(words) == 0:
return 0.0
# 词汇多样性
lexical_diversity = len(unique_words) / len(words)
# 句子复杂度
sentences = context.split('。')
avg_sentence_length = sum(len(s.split()) for s in sentences) / len(sentences) if sentences else 0
# 综合信息密度
information_density = 0.6 * lexical_diversity + 0.4 * min(avg_sentence_length / 20, 1.0)
return information_density
def _calculate_structural_completeness(self, context: str) -> float:
"""计算结构完整性"""
# 检查是否有完整的句子结构
sentences = context.split('。')
complete_sentences = 0
for sentence in sentences:
if sentence.strip() and len(sentence.split()) > 3:
complete_sentences += 1
if len(sentences) == 0:
return 0.0
return complete_sentences / len(sentences)
def get_overall_relevance_score(self, context: str, query: str) -> float:
"""获取整体相关性分数"""
metrics = self.evaluate_context_relevance(context, query)
# 加权平均
weights = {
'lexical_overlap': 0.3,
'semantic_relevance': 0.4,
'information_density': 0.2,
'structural_completeness': 0.1
}
overall_score = sum(metrics[key] * weights[key] for key in weights)
return overall_score2. 上下文效果监控
实现示例
python
class ContextEffectivenessMonitor:
def __init__(self):
self.metrics_history = []
self.performance_data = {}
def monitor_context_effectiveness(self, context: str, query: str,
response: str, expected_response: str = None) -> Dict[str, float]:
"""监控上下文效果"""
metrics = {}
# 上下文质量指标
relevance_evaluator = ContextRelevanceEvaluator()
context_metrics = relevance_evaluator.evaluate_context_relevance(context, query)
metrics.update(context_metrics)
# 响应质量指标
response_metrics = self._evaluate_response_quality(response, query, context)
metrics.update(response_metrics)
# 准确性指标（如果有期望响应）
if expected_response:
accuracy_metrics = self._evaluate_accuracy(response, expected_response)
metrics.update(accuracy_metrics)
# 记录指标
self.metrics_history.append(metrics)
return metrics
def _evaluate_response_quality(self, response: str, query: str, context: str) -> Dict[str, float]:
"""评估响应质量"""
metrics = {}
# 响应长度
metrics['response_length'] = len(response)
# 响应与查询的相关性
metrics['response_query_relevance'] = self._calculate_relevance(response, query)
# 响应与上下文的一致性
metrics['response_context_consistency'] = self._calculate_consistency(response, context)
return metrics
def _calculate_relevance(self, response: str, query: str) -> float:
"""计算相关性"""
query_words = set(query.lower().split())
response_words = set(response.lower().split())
intersection = query_words.intersection(response_words)
union = query_words.union(response_words)
if len(union) == 0:
return 0.0
return len(intersection) / len(union)
def _calculate_consistency(self, response: str, context: str) -> float:
"""计算一致性"""
context_concepts = self._extract_concepts(context)
response_concepts = self._extract_concepts(response)
intersection = context_concepts.intersection(response_concepts)
union = context_concepts.union(response_concepts)
if len(union) == 0:
return 0.0
return len(intersection) / len(union)
def _extract_concepts(self, text: str) -> set:
"""提取概念"""
words = text.lower().split()
concepts = set()
for word in words:
if len(word) > 3:
concepts.add(word)
return concepts
def _evaluate_accuracy(self, response: str, expected: str) -> Dict[str, float]:
"""评估准确性"""
from difflib import SequenceMatcher
metrics = {}
# 文本相似度
similarity = SequenceMatcher(None, response, expected).ratio()
metrics['text_similarity'] = similarity
# 概念重叠度
response_concepts = self._extract_concepts(response)
expected_concepts = self._extract_concepts(expected)
intersection = response_concepts.intersection(expected_concepts)
union = response_concepts.union(expected_concepts)
if len(union) == 0:
metrics['concept_overlap'] = 0.0
else:
metrics['concept_overlap'] = len(intersection) / len(union)
return metrics
def get_effectiveness_summary(self) -> Dict[str, float]:
"""获取效果摘要"""
if not self.metrics_history:
return {}
summary = {}
# 计算每个指标的平均值
for metric_name in self.metrics_history[0].keys():
values = [metrics[metric_name] for metrics in self.metrics_history]
summary[f'{metric_name}_avg'] = sum(values) / len(values)
summary[f'{metric_name}_max'] = max(values)
summary[f'{metric_name}_min'] = min(values)
return summary
def detect_context_issues(self) -> List[str]:
"""检测上下文问题"""
issues = []
if not self.metrics_history:
return issues
# 分析最近的指标
recent_metrics = self.metrics_history[-10:] # 最近10次
# 检查相关性趋势
relevance_scores = [m.get('lexical_overlap', 0) for m in recent_metrics]
if len(relevance_scores) > 1:
avg_relevance = sum(relevance_scores) / len(relevance_scores)
if avg_relevance < 0.3:
issues.append("上下文相关性较低")
# 检查信息密度
density_scores = [m.get('information_density', 0) for m in recent_metrics]
if len(density_scores) > 1:
avg_density = sum(density_scores) / len(density_scores)
if avg_density < 0.5:
issues.append("上下文信息密度不足")
return issues最佳实践
1. 上下文管理策略选择
python
def select_context_management_strategy(requirements: dict) -> str:
"""选择上下文管理策略"""
if requirements['accuracy'] == 'high' and requirements['latency'] == 'low':
return 'semantic_context_manager'
elif requirements['simplicity'] == 'high':
return 'simple_truncation_manager'
elif requirements['dynamism'] == 'high':
return 'dynamic_context_manager'
else:
return 'length_based_context_manager'2. 上下文优化建议
python
class ContextOptimizationAdvisor:
def __init__(self):
self.optimization_rules = {}
def get_optimization_suggestions(self, context_metrics: Dict[str, float]) -> List[str]:
"""获取优化建议"""
suggestions = []
# 基于指标提供建议
if context_metrics.get('lexical_overlap', 0) < 0.3:
suggestions.append("考虑使用更相关的文档或改进检索策略")
if context_metrics.get('information_density', 0) < 0.5:
suggestions.append("尝试压缩冗余信息，提高信息密度")
if context_metrics.get('structural_completeness', 0) < 0.7:
suggestions.append("确保上下文包含完整的句子结构")
return suggestions总结
上下文管理是RAG系统成功的关键技术之一。本文介绍了上下文管理的核心概念、策略和最佳实践，包括基础管理方法、高级技术、优化策略和质量评估等方面。
关键要点：
- 策略选择：根据应用需求选择合适的上下文管理策略
- 质量优化：通过去重、压缩等技术优化上下文质量
- 动态调整：根据查询类型和对话历史动态调整上下文
- 效果监控：建立完善的评估和监控机制
在下一篇文章中，我们将探讨生成质量控制技术，了解如何确保RAG系统生成高质量的回答。
下一步学习建议：
- 阅读《生成质量控制》，了解如何提升生成质量
- 实践不同的上下文管理策略，比较它们的效果
- 关注上下文管理技术的最新发展和创新方案