RAG增强Agent模式
RAG（Retrieval-Augmented Generation）增强Agent模式通过将外部知识检索与生成模型相结合，显著提升了Agent的知识准确性和时效性。本节将深入分析该模式的核心架构、实现细节和最佳实践。
1. 架构原理与核心组件
RAG增强Agent的核心在于构建一个检索-生成-验证的闭环系统，其架构图如下：
graph TB
A[用户查询] --> B[查询理解与重写]
B --> C[检索策略选择]
C --> D[向量检索]
C --> E[关键词检索]
C --> F[混合检索]
D --> G[结果融合与重排序]
E --> G
F --> G
G --> H[上下文构建]
H --> I[LLM生成]
I --> J[引用验证]
J --> K[最终输出]
K --> L[反馈学习]
L --> B1.1 核心组件详解
查询理解与重写模块
class QueryRewriter:
def __init__(self, llm):
self.llm = llm
self.prompt_template = """
原始查询: {original_query}
请重写查询以提高检索效果：
1. 扩展同义词和相关概念
2. 添加领域特定的关键词
3. 澄清模糊术语
4. 分解复合问题
重写后的查询:
"""
def rewrite(self, query, context=None):
prompt = self.prompt_template.format(
original_query=query,
context=context or ""
)
rewritten = self.llm.generate(prompt)
return self.post_process(rewritten)
def post_process(self, rewritten):
# 去除重复，标准化格式
queries = [q.strip() for q in rewritten.split('\n') if q.strip()]
return list(set(queries))[:3] # 最多返回3个重写版本多策略检索引擎
class HybridRetriever:
def __init__(self, vector_store, keyword_store, knowledge_graph=None):
self.vector_retriever = VectorRetriever(vector_store)
self.keyword_retriever = KeywordRetriever(keyword_store)
self.graph_retriever = KnowledgeGraphRetriever(knowledge_graph)
self.fusion_strategy = "rrf" # Reciprocal Rank Fusion
def retrieve(self, query, k=10, weights=None):
"""
混合检索策略
:param query: 查询字符串
:param k: 返回文档数量
:param weights: 各检索器权重 [vector_weight, keyword_weight, graph_weight]
"""
weights = weights or [0.6, 0.3, 0.1]
# 并行执行多种检索策略
vector_docs = self.vector_retriever.similarity_search(query, k=k*2)
keyword_docs = self.keyword_retriever.search(query, k=k*2)
graph_docs = self.graph_retriever.search(query, k=k*2) if self.graph_retriever else []
# RRF融合重排序
fused_docs = self.reciprocal_rank_fusion(
[vector_docs, keyword_docs, graph_docs],
weights,
k=k
)
return fused_docs
def reciprocal_rank_fusion(self, doc_lists, weights, k=10):
"""倒数排名融合算法"""
scores = {}
for i, docs in enumerate(doc_lists):
weight = weights[i]
for rank, doc in enumerate(docs):
doc_id = doc.id
# RRF分数计算
rrf_score = weight * (1 / (60 + rank + 1))
scores[doc_id] = scores.get(doc_id, 0) + rrf_score
# 按分数排序并返回前k个
sorted_docs = sorted(scores.items(), key=lambda x: x[1], reverse=True)
return [self.get_doc_by_id(doc_id) for doc_id, _ in sorted_docs[:k]]2. 上下文构建与引用生成
2.1 动态上下文窗口管理
class ContextBuilder:
def __init__(self, max_context_length=4000, chunk_overlap=200):
self.max_context_length = max_context_length
self.chunk_overlap = chunk_overlap
self.citation_format = "[来源{i}: {doc_id}]"
def build_context(self, query, retrieved_docs, llm):
"""
构建包含引用信息的上下文
"""
context_parts = []
citations = []
current_length = 0
# 按相关性排序文档
sorted_docs = sorted(retrieved_docs, key=lambda x: x.score, reverse=True)
for i, doc in enumerate(sorted_docs):
doc_text = self.preprocess_document(doc.content)
doc_length = len(doc_text)
# 检查是否需要截断
if current_length + doc_length > self.max_context_length:
remaining_length = self.max_context_length - current_length
if remaining_length > 100: # 至少保留100字符
truncated_text = doc_text[:remaining_length] + "..."
context_parts.append(truncated_text)
citations.append(self.citation_format.format(i=i+1, doc_id=doc.id))
break
context_parts.append(doc_text)
citations.append(self.citation_format.format(i=i+1, doc_id=doc.id))
current_length += doc_length
# 构建最终上下文
context = "\n\n".join(context_parts)
citation_section = "\n引用来源:\n" + "\n".join(citations)
return {
"context": context,
"citations": citations,
"full_context": context + citation_section
}2.2 可验证的引用生成
class CitationGenerator:
def __init__(self):
self.citation_patterns = {
"direct_quote": '"{quote}" [来源: {doc_id}, 第{page}页]',
"paraphrase": '根据{doc_id}，{content}',
"statistical": '根据{doc_id}的数据，{statistic}'
}
def generate_citations(self, generated_text, retrieved_docs, confidence_threshold=0.8):
"""
基于检索文档生成引用
"""
citations = []
sentences = self.split_into_sentences(generated_text)
for sentence in sentences:
if self.requires_citation(sentence, confidence_threshold):
# 找到最相关的文档
relevant_doc = self.find_relevant_doc(sentence, retrieved_docs)
if relevant_doc:
citation = self.create_citation(sentence, relevant_doc)
citations.append(citation)
return self.format_citations(citations)
def requires_citation(self, sentence, threshold):
"""判断句子是否需要引用"""
citation_triggers = [
"根据", "数据显示", "研究表明", "据报道",
"统计", "分析", "认为", "指出"
]
return any(trigger in sentence for trigger in citation_triggers)3. 增量索引与缓存策略
3.1 增量索引更新
class IncrementalIndexManager:
def __init__(self, vector_store, document_processor):
self.vector_store = vector_store
self.document_processor = document_processor
self.update_queue = asyncio.Queue()
self.version_manager = VersionManager()
async def add_document(self, doc_id, content, metadata=None):
"""异步添加文档到索引"""
# 预处理文档
chunks = self.document_processor.chunk_document(
content,
chunk_size=512,
overlap=50
)
# 生成嵌入向量
embeddings = await self.generate_embeddings(chunks)
# 批量更新索引
await self.vector_store.add_documents(
documents=[{
"id": f"{doc_id}_chunk_{i}",
"content": chunk,
"metadata": {**metadata, "chunk_index": i, "doc_id": doc_id},
"embedding": embedding
} for i, (chunk, embedding) in enumerate(zip(chunks, embeddings))]
)
# 更新版本信息
self.version_manager.update_version(doc_id)
async def update_document(self, doc_id, new_content, old_doc_id=None):
"""增量更新文档"""
if old_doc_id:
await self.vector_store.delete_by_doc_id(old_doc_id)
await self.add_document(doc_id, new_content, metadata={"doc_id": doc_id})
return True反思/自监督模式
模式概述
反思（Self-reflection）模式是LLM Agent架构中的重要增强模式，通过让模型对自身输出进行评估、修正和优化，显著提升任务完成质量和可靠性。该模式基于认知科学的元认知理论，模拟人类在完成任务后的自我检视和反思过程。
核心特征
- 自评估机制：模型主动评估自身输出的质量和正确性
- 迭代优化：基于评估结果进行多轮修正和改进
- 元认知能力：模型具备"思考自己思考"的高级认知能力
- 错误检测与纠正：自动识别逻辑错误并进行修正
技术实现架构
1. 自反思循环流程
graph TD
A[初始任务] --> B[生成初始解答]
B --> C[自反思评估]
C --> D{评估结果}
D -->|通过| E[输出最终结果]
D -->|不通过| F[生成修正建议]
F --> G[基于建议重试]
G --> H[生成改进解答]
H --> C2. 核心组件实现
class SelfReflectionAgent:
def __init__(self, llm_model, max_iterations=3):
self.llm = llm_model
self.max_iterations = max_iterations
self.reflection_prompt_template = """
任务：{task}
当前解答：{current_answer}
请从以下维度评估解答质量：
1. 逻辑一致性：解答是否自相矛盾？
2. 完整性：是否遗漏重要信息？
3. 准确性：事实和计算是否正确？
4. 清晰度：表达是否清楚易懂？
如果存在问题，请提供具体的修正建议。
"""
def reflect_and_improve(self, task, initial_answer):
"""反思和改进主循环"""
current_answer = initial_answer
reflection_history = []
for iteration in range(self.max_iterations):
# 生成反思评估
reflection = self.generate_reflection(task, current_answer)
reflection_history.append(reflection)
# 检查是否通过评估
if self.evaluate_quality(reflection):
break
# 生成改进解答
current_answer = self.improve_answer(task, current_answer, reflection)
return {
'final_answer': current_answer,
'reflection_history': reflection_history,
'iterations_used': len(reflection_history)
}
def generate_reflection(self, task, current_answer):
"""生成反思评估"""
prompt = self.reflection_prompt_template.format(
task=task,
current_answer=current_answer
)
return self.llm.generate(prompt)3. 思维链增强实现
class ChainOfThoughtReflection:
def __init__(self, llm_model):
self.llm = llm_model
self.cot_prompt_template = """
让我们一步一步思考：
任务：{task}
第一步：分析问题
{task_analysis}
第二步：列出已知信息
{known_info}
第三步：推理过程
{reasoning}
第四步：验证推理
{verification}
第五步：得出结论
{conclusion}
现在，请检查上述推理过程中是否存在逻辑错误：
{reflection_prompt}
"""
def enhanced_cot_with_reflection(self, task):
"""带反思的思维链推理"""
# 阶段1：基础思维链推理
reasoning_steps = self.generate_cot_reasoning(task)
# 阶段2：反思验证
reflection = self.reflexive_check(task, reasoning_steps)
# 阶段3：修正和优化
if reflection['has_issues']:
reasoning_steps = self.correct_reasoning(task, reasoning_steps, reflection)
return {
'reasoning': reasoning_steps,
'reflection': reflection,
'final_answer': reasoning_steps['conclusion']
}