Agent与RAG结合
引言
Agent与RAG结合是人工智能领域的重要发展方向，通过将RAG技术集成到智能代理中，可以实现更智能、更自主的AI系统。本文将深入探讨Agent与RAG结合的实现原理、技术架构和实际应用。
Agent与RAG结合概述
什么是Agent与RAG结合
Agent与RAG结合是一种将RAG技术集成到智能代理中的技术，它能够：
- 自主检索相关信息
- 基于检索结果进行推理
- 执行复杂的任务
- 与环境进行交互
- 学习和适应
Agent与RAG结合的核心价值
技术架构
1. 整体架构
2. 核心组件实现
python
from typing import List, Dict, Any, Optional, Tuple
import asyncio
import json
from dataclasses import dataclass
from enum import Enum
class AgentState(Enum):
IDLE = "idle"
THINKING = "thinking"
ACTING = "acting"
LEARNING = "learning"
ERROR = "error"
@dataclass
class AgentMemory:
"""代理记忆"""
short_term: Dict[str, Any]
long_term: Dict[str, Any]
episodic: List[Dict[str, Any]]
semantic: Dict[str, Any]
@dataclass
class AgentGoal:
"""代理目标"""
description: str
priority: int
deadline: Optional[str]
sub_goals: List['AgentGoal']
@dataclass
class AgentAction:
"""代理行动"""
action_type: str
parameters: Dict[str, Any]
expected_outcome: str
confidence: float
class RAGAgent:
def __init__(self, name: str, rag_system: Any, tools: List[Any]):
self.name = name
self.rag_system = rag_system
self.tools = {tool.name: tool for tool in tools}
self.memory = AgentMemory(
short_term={},
long_term={},
episodic=[],
semantic={}
)
self.goals = []
self.state = AgentState.IDLE
self.current_task = None
self.execution_history = []
async def process_task(self, task: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
"""处理任务"""
try:
self.state = AgentState.THINKING
# 1. 任务理解
task_understanding = await self._understand_task(task, context)
# 2. 目标设定
goals = await self._set_goals(task_understanding)
# 3. 计划制定
plan = await self._create_plan(goals)
# 4. 执行计划
results = await self._execute_plan(plan)
# 5. 结果评估
evaluation = await self._evaluate_results(results, goals)
# 6. 学习更新
await self._update_knowledge(evaluation)
self.state = AgentState.IDLE
return {
'task': task,
'goals': goals,
'plan': plan,
'results': results,
'evaluation': evaluation,
'success': evaluation['success']
}
except Exception as e:
self.state = AgentState.ERROR
return {
'error': str(e),
'task': task,
'success': False
}
async def _understand_task(self, task: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""理解任务"""
# 使用RAG系统检索相关信息
relevant_info = await self.rag_system.retrieve(task)
# 分析任务类型和复杂度
task_analysis = await self._analyze_task(task, relevant_info)
# 构建任务理解
understanding = {
'task_description': task,
'task_type': task_analysis['type'],
'complexity': task_analysis['complexity'],
'required_tools': task_analysis['required_tools'],
'relevant_info': relevant_info,
'context': context or {}
}
return understanding
async def _analyze_task(self, task: str, relevant_info: List[Dict[str, Any]]) -> Dict[str, Any]:
"""分析任务"""
# 使用LLM分析任务
analysis_prompt = f"""
分析以下任务：
任务：{task}
相关信息：{relevant_info}
请分析：
1. 任务类型（信息查询、计算、创作、分析等）
2. 复杂度（简单、中等、复杂）
3. 需要的工具
4. 可能的挑战
"""
# 这里应该调用LLM进行分析
# 简化实现
analysis = {
'type': 'information_query',
'complexity': 'medium',
'required_tools': ['search', 'analysis'],
'challenges': ['信息准确性', '时间限制']
}
return analysis
async def _set_goals(self, task_understanding: Dict[str, Any]) -> List[AgentGoal]:
"""设定目标"""
goals = []
# 主目标
main_goal = AgentGoal(
description=task_understanding['task_description'],
priority=1,
deadline=None,
sub_goals=[]
)
# 子目标
if task_understanding['complexity'] == 'complex':
sub_goals = await self._decompose_task(task_understanding)
main_goal.sub_goals = sub_goals
goals.append(main_goal)
return goals
async def _decompose_task(self, task_understanding: Dict[str, Any]) -> List[AgentGoal]:
"""分解任务"""
sub_goals = []
# 基于任务类型分解
if task_understanding['task_type'] == 'information_query':
sub_goals.append(AgentGoal(
description="检索相关信息",
priority=1,
deadline=None,
sub_goals=[]
))
sub_goals.append(AgentGoal(
description="分析信息质量",
priority=2,
deadline=None,
sub_goals=[]
))
sub_goals.append(AgentGoal(
description="整合信息回答",
priority=3,
deadline=None,
sub_goals=[]
))
return sub_goals
async def _create_plan(self, goals: List[AgentGoal]) -> List[AgentAction]:
"""制定计划"""
plan = []
for goal in goals:
# 为每个目标制定行动
actions = await self._plan_for_goal(goal)
plan.extend(actions)
return plan
async def _plan_for_goal(self, goal: AgentGoal) -> List[AgentAction]:
"""为目标制定计划"""
actions = []
if goal.description == "检索相关信息":
actions.append(AgentAction(
action_type="rag_retrieve",
parameters={"query": goal.description},
expected_outcome="获得相关信息",
confidence=0.8
))
elif goal.description == "分析信息质量":
actions.append(AgentAction(
action_type="analyze_quality",
parameters={"criteria": ["准确性", "相关性", "时效性"]},
expected_outcome="评估信息质量",
confidence=0.7
))
elif goal.description == "整合信息回答":
actions.append(AgentAction(
action_type="synthesize_answer",
parameters={"format": "structured"},
expected_outcome="生成完整回答",
confidence=0.9
))
return actions
async def _execute_plan(self, plan: List[AgentAction]) -> List[Dict[str, Any]]:
"""执行计划"""
results = []
for action in plan:
self.state = AgentState.ACTING
# 执行行动
result = await self._execute_action(action)
results.append(result)
# 更新记忆
self._update_memory(action, result)
return results
async def _execute_action(self, action: AgentAction) -> Dict[str, Any]:
"""执行行动"""
try:
if action.action_type == "rag_retrieve":
result = await self.rag_system.retrieve(action.parameters["query"])
elif action.action_type == "analyze_quality":
result = await self._analyze_quality(action.parameters)
elif action.action_type == "synthesize_answer":
result = await self._synthesize_answer(action.parameters)
else:
result = {"error": f"未知行动类型: {action.action_type}"}
return {
'action': action,
'result': result,
'success': 'error' not in result,
'timestamp': self._get_timestamp()
}
except Exception as e:
return {
'action': action,
'result': {'error': str(e)},
'success': False,
'timestamp': self._get_timestamp()
}
async def _analyze_quality(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
"""分析质量"""
criteria = parameters.get('criteria', [])
# 简化的质量分析
quality_analysis = {
'criteria': criteria,
'scores': {criterion: 0.8 for criterion in criteria},
'overall_score': 0.8,
'recommendations': ['信息质量良好']
}
return quality_analysis
async def _synthesize_answer(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
"""合成回答"""
format_type = parameters.get('format', 'structured')
# 使用RAG系统生成回答
answer = await self.rag_system.generate_answer(
context=self.memory.short_term.get('context', ''),
format=format_type
)
return {
'answer': answer,
'format': format_type,
'confidence': 0.9
}
async def _evaluate_results(self, results: List[Dict[str, Any]], goals: List[AgentGoal]) -> Dict[str, Any]:
"""评估结果"""
evaluation = {
'success': True,
'goal_achievement': {},
'overall_score': 0,
'improvements': []
}
# 评估每个目标的达成情况
for goal in goals:
goal_results = [r for r in results if goal.description in str(r)]
achievement_score = len([r for r in goal_results if r['success']]) / len(goal_results) if goal_results else 0
evaluation['goal_achievement'][goal.description] = {
'score': achievement_score,
'completed': achievement_score > 0.8
}
# 计算总体得分
evaluation['overall_score'] = sum(
g['score'] for g in evaluation['goal_achievement'].values()
) / len(evaluation['goal_achievement'])
# 确定成功状态
evaluation['success'] = evaluation['overall_score'] > 0.7
# 识别改进点
if not evaluation['success']:
evaluation['improvements'].append("提高任务执行成功率")
return evaluation
async def _update_knowledge(self, evaluation: Dict[str, Any]):
"""更新知识"""
# 更新长期记忆
self.memory.long_term['task_patterns'] = self.memory.long_term.get('task_patterns', {})
self.memory.long_term['success_strategies'] = self.memory.long_term.get('success_strategies', [])
# 记录经验
experience = {
'task': self.current_task,
'evaluation': evaluation,
'timestamp': self._get_timestamp()
}
self.memory.episodic.append(experience)
# 更新语义记忆
if evaluation['success']:
self.memory.semantic['successful_patterns'] = self.memory.semantic.get('successful_patterns', [])
self.memory.semantic['successful_patterns'].append(experience)
def _update_memory(self, action: AgentAction, result: Dict[str, Any]):
"""更新记忆"""
# 更新短期记忆
self.memory.short_term['last_action'] = action
self.memory.short_term['last_result'] = result
self.memory.short_term['context'] = result.get('result', {})
def _get_timestamp(self) -> str:
"""获取时间戳"""
import datetime
return datetime.datetime.now().isoformat()RAG增强的Agent能力
1. 知识检索能力
python
class RAGEnhancedAgent:
def __init__(self, rag_system: Any):
self.rag_system = rag_system
self.knowledge_cache = {}
self.retrieval_strategies = {
'semantic': SemanticRetrieval(),
'keyword': KeywordRetrieval(),
'hybrid': HybridRetrieval()
}
async def retrieve_knowledge(self, query: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
"""检索知识"""
# 选择检索策略
strategy = self._select_retrieval_strategy(query, context)
# 执行检索
results = await self.retrieval_strategies[strategy].retrieve(query, context)
# 缓存结果
self.knowledge_cache[query] = results
return results
def _select_retrieval_strategy(self, query: str, context: Dict[str, Any]) -> str:
"""选择检索策略"""
# 基于查询类型选择策略
if len(query.split()) > 5:
return 'semantic'
elif any(keyword in query.lower() for keyword in ['what', 'how', 'why']):
return 'hybrid'
else:
return 'keyword'
async def enhance_knowledge(self, retrieved_knowledge: Dict[str, Any]) -> Dict[str, Any]:
"""增强知识"""
enhanced = {
'original': retrieved_knowledge,
'summarized': await self._summarize_knowledge(retrieved_knowledge),
'related': await self._find_related_knowledge(retrieved_knowledge),
'verified': await self._verify_knowledge(retrieved_knowledge)
}
return enhanced
async def _summarize_knowledge(self, knowledge: Dict[str, Any]) -> str:
"""总结知识"""
# 使用LLM总结知识
summary_prompt = f"""
请总结以下知识：
{knowledge}
要求：
1. 简洁明了
2. 重点突出
3. 逻辑清晰
"""
# 这里应该调用LLM生成总结
summary = "知识总结：基于检索到的信息，可以得出以下结论..."
return summary
async def _find_related_knowledge(self, knowledge: Dict[str, Any]) -> List[Dict[str, Any]]:
"""查找相关知识"""
related = []
# 基于知识内容查找相关项
for key, value in knowledge.items():
if isinstance(value, str):
related_query = f"与{value}相关的信息"
related_results = await self.rag_system.retrieve(related_query)
related.extend(related_results)
return related[:5] # 返回前5个相关项
async def _verify_knowledge(self, knowledge: Dict[str, Any]) -> Dict[str, Any]:
"""验证知识"""
verification = {
'accuracy': 0.8,
'relevance': 0.9,
'completeness': 0.7,
'timeliness': 0.8,
'overall_score': 0.8
}
return verification
class SemanticRetrieval:
async def retrieve(self, query: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
"""语义检索"""
# 使用语义相似度检索
results = await self._semantic_search(query, context)
return {
'method': 'semantic',
'results': results,
'confidence': 0.8
}
async def _semantic_search(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
"""语义搜索"""
# 简化的语义搜索实现
results = [
{
'content': '语义相关的内容1',
'similarity': 0.9,
'source': '知识库1'
},
{
'content': '语义相关的内容2',
'similarity': 0.8,
'source': '知识库2'
}
]
return results
class KeywordRetrieval:
async def retrieve(self, query: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
"""关键词检索"""
# 使用关键词匹配检索
results = await self._keyword_search(query, context)
return {
'method': 'keyword',
'results': results,
'confidence': 0.7
}
async def _keyword_search(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
"""关键词搜索"""
# 简化的关键词搜索实现
results = [
{
'content': '关键词匹配的内容1',
'match_score': 0.9,
'source': '知识库1'
},
{
'content': '关键词匹配的内容2',
'match_score': 0.8,
'source': '知识库2'
}
]
return results
class HybridRetrieval:
async def retrieve(self, query: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
"""混合检索"""
# 结合语义和关键词检索
semantic_results = await SemanticRetrieval().retrieve(query, context)
keyword_results = await KeywordRetrieval().retrieve(query, context)
# 融合结果
fused_results = await self._fuse_results(semantic_results, keyword_results)
return {
'method': 'hybrid',
'results': fused_results,
'confidence': 0.85
}
async def _fuse_results(self, semantic_results: Dict[str, Any],
keyword_results: Dict[str, Any]) -> List[Dict[str, Any]]:
"""融合结果"""
# 简化的结果融合
fused = []
# 添加语义结果
for result in semantic_results['results']:
fused.append({
**result,
'method': 'semantic',
'weight': 0.6
})
# 添加关键词结果
for result in keyword_results['results']:
fused.append({
**result,
'method': 'keyword',
'weight': 0.4
})
# 按权重排序
fused.sort(key=lambda x: x.get('similarity', x.get('match_score', 0)) * x['weight'], reverse=True)
return fused2. 推理决策能力
python
class RAGReasoningAgent:
def __init__(self, rag_system: Any):
self.rag_system = rag_system
self.reasoning_engine = ReasoningEngine()
self.decision_maker = DecisionMaker()
self.knowledge_graph = KnowledgeGraph()
async def reason_and_decide(self, problem: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
"""推理和决策"""
# 1. 问题分析
problem_analysis = await self._analyze_problem(problem, context)
# 2. 知识检索
relevant_knowledge = await self.rag_system.retrieve(problem, context)
# 3. 知识图谱构建
knowledge_graph = await self._build_knowledge_graph(relevant_knowledge)
# 4. 推理链构建
reasoning_chain = await self._build_reasoning_chain(problem_analysis, knowledge_graph)
# 5. 决策生成
decision = await self._generate_decision(reasoning_chain)
# 6. 决策验证
validation = await self._validate_decision(decision, reasoning_chain)
return {
'problem': problem,
'analysis': problem_analysis,
'knowledge': relevant_knowledge,
'reasoning_chain': reasoning_chain,
'decision': decision,
'validation': validation
}
async def _analyze_problem(self, problem: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""分析问题"""
analysis = {
'type': 'unknown',
'complexity': 'medium',
'constraints': [],
'objectives': [],
'stakeholders': []
}
# 使用LLM分析问题
analysis_prompt = f"""
分析以下问题：
问题：{problem}
上下文：{context}
请分析：
1. 问题类型
2. 复杂度
3. 约束条件
4. 目标
5. 利益相关者
"""
# 这里应该调用LLM进行分析
# 简化实现
if 'how' in problem.lower():
analysis['type'] = 'how_to'
elif 'why' in problem.lower():
analysis['type'] = 'why'
elif 'what' in problem.lower():
analysis['type'] = 'what'
return analysis
async def _build_knowledge_graph(self, knowledge: Dict[str, Any]) -> Dict[str, Any]:
"""构建知识图谱"""
graph = {
'entities': [],
'relations': [],
'properties': {}
}
# 从知识中提取实体和关系
for item in knowledge.get('results', []):
entities = await self._extract_entities(item['content'])
relations = await self._extract_relations(item['content'])
graph['entities'].extend(entities)
graph['relations'].extend(relations)
return graph
async def _extract_entities(self, content: str) -> List[Dict[str, Any]]:
"""提取实体"""
# 简化的实体提取
entities = []
# 这里应该使用NER模型提取实体
# 简化实现
words = content.split()
for word in words:
if word.isupper() or word.istitle():
entities.append({
'name': word,
'type': 'concept',
'confidence': 0.8
})
return entities
async def _extract_relations(self, content: str) -> List[Dict[str, Any]]:
"""提取关系"""
# 简化的关系提取
relations = []
# 这里应该使用关系抽取模型
# 简化实现
if 'is' in content:
relations.append({
'subject': 'entity1',
'predicate': 'is',
'object': 'entity2',
'confidence': 0.7
})
return relations
async def _build_reasoning_chain(self, problem_analysis: Dict[str, Any],
knowledge_graph: Dict[str, Any]) -> List[Dict[str, Any]]:
"""构建推理链"""
reasoning_chain = []
# 基于问题类型构建推理链
if problem_analysis['type'] == 'how_to':
reasoning_chain = await self._build_how_to_chain(problem_analysis, knowledge_graph)
elif problem_analysis['type'] == 'why':
reasoning_chain = await self._build_why_chain(problem_analysis, knowledge_graph)
elif problem_analysis['type'] == 'what':
reasoning_chain = await self._build_what_chain(problem_analysis, knowledge_graph)
return reasoning_chain
async def _build_how_to_chain(self, problem_analysis: Dict[str, Any],
knowledge_graph: Dict[str, Any]) -> List[Dict[str, Any]]:
"""构建如何做推理链"""
chain = [
{
'step': 1,
'reasoning': '分析问题需求',
'evidence': knowledge_graph['entities'],
'confidence': 0.8
},
{
'step': 2,
'reasoning': '识别解决方案',
'evidence': knowledge_graph['relations'],
'confidence': 0.7
},
{
'step': 3,
'reasoning': '制定执行计划',
'evidence': problem_analysis['constraints'],
'confidence': 0.9
}
]
return chain
async def _build_why_chain(self, problem_analysis: Dict[str, Any],
knowledge_graph: Dict[str, Any]) -> List[Dict[str, Any]]:
"""构建为什么推理链"""
chain = [
{
'step': 1,
'reasoning': '识别现象',
'evidence': knowledge_graph['entities'],
'confidence': 0.8
},
{
'step': 2,
'reasoning': '分析原因',
'evidence': knowledge_graph['relations'],
'confidence': 0.7
},
{
'step': 3,
'reasoning': '验证因果关系',
'evidence': problem_analysis['constraints'],
'confidence': 0.9
}
]
return chain
async def _build_what_chain(self, problem_analysis: Dict[str, Any],
knowledge_graph: Dict[str, Any]) -> List[Dict[str, Any]]:
"""构建是什么推理链"""
chain = [
{
'step': 1,
'reasoning': '定义概念',
'evidence': knowledge_graph['entities'],
'confidence': 0.8
},
{
'step': 2,
'reasoning': '分析特征',
'evidence': knowledge_graph['relations'],
'confidence': 0.7
},
{
'step': 3,
'reasoning': '总结定义',
'evidence': problem_analysis['constraints'],
'confidence': 0.9
}
]
return chain
async def _generate_decision(self, reasoning_chain: List[Dict[str, Any]]) -> Dict[str, Any]:
"""生成决策"""
decision = {
'recommendation': '基于推理链的建议',
'confidence': 0.8,
'alternatives': [],
'risks': [],
'benefits': []
}
# 基于推理链生成决策
if reasoning_chain:
avg_confidence = sum(step['confidence'] for step in reasoning_chain) / len(reasoning_chain)
decision['confidence'] = avg_confidence
# 生成建议
if avg_confidence > 0.8:
decision['recommendation'] = '强烈推荐执行'
elif avg_confidence > 0.6:
decision['recommendation'] = '建议执行'
else:
decision['recommendation'] = '需要更多信息'
return decision
async def _validate_decision(self, decision: Dict[str, Any],
reasoning_chain: List[Dict[str, Any]]) -> Dict[str, Any]:
"""验证决策"""
validation = {
'valid': True,
'strengths': [],
'weaknesses': [],
'improvements': []
}
# 验证决策质量
if decision['confidence'] < 0.6:
validation['valid'] = False
validation['weaknesses'].append('置信度较低')
if not reasoning_chain:
validation['valid'] = False
validation['weaknesses'].append('缺乏推理链')
# 识别优势
if decision['confidence'] > 0.8:
validation['strengths'].append('高置信度')
if len(reasoning_chain) > 2:
validation['strengths'].append('推理链完整')
# 提出改进建议
if decision['confidence'] < 0.8:
validation['improvements'].append('提高推理质量')
return validation工具集成
1. 工具调用框架
python
class ToolIntegration:
def __init__(self):
self.tools = {}
self.tool_registry = ToolRegistry()
self.tool_executor = ToolExecutor()
self.tool_selector = ToolSelector()
def register_tool(self, tool: Any):
"""注册工具"""
self.tools[tool.name] = tool
self.tool_registry.register(tool)
async def select_and_execute_tool(self, task: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""选择并执行工具"""
# 选择工具
selected_tools = await self.tool_selector.select_tools(task, context)
# 执行工具
results = []
for tool in selected_tools:
result = await self.tool_executor.execute_tool(tool, task, context)
results.append(result)
return {
'selected_tools': selected_tools,
'results': results,
'success': all(r['success'] for r in results)
}
class ToolRegistry:
def __init__(self):
self.registered_tools = {}
self.tool_categories = {
'search': [],
'analysis': [],
'generation': [],
'communication': [],
'data_processing': []
}
def register(self, tool: Any):
"""注册工具"""
self.registered_tools[tool.name] = tool
# 按类别分类
category = tool.category
if category in self.tool_categories:
self.tool_categories[category].append(tool)
def get_tools_by_category(self, category: str) -> List[Any]:
"""按类别获取工具"""
return self.tool_categories.get(category, [])
def get_all_tools(self) -> List[Any]:
"""获取所有工具"""
return list(self.registered_tools.values())
class ToolSelector:
def __init__(self):
self.selection_strategies = {
'rule_based': RuleBasedSelection(),
'ml_based': MLBasedSelection(),
'hybrid': HybridSelection()
}
async def select_tools(self, task: str, context: Dict[str, Any]) -> List[Any]:
"""选择工具"""
# 选择策略
strategy = self._select_strategy(task, context)
# 执行选择
selected_tools = await self.selection_strategies[strategy].select(task, context)
return selected_tools
def _select_strategy(self, task: str, context: Dict[str, Any]) -> str:
"""选择策略"""
# 基于任务复杂度选择策略
if len(task.split()) > 10:
return 'ml_based'
elif 'search' in task.lower():
return 'rule_based'
else:
return 'hybrid'
class ToolExecutor:
def __init__(self):
self.execution_history = []
self.error_handler = ToolErrorHandler()
async def execute_tool(self, tool: Any, task: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""执行工具"""
try:
# 执行工具
result = await tool.execute(task, context)
# 记录执行历史
self.execution_history.append({
'tool': tool.name,
'task': task,
'result': result,
'timestamp': self._get_timestamp()
})
return {
'tool': tool.name,
'result': result,
'success': True,
'timestamp': self._get_timestamp()
}
except Exception as e:
# 错误处理
error_result = self.error_handler.handle_error(e, tool, task)
return {
'tool': tool.name,
'result': error_result,
'success': False,
'error': str(e),
'timestamp': self._get_timestamp()
}
def _get_timestamp(self) -> str:
"""获取时间戳"""
import datetime
return datetime.datetime.now().isoformat()
class ToolErrorHandler:
def handle_error(self, error: Exception, tool: Any, task: str) -> Dict[str, Any]:
"""处理工具错误"""
error_info = {
'error_type': type(error).__name__,
'error_message': str(error),
'tool': tool.name,
'task': task,
'suggestions': []
}
# 基于错误类型提供建议
if 'timeout' in str(error).lower():
error_info['suggestions'].append('增加超时时间')
elif 'permission' in str(error).lower():
error_info['suggestions'].append('检查权限设置')
elif 'network' in str(error).lower():
error_info['suggestions'].append('检查网络连接')
return error_info2. 具体工具实现
python
class SearchTool:
def __init__(self, name: str = "search"):
self.name = name
self.category = "search"
self.description = "搜索工具"
async def execute(self, task: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""执行搜索"""
# 简化的搜索实现
search_results = [
{
'title': '搜索结果1',
'content': '搜索结果内容1',
'url': 'https://example.com/1',
'relevance': 0.9
},
{
'title': '搜索结果2',
'content': '搜索结果内容2',
'url': 'https://example.com/2',
'relevance': 0.8
}
]
return {
'query': task,
'results': search_results,
'total_count': len(search_results)
}
class AnalysisTool:
def __init__(self, name: str = "analysis"):
self.name = name
self.category = "analysis"
self.description = "分析工具"
async def execute(self, task: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""执行分析"""
# 简化的分析实现
analysis_result = {
'summary': '分析总结',
'insights': ['洞察1', '洞察2', '洞察3'],
'recommendations': ['建议1', '建议2'],
'confidence': 0.8
}
return analysis_result
class GenerationTool:
def __init__(self, name: str = "generation"):
self.name = name
self.category = "generation"
self.description = "生成工具"
async def execute(self, task: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""执行生成"""
# 简化的生成实现
generated_content = {
'text': '生成的内容',
'format': 'structured',
'length': 100,
'quality': 0.8
}
return generated_content
class CommunicationTool:
def __init__(self, name: str = "communication"):
self.name = name
self.category = "communication"
self.description = "通信工具"
async def execute(self, task: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""执行通信"""
# 简化的通信实现
communication_result = {
'message': '通信消息',
'recipient': '接收者',
'status': 'sent',
'timestamp': self._get_timestamp()
}
return communication_result
def _get_timestamp(self) -> str:
"""获取时间戳"""
import datetime
return datetime.datetime.now().isoformat()
class DataProcessingTool:
def __init__(self, name: str = "data_processing"):
self.name = name
self.category = "data_processing"
self.description = "数据处理工具"
async def execute(self, task: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""执行数据处理"""
# 简化的数据处理实现
processing_result = {
'input_data': context.get('data', {}),
'processed_data': '处理后的数据',
'processing_method': 'standard',
'quality_score': 0.9
}
return processing_result实战应用
1. 智能助手Agent
python
class IntelligentAssistantAgent:
def __init__(self, rag_system: Any):
self.rag_system = rag_system
self.agent = RAGAgent("智能助手", rag_system, [])
self.tool_integration = ToolIntegration()
self.conversation_manager = ConversationManager()
# 注册工具
self._register_tools()
def _register_tools(self):
"""注册工具"""
tools = [
SearchTool(),
AnalysisTool(),
GenerationTool(),
CommunicationTool(),
DataProcessingTool()
]
for tool in tools:
self.tool_integration.register_tool(tool)
async def assist_user(self, user_input: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
"""协助用户"""
try:
# 1. 理解用户意图
intent = await self._understand_user_intent(user_input, context)
# 2. 制定协助计划
plan = await self._create_assistance_plan(intent)
# 3. 执行协助任务
results = await self._execute_assistance_plan(plan)
# 4. 生成响应
response = await self._generate_response(results, intent)
# 5. 更新对话状态
await self.conversation_manager.update_state(user_input, response)
return {
'user_input': user_input,
'intent': intent,
'plan': plan,
'results': results,
'response': response,
'success': True
}
except Exception as e:
return {
'user_input': user_input,
'error': str(e),
'success': False
}
async def _understand_user_intent(self, user_input: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""理解用户意图"""
# 使用RAG系统检索相关信息
relevant_info = await self.rag_system.retrieve(user_input)
# 分析用户意图
intent_analysis = {
'intent_type': 'unknown',
'entities': [],
'sentiment': 'neutral',
'urgency': 'medium',
'complexity': 'medium'
}
# 基于用户输入分析意图
if 'help' in user_input.lower():
intent_analysis['intent_type'] = 'help_request'
elif 'search' in user_input.lower():
intent_analysis['intent_type'] = 'search_request'
elif 'analyze' in user_input.lower():
intent_analysis['intent_type'] = 'analysis_request'
elif 'generate' in user_input.lower():
intent_analysis['intent_type'] = 'generation_request'
return intent_analysis
async def _create_assistance_plan(self, intent: Dict[str, Any]) -> List[Dict[str, Any]]:
"""制定协助计划"""
plan = []
if intent['intent_type'] == 'help_request':
plan.append({
'action': 'provide_help',
'parameters': {'intent': intent},
'priority': 1
})
elif intent['intent_type'] == 'search_request':
plan.append({
'action': 'search_information',
'parameters': {'query': intent.get('query', '')},
'priority': 1
})
elif intent['intent_type'] == 'analysis_request':
plan.append({
'action': 'analyze_data',
'parameters': {'data': intent.get('data', {})},
'priority': 1
})
elif intent['intent_type'] == 'generation_request':
plan.append({
'action': 'generate_content',
'parameters': {'requirements': intent.get('requirements', {})},
'priority': 1
})
return plan
async def _execute_assistance_plan(self, plan: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""执行协助计划"""
results = []
for action in plan:
if action['action'] == 'provide_help':
result = await self._provide_help(action['parameters'])
elif action['action'] == 'search_information':
result = await self._search_information(action['parameters'])
elif action['action'] == 'analyze_data':
result = await self._analyze_data(action['parameters'])
elif action['action'] == 'generate_content':
result = await self._generate_content(action['parameters'])
else:
result = {'error': f"未知行动: {action['action']}"}
results.append(result)
return results
async def _provide_help(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
"""提供帮助"""
help_content = {
'message': '我可以帮助您搜索信息、分析数据、生成内容等。请告诉我您需要什么帮助。',
'available_functions': ['搜索', '分析', '生成', '通信', '数据处理'],
'examples': [
'搜索关于RAG技术的信息',
'分析这组数据的趋势',
'生成一份报告',
'发送邮件给某人',
'处理CSV文件'
]
}
return help_content
async def _search_information(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
"""搜索信息"""
query = parameters.get('query', '')
# 使用RAG系统搜索
search_results = await self.rag_system.retrieve(query)
return {
'query': query,
'results': search_results,
'total_count': len(search_results)
}
async def _analyze_data(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
"""分析数据"""
data = parameters.get('data', {})
# 简化的数据分析
analysis_result = {
'summary': '数据分析总结',
'insights': ['洞察1', '洞察2', '洞察3'],
'recommendations': ['建议1', '建议2'],
'confidence': 0.8
}
return analysis_result
async def _generate_content(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
"""生成内容"""
requirements = parameters.get('requirements', {})
# 使用RAG系统生成内容
generated_content = await self.rag_system.generate_answer(
context=requirements.get('context', ''),
format=requirements.get('format', 'text')
)
return {
'content': generated_content,
'format': requirements.get('format', 'text'),
'length': len(generated_content),
'quality': 0.8
}
async def _generate_response(self, results: List[Dict[str, Any]], intent: Dict[str, Any]) -> str:
"""生成响应"""
response_parts = []
for result in results:
if 'error' in result:
response_parts.append(f"抱歉，处理过程中出现错误：{result['error']}")
else:
response_parts.append(self._format_result(result, intent))
return "\n".join(response_parts)
def _format_result(self, result: Dict[str, Any], intent: Dict[str, Any]) -> str:
"""格式化结果"""
if intent['intent_type'] == 'help_request':
return result.get('message', '')
elif intent['intent_type'] == 'search_request':
return f"搜索到{result.get('total_count', 0)}个结果"
elif intent['intent_type'] == 'analysis_request':
return f"分析完成，置信度：{result.get('confidence', 0)}"
elif intent['intent_type'] == 'generation_request':
return result.get('content', '')
else:
return str(result)
class ConversationManager:
def __init__(self):
self.conversation_history = []
self.context = {}
async def update_state(self, user_input: str, response: str):
"""更新对话状态"""
self.conversation_history.append({
'user_input': user_input,
'response': response,
'timestamp': self._get_timestamp()
})
# 更新上下文
self.context['last_input'] = user_input
self.context['last_response'] = response
self.context['conversation_length'] = len(self.conversation_history)
def _get_timestamp(self) -> str:
"""获取时间戳"""
import datetime
return datetime.datetime.now().isoformat()
def get_context(self) -> Dict[str, Any]:
"""获取上下文"""
return self.context.copy()
def get_history(self) -> List[Dict[str, Any]]:
"""获取历史"""
return self.conversation_history.copy()2. 任务执行Agent
python
class TaskExecutionAgent:
def __init__(self, rag_system: Any):
self.rag_system = rag_system
self.agent = RAGAgent("任务执行", rag_system, [])
self.task_planner = TaskPlanner()
self.task_executor = TaskExecutor()
self.progress_tracker = ProgressTracker()
async def execute_task(self, task_description: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
"""执行任务"""
try:
# 1. 任务规划
plan = await self.task_planner.create_plan(task_description, context)
# 2. 执行任务
execution_result = await self.task_executor.execute_plan(plan)
# 3. 跟踪进度
progress = await self.progress_tracker.track_progress(execution_result)
# 4. 生成报告
report = await self._generate_execution_report(execution_result, progress)
return {
'task': task_description,
'plan': plan,
'execution_result': execution_result,
'progress': progress,
'report': report,
'success': execution_result['success']
}
except Exception as e:
return {
'task': task_description,
'error': str(e),
'success': False
}
async def _generate_execution_report(self, execution_result: Dict[str, Any],
progress: Dict[str, Any]) -> Dict[str, Any]:
"""生成执行报告"""
report = {
'summary': '任务执行报告',
'completion_rate': progress.get('completion_rate', 0),
'success_rate': execution_result.get('success_rate', 0),
'total_time': execution_result.get('total_time', 0),
'issues': execution_result.get('issues', []),
'recommendations': []
}
# 基于执行结果生成建议
if report['success_rate'] < 0.8:
report['recommendations'].append('提高任务执行成功率')
if report['completion_rate'] < 1.0:
report['recommendations'].append('完成剩余任务')
return report
class TaskPlanner:
def __init__(self):
self.planning_strategies = {
'sequential': SequentialPlanning(),
'parallel': ParallelPlanning(),
'hybrid': HybridPlanning()
}
async def create_plan(self, task_description: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""创建计划"""
# 分析任务
task_analysis = await self._analyze_task(task_description, context)
# 选择规划策略
strategy = self._select_planning_strategy(task_analysis)
# 创建计划
plan = await self.planning_strategies[strategy].create_plan(task_analysis)
return plan
async def _analyze_task(self, task_description: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""分析任务"""
analysis = {
'description': task_description,
'type': 'unknown',
'complexity': 'medium',
'dependencies': [],
'resources': [],
'constraints': []
}
# 基于任务描述分析
if 'analyze' in task_description.lower():
analysis['type'] = 'analysis'
elif 'generate' in task_description.lower():
analysis['type'] = 'generation'
elif 'search' in task_description.lower():
analysis['type'] = 'search'
elif 'process' in task_description.lower():
analysis['type'] = 'processing'
return analysis
def _select_planning_strategy(self, task_analysis: Dict[str, Any]) -> str:
"""选择规划策略"""
if task_analysis['complexity'] == 'high':
return 'hybrid'
elif task_analysis['type'] in ['analysis', 'generation']:
return 'sequential'
else:
return 'parallel'
class TaskExecutor:
def __init__(self):
self.execution_strategies = {
'sequential': SequentialExecution(),
'parallel': ParallelExecution(),
'hybrid': HybridExecution()
}
async def execute_plan(self, plan: Dict[str, Any]) -> Dict[str, Any]:
"""执行计划"""
strategy = plan.get('strategy', 'sequential')
# 执行计划
result = await self.execution_strategies[strategy].execute(plan)
return result
class ProgressTracker:
def __init__(self):
self.tracking_history = []
async def track_progress(self, execution_result: Dict[str, Any]) -> Dict[str, Any]:
"""跟踪进度"""
progress = {
'completion_rate': 0,
'success_rate': 0,
'current_step': 0,
'total_steps': 0,
'estimated_time_remaining': 0
}
# 计算进度
if execution_result.get('steps'):
completed_steps = len([s for s in execution_result['steps'] if s.get('completed', False)])
total_steps = len(execution_result['steps'])
progress['completion_rate'] = completed_steps / total_steps if total_steps > 0 else 0
progress['current_step'] = completed_steps
progress['total_steps'] = total_steps
# 计算成功率
if execution_result.get('steps'):
successful_steps = len([s for s in execution_result['steps'] if s.get('success', False)])
total_steps = len(execution_result['steps'])
progress['success_rate'] = successful_steps / total_steps if total_steps > 0 else 0
# 记录跟踪历史
self.tracking_history.append({
'timestamp': self._get_timestamp(),
'progress': progress,
'execution_result': execution_result
})
return progress
def _get_timestamp(self) -> str:
"""获取时间戳"""
import datetime
return datetime.datetime.now().isoformat()最佳实践
1. 性能优化
python
class OptimizedRAGAgent:
def __init__(self, rag_system: Any):
self.rag_system = rag_system
self.agent = RAGAgent("优化代理", rag_system, [])
self.cache_manager = CacheManager()
self.parallel_processor = ParallelProcessor()
self.model_optimizer = ModelOptimizer()
async def optimize_performance(self) -> Dict[str, Any]:
"""优化性能"""
optimizations = {}
# 缓存优化
cache_optimization = await self.cache_manager.optimize_cache()
optimizations['cache'] = cache_optimization
# 并行处理优化
parallel_optimization = await self.parallel_processor.optimize_parallel_processing()
optimizations['parallel'] = parallel_optimization
# 模型优化
model_optimization = await self.model_optimizer.optimize_models()
optimizations['model'] = model_optimization
return optimizations
class CacheManager:
async def optimize_cache(self) -> Dict[str, Any]:
"""优化缓存"""
return {
'knowledge_cache': '启用知识缓存',
'result_cache': '启用结果缓存',
'model_cache': '启用模型缓存'
}
class ParallelProcessor:
async def optimize_parallel_processing(self) -> Dict[str, Any]:
"""优化并行处理"""
return {
'task_parallel': '启用任务并行处理',
'tool_parallel': '启用工具并行处理',
'batch_processing': '启用批量处理'
}
class ModelOptimizer:
async def optimize_models(self) -> Dict[str, Any]:
"""优化模型"""
return {
'model_quantization': '启用模型量化',
'model_pruning': '启用模型剪枝',
'model_distillation': '启用模型蒸馏'
}2. 错误处理
python
class RobustRAGAgent:
def __init__(self, rag_system: Any):
self.rag_system = rag_system
self.agent = RAGAgent("健壮代理", rag_system, [])
self.error_handler = ErrorHandler()
self.fallback_strategies = FallbackStrategies()
async def process_with_error_handling(self, task: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
"""带错误处理的处理"""
try:
# 处理任务
result = await self.agent.process_task(task, context)
# 验证结果
validation_result = await self.error_handler.validate_result(result)
if not validation_result['valid']:
# 使用降级策略
result = await self.fallback_strategies.apply_fallback(task, context, validation_result)
return result
except Exception as e:
# 记录错误
await self.error_handler.log_error(e)
# 使用降级策略
result = await self.fallback_strategies.apply_fallback(task, context, {'error': str(e)})
return result
class ErrorHandler:
async def validate_result(self, result: Dict[str, Any]) -> Dict[str, Any]:
"""验证结果"""
validation_result = {
'valid': True,
'warnings': [],
'errors': []
}
# 检查结果完整性
if 'error' in result:
validation_result['valid'] = False
validation_result['errors'].append(result['error'])
# 检查任务完成情况
if not result.get('success', False):
validation_result['warnings'].append('任务未成功完成')
return validation_result
async def log_error(self, error: Exception):
"""记录错误"""
import logging
logger = logging.getLogger(__name__)
logger.error(f"RAG代理处理错误: {str(error)}")
class FallbackStrategies:
async def apply_fallback(self, task: str, context: Dict[str, Any], error_info: Dict[str, Any]) -> Dict[str, Any]:
"""应用降级策略"""
# 简化的降级策略
fallback_result = {
'task': task,
'result': '抱歉，处理过程中出现错误，请稍后再试。',
'error': error_info.get('error', '未知错误'),
'fallback': True,
'success': False
}
return fallback_result总结
Agent与RAG结合是人工智能领域的重要发展方向，通过将RAG技术集成到智能代理中，可以实现更智能、更自主的AI系统。本文介绍了Agent与RAG结合的实现原理、技术架构和实际应用，包括RAG增强的Agent能力、工具集成、推理决策等方面。
关键要点：
- 知识检索：Agent能够自主检索相关信息
- 推理决策：基于检索结果进行智能推理
- 工具集成：集成各种工具执行复杂任务
- 任务执行：自主执行复杂的任务流程
- 学习适应：从经验中学习并适应环境
在下一篇文章中，我们将探讨RAG评估体系，了解如何评估RAG系统的性能和质量。
下一步学习建议：
- 阅读《RAG评估体系》，了解如何评估RAG系统的性能和质量
- 实践Agent与RAG结合的设计和实现
- 关注Agent与RAG结合技术的最新发展和创新方案