高级自监督策略
1. 多样性采样（Self-Consistency）
class SelfConsistencyAgent:
def __init__(self, llm_model, num_samples=5):
self.llm = llm_model
self.num_samples = num_samples
self.consensus_prompt = """
基于以下{num_samples}个不同的推理结果，找出最一致的答案：
{sample_answers}
请分析每个答案的合理性，并给出最终的一致性答案。
"""
def self_consistency_evaluation(self, task):
"""自一致性评估"""
# 多样性采样
samples = self.diverse_sampling(task, self.num_samples)
# 生成一致性评估
consensus_prompt = self.consensus_prompt.format(
num_samples=len(samples),
sample_answers="\n".join([f"{i+1}. {ans}" for i, ans in enumerate(samples)])
)
consensus = self.llm.generate(consensus_prompt)
# 计算一致性分数
consistency_score = self.calculate_consistency(samples)
return {
'samples': samples,
'consensus_answer': consensus,
'consistency_score': consistency_score
}2. 错误检测与纠正
class ErrorDetectionCorrection:
def __init__(self, llm_model):
self.llm = llm_model
self.error_patterns = [
"计算错误",
"逻辑矛盾",
"事实错误",
"推理跳跃",
"不完整回答"
]
def detect_and_correct_errors(self, task, answer):
"""错误检测与纠正"""
# 错误检测
detected_errors = self.detect_errors(answer)
# 生成纠正策略
correction_strategies = []
for error in detected_errors:
strategy = self.generate_correction_strategy(error, answer)
correction_strategies.append(strategy)
# 应用纠正
corrected_answer = self.apply_corrections(answer, correction_strategies)
return {
'original_answer': answer,
'detected_errors': detected_errors,
'correction_strategies': correction_strategies,
'corrected_answer': corrected_answer
}
def detect_errors(self, answer):
"""检测错误类型"""
error_detection_prompt = f"""
分析以下回答中可能存在的错误：
回答：{answer}
检查以下错误类型：
{self.error_patterns}
对于每种错误类型，如果存在，请详细说明。
"""
return self.llm.generate(error_detection_prompt)思维树（Tree of Thoughts）扩展
class TreeOfThoughts:
def __init__(self, llm_model, max_depth=3, branching_factor=3):
self.llm = llm_model
self.max_depth = max_depth
self.branching_factor = branching_factor
self.evaluation_prompt = """
评估以下思考路径的可行性（1-10分）：
思考路径：{thought_path}
目标：{goal}
评估维度：
1. 逻辑合理性
2. 目标达成可能性
3. 资源需求合理性
"""
def explore_thought_tree(self, task):
"""思维树探索"""
root_node = ThoughtNode(
content=task,
path=[],
score=0,
is_terminal=False
)
# 广度优先搜索
queue = [root_node]
all_nodes = [root_node]
while queue and len(all_nodes) < 100: # 限制节点数量
current_node = queue.pop(0)
if current_node.depth < self.max_depth:
# 生成子思考
child_thoughts = self.generate_child_thoughts(
current_node.content,
self.branching_factor
)
for thought in child_thoughts:
child_node = ThoughtNode(
content=thought,
path=current_node.path + [thought],
score=self.evaluate_thought(thought, task),
parent=current_node
)
current_node.children.append(child_node)
queue.append(child_node)
all_nodes.append(child_node)
# 选择最佳路径
best_path = self.select_best_path(all_nodes)
return {
'thought_tree': all_nodes,
'best_path': best_path,
'evaluation_scores': [node.score for node in all_nodes]
}高级模式分析
在本节中，我们将深入探讨LLM Agent架构中的三种高级模式，这些模式在处理复杂业务场景时展现出强大的能力和独特的技术价值。高级模式的核心在于通过更复杂的架构设计来解决单Agent模式难以处理的复杂问题。
记忆增强Agent模式
架构设计原理
记忆增强Agent模式通过引入分层记忆系统，显著提升Agent的长期任务处理能力。这种模式特别适用于需要持续学习和上下文保持的场景。
class MemoryEnhancedAgent:
def __init__(self, llm, memory_system, tools):
self.llm = llm
self.memory = memory_system
self.tools = tools
self.current_task = None
async def process_with_memory(self, user_input):
# 1. 提取相关记忆
relevant_memories = await self.memory.retrieve(
query=user_input,
context_limit=5
)
# 2. 构建增强上下文
enhanced_context = self._build_context(
user_input, relevant_memories
)
# 3. 执行推理
response = await self.llm.generate(
prompt=enhanced_context,
max_tokens=1000
)
# 4. 更新记忆
await self._update_memory(user_input, response)
return response记忆系统架构
记忆系统通常采用三层架构：
- 短期记忆（Working Memory）：当前会话上下文
- 语义记忆（Semantic Memory）：知识库和经验
- 情节记忆（Episodic Memory）：历史交互记录
class HierarchicalMemorySystem:
def __init__(self, vector_store, episodic_store):
self.working_memory = WorkingMemory()
self.semantic_memory = SemanticMemory(vector_store)
self.episodic_memory = EpisodicMemory(episodic_store)
async def store_interaction(self, interaction):
# 提取关键信息
key_info = await self._extract_key_info(interaction)
# 存储到不同层级
await self.episodic_memory.store(interaction)
await self.semantic_memory.store(key_info)
await self.working_memory.update(key_info)
async def retrieve(self, query, memory_types=['all']):
results = []
if 'semantic' in memory_types:
semantic_results = await self.semantic_memory.search(query)
results.extend(semantic_results)
if 'episodic' in memory_types:
episodic_results = await self.episodic_memory.search(query)
results.extend(episodic_results)
# 融合和排序
return self._merge_and_rank(results)记忆一致性维护
记忆冲突处理是记忆增强Agent的关键挑战：
class MemoryConsistencyManager:
def __init__(self, conflict_resolution_strategy='majority_vote'):
self.strategy = conflict_resolution_strategy
self.conflict_threshold = 0.7
async def resolve_conflicts(self, new_memory, existing_memories):
conflicts = self._detect_conflicts(new_memory, existing_memories)
if not conflicts:
return new_memory
resolution = await self._apply_resolution_strategy(
new_memory, conflicts
)
return resolution
async def _apply_resolution_strategy(self, new_memory, conflicts):
if self.strategy == 'majority_vote':
return self._majority_vote_resolution(new_memory, conflicts)
elif self.strategy == 'confidence_weighted':
return self._confidence_weighted_resolution(new_memory, conflicts)
elif self.strategy == 'temporal_preference':
return self._temporal_preference_resolution(new_memory, conflicts)应用场景与性能优化
记忆增强模式特别适用于：
- 长期客户服务场景
- 个性化推荐系统
- 持续学习的AI助手
- 复杂项目管理
性能优化策略：
- 记忆索引优化
- 缓存策略设计
- 记忆压缩与总结
- 分布式记忆存储
规则/符号与LLM融合模式
混合推理架构
规则与LLM融合模式通过将符号推理与神经语言模型结合，实现可控性和灵活性的平衡。这种模式在需要高确定性和可解释性的场景中尤为重要。
class SymbolicLLMHybrid:
def __init__(self, llm, rule_engine, knowledge_base):
self.llm = llm
self.rule_engine = rule_engine
self.kb = knowledge_base
self.fusion_strategy = 'sequential'
async def hybrid_reasoning(self, query):
if self.fusion_strategy == 'sequential':
return await self._sequential_reasoning(query)
elif self.fusion_strategy == 'parallel':
return await self._parallel_reasoning(query)
elif self.fusion_strategy == 'iterative':
return await self._iterative_reasoning(query)
async def _sequential_reasoning(self, query):
# 第一阶段：规则推理
symbolic_result = await self.rule_engine.infer(query)
# 第二阶段：LLM增强
enhanced_result = await self.llm.enhance(
symbolic_result,
knowledge=self.kb
)
return enhanced_result工作流引擎集成
class WorkflowDrivenAgent:
def __init__(self, workflow_engine, llm, tools):
self.workflow = workflow_engine
self.llm = llm
self.tools = tools
async def execute_workflow(self, workflow_id, context):
workflow = await self.workflow.load(workflow_id)
for node in workflow.nodes:
if node.type == 'rule':
result = await self._execute_rule_node(node, context)
elif node.type == 'llm':
result = await self._execute_llm_node(node, context)
elif node.type == 'tool':
result = await self._execute_tool_node(node, context)
context = self._update_context(context, result)
return context['result']约束求解集成
class ConstraintAwareAgent:
def __init__(self, llm, constraint_solver):
self.llm = llm
self.solver = constraint_solver
self.constraints = []
async def solve_with_constraints(self, problem, constraints):
# 符号约束求解
symbolic_solution = await self.solver.solve(problem, constraints)
# LLM生成解决方案
solution_prompt = self._build_solution_prompt(
problem, symbolic_solution, constraints
)
llm_solution = await self.llm.generate(solution_prompt)
# 验证解决方案
is_valid = await self._validate_solution(
llm_solution, constraints
)
if not is_valid:
return await self._repair_solution(llm_solution, constraints)
return llm_solution可解释性机制
class ExplainableAgent:
def __init__(self, hybrid_agent):
self.agent = hybrid_agent
async def explainable_process(self, query):
# 记录推理路径
reasoning_trace = []
# 执行推理并记录每一步
result = await self.agent.process(query, reasoning_trace)
# 生成解释
explanation = await self._generate_explanation(reasoning_trace, result)
return {
'result': result,
'explanation': explanation,
'confidence': self._calculate_confidence(reasoning_trace)
}
async def _generate_explanation(self, trace, result):
explanation_prompt = f"""
解释以下推理过程和结果：
推理路径：{trace}
最终结果：{result}
请用自然语言详细解释每个步骤的逻辑和原因。
"""
return await self.llm.generate(explanation_prompt)