AI Agent 架构设计与 Python 记忆工具实战技术指南

AI Agent 架构设计与 Python 记忆工具实战技术指南

1. 技术分析

1.1 AI Agent概述

AI Agent是能够自主完成任务的智能实体：

AI Agent特征
自主性: 自主决策
目标导向: 目标驱动
学习能力: 持续学习
交互能力: 与环境交互

Agent类型:
任务型Agent: 完成特定任务
对话型Agent: 自然语言交互
协作型Agent: 多Agent协作

1.2 Agent架构

Agent架构组件
感知模块: 获取信息
决策模块: 制定计划
执行模块: 执行动作
学习模块: 优化行为

核心能力:
记忆管理
工具使用
反思能力
多步推理

1.3 Agent应用场景

应用场景
自动化办公: 自动处理任务
智能助手: 个人助手
代码生成: 自动编程
数据分析: 自动分析

发展趋势:
多模态能力
长上下文理解
工具调用扩展
自主学习

2. 核心功能实现

2.1 Agent框架

import json

class AIAgent:
def __init__(self, name, model):
self.name = name
self.model = model
self.memory = []
self.tools = {}

def add_tool(self, tool_name, tool_func, description):
self.tools[tool_name] = {
'func': tool_func,
'description': description
}

def add_memory(self, content):
self.memory.append({
'timestamp': '2024-01-01',
'content': content
})

def get_relevant_memory(self, query):
return [m for m in self.memory if any(word in query.lower() for word in m['content'].lower())]

def think(self, prompt):
context = {
'memory': self.get_relevant_memory(prompt),
'available_tools': [{'name': name, 'description': desc['description']} for name, desc in self.tools.items()]
}

full_prompt = f"""
Agent: {self.name}
Context: {json.dumps(context)}
Prompt: {prompt}

Think step by step and decide what to do.
"""

return self.model.generate(full_prompt)

def act(self, action):
if action['type'] == 'tool_call':
tool_name = action['tool']
params = action['params']

if tool_name in self.tools:
result = self.tools[tool_name]['func'](**params)
return result

elif action['type'] == 'response':
return action['content']

return None

def execute(self, prompt):
thought = self.think(prompt)
action = self._parse_action(thought)
result = self.act(action)

return result

def _parse_action(self, thought):
return {'type': 'response', 'content': thought}

2.2 工具调用系统

class ToolSystem:
def __init__(self):
self.registry = {}

def register_tool(self, name, func, schema):
self.registry[name] = {
'func': func,
'schema': schema
}

def call_tool(self, name, **kwargs):
tool = self.registry.get(name)

if not tool:
raise ValueError(f"Tool {name} not found")

return tool['func'](**kwargs)

def get_tool_info(self, name):
return self.registry.get(name, {}).get('schema', {})

def list_tools(self):
return list(self.registry.keys())

2.3 记忆系统

class MemorySystem:
def __init__(self):
self.short_term = []
self.long_term = {}

def add_short_term(self, content):
self.short_term.append(content)

if len(self.short_term) > 100:
self.short_term = self.short_term[-50:]

def add_long_term(self, key, content):
self.long_term[key] = content

def retrieve(self, query):
results = []

for item in self.short_term:
if self._similarity(query, item) > 0.5:
results.append(item)

for key, content in self.long_term.items():
if self._similarity(query, key) > 0.5:
results.append(content)

return results

def _similarity(self, s1, s2):
words1 = set(s1.lower().split())
words2 = set(s2.lower().split())

if not words1 or not words2:
return 0

return len(words1 & words2) / len(words1 | words2)

3. 性能对比

3.1 Agent类型对比

3.2 Agent平台对比

3.3 工具能力对比

4. 最佳实践

4.1 创建AI Agent

def create_agent_example():
class DummyModel:
def generate(self, prompt):
return "I need to use the search tool to find information."

agent = AIAgent('ResearchAgent', DummyModel())

def search_tool(query):
return f"Search results for: {query}"

agent.add_tool('search', search_tool, 'Search the web for information')

result = agent.execute('What is AI Agent?')
print(f"Agent response: {result}")

4.2 工具系统示例

def tool_system_example():
tools = ToolSystem()

def get_weather(city):
return {'city': city, 'temperature': 25, 'condition': 'sunny'}

def calculate(a, b, operation):
if operation == 'add':
return a + b
elif operation == 'multiply':
return a * b

tools.register_tool('get_weather', get_weather, {'city': 'string'})
tools.register_tool('calculate', calculate, {'a': 'number', 'b': 'number', 'operation': 'string'})

weather = tools.call_tool('get_weather', city='Beijing')
print(f"Weather: {weather}")

result = tools.call_tool('calculate', a=10, b=5, operation='multiply')
print(f"Calculation result: {result}")

5. 总结

AI Agent是AI的下一个发展阶段：

对比数据如下：

• LangChain最灵活
• 任务型Agent最成熟
• API工具最常用
• 推荐从简单任务开始

AI Agent将在自动化、客服、编程等领域带来革命性变化。