AI Agent系统开发指南

Agent系统简介

Agent是一个能够自主执行任务的AI系统，它具有以下特点： - 自主决策能力 - 环境感知能力 - 目标导向行为 - 持续学习能力 - 交互通信能力

Agent的核心组件

1. 记忆系统

python
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import RedisChatMessageHistory

# 短期记忆
short_term_memory = ConversationBufferMemory(
    memory_key="chat_history",
    return_messages=True
)

# 长期记忆（使用Redis存储）
long_term_memory = RedisChatMessageHistory(
    url="redis://localhost:6379/0",
    ttl=3600,  # 1小时过期
    session_id="user_123"
)

2. 知识库

python
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter

# 创建知识库
class KnowledgeBase:
    def __init__(self):
        self.embeddings = OpenAIEmbeddings()
        self.vectorstore = None

    def add_documents(self, documents):
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=200
        )
        texts = text_splitter.split_documents(documents)
        self.vectorstore = Chroma.from_documents(
            texts,
            self.embeddings
        )

    def query(self, question, k=4):
        return self.vectorstore.similarity_search(question, k=k)

3. 工具集成

python
from langchain.agents import Tool, initialize_agent
from langchain.agents import AgentType

# 定义工具
class ToolSet:
    def __init__(self):
        self.tools = [
            Tool(
                name="Calculator",
                func=self._calculator,
                description="用于执行数学计算"
            ),
            Tool(
                name="WebSearch",
                func=self._web_search,
                description="用于搜索网络信息"
            ),
            Tool(
                name="DatabaseQuery",
                func=self._query_database,
                description="用于查询数据库"
            )
        ]

    def _calculator(self, expression):
        return eval(expression)

    def _web_search(self, query):
        # 实现网络搜索逻辑
        pass

    def _query_database(self, query):
        # 实现数据库查询逻辑
        pass

Agent架构设计

1. 基础Agent

python
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

class BaseAgent:
    def __init__(self):
        self.llm = OpenAI(temperature=0.7)
        self.memory = ConversationBufferMemory()
        self.knowledge_base = KnowledgeBase()
        self.tools = ToolSet().tools

        self.agent = initialize_agent(
            self.tools,
            self.llm,
            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
            memory=self.memory,
            verbose=True
        )

    def process(self, input_text):
        return self.agent.run(input_text)

2. 专业Agent

python
class ExpertAgent(BaseAgent):
    def __init__(self, domain):
        super().__init__()
        self.domain = domain
        self.prompt_template = PromptTemplate(
            input_variables=["domain", "query"],
            template="作为{domain}专家，{query}"
        )

    def process(self, input_text):
        prompt = self.prompt_template.format(
            domain=self.domain,
            query=input_text
        )
        return super().process(prompt)

多Agent系统

1. Agent协作系统

python
class AgentSystem:
    def __init__(self):
        self.agents = {
            "analyst": ExpertAgent("数据分析"),
            "programmer": ExpertAgent("编程"),
            "writer": ExpertAgent("写作")
        }
        self.coordinator = BaseAgent()

    def process_task(self, task):
        # 任务分配
        agent_assignment = self.coordinator.process(
            f"将任务分配给合适的专家：{task}"
        )

        # 执行任务
        results = {}
        for agent_name, subtask in agent_assignment.items():
            if agent_name in self.agents:
                results[agent_name] = self.agents[agent_name].process(subtask)

        # 整合结果
        return self.coordinator.process(f"整合以下结果：{results}")

2. 竞争机制

python
class CompetitiveAgentSystem:
    def __init__(self, num_agents=3):
        self.agents = [BaseAgent() for _ in range(num_agents)]

    def process_with_voting(self, task):
        # 收集所有agent的答案
        responses = [agent.process(task) for agent in self.agents]

        # 实现投票机制
        return self._vote_best_response(responses)

    def _vote_best_response(self, responses):
        # 实现投票逻辑
        pass

高级特性

1. 自主学习

python
class LearningAgent(BaseAgent):
    def __init__(self):
        super().__init__()
        self.experience_buffer = []

    def process(self, input_text):
        result = super().process(input_text)
        self.experience_buffer.append({
            "input": input_text,
            "output": result
        })
        self._update_knowledge()
        return result

    def _update_knowledge(self):
        # 实现学习机制
        pass

2. 情感识别

python
from transformers import pipeline

class EmpatheticAgent(BaseAgent):
    def __init__(self):
        super().__init__()
        self.sentiment_analyzer = pipeline("sentiment-analysis")

    def process(self, input_text):
        # 分析情感
        sentiment = self.sentiment_analyzer(input_text)[0]

        # 根据情感调整响应
        response = super().process(
            f"考虑到用户情感是{sentiment['label']}，回应：{input_text}"
        )
        return response

最佳实践

  1. 系统设计
  2. 模块化架构
  3. 清晰的责任划分

  4. 可扩展性设计

  5. 性能优化

  6. 并行处理
  7. 缓存机制

  8. 资源管理

  9. 安全考虑

  10. 输入验证
  11. 权限控制
  12. 行为监控

应用场景

  1. 智能客服
  2. 自动化运维
  3. 个人助理
  4. 教育辅导
  5. 金融分析

未来展望

  1. 多模态集成
  2. 强化学习应用
  3. 群体智能
  4. 自主决策
  5. 伦理约束
3. Langchain 5. Prompt