基于LangChain的多语言Agent开发实战（Python & Java）

1. LangChain Agent核心概念

1.1 LangChain架构概述

LangChain是一个用于构建基于LLM的应用程序的框架，其核心模块包括：

• Models：对接多种LLM（如GPT-4、Claude）
• Prompts：提示词管理与模板化
• Chains：任务流水线编排
• Agents：自主决策系统
• Memory：短期/长期记忆存储

1.2 Agent的核心组件

组件	功能	实现类（Python）
AgentExecutor	运行Agent逻辑	langchain.agents.AgentExecutor
Tool	扩展外部能力	langchain.tools.BaseTool
Memory	历史记录存储	langchain.memory.ConversationBufferMemory

1.3 工具与记忆

• 工具示例：
  • 搜索引擎（SerpAPI）
  • 数学计算（WolframAlpha）
  • 自定义Python函数
• 记忆类型：
  • 短期记忆：对话上下文
  • 长期记忆：向量数据库（Pinecone）

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2. Python版LangChain Agent开发

2.1 环境配置

pip install langchain openai python-dotenv

创建.env文件存储API密钥：

OPENAI_API_KEY=sk-xxxSERPAPI_API_KEY=xxx

2.2 基础Agent实现

from langchain.agents import AgentType, initialize_agentfrom langchain.llms import OpenAIfrom langchain.tools import Tooldef search(query: str) -> str:    return "Results: " + query  # 模拟搜索tools = [    Tool(        name="Search",        func=search,        description="Useful for answering questions"    )]llm = OpenAI(temperature=0)agent = initialize_agent(    tools, llm,     agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,    verbose=True)agent.run("What is LangChain?")

2.3 自定义工具开发

from langchain.tools import BaseToolfrom typing import Optionalclass CalculatorTool(BaseTool):    name = "Calculator"    description = "Performs math calculations"    def _run(self, expression: str) -> str:        try:            return str(eval(expression))        except:            return "Invalid expression"tools.append(CalculatorTool())

2.4 记忆增强型Agent

from langchain.memory import ConversationBufferMemorymemory = ConversationBufferMemory(memory_key="chat_history")agent = initialize_agent(    tools, llm,     agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,    memory=memory,    verbose=True)agent.run("Calculate 2+2")  # 记忆会被保留

2.5 多Agent协作系统

from langchain.agents import AgentExecutorfrom langchain.agents.agent import AgentOutputParserfrom langchain.schema import AgentActionclass CustomAgent(AgentExecutor):    def plan(self, inputs):        # 自定义决策逻辑        return AgentAction(tool="Search", tool_input=inputs["input"])agent1 = CustomAgent(...)agent2 = initialize_agent(...)

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3. Java版LangChain集成开发

3.1 通过Jython调用Python Agent

import org.python.util.PythonInterpreter;public class JythonAgent {    public static void main(String[] args) {        PythonInterpreter py = new PythonInterpreter();        py.exec("from langchain.agents import load_tools");        py.exec("agent = load_tools(['serpapi'])");        py.exec("print(agent.run('What is Java?'))");    }}

3.2 使用LangChain4j（Java版）

<dependency>    <groupId>io.github.langchain4jgroupId>    <artifactId>langchain4jartifactId>    <version>0.1.0version>dependency>

import dev.langchain4j.agent.tool.Tool;import dev.langchain4j.model.openai.OpenAiChatModel;public class JavaAgent {    @Tool("Searches the web")    String search(String query) {        return "Results for " + query;    }    public static void main(String[] args) {        OpenAiChatModel model = OpenAiChatModel.withApiKey("sk-xxx");        JavaAgent agent = new JavaAgent();        String result = model.generate("Search: LangChain4j");        System.out.println(result);    }}

3.3 Spring Boot集成案例

@RestControllerpublic class AgentController {    @PostMapping("/ask")    public String ask(@RequestBody String question) {        OpenAiChatModel model = OpenAiChatModel.withApiKey("sk-xxx");        return model.generate(question);    }}

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4. 高级功能实现

4.1 多模态Agent

from langchain.tools import MultimodalToolfrom PIL import Imageclass ImageAnalyzer(MultimodalTool):    def _run(self, image_path: str) -> str:        img = Image.open(image_path)        return f"Image size: {img.size}"tools.append(ImageAnalyzer())

4.2 自主任务分解

from langchain.experimental import AutoGPTagent = AutoGPT.from_llm_and_tools(    llm=OpenAI(temperature=0),    tools=tools,    memory=memory)agent.run("Write a report about AI trends")

4.3 分布式Agent通信

import redisfrom langchain.agents import Agentclass DistributedAgent(Agent):    def __init__(self, redis_client):        self.redis = redis_client    def send_message(self, agent_id: str, message: str):        self.redis.publish(agent_id, message)

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5. 性能优化与生产部署

5.1 异步处理优化

import asynciofrom langchain.agents import AgentExecutorasync def async_agent_run():    agent = AgentExecutor(...)    await agent.arun("Async query")asyncio.run(async_agent_run())

5.2 向量数据库集成

from langchain.vectorstores import Pineconefrom langchain.embeddings import OpenAIEmbeddingsembeddings = OpenAIEmbeddings()vectorstore = Pinecone.from_documents(    documents, embeddings, index_name="my-index")

5.3 Kubernetes部署

apiVersion: apps/v1kind: Deploymentmetadata:  name: langchain-agentspec:  containers:  - name: agent    image: python:3.9    command: ["python", "/app/agent.py"]

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6. 完整项目代码仓库

• Python版：https://github.com/your-repo/langchain-agent-python
• Java版：https://github.com/your-repo/langchain4j-spring

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7. 未来扩展方向

1. 强化学习优化：让Agent自主改进策略
2. 边缘计算：在IoT设备上运行轻量级Agent
3. 区块链集成：构建去中心化Agent网络