传统方式：直接调用大模型API
import requests

def ask_llm(question):
    response = requests.post(
        "https://api.llm.com/v1/chat",
        headers={"Authorization": "Bearer your-api-key"},
        json={"messages": [{"role": "user", "content": question}]}
    )
    return response.json()["choices"][0]["message"]["content"]

 RAG系统简化实现示例
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI

 1. 文档处理与向量化（ETL Pipeline）
documents = load_documents("knowledge_base/")   加载文档
embeddings = OpenAIEmbeddings()   生成向量嵌入
vector_store = Chroma.from_documents(documents, embeddings)   存入向量数据库

 2. 检索增强生成
def rag_query(question):
     检索相关文档
    relevant_docs = vector_store.similarity_search(question, k=3)
    context = "\n".join([doc.page_content for doc in relevant_docs])
    
     构建增强提示词
    prompt = f"""基于以下参考信息回答用户问题：
    参考信息：{context}
    用户问题：{question}
    回答："""
    
     调用模型生成答案
    llm = ChatOpenAI()
    return llm.predict(prompt)

 Agent任务分解示例
def travel_agent(request):
     步骤1：解析用户意图
    if "巴黎旅行" in request:
         步骤2：规划子任务
        tasks = ["查询天气", "筛选景点", "预订酒店"]
        
         步骤3：执行子任务
        weather = call_api("weather_api", {"city": "巴黎"})
        attractions = search_database("巴黎景点")
        hotels = call_api("hotel_api", {"city": "巴黎"})
        
         步骤4：整合结果
        return generate_response(weather, attractions, hotels)

 Function Calling 实现示例
import json

 1. 定义工具函数描述
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "获取指定城市的天气信息",
        "parameters": {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "城市名称"}
            },
            "required": ["city"]
        }
    }
}]

 2. 模型决策调用
response = llm.chat(
    messages=[{"role": "user", "content": "北京今天天气如何？"}],
    tools=tools
)
 模型返回：{"name": "get_weather", "arguments": {"city": "北京"}}

 3. 你的程序执行函数
def get_weather(city):
    return call_weather_api(city)

 4. 将结果回填给模型
result = get_weather("北京")
final_response = llm.chat(
    messages=[...], 
    tool_result=result
)

<!-- pom.xml -->
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter</artifactId>
    <version>0.4.0</version>
</dependency>

 application.yml
spring:
  ai:
    chat:
      providers:
        - name: "openai"
          endpoint: "https://api.openai.com/v1/chat"
          api-key: "${OPENAI_API_KEY}"
          model: "gpt-4"

@Service
public class AIAssistantService {
    
    @Autowired
    private ChatClient chatClient;
    
    @Autowired
    private VectorStore vectorStore;  // 向量数据库（RAG支撑）
    
    /
      RAG增强问答
     /
    public String askWithRAG(String question) {
        // 1. 从知识库检索相关文档
        List<Document> relevantDocs = vectorStore.similaritySearch(question, 3);
        String context = relevantDocs.stream()
            .map(Document::getContent)
            .collect(Collectors.joining("\n"));
        
        // 2. 构建增强提示词
        String prompt = String.format(
            "基于以下参考信息回答：\n参考：%s\n问题：%s\n回答：",
            context, question
        );
        
        // 3. 调用大模型生成答案
        return chatClient.call(prompt);
    }
    
    /
      函数调用（让AI能操作外部系统）
     /
    public ChatResponse functionCalling(String userRequest) {
        // 定义可用工具
        List<Tool> tools = Arrays.asList(
            new Tool("query_order", "查询订单状态", 
                     "{\"type\":\"object\",\"properties\":{\"orderId\":{\"type\":\"string\"}}}"),
            new Tool("send_email", "发送邮件",
                     "{\"type\":\"object\",\"properties\":{\"to\":{\"type\":\"string\"}}}")
        );
        
        // 模型决策调用哪个工具
        return chatClient.chat(userRequest, tools);
    }
}