基础概念
快速入门
so eary
相关概念
- token
- 模型参数:
- 温度: 控制文本生成的随机性，值越低越准确，
- 核采样
- 流式输出
- message
- prompt
SpringAIAlibaba
基础组件
Message
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| @GetMapping("/message")
public String message(@RequestParam(name = "query") String query){
SystemMessage systemMessage = new SystemMessage("你是一个智能机器人");
UserMessage userMessage = new UserMessage(query);
String call = chatModel.call(systemMessage, userMessage );
return call;
}
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Prompt
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| @GetMapping("/chatOptions")
public ChatResponse chatOptions(@RequestParam(name = "query") String query){
SystemMessage systemMessage = new SystemMessage("你是一个智能机器人");
UserMessage userMessage = new UserMessage(query);
var options = new ZhiPuAiChatOptions.Builder()
.model("glm-4.5")
.temperature(0.0)
.maxTokens(15536)
.build();
// 这里的配置会被application.yaml覆盖，目前不知道为啥，学到后面再说
return chatModel.call(new Prompt(List.of(systemMessage,
userMessage),options));
// ChatResponse chatResponse = chatModel.call(new Prompt(List.of(systemMessage,
// userMessage),options));
// var result = chatResponse.getResult().getOutput().getText();
// return result;
}
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ChatModel
流式
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// 注意：一定要配置字符集
@GetMapping("/flux")
public Flux<String> flux(@RequestParam(name = "query") String query){
SystemMessage systemMessage = new SystemMessage("你是一个智能机器人");
UserMessage userMessage = new UserMessage(query);
var call = chatModel.stream(systemMessage, userMessage );
return call;
}
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ChatClient
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// constructor 注入
private final ChatClient chatClient;
public ZhiPuAIClientController(ChatClient.Builder builder ) {
this.chatClient = builder.build();
}
@GetMapping("/simple")
public String simple(@RequestParam(name = "query") String query){
var options = new ZhiPuAiChatOptions.Builder()
.model("glm-4.5")
.temperature(0.0)
.maxTokens(15536)
.build();
// 不优雅
// SystemMessage systemMessage = new SystemMessage("你是一个智能机器人");
// UserMessage userMessage = new UserMessage(query);
// var result = chatClient.prompt(new Prompt(List.of(systemMessage,
// userMessage),options))
// .call().content();
var result = chatClient.prompt()
.system("你是一个ai助手")
.user(query)
.options(options)
.call().content();
//当然这里也可以返回 ChatResponse
return result;
}
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Advisors
介绍
其实就是一个请求前后增强器
- getOrder值越小越早请求
- 这是一个调用链
CallAdvisor 和 StreamAdvisorBaseAdvisor 是上面俩合起来的封装
记忆增强
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| // 简单写一下，很多功能没有打磨
public class SimpleMessageChatAdvisor implements BaseAdvisor {
public static List<Advisor> SimpleMessageChatAdvisor;
private static Map<String, List<Message>> chatMemory = new HashMap<String, List<Message>>();
@Override
public ChatClientRequest before(ChatClientRequest chatClientRequest, AdvisorChain advisorChain) {
//
//
List<Message> messages = chatMemory.get("haiziyao");
if (messages == null) {
messages = new ArrayList<Message>();
chatMemory.put("haiziyao", messages);
}
List<Message> instructions = chatClientRequest.prompt().getInstructions();
messages.addAll(instructions);
//
return chatClientRequest.mutate()
.prompt(chatClientRequest.prompt().mutate().messages(messages).build())
.build();
}
@Override
public ChatClientResponse after(ChatClientResponse chatClientResponse, AdvisorChain advisorChain) {
// 找到会话记录
List<Message> messages = chatMemory.get("haiziyao");
if (messages == null) {
messages = new ArrayList<>();
}
// 获取response中ai的消息
if(Objects.isNull(chatClientResponse)){
return chatClientResponse;
}
// 存入
AssistantMessage output = chatClientResponse.chatResponse()
.getResult()
.getOutput();
messages.add(output);
return chatClientResponse;
}
@Override
public int getOrder() {
return 0;
}
}
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@GetMapping("simplemessageclient")
public String demo(@RequestParam(name ="query")String query){
return chatClient.prompt()
.user(query)
.system("你是个“疯狂星期四文案”生成器，你需要做的就是生成vivo50的文案，无论用户说啥，你都用文案回复")
.advisors(advisorSpec -> advisorSpec.param("conID","haiziyao"))
.advisors(new SimpleMessageChatAdvisor())
.call()
.content();
}
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使用官方Advisor
MessageCharMemoryAdvisor
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@RestController
@RequestMapping("/chatclientplus")
public class ZhiPuCharMemoryController {
private final ChatClient chatClient;
public ZhiPuCharMemoryController(ChatClient.Builder builder) {
MessageWindowChatMemory memory = MessageWindowChatMemory.builder()
.maxMessages(20)
.build();
MessageChatMemoryAdvisor advisor = MessageChatMemoryAdvisor.builder(memory).build();
this.chatClient = builder
.defaultAdvisors(advisor)
.build();
}
@GetMapping("simplemessageclient")
public String demo(@RequestParam(name ="query")String query,
@RequestParam(name = "conversationId")String conversationId){
return chatClient.prompt()
.user(query)
.advisors(advisorSpec -> advisorSpec.param(ChatMemory.CONVERSATION_ID,conversationId))
.call()
.content();
}
}
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- 这里其实几句话说不完
- 如果想自定义，就去实现
ChatMemoryRepository接口，实现所有方法
MessageWindowMemory的局限性或特性
Prompt Template
- 提示词拼接不够优雅，所以封装起来
- 不就是
format吗
- 暂时不给太多例子，因为这个也不好用。
- 我更倾向于那种
read from file
RAG
quick-start
构建向量知识库
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| @GetMapping("/import")
public String importCoffee() {
try {
ClassPathResource resource = new ClassPathResource("QA.csv");
InputStreamReader isr = new InputStreamReader(resource.getInputStream());
CSVParser csvParser = CSVFormat.DEFAULT.builder()
.setHeader()
.setSkipHeaderRecord(true)
.build()
.parse(isr);
List<Document> documents = new ArrayList<Document>();
for (CSVRecord record : csvParser) {
String question = record.get("问题");
String answer = record.get("回答");
String content = "问题:" + question + "\n回答"+ answer;
Document document = new Document(content);
documents.add(document);
}
csvParser.close();
vectorStore.add(documents);
return "成功导入 " + documents.size()+ "条数据到向量数据库";
}catch (Exception e) {
e.printStackTrace();
return "导入失败"+e.getMessage();
}
}
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| @Tool(description = "通过时区id获取当前时间")
public String getTimeByZoneId(@ToolParam(description = "时区id, 比如 Asia/Shanghai")
String zoneId) {
ZoneId zid = ZoneId.of(zoneId);
ZonedDateTime zonedDateTime = ZonedDateTime.now(zid);
DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss z");
return zonedDateTime.format(formatter);
}
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MCP
- MCP host
- MCP client
- MCP server
调包侠进阶
Graph
KeyStrategyFactory
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KeyStrategyFactory keyStrategyFactory = new KeyStrategyFactoryBuilder()
.addStrategy("input1", KeyStrategy.REPLACE)
.addStrategy("input2",KeyStrategy.MERGE)
.addStrategy("input3",KeyStrategy.APPEND)
.build();
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NodeAction & AsyncNodeAction
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stateGraph.addNode("node1", AsyncNodeAction.node_async(node ->{
return Map.of("input1",1);
}));
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StateGraph
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@Bean("simpleGraph")
public CompiledGraph simpleGraph() throws GraphStateException {
KeyStrategyFactory keyStrategyFactory = new KeyStrategyFactoryBuilder()
.addStrategy("sentence",KeyStrategy.REPLACE)
.addStrategy("word",KeyStrategy.REPLACE)
.build();
StateGraph stateGraph = new StateGraph("simpleGraph", keyStrategyFactory);
stateGraph.addNode("SentenceConstructionNode",AsyncNodeAction.node_async(new SentenceConstrucrionNode(chatClient)));
stateGraph.addNode("translateNode",AsyncNodeAction.node_async(new TranslateNode(chatClient)));
stateGraph.addEdge(StateGraph.START,"SentenceConstructionNode");
stateGraph.addEdge("SentenceConstructionNode","translateNode");
stateGraph.addEdge("translateNode",StateGraph.END);
return stateGraph.compile();
}
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循环分支控制语句
条件边
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stateGraph.addConditionalEdges("评估笑话", AsyncEdgeAction.edge_async(
state -> state.value("result","优秀")),
Map.of("优秀",StateGraph.END,
"不够优秀","优化笑话"));
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循环
就是基于条件实现的，只不过在状态中存储了一个用来检验循环次数的值而已
状态存储
对于一些信息或者数据，我们可以使用追加，就是定义keystrateFactory的时候
也可以使用我们的状态存储，可以做对话区分
默认使用的都是ThreadLocal
可视化
项目
