feat(XL13): 支持工作流大模型流式输出
- 增加文本与思考增量事件及取消终态收口 - 支持图片输入解析并修复死信定义查找空值 - 补充并发与模型流式回归测试
This commit is contained in:
@@ -28,7 +28,11 @@
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<artifactId>easy-agents-core</artifactId>
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</dependency>
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<dependency>
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<groupId>junit</groupId>
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<artifactId>junit</artifactId>
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<scope>test</scope>
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</dependency>
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</dependencies>
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</project>
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@@ -2,46 +2,240 @@ package com.easyagents.flow.support.provider;
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import com.easyagents.core.message.AiMessage;
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import com.easyagents.core.message.SystemMessage;
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import com.easyagents.core.model.chat.BaseChatModel;
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import com.easyagents.core.model.chat.ChatModel;
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import com.easyagents.core.model.chat.StreamResponseListener;
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import com.easyagents.core.model.client.StreamContext;
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import com.easyagents.core.model.chat.response.AiMessageResponse;
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import com.easyagents.core.prompt.SimplePrompt;
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import com.easyagents.core.util.ImageUtil;
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import com.easyagents.flow.core.chain.Chain;
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import com.easyagents.flow.core.chain.ChainStatus;
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import com.easyagents.flow.core.chain.event.ChainStatusChangeEvent;
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import com.easyagents.flow.core.chain.event.LlmStreamEvent;
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import com.easyagents.flow.core.chain.listener.ChainEventListener;
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import com.easyagents.flow.core.llm.Llm;
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import com.easyagents.flow.core.node.LlmNode;
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import com.easyagents.flow.core.util.StringUtil;
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import java.io.File;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.List;
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import java.util.UUID;
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import java.util.concurrent.CountDownLatch;
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import java.util.concurrent.atomic.AtomicReference;
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/**
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* 基于 Easy-Agents 聊天模型实现工作流 LLM 调用。
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*/
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public class EasyAgentsLlm implements Llm {
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private ChatModel chatModel;
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private ImageInputResolver imageInputResolver;
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/**
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* 获取聊天模型。
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*
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* @return 聊天模型
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*/
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public ChatModel getChatModel() {
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return chatModel;
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}
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/**
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* 设置聊天模型。
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*
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* @param chatModel 聊天模型
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*/
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public void setChatModel(ChatModel chatModel) {
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this.chatModel = chatModel;
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}
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/**
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* 获取图片输入解析器。
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*
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* @return 图片输入解析器
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*/
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public ImageInputResolver getImageInputResolver() {
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return imageInputResolver;
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}
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/**
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* 设置图片输入解析器。
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*
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* @param imageInputResolver 图片输入解析器
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*/
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public void setImageInputResolver(ImageInputResolver imageInputResolver) {
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this.imageInputResolver = imageInputResolver;
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}
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/**
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* 调用聊天模型并返回文本结果。
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*
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* @param messageInfo 消息信息
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* @param options 模型参数
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* @param llmNode 当前 LLM 节点
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* @param chain 工作流链
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* @return 模型文本结果
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*/
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@Override
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public String chat(MessageInfo messageInfo, ChatOptions options, LlmNode llmNode, Chain chain) {
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SimplePrompt prompt = buildPrompt(messageInfo);
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com.easyagents.core.model.chat.ChatOptions chatOptions = buildChatOptions(options);
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CountDownLatch completion = new CountDownLatch(1);
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AtomicReference<StreamContext> streamContext = new AtomicReference<>();
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AtomicReference<String> result = new AtomicReference<>();
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AtomicReference<Throwable> failure = new AtomicReference<>();
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String streamId = UUID.randomUUID().toString();
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ChainEventListener cancellationListener = (event, eventChain) -> {
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if (!(event instanceof ChainStatusChangeEvent statusEvent)
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|| statusEvent.getStatus() != ChainStatus.CANCELLED
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|| !chain.getStateInstanceId().equals(eventChain.getStateInstanceId())) {
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return;
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}
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StreamContext context = streamContext.get();
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if (context != null) {
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context.getClient().stop();
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}
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};
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chain.getEventManager().addEventListener(
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ChainStatusChangeEvent.class, cancellationListener);
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try {
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chatModel.chatStream(prompt, new StreamResponseListener() {
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/**
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* 记录流客户端，供工作流取消时立即关闭模型连接。
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*
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* @param context 流上下文
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*/
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@Override
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public void onStart(StreamContext context) {
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streamContext.set(context);
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}
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/**
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* 将模型文本增量转发为工作流事件。
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*
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* @param context 流上下文
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* @param response 本次模型响应
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*/
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@Override
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public void onMessage(StreamContext context, AiMessageResponse response) {
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AiMessage message = response == null ? null : response.getMessage();
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if (message == null) {
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return;
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}
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String reasoningDelta = message.getReasoningContent();
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if (StringUtil.hasText(reasoningDelta)) {
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chain.notifyEvent(new LlmStreamEvent(
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chain,
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llmNode,
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streamId,
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reasoningDelta,
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LlmStreamEvent.ContentType.REASONING
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));
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}
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String textDelta = message.getContent();
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if (StringUtil.hasText(textDelta)) {
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chain.notifyEvent(new LlmStreamEvent(
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chain,
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llmNode,
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streamId,
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textDelta,
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LlmStreamEvent.ContentType.TEXT
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));
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}
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}
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/**
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* 收集完整响应并结束同步节点等待。
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*
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* @param context 流上下文
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*/
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@Override
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public void onStop(StreamContext context) {
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try {
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if (failure.get() == null) {
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AiMessage message = context.getFullMessage();
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if (message == null || StringUtil.noText(message.getFullContent())) {
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failure.compareAndSet(
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null,
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new IllegalStateException(
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"EasyAgentsLlm can not get aiMessage!"));
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} else {
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result.set(message.getFullContent());
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}
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}
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} finally {
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completion.countDown();
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}
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}
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/**
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* 记录模型流异常并结束等待。
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*
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* @param context 流上下文
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* @param throwable 模型异常
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*/
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@Override
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public void onFailure(StreamContext context, Throwable throwable) {
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failure.compareAndSet(
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null,
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throwable == null
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? new IllegalStateException("EasyAgentsLlm stream failed")
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: throwable);
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completion.countDown();
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}
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}, chatOptions);
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awaitCompletion(completion, streamContext);
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} finally {
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chain.getEventManager().removeEventListener(
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ChainStatusChangeEvent.class, cancellationListener);
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}
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Throwable throwable = failure.get();
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if (throwable != null) {
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throw new RuntimeException("EasyAgentsLlm stream failed", throwable);
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}
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if (StringUtil.noText(result.get())) {
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throw new RuntimeException("EasyAgentsLlm can not get response!");
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}
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return result.get();
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}
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/**
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* 构建模型提示词，并解析图片输入。
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*
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* @param messageInfo 消息信息
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* @return 模型提示词
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*/
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private SimplePrompt buildPrompt(MessageInfo messageInfo) {
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SimplePrompt prompt = new SimplePrompt(messageInfo.getMessage());
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// 系统提示词
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if (StringUtil.hasText(messageInfo.getSystemMessage())) {
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prompt.setSystemMessage(SystemMessage.of(messageInfo.getSystemMessage()));
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}
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// 图片
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List<String> images = messageInfo.getImages();
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List<String> images = resolveImages(messageInfo);
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if (images != null && !images.isEmpty()) {
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assertImageSupported();
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for (String image : images) {
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prompt.addImageUrl(image);
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}
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}
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return prompt;
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}
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/**
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* 构建 Easy-Agents 模型参数。
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*
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* @param options 工作流模型参数
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* @return Easy-Agents 模型参数
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*/
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private com.easyagents.core.model.chat.ChatOptions buildChatOptions(ChatOptions options) {
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com.easyagents.core.model.chat.ChatOptions chatOptions = new com.easyagents.core.model.chat.ChatOptions();
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chatOptions.setSeed(options.getSeed());
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chatOptions.setTemperature(options.getTemperature());
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@@ -49,21 +243,89 @@ public class EasyAgentsLlm implements Llm {
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chatOptions.setTopK(options.getTopK());
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chatOptions.setMaxTokens(options.getMaxTokens());
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chatOptions.setStop(options.getStop());
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return chatOptions;
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}
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AiMessageResponse response = chatModel.chat(prompt, chatOptions);
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if (response == null) {
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throw new RuntimeException("EasyAgentsLlm can not get response!");
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/**
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* 等待异步模型流结束。
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*
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* @param completion 完成信号
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* @param streamContext 当前模型流上下文
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*/
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private void awaitCompletion(
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CountDownLatch completion,
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AtomicReference<StreamContext> streamContext) {
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try {
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completion.await();
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} catch (InterruptedException exception) {
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Thread.currentThread().interrupt();
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StreamContext context = streamContext.get();
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if (context != null) {
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context.getClient().stop();
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}
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throw new RuntimeException("EasyAgentsLlm stream interrupted", exception);
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}
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}
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/**
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* 将原始图片输入解析为模型图片 URL。
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*
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* @param messageInfo 消息信息
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* @return 已解析图片 URL 列表
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*/
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List<String> resolveImages(MessageInfo messageInfo) {
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if (messageInfo == null) {
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return Collections.emptyList();
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}
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List<?> inputs = messageInfo.getImageInputs();
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if (inputs == null || inputs.isEmpty()) {
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inputs = messageInfo.getImages();
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}
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if (inputs == null || inputs.isEmpty()) {
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return Collections.emptyList();
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}
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if (response.isError()) {
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throw new RuntimeException("EasyAgentsLlm error: " + response.getErrorMessage());
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List<String> resolvedImages = new ArrayList<>(inputs.size());
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for (Object input : inputs) {
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if (input == null) {
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continue;
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}
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String resolvedImage = resolveImage(input);
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if (StringUtil.noText(resolvedImage)) {
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throw new IllegalArgumentException("Resolved image input must not be blank");
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}
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resolvedImages.add(resolvedImage);
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}
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return resolvedImages;
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}
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AiMessage aiMessage = response.getMessage();
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if (aiMessage != null) {
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return aiMessage.getTextContent();
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/**
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* 解析单个图片输入。
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*
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* @param imageInput 原始图片输入
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* @return 图片 URL 或 Data URI
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*/
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private String resolveImage(Object imageInput) {
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if (imageInputResolver != null) {
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return imageInputResolver.resolve(imageInput);
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}
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if (imageInput instanceof String value) {
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return value;
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}
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if (imageInput instanceof File file) {
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return ImageUtil.imageFileToDataUri(file);
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}
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throw new IllegalArgumentException(
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"Unsupported image input type: " + imageInput.getClass().getName());
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}
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throw new RuntimeException("EasyAgentsLlm can not get aiMessage!");
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/**
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* 校验当前聊天模型是否明确支持图片。
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*/
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private void assertImageSupported() {
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if (chatModel instanceof BaseChatModel<?> baseChatModel
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&& Boolean.FALSE.equals(baseChatModel.getConfig().getSupportImage())) {
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throw new IllegalArgumentException("当前模型不支持图片输入，请选择支持视觉能力的模型");
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}
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}
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}
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@@ -0,0 +1,16 @@
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package com.easyagents.flow.support.provider;
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/**
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* 将工作流运行态图片输入解析为模型可消费的图片 URL 或 Data URI。
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*/
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@FunctionalInterface
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public interface ImageInputResolver {
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/**
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* 解析单个图片输入。
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*
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* @param imageInput 原始图片输入
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* @return 图片 URL 或带 MIME 的 Data URI
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*/
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String resolve(Object imageInput);
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}
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@@ -0,0 +1,314 @@
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package com.easyagents.flow.support.provider;
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import com.easyagents.core.model.chat.BaseChatModel;
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import com.easyagents.core.model.chat.ChatConfig;
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import com.easyagents.core.model.chat.ChatModel;
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import com.easyagents.core.model.chat.ChatOptions;
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import com.easyagents.core.model.chat.StreamResponseListener;
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import com.easyagents.core.model.chat.response.AiMessageResponse;
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import com.easyagents.core.model.client.StreamClient;
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import com.easyagents.core.model.client.StreamContext;
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import com.easyagents.core.prompt.Prompt;
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import com.easyagents.flow.core.chain.Chain;
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import com.easyagents.flow.core.chain.ChainDefinition;
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import com.easyagents.flow.core.chain.EventManager;
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import com.easyagents.flow.core.chain.event.LlmStreamEvent;
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import com.easyagents.flow.core.llm.Llm;
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import com.easyagents.flow.core.node.LlmNode;
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import org.junit.Assert;
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import org.junit.Test;
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import java.nio.file.Files;
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import java.nio.file.Path;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Map;
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/**
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* 工作流 LLM 图片输入解析测试。
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*/
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public class EasyAgentsLlmTest {
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/**
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* 验证结构化图片对象通过注入的解析器转换。
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*/
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@Test
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public void shouldResolveRawImageInput() {
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EasyAgentsLlm llm = new EasyAgentsLlm();
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llm.setImageInputResolver(input -> {
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Assert.assertTrue(input instanceof Map<?, ?>);
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return "data:image/png;base64,AQID";
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});
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Llm.MessageInfo messageInfo = new Llm.MessageInfo();
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messageInfo.setImageInputs(List.of(Map.of(
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"sourceType", "upload",
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"fileName", "image.png",
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"filePath", "workflow/image.png")));
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Assert.assertEquals(
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List.of("data:image/png;base64,AQID"),
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llm.resolveImages(messageInfo));
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}
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/**
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* 验证旧版图片字符串仍然可直接传递。
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*/
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@Test
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public void shouldKeepLegacyImageString() {
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EasyAgentsLlm llm = new EasyAgentsLlm();
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Llm.MessageInfo messageInfo = new Llm.MessageInfo();
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messageInfo.setImages(List.of("https://example.com/image.png"));
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Assert.assertEquals(
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List.of("https://example.com/image.png"),
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llm.resolveImages(messageInfo));
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}
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/**
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* 验证 File 图片会补全 MIME 和 Data URI 前缀。
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*
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* @throws Exception 临时文件写入失败
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*/
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@Test
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public void shouldConvertFileToCompleteDataUri() throws Exception {
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Path image = Files.createTempFile("easy-agents-image-", ".png");
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try {
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Files.write(image, new byte[]{1, 2, 3});
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EasyAgentsLlm llm = new EasyAgentsLlm();
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Llm.MessageInfo messageInfo = new Llm.MessageInfo();
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messageInfo.setImageInputs(List.of(image.toFile()));
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Assert.assertEquals(
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List.of("data:image/png;base64,AQID"),
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llm.resolveImages(messageInfo));
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} finally {
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Files.deleteIfExists(image);
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}
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}
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/**
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* 验证明确不支持图片的模型会在发送请求前失败。
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*/
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@Test
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public void shouldRejectImageForUnsupportedModel() {
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ChatConfig config = new ChatConfig();
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config.setSupportImage(Boolean.FALSE);
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EasyAgentsLlm llm = new EasyAgentsLlm();
|
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llm.setChatModel(new BaseChatModel<>(config) {
|
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});
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Llm.MessageInfo messageInfo = new Llm.MessageInfo();
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messageInfo.setImages(List.of("data:image/png;base64,AQID"));
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||||
try {
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llm.chat(messageInfo, new Llm.ChatOptions(), null, null);
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Assert.fail("expected IllegalArgumentException");
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||||
} catch (IllegalArgumentException exception) {
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||||
Assert.assertEquals(
|
||||
"当前模型不支持图片输入，请选择支持视觉能力的模型",
|
||||
exception.getMessage());
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 验证重复执行同一 LLM 节点时，每次调用拥有独立流标识且增量不会被覆盖。
|
||||
*/
|
||||
@Test
|
||||
public void shouldEmitIndependentStreamForRepeatedNodeInvocation() {
|
||||
EasyAgentsLlm llm = new EasyAgentsLlm();
|
||||
llm.setChatModel(new DeterministicStreamChatModel());
|
||||
|
||||
Chain chain = new Chain(new ChainDefinition(), "stream-test");
|
||||
EventManager eventManager = new EventManager();
|
||||
chain.setEventManager(eventManager);
|
||||
List<LlmStreamEvent> events = new ArrayList<>();
|
||||
eventManager.addEventListener(
|
||||
LlmStreamEvent.class,
|
||||
(event, currentChain) -> events.add((LlmStreamEvent) event));
|
||||
|
||||
LlmNode node = new LlmNode();
|
||||
node.setId("llm-1");
|
||||
Llm.MessageInfo messageInfo = new Llm.MessageInfo();
|
||||
messageInfo.setMessage("请回答");
|
||||
|
||||
Assert.assertEquals(
|
||||
"你好",
|
||||
llm.chat(messageInfo, new Llm.ChatOptions(), node, chain));
|
||||
Assert.assertEquals(
|
||||
"你好",
|
||||
llm.chat(messageInfo, new Llm.ChatOptions(), node, chain));
|
||||
|
||||
Assert.assertEquals(4, events.size());
|
||||
Assert.assertEquals(List.of("你", "好", "你", "好"), events.stream()
|
||||
.map(LlmStreamEvent::getDelta)
|
||||
.toList());
|
||||
Assert.assertTrue(events.stream().noneMatch(LlmStreamEvent::isReasoning));
|
||||
Assert.assertEquals(events.get(0).getStreamId(), events.get(1).getStreamId());
|
||||
Assert.assertEquals(events.get(2).getStreamId(), events.get(3).getStreamId());
|
||||
Assert.assertNotEquals(events.get(0).getStreamId(), events.get(2).getStreamId());
|
||||
}
|
||||
|
||||
/**
|
||||
* 验证模型思考与正式回答使用同一流标识并按内容类型分别发布。
|
||||
*/
|
||||
@Test
|
||||
public void shouldEmitReasoningAndTextDeltasSeparately() {
|
||||
EasyAgentsLlm llm = new EasyAgentsLlm();
|
||||
llm.setChatModel(new ReasoningStreamChatModel());
|
||||
|
||||
Chain chain = new Chain(new ChainDefinition(), "reasoning-stream-test");
|
||||
EventManager eventManager = new EventManager();
|
||||
chain.setEventManager(eventManager);
|
||||
List<LlmStreamEvent> events = new ArrayList<>();
|
||||
eventManager.addEventListener(
|
||||
LlmStreamEvent.class,
|
||||
(event, currentChain) -> events.add((LlmStreamEvent) event));
|
||||
|
||||
LlmNode node = new LlmNode();
|
||||
node.setId("llm-reasoning");
|
||||
Llm.MessageInfo messageInfo = new Llm.MessageInfo();
|
||||
messageInfo.setMessage("请回答");
|
||||
|
||||
Assert.assertEquals(
|
||||
"答案",
|
||||
llm.chat(messageInfo, new Llm.ChatOptions(), node, chain));
|
||||
Assert.assertEquals(4, events.size());
|
||||
Assert.assertEquals(
|
||||
List.of("先", "想", "答", "案"),
|
||||
events.stream().map(LlmStreamEvent::getDelta).toList());
|
||||
Assert.assertEquals(
|
||||
List.of(
|
||||
LlmStreamEvent.ContentType.REASONING,
|
||||
LlmStreamEvent.ContentType.REASONING,
|
||||
LlmStreamEvent.ContentType.TEXT,
|
||||
LlmStreamEvent.ContentType.TEXT
|
||||
),
|
||||
events.stream().map(LlmStreamEvent::getContentType).toList());
|
||||
Assert.assertEquals(
|
||||
1,
|
||||
events.stream().map(LlmStreamEvent::getStreamId).distinct().count());
|
||||
}
|
||||
|
||||
/**
|
||||
* 固定输出两个文本增量的测试聊天模型。
|
||||
*/
|
||||
private static final class DeterministicStreamChatModel implements ChatModel {
|
||||
|
||||
/**
|
||||
* 同步聊天接口不参与本测试。
|
||||
*
|
||||
* @param prompt 提示词
|
||||
* @param options 模型参数
|
||||
* @return 无
|
||||
*/
|
||||
@Override
|
||||
public AiMessageResponse chat(Prompt prompt, ChatOptions options) {
|
||||
throw new UnsupportedOperationException("sync chat is not used");
|
||||
}
|
||||
|
||||
/**
|
||||
* 连续发送两个增量及完整消息。
|
||||
*
|
||||
* @param prompt 提示词
|
||||
* @param listener 流监听器
|
||||
* @param options 模型参数
|
||||
*/
|
||||
@Override
|
||||
public void chatStream(
|
||||
Prompt prompt,
|
||||
StreamResponseListener listener,
|
||||
ChatOptions options) {
|
||||
StreamContext context = new StreamContext(this, null, new NoopStreamClient());
|
||||
listener.onStart(context);
|
||||
for (String content : List.of("你", "好")) {
|
||||
com.easyagents.core.message.AiMessage delta =
|
||||
new com.easyagents.core.message.AiMessage();
|
||||
delta.setContent(content);
|
||||
listener.onMessage(context, new AiMessageResponse(null, content, delta));
|
||||
}
|
||||
context.setFullMessage(new com.easyagents.core.message.AiMessage("你好"));
|
||||
listener.onStop(context);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 固定输出思考与回答增量的测试聊天模型。
|
||||
*/
|
||||
private static final class ReasoningStreamChatModel implements ChatModel {
|
||||
|
||||
/**
|
||||
* 同步聊天接口不参与本测试。
|
||||
*
|
||||
* @param prompt 提示词
|
||||
* @param options 模型参数
|
||||
* @return 无
|
||||
*/
|
||||
@Override
|
||||
public AiMessageResponse chat(Prompt prompt, ChatOptions options) {
|
||||
throw new UnsupportedOperationException("sync chat is not used");
|
||||
}
|
||||
|
||||
/**
|
||||
* 依次发送思考增量、回答增量及完整消息。
|
||||
*
|
||||
* @param prompt 提示词
|
||||
* @param listener 流监听器
|
||||
* @param options 模型参数
|
||||
*/
|
||||
@Override
|
||||
public void chatStream(
|
||||
Prompt prompt,
|
||||
StreamResponseListener listener,
|
||||
ChatOptions options) {
|
||||
StreamContext context = new StreamContext(this, null, new NoopStreamClient());
|
||||
listener.onStart(context);
|
||||
for (String reasoning : List.of("先", "想")) {
|
||||
com.easyagents.core.message.AiMessage delta =
|
||||
new com.easyagents.core.message.AiMessage();
|
||||
delta.setReasoningContent(reasoning);
|
||||
listener.onMessage(context, new AiMessageResponse(null, reasoning, delta));
|
||||
}
|
||||
for (String content : List.of("答", "案")) {
|
||||
com.easyagents.core.message.AiMessage delta =
|
||||
new com.easyagents.core.message.AiMessage();
|
||||
delta.setContent(content);
|
||||
listener.onMessage(context, new AiMessageResponse(null, content, delta));
|
||||
}
|
||||
com.easyagents.core.message.AiMessage fullMessage =
|
||||
new com.easyagents.core.message.AiMessage("答案");
|
||||
fullMessage.setFullReasoningContent("先想");
|
||||
context.setFullMessage(fullMessage);
|
||||
listener.onStop(context);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 测试使用的空流客户端。
|
||||
*/
|
||||
private static final class NoopStreamClient implements StreamClient {
|
||||
|
||||
/**
|
||||
* 测试模型直接分发事件，无需启动网络请求。
|
||||
*
|
||||
* @param url 请求地址
|
||||
* @param headers 请求头
|
||||
* @param payload 请求体
|
||||
* @param listener 客户端监听器
|
||||
* @param config 模型配置
|
||||
*/
|
||||
@Override
|
||||
public void start(
|
||||
String url,
|
||||
Map<String, String> headers,
|
||||
String payload,
|
||||
com.easyagents.core.model.client.StreamClientListener listener,
|
||||
ChatConfig config) {
|
||||
}
|
||||
|
||||
/**
|
||||
* 测试客户端没有需要关闭的网络资源。
|
||||
*/
|
||||
@Override
|
||||
public void stop() {
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user