fix: 让 LLM 节点应用模型消息格式配置
- 从模型高级配置解析内容块数组模式并兼容旧配置项 - 向 OpenAI 兼容、DeepSeek 与 Ollama 聊天配置透传消息格式 - 补充配置优先级、回退和请求序列化测试
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@@ -2,6 +2,7 @@
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package tech.easyflow.ai.entity;
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import cn.hutool.core.util.StrUtil;
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import com.easyagents.core.model.chat.ChatMessageContentFormat;
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import com.easyagents.core.model.chat.ChatModel;
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import com.easyagents.core.model.embedding.EmbeddingModel;
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import com.easyagents.core.model.rerank.RerankModel;
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@@ -27,6 +28,9 @@ import tech.easyflow.ai.entity.base.ModelBase;
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import tech.easyflow.common.util.StringUtil;
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import tech.easyflow.common.web.exceptions.BusinessException;
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import java.util.Locale;
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import java.util.Map;
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/**
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* 实体类。
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*
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@@ -118,6 +122,7 @@ public class Model extends ModelBase {
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ollamaChatConfig.setProvider(getModelProvider().getProviderName());
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ollamaChatConfig.setSupportImage(getSupportImage());
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ollamaChatConfig.setSupportImageBase64Only(getSupportImageB64Only());
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ollamaChatConfig.setMessageContentFormat(resolveMessageContentFormat());
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return new OllamaChatModel(ollamaChatConfig);
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case "deepseek":
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DeepseekConfig deepseekConfig = new DeepseekConfig();
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@@ -131,6 +136,7 @@ public class Model extends ModelBase {
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deepseekConfig.setNeedReasoningContentForToolMessage(Boolean.TRUE);
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deepseekConfig.setSupportImage(getSupportImage());
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deepseekConfig.setSupportImageBase64Only(getSupportImageB64Only());
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deepseekConfig.setMessageContentFormat(resolveMessageContentFormat());
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if (getSupportTool() != null) {
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deepseekConfig.setSupportToolMessage(getSupportTool());
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}
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@@ -144,6 +150,7 @@ public class Model extends ModelBase {
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openAIChatConfig.setRequestPath(checkAndGetRequestPath());
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openAIChatConfig.setSupportImage(getSupportImage());
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openAIChatConfig.setSupportImageBase64Only(getSupportImageB64Only());
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openAIChatConfig.setMessageContentFormat(resolveMessageContentFormat());
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if (getSupportTool() != null) {
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openAIChatConfig.setSupportToolMessage(getSupportTool());
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}
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@@ -151,6 +158,37 @@ public class Model extends ModelBase {
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}
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}
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/**
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* 解析模型高级配置中的 OpenAI-compatible 消息 content 格式。
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* 新配置优先，旧 system 配置用于兼容历史数据。
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*
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* @return 消息 content 格式；缺失或非法时返回标准格式
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*/
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private ChatMessageContentFormat resolveMessageContentFormat() {
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Map<String, Object> modelOptions = getOptions();
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if (modelOptions == null || modelOptions.isEmpty()) {
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return ChatMessageContentFormat.STANDARD;
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}
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Object rawFormat = modelOptions.get("agentMessageContentFormat");
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if (rawFormat == null || String.valueOf(rawFormat).isBlank()) {
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rawFormat = modelOptions.get("agentSystemContentFormat");
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}
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if (rawFormat == null || String.valueOf(rawFormat).isBlank()) {
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return ChatMessageContentFormat.STANDARD;
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}
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String normalizedFormat = String.valueOf(rawFormat).trim().toUpperCase(Locale.ROOT);
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if ("STRING".equals(normalizedFormat)) {
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return ChatMessageContentFormat.STANDARD;
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}
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try {
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return ChatMessageContentFormat.valueOf(normalizedFormat);
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} catch (IllegalArgumentException ignored) {
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return ChatMessageContentFormat.STANDARD;
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}
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}
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public RerankModel toRerankModel() {
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switch (modelProvider.getProviderType().toLowerCase()) {
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case "gitee":
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@@ -0,0 +1,131 @@
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package tech.easyflow.ai.entity;
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import com.easyagents.core.message.SystemMessage;
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import com.easyagents.core.message.UserMessage;
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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.ChatMessageContentFormat;
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import com.easyagents.core.model.client.OpenAIChatMessageSerializer;
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import org.junit.Assert;
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import org.junit.Test;
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import java.util.List;
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import java.util.Map;
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/**
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* 模型高级配置向工作流聊天模型透传的消息格式测试。
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*/
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public class ModelMessageContentFormatTest {
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/**
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* 验证 OpenAI-compatible、DeepSeek 与 Ollama 模型都会接收内容块数组配置。
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*/
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@Test
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public void shouldApplyTextPartsFormatToOpenAiCompatibleModels() {
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Map<String, Object> options = Map.of("agentMessageContentFormat", "TEXT_PARTS");
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Assert.assertEquals(ChatMessageContentFormat.TEXT_PARTS,
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chatConfig(model("custom", options)).getMessageContentFormat());
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Assert.assertEquals(ChatMessageContentFormat.TEXT_PARTS,
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chatConfig(model("deepseek", options)).getMessageContentFormat());
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Assert.assertEquals(ChatMessageContentFormat.TEXT_PARTS,
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chatConfig(model("ollama", options)).getMessageContentFormat());
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}
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/**
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* 验证旧 system 内容块配置会迁移到消息级内容块格式。
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*/
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@Test
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public void shouldMigrateLegacySystemTextPartsFormat() {
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Model model = model("custom", Map.of("agentSystemContentFormat", "TEXT_PARTS"));
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Assert.assertEquals(ChatMessageContentFormat.TEXT_PARTS,
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chatConfig(model).getMessageContentFormat());
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}
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/**
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* 验证新旧配置并存时使用新的消息级配置。
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*/
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@Test
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public void shouldPreferCurrentMessageFormatSetting() {
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Model model = model("custom", Map.of(
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"agentMessageContentFormat", "STANDARD",
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"agentSystemContentFormat", "TEXT_PARTS"));
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Assert.assertEquals(ChatMessageContentFormat.STANDARD,
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chatConfig(model).getMessageContentFormat());
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}
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/**
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* 验证非法配置会安全回退到标准格式。
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*/
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@Test
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public void shouldFallbackToStandardForUnknownFormat() {
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Model model = model("custom", Map.of("agentMessageContentFormat", "PARTS"));
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Assert.assertEquals(ChatMessageContentFormat.STANDARD,
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chatConfig(model).getMessageContentFormat());
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}
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/**
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* 验证工作流选择内容块模式模型后，请求消息按数组格式序列化。
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*/
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@Test
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public void shouldSerializeWorkflowModelMessagesAsTextParts() {
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ChatConfig config = chatConfig(model(
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"custom", Map.of("agentMessageContentFormat", "TEXT_PARTS")));
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List<Map<String, Object>> messages = new OpenAIChatMessageSerializer().serializeMessages(
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List.of(SystemMessage.of("系统提示"), new UserMessage("用户问题")),
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config);
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assertTextPart(messages.get(0), "系统提示");
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assertTextPart(messages.get(1), "用户问题");
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}
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/**
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* 创建指定供应商和高级配置的聊天模型记录。
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*
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* @param providerType 供应商类型
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* @param options 模型高级配置
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* @return 模型记录
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*/
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private Model model(String providerType, Map<String, Object> options) {
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ModelProvider provider = new ModelProvider();
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provider.setProviderType(providerType);
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provider.setProviderName(providerType);
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Model model = new Model();
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model.setModelProvider(provider);
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model.setEndpoint("https://model.example.com");
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model.setApiKey("sk-test");
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model.setModelName("test-model");
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model.setRequestPath("/v1/chat/completions");
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model.setOptions(options);
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return model;
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}
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/**
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* 获取模型生成的聊天配置。
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*
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* @param model 模型记录
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* @return 聊天配置
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*/
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private ChatConfig chatConfig(Model model) {
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return ((BaseChatModel<?>) model.toChatModel()).getConfig();
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}
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/**
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* 断言消息 content 只包含指定文本内容块。
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*
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* @param message 已序列化消息
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* @param expectedText 预期文本
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*/
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private void assertTextPart(Map<String, Object> message, String expectedText) {
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List<?> content = (List<?>) message.get("content");
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Assert.assertEquals(1, content.size());
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Map<?, ?> textPart = (Map<?, ?>) content.get(0);
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Assert.assertEquals("text", textPart.get("type"));
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Assert.assertEquals(expectedText, textPart.get("text"));
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}
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}
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