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Add builtin customized RenderTemplate implementation. (#3935)
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‎examples/documentation/src/main/java/com/alibaba/cloud/ai/examples/documentation/framework/advanced/MultiAgentExample.java‎

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@@ -32,6 +32,8 @@
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import org.springframework.ai.chat.messages.AssistantMessage;
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import org.springframework.ai.chat.messages.Message;
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import org.springframework.ai.chat.model.ChatModel;
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import org.springframework.ai.template.StringTemplateRenderer;
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import org.springframework.ai.template.TemplateRenderer;
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import java.util.List;
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import java.util.Map;
@@ -393,6 +395,112 @@ public void example5_llmRoutingAgent() throws Exception {
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System.out.println("LLM路由示例执行完成");
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}
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/**
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* 示例5.5：使用自定义 TemplateRenderer 与多智能体协作
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*
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* 展示如何在多智能体场景中使用 StringTemplateRenderer.builder() 来定制占位符分隔符。
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* 使用 [[variable]] 替代默认的 {variable} 作为占位符格式。
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*/
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public void example5_5_customTemplateRenderer() throws Exception {
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// 使用 StringTemplateRenderer.builder() 创建自定义分隔符的 TemplateRenderer
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// 使用 [[ 和 ]] 作为占位符分隔符
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TemplateRenderer customRenderer = StringTemplateRenderer.builder()
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.startDelimiterToken("[[")
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.endDelimiterToken("]]")
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.build();
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// 创建专业化的子Agent - 注意 instruction 中使用 [[variable]] 格式
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ReactAgent writerAgent = ReactAgent.builder()
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.name("writer_agent")
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.model(chatModel)
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.description("擅长创作各类文章，包括散文、诗歌等文学作品")
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.instruction("""
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你是一个知名的作家，擅长写作和创作。
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当前主题：[[topic]]
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文体要求：[[style]]
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字数要求：[[word_count]]
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请根据用户的提问进行回答。
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""")
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.templateRenderer(customRenderer)
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.outputKey("writer_output")
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.build();
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ReactAgent reviewerAgent = ReactAgent.builder()
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.name("reviewer_agent")
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.model(chatModel)
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.description("擅长对文章进行评论、修改和润色")
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.instruction("""
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你是一个知名的评论家，擅长对文章进行评论和修改。
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评审标准：[[review_criteria]]
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关注要点：[[focus_points]]
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对于散文类文章，请确保文章中必须包含对于风景的描述。
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""")
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.templateRenderer(customRenderer)
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.outputKey("reviewer_output")
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.build();
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ReactAgent translatorAgent = ReactAgent.builder()
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.name("translator_agent")
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.model(chatModel)
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.description("擅长将文章翻译成各种语言")
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.instruction("""
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你是一个专业的翻译家，能够准确地将文章翻译成目标语言。
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目标语言：[[target_language]]
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翻译风格：[[translation_style]]
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""")
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.templateRenderer(customRenderer)
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.outputKey("translator_output")
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.build();
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// 创建路由Agent
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LlmRoutingAgent routingAgent = LlmRoutingAgent.builder()
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.name("content_routing_agent")
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.description("根据用户需求智能路由到合适的专家Agent")
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.model(chatModel)
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.subAgents(List.of(writerAgent, reviewerAgent, translatorAgent))
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.build();
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// 使用 - 传入带有自定义占位符变量的输入
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System.out.println("自定义模板路由测试1: 写作请求");
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Map<String, Object> writerInput = Map.of(
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"input", "帮我写一篇关于春天的散文",
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"topic", "春天",
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"style", "散文",
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"word_count", "200字左右"
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);
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Optional<OverAllState> result1 = routingAgent.invoke(writerInput);
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if (result1.isPresent()) {
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result1.get().value("writer_output").ifPresent(output ->
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System.out.println("写作输出: " + output));
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}
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System.out.println("\n自定义模板路由测试2: 评审请求");
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Map<String, Object> reviewerInput = Map.of(
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"input", "请帮我修改这篇文章：春天来了，花开了。",
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"review_criteria", "语言流畅、描述生动",
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"focus_points", "修辞手法、意境营造"
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);
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Optional<OverAllState> result2 = routingAgent.invoke(reviewerInput);
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if (result2.isPresent()) {
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result2.get().value("reviewer_output").ifPresent(output ->
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System.out.println("评审输出: " + output));
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}
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System.out.println("\n自定义模板路由测试3: 翻译请求");
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Map<String, Object> translatorInput = Map.of(
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"input", "请将以下内容翻译成英文：春暖花开",
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"target_language", "英文",
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"translation_style", "文学性翻译"
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);
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Optional<OverAllState> result3 = routingAgent.invoke(translatorInput);
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if (result3.isPresent()) {
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result3.get().value("translator_output").ifPresent(output ->
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System.out.println("翻译输出: " + output));
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}
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System.out.println("\n自定义TemplateRenderer多智能体示例执行完成");
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}
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/**
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* 示例6：优化路由准确性
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*

‎examples/documentation/src/main/java/com/alibaba/cloud/ai/examples/documentation/framework/tutorials/AgentsExample.java‎

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@@ -46,6 +46,9 @@
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import org.springframework.ai.chat.model.ChatModel;
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import org.springframework.ai.chat.model.ToolContext;
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import org.springframework.ai.converter.BeanOutputConverter;
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import org.springframework.ai.template.StringTemplateRenderer;
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import org.springframework.ai.template.TemplateRenderer;
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import org.springframework.ai.template.st.StTemplateRenderer;
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import org.springframework.ai.tool.ToolCallback;
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import org.springframework.ai.tool.annotation.ToolParam;
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import org.springframework.ai.tool.function.FunctionToolCallback;
@@ -183,6 +186,76 @@ public static void instructionUsage() {
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// ==================== System Prompt ====================
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/**
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* 示例6.5：使用自定义 TemplateRenderer 定制占位符分隔符
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*
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* 展示如何使用 StringTemplateRenderer.builder() 来定制占位符的起始和结束分隔符。
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* 默认使用 {variable}，这里演示使用 {{variable}} 作为占位符。
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*/
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public static void customTemplateRendererExample() throws GraphRunnerException {
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DashScopeApi dashScopeApi = DashScopeApi.builder()
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.apiKey(System.getenv("AI_DASHSCOPE_API_KEY"))
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.build();
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ChatModel chatModel = DashScopeChatModel.builder()
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.dashScopeApi(dashScopeApi)
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.build();
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// 使用 StringTemplateRenderer.builder() 创建自定义分隔符的 TemplateRenderer
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// 使用 {{ 和 }} 作为占位符分隔符
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TemplateRenderer customRenderer = StringTemplateRenderer.builder()
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.startDelimiterToken("{{")
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.endDelimiterToken("}}")
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.build();
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// 使用自定义分隔符的 systemPrompt
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String systemPrompt = """
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你是一个专业的{{role}}助手。
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你的专业领域是{{domain}}。
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请用{{language}}语言回答用户的问题。
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""";
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// 使用自定义分隔符的 instruction
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String instruction = """
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用户询问的主题是：{{topic}}
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请根据以下要求回答：
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1. 保持专业性
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2. 提供具体示例
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3. 语言要{{style}}
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""";
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ReactAgent agent = ReactAgent.builder()
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.name("custom_template_agent")
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.model(chatModel)
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.systemPrompt(systemPrompt)
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.instruction(instruction)
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.templateRenderer(customRenderer)
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.build();
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// 使用时，状态中的变量会自动替换 {{ }} 包裹的占位符
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Map<String, Object> inputs = Map.of(
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"input", "请介绍一下Spring框架的核心特性",
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"role", "技术专家",
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"domain", "Java企业级开发",
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"language", "中文",
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"topic", "Spring框架",
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"style", "简洁易懂"
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);
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Optional<OverAllState> result = agent.invoke(inputs);
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if (result.isPresent()) {
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List<Message> messages = (List<Message>) result.get().value("messages").orElse(List.of());
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for (Message message : messages) {
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if (message instanceof AssistantMessage) {
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System.out.println("Agent回复: " + ((AssistantMessage) message).getText());
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}
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}
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}
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}
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/**
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* 示例7：动态 System Prompt
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*/
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public static void dynamicSystemPrompt() {
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DashScopeApi dashScopeApi = DashScopeApi.builder()
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.apiKey(System.getenv("AI_DASHSCOPE_API_KEY"))

‎spring-ai-alibaba-agent-framework/src/main/java/com/alibaba/cloud/ai/graph/agent/Builder.java‎

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import org.springframework.ai.chat.model.ChatModel;
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import org.springframework.ai.chat.prompt.ChatOptions;
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import org.springframework.ai.support.ToolCallbacks;
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import org.springframework.ai.template.TemplateRenderer;
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import org.springframework.ai.tool.ToolCallback;
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import org.springframework.ai.tool.ToolCallbackProvider;
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import org.springframework.ai.tool.execution.ToolExecutionExceptionProcessor;
@@ -59,6 +60,10 @@ public abstract class Builder {
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protected String systemPrompt;
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// Provide customized template renderer instance, for example
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// SaaStTemplateRenderer.builder().startDelimiterToken("{{").endDelimiterToken("}}").build()
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protected TemplateRenderer templateRenderer;
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protected ChatModel model;
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protected ChatOptions chatOptions;
@@ -219,6 +224,11 @@ public Builder systemPrompt(String systemPrompt) {
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return this;
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}
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public Builder templateRenderer(TemplateRenderer templateRenderer) {
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this.templateRenderer = templateRenderer;
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return this;
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}
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public Builder outputKey(String outputKey) {
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this.outputKey = outputKey;
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return this;

‎spring-ai-alibaba-agent-framework/src/main/java/com/alibaba/cloud/ai/graph/agent/DefaultBuilder.java‎

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@@ -84,6 +84,10 @@ public ReactAgent build() {
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llmNodeBuilder.systemPrompt(systemPrompt);
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}
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if (templateRenderer != null) {
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llmNodeBuilder.templateRenderer(templateRenderer);
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}
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if (instruction != null) {
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llmNodeBuilder.instruction(instruction);
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}

‎spring-ai-alibaba-agent-framework/src/main/java/com/alibaba/cloud/ai/graph/agent/node/AgentLlmNode.java‎

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@@ -39,6 +39,7 @@
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import org.springframework.ai.chat.prompt.ChatOptions;
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import org.springframework.ai.chat.prompt.PromptTemplate;
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import org.springframework.ai.model.tool.ToolCallingChatOptions;
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import org.springframework.ai.template.TemplateRenderer;
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import org.springframework.ai.tool.ToolCallback;
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import org.springframework.lang.Nullable;
@@ -79,6 +80,8 @@ public class AgentLlmNode implements NodeActionWithConfig {
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private String systemPrompt;
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private TemplateRenderer templateRenderer;
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private String instruction;
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private ToolCallingChatOptions chatOptions;
@@ -91,6 +94,7 @@ public AgentLlmNode(Builder builder) {
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this.outputSchema = builder.outputSchema;
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this.systemPrompt = builder.systemPrompt;
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this.instruction = builder.instruction;
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this.templateRenderer = builder.templateRenderer;
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if (builder.advisors != null) {
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this.advisors = builder.advisors;
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}
@@ -353,8 +357,11 @@ private ToolCallingChatOptions buildChatOptions(ChatOptions chatOptions, List<To
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}
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private String renderPromptTemplate(String prompt, Map<String, Object> params) {
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PromptTemplate promptTemplate = new PromptTemplate(prompt);
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return promptTemplate.render(params);
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PromptTemplate.Builder builder = PromptTemplate.builder().template(prompt);
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if (templateRenderer != null) {
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builder.renderer(templateRenderer);
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}
364+
return builder.build().render(params);
358365
}
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360367
public void augmentUserMessage(List<Message> messages, String outputSchema) {
@@ -506,6 +513,8 @@ public static class Builder {
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private String systemPrompt;
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private TemplateRenderer templateRenderer;
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private ChatClient chatClient;
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private List<Advisor> advisors;
@@ -540,6 +549,11 @@ public Builder systemPrompt(String systemPrompt) {
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return this;
541550
}
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public Builder templateRenderer(TemplateRenderer templateRenderer) {
553+
this.templateRenderer = templateRenderer;
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return this;
555+
}
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543557
public Builder advisors(List<Advisor> advisors) {
544558
this.advisors = advisors;
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return this;

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