Self-contained prompt templates in advisors

advisors/spring-ai-advisors-vector-store/src/main/java/org/springframework/ai/chat/client/advisor/vectorstore/QuestionAnswerAdvisor.java

@@ -25,6 +25,7 @@
 import org.springframework.ai.chat.client.ChatClientResponse;
 import org.springframework.ai.chat.client.advisor.api.AdvisorChain;
 import org.springframework.ai.chat.client.advisor.api.BaseAdvisor;
+import org.springframework.ai.chat.messages.UserMessage;
 import reactor.core.scheduler.Scheduler;
 import reactor.core.scheduler.Schedulers;
 
@@ -56,6 +57,7 @@ public class QuestionAnswerAdvisor implements BaseAdvisor {
 	public static final String FILTER_EXPRESSION = "qa_filter_expression";
 
 	private static final PromptTemplate DEFAULT_PROMPT_TEMPLATE = new PromptTemplate("""
+			{query}
 
 			Context information is below, surrounded by ---------------------
 
@@ -124,12 +126,9 @@ public ChatClientRequest before(ChatClientRequest chatClientRequest, AdvisorChai
 				: documents.stream().map(Document::getText).collect(Collectors.joining(System.lineSeparator()));
 
 		// 3. Augment the user prompt with the document context.
-		String augmentedUserText = this.promptTemplate.mutate()
-			.template(chatClientRequest.prompt().getUserMessage().getText() + System.lineSeparator()
-					+ this.promptTemplate.getTemplate())
-			.variables(Map.of("question_answer_context", documentContext))
-			.build()
-			.render();
+		UserMessage userMessage = chatClientRequest.prompt().getUserMessage();
+		String augmentedUserText = this.promptTemplate
+			.render(Map.of("query", userMessage.getText(), "question_answer_context", documentContext));
 
 		// 4. Update ChatClientRequest with augmented prompt.
 		return chatClientRequest.mutate()

advisors/spring-ai-advisors-vector-store/src/main/java/org/springframework/ai/chat/client/advisor/vectorstore/VectorStoreChatMemoryAdvisor.java

@@ -56,32 +56,23 @@ public class VectorStoreChatMemoryAdvisor extends AbstractChatMemoryAdvisor<Vect
 
 	private static final String DOCUMENT_METADATA_MESSAGE_TYPE = "messageType";
 
-	private static final String DEFAULT_SYSTEM_TEXT_ADVISE = """
+	private static final PromptTemplate DEFAULT_SYSTEM_PROMPT_TEMPLATE = new PromptTemplate("""
+			{instructions}
 
 			Use the long term conversation memory from the LONG_TERM_MEMORY section to provide accurate answers.
 
 			---------------------
 			LONG_TERM_MEMORY:
 			{long_term_memory}
 			---------------------
-			""";
-
-	private final String systemTextAdvise;
-
-	/**
-	 * Constructor for VectorStoreChatMemoryAdvisor.
-	 * @param vectorStore the vector store instance used for managing and querying
-	 * documents.
-	 * @param defaultConversationId the default conversation ID used if none is provided
-	 * in the context.
-	 * @param chatHistoryWindowSize the window size for the chat history retrieval.
-	 * @param systemTextAdvise the system text advice used for the chat advisor system.
-	 * @param order the order of precedence for this advisor in the chain.
-	 */
+			""");
+
+	private final PromptTemplate systemPromptTemplate;
+
 	private VectorStoreChatMemoryAdvisor(VectorStore vectorStore, String defaultConversationId,
-			int chatHistoryWindowSize, String systemTextAdvise, int order) {
+			int chatHistoryWindowSize, PromptTemplate systemPromptTemplate, int order) {
 		super(vectorStore, defaultConversationId, chatHistoryWindowSize, true, order);
-		this.systemTextAdvise = systemTextAdvise;
+		this.systemPromptTemplate = systemPromptTemplate;
 	}
 
 	public static Builder builder(VectorStore chatMemory) {
@@ -127,11 +118,8 @@ private ChatClientRequest before(ChatClientRequest chatClientRequest) {
 
 		// 2. Augment the system message.
 		SystemMessage systemMessage = chatClientRequest.prompt().getSystemMessage();
-		String augmentedSystemText = PromptTemplate.builder()
-			.template(systemMessage.getText() + System.lineSeparator() + this.systemTextAdvise)
-			.variables(Map.of("long_term_memory", longTermMemory))
-			.build()
-			.render();
+		String augmentedSystemText = this.systemPromptTemplate
+			.render(Map.of("instructions", systemMessage.getText(), "long_term_memory", longTermMemory));
 
 		// 3. Create a new request with the augmented system message.
 		ChatClientRequest processedChatClientRequest = chatClientRequest.mutate()
@@ -187,21 +175,26 @@ else if (message instanceof AssistantMessage assistantMessage) {
 
 	public static class Builder extends AbstractChatMemoryAdvisor.AbstractBuilder<VectorStore> {
 
-		private String systemTextAdvise = DEFAULT_SYSTEM_TEXT_ADVISE;
+		private PromptTemplate systemPromptTemplate = DEFAULT_SYSTEM_PROMPT_TEMPLATE;
 
 		protected Builder(VectorStore chatMemory) {
 			super(chatMemory);
 		}
 
 		public Builder systemTextAdvise(String systemTextAdvise) {
-			this.systemTextAdvise = systemTextAdvise;
+			this.systemPromptTemplate = new PromptTemplate(systemTextAdvise);
+			return this;
+		}
+
+		public Builder systemPromptTemplate(PromptTemplate systemPromptTemplate) {
+			this.systemPromptTemplate = systemPromptTemplate;
 			return this;
 		}
 
 		@Override
 		public VectorStoreChatMemoryAdvisor build() {
 			return new VectorStoreChatMemoryAdvisor(this.chatMemory, this.conversationId, this.chatMemoryRetrieveSize,
-					this.systemTextAdvise, this.order);
+					this.systemPromptTemplate, this.order);
 		}
 
 	}

spring-ai-client-chat/src/main/java/org/springframework/ai/chat/client/advisor/PromptChatMemoryAdvisor.java

@@ -46,7 +46,8 @@
  */
 public class PromptChatMemoryAdvisor extends AbstractChatMemoryAdvisor<ChatMemory> {
 
-	private static final String DEFAULT_SYSTEM_TEXT_ADVISE = """
+	private static final PromptTemplate DEFAULT_SYSTEM_PROMPT_TEMPLATE = new PromptTemplate("""
+			{instructions}
 
 			Use the conversation memory from the MEMORY section to provide accurate answers.
 
@@ -55,29 +56,34 @@ public class PromptChatMemoryAdvisor extends AbstractChatMemoryAdvisor<ChatMemor
 			{memory}
 			---------------------
 
-			""";
+			""");
 
-	private final String systemTextAdvise;
+	private final PromptTemplate systemPromptTemplate;
 
 	public PromptChatMemoryAdvisor(ChatMemory chatMemory) {
-		this(chatMemory, DEFAULT_SYSTEM_TEXT_ADVISE);
+		this(chatMemory, DEFAULT_SYSTEM_PROMPT_TEMPLATE.getTemplate());
 	}
 
-	public PromptChatMemoryAdvisor(ChatMemory chatMemory, String systemTextAdvise) {
+	public PromptChatMemoryAdvisor(ChatMemory chatMemory, String systemPromptTemplate) {
 		super(chatMemory);
-		this.systemTextAdvise = systemTextAdvise;
+		this.systemPromptTemplate = new PromptTemplate(systemPromptTemplate);
 	}
 
 	public PromptChatMemoryAdvisor(ChatMemory chatMemory, String defaultConversationId, int chatHistoryWindowSize,
-			String systemTextAdvise) {
-		this(chatMemory, defaultConversationId, chatHistoryWindowSize, systemTextAdvise,
+			String systemPromptTemplate) {
+		this(chatMemory, defaultConversationId, chatHistoryWindowSize, new PromptTemplate(systemPromptTemplate),
 				Advisor.DEFAULT_CHAT_MEMORY_PRECEDENCE_ORDER);
 	}
 
 	public PromptChatMemoryAdvisor(ChatMemory chatMemory, String defaultConversationId, int chatHistoryWindowSize,
-			String systemTextAdvise, int order) {
+			String systemPromptTemplate, int order) {
+		this(chatMemory, defaultConversationId, chatHistoryWindowSize, new PromptTemplate(systemPromptTemplate), order);
+	}
+
+	private PromptChatMemoryAdvisor(ChatMemory chatMemory, String defaultConversationId, int chatHistoryWindowSize,
+			PromptTemplate systemPromptTemplate, int order) {
 		super(chatMemory, defaultConversationId, chatHistoryWindowSize, true, order);
-		this.systemTextAdvise = systemTextAdvise;
+		this.systemPromptTemplate = systemPromptTemplate;
 	}
 
 	public static Builder builder(ChatMemory chatMemory) {
@@ -119,11 +125,8 @@ private ChatClientRequest before(ChatClientRequest chatClientRequest) {
 
 		// 2. Augment the system message.
 		SystemMessage systemMessage = chatClientRequest.prompt().getSystemMessage();
-		String augmentedSystemText = PromptTemplate.builder()
-			.template(systemMessage.getText() + System.lineSeparator() + this.systemTextAdvise)
-			.variables(Map.of("memory", memory))
-			.build()
-			.render();
+		String augmentedSystemText = this.systemPromptTemplate
+			.render(Map.of("instructions", systemMessage.getText(), "memory", memory));
 
 		// 3. Create a new request with the augmented system message.
 		ChatClientRequest processedChatClientRequest = chatClientRequest.mutate()
@@ -151,20 +154,25 @@ private void after(ChatClientResponse chatClientResponse) {
 
 	public static class Builder extends AbstractChatMemoryAdvisor.AbstractBuilder<ChatMemory> {
 
-		private String systemTextAdvise = DEFAULT_SYSTEM_TEXT_ADVISE;
+		private PromptTemplate systemPromptTemplate = DEFAULT_SYSTEM_PROMPT_TEMPLATE;
 
 		protected Builder(ChatMemory chatMemory) {
 			super(chatMemory);
 		}
 
 		public Builder systemTextAdvise(String systemTextAdvise) {
-			this.systemTextAdvise = systemTextAdvise;
+			this.systemPromptTemplate = new PromptTemplate(systemTextAdvise);
+			return this;
+		}
+
+		public Builder systemPromptTemplate(PromptTemplate systemPromptTemplate) {
+			this.systemPromptTemplate = systemPromptTemplate;
 			return this;
 		}
 
 		public PromptChatMemoryAdvisor build() {
 			return new PromptChatMemoryAdvisor(this.chatMemory, this.conversationId, this.chatMemoryRetrieveSize,
-					this.systemTextAdvise, this.order);
+					this.systemPromptTemplate, this.order);
 		}
 
 	}

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat-memory.adoc

@@ -244,6 +244,32 @@ chatClient.prompt()
     .content();
 ----
 
+=== PromptChatMemoryAdvisor
+
+==== Custom Template
+
+The `PromptChatMemoryAdvisor` uses a default template to augment the system message with the retrieved conversation memory. You can customize this behavior by providing your own `PromptTemplate` object via the `.promptTemplate()` builder method.
+
+NOTE: The `PromptTemplate` provided here customizes how the advisor merges retrieved memory with the system message. This is distinct from configuring a `TemplateRenderer` on the `ChatClient` itself (using `.templateRenderer()`), which affects the rendering of the initial user/system prompt content *before* the advisor runs. See xref:api/chatclient.adoc#_prompt_templates[ChatClient Prompt Templates] for more details on client-level template rendering.
+
+The custom `PromptTemplate` can use any `TemplateRenderer` implementation (by default, it uses `StPromptTemplate` based on the https://www.stringtemplate.org/[StringTemplate] engine). The important requirement is that the template must contain the following two placeholders:
+
+* an `instructions` placeholder to receive the original system message.
+* a `memory` placeholder to receive the retrieved conversation memory.
+
+=== VectorStoreChatMemoryAdvisor
+
+==== Custom Template
+
+The `VectorStoreChatMemoryAdvisor` uses a default template to augment the system message with the retrieved conversation memory. You can customize this behavior by providing your own `PromptTemplate` object via the `.promptTemplate()` builder method.
+
+NOTE: The `PromptTemplate` provided here customizes how the advisor merges retrieved memory with the system message. This is distinct from configuring a `TemplateRenderer` on the `ChatClient` itself (using `.templateRenderer()`), which affects the rendering of the initial user/system prompt content *before* the advisor runs. See xref:api/chatclient.adoc#_prompt_templates[ChatClient Prompt Templates] for more details on client-level template rendering.
+
+The custom `PromptTemplate` can use any `TemplateRenderer` implementation (by default, it uses `StPromptTemplate` based on the https://www.stringtemplate.org/[StringTemplate] engine). The important requirement is that the template must contain the following two placeholders:
+
+* an `instructions` placeholder to receive the original system message.
+* a `long_term_memory` placeholder to receive the retrieved conversation memory.
+
 == Memory in Chat Model
 
 If you're working directly with a `ChatModel` instead of a `ChatClient`, you can manage the memory explicitly:

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/retrieval-augmented-generation.adoc

@@ -82,13 +82,18 @@ The `QuestionAnswerAdvisor` uses a default template to augment the user question
 
 NOTE: The `PromptTemplate` provided here customizes how the advisor merges retrieved context with the user query. This is distinct from configuring a `TemplateRenderer` on the `ChatClient` itself (using `.templateRenderer()`), which affects the rendering of the initial user/system prompt content *before* the advisor runs. See xref:api/chatclient.adoc#_prompt_templates[ChatClient Prompt Templates] for more details on client-level template rendering.
 
-The custom `PromptTemplate` can use any `TemplateRenderer` implementation (by default, it uses `StPromptTemplate` based on the https://www.stringtemplate.org/[StringTemplate] engine). The important requirement is that the template must contain a placeholder to receive the retrieved context, which the advisor provides under the key `question_answer_context`.
+The custom `PromptTemplate` can use any `TemplateRenderer` implementation (by default, it uses `StPromptTemplate` based on the https://www.stringtemplate.org/[StringTemplate] engine). The important requirement is that the template must contain the following two placeholders:
+
+* a `query` placeholder to receive the user question.
+* a `question_answer_context` placeholder to receive the retrieved context.
 
 [source,java]
 ----
 PromptTemplate customPromptTemplate = PromptTemplate.builder()
     .renderer(StTemplateRenderer.builder().startDelimiterToken('<').endDelimiterToken('>').build())
     .template("""
+            <query>
+
             Context information is below.
 
 			---------------------

spring-ai-docs/src/main/antora/modules/ROOT/pages/upgrade-notes.adoc

@@ -36,6 +36,21 @@ For details, refer to:
 * `MessageAggregator` has a new method to aggregate messages from `ChatClientRequest`. The previous method aggregating messages from the old `AdvisedRequest` has been removed, since it was already marked as deprecated in M8.
 * In `SimpleLoggerAdvisor`, the `requestToString` input argument needs to be updated to use `ChatClientRequest`. It’s a breaking change since the alternative was not part of M8 yet. Same thing about the constructor.
 
+==== Self-contained Templates in Advisors
+
+The built-in advisors that perform prompt augmentation have been updated to use self-contained templates. The goal is for each advisor to be able to perform templating operations without affecting nor being affected by templating and prompt decisions in other advisors.
+If you were providing custom templates for the following advisors, you'll need to update them to ensure all expected placeholders are included.
+
+* The `QuestionAnswerAdvisor` expects a template with the following placeholders (see xref:api/retrieval-augmented-generation.adoc#_questionansweradvisor[more details]):
+** a `query` placeholder to receive the user question.
+** a `question_answer_context` placeholder to receive the retrieved context.
+* The `PromptChatMemoryAdvisor` expects a template with the following placeholders (see xref:api/chat-memory.adoc#_promptchatmemoryadvisor[more details]):
+** an `instructions` placeholder to receive the original system message.
+** a `memory` placeholder to receive the retrieved conversation memory.
+* The `VectorStoreChatMemoryAdvisor` expects a template with the following placeholders (see xref:api/chat-memory.adoc#_vectorstorechatmemoryadvisor[more details]):
+** an `instructions` placeholder to receive the original system message.
+** a `long_term_memory` placeholder to receive the retrieved conversation memory.
+
 === Breaking Changes
 The Watson AI model was removed as it was based on the older text generation that is considered outdated as there is a new chat generation model available.
 Hopefully Watson will reappear in a future version of Spring AI

spring-ai-integration-tests/src/test/java/org/springframework/ai/integration/tests/client/advisor/QuestionAnswerAdvisorIT.java

@@ -128,6 +128,7 @@ void qaCustomPromptTemplate() {
 		PromptTemplate customPromptTemplate = PromptTemplate.builder()
 			.renderer(StTemplateRenderer.builder().startDelimiterToken('$').endDelimiterToken('$').build())
 			.template("""
+					$query$
 
 					Context information is below, surrounded by ---------------------