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Merge pull request #1643 from cescoffier/doc-semantic-compression
Semantic compression guide
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‎docs/modules/ROOT/examples/io/quarkiverse/langchain4j/samples/compression/Assistant.java‎

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package io.quarkiverse.langchain4j.samples.compression;
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import jakarta.enterprise.context.ApplicationScoped;
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import dev.langchain4j.service.MemoryId;
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import dev.langchain4j.service.SystemMessage;
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import dev.langchain4j.service.UserMessage;
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import io.quarkiverse.langchain4j.RegisterAiService;
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@RegisterAiService
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@SystemMessage("""
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You are a police and helpful assistant.
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""")
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@ApplicationScoped // For demo purpose.
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public interface Assistant {
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String answer(@MemoryId String id, @UserMessage String question);
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}

‎docs/modules/ROOT/examples/io/quarkiverse/langchain4j/samples/compression/ChatResource.java‎

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package io.quarkiverse.langchain4j.samples.compression;
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import jakarta.inject.Inject;
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import jakarta.ws.rs.POST;
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import jakarta.ws.rs.Path;
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import jakarta.ws.rs.Produces;
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import jakarta.ws.rs.core.MediaType;
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@Path("/chat")
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public class ChatResource {
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@Inject
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Assistant assistant;
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@POST
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@Produces(MediaType.TEXT_PLAIN)
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public String chat(String question) {
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// Use the same memory ID for all questions in this demo.
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// This is just to trigger the compression logic.
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return assistant.answer("abc", question);
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}
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}

‎docs/modules/ROOT/examples/io/quarkiverse/langchain4j/samples/compression/CompressingChatMemoryStore.java‎

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package io.quarkiverse.langchain4j.samples.compression;
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import java.util.ArrayList;
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import java.util.List;
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import jakarta.enterprise.context.ApplicationScoped;
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import org.eclipse.microprofile.config.inject.ConfigProperty;
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import dev.langchain4j.data.message.AiMessage;
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import dev.langchain4j.data.message.ChatMessage;
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import dev.langchain4j.data.message.ChatMessageType;
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import dev.langchain4j.data.message.SystemMessage;
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import dev.langchain4j.data.message.UserMessage;
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import dev.langchain4j.model.chat.ChatModel;
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import dev.langchain4j.store.memory.chat.ChatMemoryStore;
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import dev.langchain4j.store.memory.chat.InMemoryChatMemoryStore;
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import io.quarkus.logging.Log;
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@ApplicationScoped
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public class CompressingChatMemoryStore implements ChatMemoryStore {
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/**
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* The delegate store that will hold the chat messages.
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* This could be a database-backed store, but for simplicity,
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* we are using an in-memory store here.
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*/
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private final ChatMemoryStore delegate;
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/**
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* The chat model used for summarization.
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*/
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private final ChatModel chatModel;
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/**
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* The threshold for the number of messages before compression is triggered.
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*/
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private final int threshold;
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/**
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* The prefix used to identify the summary in the system message.
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* This is used to ensure that we can extract and update the summary correctly.
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*/
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private static final String SUMMARY_PREFIX = "Context: The following is a summary of the previous conversation:";
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public CompressingChatMemoryStore(ChatModel model, // We use the default chat model, but you can select any chat model
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@ConfigProperty(name = "semantic-compression-threshold", defaultValue = "5") int threshold) {
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this.delegate = new InMemoryChatMemoryStore();
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this.chatModel = model;
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this.threshold = threshold;
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}
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@Override
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public void updateMessages(Object memoryId, List<ChatMessage> messages) {
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// Extract the last message if any, as we do not want to compress during function calls
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if (messages.isEmpty()) {
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Log.warnf("No messages to compress for memory ID: %s", memoryId);
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return;
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}
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ChatMessage lastMessage = messages.get(messages.size() - 1);
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if (lastMessage.type() == ChatMessageType.AI && ((AiMessage) lastMessage).hasToolExecutionRequests()) {
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Log.infof("Skipping compression for memory ID: %s due to function call in the last message", memoryId);
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delegate.updateMessages(memoryId, messages);
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return;
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}
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// Also skip compression if the last message is a system message or a function call response
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if (lastMessage.type() == ChatMessageType.SYSTEM || lastMessage.type() == ChatMessageType.TOOL_EXECUTION_RESULT) {
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Log.infof(
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"Skipping compression for memory ID: %s due to system message or function call response in the last message",
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memoryId);
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delegate.updateMessages(memoryId, messages);
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return;
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}
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// If the number of messages exceeds the threshold, compress them
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if (messages.size() > threshold) {
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Log.infof("Triggering semantic compression for memory ID: %s with %d messages", memoryId, messages.size());
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List<ChatMessage> compressed = new ArrayList<>();
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// Retain the first system message if present
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SystemMessage systemMsg = (SystemMessage) messages.stream()
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.filter(m -> m.type() == ChatMessageType.SYSTEM)
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.findFirst().orElse(null);
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// Collect messages since last compression and extract any existing summary
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for (ChatMessage msg : messages) {
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if (msg.type() == ChatMessageType.SYSTEM) {
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// We found a system message, we need to check if it contains a previous summary
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extractSummaryFromSystemMessageIfAny((SystemMessage) msg, compressed);
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} else {
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compressed.add(msg);
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}
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}
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// compressed now contains a "fake" system message with the previous summary if it existed, and all other messages.
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// Build compression prompt
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StringBuilder sb = new StringBuilder(
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"Summarize the following dialogue into a brief summary, preserving context and tone:\n\n");
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for (ChatMessage msg : messages) {
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switch (msg.type()) {
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case SYSTEM ->
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// This is the previous summary
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sb.append("Context: ").append(((SystemMessage) msg).text()).append("\n");
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case USER -> sb.append("User: ").append(((UserMessage) msg).singleText()).append("\n");
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case AI -> sb.append("Assistant: ").append(((AiMessage) msg).text()).append("\n");
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default -> {
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// Ignore other message types for compression
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}
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}
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}
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String summary = chatModel.chat(sb.toString());
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systemMsg = appendSummaryToSystemMessage(systemMsg, summary);
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Log.infof("Generated system message with summary: %s", systemMsg.text());
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delegate.updateMessages(memoryId, List.of(systemMsg));
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} else {
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delegate.updateMessages(memoryId, messages);
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}
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}
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private SystemMessage appendSummaryToSystemMessage(SystemMessage systemMsg, String summary) {
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if (systemMsg == null) {
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// If no system message exists, create a new one with the summary
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return SystemMessage.systemMessage(SUMMARY_PREFIX + "\n" + summary);
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}
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// Check if the system message already contains a summary
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String content = systemMsg.text();
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if (content.contains(SUMMARY_PREFIX)) {
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// Replace the existing summary with the new one
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int startIndex = content.indexOf(SUMMARY_PREFIX) + SUMMARY_PREFIX.length();
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String newContent = content.substring(0, startIndex) + "\n\n";
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newContent = newContent + "\n\n" + SUMMARY_PREFIX + "\n" + summary;
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return SystemMessage.systemMessage(newContent);
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} else {
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// If no summary exists, append the new summary
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String newContent = content + "\n\n" + SUMMARY_PREFIX + "\n" + summary;
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return SystemMessage.systemMessage(newContent);
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}
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}
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private void extractSummaryFromSystemMessageIfAny(SystemMessage systemMsg, List<ChatMessage> compressed) {
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String content = systemMsg.text();
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if (content.contains(SUMMARY_PREFIX)) {
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// Extract the summary part
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int startIndex = content.indexOf(SUMMARY_PREFIX) + SUMMARY_PREFIX.length();
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String summary = content.substring(startIndex).trim();
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// Add the sanitized summary to the compressed messages
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compressed.add(SystemMessage.systemMessage(sanitize(summary)));
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}
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// Otherwise, do nothing, as we don't want to include the system message in the compressed messages.
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}
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private String sanitize(String text) {
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// Remove the previous summary if it exists
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int index = text.indexOf("Context: The following is a summary of the previous conversation:");
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if (index != -1) {
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return text.substring(0, index).trim();
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}
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return text.trim();
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}
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@Override
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public List<ChatMessage> getMessages(Object memoryId) {
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return delegate.getMessages(memoryId);
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}
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@Override
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public void deleteMessages(Object memoryId) {
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delegate.deleteMessages(memoryId);
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}
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}

‎docs/modules/ROOT/nav.adoc‎

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* xref:guide-web-search.adoc[Using Tavily Web Search]
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* xref:guide-passing-image.adoc[Passing Images to Models]
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* xref:guide-generating-image.adoc[Generating Images]
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* xref:guide-semantic-compression.adoc[Compressing Chat History]
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// * xref:guide-agentic-patterns.adoc[Implementing Agentic patterns]
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// * xref:guide-structured-output.adoc[Returning structured data from a model]
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// * xref:guide-streamed-responses.adoc[Using function calling]

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