Fix problem with VectorStoreChatMemoryAdvisor using pgvector

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

@@ -87,8 +87,7 @@ public AdvisedRequest adviseRequest(AdvisedRequest request, Map<String, Object>
 
 		var searchRequest = SearchRequest.query(request.userText())
 			.withTopK(this.doGetChatMemoryRetrieveSize(context))
-			.withFilterExpression(
-					"'" + DOCUMENT_METADATA_CONVERSATION_ID + "'=='" + this.doGetConversationId(context) + "'");
+			.withFilterExpression(DOCUMENT_METADATA_CONVERSATION_ID + "=='" + this.doGetConversationId(context) + "'");
 
 		List<Document> documents = this.getChatMemoryStore().similaritySearch(searchRequest);
 

vector-stores/spring-ai-pgvector-store/src/test/java/org/springframework/ai/vectorstore/PgVectorStoreWithChatMemoryAdvisorIT.java

@@ -0,0 +1,145 @@
+/*
+ * Copyright 2023 - 2024 the original author or authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.springframework.ai.vectorstore;
+
+import org.jetbrains.annotations.NotNull;
+import org.junit.jupiter.api.Assertions;
+import org.junit.jupiter.api.DisplayName;
+import org.junit.jupiter.api.Test;
+import org.mockito.ArgumentCaptor;
+import org.postgresql.ds.PGSimpleDataSource;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.client.advisor.VectorStoreChatMemoryAdvisor;
+import org.springframework.ai.chat.messages.AssistantMessage;
+import org.springframework.ai.chat.messages.SystemMessage;
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.chat.model.ChatResponse;
+import org.springframework.ai.chat.model.Generation;
+import org.springframework.ai.chat.prompt.Prompt;
+import org.springframework.ai.document.Document;
+import org.springframework.ai.embedding.EmbeddingModel;
+import org.springframework.jdbc.core.JdbcTemplate;
+import org.testcontainers.containers.PostgreSQLContainer;
+import org.testcontainers.junit.jupiter.Container;
+import org.testcontainers.junit.jupiter.Testcontainers;
+
+import java.util.List;
+import java.util.Map;
+
+import static org.assertj.core.api.Assertions.assertThat;
+import static org.mockito.Mockito.*;
+
+/**
+ * @author Fabian Krüger
+ */
+@Testcontainers
+class PgVectorStoreWithChatMemoryAdvisorIT {
+
+	float[] embed = { 0.003961659F, -0.0073295482F, 0.02663665F };
+
+	@Container
+	@SuppressWarnings("resource")
+	static PostgreSQLContainer<?> postgresContainer = new PostgreSQLContainer<>("pgvector/pgvector:pg16")
+		.withUsername("postgres")
+		.withPassword("postgres");
+
+	/**
+	 * Test that chats with {@link VectorStoreChatMemoryAdvisor} get advised with similar
+	 * messages from the (gp)vector store.
+	 */
+	@Test
+	@DisplayName("Advised chat should have similar messages from vector store")
+	void advisedChatShouldHaveSimilarMessagesFromVectorStore() throws Exception {
+		// faked ChatModel
+		ChatModel chatModel = chatModelAlwaysReturnsTheSameReply();
+		// faked embedding model
+		EmbeddingModel embeddingModel = embeddingNModelShouldAlwaysReturnFakedEmbed();
+		PgVectorStore store = createPgVectorStoreUsingTestcontainer(embeddingModel);
+
+		// do the chat
+		ChatClient.builder(chatModel)
+			.build()
+			.prompt()
+			.user("joke")
+			.advisors(new VectorStoreChatMemoryAdvisor(store))
+			.call()
+			.chatResponse();
+
+		verifyRequestHasBeenAdvisedWithMessagesFromVectorStore(chatModel);
+	}
+
+	private static @NotNull ChatModel chatModelAlwaysReturnsTheSameReply() {
+		ChatModel chatModel = mock(ChatModel.class);
+		ArgumentCaptor<Prompt> argumentCaptor = ArgumentCaptor.forClass(Prompt.class);
+		ChatResponse chatResponse = new ChatResponse(List.of(new Generation(new AssistantMessage("""
+				Why don't scientists trust atoms?
+				Because they make up everything!
+				"""))));
+		when(chatModel.call(argumentCaptor.capture())).thenReturn(chatResponse);
+		return chatModel;
+	}
+
+	private static void initStore(PgVectorStore store) throws Exception {
+		store.afterPropertiesSet();
+		// fill the store
+		store.add(List.of(new Document("Tell me a good joke", Map.of("conversationId", "default")),
+				new Document("Tell me a bad joke", Map.of("conversationId", "default", "messageType", "USER"))));
+	}
+
+	private static PgVectorStore createPgVectorStoreUsingTestcontainer(EmbeddingModel embeddingModel) throws Exception {
+		JdbcTemplate jdbcTemplate = createJdbcTemplateWithConnectionToTestcontainer();
+		PgVectorStore vectorStore = new PgVectorStore.Builder(jdbcTemplate, embeddingModel).withDimensions(3) // match
+																												// embeddings
+			.withInitializeSchema(true)
+			.build();
+		initStore(vectorStore);
+		return vectorStore;
+	}
+
+	private static @NotNull JdbcTemplate createJdbcTemplateWithConnectionToTestcontainer() {
+		PGSimpleDataSource ds = new PGSimpleDataSource();
+		ds.setUrl("jdbc:postgresql://localhost:" + postgresContainer.getMappedPort(5432) + "/postgres");
+		ds.setUser(postgresContainer.getUsername());
+		ds.setPassword(postgresContainer.getPassword());
+		return new JdbcTemplate(ds);
+	}
+
+	private @NotNull EmbeddingModel embeddingNModelShouldAlwaysReturnFakedEmbed() {
+		EmbeddingModel embeddingModel = mock(EmbeddingModel.class);
+		when(embeddingModel.embed(any(Document.class))).thenReturn(embed);
+		when(embeddingModel.embed(any(String.class))).thenReturn(embed);
+		return embeddingModel;
+	}
+
+	private static void verifyRequestHasBeenAdvisedWithMessagesFromVectorStore(ChatModel chatModel) {
+		ArgumentCaptor<Prompt> promptCaptor = ArgumentCaptor.forClass(Prompt.class);
+		verify(chatModel).call(promptCaptor.capture());
+		assertThat(promptCaptor.getValue().getInstructions().get(0)).isInstanceOf(SystemMessage.class);
+		assertThat(promptCaptor.getValue().getInstructions().get(0).getContent()).isEqualTo("""
+
+
+				Use the long term conversation memory from the LONG_TERM_MEMORY section to provide accurate answers.
+
+				---------------------
+				LONG_TERM_MEMORY:
+				Tell me a good joke
+				Tell me a bad joke
+				---------------------
+
+				""");
+	}
+
+}