Merge SimplePersistentVectorStore and InMemoryVector store to be SimpleVectorStore

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/acme/AcmeIT.java

@@ -22,7 +22,7 @@
 import org.springframework.ai.reader.JsonReader;
 import org.springframework.ai.retriever.VectorStoreRetriever;
 import org.springframework.ai.transformer.splitter.TokenTextSplitter;
-import org.springframework.ai.vectorstore.InMemoryVectorStore;
+import org.springframework.ai.vectorstore.SimpleVectorStore;
 import org.springframework.ai.vectorstore.VectorStore;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.beans.factory.annotation.Value;
@@ -67,7 +67,7 @@ void acmeChain() {
 		// Step 2 - Create embeddings and save to vector store
 
 		logger.info("Creating Embeddings...");
-		VectorStore vectorStore = new InMemoryVectorStore(embeddingClient);
+		VectorStore vectorStore = new SimpleVectorStore(embeddingClient);
 
 		vectorStore.accept(textSplitter.apply(jsonReader.get()));
 

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/vectorstore/SimplePersistentVectorStoreIT.java

@@ -6,7 +6,7 @@
 import org.springframework.ai.document.Document;
 import org.springframework.ai.embedding.EmbeddingClient;
 import org.springframework.ai.reader.JsonReader;
-import org.springframework.ai.vectorstore.SimplePersistentVectorStore;
+import org.springframework.ai.vectorstore.SimpleVectorStore;
 import org.springframework.ai.reader.JsonMetadataGenerator;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.beans.factory.annotation.Value;
@@ -34,14 +34,14 @@ void persist(@TempDir(cleanup = CleanupMode.ON_SUCCESS) Path workingDir) {
 		JsonReader jsonReader = new JsonReader(bikesJsonResource, new ProductMetadataGenerator(), "price", "name",
 				"shortDescription", "description", "tags");
 		List<Document> documents = jsonReader.get();
-		SimplePersistentVectorStore vectorStore = new SimplePersistentVectorStore(this.embeddingClient);
+		SimpleVectorStore vectorStore = new SimpleVectorStore(this.embeddingClient);
 		vectorStore.add(documents);
 
 		File tempFile = new File(workingDir.toFile(), "temp.txt");
 		vectorStore.save(tempFile);
 		assertThat(tempFile).isNotEmpty();
 		assertThat(tempFile).content().contains("Velo 99 XR1 AXS");
-		SimplePersistentVectorStore vectorStore2 = new SimplePersistentVectorStore(this.embeddingClient);
+		SimpleVectorStore vectorStore2 = new SimpleVectorStore(this.embeddingClient);
 
 		vectorStore2.load(tempFile);
 		List<Document> similaritySearch = vectorStore2.similaritySearch("Velo 99 XR1 AXS");

spring-ai-core/src/main/java/org/springframework/ai/vectorstore/InMemoryVectorStore.java → spring-ai-core/src/main/java/org/springframework/ai/vectorstore/SimpleVectorStore.java

@@ -1,10 +1,21 @@
 package org.springframework.ai.vectorstore;
 
+import com.fasterxml.jackson.core.JsonProcessingException;
+import com.fasterxml.jackson.core.type.TypeReference;
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.fasterxml.jackson.databind.ObjectWriter;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 import org.springframework.ai.document.Document;
 import org.springframework.ai.embedding.EmbeddingClient;
-
+import org.springframework.core.io.Resource;
+import org.springframework.util.StreamUtils;
+
+import java.io.File;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.OutputStream;
+import java.nio.charset.Charset;
 import java.util.*;
 import java.util.concurrent.ConcurrentHashMap;
 
@@ -14,15 +25,15 @@
  * @author Mark Pollack
  * @author Christian Tzolov
  */
-public class InMemoryVectorStore implements VectorStore {
+public class SimpleVectorStore implements VectorStore {
 
-	private static final Logger logger = LoggerFactory.getLogger(InMemoryVectorStore.class);
+	private static final Logger logger = LoggerFactory.getLogger(SimpleVectorStore.class);
 
 	protected Map<String, Document> store = new ConcurrentHashMap<>();
 
 	protected EmbeddingClient embeddingClient;
 
-	public InMemoryVectorStore(EmbeddingClient embeddingClient) {
+	public SimpleVectorStore(EmbeddingClient embeddingClient) {
 		Objects.requireNonNull(embeddingClient, "EmbeddingClient must not be null");
 		this.embeddingClient = embeddingClient;
 	}
@@ -66,6 +77,81 @@ public List<Document> similaritySearch(SearchRequest request) {
 		return similarities;
 	}
 
+	/**
+	 * Serialize the vector store content into a file in JSON format.
+	 * @param file the file to save the vector store content
+	 */
+	public void save(File file) {
+		String json = getVectorDbAsJson();
+		try {
+			if (!file.exists()) {
+				logger.info("Creating new vector store file: " + file);
+				file.createNewFile();
+			}
+			else {
+				logger.info("Replacing existing vector store file: " + file);
+				file.delete();
+				file.createNewFile();
+			}
+		}
+		catch (IOException ex) {
+			throw new RuntimeException(ex);
+		}
+		try (OutputStream stream = new FileOutputStream(file)) {
+			StreamUtils.copy(json, Charset.forName("UTF-8"), stream);
+		}
+		catch (IOException e) {
+			throw new RuntimeException(e);
+		}
+	}
+
+	/**
+	 * Deserialize the vector store content from a file in JSON format into memory.
+	 * @param file the file to load the vector store content
+	 */
+	public void load(File file) {
+		TypeReference<HashMap<String, Document>> typeRef = new TypeReference<>() {
+		};
+		ObjectMapper objectMapper = new ObjectMapper();
+		try {
+			Map<String, Document> deserializedMap = objectMapper.readValue(file, typeRef);
+			this.store = deserializedMap;
+		}
+		catch (IOException ex) {
+			throw new RuntimeException(ex);
+		}
+	}
+
+	/**
+	 * Deserialize the vector store content from a resource in JSON format into memory.
+	 * @param resource the resource to load the vector store content
+	 */
+	public void load(Resource resource) {
+		TypeReference<HashMap<String, Document>> typeRef = new TypeReference<>() {
+		};
+		ObjectMapper objectMapper = new ObjectMapper();
+		try {
+			Map<String, Document> deserializedMap = objectMapper.readValue(resource.getInputStream(), typeRef);
+			this.store = deserializedMap;
+		}
+		catch (IOException ex) {
+			throw new RuntimeException(ex);
+		}
+	}
+
+	private String getVectorDbAsJson() {
+		ObjectMapper objectMapper = new ObjectMapper();
+		ObjectWriter objectWriter = objectMapper.writerWithDefaultPrettyPrinter();
+		String json;
+		try {
+			json = objectWriter.writeValueAsString(this.store);
+		}
+		catch (JsonProcessingException e) {
+			throw new RuntimeException("Error serializing documentMap to JSON.", e);
+		}
+		return json;
+	}
+
 	private List<Double> getUserQueryEmbedding(String query) {
 		List<Double> userQueryEmbedding = this.embeddingClient.embed(query);
 		return userQueryEmbedding;

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/etl-pipeline.adoc

@@ -84,7 +84,7 @@ public interface DocumentWriter extends Consumer<List<Document>> {
 *PgVectorStore*::
 + Provides vector storage capabilities using PostgreSQL.
 
-*SimplePersistentVectorStore*::
+*SimpleVectorStore*::
 + A straightforward approach to persistent vector storage.
 
 *InMemoryVectorStore*::

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

@@ -87,7 +87,7 @@ country == 'UK' && year >= 2020 && isActive == true.
 
 These are the available implementations of the `VectorStore` interface:
 
-* `InMemoryVectorStore` and `SimplePersistentVectorStore`.
+* `SimpleVectorStore`
 * Pinecone: https://www.pinecone.io/[PineCone] vector store.
 * PgVector [`PgVectorStore`]: The https://github.com/pgvector/pgvector[PostgreSQL/PGVector] vector store.
 * Milvus [`MilvusVectorStore`]: The https://milvus.io/[Milvus] vector store