feat(google-cloud-sql): Add example with filtering

google-cloud-sql-pg-example/pom.xml

@@ -0,0 +1,34 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>dev.langchain4j</groupId>
+    <artifactId>cloud-sql-example</artifactId>
+    <version>1.1.0-beta7</version>
+
+    <properties>
+        <maven.compiler.source>17</maven.compiler.source>
+        <maven.compiler.target>17</maven.compiler.target>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+    </properties>
+
+    <dependencies>
+
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-community-cloud-sql-pg</artifactId>
+            <version>1.0.0-beta4</version>
+        </dependency>
+
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-embeddings-all-minilm-l6-v2</artifactId>
+            <version>1.1.0-beta7</version>
+        </dependency>
+
+
+    </dependencies>
+
+</project>

google-cloud-sql-pg-example/src/main/java/PostgresEmbeddingStoreWithMetadataExample.java

@@ -0,0 +1,105 @@
+import dev.langchain4j.data.document.Metadata;
+import dev.langchain4j.data.embedding.Embedding;
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.model.embedding.onnx.allminilml6v2.AllMiniLmL6V2EmbeddingModel;
+import dev.langchain4j.model.embedding.EmbeddingModel;
+import dev.langchain4j.store.embedding.EmbeddingMatch;
+import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
+import dev.langchain4j.store.embedding.EmbeddingSearchResult;
+import dev.langchain4j.engine.EmbeddingStoreConfig;
+import dev.langchain4j.engine.PostgresEngine;
+import dev.langchain4j.engine.MetadataColumn;
+import dev.langchain4j.store.embedding.cloudsql.PostgresEmbeddingStore;
+import dev.langchain4j.store.embedding.filter.Filter;
+import java.sql.SQLException;
+
+import static dev.langchain4j.store.embedding.filter.MetadataFilterBuilder.metadataKey;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.UUID;
+import java.util.stream.Collectors;
+
+public class PostgresEmbeddingStoreWithMetadataExample {
+
+        private static final String TABLE_NAME = "my_test_table";
+        private static final Integer VECTOR_SIZE = 384;
+
+        public static void main(String[] args) throws SQLException {
+
+                EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel();
+                // Create connection pool
+                PostgresEngine engine = new PostgresEngine.Builder()
+                                .projectId(System.getenv("PROJECT_ID"))
+                                .region(System.getenv("REGION"))
+                                .instance(System.getenv("INSTANCE"))
+                                .database(System.getenv("DB_NAME"))
+                                .user(System.getenv("USER"))
+                                .password(System.getenv("PASSWORD"))
+                                .ipType("public")
+                                .build();
+
+                // Create embedding store table with metadata columns
+                List<MetadataColumn> metadataColumns = new ArrayList<>();
+                metadataColumns.add(new MetadataColumn("userId", "uuid", true));
+                EmbeddingStoreConfig embeddingStoreConfig =
+                                new EmbeddingStoreConfig.Builder(TABLE_NAME, VECTOR_SIZE)
+                                                .metadataColumns(metadataColumns)
+                                                .overwriteExisting(true).storeMetadata(true)
+                                                .build();
+
+                engine.initVectorStoreTable(embeddingStoreConfig);
+
+                // Initialize embedding store to use metadata columns
+                List<String> metaColumnNames = metadataColumns.stream().map(c -> c.getName())
+                                .collect(Collectors.toList());
+
+                PostgresEmbeddingStore embeddingStore =
+                                new PostgresEmbeddingStore.Builder(engine, TABLE_NAME)
+                                                .metadataColumns(metaColumnNames).build();
+
+                // Add entries to embedding store
+                Metadata metadata1 = new Metadata();
+                UUID user1 = UUID.randomUUID();
+                metadata1.put("userId", user1);
+                TextSegment segment1 = TextSegment.from("I like turtles.", metadata1);
+                Embedding embedding1 = embeddingModel.embed(segment1).content();
+                embeddingStore.add(embedding1, segment1);
+
+                Metadata metadata2 = new Metadata();
+                UUID user2 = UUID.randomUUID();
+                metadata2.put("userId", user2);
+                TextSegment segment2 = TextSegment.from(
+                                "I do not like fish. My favorite animal is a horse!", metadata2);
+                Embedding embedding2 = embeddingModel.embed(segment2).content();
+                embeddingStore.add(embedding2, segment2);
+
+                // Search embedding store
+                Embedding queryEmbedding =
+                                embeddingModel.embed("What is your favorite animal?").content();
+                EmbeddingSearchRequest searchRequest = EmbeddingSearchRequest.builder()
+                                .queryEmbedding(queryEmbedding).maxResults(1).build();
+                EmbeddingSearchResult<TextSegment> searchResult =
+                                embeddingStore.search(searchRequest);
+
+                EmbeddingMatch<TextSegment> embeddingMatch = searchResult.matches().get(0);
+                System.out.println("Unfiltered match:");
+                System.out.println(embeddingMatch.score());
+                System.out.println(embeddingMatch.embedded().text());
+
+                // Search embedding store with filter
+                Filter onlyForUser1 = metadataKey("userId").isEqualTo(user1);
+
+                EmbeddingSearchRequest embeddingSearchRequest1 = EmbeddingSearchRequest.builder()
+                                .queryEmbedding(queryEmbedding).filter(onlyForUser1).build();
+
+                EmbeddingSearchResult<TextSegment> embeddingSearchResult1 =
+                                embeddingStore.search(embeddingSearchRequest1);
+                EmbeddingMatch<TextSegment> embeddingMatch1 =
+                                embeddingSearchResult1.matches().get(0);
+                System.out.println("Filtered match:");
+                System.out.println(embeddingMatch1.score());
+                System.out.println(embeddingMatch1.embedded().text());
+
+                engine.close();
+        }
+}

pom.xml

@@ -48,6 +48,7 @@
         <module>oracle-example</module>
         <module>voyage-ai-examples</module>
         <module>google-alloydb-example</module>
+        <module>google-cloud-sql-pg-example</module>
         <module>wildfly-example</module>
         <module>helidon-examples</module>
         <module>payara-micro-example</module>