MongoDB Atlas

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-mongodb-atlas-store-spring-boot-starter</artifactId>
</dependency>

dependencies {
    implementation 'org.springframework.ai:spring-ai-mongodb-atlas-store-spring-boot-starter'
}

@Autowired VectorStore vectorStore;

// ...

List<Document> documents = List.of(
    new Document("Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!!", Map.of("meta1", "meta1")),
    new Document("The World is Big and Salvation Lurks Around the Corner"),
    new Document("You walk forward facing the past and you turn back toward the future.", Map.of("meta2", "meta2")));

// Add the documents to MongoDB Atlas
vectorStore.add(documents);

// Retrieve documents similar to a query
List<Document> results = vectorStore.similaritySearch(SearchRequest.builder().query("Spring").topK(5).build());

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-mongodb-atlas-store</artifactId>
</dependency>

dependencies {
    implementation 'org.springframework.ai:spring-ai-mongodb-atlas-store'
}

@Bean
public MongoTemplate mongoTemplate() {
    return new MongoTemplate(MongoClients.create("<mongodb atlas connection string>"), "<database name>");
}

@Bean
public VectorStore vectorStore(MongoTemplate mongoTemplate, EmbeddingModel embeddingModel) {
    return MongoDBAtlasVectorStore.builder(mongoTemplate, embeddingModel)
        .collectionName("custom_vector_store")           // Optional: defaults to "vector_store"
        .vectorIndexName("custom_vector_index")          // Optional: defaults to "vector_index"
        .pathName("custom_embedding")                    // Optional: defaults to "embedding"
        .numCandidates(500)                             // Optional: defaults to 200
        .metadataFieldsToFilter(List.of("author", "year")) // Optional: defaults to empty list
        .initializeSchema(true)                         // Optional: defaults to false
        .batchingStrategy(new TokenCountBatchingStrategy()) // Optional: defaults to TokenCountBatchingStrategy
        .build();
}

// This can be any EmbeddingModel implementation
@Bean
public EmbeddingModel embeddingModel() {
    return new OpenAiEmbeddingModel(new OpenAiApi(System.getenv("OPENAI_API_KEY")));
}

vectorStore.similaritySearch(SearchRequest.builder()
        .query("The World")
        .topK(5)
        .similarityThreshold(0.7)
        .filterExpression("author in ['john', 'jill'] && article_type == 'blog'").build());

FilterExpressionBuilder b = new FilterExpressionBuilder();

vectorStore.similaritySearch(SearchRequest.builder()
        .query("The World")
        .topK(5)
        .similarityThreshold(0.7)
        .filterExpression(b.and(
                b.in("author", "john", "jill"),
                b.eq("article_type", "blog")).build()).build());

author in ['john', 'jill'] && article_type == 'blog'

{
  "$and": [
    {
      "$or": [
        { "metadata.author": "john" },
        { "metadata.author": "jill" }
      ]
    },
    {
      "metadata.article_type": "blog"
    }
  ]
}

MongoDBAtlasVectorStore vectorStore = context.getBean(MongoDBAtlasVectorStore.class);
Optional<MongoClient> nativeClient = vectorStore.getNativeClient();

if (nativeClient.isPresent()) {
    MongoClient client = nativeClient.get();
    // Use the native client for MongoDB-specific operations
}