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

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>

# Weaviate 配置
spring.ai.vectorstore.weaviate.scheme=http
spring.ai.vectorstore.weaviate.host=localhost:8080 # 假设 Weaviate 在 8080 端口运行
spring.ai.vectorstore.weaviate.object-class=MyDocuments
# spring.ai.vectorstore.weaviate.api-key=YOUR_WEAVIATE_API_KEY # 如果需要

# 嵌入客户端配置 (例如 OpenAI)
spring.ai.openai.api-key=YOUR_OPENAI_API_KEY
spring.ai.openai.embedding.options.model=text-embedding-ada-002

import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.document.Document;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

import java.util.List;

@Service
public class MyVectorStoreService {

    private final VectorStore vectorStore; // 将自动配置 WeaviateVectorStore

    @Autowired
    public MyVectorStoreService(VectorStore vectorStore) {
        this.vectorStore = vectorStore;
    }

    public void addDocuments(List<Document> documents) {
        vectorStore.add(documents);
    }

    public List<Document> findSimilarDocuments(String query, int k) {
        return vectorStore.similaritySearch(query, k);
    }
}

import io.weaviate.client.WeaviateClient;
import io.weaviate.client.Config;
import org.springframework.ai.embedding.EmbeddingClient;
import org.springframework.ai.vectorstore.WeaviateVectorStore;
import org.springframework.ai.vectorstore.WeaviateVectorStore.WeaviateVectorStoreConfig;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

import java.util.Map;

@Configuration
public class MyWeaviateConfig {

    @Bean
    public WeaviateClient weaviateClient() {
        // 从属性或直接创建 WeaviateClient
        // 例如: new Config("http", "localhost:8080");
        // 如果需要 API 密钥: new Config("https", "your-weaviate-host.com", "YOUR_API_KEY");
        Config config = new Config("http", "localhost:8080");
        return new WeaviateClient(config);
    }

    @Bean
    public WeaviateVectorStore weaviateVectorStore(WeaviateClient weaviateClient, EmbeddingClient embeddingClient) {
        WeaviateVectorStoreConfig config = WeaviateVectorStoreConfig.builder()
            .withObjectClass("MyCustomDocuments")
            // .withConsistencyLevel(WeaviateVectorStoreConfig.ConsistencyLevel.QUORUM) // 按需设置
            // .withHeaders(Map.of("X-Custom-Header", "value")) // 按需设置
            .build();
        return new WeaviateVectorStore(weaviateClient, embeddingClient, config);
    }
}

import org.springframework.ai.vectorstore.SearchRequest;
import org.springframework.ai.vectorstore.filter.Filter;
import org.springframework.ai.vectorstore.filter.FilterExpressionBuilder;

// ...

FilterExpressionBuilder b = new FilterExpressionBuilder();

// path: ["genre"], operator: Equal, valueText: "drama"
// AND path: ["year"], operator: GreaterThan, valueInt: 2020
Filter filter = new Filter(b.eq("genre", "drama").and(b.gt("year", 2020)).build());

SearchRequest request = SearchRequest.query("A dramatic film released after 2020") // 示例查询通常保留英文
    .withTopK(5)
    .withFilter(filter);

List<Document> results = vectorStore.similaritySearch(request);

boolean deleted = vectorStore.delete(List.of("documentId1", "documentId2"));