Azure AI 搜索向量数据库

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-azure-ai-search-store</artifactId>  <!-- 注意 artifactId 更新 -->
    <version>${spring-ai.version}</version>
</dependency>

# Azure AI 搜索向量数据库配置
spring.ai.vectorstore.azure.aisearch.endpoint=YOUR_AZURE_AI_SEARCH_ENDPOINT # 例如 https://your-service-name.search.windows.net
spring.ai.vectorstore.azure.aisearch.api-key=YOUR_AZURE_AI_SEARCH_API_KEY
spring.ai.vectorstore.azure.aisearch.index-name=vector-store-index # 要使用的索引名称
# spring.ai.vectorstore.azure.aisearch.create-index-if-not-exists=true # 如果索引不存在则创建 (可选, 默认为 false)
# spring.ai.vectorstore.azure.aisearch.embedding-dimension=1536 # 嵌入维度 (如果 create-index-if-not-exists=true 则需要)

@Service
public class VectorService {
    private final AzureAiSearchVectorStore vectorStore;

    public VectorService(AzureAiSearchVectorStore vectorStore) {
        this.vectorStore = vectorStore;
    }

    public void storeVector(String id, float[] vector, Map<String, Object> metadata) {
        // 注意：Azure AI 搜索通常从嵌入客户端获取嵌入
        // 此处仅为示例，实际使用时通常直接添加 Document 对象
        Document document = new Document(id, "Content related to vector", metadata);
        // 假设嵌入由 AzureAiSearchVectorStore 内部处理或您已配置 EmbeddingClient
        vectorStore.add(List.of(document));
    }

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

@Configuration
public class VectorStorageConfig {
    // AzureAiSearchVectorStore bean 通常由 Spring Boot 自动配置
    // 如果需要，您可以自定义它
}

@Service
public class SimilaritySearchService {
    private final AzureAiSearchVectorStore vectorStore;

    public SimilaritySearchService(AzureAiSearchVectorStore vectorStore) {
        this.vectorStore = vectorStore;
    }

    public List<Document> findSimilar(String queryText, int topK) {
        return vectorStore.similaritySearch(SearchRequest.query(queryText).withTopK(topK));
    }
}

@Service
public class BatchVectorService {
    private final AzureAiSearchVectorStore vectorStore;

    public BatchVectorService(AzureAiSearchVectorStore vectorStore) {
        this.vectorStore = vectorStore;
    }

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