What is the recommended way to configure RAG pipeline in Spring AI Alibaba?

[however-yir]

I am new to Spring AI Alibaba and want to build a RAG pipeline. What are the best practices for configuring the embedding model, vector store, and retrieval chain?

[however-yir]

Here is the recommended approach for configuring a RAG pipeline with Spring AI Alibaba:

1. Add Dependencies

<dependency>
    <groupId>com.alibaba.cloud.ai</groupId>
    <artifactId>spring-ai-alibaba-starter</artifactId>
</dependency>

2. Configure Application Properties

spring:
  ai:
    dashscope:
      api-key: ${DASHSCOPE_API_KEY}
    vectorstore:
      elasticsearch:
        uris: ${ES_URIS}

3. Build RAG Pipeline

@Bean
public ChatClient chatClient(ChatClient.Builder builder,
                               VectorStore vectorStore) {
    return builder
        .defaultAdvisors(new QuestionAnswerAdvisor(vectorStore))
        .build();
}

4. Best Practices

• Use async document ingestion for large datasets
• Enable prompt caching for repeated queries
• Implement retrieval evaluation with hit rate metrics

For a complete working example, check the spring-ai-alibaba/examples repo.