聊聊Spring AI的RAG

最新推荐文章于 2025-07-11 23:22:26 发布

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#人工智能 #spring #数据库

序

本文主要研究一下Spring AI的RAG

Sequential RAG Flows

Naive RAG

Advisor retrievalAugmentationAdvisor = RetrievalAugmentationAdvisor.builder()
        .documentRetriever(VectorStoreDocumentRetriever.builder()
                .similarityThreshold(0.50)
                .vectorStore(vectorStore)
                .build())
        .queryAugmenter(ContextualQueryAugmenter.builder()
                .allowEmptyContext(true)
                .build())
        .build();

String answer = chatClient.prompt()
        .advisors(retrievalAugmentationAdvisor)
        .user(question)
        .call()
        .content();

allowEmptyContext为true告诉大模型不回答context为empty的问题

Advanced RAG

Advisor retrievalAugmentationAdvisor = RetrievalAugmentationAdvisor.builder()
        .queryTransformers(RewriteQueryTransformer.builder()
                .chatClientBuilder(chatClientBuilder.build().mutate())
                .build())
        .documentRetriever(VectorStoreDocumentRetriever.builder()
                .similarityThreshold(0.50)
                .vectorStore(vectorStore)
                .build())
        .build();

String answer = chatClient.prompt()
        .advisors(retrievalAugmentationAdvisor)
        .user(question)
        .call()
        .content();

Advanced RAG可以设置queryTransformers来进行查询转换

Modular RAG

Spring AI受Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks启发实现了Modular RAG，主要分为如下几个阶段：Pre-Retrieval、Retrieval、Post-Retrieval、Generation

Pre-Retrieval

增强和转换用户输入，使其更有效地执行检索任务，解决格式不正确的查询、query 语义不清晰、或不受支持的语言等。

1. QueryAugmenter 查询增强

使用附加的上下文数据信息增强用户query，提供大模型回答问题时的必要上下文信息；

ContextualQueryAugmenter使用上下文来增强query

QueryAugmenter augmenter = ContextualQueryAugmenter. builder()    
		.allowEmptyContext(false)    
		.build(); 
Query augmentedQuery = augmenter.augment(query, documents);

2. QueryTransformer 查询改写

因为用户的输入通常是片面的，关键信息较少，不便于大模型理解和回答问题。因此需要使用prompt调优手段或者大模型改写用户query；
当使用QueryTransformer时建议设置比较低的temperature(比如0.0)来确保结果的准确性
它有CompressionQueryTransformer、RewriteQueryTransformer、TranslationQueryTransformer三种实现

CompressionQueryTransformer使用大模型来压缩会话历史

Query query = Query.builder()
        .text("And what is its second largest city?")
        .history(new UserMessage("What is the capital of Denmark?"),
                new AssistantMessage("Copenhagen is the capital of Denmark."))
        .build();

QueryTransformer queryTransformer = CompressionQueryTransformer.builder()
        .chatClientBuilder(chatClientBuilder)
        .build();

Query transformedQuery = queryTransformer.transform(query);

RewriteQueryTransformer使用大模型来重写query

Query query = new Query("I'm studying machine learning. What is an LLM?");

QueryTransformer queryTransformer = RewriteQueryTransformer.builder()
        .chatClientBuilder(chatClientBuilder)
        .build();

Query transformedQuery = queryTransformer.transform(query);

TranslationQueryTransformer使用大模型来翻译query

Query query = new Query("Hvad er Danmarks hovedstad?");

QueryTransformer queryTransformer = TranslationQueryTransformer.builder()
        .chatClientBuilder(chatClientBuilder)
        .targetLanguage("english")
        .build();

Query transformedQuery = queryTransformer.transform(query);

3. QueryExpander 查询扩展

将用户 query 扩展为多个语义不同的变体以获得不同视角，有助于检索额外的上下文信息并增加找到相关结果的机会。

MultiQueryExpander使用大模型扩展query

MultiQueryExpander queryExpander = MultiQueryExpander.builder()
    .chatClientBuilder(chatClientBuilder)
    .numberOfQueries(3)
    .includeOriginal(false) // 默认会包含原始query，设置为false表示不包含
    .build();
List<Query> queries = expander.expand(new Query("How to run a Spring Boot app?"));

Retrieval

负责查询向量存储等数据系统并检索和用户query相关性最高的Document。

1. DocumentRetriever 检索器

根据 QueryExpander 使用不同的数据源进行检索，例如搜索引擎、向量存储、数据库或知识图等；它主要有VectorStoreDocumentRetriever、WebSearchRetriever两个实现

VectorStoreDocumentRetriever

DocumentRetriever retriever = VectorStoreDocumentRetriever.builder()
    .vectorStore(vectorStore)
    .similarityThreshold(0.73)
    .topK(5)
    .filterExpression(new FilterExpressionBuilder()
        .eq("genre", "fairytale")
        .build())
    .build();
List<Document> documents = retriever.retrieve(new Query("What is the main character of the story?"));

2. DocumentJoiner

将从多个query和从多个数据源检索到的Document合并为一个Document集合；它有ConcatenationDocumentJoiner实现

ConcatenationDocumentJoiner

Map<Query, List<List<Document>>> documentsForQuery = ...
DocumentJoiner documentJoiner = new ConcatenationDocumentJoiner();
List<Document> documents = documentJoiner.join(documentsForQuery);

Post-Retrieval

负责处理检索到的 Document 以获得最佳的输出结果，解决模型中的中间丢失和上下文长度限制等。

DocumentRanker：根据Document和用户query的相关性对Document进行排序和排名；
DocumentSelector：用于从检索到的Document列表中删除不相关或冗余文档；
DocumentCompressor：用于压缩每个Document，减少检索到的信息中的噪音和冗余。

Generation

生成用户 Query 对应的大模型输出。

源码

org/springframework/ai/chat/client/advisor/RetrievalAugmentationAdvisor.java

	public static final class Builder {

		private List<QueryTransformer> queryTransformers;

		private QueryExpander queryExpander;

		private DocumentRetriever documentRetriever;

		private DocumentJoiner documentJoiner;

		private QueryAugmenter queryAugmenter;

		private TaskExecutor taskExecutor;

		private Scheduler scheduler;

		private Integer order;

		private Builder() {
		}

		//......
	}

RetrievalAugmentationAdvisor的Builder提供了Pre-Retrieval(queryAugmenter、queryTransformers、queryExpander)、Retrieval(documentRetriever、documentJoiner)这几个组件的配置。

示例

ModuleRAGBasicController

@RestController
@RequestMapping("/module-rag")
public class ModuleRAGBasicController {

	private final ChatClient chatClient;
	private final RetrievalAugmentationAdvisor retrievalAugmentationAdvisor;

	public ModuleRAGBasicController(ChatClient.Builder chatClientBuilder, VectorStore vectorStore) {

		this.chatClient = chatClientBuilder.build();
		this.retrievalAugmentationAdvisor = RetrievalAugmentationAdvisor.builder()
				.documentRetriever(VectorStoreDocumentRetriever.builder()
						.similarityThreshold(0.50)
						.vectorStore(vectorStore)
						.build()
				).build();
	}

	@GetMapping("/rag/basic")
	public String chatWithDocument(@RequestParam("prompt") String prompt) {

		return chatClient.prompt()
				.advisors(retrievalAugmentationAdvisor)
				.user(prompt)
				.call()
				.content();
	}

}

ModuleRAGCompressionController

@RestController
@RequestMapping("/module-rag")
public class ModuleRAGCompressionController {

	private final ChatClient chatClient;

	private final MessageChatMemoryAdvisor chatMemoryAdvisor;

	private final RetrievalAugmentationAdvisor retrievalAugmentationAdvisor;

	public ModuleRAGCompressionController(
			ChatClient.Builder chatClientBuilder,
			ChatMemory chatMemory,
			VectorStore vectorStore) {

		this.chatClient = chatClientBuilder.build();

		this.chatMemoryAdvisor = MessageChatMemoryAdvisor.builder(chatMemory)
				.build();

		var documentRetriever = VectorStoreDocumentRetriever.builder()
				.vectorStore(vectorStore)
				.similarityThreshold(0.50)
				.build();

		var queryTransformer = CompressionQueryTransformer.builder()
				.chatClientBuilder(chatClientBuilder.build().mutate())
				.build();

		this.retrievalAugmentationAdvisor = RetrievalAugmentationAdvisor.builder()
				.documentRetriever(documentRetriever)
				.queryTransformers(queryTransformer)
				.build();
	}

	@PostMapping("/rag/compression/{chatId}")
	public String rag(
			@RequestBody String prompt,
			@PathVariable("chatId") String conversationId
	) {

		return chatClient.prompt()
				.advisors(chatMemoryAdvisor, retrievalAugmentationAdvisor)
				.advisors(advisors -> advisors.param(
						AbstractChatMemoryAdvisor.CHAT_MEMORY_CONVERSATION_ID_KEY, conversationId))
				.user(prompt)
				.call()
				.content();
	}

}

ModuleRAGMemoryController

@RestController
@RequestMapping("/module-rag")
public class ModuleRAGMemoryController {

	private final ChatClient chatClient;

	private final MessageChatMemoryAdvisor chatMemoryAdvisor;

	private final RetrievalAugmentationAdvisor retrievalAugmentationAdvisor;

	public ModuleRAGMemoryController(
			ChatClient.Builder chatClientBuilder,
			ChatMemory chatMemory,
			VectorStore vectorStore
	) {

		this.chatClient = chatClientBuilder.build();
		this.chatMemoryAdvisor = MessageChatMemoryAdvisor.builder(chatMemory)
				.build();

		this.retrievalAugmentationAdvisor = RetrievalAugmentationAdvisor.builder()
				.documentRetriever(VectorStoreDocumentRetriever.builder()
						.similarityThreshold(0.50)
						.vectorStore(vectorStore)
						.build())
				.build();
	}

	@PostMapping("/rag/memory/{chatId}")
	public String chatWithDocument(
			@RequestBody String prompt,
			@PathVariable("chatId") String conversationId
	) {

		return chatClient.prompt()
				.advisors(chatMemoryAdvisor, retrievalAugmentationAdvisor)
				.advisors(advisors -> advisors.param(
						AbstractChatMemoryAdvisor.CHAT_MEMORY_CONVERSATION_ID_KEY, conversationId))
				.user(prompt)
				.call()
				.content();
	}

}

ModuleRAGRewriteController

@RestController
@RequestMapping("/module-rag")
public class ModuleRAGRewriteController {

	private final ChatClient chatClient;

	private final RetrievalAugmentationAdvisor retrievalAugmentationAdvisor;

	public ModuleRAGRewriteController(
			ChatClient.Builder chatClientBuilder,
			VectorStore vectorStore
	) {

		this.chatClient = chatClientBuilder.build();

		var documentRetriever = VectorStoreDocumentRetriever.builder()
				.vectorStore(vectorStore)
				.similarityThreshold(0.50)
				.build();

		var queryTransformer = RewriteQueryTransformer.builder()
				.chatClientBuilder(chatClientBuilder.build().mutate())
				.targetSearchSystem("vector store")
				.build();

		this.retrievalAugmentationAdvisor = RetrievalAugmentationAdvisor.builder()
				.documentRetriever(documentRetriever)
				.queryTransformers(queryTransformer)
				.build();
	}

	@PostMapping("/rag/rewrite")
	public String rag(@RequestBody String prompt) {

		return chatClient.prompt()
				.advisors(retrievalAugmentationAdvisor)
				.user(prompt)
				.call()
				.content();
	}
}

ModuleRAGTranslationController

@RestController
@RequestMapping("/module-rag")
public class ModuleRAGTranslationController {

	private final ChatClient chatClient;

	private final RetrievalAugmentationAdvisor retrievalAugmentationAdvisor;

	public ModuleRAGTranslationController(ChatClient.Builder chatClientBuilder, VectorStore vectorStore) {
		this.chatClient = chatClientBuilder.build();

		var documentRetriever = VectorStoreDocumentRetriever.builder()
				.vectorStore(vectorStore)
				.similarityThreshold(0.50)
				.build();

		var queryTransformer = TranslationQueryTransformer.builder()
				.chatClientBuilder(chatClientBuilder.build().mutate())
				.targetLanguage("english")
				.build();

		this.retrievalAugmentationAdvisor = RetrievalAugmentationAdvisor.builder()
				.documentRetriever(documentRetriever)
				.queryTransformers(queryTransformer)
				.build();
	}

	@PostMapping("/rag/translation")
	public String rag(@RequestBody String prompt) {

		return chatClient.prompt()
				.advisors(retrievalAugmentationAdvisor)
				.user(prompt)
				.call()
				.content();
	}

}

小结

Spring AI通过RetrievalAugmentationAdvisor提供了开箱即用的RAG flows，主要有两大类，一是Sequential RAG Flows(Naive RAG、Advanced RAG)，另外Spring AI受Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks启发实现了Modular RAG，主要分为如下几个阶段：Pre-Retrieval、Retrieval、Post-Retrieval、Generation这几个阶段。RetrievalAugmentationAdvisor的Builder提供了Pre-Retrieval(queryAugmenter、queryTransformers、queryExpander)、Retrieval(documentRetriever、documentJoiner)这几个组件的配置。