聊聊Spring AI的RAG

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    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。
    1. DocumentRanker：根据Document和用户query的相关性对Document进行排序和排名；
    2. DocumentSelector：用于从检索到的Document列表中删除不相关或冗余文档；
    3. 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。

    doc

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