本文主要研究一下Spring AI的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
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
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
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。
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;
QueryAugmenter augmenter = ContextualQueryAugmenter. builder()
.allowEmptyContext(false)
.build();
Query augmentedQuery = augmenter.augment(query, documents);
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
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);
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);
Query query = new Query("Hvad er Danmarks hovedstad?");
QueryTransformer queryTransformer = TranslationQueryTransformer.builder()
.chatClientBuilder(chatClientBuilder)
.targetLanguage("english")
.build();
Query transformedQuery = queryTransformer.transform(query);
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 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?"));
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。
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
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?"));
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
Map<Query, List<List<Document>>> documentsForQuery = ...
DocumentJoiner documentJoiner = new ConcatenationDocumentJoiner();
List<Document> documents = documentJoiner.join(documentsForQuery);
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。
生成用户 Query 对应的大模型输出。
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。
@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();
}
}
@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();
}
}
@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();
}
}
@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();
}
}
@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();
}
}
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。
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。