add query compressing

README.md

@@ -144,6 +144,10 @@ docker run -d \
 
 高级RAG
 
+* 查询压缩 √
+* 查询路由
+* Re-rank
+
 增加搜索引擎（BING、百度）
 
 ## 截图

adi-common/src/main/java/com/moyz/adi/common/config/BeanConfig.java

@@ -131,4 +131,10 @@ public class BeanConfig {
         return ragService;
     }
 
+    @Bean(name = "queryCompressingRagService")
+    public RAGService queryCompressingRagService() {
+        RAGService ragService = new RAGService("adi_advanced_rag_query_embedding", dataBaseUrl, dataBaseUserName, dataBasePassword);
+        ragService.init();
+        return ragService;
+    }
 }

adi-common/src/main/java/com/moyz/adi/common/interfaces/AbstractLLMService.java

@@ -1,6 +1,7 @@
 package com.moyz.adi.common.interfaces;
 
 import com.moyz.adi.common.exception.BaseException;
+import com.moyz.adi.common.service.RAGService;
 import com.moyz.adi.common.util.JsonUtil;
 import com.moyz.adi.common.util.LocalCache;
 import com.moyz.adi.common.util.MapDBChatMemoryStore;
@@ -15,6 +16,10 @@ import dev.langchain4j.memory.chat.MessageWindowChatMemory;
 import dev.langchain4j.model.chat.ChatLanguageModel;
 import dev.langchain4j.model.chat.StreamingChatLanguageModel;
 import dev.langchain4j.model.output.Response;
+import dev.langchain4j.rag.DefaultRetrievalAugmentor;
+import dev.langchain4j.rag.RetrievalAugmentor;
+import dev.langchain4j.rag.query.transformer.CompressingQueryTransformer;
+import dev.langchain4j.rag.query.transformer.QueryTransformer;
 import dev.langchain4j.service.AiServices;
 import dev.langchain4j.service.TokenStream;
 import lombok.extern.slf4j.Slf4j;
@@ -43,7 +48,7 @@ public abstract class AbstractLLMService<T> {
 
     private IChatAssistantWithoutMemory chatAssistantWithoutMemory;
 
-    private MapDBChatMemoryStore mapDBChatMemoryStore;
+    private RAGService queryCompressingRagService;
 
     public AbstractLLMService(String modelName, String settingName, Class<T> clazz) {
         this.modelName = modelName;
@@ -56,6 +61,11 @@ public abstract class AbstractLLMService<T> {
         return this;
     }
 
+    public AbstractLLMService setQueryCompressingRAGService(RAGService ragService){
+        queryCompressingRagService = ragService;
+        return this;
+    }
+
     /**
      * 检测该service是否可用（不可用的情况通常是没有配置key）
      *
@@ -99,6 +109,15 @@ public abstract class AbstractLLMService<T> {
             throw new BaseException(B_LLM_SERVICE_DISABLED);
         }
         log.info("sseChat,messageId:{}", params.getMessageId());
+
+        //Query compressing
+        QueryTransformer queryTransformer = new CompressingQueryTransformer(getChatLLM());
+
+        RetrievalAugmentor retrievalAugmentor = DefaultRetrievalAugmentor.builder()
+                .queryTransformer(queryTransformer)
+                .contentRetriever(queryCompressingRagService.buildContentRetriever())
+                .build();
+
         //create chat assistant
         if (null == chatAssistant && StringUtils.isNotBlank(params.getMessageId())) {
             ChatMemoryProvider chatMemoryProvider = memoryId -> MessageWindowChatMemory.builder()
@@ -109,6 +128,7 @@ public abstract class AbstractLLMService<T> {
             chatAssistant = AiServices.builder(IChatAssistant.class)
                     .streamingChatLanguageModel(getStreamingChatLLM())
                     .chatMemoryProvider(chatMemoryProvider)
+                    .retrievalAugmentor(retrievalAugmentor)
                     .build();
         } else if (null == chatAssistantWithoutMemory && StringUtils.isBlank(params.getMessageId())) {
             chatAssistantWithoutMemory = AiServices.builder(IChatAssistantWithoutMemory.class)

adi-common/src/main/java/com/moyz/adi/common/service/Initializer.java

@@ -32,7 +32,7 @@ public class Initializer {
     private SysConfigService sysConfigService;
 
     @Resource
-    private RAGService ragService;
+    private RAGService queryCompressingRagService;
 
     @PostConstruct
     public void init() {
@@ -49,7 +49,7 @@ public class Initializer {
             log.warn("openai service is disabled");
         }
         for (String model : openaiModels) {
-            LLMContext.addLLMService(model, new OpenAiLLMService(model).setProxy(proxy));
+            LLMContext.addLLMService(model, new OpenAiLLMService(model).setProxy(proxy).setQueryCompressingRAGService(queryCompressingRagService));
         }
 
         //dashscope
@@ -58,7 +58,7 @@ public class Initializer {
             log.warn("dashscope service is disabled");
         }
         for (String model : dashscopeModels) {
-            LLMContext.addLLMService(model, new DashScopeLLMService(model));
+            LLMContext.addLLMService(model, new DashScopeLLMService(model).setQueryCompressingRAGService(queryCompressingRagService));
         }
 
         //qianfan
@@ -67,7 +67,7 @@ public class Initializer {
             log.warn("qianfan service is disabled");
         }
         for (String model : qianfanModels) {
-            LLMContext.addLLMService(model, new QianFanLLMService(model));
+            LLMContext.addLLMService(model, new QianFanLLMService(model).setQueryCompressingRAGService(queryCompressingRagService));
         }
 
         //ollama
@@ -76,16 +76,13 @@ public class Initializer {
             log.warn("ollama service is disabled");
         }
         for (String model : ollamaModels) {
-            LLMContext.addLLMService("ollama:" + model, new OllamaLLMService(model));
+            LLMContext.addLLMService("ollama:" + model, new OllamaLLMService(model).setQueryCompressingRAGService(queryCompressingRagService));
         }
 
         ImageModelContext.addImageModelService(OpenAiModelName.DALL_E_2, new OpenAiImageModelService(OpenAiModelName.DALL_E_2).setProxy(proxy));
 
-
         //search engine
         SearchEngineContext.addEngine(AdiConstant.SearchEngineName.GOOGLE, new GoogleSearchEngine().setProxy(proxy));
 
-
-        ragService.init();
     }
 }

adi-common/src/main/java/com/moyz/adi/common/service/RAGService.java

@@ -13,6 +13,8 @@ import dev.langchain4j.model.embedding.EmbeddingModel;
 import dev.langchain4j.model.input.Prompt;
 import dev.langchain4j.model.openai.OpenAiTokenizer;
 import dev.langchain4j.model.output.Response;
+import dev.langchain4j.rag.content.retriever.ContentRetriever;
+import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
 import dev.langchain4j.store.embedding.EmbeddingMatch;
 import dev.langchain4j.store.embedding.EmbeddingStore;
 import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
@@ -32,6 +34,10 @@ import static java.util.stream.Collectors.joining;
 
 @Slf4j
 public class RAGService {
+
+    private static final int MAX_RESULTS = 3;
+    private static final double MIN_SCORE = 0.6;
+
     private String dataBaseUrl;
 
     private String dataBaseUserName;
@@ -91,23 +97,35 @@ public class RAGService {
         return embeddingStore;
     }
 
-    private EmbeddingStoreIngestor getEmbeddingStoreIngestor() {
+    /**
+     * 对文档切块、向量化并存储到数据库
+     *
+     * @param document 知识库文档
+     */
+    public void ingest(Document document) {
         DocumentSplitter documentSplitter = DocumentSplitters.recursive(1000, 0, new OpenAiTokenizer(GPT_3_5_TURBO));
         EmbeddingStoreIngestor embeddingStoreIngestor = EmbeddingStoreIngestor.builder()
                 .documentSplitter(documentSplitter)
                 .embeddingModel(embeddingModel)
                 .embeddingStore(embeddingStore)
                 .build();
-        return embeddingStoreIngestor;
+        embeddingStoreIngestor.ingest(document);
     }
 
     /**
-     * 对文档切块并向量化
+     * There are two methods for retrieve documents:
+     * 1. ContentRetriever.retrieve()
+     * 2. retrieveAndCreatePrompt()
      *
-     * @param document 知识库文档
+     * @return ContentRetriever
      */
-    public void ingest(Document document) {
+    public ContentRetriever buildContentRetriever() {
-        getEmbeddingStoreIngestor().ingest(document);
+        return EmbeddingStoreContentRetriever.builder()
+                .embeddingStore(embeddingStore)
+                .embeddingModel(embeddingModel)
+                .maxResults(MAX_RESULTS)
+                .minScore(MIN_SCORE)
+                .build();
     }
 
     /**