update

2026-09-09 02:59:28 +00:00 · 2025-05-31 19:20:51 +08:00
parent 660cf072cb
commit 38b8d36bd5
17 changed files with 595 additions and 110 deletions
--- a/modules/ai/SpringAI_RAG_Refactor.md
+++ b/modules/ai/SpringAI_RAG_Refactor.md
@@ -0,0 +1,47 @@
+# SpringAI RAG Controller 重构说明
+
+## 问题描述
+原有的 `SpringAIRagController` 使用了特定的 `OllamaChatModel`，当 Ollama 没有启用时会导致以下错误：
+```
+Parameter 2 of constructor in com.bytedesk.ai.springai.rag.SpringAIRagController required a bean of type 'org.springframework.ai.ollama.OllamaChatModel' that could not be found.
+```
+
+## 解决方案
+将特定的 `OllamaChatModel` 替换为通用的 `ChatModel` 接口，使控制器能够适配任何 ChatModel 实现。
+
+## 主要更改
+
+### 1. SpringAIRagController.java
+- **字段更改**: 将 `private final OllamaChatModel bytedeskOllamaChatModel` 改为 `private final ChatModel chatModel`
+- **导入更改**: 移除 `OllamaChatModel` 导入，添加 `ChatModel` 导入
+- **注解更改**: 添加 `@ConditionalOnBean(ChatModel.class)` 确保只有在有 ChatModel 可用时才创建控制器
+- **方法更新**: 所有使用 `bytedeskOllamaChatModel` 的地方都替换为 `chatModel`
+
+### 2. 新增配置类
+
+#### ChatModelConfig.java
+- 提供备用的 ChatModel 配置
+- 只有在没有其他 ChatModel 实现时才创建 FallbackChatModel
+- 受 `bytedesk.features.java-ai=true` 配置控制
+
+#### FallbackChatModel.java
+- 简单的 ChatModel 实现作为备用
+- 当没有其他 ChatModel 可用时返回友好的错误消息
+- 提醒用户配置正确的 ChatModel 实现
+
+## 兼容性
+此更改向后兼容：
+- 当 Ollama 启用时，会优先使用 OllamaChatModel
+- 当其他 ChatModel 实现（如 OpenAI）可用时，会使用相应的实现
+- 当没有任何 ChatModel 可用时，会使用 FallbackChatModel 避免启动错误
+
+## 配置要求
+确保在 `application.properties` 中设置：
+```properties
+bytedesk.features.java-ai=true
+```
+
+## 测试建议
+1. 测试 Ollama 启用的情况
+2. 测试 Ollama 禁用但有其他 ChatModel 的情况
+3. 测试完全没有 ChatModel 的情况（应该使用 FallbackChatModel）
--- a/modules/ai/src/main/java/com/bytedesk/ai/springai/config/ChatModelConfig.java
+++ b/modules/ai/src/main/java/com/bytedesk/ai/springai/config/ChatModelConfig.java
@@ -0,0 +1,47 @@
+/*
+ * @Author: jackning 270580156@qq.com
+ * @Date: 2025-05-31 10:00:00
+ * @LastEditors: jackning 270580156@qq.com
+ * @LastEditTime: 2025-05-31 13:49:47
+ * @Description: bytedesk.com https://github.com/Bytedesk/bytedesk
+ *   Please be aware of the BSL license restrictions before installing Bytedesk IM – 
+ *  selling, reselling, or hosting Bytedesk IM as a service is a breach of the terms and automatically terminates your rights under the license. 
+ *  Business Source License 1.1: https://github.com/Bytedesk/bytedesk/blob/main/LICENSE 
+ *  contact: 270580156@qq.com 
+ * 
+ * Copyright (c) 2025 by bytedesk.com, All Rights Reserved. 
+ */
+package com.bytedesk.ai.springai.config;
+
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.boot.autoconfigure.condition.ConditionalOnMissingBean;
+import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+import org.springframework.context.annotation.Primary;
+
+import lombok.extern.slf4j.Slf4j;
+
+/**
+ * ChatModel配置类
+ * 提供ChatModel的默认实现，当没有其他ChatModel可用时作为备用
+ */
+@Slf4j
+@Configuration
+public class ChatModelConfig {
+
+    /**
+     * 当没有其他ChatModel bean时，提供一个默认的ChatModel实现
+     * 这个bean只有在启用Java AI功能时才会创建
+     */
+    @Bean
+    @Primary
+    @ConditionalOnMissingBean(ChatModel.class)
+    @ConditionalOnProperty(name = "bytedesk.features.java-ai", havingValue = "true", matchIfMissing = false)
+    public ChatModel defaultChatModel() {
+        log.warn("No ChatModel implementation found, using fallback implementation");
+        
+        // 返回一个简单的ChatModel实现作为备用
+        return new FallbackChatModel();
+    }
+}
--- a/modules/ai/src/main/java/com/bytedesk/ai/springai/config/FallbackChatModel.java
+++ b/modules/ai/src/main/java/com/bytedesk/ai/springai/config/FallbackChatModel.java
@@ -0,0 +1,50 @@
+/*
+ * @Author: jackning 270580156@qq.com
+ * @Date: 2025-05-31 10:00:00
+ * @LastEditors: jackning 270580156@qq.com
+ * @LastEditTime: 2025-05-31 10:00:00
+ * @Description: bytedesk.com https://github.com/Bytedesk/bytedesk
+ *   Please be aware of the BSL license restrictions before installing Bytedesk IM – 
+ *  selling, reselling, or hosting Bytedesk IM as a service is a breach of the terms and automatically terminates your rights under the license. 
+ *  Business Source License 1.1: https://github.com/Bytedesk/bytedesk/blob/main/LICENSE 
+ *  contact: 270580156@qq.com 
+ * 
+ * Copyright (c) 2025 by bytedesk.com, All Rights Reserved. 
+ */
+package com.bytedesk.ai.springai.config;
+
+import org.springframework.ai.chat.messages.AssistantMessage;
+import org.springframework.ai.chat.messages.UserMessage;
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.chat.model.ChatResponse;
+import org.springframework.ai.chat.prompt.Prompt;
+import org.springframework.ai.chat.model.Generation;
+
+import lombok.extern.slf4j.Slf4j;
+
+import java.util.List;
+
+/**
+ * 备用ChatModel实现
+ * 当没有其他ChatModel可用时使用此实现
+ */
+@Slf4j
+public class FallbackChatModel implements ChatModel {
+
+    @Override
+    public ChatResponse call(Prompt prompt) {
+        log.warn("FallbackChatModel is being used - please configure a proper ChatModel implementation");
+        
+        // 创建一个简单的响应
+        String responseText = "AI服务暂时不可用，请稍后再试。请确保已正确配置ChatModel实现（如Ollama、OpenAI等）。";
+        
+        AssistantMessage assistantMessage = new AssistantMessage(responseText);
+        Generation generation = new Generation(assistantMessage);
+        return new ChatResponse(List.of(generation));
+    }
+
+    @Override
+    public String call(String message) {
+        return call(new Prompt(new UserMessage(message))).getResult().getOutput().getText();
+    }
+}
--- a/modules/ai/src/main/java/com/bytedesk/ai/springai/rag/SpringAIRagController.java
+++ b/modules/ai/src/main/java/com/bytedesk/ai/springai/rag/SpringAIRagController.java
@@ -14,6 +14,8 @@
 package com.bytedesk.ai.springai.rag;

 import java.util.List;
+import java.util.Map;
+import java.util.HashMap;

 import org.springframework.ai.chat.client.ChatClient;
 import org.springframework.ai.chat.client.advisor.api.Advisor;
@@ -22,7 +24,7 @@ import org.springframework.ai.chat.messages.AssistantMessage;
 import org.springframework.ai.chat.messages.UserMessage;
 import org.springframework.ai.chat.model.ChatResponse;
 import org.springframework.ai.document.Document;
-import org.springframework.ai.ollama.OllamaChatModel;
+import org.springframework.ai.chat.model.ChatModel;
 import org.springframework.ai.rag.Query;
 import org.springframework.ai.rag.advisor.RetrievalAugmentationAdvisor;
 import org.springframework.ai.rag.generation.augmentation.ContextualQueryAugmenter;
@@ -36,8 +38,10 @@ import org.springframework.ai.rag.retrieval.search.VectorStoreDocumentRetriever;
 import org.springframework.ai.vectorstore.SearchRequest;
 import org.springframework.ai.vectorstore.VectorStore;
 import org.springframework.ai.vectorstore.filter.FilterExpressionBuilder;
+import org.springframework.ai.embedding.EmbeddingModel;
+import org.springframework.ai.vectorstore.elasticsearch.ElasticsearchVectorStore;
 import io.micrometer.observation.ObservationRegistry;
-import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
+import org.springframework.boot.autoconfigure.condition.ConditionalOnBean;
 import org.springframework.http.ResponseEntity;
 import org.springframework.web.bind.annotation.GetMapping;
 import org.springframework.web.bind.annotation.RequestMapping;
@@ -59,14 +63,14 @@ import lombok.extern.slf4j.Slf4j;
@RestController
@RequestMapping("/spring/ai/rag")
@RequiredArgsConstructor
-@ConditionalOnProperty(name = "spring.ai.ollama.chat.enabled", havingValue = "true", matchIfMissing = false)
+@ConditionalOnBean(ChatModel.class)
 public class SpringAIRagController {

    private final VectorStore vectorStore;

-    private final OllamaChatModel bytedeskOllamaChatModel;
+    private final EmbeddingModel embeddingModel;

-//     private final SpringAIVectorStoreService springAIVectorService;
+    private final ChatModel chatModel;

    private final ObservationRegistry observationRegistry;

@@ -87,7 +91,7 @@ public class SpringAIRagController {
                        .build())
                .build();
        // 使用chatClient，添加ObservationRegistry
-        ChatResponse response = ChatClient.builder(bytedeskOllamaChatModel, observationRegistry, null)
+        ChatResponse response = ChatClient.builder(chatModel, observationRegistry, null)
                .build()
                .prompt()
                .advisors(qaAdvisor)
@@ -107,7 +111,7 @@ public class SpringAIRagController {
            @RequestParam(value = "message", defaultValue = "什么时间考试？") String message,
            @RequestParam(value = "kbUid", defaultValue = "") String kbUid) {

-        ChatClient chatClient = ChatClient.builder(bytedeskOllamaChatModel)
+        ChatClient chatClient = ChatClient.builder(chatModel)
                .defaultAdvisors(QuestionAnswerAdvisor.builder(vectorStore)
                        .searchRequest(SearchRequest.builder().build())
                        .build())
@@ -136,13 +140,13 @@ public class SpringAIRagController {
                        .similarityThreshold(0.50)
                        .vectorStore(vectorStore)
                        .build())
-                // 允许为空
-                // .queryAugmenter(ContextualQueryAugmenter.builder()
-                // .allowEmptyContext(true)
-                // .build())
+        // 允许为空
+        // .queryAugmenter(ContextualQueryAugmenter.builder()
+        // .allowEmptyContext(true)
+        // .build())
                .build();

-        String answer = ChatClient.builder(bytedeskOllamaChatModel)
+        String answer = ChatClient.builder(chatModel)
                .defaultAdvisors(retrievalAugmentationAdvisor)
                .build()
                .prompt()
@@ -163,7 +167,7 @@ public class SpringAIRagController {

        Advisor retrievalAugmentationAdvisor = RetrievalAugmentationAdvisor.builder()
                .queryTransformers(RewriteQueryTransformer.builder()
-                        .chatClientBuilder(ChatClient.builder(bytedeskOllamaChatModel).build().mutate())
+                        .chatClientBuilder(ChatClient.builder(chatModel).build().mutate())
                        .build())
                .documentRetriever(VectorStoreDocumentRetriever.builder()
                        .similarityThreshold(0.50)
@@ -171,7 +175,7 @@ public class SpringAIRagController {
                        .build())
                .build();

-        String answer = ChatClient.builder(bytedeskOllamaChatModel)
+        String answer = ChatClient.builder(chatModel)
                .defaultAdvisors(retrievalAugmentationAdvisor)
                .build()
                .prompt()
@@ -206,13 +210,13 @@ public class SpringAIRagController {
        // conversation history and a follow-up query into a standalone query that
        // captures the essence of the conversation.
        CompressionQueryTransformer queryTransformer = CompressionQueryTransformer.builder()
-                .chatClientBuilder(ChatClient.builder(bytedeskOllamaChatModel).build().mutate())
+                .chatClientBuilder(ChatClient.builder(chatModel).build().mutate())
                .build();

        Query transformedQuery = queryTransformer.transform(query);

        // 使用chatClient
-        String answer = ChatClient.builder(bytedeskOllamaChatModel)
+        String answer = ChatClient.builder(chatModel)
                // .defaultAdvisors(retrievalAugmentationAdvisor)
                .build()
                .prompt()
@@ -241,13 +245,13 @@ public class SpringAIRagController {
        // to provide better results when querying a target system, such as a vector
        // store or a web search engine.
        QueryTransformer queryTransformer = RewriteQueryTransformer.builder()
-                .chatClientBuilder(ChatClient.builder(bytedeskOllamaChatModel).build().mutate())
+                .chatClientBuilder(ChatClient.builder(chatModel).build().mutate())
                .build();

        Query transformedQuery = queryTransformer.transform(query);

        // 使用chatClient
-        String answer = ChatClient.builder(bytedeskOllamaChatModel)
+        String answer = ChatClient.builder(chatModel)
                // .defaultAdvisors(retrievalAugmentationAdvisor)
                .build()
                .prompt()
@@ -272,14 +276,14 @@ public class SpringAIRagController {
        Query query = new Query("Hvad er Danmarks hovedstad?");

        QueryTransformer queryTransformer = TranslationQueryTransformer.builder()
-                .chatClientBuilder(ChatClient.builder(bytedeskOllamaChatModel).build().mutate())
+                .chatClientBuilder(ChatClient.builder(chatModel).build().mutate())
                .targetLanguage("english")
                .build();

        Query transformedQuery = queryTransformer.transform(query);

        // 使用chatClient
-        String answer = ChatClient.builder(bytedeskOllamaChatModel)
+        String answer = ChatClient.builder(chatModel)
                // .defaultAdvisors(retrievalAugmentationAdvisor)
                .build()
                .prompt()
@@ -302,14 +306,14 @@ public class SpringAIRagController {
            @RequestParam(value = "kbUid", defaultValue = "") String kbUid) {

        MultiQueryExpander queryExpander = MultiQueryExpander.builder()
-                .chatClientBuilder(ChatClient.builder(bytedeskOllamaChatModel).build().mutate())
+                .chatClientBuilder(ChatClient.builder(chatModel).build().mutate())
                .numberOfQueries(3)
                // .includeOriginal(false)
                .build();
        List<Query> queries = queryExpander.expand(new Query("How to run a Spring Boot app?"));

        // 使用chatClient
-        String answer = ChatClient.builder(bytedeskOllamaChatModel)
+        String answer = ChatClient.builder(chatModel)
                // .defaultAdvisors(retrievalAugmentationAdvisor)
                .build()
                .prompt()
@@ -393,7 +397,7 @@ public class SpringAIRagController {
    ResponseEntity<JsonResult<?>> observedChat(
            @RequestParam(value = "message", defaultValue = "什么时间考试？") String message) {
            
-        ChatClient chatClient = ChatClient.builder(bytedeskOllamaChatModel, observationRegistry, null)
+        ChatClient chatClient = ChatClient.builder(chatModel, observationRegistry, null)
                .build();
                
        ChatResponse response = chatClient.prompt()
@@ -406,4 +410,86 @@ public class SpringAIRagController {
        return ResponseEntity.ok(JsonResult.success(response));
    }

+    // 查看当前ElasticsearchVectorStore详情
+    // http://127.0.0.1:9003/spring/ai/rag/vector-store-info
+    @GetMapping("/vector-store-info")
+    ResponseEntity<JsonResult<?>> getVectorStoreInfo() {
+        log.info("Getting VectorStore info");
+        
+        Map<String, Object> vectorStoreInfo = new HashMap<>();
+        
+        try {
+            vectorStoreInfo.put("type", vectorStore.getClass().getSimpleName());
+            vectorStoreInfo.put("className", vectorStore.getClass().getName());
+            
+            // 如果是ElasticsearchVectorStore，获取更详细的信息
+            if (vectorStore instanceof ElasticsearchVectorStore) {
+                vectorStoreInfo.put("vectorStoreType", "ElasticsearchVectorStore");
+                vectorStoreInfo.put("status", "Active");
+                // 注意：由于ElasticsearchVectorStore的内部属性可能是私有的，
+                // 这里只能获取到公开的信息
+                vectorStoreInfo.put("description", "Elasticsearch-based vector store for semantic search");
+            } else {
+                vectorStoreInfo.put("vectorStoreType", "Unknown");
+            }
+            
+            vectorStoreInfo.put("timestamp", System.currentTimeMillis());
+            
+        } catch (Exception e) {
+            log.error("Error getting vector store info: {}", e.getMessage());
+            vectorStoreInfo.put("error", e.getMessage());
+            vectorStoreInfo.put("status", "Error");
+        }
+        
+        return ResponseEntity.ok(JsonResult.success(vectorStoreInfo));
+    }
+
+    // 查看当前EmbeddingModel详情
+    // http://127.0.0.1:9003/spring/ai/rag/embedding-model-info
+    @GetMapping("/embedding-model-info")
+    ResponseEntity<JsonResult<?>> getEmbeddingModelInfo() {
+        log.info("Getting EmbeddingModel info");
+        
+        Map<String, Object> embeddingModelInfo = new HashMap<>();
+        
+        try {
+            embeddingModelInfo.put("type", embeddingModel.getClass().getSimpleName());
+            embeddingModelInfo.put("className", embeddingModel.getClass().getName());
+            
+            // 获取embedding维度信息
+            try {
+                // 使用一个简单的测试文本来获取embedding维度
+                var testEmbedding = embeddingModel.embed("test");
+                embeddingModelInfo.put("dimensions", testEmbedding.length);
+                embeddingModelInfo.put("status", "Active");
+                embeddingModelInfo.put("testEmbeddingSize", testEmbedding.length);
+            } catch (Exception e) {
+                log.warn("Could not get embedding dimensions: {}", e.getMessage());
+                embeddingModelInfo.put("dimensionsError", e.getMessage());
+                embeddingModelInfo.put("status", "Error");
+            }
+            
+            // 根据类名判断embedding模型类型
+            String className = embeddingModel.getClass().getSimpleName();
+            if (className.contains("Ollama")) {
+                embeddingModelInfo.put("modelType", "Ollama Embedding Model");
+                embeddingModelInfo.put("description", "Local Ollama embedding model (e.g., bgm-m3)");
+            } else if (className.contains("Zhipuai") || className.contains("ZhiPu")) {
+                embeddingModelInfo.put("modelType", "Zhipuai Embedding Model");
+                embeddingModelInfo.put("description", "Zhipuai cloud embedding model");
+            } else {
+                embeddingModelInfo.put("modelType", "Unknown Embedding Model");
+            }
+            
+            embeddingModelInfo.put("timestamp", System.currentTimeMillis());
+            
+        } catch (Exception e) {
+            log.error("Error getting embedding model info: {}", e.getMessage());
+            embeddingModelInfo.put("error", e.getMessage());
+            embeddingModelInfo.put("status", "Error");
+        }
+        
+        return ResponseEntity.ok(JsonResult.success(embeddingModelInfo));
+    }
+
 }
--- a/modules/ai/src/main/java/com/bytedesk/ai/springai/rag/readme.md
+++ b/modules/ai/src/main/java/com/bytedesk/ai/springai/rag/readme.md
@@ -1,4 +0,0 @@
-# RAG
-
- [spring ai alibaba](https://java2ai.com/blog/spring-ai-alibaba-module-rag)
- [spring ai rag](https://docs.spring.io/spring-ai/reference/api/retrieval-augmented-generation.html?spm=0.29160081.0.0.75c73b5blqQmqQ)