添加模型管理接口，模型采用配置

ask-data-ai/ask-data-ai-api/src/main/java/com/ask/api/entity/AskModel.java

+package com.ask.api.entity;
+
+import com.ask.common.base.BaseEntity;
+import com.baomidou.mybatisplus.annotation.IdType;
+import com.baomidou.mybatisplus.annotation.TableId;
+import com.baomidou.mybatisplus.annotation.TableName;
+import io.swagger.v3.oas.annotations.media.Schema;
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+
+@EqualsAndHashCode(callSuper = true)
+@Data
+@TableName("ask_model")
+@Schema(description = "智能模型实体")
+public class AskModel extends BaseEntity {
+
+    /**
+     * 主键ID
+     */
+    @TableId(type = IdType.AUTO)
+    @Schema(description = "主键ID")
+    private Long id;
+
+    @Schema(description = "模型名称")
+    private String name;
+
+    @Schema(description = "模型类型")
+    private String modelType;
+
+    @Schema(description = "具体模型名称")
+    private String modelName;
+
+    @Schema(description = "模型提供商")
+    private String provider;
+
+    @Schema(description = "用户ID")
+    private Long userId;
+
+    @Schema(description = "模型状态", example = "1")
+    private Integer status;
+
+    @Schema(description = "基础URL地址")
+    private String baseUrl;
+
+    @Schema(description = "API密钥")
+    private String key;
+
+    @Schema(description = "最大token数", example = "4096")
+    private Integer maxTokens;
+
+    @Schema(description = "温度参数", example = "0.7")
+    private Float temperature;
+
+}

ask-data-ai/ask-data-ai-biz/pom.xml

@@ -61,7 +61,7 @@
         </dependency>
         <dependency>
             <groupId>org.springframework.ai</groupId>
-            <artifactId>spring-ai-starter-model-ollama</artifactId>
+            <artifactId>spring-ai-ollama</artifactId>
         </dependency>
         <!-- undertow容器 -->
         <dependency>
@@ -100,13 +100,13 @@
         </dependency>
 
         <!-- Spring AI -->
+<!--        <dependency>-->
+<!--            <groupId>org.springframework.ai</groupId>-->
+<!--            <artifactId>spring-ai-starter-model-deepseek</artifactId>-->
+<!--        </dependency>-->
         <dependency>
             <groupId>org.springframework.ai</groupId>
-            <artifactId>spring-ai-starter-model-deepseek</artifactId>
-        </dependency>
-        <dependency>
-            <groupId>org.springframework.ai</groupId>
-            <artifactId>spring-ai-starter-model-openai</artifactId>
+            <artifactId>spring-ai-openai</artifactId>
         </dependency>
         <dependency>
             <groupId>org.springframework.ai</groupId>
@@ -114,7 +114,7 @@
         </dependency>
         <dependency>
             <groupId>org.springframework.ai</groupId>
-            <artifactId>spring-ai-starter-vector-store-pgvector</artifactId>
+            <artifactId>spring-ai-pgvector-store</artifactId>
         </dependency>
         <dependency>
             <groupId>org.springframework.ai</groupId>
@@ -152,6 +152,15 @@
             <artifactId>poi-tl</artifactId>
             <version>1.12.1</version>
         </dependency>
+        <dependency>
+            <groupId>com.github.ben-manes.caffeine</groupId>
+            <artifactId>caffeine</artifactId>
+            <version>2.9.0</version>
+        </dependency>
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-deepseek</artifactId>
+        </dependency>
 	</dependencies>
 	<repositories>
 		<repository>

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/config/CaffeineCacheConfiguration.java

+package com.ask.config;
+
+import com.github.benmanes.caffeine.cache.Caffeine;
+import org.springframework.cache.CacheManager;
+import org.springframework.cache.annotation.EnableCaching;
+import org.springframework.cache.caffeine.CaffeineCacheManager;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+import java.util.concurrent.TimeUnit;
+
+/**
+ * 本地缓存Caffeine配置类
+ */
+@EnableCaching
+@Configuration
+public class CaffeineCacheConfiguration {
+
+	@Bean("caffeineCacheManager")
+	public CacheManager caffeineCacheManager() {
+		CaffeineCacheManager cacheManager = new CaffeineCacheManager();
+		cacheManager.setCaffeine(Caffeine.newBuilder()
+				.expireAfterWrite(30, TimeUnit.MINUTES)
+				.maximumSize(10000));
+		return cacheManager;
+	}
+
+}

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/config/CommonConfiguration.java

@@ -9,13 +9,14 @@ import org.springframework.ai.chat.memory.ChatMemoryRepository;
 import org.springframework.ai.chat.memory.MessageWindowChatMemory;
 import org.springframework.ai.chat.memory.repository.jdbc.JdbcChatMemoryRepository;
 import org.springframework.ai.chat.prompt.PromptTemplate;
-import org.springframework.ai.deepseek.DeepSeekChatModel;
+
 import org.springframework.ai.openai.OpenAiChatModel;
 import org.springframework.ai.openai.api.OpenAiApi;
 import org.springframework.ai.rag.advisor.RetrievalAugmentationAdvisor;
 import org.springframework.ai.rag.generation.augmentation.ContextualQueryAugmenter;
 import org.springframework.ai.rag.retrieval.search.VectorStoreDocumentRetriever;
 import org.springframework.ai.vectorstore.VectorStore;
+import org.springframework.ai.vectorstore.pgvector.PgVectorStore;
 import org.springframework.context.annotation.Bean;
 import org.springframework.context.annotation.Configuration;
 import org.springframework.http.client.reactive.ReactorClientHttpConnector;
@@ -27,6 +28,8 @@ import reactor.netty.http.client.HttpClient;
 import java.util.ArrayList;
 import java.util.List;
 
+import static org.springframework.ai.vectorstore.pgvector.PgVectorStore.PgIndexType.HNSW;
+
 @Configuration
 public class CommonConfiguration {
 
@@ -49,19 +52,19 @@ public class CommonConfiguration {
                 .build();
     }
 
-    @Bean
-    public ChatClient openAiChatClient(OpenAiChatModel model) {
-        return ChatClient.builder(model)
-                .defaultAdvisors()
-                .build();
-    }
+//    @Bean
+//    public ChatClient openAiChatClient(OpenAiChatModel model) {
+//        return ChatClient.builder(model)
+//                .defaultAdvisors()
+//                .build();
+//    }
 
-    @Bean
-    public ChatClient deepseekChatClient(DeepSeekChatModel model) {
-        return ChatClient.builder(model)
-                .defaultAdvisors()
-                .build();
-    }
+//    @Bean
+//    public ChatClient deepseekChatClient(DeepSeekChatModel model) {
+//        return ChatClient.builder(model)
+//                .defaultAdvisors()
+//                .build();
+//    }
     @Bean
     public PromptChatMemoryAdvisor promptChatMemoryAdvisor(ChatMemory chatMemory){
         return PromptChatMemoryAdvisor.builder(chatMemory).build();
@@ -72,20 +75,28 @@ public class CommonConfiguration {
         return MessageChatMemoryAdvisor.builder(chatMemory).build();
     }
 
-    @Bean
-    public RetrievalAugmentationAdvisor retrievalAugmentationAdvisor(VectorStore vectorStore) {
+//    @Bean
+//    public VectorStore vectorStore(JdbcTemplate jdbcTemplate, EmbeddingModel embeddingModel) {
+//        return PgVectorStore.builder(jdbcTemplate, embeddingModel)
+//                .vectorTableName("ask_vector_store")     // Optional: defaults to "vector_store"
+//                .maxDocumentBatchSize(10000)         // Optional: defaults to 10000
+//                .build();
+//    }
 
-        return RetrievalAugmentationAdvisor.builder()
-                .documentRetriever(VectorStoreDocumentRetriever.builder()
-                        .similarityThreshold(0.70)
-                        .topK(5)
-                        .vectorStore(vectorStore)
-                        .build())
-                .documentPostProcessors(new MyDocumentPostProcessor())
-                .queryAugmenter(ContextualQueryAugmenter.builder()
-                        .allowEmptyContext(true)
-                        .build())
-                .build();
-    }
+//    @Bean
+//    public RetrievalAugmentationAdvisor retrievalAugmentationAdvisor(VectorStore vectorStore) {
+//
+//        return RetrievalAugmentationAdvisor.builder()
+//                .documentRetriever(VectorStoreDocumentRetriever.builder()
+//                        .similarityThreshold(0.70)
+//                        .topK(5)
+//                        .vectorStore(vectorStore)
+//                        .build())
+//                .documentPostProcessors(new MyDocumentPostProcessor())
+//                .queryAugmenter(ContextualQueryAugmenter.builder()
+//                        .allowEmptyContext(true)
+//                        .build())
+//                .build();
+//    }
 
 }

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/controller/AskModelController.java

+package com.ask.controller;
+
+
+import com.ask.api.entity.AskModel;
+import com.ask.api.vo.KeyAndValueVO;
+import com.ask.common.core.R;
+import com.ask.enums.ModelProviderEnum;
+import com.ask.enums.ModelTypeEnum;
+import com.ask.service.AskModelService;
+import io.swagger.v3.oas.annotations.Operation;
+import io.swagger.v3.oas.annotations.Parameter;
+import io.swagger.v3.oas.annotations.tags.Tag;
+import lombok.AllArgsConstructor;
+import org.apache.poi.util.StringUtil;
+import org.springframework.web.bind.annotation.*;
+
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.stream.Collectors;
+
+/**
+ * @author tarzan
+ * @date 2024-12-25 12:22:22
+ */
+@Tag(description = "智能模型管理", name = "智能模型管理")
+@RestController
+@RequestMapping("/ai/model")
+@AllArgsConstructor
+public class AskModelController {
+
+	private final AskModelService askModelService;
+
+	@Operation(summary = "创建模型")
+	@PostMapping("/model")
+	public R<Boolean> createModel(@Parameter(description = "模型信息") @RequestBody AskModel model){
+		return R.ok(askModelService.createModel(model));
+	}
+
+	@Operation(summary = "获取模型列表")
+	@GetMapping("/model")
+	public R<List<AskModel>> models(@Parameter(description = "模型名称") String name, @Parameter(description = "模型类型") String modelType, @Parameter(description = "提供商") String provider){
+		return R.ok(askModelService.models(name,modelType,provider));
+	}
+
+	@Operation(summary = "根据ID获取模型")
+	@GetMapping("/model/{id}")
+	public R<AskModel> get(@Parameter(description = "模型ID") @PathVariable Long id){
+		return R.ok(askModelService.getById(id));
+	}
+
+	@Operation(summary = "删除模型")
+	@DeleteMapping("/model/{id}")
+	public R<Boolean> delete(@Parameter(description = "模型ID") @PathVariable Long id){
+		return R.ok(askModelService.removeById(id));
+	}
+
+	@Operation(summary = "更新模型")
+	@PutMapping("/model/{id}")
+	public R<AskModel> update(@Parameter(description = "模型ID") @PathVariable Long id, @Parameter(description = "模型信息") @RequestBody AskModel model){
+		return R.ok(askModelService.updateModel(id,model));
+	}
+
+	@Operation(summary = "获取厂商列表")
+	@GetMapping("/providers")
+	public R<List<Map<String, String>>> getProviders(){
+		List<Map<String, String>> providers = Arrays.stream(ModelProviderEnum.values())
+				.map(provider -> {
+					Map<String, String> providerMap = new HashMap<>();
+					providerMap.put("provider", provider.getProvider());
+					providerMap.put("name", provider.getName());
+					return providerMap;
+				})
+				.collect(Collectors.toList());
+		return R.ok(providers);
+	}
+
+	@Operation(summary = "获取模型类型列表")
+	@GetMapping("/types")
+	public R<List<Map<String, String>>> getModelTypes(){
+		List<Map<String, String>> types = Arrays.stream(ModelTypeEnum.values())
+				.map(type -> {
+					Map<String, String> typeMap = new HashMap<>();
+					typeMap.put("code", type.getCode());
+					typeMap.put("name", type.getName());
+					typeMap.put("description", type.getDescription());
+					return typeMap;
+				})
+				.collect(Collectors.toList());
+		return R.ok(types);
+	}
+
+
+}

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/controller/AskVectorStoreController.java

@@ -41,7 +41,6 @@ import java.util.UUID;
 public class AskVectorStoreController {
 
     private final AskVectorStoreService askVectorStoreService;
-    private final VectorStore vectorStore;
 
     /**
      * 分页查询向量存储
@@ -144,7 +143,7 @@ public class AskVectorStoreController {
             return R.failed("ID列表不能为空");
         }
         try {
-            vectorStore.delete(ids);
+            askVectorStoreService.removeBatchByIds(ids);
             return R.ok(true);
         } catch (Exception e) {
             log.error("批量删除向量存储失败，IDs: {}, 错误: {}", ids, e.getMessage(), e);

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/controller/ChatController.java

@@ -5,6 +5,7 @@ import cn.hutool.json.JSONArray;
 import cn.hutool.json.JSONObject;
 import com.ask.api.entity.ChatConversation;
 import com.ask.common.core.R;
+import com.ask.service.AskModelService;
 import com.ask.service.ChatConversationService;
 import com.ask.service.impl.ChatService;
 import com.ask.service.impl.RagPromptService;
@@ -23,9 +24,13 @@ import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
 import org.springframework.ai.chat.client.advisor.PromptChatMemoryAdvisor;
 import org.springframework.ai.chat.memory.ChatMemory;
 import org.springframework.ai.chat.messages.*;
+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.deepseek.DeepSeekAssistantMessage;
+
+import org.springframework.ai.deepseek.DeepSeekChatModel;
+import org.springframework.ai.deepseek.DeepSeekChatOptions;
+import org.springframework.ai.deepseek.api.DeepSeekApi;
 import org.springframework.ai.document.Document;
 import org.springframework.ai.model.tool.ToolCallingChatOptions;
 import org.springframework.ai.model.tool.ToolExecutionResult;
@@ -52,32 +57,24 @@ import java.util.concurrent.atomic.AtomicBoolean;
 @Tag(description = "ai", name = "AI对话模块")
 public class ChatController {
 
-    private final ChatClient openAiChatClient;
-
-    private final ChatClient deepseekChatClient;
+    private final AskModelService askModelService;
 
     private final ChatConversationService chatConversationService;
 
-    private final RetrievalAugmentationAdvisor retrievalAugmentationAdvisor;
 
     private final MessageChatMemoryAdvisor messageChatMemoryAdvisor;
 
-    private final PromptChatMemoryAdvisor promptChatMemoryAdvisor;
 
-    private final VectorStore vectorStore;
 
     private final ChatService chatService;
 
     private final RagPromptService ragPromptService;
 
-    private final ChatMemory chatMemory;
-    private final OpenAiChatModel openAiChatModel;
+
 
     private final ExcelTools excelTools;
 
-    private final SqlTools sqlTools;
 
-//    private final ToolCallbackProvider toolCallbackProvider;
 
     /**
      * 获取会话ID
@@ -109,13 +106,18 @@ public class ChatController {
     @Operation(summary = "普通对话", description = "普通对话")
     @GetMapping(value = "/chat", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
     public Flux<String> chat(@RequestParam String message,
-                             @RequestParam String conversationId) {
+                             @RequestParam String conversationId,
+                             @RequestParam(required = false) Optional<Long> modelId) {
+        Long actualModelId = modelId.orElse(1L);
 
         Message systemMessage = new SystemMessage("你是一个AI问答助手，请准确回答用户问题,回答要求:请使用markdown格式输出");
         Message userMessage = new UserMessage(message);
         Prompt prompt = new Prompt(List.of(systemMessage, userMessage));
-
-        return FluxUtils.wrapDeepSeekStream(deepseekChatClient.prompt(prompt)
+        ChatClient chatClient = askModelService.getChatClientById(actualModelId);
+        if (Objects.isNull(chatClient)) {
+            return Flux.error(new Throwable("模型创建失败"));
+        }
+        return FluxUtils.wrapDeepSeekStream(chatClient.prompt(prompt)
                 .advisors(messageChatMemoryAdvisor)
                 .advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
                 .stream()
@@ -132,7 +134,10 @@ public class ChatController {
      */
     @Operation(summary = "知识库对话", description = "知识库对话")
     @GetMapping(value = "/rag/chat", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
-    public Flux<String> ragChat(@RequestParam @Parameter(description = "对话内容") String message, @RequestParam @Parameter(description = "会话ID") String conversationId) {
+    public Flux<String> ragChat(@RequestParam @Parameter(description = "对话内容") String message,
+                                @RequestParam @Parameter(description = "会话ID") String conversationId,
+                                @RequestParam(required = false) Optional<Long> modelId) {
+        Long actualModelId = modelId.orElse(1L);
 
         //获取对话历史
         String historyMemory = chatService.getHistoryMemoryAsString(conversationId);
@@ -151,7 +156,11 @@ public class ChatController {
         String userPrompt = ragPromptService.createRagPrompt(message, context, historyMemory);
 
         StringBuilder contentBuilder = new StringBuilder();
-        return FluxUtils.wrapDeepSeekStream(deepseekChatClient.prompt()
+        ChatClient chatClient = askModelService.getChatClientById(actualModelId);
+        if (Objects.isNull(chatClient)) {
+            return Flux.error(new Throwable("模型创建失败"));
+        }
+        return FluxUtils.wrapDeepSeekStream(chatClient.prompt()
                         .user(userPrompt)
                         .system("你是一个智能助手，基于以下上下文和历史对话回答问题,请用简洁的语言回答问题，并确保答案准确,要求" +
                                 "1.以 Markdown 格式输出")
@@ -169,13 +178,17 @@ public class ChatController {
     @Operation(summary = "智能数据报表对话", description = "智能数据报表对话")
     @GetMapping(value = "/chat/report", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
     public Flux<String> reportChat(@RequestParam String message,
-                                   @RequestParam String conversationId) {
-
+                                   @RequestParam String conversationId,
+                                   @RequestParam(required = false) Optional<Long> modelId) {
+        Long actualModelId = modelId.orElse(1L);
         Message systemMessage = new SystemMessage("你是一个AI问答助手，请用回答用户问题,使用相关工具");
         Message userMessage = new UserMessage(message);
         Prompt prompt = new Prompt(List.of(systemMessage, userMessage));
-
-        return FluxUtils.wrapDeepSeekStream(deepseekChatClient.prompt(prompt)
+        ChatClient chatClient = askModelService.getChatClientById(actualModelId);
+        if (Objects.isNull(chatClient)) {
+            return Flux.error(new Throwable("模型创建失败"));
+        }
+        return FluxUtils.wrapDeepSeekStream(chatClient.prompt(prompt)
                 .advisors(messageChatMemoryAdvisor)
                 .advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
                 .tools(excelTools)
@@ -185,26 +198,16 @@ public class ChatController {
 
     }
 
-//    @Operation(summary = "智能问数据对话", description = "智能问数据对话")
-//    @GetMapping(value = "/chat/data", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
-//    public Flux<String> dataChat(@RequestParam String message,
-//                                 @RequestParam Long knowledgeBaseId,
-//                                 @RequestParam String conversationId) {
-//
-//        Message systemMessage = new SystemMessage("你是一个AI智能问数助手，数据库采用postgres 16,请使用相关工具回答用户问题");
-//        Message userMessage = new UserMessage(message);
-//        Prompt prompt = new Prompt(List.of(systemMessage, userMessage));
-//
-//        return FluxUtils.wrapDeepSeekStream(deepseekChatClient.prompt(prompt)
-//                .advisors(messageChatMemoryAdvisor)
-//                .advisors(a -> a.param(ChatMemory.CONVERSATION_ID, conversationId))
-////                .advisors(retrievalAugmentationAdvisor)
-////                .advisors(a -> a.param(VectorStoreDocumentRetriever.FILTER_EXPRESSION, "knowledgeBaseId == "+knowledgeBaseId))
-//                .tools(sqlTools)
-//                .toolCallbacks(toolCallbackProvider)
-//                .advisors()
-//                .stream()
-//                .chatResponse());
-//
-//    }
+
+    public void test() {
+        ChatModel chatModel = DeepSeekChatModel.builder()
+                .deepSeekApi(DeepSeekApi.builder().baseUrl("").apiKey("TEST").build())
+                .defaultOptions(DeepSeekChatOptions.builder().model("deepseek-r1").temperature(66.6).maxTokens(10000).build())
+                .build();
+        ChatClient chatClient = ChatClient.builder(chatModel)
+                .defaultAdvisors()
+                .build();
+    }
+
+
 }
\ No newline at end of file

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/controller/KnowledgeBaseController.java

@@ -2,6 +2,7 @@ package com.ask.controller;
 
 import com.ask.api.entity.KnowledgeBase;
 import com.ask.common.core.R;
+import com.ask.service.AskModelService;
 import com.ask.service.KnowledgeBaseService;
 import com.baomidou.mybatisplus.core.metadata.IPage;
 import com.baomidou.mybatisplus.core.toolkit.Wrappers;
@@ -13,16 +14,20 @@ import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
 import org.springdoc.core.annotations.ParameterObject;
 import org.springframework.ai.document.Document;
+import org.springframework.ai.embedding.EmbeddingModel;
 import org.springframework.ai.vectorstore.SearchRequest;
 import org.springframework.ai.vectorstore.VectorStore;
 import org.springframework.ai.vectorstore.filter.Filter;
 import org.springframework.ai.vectorstore.filter.FilterExpressionBuilder;
+import org.springframework.ai.vectorstore.pgvector.PgVectorStore;
 import org.springframework.http.HttpHeaders;
+import org.springframework.jdbc.core.JdbcTemplate;
 import org.springframework.util.StringUtils;
 import org.springframework.web.bind.annotation.*;
 
 import java.util.List;
 import java.util.Map;
+import java.util.Objects;
 import java.util.stream.Collectors;
 
 /**
@@ -39,10 +44,12 @@ import java.util.stream.Collectors;
 public class KnowledgeBaseController {
 
     private final KnowledgeBaseService knowledgeBaseService;
-    private final VectorStore vectorStore;
+
+    private final AskModelService askModelService;
 
     /**
      * 分页查询
+     *
      * @param page 分页对象
      * @param name 查询条件
      * @return 分页数据
@@ -53,12 +60,13 @@ public class KnowledgeBaseController {
                                            @Parameter(description = "知识库名称") @RequestParam(required = false) String name) {
         return R.ok(knowledgeBaseService.page(page,
                 Wrappers.lambdaQuery(KnowledgeBase.class)
-                        .like(org.apache.commons.lang3.StringUtils.isNotBlank(name),KnowledgeBase::getName, name)
+                        .like(org.apache.commons.lang3.StringUtils.isNotBlank(name), KnowledgeBase::getName, name)
                         .orderByAsc(KnowledgeBase::getId)));
     }
 
     /**
      * 通过id查询知识库
+     *
      * @param id id
      * @return R
      */
@@ -70,6 +78,7 @@ public class KnowledgeBaseController {
 
     /**
      * 新增知识库
+     *
      * @param knowledgeBase 知识库
      * @return R
      */
@@ -91,6 +100,7 @@ public class KnowledgeBaseController {
 
     /**
      * 修改知识库
+     *
      * @param knowledgeBase 知识库
      * @return R
      */
@@ -112,6 +122,7 @@ public class KnowledgeBaseController {
 
     /**
      * 通过id删除知识库
+     *
      * @param id id
      * @return R
      */
@@ -123,6 +134,7 @@ public class KnowledgeBaseController {
 
     /**
      * 校验知识库名称是否重复
+     *
      * @param name 知识库名称
      * @param id   知识库ID（可选，修改时传入）
      * @return R
@@ -145,6 +157,7 @@ public class KnowledgeBaseController {
 
     /**
      * 向量搜索命中测试
+     *
      * @param knowledgeBaseId     知识库ID
      * @param content             搜索内容
      * @param topK                返回最相似的K个结果（可选，默认5）
@@ -192,8 +205,10 @@ public class KnowledgeBaseController {
                             new Filter.Value(1)
                     )
             );
-
-
+            VectorStore vectorStore = askModelService.getVectorStoreById(2L);
+            if(Objects.isNull(vectorStore)){
+                return R.failed("向量模型获取失败");
+            }
             // 执行向量搜索
             List<Document> searchResults = vectorStore.similaritySearch(SearchRequest.builder().query(content).filterExpression(filterExpression).similarityThreshold(similarityThreshold).topK(topK).build());
 

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/controller/KnowledgeDocumentController.java

@@ -54,8 +54,6 @@ public class KnowledgeDocumentController {
 
     private final KnowledgeDocumentService knowledgeDocumentService;
 
-    private final VectorStore vectorStore;
-
     private final AskVectorStoreService askVectorStoreService;
 
 
@@ -153,15 +151,14 @@ public class KnowledgeDocumentController {
                 // 删除知识库文档
                 knowledgeDocumentService.removeById(id);
                 
-                // 构建基于documentId的过滤条件
-                Filter.Expression filterExpression = new Filter.Expression(
-                    Filter.ExpressionType.EQ,
-                    new Filter.Key("documentId"),
-                    new Filter.Value(id)
-                );
-                
-                // 删除向量存储中对应的文档切片
-                vectorStore.delete(filterExpression);
+//                // 构建基于documentId的过滤条件
+//                Filter.Expression filterExpression = new Filter.Expression(
+//                    Filter.ExpressionType.EQ,
+//                    new Filter.Key("documentId"),
+//                    new Filter.Value(id)
+//                );
+                askVectorStoreService.remove(Wrappers.lambdaQuery(AskVectorStore.class)
+                        .apply("metadata->>'documentId' = {0}", id));                // 删除向量存储中对应的文档切片
                 
                 log.info("成功删除文档及其向量数据，文档ID: {}", id);
             } catch (Exception e) {

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/enums/ModelProviderEnum.java

+package com.ask.enums;
+
+import com.ask.model.IBaseModel;
+import com.ask.model.IDeepSeekModel;
+import com.ask.model.IOllamaModel;
+import com.ask.model.IOpenAiModel;
+import lombok.Getter;
+
+import java.util.HashMap;
+import java.util.Map;
+
+@Getter
+public enum ModelProviderEnum {
+
+    DeepSeek("DeepSeek", "DeepSeek",new IDeepSeekModel()),
+
+    Openai("OpenAI", "Openai",new IOpenAiModel()),
+
+    OLlama("OLlama", "model_ollama_provider",new IOllamaModel()),
+    ;
+
+    private final String name;
+    private final String provider;
+    private final IBaseModel iBaseModel;
+
+    ModelProviderEnum(String name, String provider,IBaseModel iBaseModel) {
+        this.name = name;
+        this.provider = provider;
+        this.iBaseModel = iBaseModel;
+    }
+
+    public static IBaseModel get(String provider) {
+        if (provider == null || provider.isEmpty()) {
+            throw new IllegalArgumentException("Provider name cannot be null or empty.");
+        }
+        return getMap().getOrDefault(provider, null);
+    }
+    /**
+     * 获取模型提供者的映射。
+     *
+     * @return 包含所有模型提供者名称和其实例的映射
+     */
+    public static Map<String, IBaseModel> getMap() {
+        Map<String, IBaseModel> map = new HashMap<>();
+        for (ModelProviderEnum providerEnum : values()) {
+            map.put(providerEnum.getProvider(), providerEnum.getIBaseModel());
+        }
+        return map;
+    }
+
+}

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/enums/ModelTypeEnum.java

+package com.ask.enums;
+import lombok.AllArgsConstructor;
+import lombok.Getter;
+
+/**
+ * AI模型类型枚举
+ *
+ * @author ai
+ * @date 2024/12/20
+ */
+@Getter
+@AllArgsConstructor
+public enum ModelTypeEnum {
+
+    /**
+     * 大语言模型
+     */
+    LLM("LLM", "大语言模型", "用于文本理解、生成和对话的大型语言模型"),
+
+    /**
+     * 文本向量模型
+     */
+    EMBEDDING("EMBEDDING", "文本向量模型", "将文本转换为向量表示的嵌入模型"),
+
+    /**
+     * 语音转文本模型
+     */
+    STT("STT", "语音识别模型", "将语音转换为文本的语音识别模型"),
+
+    /**
+     * 文本转语音模型
+     */
+    TTS("TTS", "语音生成模型", "将文本转换为语音的语音合成模型"),
+
+    /**
+     * AI视觉模型
+     */
+    IMAGE("IMAGE", "AI视觉模型", "用于图像理解和分析的视觉模型"),
+
+    /**
+     * 文生图模型
+     */
+    TTI("TTI", "文生图模型", "根据文本描述生成图像的模型"),
+
+    /**
+     * 重排序模型
+     */
+    RERANKER("RERANKER", "重排序模型", "用于搜索结果重新排序的模型");
+
+    /**
+     * 类型编码
+     */
+    private final String code;
+
+    /**
+     * 类型名称
+     */
+    private final String name;
+
+    /**
+     * 类型描述
+     */
+    private final String description;
+
+    /**
+     * 根据编码获取枚举
+     */
+    public static ModelTypeEnum getByCode(String code) {
+        for (ModelTypeEnum type : values()) {
+            if (type.getCode().equals(code)) {
+                return type;
+            }
+        }
+        return null;
+    }
+
+    /**
+     * 根据名称获取枚举
+     */
+    public static ModelTypeEnum getByName(String name) {
+        for (ModelTypeEnum type : values()) {
+            if (type.getName().equals(name)) {
+                return type;
+            }
+        }
+        return null;
+    }
+
+    /**
+     * 判断是否为有效的模型类型
+     */
+    public static boolean isValidType(String code) {
+        return getByCode(code) != null;
+    }
+}
\ No newline at end of file

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/mapper/AskModelMapper.java

+package com.ask.mapper;
+
+import com.ask.api.entity.AskModel;
+import com.baomidou.mybatisplus.core.mapper.BaseMapper;
+import org.apache.ibatis.annotations.Mapper;
+
+
+/**
+ * @author tarzan
+ * @date 2024-12-25 12:22:22
+ */
+@Mapper
+public interface AskModelMapper extends BaseMapper<AskModel>{
+
+}

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/model/IBaseModel.java

+package com.ask.model;
+
+import com.ask.api.entity.AskModel;
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.embedding.EmbeddingModel;
+
+public interface IBaseModel {
+
+    public ChatModel buildChatModel(AskModel askModel);
+    public EmbeddingModel buildEmbedding(AskModel askModel);
+}

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/model/IDeepSeekModel.java

+package com.ask.model;
+
+import com.ask.api.entity.AskModel;
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.deepseek.DeepSeekChatModel;
+import org.springframework.ai.deepseek.DeepSeekChatOptions;
+import org.springframework.ai.deepseek.api.DeepSeekApi;
+import org.springframework.ai.embedding.EmbeddingModel;
+
+public class IDeepSeekModel implements IBaseModel {
+    @Override
+    public ChatModel buildChatModel(AskModel askModel) {
+        return DeepSeekChatModel.builder()
+                .deepSeekApi(DeepSeekApi.builder().baseUrl(askModel.getBaseUrl()).apiKey(askModel.getKey()).build())
+                .defaultOptions(DeepSeekChatOptions.builder().model(askModel.getModelName()).temperature(Double.valueOf(askModel.getTemperature())).maxTokens(askModel.getMaxTokens()).build())
+                .build();
+    }
+
+    @Override
+    public EmbeddingModel buildEmbedding(AskModel askModel) {
+        return null;
+    }
+}

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/model/IOllamaModel.java

+package com.ask.model;
+
+import com.ask.api.entity.AskModel;
+import io.micrometer.observation.ObservationRegistry;
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.deepseek.DeepSeekChatModel;
+import org.springframework.ai.deepseek.DeepSeekChatOptions;
+import org.springframework.ai.deepseek.api.DeepSeekApi;
+import org.springframework.ai.embedding.EmbeddingModel;
+import org.springframework.ai.ollama.OllamaChatModel;
+import org.springframework.ai.ollama.OllamaEmbeddingModel;
+import org.springframework.ai.ollama.api.OllamaApi;
+import org.springframework.ai.ollama.api.OllamaModel;
+import org.springframework.ai.ollama.api.OllamaOptions;
+import org.springframework.ai.ollama.management.ModelManagementOptions;
+import org.springframework.ai.retry.RetryUtils;
+
+public class IOllamaModel implements IBaseModel {
+    @Override
+    public ChatModel buildChatModel(AskModel askModel) {
+        OllamaApi ollamaApi = OllamaApi.builder()
+                .baseUrl(askModel.getBaseUrl()).build();
+
+        return OllamaChatModel.builder()
+                .ollamaApi(ollamaApi)
+                .defaultOptions(
+                        OllamaOptions.builder()
+                                .model(askModel.getModelName())
+                                .temperature(Double.valueOf(askModel.getTemperature()))
+                                .build())
+                .build();
+    }
+
+    @Override
+    public EmbeddingModel buildEmbedding(AskModel askModel) {
+        OllamaApi ollamaApi = OllamaApi.builder()
+                .baseUrl(askModel.getBaseUrl())
+                .build();
+        return OllamaEmbeddingModel.builder().defaultOptions(OllamaOptions.builder()
+                .model(askModel.getModelName())
+                .truncate(false)
+                .build()).ollamaApi(ollamaApi).build();
+    }
+}

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/model/IOpenAiModel.java

+package com.ask.model;
+
+import com.ask.api.entity.AskModel;
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.document.MetadataMode;
+import org.springframework.ai.embedding.EmbeddingModel;
+import org.springframework.ai.openai.OpenAiEmbeddingModel;
+import org.springframework.ai.openai.OpenAiEmbeddingOptions;
+import org.springframework.ai.openai.api.OpenAiApi;
+import org.springframework.ai.retry.RetryUtils;
+
+public class IOpenAiModel implements IBaseModel {
+    @Override
+    public ChatModel buildChatModel(AskModel askModel) {
+        return null;
+    }
+
+    @Override
+    public EmbeddingModel buildEmbedding(AskModel askModel) {
+        OpenAiApi openAiApi = OpenAiApi.builder()
+                .baseUrl(askModel.getBaseUrl())
+                .apiKey(askModel.getKey())
+                .build();
+
+        return new OpenAiEmbeddingModel(
+                openAiApi,
+                MetadataMode.EMBED,
+                OpenAiEmbeddingOptions.builder()
+                        .model(askModel.getModelName())
+                        .dimensions(askModel.getMaxTokens())
+                        .build(),
+                RetryUtils.DEFAULT_RETRY_TEMPLATE);
+    }
+}

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/service/AskModelService.java

+package com.ask.service;
+
+import com.ask.api.entity.AskModel;
+import com.baomidou.mybatisplus.extension.service.IService;
+import org.apache.poi.ss.formula.functions.T;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.embedding.EmbeddingModel;
+import org.springframework.ai.vectorstore.VectorStore;
+
+import java.util.List;
+
+public interface AskModelService extends IService<AskModel> {
+    public Boolean createModel(AskModel askModel);
+
+    AskModel updateModel(Long id, AskModel model);
+
+    List<AskModel> models(String name, String modelType, String provider);
+    public AskModel getModelInfoById(Long modelId);
+    public ChatClient  getChatClientById(Long modelId);
+    public EmbeddingModel getEmbeddingModelById(Long modelId);
+    public VectorStore getVectorStoreById(Long modelId);
+}

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/service/impl/AskModelServiceImpl.java

+package com.ask.service.impl;
+
+import com.ask.api.entity.AskModel;
+import com.ask.enums.ModelProviderEnum;
+import com.ask.mapper.AskModelMapper;
+import com.ask.model.IBaseModel;
+import com.ask.service.AskModelService;
+import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
+import com.baomidou.mybatisplus.core.toolkit.Wrappers;
+import com.baomidou.mybatisplus.extension.service.impl.ServiceImpl;
+import lombok.AllArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.lang3.StringUtils;
+import org.springframework.ai.chat.client.ChatClient;
+import org.springframework.ai.chat.model.ChatModel;
+import org.springframework.ai.embedding.EmbeddingModel;
+import org.springframework.ai.vectorstore.VectorStore;
+import org.springframework.ai.vectorstore.pgvector.PgVectorStore;
+import org.springframework.cache.annotation.Cacheable;
+import org.springframework.jdbc.core.JdbcTemplate;
+import org.springframework.stereotype.Service;
+
+import java.util.List;
+import java.util.Objects;
+
+@Slf4j
+@Service
+@AllArgsConstructor
+public class AskModelServiceImpl extends ServiceImpl<AskModelMapper, AskModel> implements AskModelService {
+
+    private final JdbcTemplate jdbcTemplate;
+
+    @Override
+    public Boolean createModel(AskModel AskModel) {
+//        Long userId = StpUtil.getLoginIdAsLong();
+//       long count = this.lambdaQuery().eq(AskModel::getName, AskModel.getName()).count();
+//        if (count > 0) {
+//            return false;
+//        }
+        //AskModel.setUserId(userId);
+        AskModel.setStatus(1);
+        return save(AskModel);
+    }
+
+    @Override
+    public AskModel getModelInfoById(Long modelId) {
+        return this.getById(modelId);
+    }
+
+    @Cacheable(cacheNames = "chatClient", key = "#modelId")
+    @Override
+    public ChatClient getChatClientById(Long modelId) {
+        AskModel askModel = this.getById(modelId);
+        IBaseModel baseModel = ModelProviderEnum.get(askModel.getProvider());
+        if (Objects.isNull(baseModel)) {
+            return null;
+        }
+        ChatModel chatModel = baseModel.buildChatModel(askModel);
+        return ChatClient.builder(chatModel).build();
+    }
+
+    @Cacheable(cacheNames = "embeddingMode", key = "#modelId")
+    @Override
+    public EmbeddingModel getEmbeddingModelById(Long modelId) {
+        AskModel askModel = this.getById(modelId);
+        IBaseModel baseModel = ModelProviderEnum.get(askModel.getProvider());
+        if (Objects.isNull(baseModel)) {
+            return null;
+        }
+        return baseModel.buildEmbedding(askModel);
+
+    }
+
+    @Override
+    @Cacheable(cacheNames = "vectorStore", key = "#modelId")
+    public VectorStore getVectorStoreById(Long modelId) {
+
+        EmbeddingModel embeddingModel = this.getEmbeddingModelById(modelId);
+        if (Objects.isNull(embeddingModel)) {
+            return null;
+        }
+        return PgVectorStore.builder(jdbcTemplate, embeddingModel)
+                .vectorTableName("ask_vector_store")     // Optional: defaults to "vector_store"
+                .maxDocumentBatchSize(10000)         // Optional: defaults to 10000
+                .build();
+    }
+
+    @Override
+    public AskModel updateModel(Long id, AskModel model) {
+        model.setId(id);
+        this.updateById(model);
+        return model;
+    }
+
+    @Override
+    public List<AskModel> models(String name, String modelType, String provider) {
+        LambdaQueryWrapper<AskModel> wrapper = Wrappers.<AskModel>lambdaQuery()
+                .like(StringUtils.isNotBlank(name), AskModel::getName, name)
+                .eq(StringUtils.isNotBlank(provider), AskModel::getProvider, provider)
+                .eq(StringUtils.isNotBlank(modelType), AskModel::getModelType, modelType)
+                .orderByAsc(AskModel::getId);
+        return baseMapper.selectList(wrapper);
+    }
+}

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/service/impl/AskVectorStoreServiceImpl.java

@@ -3,6 +3,7 @@ package com.ask.service.impl;
 import com.ask.api.entity.AskVectorStore;
 import com.ask.api.vo.KeyAndValueVO;
 import com.ask.mapper.AskVectorStoreMapper;
+import com.ask.service.AskModelService;
 import com.ask.service.AskVectorStoreService;
 import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
 import com.baomidou.mybatisplus.core.conditions.update.LambdaUpdateWrapper;
@@ -41,13 +42,11 @@ import java.util.stream.Collectors;
 @Service
 public class AskVectorStoreServiceImpl extends ServiceImpl<AskVectorStoreMapper, AskVectorStore> implements AskVectorStoreService {
 
-    @Autowired
-    private EmbeddingModel embeddingModel;
-
     @Autowired
     private JdbcTemplate jdbcTemplate;
 
 
+    private AskModelService askModelService;
 
 
 
@@ -74,7 +73,7 @@ public class AskVectorStoreServiceImpl extends ServiceImpl<AskVectorStoreMapper,
                     String result = (title == null || title.trim().isEmpty()) ?
                             (content == null ? "" : content) :
                             title.trim() + "\n" + (content == null ? "" : content);
-                    return embeddingModel.embed(result);
+                    return askModelService.getEmbeddingModelById(2L).embed(result);
                 })
                 .toList();
 

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/service/impl/ChatService.java

@@ -2,6 +2,7 @@ package com.ask.service.impl;
 
 import cn.hutool.json.JSONArray;
 import cn.hutool.json.JSONObject;
+import com.ask.service.AskModelService;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.lang3.StringUtils;
@@ -33,11 +34,13 @@ import static org.springframework.ai.chat.messages.MessageType.ASSISTANT;
 @Service
 public class ChatService {
 
-    private final VectorStore vectorStore;
 
     private final ChatMemory chatMemory;
     @Value("${file.local.base-url:http://8.152.98.45/api}")
     private String baseUrl;
+
+    private final AskModelService askModelService;
+
     /**
      * rag召回
      *
@@ -48,6 +51,9 @@ public class ChatService {
      * @return 召回文档
      */
     public List<Document> retrieveDocuments(String query, double threshold, int topK, Filter.Expression filter) {
+
+        VectorStore vectorStore = askModelService.getVectorStoreById(2L);
+
         List<Document> documents = vectorStore.similaritySearch(SearchRequest.builder()
                 .query(query)
                 .filterExpression(filter)
@@ -57,7 +63,6 @@ public class ChatService {
         if (CollectionUtils.isEmpty(documents)) {
             return List.of();
         }
-
         // 根据ID去重
         return documents.stream()
                 .filter(distinctByKey(Document::getId)) // 根据ID去重

ask-data-ai/ask-data-ai-biz/src/main/java/com/ask/tools/EchartsTools.java

+package com.ask.tools;
+
+import cn.hutool.json.JSONArray;
+import cn.hutool.json.JSONObject;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.ai.tool.annotation.Tool;
+import org.springframework.ai.tool.annotation.ToolParam;
+import org.springframework.stereotype.Component;
+
+@Component
+@Slf4j
+public class EchartsTools {
+
+    @Tool(description = "生成ECharts饼图配置，入参：标题、数据列表(name,value格式)")
+    public String generatePieChartConfig(
+            @ToolParam(description = "图表标题") String title,
+            @ToolParam(description = "数据列表，格式：name1,value1;name2,value2") String dataStr) {
+        
+        log.info("生成饼图配置 - 标题: {}, 数据: {}", title, dataStr);
+        
+        try {
+            JSONObject config = new JSONObject();
+            
+            // 标题配置
+            JSONObject titleObj = new JSONObject();
+            titleObj.set("text", title);
+            titleObj.set("left", "center");
+            config.set("title", titleObj);
+            
+            // 提示框配置
+            JSONObject tooltip = new JSONObject();
+            tooltip.set("trigger", "item");
+            tooltip.set("formatter", "{a} <br/>{b}: {c} ({d}%)");
+            config.set("tooltip", tooltip);
+            
+            // 图例配置
+            JSONObject legend = new JSONObject();
+            legend.set("orient", "vertical");
+            legend.set("left", "left");
+            config.set("legend", legend);
+            
+            // 系列配置
+            JSONArray series = new JSONArray();
+            JSONObject seriesItem = new JSONObject();
+            seriesItem.set("name", title);
+            seriesItem.set("type", "pie");
+            seriesItem.set("radius", "50%");
+            
+            // 数据解析
+            JSONArray data = new JSONArray();
+            if (dataStr != null && !dataStr.trim().isEmpty()) {
+                String[] items = dataStr.split(";");
+                for (String item : items) {
+                    String[] parts = item.split(",");
+                    if (parts.length == 2) {
+                        JSONObject dataItem = new JSONObject();
+                        dataItem.set("name", parts[0].trim());
+                        dataItem.set("value", Double.parseDouble(parts[1].trim()));
+                        data.add(dataItem);
+                    }
+                }
+            }
+            seriesItem.set("data", data);
+            
+            JSONObject emphasis = new JSONObject();
+            JSONObject itemStyle = new JSONObject();
+            itemStyle.set("shadowBlur", 10);
+            itemStyle.set("shadowOffsetX", 0);
+            itemStyle.set("shadowColor", "rgba(0, 0, 0, 0.5)");
+            emphasis.set("itemStyle", itemStyle);
+            seriesItem.set("emphasis", emphasis);
+            
+            series.add(seriesItem);
+            config.set("series", series);
+            
+            return config.toString();
+            
+        } catch (Exception e) {
+            log.error("生成饼图配置失败", e);
+            return "{\"error\": \"生成饼图配置失败: " + e.getMessage() + "\"}";
+        }
+    }
+
+    @Tool(description = "生成ECharts柱状图配置，入参：标题、X轴分类、Y轴数据")
+    public String generateBarChartConfig(
+            @ToolParam(description = "图表标题") String title,
+            @ToolParam(description = "X轴分类，用逗号分隔") String categories,
+            @ToolParam(description = "Y轴数据，用逗号分隔") String values) {
+        
+        log.info("生成柱状图配置 - 标题: {}, 分类: {}, 数据: {}", title, categories, values);
+        
+        try {
+            JSONObject config = new JSONObject();
+            
+            // 标题配置
+            JSONObject titleObj = new JSONObject();
+            titleObj.set("text", title);
+            titleObj.set("left", "center");
+            config.set("title", titleObj);
+            
+            // 提示框配置
+            JSONObject tooltip = new JSONObject();
+            tooltip.set("trigger", "axis");
+            tooltip.set("axisPointer", new JSONObject().set("type", "shadow"));
+            config.set("tooltip", tooltip);
+            
+            // 网格配置
+            JSONObject grid = new JSONObject();
+            grid.set("left", "3%");
+            grid.set("right", "4%");
+            grid.set("bottom", "3%");
+            grid.set("containLabel", true);
+            config.set("grid", grid);
+            
+            // X轴配置
+            JSONArray xAxis = new JSONArray();
+            JSONObject xAxisItem = new JSONObject();
+            xAxisItem.set("type", "category");
+            
+            JSONArray xAxisData = new JSONArray();
+            if (categories != null && !categories.trim().isEmpty()) {
+                String[] cats = categories.split(",");
+                for (String cat : cats) {
+                    xAxisData.add(cat.trim());
+                }
+            }
+            xAxisItem.set("data", xAxisData);
+            xAxis.add(xAxisItem);
+            config.set("xAxis", xAxis);
+            
+            // Y轴配置
+            JSONArray yAxis = new JSONArray();
+            JSONObject yAxisItem = new JSONObject();
+            yAxisItem.set("type", "value");
+            yAxis.add(yAxisItem);
+            config.set("yAxis", yAxis);
+            
+            // 系列配置
+            JSONArray series = new JSONArray();
+            JSONObject seriesItem = new JSONObject();
+            seriesItem.set("name", title);
+            seriesItem.set("type", "bar");
+            
+            JSONArray data = new JSONArray();
+            if (values != null && !values.trim().isEmpty()) {
+                String[] vals = values.split(",");
+                for (String val : vals) {
+                    data.add(Double.parseDouble(val.trim()));
+                }
+            }
+            seriesItem.set("data", data);
+            series.add(seriesItem);
+            config.set("series", series);
+            
+            return config.toString();
+            
+        } catch (Exception e) {
+            log.error("生成柱状图配置失败", e);
+            return "{\"error\": \"生成柱状图配置失败: " + e.getMessage() + "\"}";
+        }
+    }
+
+    @Tool(description = "生成ECharts折线图配置，入参：标题、X轴分类、Y轴数据")
+    public String generateLineChartConfig(
+            @ToolParam(description = "图表标题") String title,
+            @ToolParam(description = "X轴分类，用逗号分隔") String categories,
+            @ToolParam(description = "Y轴数据，用逗号分隔") String values) {
+        
+        log.info("生成折线图配置 - 标题: {}, 分类: {}, 数据: {}", title, categories, values);
+        
+        try {
+            JSONObject config = new JSONObject();
+            
+            // 标题配置
+            JSONObject titleObj = new JSONObject();
+            titleObj.set("text", title);
+            titleObj.set("left", "center");
+            config.set("title", titleObj);
+            
+            // 提示框配置
+            JSONObject tooltip = new JSONObject();
+            tooltip.set("trigger", "axis");
+            config.set("tooltip", tooltip);
+            
+            // 图例配置
+            JSONObject legend = new JSONObject();
+            legend.set("data", new JSONArray().put(title));
+            config.set("legend", legend);
+            
+            // 网格配置
+            JSONObject grid = new JSONObject();
+            grid.set("left", "3%");
+            grid.set("right", "4%");
+            grid.set("bottom", "3%");
+            grid.set("containLabel", true);
+            config.set("grid", grid);
+            
+            // X轴配置
+            JSONArray xAxis = new JSONArray();
+            JSONObject xAxisItem = new JSONObject();
+            xAxisItem.set("type", "category");
+            xAxisItem.set("boundaryGap", false);
+            
+            JSONArray xAxisData = new JSONArray();
+            if (categories != null && !categories.trim().isEmpty()) {
+                String[] cats = categories.split(",");
+                for (String cat : cats) {
+                    xAxisData.add(cat.trim());
+                }
+            }
+            xAxisItem.set("data", xAxisData);
+            xAxis.add(xAxisItem);
+            config.set("xAxis", xAxis);
+            
+            // Y轴配置
+            JSONArray yAxis = new JSONArray();
+            JSONObject yAxisItem = new JSONObject();
+            yAxisItem.set("type", "value");
+            yAxis.add(yAxisItem);
+            config.set("yAxis", yAxis);
+            
+            // 系列配置
+            JSONArray series = new JSONArray();
+            JSONObject seriesItem = new JSONObject();
+            seriesItem.set("name", title);
+            seriesItem.set("type", "line");
+            seriesItem.set("stack", "Total");
+            
+            JSONArray data = new JSONArray();
+            if (values != null && !values.trim().isEmpty()) {
+                String[] vals = values.split(",");
+                for (String val : vals) {
+                    data.add(Double.parseDouble(val.trim()));
+                }
+            }
+            seriesItem.set("data", data);
+            series.add(seriesItem);
+            config.set("series", series);
+            
+            return config.toString();
+            
+        } catch (Exception e) {
+            log.error("生成折线图配置失败", e);
+            return "{\"error\": \"生成折线图配置失败: " + e.getMessage() + "\"}";
+        }
+    }
+}

ask-data-ai/ask-data-ai-boot/src/main/resources/application.yml

@@ -17,53 +17,15 @@ spring:
     username: postgres
     password: e5d039e4ba5246068
     driver-class-name: org.postgresql.Driver
+  cache:
+    type: caffeine
   ai:
-#    mcp:
-#      client:
-#        sse:
-#          connections:
-#            charts:
-#              url: http://81.70.183.25:18000
-
-    model:
-      embedding: openai
-    vectorstore:
-      pgvector:
-        index-type: HNSW
-        distance-type: COSINE_DISTANCE
-        dimensions: 1024
-        max-document-batch-size: 10000 # Optional: Maximum number of documents per batch
-        schema-name: public
-        table-name: ask_vector_store
     chat:
       memory:
         repository:
           jdbc:
             initialize-schema: never  # 开发环境可以使用 always，方便调试
             platform: postgresql
-    openai:
-      base-url: https://dashscope.aliyuncs.com/compatible-mode
-      api-key: sk-ae96ff281ff644c992843c64a711a950
-      chat:
-        options:
-          model: deepseek-r1-0528
-      embedding:
-        base-url: https://dashscope.aliyuncs.com/compatible-mode
-        options:
-          model: text-embedding-v4
-
-    deepseek:
-      base-url: https://dashscope.aliyuncs.com/compatible-mode/v1
-      api-key: sk-ae96ff281ff644c992843c64a711a950
-      chat:
-        enabled: true
-        options:
-          model: deepseek-r1-0528
-    ollama:
-      base-url: http://127.0.0.1:11434
-      embedding:
-        options:
-          model: nomic-embed-text
 
 mybatis-plus:
   mapper-locations: classpath*:/mapper/*Mapper.xml # mapper文件位置