RAG代码提交

know_sub_etl/pom.xml

@@ -30,10 +30,12 @@
             <version>4.5.13</version>
         </dependency>
 
-        <dependency>
+<!--        <dependency>-->
-            <groupId>org.springframework.ai</groupId>
+<!--            <groupId>io.springboot.ai</groupId>-->
-            <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
+<!--            <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>-->
-        </dependency>
+<!--        </dependency>-->
+
+
 
         <dependency>
             <groupId>org.springframework.ai</groupId>

know_sub_etl/src/main/java/som/supervision/knowsub/config/ElasticsearchVectorStoreConfig.java

@@ -1,7 +1,6 @@
 package som.supervision.knowsub.config;
 
 import org.elasticsearch.client.RestClient;
-import org.springframework.ai.autoconfigure.vectorstore.elasticsearch.ElasticsearchVectorStoreProperties;
 import org.springframework.ai.embedding.EmbeddingModel;
 import org.springframework.ai.vectorstore.ElasticsearchVectorStore;
 import org.springframework.ai.vectorstore.ElasticsearchVectorStoreOptions;
@@ -24,9 +23,9 @@ public class ElasticsearchVectorStoreConfig {
 
     @Bean
     @ConditionalOnProperty(prefix = "embedding", name = "url")
-    public ElasticsearchVectorStore vectorStore(ElasticsearchVectorStoreProperties properties,EmbeddingModel embeddingModel, RestClient restClient) {
+    public ElasticsearchVectorStore vectorStore(EmbeddingModel embeddingModel, RestClient restClient) {
         ElasticsearchVectorStoreOptions options = new ElasticsearchVectorStoreOptions();
-        options.setIndexName(properties.getIndexName());
+        options.setIndexName("know-sub-rag-store");
         options.setDimensions(1024);
         return new ElasticsearchVectorStore(options, restClient, embeddingModel, true);
     }

know_sub_etl/src/main/java/som/supervision/knowsub/controller/KnowledgeEtlController.java

@@ -0,0 +1,26 @@
+package som.supervision.knowsub.controller;
+
+import io.swagger.v3.oas.annotations.Operation;
+import io.swagger.v3.oas.annotations.tags.Tag;
+import lombok.RequiredArgsConstructor;
+import org.apache.ibatis.annotations.Param;
+import org.springframework.web.bind.annotation.*;
+import org.springframework.web.multipart.MultipartFile;
+import som.supervision.knowsub.service.KnowledgeEtlService;
+
+import java.io.IOException;
+
+@Tag(name = "知识ETL类")
+@RestController
+@RequestMapping("etl")
+@RequiredArgsConstructor
+public class KnowledgeEtlController {
+
+    private final KnowledgeEtlService knowledgeEtlService;
+
+    @Operation(summary = "对知识进行ETL")
+    @PostMapping("knowledgeEtl")
+    public void knowledgeEtl(@RequestParam("files") MultipartFile[] files) {
+        knowledgeEtlService.knowledgeEtl(files);
+    }
+}

know_sub_etl/src/main/java/som/supervision/knowsub/service/KnowledgeEtlService.java

@@ -0,0 +1,11 @@
+package som.supervision.knowsub.service;
+
+import org.springframework.web.bind.annotation.RequestParam;
+import org.springframework.web.multipart.MultipartFile;
+
+import java.io.IOException;
+
+public interface KnowledgeEtlService {
+
+    void knowledgeEtl(MultipartFile[] files);
+}

know_sub_etl/src/main/java/som/supervision/knowsub/service/impl/KnowledgeEtlServiceImpl.java

@@ -1,6 +1,5 @@
-package com.supervision.knowsub.etl;
+package som.supervision.knowsub.service.impl;
 
-import com.supervision.knowsub.dto.HtmlContext;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.ai.document.Document;
@@ -8,16 +7,18 @@ import org.springframework.ai.reader.tika.TikaDocumentReader;
 import org.springframework.ai.transformer.splitter.TokenTextSplitter;
 import org.springframework.ai.vectorstore.ElasticsearchVectorStore;
 import org.springframework.core.io.InputStreamResource;
-import org.springframework.stereotype.Component;
+import org.springframework.stereotype.Service;
-import org.springframework.web.bind.annotation.RequestBody;
+import org.springframework.web.multipart.MultipartFile;
+import som.supervision.knowsub.service.KnowledgeEtlService;
 
+import java.io.IOException;
 import java.io.InputStream;
 import java.util.List;
 
 @Slf4j
-@Component
+@Service
 @RequiredArgsConstructor
-public class EtlProcessor {
+public class KnowledgeEtlServiceImpl implements KnowledgeEtlService {
 
     private final ElasticsearchVectorStore elasticsearchVectorStore;
 
@@ -26,23 +27,32 @@ public class EtlProcessor {
      *
      * @param inputStream 输入流
      */
-    public void loadFile(InputStream inputStream) {
+    private void loadFile(InputStream inputStream, String fileName) {
         // 首先使用tika进行文件切分操作
-        log.info("首先进行内容切分");
+        log.info("{} 进行内容切分", fileName);
         TikaDocumentReader tikaDocumentReader = new TikaDocumentReader(new InputStreamResource(inputStream));
         List<Document> documents = tikaDocumentReader.read();
-        log.info("切分完成,开始进行chunk分割");
+        log.info("{} 切分完成,开始进行chunk分割", fileName);
         // 然后切分为chunk
-        TokenTextSplitter tokenTextSplitter = new TokenTextSplitter();
+        TokenTextSplitter tokenTextSplitter = new TokenTextSplitter(200, 100, 10, 1000, true);
         List<Document> apply = tokenTextSplitter.apply(documents);
-        log.info("切分完成,开始进行保存到向量库中");
+        log.info("{} 切分完成,开始进行保存到向量库中", fileName);
         // 保存到向量数据库中
         elasticsearchVectorStore.accept(apply);
-        log.info("保存完成");
+        log.info("{} 保存完成", fileName);
 
     }
 
-    public void loadHtml(HtmlContext htmlContext) {
+
-        // 使用Html工具进行读取
+    @Override
+    public void knowledgeEtl(MultipartFile[] files) {
+        for (MultipartFile file : files) {
+            try {
+                loadFile(file.getInputStream(), file.getOriginalFilename());
+            } catch (Exception e) {
+                log.error("{}文件处理失败", file.getOriginalFilename(), e);
+            }
+
+        }
     }
 }

know_sub_etl/src/main/resources/application.yml

@@ -14,10 +14,6 @@ server:
 spring:
   elasticsearch:
     uris: http://192.168.10.137:9200
-  ai:
-    vectorstore:
-      elasticsearch:
-        index-name: know-sub-rag-store
 
   main:
     allow-bean-definition-overriding: true

know_sub_rag/pom.xml

@@ -40,6 +40,8 @@
             <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
         </dependency>
 
+
+
         <dependency>
             <groupId>org.springframework.ai</groupId>
             <artifactId>spring-ai-elasticsearch-store</artifactId>

know_sub_rag/src/main/java/com/supervision/knowsub/config/ElasticsearchVectorStoreConfig.java

@@ -0,0 +1,34 @@
+package com.supervision.knowsub.config;
+
+import org.elasticsearch.client.RestClient;
+import org.springframework.ai.autoconfigure.vectorstore.elasticsearch.ElasticsearchVectorStoreProperties;
+import org.springframework.ai.embedding.EmbeddingModel;
+import org.springframework.ai.vectorstore.ElasticsearchVectorStore;
+import org.springframework.ai.vectorstore.ElasticsearchVectorStoreOptions;
+import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
+import org.springframework.boot.context.properties.EnableConfigurationProperties;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+import org.springframework.util.Assert;
+
+@Configuration
+@EnableConfigurationProperties(EmbeddingProperties.class)
+public class ElasticsearchVectorStoreConfig {
+
+    @Bean
+    @ConditionalOnProperty(prefix = "embedding", name = "url")
+    public EmbeddingModel embeddingModel(EmbeddingProperties embeddingProperties) {
+        Assert.notNull(embeddingProperties.getUrl(), "配置文件embedding:url未找到");
+        return new VectorEmbeddingModel(embeddingProperties.getUrl());
+    }
+
+    @Bean
+    @ConditionalOnProperty(prefix = "embedding", name = "url")
+    public ElasticsearchVectorStore vectorStore(ElasticsearchVectorStoreProperties properties,EmbeddingModel embeddingModel, RestClient restClient) {
+        ElasticsearchVectorStoreOptions options = new ElasticsearchVectorStoreOptions();
+        options.setIndexName(properties.getIndexName());
+        options.setDimensions(1024);
+        return new ElasticsearchVectorStore(options, restClient, embeddingModel, true);
+    }
+
+}

know_sub_rag/src/main/java/com/supervision/knowsub/config/EmbeddingProperties.java

@@ -0,0 +1,12 @@
+package com.supervision.knowsub.config;
+
+import lombok.Data;
+import org.springframework.boot.context.properties.ConfigurationProperties;
+
+@Data
+@ConfigurationProperties(prefix = "embedding")
+public class EmbeddingProperties {
+
+    private String url;
+
+}

know_sub_rag/src/main/java/com/supervision/knowsub/config/VectorEmbeddingModel.java

@@ -0,0 +1,57 @@
+package com.supervision.knowsub.config;
+
+import cn.hutool.http.HttpUtil;
+import cn.hutool.json.JSONUtil;
+import lombok.Data;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.ai.document.Document;
+import org.springframework.ai.embedding.*;
+import org.springframework.util.Assert;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.atomic.AtomicInteger;
+
+@Slf4j
+public class VectorEmbeddingModel implements EmbeddingModel {
+
+    private final String embeddingUrl;
+
+    public VectorEmbeddingModel(String embeddingUrl) {
+        this.embeddingUrl = embeddingUrl;
+    }
+
+    @Override
+    public List<Double> embed(Document document) {
+        List<List<Double>> list = this.call(new EmbeddingRequest(List.of(document.getContent()), EmbeddingOptions.EMPTY))
+                .getResults()
+                .stream()
+                .map(Embedding::getOutput)
+                .toList();
+        return list.iterator().next();
+    }
+
+    @Override
+    public EmbeddingResponse call(EmbeddingRequest request) {
+        Assert.notEmpty(request.getInstructions(), "At least one text is required!");
+        List<List<Double>> embeddingList = new ArrayList<>();
+
+        for (String inputContent : request.getInstructions()) {
+            // 这里需要吧inputContent转化为向量数据
+            String post = HttpUtil.post(embeddingUrl, JSONUtil.toJsonStr(Map.of("text", inputContent)));
+            EmbeddingData bean = JSONUtil.toBean(post, EmbeddingData.class);
+            embeddingList.add(bean.embeddings);
+        }
+        var indexCounter = new AtomicInteger(0);
+        List<Embedding> embeddings = embeddingList.stream()
+                .map(e -> new Embedding(e, indexCounter.getAndIncrement()))
+                .toList();
+        return new EmbeddingResponse(embeddings);
+    }
+
+    @Data
+    private static class EmbeddingData {
+        private List<Double> embeddings;
+    }
+}

know_sub_rag/src/main/java/com/supervision/knowsub/controller/EtlController.java

@@ -1,27 +0,0 @@
-package com.supervision.knowsub.controller;
-
-import com.supervision.knowsub.dto.HtmlContext;
-import com.supervision.knowsub.etl.EtlProcessor;
-import org.springframework.beans.factory.annotation.Autowired;
-import org.springframework.web.bind.annotation.*;
-import org.springframework.web.multipart.MultipartFile;
-
-import java.io.IOException;
-
-@RestController
-@RequestMapping("etl")
-public class EtlController {
-
-    @Autowired
-    private EtlProcessor etlProcessor;
-
-    @PostMapping("testLoadFile")
-    public void testLoadFile(@RequestParam(name = "file") MultipartFile file) throws IOException {
-        etlProcessor.loadFile(file.getInputStream());
-    }
-
-    @PostMapping("testLoadHtml")
-    public void testLoadHtml(@RequestBody HtmlContext htmlContext){
-        etlProcessor.loadHtml(htmlContext);
-    }
-}

know_sub_rag/src/main/java/com/supervision/knowsub/dto/HtmlContext.java

@@ -1,9 +0,0 @@
-package com.supervision.knowsub.dto;
-
-import lombok.Data;
-
-@Data
-public class HtmlContext {
-
-    private String htmlContext;
-}

know_sub_rag/src/main/java/com/supervision/knowsub/service/impl/RagServiceImpl.java

@@ -1,17 +1,17 @@
 package com.supervision.knowsub.service.impl;
 
 import com.supervision.knowsub.service.RagService;
-import com.supervision.knowsub.util.SpringBeanUtil;
 import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
-import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.messages.Message;
 import org.springframework.ai.chat.messages.UserMessage;
+import org.springframework.ai.chat.model.ChatResponse;
 import org.springframework.ai.chat.prompt.Prompt;
 import org.springframework.ai.chat.prompt.SystemPromptTemplate;
 import org.springframework.ai.document.Document;
-import org.springframework.ai.ollama.OllamaChatClient;
+import org.springframework.ai.ollama.OllamaChatModel;
 import org.springframework.ai.vectorstore.ElasticsearchVectorStore;
+import org.springframework.ai.vectorstore.SearchRequest;
 import org.springframework.stereotype.Service;
 
 import java.util.List;
@@ -25,14 +25,18 @@ public class RagServiceImpl implements RagService {
 
     private final ElasticsearchVectorStore elasticsearchVectorStore;
 
-    private static final OllamaChatClient chatClient = SpringBeanUtil.getBean(OllamaChatClient.class);
+//    private final OllamaChatClient chatClient ;
+
+    private final OllamaChatModel ollamaChatModel;
+
 
     private static final String springDemoSystemPrompt = """
             您正在协助解答有关卡琳娜所提供服务的问题。
             卡琳娜是一个政府政策解决中心，专注于政策问题解答。
             卡琳娜的使命是根据政策文档，尽可能回答用户关于政策上的疑问。
                  
-            利用来自“上下文”部分的信息提供准确的答案，但表现得好像您天生就知道这些信息。
+            利用来自“上下文”部分的信息提供准确的答案。
+            您只提供重新表述的查询，且别无其他！不要在查询前后添加任何内容！
             如果不确定，只需说明您不知道。
             
             政策内容:
@@ -40,31 +44,41 @@ public class RagServiceImpl implements RagService {
             """;
 
     private static final String systemPrompt = """
-            使用下面的语料来回答本模板最末尾的问题。如果你不知道问题的答案，直接回答"我不知道"，禁止随意编造答案。
+            使用下面的语料来回答问题。如果你不知道问题的答案，直接回答"我不知道"，禁止随意编造答案。
-            为了保证答案尽可能简洁，你的回答必须不超过三句话，你的回答中不可以带有星号。请注意!在每次回答结束之后，你都必须接上"感谢你的提问"作为结束语.
+            保证答案尽可能简洁，你的回答中不可以带有星号。请注意!在每次回答结束之后，你都必须接上"感谢你的提问"作为结束语.
-            以下是一对问题和答案的样例:请问:秦始皇的原名是什么秦始皇原名赢政。感谢你的提问。
+            您只提供重新表述的查询，且别无其他！不要在查询前后添加任何内容！
+            以下是一对问题和答案的样例:请问:秦始皇的原名是什么?答:秦始皇原名赢政。感谢你的提问。
             以下是语料:
             <context>{context}</context>
-            Question: {input}
+            """;
+
+    public static final String systemPrompt1 = """
+            您是政务知识领域的权威专家，能够根据给定的内容，为用户从给定内容中提炼出问题的准确、详尽、合规且具有权威性的回答。
+            若给定内容中信息不足或不明确，应向用户说明情况，并告知无法给出确切答案的原因。
+            
+            回答内容中,不要出现:"请注意，具体的政策和流程可能会有所变化，因此建议您咨询当地的人力资源和社会保障部门或访问官方网站以获取最新信息。"!
+            回答内容中也不要出现"根据您提供的信息"字样!
+            
+            以下是参考资料,请围绕参考资料回答:
+            <context>{context}</context>
             """;
 
     @Override
     public String ask(String question) {
-
         log.info("检索相关文档");
-        List<Document> similarDocuments = elasticsearchVectorStore.similaritySearch(question);
+        List<Document> similarDocuments = elasticsearchVectorStore.similaritySearch(SearchRequest.query(question).withTopK(10));
         log.info("找到:{}条相关文档", similarDocuments.size());
         // 构建系统消息
         String relevantDocument = similarDocuments.stream().map(Document::getContent).collect(Collectors.joining("\n"));
-        SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(springDemoSystemPrompt);
+        SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(systemPrompt1);
         Message systemMessage = systemPromptTemplate.createMessage(Map.of("context", relevantDocument));
         // 构建用户消息
         UserMessage userMessage = new UserMessage(question);
         Prompt prompt = new Prompt(List.of(systemMessage, userMessage));
 
-        log.info("Asking AI model to reply to question.");
+        log.info("开始询问GPT问题");
-        ChatResponse chatResponse = chatClient.call(prompt);
+        ChatResponse call = ollamaChatModel.call(prompt);
         log.info("AI responded.");
-        return chatResponse.getResult().getOutput().getContent();
+        return call.getResult().getOutput().getContent();
     }
 }

know_sub_rag/src/main/java/com/supervision/knowsub/util/AiChatUtil.java

@@ -1,106 +1,94 @@
 package com.supervision.knowsub.util;
 
-import cn.hutool.core.thread.ThreadUtil;
-import cn.hutool.json.JSONObject;
-import cn.hutool.json.JSONUtil;
 import lombok.extern.slf4j.Slf4j;
-import org.springframework.ai.chat.ChatResponse;
-import org.springframework.ai.chat.messages.Message;
-import org.springframework.ai.chat.messages.UserMessage;
-import org.springframework.ai.chat.prompt.Prompt;
-import org.springframework.ai.ollama.OllamaChatClient;
-
-import java.util.List;
-import java.util.Optional;
-import java.util.concurrent.*;
 
 @Slf4j
 public class AiChatUtil {
 
-    private static final ExecutorService chatExecutor = ThreadUtil.newFixedExecutor(5, 5, "chat", new ThreadPoolExecutor.CallerRunsPolicy());
+//    private static final ExecutorService chatExecutor = ThreadUtil.newFixedExecutor(5, 5, "chat", new ThreadPoolExecutor.CallerRunsPolicy());
-
+//
-    private static final OllamaChatClient chatClient = SpringBeanUtil.getBean(OllamaChatClient.class);
+//    private static final OllamaChatClient chatClient = SpringBeanUtil.getBean(OllamaChatClient.class);
-
+//
-    /**
+//    /**
-     * 单轮对话
+//     * 单轮对话
-     *
+//     *
-     * @param chat 对话的内容
+//     * @param chat 对话的内容
-     * @return jsonObject
+//     * @return jsonObject
-     */
+//     */
-    public static Optional<JSONObject> chat(String chat) {
+//    public static Optional<JSONObject> chat(String chat) {
-        Prompt prompt = new Prompt(List.of(new UserMessage(chat)));
+//        Prompt prompt = new Prompt(List.of(new UserMessage(chat)));
-        Future<String> submit = chatExecutor.submit(new ChatTask(chatClient, prompt));
+//        Future<String> submit = chatExecutor.submit(new ChatTask(chatClient, prompt));
-        try {
+//        try {
-            return Optional.of(JSONUtil.parseObj(submit.get()));
+//            return Optional.of(JSONUtil.parseObj(submit.get()));
-        } catch (ExecutionException | InterruptedException e) {
+//        } catch (ExecutionException | InterruptedException e) {
-            log.error("调用大模型生成失败");
+//            log.error("调用大模型生成失败");
-        }
+//        }
-        return Optional.empty();
+//        return Optional.empty();
-    }
+//    }
-
+//
-    /**
+//    /**
-     * 支持多轮对话,自定义消息
+//     * 支持多轮对话,自定义消息
-     *
+//     *
-     * @param messageList 消息列表
+//     * @param messageList 消息列表
-     * @return jsonObject
+//     * @return jsonObject
-     */
+//     */
-    public static Optional<JSONObject> chat(List<Message> messageList) {
+//    public static Optional<JSONObject> chat(List<Message> messageList) {
-        Prompt prompt = new Prompt(messageList);
+//        Prompt prompt = new Prompt(messageList);
-        Future<String> submit = chatExecutor.submit(new ChatTask(chatClient, prompt));
+//        Future<String> submit = chatExecutor.submit(new ChatTask(chatClient, prompt));
-        try {
+//        try {
-            return Optional.of(JSONUtil.parseObj(submit.get()));
+//            return Optional.of(JSONUtil.parseObj(submit.get()));
-        } catch (ExecutionException | InterruptedException e) {
+//        } catch (ExecutionException | InterruptedException e) {
-            log.error("调用大模型生成失败");
+//            log.error("调用大模型生成失败");
-        }
+//        }
-        return Optional.empty();
+//        return Optional.empty();
-    }
+//    }
-
+//
-    /**
+//    /**
-     * 支持序列化的方式
+//     * 支持序列化的方式
-     *
+//     *
-     * @param messageList 消息列表
+//     * @param messageList 消息列表
-     * @param clazz       需要序列化的对象
+//     * @param clazz       需要序列化的对象
-     * @param <T>         需要序列化的对象的泛型
+//     * @param <T>         需要序列化的对象的泛型
-     * @return 对应对象类型, 不支持列表类型
+//     * @return 对应对象类型, 不支持列表类型
-     */
+//     */
-    public static <T> Optional<T> chat(List<Message> messageList, Class<T> clazz) {
+//    public static <T> Optional<T> chat(List<Message> messageList, Class<T> clazz) {
-        Prompt prompt = new Prompt(messageList);
+//        Prompt prompt = new Prompt(messageList);
-        Future<String> submit = chatExecutor.submit(new ChatTask(chatClient, prompt));
+//        Future<String> submit = chatExecutor.submit(new ChatTask(chatClient, prompt));
-        try {
+//        try {
-            String s = submit.get();
+//            String s = submit.get();
-            return Optional.ofNullable(JSONUtil.toBean(s, clazz));
+//            return Optional.ofNullable(JSONUtil.toBean(s, clazz));
-        } catch (ExecutionException | InterruptedException e) {
+//        } catch (ExecutionException | InterruptedException e) {
-            log.error("调用大模型生成失败", e);
+//            log.error("调用大模型生成失败", e);
-        }
+//        }
-        return Optional.empty();
+//        return Optional.empty();
-    }
+//    }
-
+//
-    /**
+//    /**
-     * 支持序列化的方式的对话
+//     * 支持序列化的方式的对话
-     *
+//     *
-     * @param chat  对话的消息
+//     * @param chat  对话的消息
-     * @param clazz 需要序列化的对象
+//     * @param clazz 需要序列化的对象
-     * @param <T>   需要序列化的对象的泛型
+//     * @param <T>   需要序列化的对象的泛型
-     * @return 对应对象类型, 不支持列表类型
+//     * @return 对应对象类型, 不支持列表类型
-     */
+//     */
-    public static <T> Optional<T> chat(String chat, Class<T> clazz) {
+//    public static <T> Optional<T> chat(String chat, Class<T> clazz) {
-        Prompt prompt = new Prompt(List.of(new UserMessage(chat)));
+//        Prompt prompt = new Prompt(List.of(new UserMessage(chat)));
-        Future<String> submit = chatExecutor.submit(new ChatTask(chatClient, prompt));
+//        Future<String> submit = chatExecutor.submit(new ChatTask(chatClient, prompt));
-        try {
+//        try {
-            String s = submit.get();
+//            String s = submit.get();
-            return Optional.ofNullable(JSONUtil.toBean(s, clazz));
+//            return Optional.ofNullable(JSONUtil.toBean(s, clazz));
-        } catch (ExecutionException | InterruptedException e) {
+//        } catch (ExecutionException | InterruptedException e) {
-            log.error("调用大模型生成失败");
+//            log.error("调用大模型生成失败");
-        }
+//        }
-        return Optional.empty();
+//        return Optional.empty();
-    }
+//    }
-
+//
-    private record ChatTask(OllamaChatClient chatClient, Prompt prompt) implements Callable<String> {
+//    private record ChatTask(OllamaChatClient chatClient, Prompt prompt) implements Callable<String> {
-        @Override
+//        @Override
-        public String call() {
+//        public String call() {
-            ChatResponse call = chatClient.call(prompt);
+//            ChatResponse call = chatClient.call(prompt);
-            return call.getResult().getOutput().getContent();
+//            return call.getResult().getOutput().getContent();
-        }
+//        }
-    }
+//    }
 
 }

know_sub_rag/src/main/resources/application.yml

@@ -15,6 +15,15 @@ spring:
   elasticsearch:
     uris: http://192.168.10.137:9200
   ai:
+    ollama:
+      base-url: http://192.168.10.70:11434
+      chat:
+        enabled: true
+        options:
+          model: llama3-chinese:8b
+          keep-alive: 1000m
+          temperature: 0.1
+
     vectorstore:
       elasticsearch:
         index-name: know-sub-rag-store