feat: open-ai

.gitignore

@@ -35,4 +35,6 @@ build/
 .vscode/
 
 ### Mac OS ###
-.DS_Store
+.DS_Store
+/ai-rag-app/cloned-repo/
+/.idea/

ai-rag-api/src/main/java/com/storm/dev/api/IAiService.java

@@ -23,4 +23,6 @@ public interface IAiService {
      * @return
      */
     Flux<ChatResponse> generateStream(String model, String message);
+
+    Flux<ChatResponse> generateStreamRag(String model, String ragTag, String message);
 }

ai-rag-api/src/main/java/com/storm/dev/api/IRAGService.java

@@ -0,0 +1,36 @@
+package com.storm.dev.api;
+
+import com.storm.dev.api.response.Response;
+import org.springframework.ai.chat.ChatResponse;
+import org.springframework.web.bind.annotation.RequestParam;
+import org.springframework.web.multipart.MultipartFile;
+import reactor.core.publisher.Flux;
+
+import java.util.List;
+
+/**
+ * @author: lyd
+ * @date: 2026/1/14 23:41
+ */
+public interface IRAGService {
+    /**
+     * 获取标签列表
+     *
+     * @return
+     */
+    Response<List<String>> queryRagTagList();
+
+    /**
+     * 上传知识库
+     *
+     * @param ragTag
+     * @param files
+     * @return
+     */
+    Response<String> uploadFile(String ragTag, List<MultipartFile> files);
+
+    ChatResponse generateStreamRag(String model, String ragTag, String message);
+
+    Response<String> analyzeGitRepository(String repoUrl, String userName, String token) throws Exception;
+
+}

ai-rag-api/src/main/java/com/storm/dev/api/response/Response.java

@@ -0,0 +1,20 @@
+package com.storm.dev.api.response;
+
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+
+import java.io.Serializable;
+
+@Data
+@Builder
+@NoArgsConstructor
+@AllArgsConstructor
+public class Response<T> implements Serializable {
+
+    private String code;
+    private String info;
+    private T data;
+
+}

ai-rag-app/pom.xml

@@ -24,21 +24,21 @@
             <scope>test</scope>
         </dependency>
 
-<!--        <dependency>-->
-<!--            <groupId>org.springframework.ai</groupId>-->
-<!--            <artifactId>spring-ai-openai-spring-boot-starter</artifactId>-->
-<!--        </dependency>-->
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
+        </dependency>
 
-<!--        <dependency>-->
-<!--            <groupId>org.springframework.ai</groupId>-->
-<!--            <artifactId>spring-ai-tika-document-reader</artifactId>-->
-<!--        </dependency>-->
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-tika-document-reader</artifactId>
+        </dependency>
 
-<!--        &lt;!&ndash; 处理知识库：向量库 &ndash;&gt;-->
-<!--        <dependency>-->
-<!--            <groupId>org.springframework.ai</groupId>-->
-<!--            <artifactId>spring-ai-pgvector-store-spring-boot-starter</artifactId>-->
-<!--        </dependency>-->
+        <!-- 处理知识库：向量库 -->
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-pgvector-store-spring-boot-starter</artifactId>
+        </dependency>
 
         <!-- 使用ollama的api -->
         <dependency>

ai-rag-app/src/main/java/com/storm/dev/config/OllamaConfig.java

@@ -1,10 +1,18 @@
 package com.storm.dev.config;
 
 import org.springframework.ai.ollama.OllamaChatClient;
+import org.springframework.ai.ollama.OllamaEmbeddingClient;
 import org.springframework.ai.ollama.api.OllamaApi;
+import org.springframework.ai.ollama.api.OllamaOptions;
+import org.springframework.ai.openai.OpenAiEmbeddingClient;
+import org.springframework.ai.openai.api.OpenAiApi;
+import org.springframework.ai.transformer.splitter.TokenTextSplitter;
+import org.springframework.ai.vectorstore.PgVectorStore;
+import org.springframework.ai.vectorstore.SimpleVectorStore;
 import org.springframework.beans.factory.annotation.Value;
 import org.springframework.context.annotation.Bean;
 import org.springframework.context.annotation.Configuration;
+import org.springframework.jdbc.core.JdbcTemplate;
 
 /**
  * 注入OllamaApi、OllamaChatClient对象
@@ -19,9 +27,44 @@ public class OllamaConfig {
         return new OllamaApi(baseUrl);
     }
 
+    @Bean
+    public OpenAiApi openAiApi(@Value("${spring.ai.openai.base-url}") String baseUrl, @Value("${spring.ai.openai.api-key}") String apikey) {
+        return new OpenAiApi(baseUrl, apikey);
+    }
+
     @Bean
     public OllamaChatClient ollamaChatClient(OllamaApi ollamaApi) {
         return new OllamaChatClient(ollamaApi);
     }
 
+    @Bean
+    public TokenTextSplitter tokenTextSplitter() {
+        return new TokenTextSplitter();
+    }
+
+    @Bean
+    public SimpleVectorStore vectorStore(@Value("${spring.ai.rag.embed}") String model, OllamaApi ollamaApi, OpenAiApi openAiApi) {
+        if ("nomic-embed-text".equalsIgnoreCase(model)) {
+            OllamaEmbeddingClient embeddingClient = new OllamaEmbeddingClient(ollamaApi);
+            embeddingClient.withDefaultOptions(OllamaOptions.create().withModel("nomic-embed-text"));
+            return new SimpleVectorStore(embeddingClient);
+        } else {
+            OpenAiEmbeddingClient embeddingClient = new OpenAiEmbeddingClient(openAiApi);
+            return new SimpleVectorStore(embeddingClient);
+        }
+    }
+
+    @Bean
+    public PgVectorStore pgVectorStore(@Value("${spring.ai.rag.embed}") String model, OllamaApi ollamaApi, OpenAiApi openAiApi, JdbcTemplate jdbcTemplate) {
+        if ("nomic-embed-text".equalsIgnoreCase(model)) {
+            OllamaEmbeddingClient embeddingClient = new OllamaEmbeddingClient(ollamaApi);
+            embeddingClient.withDefaultOptions(OllamaOptions.create().withModel("nomic-embed-text"));
+            return new PgVectorStore(jdbcTemplate, embeddingClient);
+        } else {
+            OpenAiEmbeddingClient embeddingClient = new OpenAiEmbeddingClient(openAiApi);
+            return new PgVectorStore(jdbcTemplate, embeddingClient);
+        }
+    }
+
+
 }

ai-rag-app/src/main/resources/application-dev.yml

@@ -2,16 +2,31 @@ server:
   port: 8090
 
 spring:
+  datasource:
+    driver-class-name: org.postgresql.Driver
+    username: postgres
+    password: postgres
+    url: jdbc:postgresql://192.168.109.134:15432/ai-rag-knowledge
+    type: com.zaxxer.hikari.HikariDataSource
   ai:
     ollama:
-      base-url: http://117.72.202.142:11434
-
+      base-url: http://192.168.109.134:11434
+      embedding:
+        options:
+          num-batch: 512
+        model: nomic-embed-text
+    openai:
+      base-url: xxx
+      api-key: xxx
+      embedding-model: text-embedding-ada-002
+    rag:
+      embed: nomic-embed-text #nomic-embed-text、text-embedding-ada-002
 # Redis
 redis:
   sdk:
     config:
-      host: 117.72.202.142
-      port: 16379
+      host: 127.0.0.1
+      port: 6379
       pool-size: 10
       min-idle-size: 5
       idle-timeout: 30000

ai-rag-app/src/test/java/com/storm/dev/text/GitTest.java

@@ -0,0 +1,86 @@
+package com.storm.dev.text;
+
+import jakarta.annotation.Resource;
+import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.io.FileUtils;
+import org.eclipse.jgit.api.Git;
+import org.eclipse.jgit.transport.UsernamePasswordCredentialsProvider;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.springframework.ai.document.Document;
+import org.springframework.ai.ollama.OllamaChatClient;
+import org.springframework.ai.reader.tika.TikaDocumentReader;
+import org.springframework.ai.transformer.splitter.TokenTextSplitter;
+import org.springframework.ai.vectorstore.PgVectorStore;
+import org.springframework.ai.vectorstore.SimpleVectorStore;
+import org.springframework.boot.test.context.SpringBootTest;
+import org.springframework.core.io.PathResource;
+import org.springframework.test.context.junit4.SpringRunner;
+
+import java.io.File;
+import java.io.IOException;
+import java.nio.file.FileVisitResult;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.SimpleFileVisitor;
+import java.nio.file.attribute.BasicFileAttributes;
+import java.util.List;
+
+/**
+ * @author: lyd
+ * @date: 2026/1/18 14:55
+ */
+@Slf4j
+@RunWith(SpringRunner.class)
+@SpringBootTest
+public class GitTest {
+    @Resource
+    private OllamaChatClient ollamaChatClient;
+    @Resource
+    private TokenTextSplitter tokenTextSplitter;
+    @Resource
+    private SimpleVectorStore simpleVectorStore;
+    @Resource
+    private PgVectorStore pgVectorStore;
+
+    public final String LOCALPATH = "./cloned-repo";
+    @Test
+    public void test() throws Exception {
+        String repoUrl = "https://gitee.com/liyongde/java-trial.git";
+        String username = "liyongde";
+        String password = "a1c280a3bfe97eb5a53f7f04a01e7fca";
+
+
+        log.info("克隆路径：" + new File(LOCALPATH).getAbsolutePath());
+
+        FileUtils.deleteDirectory(new File(LOCALPATH));
+
+        Git git = Git.cloneRepository()
+                .setURI(repoUrl)
+                .setDirectory(new File(LOCALPATH))
+                .setCredentialsProvider(new UsernamePasswordCredentialsProvider(username, password))
+                .call();
+
+        git.close();
+    }
+
+    @Test
+    public void test_file() throws IOException {
+        Files.walkFileTree(Path.of(LOCALPATH), new SimpleFileVisitor<>() {
+            @Override
+            public FileVisitResult visitFile(Path file, BasicFileAttributes attrs) throws IOException {
+                log.info("文件路径:{}", file.toString());
+                PathResource resource = new PathResource(file);
+                TikaDocumentReader reader = new TikaDocumentReader(resource);
+
+                List<Document> documents = reader.get();
+                List<Document> documentSplitterList = tokenTextSplitter.apply(documents);
+                documents.forEach(doc -> doc.getMetadata().put("knowledge", "java-trial"));
+                documentSplitterList.forEach(doc -> doc.getMetadata().put("knowledge", "java-trial"));
+
+                pgVectorStore.accept(documentSplitterList);
+                return super.visitFile(file, attrs);
+            }
+        });
+    }
+}

ai-rag-app/src/test/java/com/storm/dev/text/RAGApiTest.java

@@ -0,0 +1,92 @@
+package com.storm.dev.text;
+
+import com.alibaba.fastjson.JSON;
+import jakarta.annotation.Resource;
+import lombok.extern.slf4j.Slf4j;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+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.chat.prompt.SystemPromptTemplate;
+import org.springframework.ai.document.Document;
+import org.springframework.ai.ollama.OllamaChatClient;
+import org.springframework.ai.ollama.api.OllamaApi;
+import org.springframework.ai.ollama.api.OllamaOptions;
+import org.springframework.ai.reader.tika.TikaDocumentReader;
+import org.springframework.ai.transformer.splitter.TokenTextSplitter;
+import org.springframework.ai.vectorstore.PgVectorStore;
+import org.springframework.ai.vectorstore.SearchRequest;
+import org.springframework.ai.vectorstore.SimpleVectorStore;
+import org.springframework.boot.test.context.SpringBootTest;
+import org.springframework.test.context.junit4.SpringRunner;
+import reactor.core.publisher.Flux;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+
+/**
+ * @author: lyd
+ * @date: 2026/1/14 21:47
+ */
+@Slf4j
+@RunWith(SpringRunner.class)
+@SpringBootTest
+public class RAGApiTest {
+    @Resource
+    private OllamaChatClient ollamaChatClient;
+    @Resource
+    private TokenTextSplitter tokenTextSplitter;
+    @Resource
+    private SimpleVectorStore simpleVectorStore;
+    @Resource
+    private PgVectorStore pgVectorStore;
+
+    @Test
+    public void upload() {
+        // 上传
+        TikaDocumentReader reader = new TikaDocumentReader("./data/file.text");
+        List<Document> documents = reader.get();
+        List<Document> documentSplitterList = tokenTextSplitter.apply(documents);
+        // 打标
+        documents.forEach(document -> document.getMetadata().put("knowledge", "德德"));
+        documentSplitterList.forEach(document -> document.getMetadata().put("knowledge", "德德"));
+
+        pgVectorStore.accept(documentSplitterList);
+        log.info("上传完成!");
+    }
+
+    @Test
+    public void chat() {
+        // 构建提问
+        String message = "拆装出库的操作流程是什么？";
+
+        // 构建推理模板
+        String SYSTEM_PROMPT = """
+                Use the information from the DOCUMENTS section to provide accurate answers but act as if you knew this information innately.
+                If unsure, simply state that you don't know.
+                Another thing you need to note is that your reply must be in Chinese!
+                DOCUMENTS:
+                    {documents}
+                """;
+        // 读取向量库信息
+        SearchRequest request = SearchRequest.query(message).withTopK(5).withFilterExpression("knowledge == '富士迈泰国项目软件方案'");
+        // 相似性搜索
+        List<Document> documents = pgVectorStore.similaritySearch(request);
+        String documentsCollectors = documents.stream().map(Document::getContent).collect(Collectors.joining());
+
+        // 推理：RAG
+        Message ragMessage = new SystemPromptTemplate(SYSTEM_PROMPT).createMessage(Map.of("documents", documentsCollectors));
+        ArrayList<Message> messages = new ArrayList<>();
+        messages.add(new UserMessage(message));
+        messages.add(ragMessage);
+
+        // 提问
+//        ChatResponse chatResponse = ollamaChatClient.call(new Prompt(messages, OllamaOptions.create().withModel("deepseek-r1:7b")));
+        Flux<ChatResponse> stream = ollamaChatClient.stream(new Prompt(messages, OllamaOptions.create().withModel("deepseek-r1:7b")));
+        log.info("测试结果:{}", JSON.toJSONString(stream));
+    }
+}

ai-rag-app/src/test/resources/data/file.text

@@ -0,0 +1 @@
+李永德，1999年12月31日出生，福建泉州人。

ai-rag-trigger/pom.xml

@@ -23,18 +23,18 @@
             <artifactId>spring-boot-starter-web</artifactId>
         </dependency>
 
-<!--        <dependency>-->
-<!--            <groupId>org.springframework.ai</groupId>-->
-<!--            <artifactId>spring-ai-openai-spring-boot-starter</artifactId>-->
-<!--        </dependency>-->
-<!--        <dependency>-->
-<!--            <groupId>org.springframework.ai</groupId>-->
-<!--            <artifactId>spring-ai-tika-document-reader</artifactId>-->
-<!--        </dependency>-->
-<!--        <dependency>-->
-<!--            <groupId>org.springframework.ai</groupId>-->
-<!--            <artifactId>spring-ai-pgvector-store</artifactId>-->
-<!--        </dependency>-->
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-tika-document-reader</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>org.springframework.ai</groupId>
+            <artifactId>spring-ai-pgvector-store</artifactId>
+        </dependency>
         <dependency>
             <groupId>org.springframework.ai</groupId>
             <artifactId>spring-ai-ollama</artifactId>

ai-rag-trigger/src/main/java/com/storm/dev/trigger/http/OllamaController.java

@@ -2,17 +2,30 @@ package com.storm.dev.trigger.http;
 
 import com.storm.dev.api.IAiService;
 import jakarta.annotation.Resource;
+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.chat.prompt.SystemPromptTemplate;
+import org.springframework.ai.document.Document;
 import org.springframework.ai.ollama.OllamaChatClient;
 import org.springframework.ai.ollama.api.OllamaOptions;
+import org.springframework.ai.vectorstore.PgVectorStore;
+import org.springframework.ai.vectorstore.SearchRequest;
 import org.springframework.web.bind.annotation.*;
 import reactor.core.publisher.Flux;
 
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+
 /**
  * @author: lyd
  * @date: 2025/6/7 22:18
  */
+@Slf4j
 @RestController()
 @CrossOrigin("*")
 @RequestMapping("/api/v1/ollama/")
@@ -20,9 +33,11 @@ public class OllamaController implements IAiService {
 
     @Resource
     private OllamaChatClient chatClient;
+    @Resource
+    private PgVectorStore pgVectorStore;
 
     /**
-     * http://localhost:8090/api/v1/ollama/generate?model=deepseek-r1:1.5b&message=1+1
+     * http://localhost:8090/api/v1/ollama/generate?model=deepseek-r1:7b&message=1+1
      */
     @GetMapping("generate")
     @Override
@@ -31,11 +46,40 @@ public class OllamaController implements IAiService {
     }
 
     /**
-     * http://localhost:8090/api/v1/ollama/generate_stream?model=deepseek-r1:1.5b&message=hi
+     * http://localhost:8090/api/v1/ollama/generate_stream?model=deepseek-r1:7b&message=hi
      */
     @GetMapping("generate_stream")
     @Override
     public Flux<ChatResponse> generateStream(@RequestParam String model, @RequestParam String message) {
         return chatClient.stream(new Prompt(message, OllamaOptions.create().withModel(model)));
     }
+
+    @Override
+    @RequestMapping(value = "generate_stream_rag", method = RequestMethod.GET)
+    public Flux<ChatResponse> generateStreamRag(@RequestParam String model, @RequestParam String ragTag, @RequestParam String message) {
+        log.info("用户选择模型：{}，知识库：{}，提问问题：{}", model, ragTag, message);
+        // 构建推理模板
+        String SYSTEM_PROMPT = """
+                Use the information from the DOCUMENTS section to provide accurate answers but act as if you knew this information innately.
+                If unsure, simply state that you don't know.
+                Another thing you need to note is that your reply must be in Chinese!
+                DOCUMENTS:
+                    {documents}
+                """;
+        // 读取向量库信息
+        SearchRequest request = SearchRequest.query(message).withTopK(5).withFilterExpression("knowledge == '" + ragTag + "'");
+        // 相似性搜索
+        List<Document> documents = pgVectorStore.similaritySearch(request);
+        String documentsCollectors = documents.stream().map(Document::getContent).collect(Collectors.joining());
+
+        // 推理：RAG
+        Message ragMessage = new SystemPromptTemplate(SYSTEM_PROMPT).createMessage(Map.of("documents", documentsCollectors));
+        ArrayList<Message> messages = new ArrayList<>();
+        messages.add(new UserMessage(message));
+        messages.add(ragMessage);
+
+        // 提问
+        Flux<ChatResponse> chatResponse = chatClient.stream(new Prompt(messages, OllamaOptions.create().withModel(model)));
+        return chatResponse;
+    }
 }

ai-rag-trigger/src/main/java/com/storm/dev/trigger/http/OpenAiController.java

@@ -0,0 +1,86 @@
+package com.storm.dev.trigger.http;
+
+import com.storm.dev.api.IAiService;
+import jakarta.annotation.Resource;
+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.chat.prompt.SystemPromptTemplate;
+import org.springframework.ai.document.Document;
+import org.springframework.ai.openai.OpenAiChatClient;
+import org.springframework.ai.openai.OpenAiChatOptions;
+import org.springframework.ai.vectorstore.PgVectorStore;
+import org.springframework.ai.vectorstore.SearchRequest;
+import org.springframework.web.bind.annotation.CrossOrigin;
+import org.springframework.web.bind.annotation.RequestMapping;
+import org.springframework.web.bind.annotation.RestController;
+import reactor.core.publisher.Flux;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+
+/**
+ * @author: lyd
+ * @date: 2026/1/18 17:08
+ */
+@RestController()
+@CrossOrigin("*")
+@RequestMapping("/api/v1/openai/")
+public class OpenAiController implements IAiService {
+
+    @Resource
+    private OpenAiChatClient chatClient;
+    @Resource
+    private PgVectorStore pgVectorStore;
+
+    @Override
+    public ChatResponse generate(String model, String message) {
+        return chatClient.call(new Prompt(message, OpenAiChatOptions.builder().withModel(model).build()));
+    }
+
+    @Override
+    public Flux<ChatResponse> generateStream(String model, String message) {
+        return chatClient.stream(new Prompt(
+                message,
+                OpenAiChatOptions.builder()
+                        .withModel(model)
+                        .build()
+        ));
+    }
+
+    @Override
+    public Flux<ChatResponse> generateStreamRag(String model, String ragTag, String message) {
+        String SYSTEM_PROMPT = """
+                Use the information from the DOCUMENTS section to provide accurate answers but act as if you knew this information innately.
+                If unsure, simply state that you don't know.
+                Another thing you need to note is that your reply must be in Chinese!
+                DOCUMENTS:
+                    {documents}
+                """;
+
+        // 指定文档搜索
+        SearchRequest request = SearchRequest.query(message)
+                .withTopK(5)
+                .withFilterExpression("knowledge == '" + ragTag + "'");
+
+        List<Document> documents = pgVectorStore.similaritySearch(request);
+        String documentCollectors = documents.stream().map(Document::getContent).collect(Collectors.joining());
+        Message ragMessage = new SystemPromptTemplate(SYSTEM_PROMPT).createMessage(Map.of("documents", documentCollectors));
+
+        List<Message> messages = new ArrayList<>();
+        messages.add(new UserMessage(message));
+        messages.add(ragMessage);
+
+        return chatClient.stream(new Prompt(
+                messages,
+                OpenAiChatOptions.builder()
+                        .withModel(model)
+                        .build()
+        ));
+    }
+
+
+}

ai-rag-trigger/src/main/java/com/storm/dev/trigger/http/RAGController.java

@@ -0,0 +1,187 @@
+package com.storm.dev.trigger.http;
+
+import com.alibaba.fastjson.JSON;
+import com.storm.dev.api.IRAGService;
+import com.storm.dev.api.response.Response;
+import jakarta.annotation.Resource;
+import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.io.FileUtils;
+import org.eclipse.jgit.api.Git;
+import org.eclipse.jgit.transport.UsernamePasswordCredentialsProvider;
+import org.redisson.api.RList;
+import org.redisson.api.RedissonClient;
+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.chat.prompt.SystemPromptTemplate;
+import org.springframework.ai.document.Document;
+import org.springframework.ai.ollama.OllamaChatClient;
+import org.springframework.ai.ollama.api.OllamaOptions;
+import org.springframework.ai.reader.tika.TikaDocumentReader;
+import org.springframework.ai.transformer.splitter.TokenTextSplitter;
+import org.springframework.ai.vectorstore.PgVectorStore;
+import org.springframework.ai.vectorstore.SearchRequest;
+import org.springframework.ai.vectorstore.SimpleVectorStore;
+import org.springframework.core.io.PathResource;
+import org.springframework.web.bind.annotation.*;
+import org.springframework.web.multipart.MultipartFile;
+import reactor.core.publisher.Flux;
+
+import java.io.File;
+import java.io.IOException;
+import java.nio.file.*;
+import java.nio.file.attribute.BasicFileAttributes;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+
+/**
+ * @author: lyd
+ * @date: 2026/1/14 23:43
+ */
+@Slf4j
+@RestController()
+@CrossOrigin("*")
+@RequestMapping("/api/v1/rag/")
+public class RAGController implements IRAGService {
+    @Resource
+    private RedissonClient redissonClient;
+    @Resource
+    private OllamaChatClient ollamaChatClient;
+    @Resource
+    private TokenTextSplitter tokenTextSplitter;
+    @Resource
+    private SimpleVectorStore simpleVectorStore;
+    @Resource
+    private PgVectorStore pgVectorStore;
+    @Override
+    @RequestMapping(value = "query_rag_tag_list", method = RequestMethod.GET)
+    public Response<List<String>> queryRagTagList() {
+        RList<String> ragTag = redissonClient.getList("ragTag");
+        return Response.<List<String>>builder()
+                .code("0000")
+                .info("调用成功")
+                .data(ragTag)
+                .build();
+    }
+
+    @Override
+    @RequestMapping(value = "file/upload", method = RequestMethod.POST, headers = "content-type=multipart/form-data")
+    public Response<String> uploadFile(@RequestParam String ragTag, @RequestParam("file") List<MultipartFile> files) {
+        log.info("上传知识库开始 {}", ragTag);
+        for (MultipartFile file : files) {
+            // 上传
+            TikaDocumentReader reader = new TikaDocumentReader(file.getResource());
+            List<Document> documents = reader.get();
+            List<Document> documentSplitterList = tokenTextSplitter.apply(documents);
+            // 打标
+            documents.forEach(document -> document.getMetadata().put("knowledge", ragTag));
+            documentSplitterList.forEach(document -> document.getMetadata().put("knowledge", ragTag));
+
+            pgVectorStore.accept(documentSplitterList);
+            // 可以用MySQL存储
+            RList<String> elements = redissonClient.getList("ragTag");
+            if (!elements.contains(ragTag)){
+                elements.add(ragTag);
+            }
+            log.info("上传完成!");
+        }
+        return Response.<String>builder().code("0000").info("调用成功").build();
+    }
+
+    @Override
+    @RequestMapping(value = "generate_stream_rag", method = RequestMethod.GET)
+    public ChatResponse generateStreamRag(@RequestParam String model, @RequestParam String ragTag, @RequestParam String message) {
+        log.info("用户选择模型：{}，知识库：{}，提问问题：{}", model, ragTag, message);
+        // 构建推理模板
+        String SYSTEM_PROMPT = """
+                Use the information from the DOCUMENTS section to provide accurate answers but act as if you knew this information innately.
+                If unsure, simply state that you don't know.
+                Another thing you need to note is that your reply must be in Chinese!
+                DOCUMENTS:
+                    {documents}
+                """;
+        // 读取向量库信息
+        SearchRequest request = SearchRequest.query(message).withTopK(5).withFilterExpression("knowledge == '" + ragTag + "'");
+        // 相似性搜索
+        List<Document> documents = pgVectorStore.similaritySearch(request);
+        String documentsCollectors = documents.stream().map(Document::getContent).collect(Collectors.joining());
+
+        // 推理：RAG
+        Message ragMessage = new SystemPromptTemplate(SYSTEM_PROMPT).createMessage(Map.of("documents", documentsCollectors));
+        ArrayList<Message> messages = new ArrayList<>();
+        messages.add(new UserMessage(message));
+        messages.add(ragMessage);
+
+        // 提问
+//        Flux<ChatResponse> chatResponse = ollamaChatClient.stream(new Prompt(messages, OllamaOptions.create().withModel(model)));
+        ChatResponse call = ollamaChatClient.call(new Prompt(messages, OllamaOptions.create().withModel(model)));
+        log.info("测试结果:{}", call);
+        return call;
+    }
+
+    @RequestMapping(value = "analyze_git_repository", method = RequestMethod.POST)
+    @Override
+    public Response<String> analyzeGitRepository(@RequestParam String repoUrl, @RequestParam String userName, @RequestParam String token) throws Exception {
+        String localPath = "./git-cloned-repo";
+        String repoProjectName = extractProjectName(repoUrl);
+        log.info("克隆路径：{}", new File(localPath).getAbsolutePath());
+
+        FileUtils.deleteDirectory(new File(localPath));
+
+        Git git = Git.cloneRepository()
+                .setURI(repoUrl)
+                .setDirectory(new File(localPath))
+                .setCredentialsProvider(new UsernamePasswordCredentialsProvider(userName, token))
+                .call();
+
+        Files.walkFileTree(Paths.get(localPath), new SimpleFileVisitor<>() {
+            @Override
+            public FileVisitResult visitFile(Path file, BasicFileAttributes attrs) throws IOException {
+                log.info("{} 遍历解析路径，上传知识库:{}", repoProjectName, file.getFileName());
+                try {
+                    TikaDocumentReader reader = new TikaDocumentReader(new PathResource(file));
+                    List<Document> documents = reader.get();
+                    List<Document> documentSplitterList = tokenTextSplitter.apply(documents);
+
+                    documents.forEach(doc -> doc.getMetadata().put("knowledge", repoProjectName));
+
+                    documentSplitterList.forEach(doc -> doc.getMetadata().put("knowledge", repoProjectName));
+
+                    pgVectorStore.accept(documentSplitterList);
+                } catch (Exception e) {
+                    log.error("遍历解析路径，上传知识库失败:{}", file.getFileName());
+                }
+
+                return FileVisitResult.CONTINUE;
+            }
+
+            @Override
+            public FileVisitResult visitFileFailed(Path file, IOException exc) throws IOException {
+                log.info("Failed to access file: {} - {}", file.toString(), exc.getMessage());
+                return FileVisitResult.CONTINUE;
+            }
+        });
+
+        FileUtils.deleteDirectory(new File(localPath));
+
+        RList<String> elements = redissonClient.getList("ragTag");
+        if (!elements.contains(repoProjectName)) {
+            elements.add(repoProjectName);
+        }
+
+        git.close();
+
+        log.info("遍历解析路径，上传完成:{}", repoUrl);
+
+        return Response.<String>builder().code("0000").info("调用成功").build();
+    }
+
+    private String extractProjectName(String repoUrl) {
+        String[] parts = repoUrl.split("/");
+        String projectNameWithGit = parts[parts.length - 1];
+        return projectNameWithGit.replace(".git", "");
+    }
+}

docs/docker-compose-environment-aliyun2.yml

@@ -0,0 +1,43 @@
+# docker-compose -f docker-compose-environment-aliyun.yml up -d
+version: '3'
+services:
+  # 对话模型
+  # ollama pull deepseek-r1:1.5b
+  # 运行模型
+  # ollama run deepseek-r1:1.5b
+  # 联网模型
+  # ollama pull nomic-embed-text
+  ollama:
+    image: registry.cn-hangzhou.aliyuncs.com/xfg-studio/ollama:0.5.10
+    container_name: ollama
+    restart: unless-stopped
+    ports:
+      - "11434:11434"
+  vector_db:
+    image: registry.cn-hangzhou.aliyuncs.com/xfg-studio/pgvector:v0.5.0
+    container_name: vector_db
+    restart: always
+    environment:
+      - POSTGRES_USER=postgres
+      - POSTGRES_PASSWORD=postgres
+      - POSTGRES_DB=ai-rag-knowledge
+      - PGPASSWORD=postgres
+    volumes:
+      - ./pgvector/sql/init.sql:/docker-entrypoint-initdb.d/init.sql
+    logging:
+      options:
+        max-size: 10m
+        max-file: "3"
+    ports:
+      - '15432:5432'
+    healthcheck:
+      test: "pg_isready -U postgres -d ai-rag-knowledge"
+      interval: 2s
+      timeout: 20s
+      retries: 10
+    networks:
+      - my-network
+
+networks:
+  my-network:
+    driver: bridge

docs/nginx/html/ai-case-04.html

@@ -0,0 +1,121 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>AI Chat</title>
+    <script src="https://cdn.tailwindcss.com"></script>
+</head>
+<body class="bg-gray-100 h-screen">
+<div class="container mx-auto max-w-3xl h-screen flex flex-col">
+    <!-- 消息容器 -->
+    <div id="messageContainer" class="flex-1 overflow-y-auto p-4 space-y-4 bg-white rounded-lg shadow-lg">
+        <!-- 消息历史将在此动态生成 -->
+    </div>
+
+    <!-- 输入区域 -->
+    <div class="p-4 bg-white rounded-lg shadow-lg mt-4">
+        <div class="flex space-x-2">
+            <input
+                    type="text"
+                    id="messageInput"
+                    placeholder="输入消息..."
+                    class="flex-1 p-2 border rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500"
+                    onkeypress="handleKeyPress(event)"
+            >
+            <button
+                    onclick="sendMessage()"
+                    class="px-4 py-2 bg-blue-500 text-white rounded-lg hover:bg-blue-600 transition-colors"
+            >
+                发送
+            </button>
+        </div>
+    </div>
+</div>
+
+<script>
+        // 添加消息到容器
+        function addMessage(content, isUser = false) {
+            const container = document.getElementById('messageContainer');
+            const messageDiv = document.createElement('div');
+
+            messageDiv.className = `flex ${isUser ? 'justify-end' : 'justify-start'}`;
+            messageDiv.innerHTML = `
+                <div class="max-w-[80%] p-3 rounded-lg ${
+                    isUser ? 'bg-blue-500 text-white' : 'bg-gray-200 text-gray-800'
+                }">
+                    ${content}
+                </div>
+            `;
+
+            container.appendChild(messageDiv);
+            container.scrollTop = container.scrollHeight; // 滚动到底部
+        }
+
+        // 发送消息
+        async function sendMessage() {
+            const input = document.getElementById('messageInput');
+            const message = input.value.trim();
+
+            if (!message) return;
+
+            // 清空输入框
+            input.value = '';
+
+            // 添加用户消息
+            addMessage(message, true);
+
+            // 添加初始AI消息占位
+            addMessage('<span class="animate-pulse">▍</span>');
+
+            // 构建API URL
+            const apiUrl = `http://localhost:8090/api/v1/ollama/generate_stream?model=deepseek-r1:7b&message=${encodeURIComponent(message)}`;
+
+            // 使用EventSource接收流式响应
+            const eventSource = new EventSource(apiUrl);
+            let buffer = '';
+
+            eventSource.onmessage = (event) => {
+                try {
+                    const data = JSON.parse(event.data);
+                    const content = data.result?.output?.content || '';
+                    const finishReason = data.result?.metadata?.finishReason;
+
+                    if (content) {
+                        buffer += content;
+                        updateLastMessage(buffer + '<span class="animate-pulse">▍</span>');
+                    }
+
+                    if (finishReason === 'STOP') {
+                        eventSource.close();
+                        updateLastMessage(buffer); // 移除加载动画
+                    }
+                } catch (error) {
+                    console.error('解析错误:', error);
+                }
+            };
+
+            eventSource.onerror = (error) => {
+                console.error('EventSource错误:', error);
+                eventSource.close();
+            };
+        }
+
+        // 更新最后一条消息
+        function updateLastMessage(content) {
+            const container = document.getElementById('messageContainer');
+            const lastMessage = container.lastChild.querySelector('div');
+            lastMessage.innerHTML = content;
+            container.scrollTop = container.scrollHeight;
+        }
+
+        // 回车发送
+        function handleKeyPress(event) {
+            if (event.key === 'Enter' && !event.shiftKey) {
+                event.preventDefault();
+                sendMessage();
+            }
+        }
+    </script>
+</body>
+</html>

docs/nginx/html/index.html

@@ -0,0 +1,190 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>AI Chat</title>
+  <script src="https://cdn.tailwindcss.com"></script>
+  <style>
+    /* Custom scrollbar for chat container */
+    .chat-container::-webkit-scrollbar {
+      width: 6px;
+    }
+    .chat-container::-webkit-scrollbar-track {
+      background: #f3f4f6;
+    }
+    .chat-container::-webkit-scrollbar-thumb {
+      background: #d1d5db;
+      border-radius: 3px;
+    }
+    .chat-container::-webkit-scrollbar-thumb:hover {
+      background: #9ca3af;
+    }
+  </style>
+</head>
+<body class="bg-gray-100 min-h-screen flex flex-col">
+<div class="container mx-auto px-4 py-8 max-w-4xl">
+  <!-- Header -->
+  <div class="text-center mb-8">
+    <h1 class="text-3xl font-bold text-gray-800">AI Chat</h1>
+    <p class="text-gray-600 mt-2">Simple AI conversation powered by Ollama</p>
+  </div>
+
+  <!-- Chat Container -->
+  <div id="chatContainer" class="chat-container bg-white rounded-lg shadow-lg h-96 overflow-y-auto mb-4 p-4 space-y-4 flex flex-col">
+    <!-- Messages will be appended here -->
+  </div>
+
+  <!-- Input Form -->
+  <form id="messageForm" class="flex space-x-2">
+    <input
+            type="text"
+            id="messageInput"
+            placeholder="Type your message..."
+            class="flex-1 px-4 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-transparent"
+            required
+    >
+    <button
+            type="submit"
+            class="px-6 py-2 bg-blue-500 text-white rounded-lg hover:bg-blue-600 focus:outline-none focus:ring-2 focus:ring-blue-500 focus:ring-offset-2 disabled:opacity-50 disabled:cursor-not-allowed"
+            id="sendButton"
+    >
+      Send
+    </button>
+  </form>
+</div>
+
+<script>
+  const API_BASE = 'http://localhost:8090/api/v1/ollama/generate_stream';
+  const MODEL = 'deepseek-r1:7b';
+  const chatContainer = document.getElementById('chatContainer');
+  const messageInput = document.getElementById('messageInput');
+  const sendButton = document.getElementById('sendButton');
+  const messageForm = document.getElementById('messageForm');
+
+  let currentEventSource = null;
+  let currentAIMessageElement = null;
+
+  // Function to add user message to chat
+  function addUserMessage(message) {
+    const messageDiv = document.createElement('div');
+    messageDiv.className = 'flex justify-end mb-4';
+    messageDiv.innerHTML = `
+                <div class="max-w-xs lg:max-w-md px-4 py-2 bg-blue-500 text-white rounded-lg rounded-tr-sm">
+                    ${escapeHtml(message)}
+                </div>
+            `;
+    chatContainer.appendChild(messageDiv);
+    chatContainer.scrollTop = chatContainer.scrollHeight;
+  }
+
+  // Function to add AI message container
+  function addAIMessageContainer() {
+    const messageDiv = document.createElement('div');
+    messageDiv.className = 'flex justify-start mb-4';
+    messageDiv.id = 'aiMessage';
+    messageDiv.innerHTML = `
+                <div class="max-w-xs lg:max-w-md px-4 py-2 bg-gray-200 text-gray-800 rounded-lg rounded-tl-sm">
+                    <span id="aiContent"></span>
+                </div>
+            `;
+    chatContainer.appendChild(messageDiv);
+    currentAIMessageElement = document.getElementById('aiContent');
+    chatContainer.scrollTop = chatContainer.scrollHeight;
+  }
+
+  // Function to append text to AI message
+  function appendToAIMessage(text) {
+    if (currentAIMessageElement && text) {
+      currentAIMessageElement.textContent += text;
+      chatContainer.scrollTop = chatContainer.scrollHeight;
+    }
+  }
+
+  // Function to escape HTML
+  function escapeHtml(text) {
+    const div = document.createElement('div');
+    div.textContent = text;
+    return div.innerHTML;
+  }
+
+  // Function to close EventSource
+  function closeEventSource() {
+    if (currentEventSource) {
+      currentEventSource.close();
+      currentEventSource = null;
+    }
+  }
+
+  // Form submit handler
+  messageForm.addEventListener('submit', async (e) => {
+    e.preventDefault();
+    const message = messageInput.value.trim();
+    if (!message || currentEventSource) return; // Prevent multiple requests
+
+    // Add user message
+    addUserMessage(message);
+
+    // Clear input
+    messageInput.value = '';
+
+    // Disable send button
+    sendButton.disabled = true;
+    sendButton.textContent = 'Sending...';
+
+    // Add AI message container
+    addAIMessageContainer();
+
+    // Prepare API URL
+    const encodedMessage = encodeURIComponent(message);
+    const apiUrl = `${API_BASE}?model=${MODEL}&message=${encodedMessage}`;
+
+    // Create EventSource for streaming
+    currentEventSource = new EventSource(apiUrl);
+
+    currentEventSource.onmessage = (event) => {
+      try {
+        const data = JSON.parse(event.data);
+        if (data && data.result) {
+          const content = data.result.output?.content || '';
+          const finishReason = data.result.metadata?.finishReason;
+
+          // Append content if not empty
+          if (content) {
+            appendToAIMessage(content);
+          }
+
+          // Check for end of stream
+          if (finishReason === 'STOP') {
+            closeEventSource();
+            sendButton.disabled = false;
+            sendButton.textContent = 'Send';
+            currentAIMessageElement = null;
+          }
+        }
+      } catch (error) {
+        console.error('Error parsing SSE data:', error);
+      }
+    };
+
+    currentEventSource.onerror = (error) => {
+      console.error('EventSource failed:', error);
+      closeEventSource();
+      sendButton.disabled = false;
+      sendButton.textContent = 'Send';
+      console.log(currentAIMessageElement)
+      // if (currentAIMessageElement) {
+      //   currentAIMessageElement.textContent += ' (Error: Connection failed)';
+      // }
+    };
+  });
+
+  // Handle input focus to scroll to bottom
+  messageInput.addEventListener('focus', () => {
+    setTimeout(() => {
+      chatContainer.scrollTop = chatContainer.scrollHeight;
+    }, 100);
+  });
+</script>
+</body>
+</html>