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

@@ -24,10 +24,10 @@
             <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>

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

@@ -4,6 +4,8 @@ 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;
@@ -25,6 +27,11 @@ 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);
@@ -36,17 +43,27 @@ public class OllamaConfig {
     }
 
     @Bean
-    public SimpleVectorStore simpleVectorStore(OllamaApi ollamaApi) {
-        OllamaEmbeddingClient embeddingClient = new OllamaEmbeddingClient(ollamaApi);
-        embeddingClient.withDefaultOptions(OllamaOptions.create().withModel("nomic-embed-text"));
-        return new SimpleVectorStore(embeddingClient);
+    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(OllamaApi ollamaApi, JdbcTemplate jdbcTemplate) {
-        OllamaEmbeddingClient embeddingClient = new OllamaEmbeddingClient(ollamaApi);
-        embeddingClient.withDefaultOptions(OllamaOptions.create().withModel("nomic-embed-text"));
-        return new PgVectorStore(jdbcTemplate, embeddingClient);
+    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

@@ -15,6 +15,12 @@ spring:
         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:

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

@@ -21,6 +21,7 @@ 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;
@@ -61,7 +62,7 @@ public class RAGApiTest {
     @Test
     public void chat() {
         // 构建提问
-        String message = "李永德，哪年出生的";
+        String message = "拆装出库的操作流程是什么？";
 
         // 构建推理模板
         String SYSTEM_PROMPT = """
@@ -72,7 +73,7 @@ public class RAGApiTest {
                     {documents}
                 """;
         // 读取向量库信息
-        SearchRequest request = SearchRequest.query(message).withTopK(5).withFilterExpression("knowledge == '德德'");
+        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());
@@ -84,7 +85,8 @@ public class RAGApiTest {
         messages.add(ragMessage);
 
         // 提问
-        ChatResponse chatResponse = ollamaChatClient.call(new Prompt(messages, OllamaOptions.create().withModel("deepseek-r1:7b")));
-        log.info("测试结果:{}", JSON.toJSONString(chatResponse));
+//        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-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/")
@@ -20,6 +33,8 @@ public class OllamaController implements IAiService {
 
     @Resource
     private OllamaChatClient chatClient;
+    @Resource
+    private PgVectorStore pgVectorStore;
 
     /**
      * http://localhost:8090/api/v1/ollama/generate?model=deepseek-r1:7b&message=1+1
@@ -38,4 +53,33 @@ public class OllamaController implements IAiService {
     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", "");
+    }
+}