feat: rag的api

ai-rag-trigger/pom.xml

@@ -27,14 +27,14 @@
 <!--            <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-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/RAGController.java

@@ -0,0 +1,116 @@
+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.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.web.bind.annotation.*;
+import org.springframework.web.multipart.MultipartFile;
+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 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;
+    }
+}