feat: open-ai

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-app/pom.xml

@@ -24,10 +24,10 @@
             <scope>test</scope>
         </dependency>
 
-<!--        <dependency>-->
+        <dependency>
-<!--            <groupId>org.springframework.ai</groupId>-->
+            <groupId>org.springframework.ai</groupId>
-<!--            <artifactId>spring-ai-openai-spring-boot-starter</artifactId>-->
+            <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
-<!--        </dependency>-->
+        </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) {
+    public SimpleVectorStore vectorStore(@Value("${spring.ai.rag.embed}") String model, OllamaApi ollamaApi, OpenAiApi openAiApi) {
-        OllamaEmbeddingClient embeddingClient = new OllamaEmbeddingClient(ollamaApi);
+        if ("nomic-embed-text".equalsIgnoreCase(model)) {
-        embeddingClient.withDefaultOptions(OllamaOptions.create().withModel("nomic-embed-text"));
+            OllamaEmbeddingClient embeddingClient = new OllamaEmbeddingClient(ollamaApi);
-        return new SimpleVectorStore(embeddingClient);
+            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) {
+    public PgVectorStore pgVectorStore(@Value("${spring.ai.rag.embed}") String model, OllamaApi ollamaApi, OpenAiApi openAiApi, JdbcTemplate jdbcTemplate) {
-        OllamaEmbeddingClient embeddingClient = new OllamaEmbeddingClient(ollamaApi);
+        if ("nomic-embed-text".equalsIgnoreCase(model)) {
-        embeddingClient.withDefaultOptions(OllamaOptions.create().withModel("nomic-embed-text"));
+            OllamaEmbeddingClient embeddingClient = new OllamaEmbeddingClient(ollamaApi);
-        return new PgVectorStore(jdbcTemplate, embeddingClient);
+            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-trigger/pom.xml

@@ -23,10 +23,10 @@
             <artifactId>spring-boot-starter-web</artifactId>
         </dependency>
 
-<!--        <dependency>-->
+        <dependency>
-<!--            <groupId>org.springframework.ai</groupId>-->
+            <groupId>org.springframework.ai</groupId>
-<!--            <artifactId>spring-ai-openai-spring-boot-starter</artifactId>-->
+            <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
-<!--        </dependency>-->
+        </dependency>
         <dependency>
             <groupId>org.springframework.ai</groupId>
             <artifactId>spring-ai-tika-document-reader</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()
+        ));
+    }
+
+
+}