Spring AI 来了，打造Java生态大模型应用开发新框架！
package org.springframework.samples.ai.azure.openai.rag;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.client.AiClient;
import org.springframework.ai.client.AiResponse;
import org.springframework.ai.client.Generation;
import org.springframework.ai.document.Document;
import org.springframework.ai.embedding.EmbeddingClient;
import org.springframework.ai.loader.impl.JsonLoader;
import org.springframework.ai.prompt.Prompt;
import org.springframework.ai.prompt.SystemPromptTemplate;
import org.springframework.ai.prompt.messages.Message;
import org.springframework.ai.prompt.messages.UserMessage;
import org.springframework.ai.retriever.impl.VectorStoreRetriever;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.impl.InMemoryVectorStore;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.core.io.Resource;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;
public class RagService {
private static final Logger logger = LoggerFactory.getLogger(RagService.class);
@Value("classpath:/data/")
private Resource bikesResource;
@Value("classpath:/prompts/")
private Resource systemBikePrompt;
private final AiClient aiClient;
private final EmbeddingClient embeddingClient;
public RagService(AiClient aiClient, EmbeddingClient embeddingClient) {
this.aiClient = aiClient;
this.embeddingClient = embeddingClient;
}
public Generation retrieve(String message) {
// Step 1 - Load JSON document as Documents
logger.info("Loading JSON as Documents");
JsonLoader jsonLoader = new JsonLoader(bikesResource,
"name", "price", "shortDescription", "description");
List<Document> documents = jsonLoader.load();
logger.info("Loading JSON as Documents");
// Step 2 - Create embeddings and save to vector store
logger.info("Creating Embeddings...");
VectorStore vectorStore = new InMemoryVectorStore(embeddingClient);
vectorStore.add(documents);
logger.info("Embeddings created.");
// Step 3 retrieve related documents to query
VectorStoreRetriever vectorStoreRetriever = new VectorStoreRetriever(vectorStore);
logger.info("Retrieving relevant documents");
List<Document> similarDocuments = vectorStoreRetriever.retrieve(message);
logger.info(String.format("Found %s relevant documents.", similarDocuments.size()));
// Step 4 Embed documents into SystemMessage with the `` prompt template
Message systemMessage = getSystemMessage(similarDocuments);
UserMessage userMessage = new UserMessage(message);
// Step 4 - Ask the AI model
logger.info("Asking AI model to reply to question.");
Prompt prompt = new Prompt(List.of(systemMessage, userMessage));
logger.info(prompt.toString());
AiResponse response = aiClient.generate(prompt);
logger.info("AI responded.");
logger.info(response.getGeneration().toString());
return response.getGeneration();
}
private Message getSystemMessage(List<Document> similarDocuments) {
String documents = similarDocuments.stream().map(entry -> entry.getContent()).collect(Collectors.joining("\n"));
SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(systemBikePrompt);
Message systemMessage = systemPromptTemplate.createMessage(Map.of("documents", documents));
return systemMessage;
}
}