将 Spring AI 和 Elasticsearch 作为矢量数据库

 curl -fsSL https://elastic.co/start-local | sh 
  ______ _           _   _      
 |  ____| |         | | (_)     
 | |__  | | __ _ ___| |_ _  ___ 
 |  __| | |/ _` / __| __| |/ __|
 | |____| | (_| \__ \ |_| | (__ 
 |______|_|\__,_|___/\__|_|\___|
-------------------------------------------------
🚀 Run Elasticsearch and Kibana for local testing
-------------------------------------------------
ℹ️  Do not use this script in a production environment
⌛️ Setting up Elasticsearch and Kibana v9.0.0...
- Generated random passwords
- Created the elastic-start-local folder containing the files:
  - .env, with settings
  - docker-compose.yml, for Docker services
  - start/stop/uninstall commands
- Running docker compose up --wait
[+] Running 25/26
 ✔ kibana_settings Pulled                                                 16.7s 
 ✔ kibana Pulled                                                          26.8s 
 ✔ elasticsearch Pulled                                                   17.4s                                                                     
[+] Running 6/6
 ✔ Network elastic-start-local_default             Created                 0.0s 
 ✔ Volume "elastic-start-local_dev-elasticsearch"  Created                 0.0s 
 ✔ Volume "elastic-start-local_dev-kibana"         Created                 0.0s 
 ✔ Container es-local-dev                          Healthy                12.9s 
 ✔ Container kibana_settings                       Exited                 11.9s 
 ✔ Container kibana-local-dev                      Healthy                21.8s 
🎉 Congrats, Elasticsearch and Kibana are installed and running in Docker!
🌐 Open your browser at http://localhost:5601
   Username: elastic
   Password: w1GB15uQ
🔌 Elasticsearch API endpoint: http://localhost:9200
🔑 API key: SERqaGlKWUJLNVJDODc1UGxjLWE6WFdxSTNvMU5SbVc5NDlKMEhpMzJmZw==
Learn more at https://github.com/elastic/start-local
➜  ~

spring.elasticsearch.uris=http://localhost:9200
spring.elasticsearch.username=elastic
spring.elasticsearch.password=w1GB15uQ

spring.ai.vectorstore.elasticsearch.initialize-schema=true

spring.ai.openai.api-key=...

spring.servlet.multipart.max-file-size=20MB
spring.servlet.multipart.max-request-size=20MB

http --form POST http://localhost:8080/rag/ingest path@/Users/jlong/Downloads/test.pdf

http :8080/rag/query question=="where do you place the reward card after obtaining it?"

After obtaining a Reward card, you place it facedown under the Hero card of the hero who received it.
Found at page: 28 of the manual

@SpringBootApplication
public class DemoApplication {
 	public static void main(String[] args) { 
     		SpringApplication.run(DemoApplication.class, args);
 	}
}

@RestController
class RagController {

   private final RagService rag;

   RagController(RagService rag) {
       this.rag = rag;
   }

   @PostMapping("/rag/ingest")
   ResponseEntity<?> ingestPDF(@RequestBody MultipartFile path) {
       rag.ingest(path.getResource());
       return ResponseEntity.ok().body("Done!");
   }

   @GetMapping("/rag/query")
   ResponseEntity<?> query(@RequestParam String question) {
       String response = rag.directRag(question);
       return ResponseEntity.ok().body(response);
   }
}

@Service
class RagService {

   private final ElasticsearchVectorStore vectorStore;

   private final ChatClient ai;

   RagService(ElasticsearchVectorStore vectorStore, ChatClient.Builder clientBuilder) {
       this.vectorStore = vectorStore;
       this.ai = clientBuilder.build();
   }

   void ingest(Resource path) {
       PagePdfDocumentReader pdfReader = new PagePdfDocumentReader(path);
       List<Document> batch = new TokenTextSplitter().apply(pdfReader.read());
       vectorStore.add(batch);
   }

  // TBD
}

String directRag(String question) {
   // Query the vector store for documents related to the question
   List<Document> vectorStoreResult =
           vectorStore.doSimilaritySearch(SearchRequest.builder().query(question).topK(5)
                   .similarityThreshold(0.7).build());

   // Merging the documents into a single string
   String documents = vectorStoreResult.stream()
           .map(Document::getText)
           .collect(Collectors.joining(System.lineSeparator()));

   // Exit if the vector search didn't find any results
   if (documents.isEmpty()) {
       return "No relevant context found. Please change your question.";
   }

   // Setting the prompt with the context
   String prompt = """
           You're assisting with providing the rules of the tabletop game Runewars.
           Use the information from the DOCUMENTS section to provide accurate answers to the
           question in the QUESTION section.
           If unsure, simply state that you don't know.
          
           DOCUMENTS:
           """ + documents
           + """
           QUESTION:
           """ + question;


   // Calling the chat model with the question
   String response = ai
           .prompt()
           .user(prompt)
           .call()
           .content();

   return response +
           System.lineSeparator() +
           "Found at page: " +
           // Retrieving the first ranked page number from the document metadata
           vectorStoreResult.getFirst().getMetadata().get(PagePdfDocumentReader.METADATA_START_PAGE_NUMBER) +
           " of the manual";

}

<dependency>
   <groupId>org.springframework.ai</groupId>
   <artifactId>spring-ai-advisors-vector-store</artifactId>
</dependency>

String advisedRag(String question) {
   return this.ai
           .prompt()
           .user(question)
           .advisors(new QuestionAnswerAdvisor(vectorStore))
           .call()
           .content();
}

management.endpoints.web.exposure.include=*

spring.threads.virtual.enabled=true