Initial commit
This commit is contained in:
Zhongwei Li
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skills/langchain4j/qdrant/references/examples.md
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skills/langchain4j/qdrant/references/examples.md
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# Qdrant for Java: Complete Examples
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This file provides comprehensive code examples for integrating Qdrant with Java and Spring Boot applications.
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## 1. Complete Spring Boot Application with Qdrant
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This example demonstrates a full Spring Boot application with Qdrant integration for vector search.
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### Project Structure
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```
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/src/main/java/com/example/qdrantdemo/
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├── QdrantDemoApplication.java
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├── config/
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│ ├── QdrantConfig.java
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│ └── Langchain4jConfig.java
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├── controller/
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│ ├── SearchController.java
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│ └── RagController.java
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├── service/
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│ ├── VectorSearchService.java
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│ └── RagService.java
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└── Application.properties
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```
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### Dependencies (pom.xml)
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```xml
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<dependencies>
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<!-- Spring Boot -->
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<dependency>
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<groupId>org.springframework.boot</groupId>
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<artifactId>spring-boot-starter-web</artifactId>
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</dependency>
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<!-- Qdrant Java Client -->
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<dependency>
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<groupId>io.qdrant</groupId>
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<artifactId>client</artifactId>
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<version>1.15.0</version>
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</dependency>
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<!-- LangChain4j -->
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j</artifactId>
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<version>1.7.0</version>
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</dependency>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-qdrant</artifactId>
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<version>1.7.0</version>
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</dependency>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-all-minilm-l6-v2</artifactId>
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<version>1.7.0</version>
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</dependency>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-open-ai</artifactId>
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<version>1.7.0</version>
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</dependency>
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</dependencies>
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```
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### Application Configuration (application.properties)
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```properties
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# Qdrant Configuration
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qdrant.host=localhost
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qdrant.port=6334
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qdrant.api-key=
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# OpenAI Configuration (for RAG)
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openai.api-key=YOUR_OPENAI_API_KEY
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```
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### Qdrant Configuration
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```java
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package com.example.qdrantdemo.config;
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import io.qdrant.client.QdrantClient;
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import io.qdrant.client.QdrantGrpcClient;
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import org.springframework.beans.factory.annotation.Value;
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import org.springframework.context.annotation.Bean;
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import org.springframework.context.annotation.Configuration;
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@Configuration
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public class QdrantConfig {
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@Value("${qdrant.host:localhost}")
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private String host;
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@Value("${qdrant.port:6334}")
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private int port;
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@Value("${qdrant.api-key:}")
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private String apiKey;
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@Bean
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public QdrantClient qdrantClient() {
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QdrantGrpcClient grpcClient = QdrantGrpcClient.newBuilder(host, port, false)
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.withApiKey(apiKey)
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.build();
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return new QdrantClient(grpcClient);
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}
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}
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```
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### Vector Search Service
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```java
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package com.example.qdrantdemo.service;
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import io.qdrant.client.QdrantClient;
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import io.qdrant.client.grpc.Collections.Distance;
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import io.qdrant.client.grpc.Collections.VectorParams;
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import io.qdrant.client.grpc.Points.PointStruct;
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import io.qdrant.client.grpc.Points.QueryPoints;
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import io.qdrant.client.grpc.Points.ScoredPoint;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.stereotype.Service;
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import jakarta.annotation.PostConstruct;
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import java.util.List;
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import java.util.Map;
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import java.util.concurrent.ExecutionException;
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import static io.qdrant.client.PointIdFactory.id;
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import static io.qdrant.client.ValueFactory.value;
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import static io.qdrant.client.VectorsFactory.vectors;
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import static io.qdrant.client.QueryFactory.nearest;
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@Service
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public class VectorSearchService {
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private final QdrantClient client;
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@Autowired
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private EmbeddingService embeddingService; // Helper service for embeddings
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public static final String COLLECTION_NAME = "document-search";
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public static final int VECTOR_SIZE = 384; // For AllMiniLM-L6-v2
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public VectorSearchService(QdrantClient client) {
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this.client = client;
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}
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@PostConstruct
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public void initializeCollection() throws ExecutionException, InterruptedException {
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// Create collection if it doesn't exist
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client.createCollectionAsync(COLLECTION_NAME,
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VectorParams.newBuilder()
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.setDistance(Distance.Cosine)
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.setSize(VECTOR_SIZE)
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.build()
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).get();
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}
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public List<ScoredPoint> search(String query, int limit) {
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try {
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List<Float> queryVector = embeddingService.embedQuery(query);
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return client.queryAsync(
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QueryPoints.newBuilder()
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.setCollectionName(COLLECTION_NAME)
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.setLimit(limit)
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.setQuery(nearest(queryVector))
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.setWithPayload(true)
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.build()
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).get();
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} catch (InterruptedException | ExecutionException e) {
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throw new RuntimeException("Qdrant search failed", e);
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}
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}
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public void addDocument(String documentId, String title, String content) {
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try {
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List<Float> contentVector = embeddingService.embedText(content);
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PointStruct point = PointStruct.newBuilder()
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.setId(id(documentId))
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.setVectors(vectors(contentVector))
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.putAllPayload(Map.of(
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"title", value(title),
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"content", value(content),
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"created_at", value(System.currentTimeMillis())
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))
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.build();
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client.upsertAsync(COLLECTION_NAME, List.of(point)).get();
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} catch (InterruptedException | ExecutionException e) {
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throw new RuntimeException("Qdrant document insertion failed", e);
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}
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}
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}
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```
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### Search Controller
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```java
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package com.example.qdrantdemo.controller;
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import com.example.qdrantdemo.service.VectorSearchService;
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import io.qdrant.client.grpc.Points.ScoredPoint;
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import org.springframework.web.bind.annotation.*;
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import java.util.List;
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@RestController
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@RequestMapping("/api/search")
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public class SearchController {
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private final VectorSearchService searchService;
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public SearchController(VectorSearchService searchService) {
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this.searchService = searchService;
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}
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@GetMapping
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public List<ScoredPoint> search(@RequestParam String query,
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@RequestParam(defaultValue = "5") int limit) {
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return searchService.search(query, limit);
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}
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@PostMapping("/document")
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public String addDocument(@RequestBody AddDocumentRequest request) {
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searchService.addDocument(request.getDocumentId(), request.getTitle(), request.getContent());
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return "Document added successfully";
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}
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public static class AddDocumentRequest {
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private String documentId;
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private String title;
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private String content;
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// Getters and setters
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public String getDocumentId() { return documentId; }
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public void setDocumentId(String documentId) { this.documentId = documentId; }
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public String getTitle() { return title; }
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public void setTitle(String title) { this.title = title; }
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public String getContent() { return content; }
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public void setContent(String content) { this.content = content; }
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}
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}
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```
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## 2. Advanced RAG with LangChain4j
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This example demonstrates a complete RAG system with Qdrant and LLM integration.
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### LangChain4j Configuration
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```java
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package com.example.qdrantdemo.config;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.embedding.EmbeddingModel;
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import dev.langchain4j.embedding.allminilml6v2.AllMiniLmL6V2EmbeddingModel;
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import dev.langchain4j.model.chat.ChatLanguageModel;
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import dev.langchain4j.model.openai.OpenAiChatModel;
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import dev.langchain4j.store.embedding.EmbeddingStore;
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import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
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import dev.langchain4j.store.embedding.qdrant.QdrantEmbeddingStore;
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import org.springframework.beans.factory.annotation.Value;
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import org.springframework.context.annotation.Bean;
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import org.springframework.context.annotation.Configuration;
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@Configuration
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public class Langchain4jConfig {
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@Value("${qdrant.host:localhost}")
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private String host;
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@Value("${qdrant.port:6334}")
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private int port;
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@Value("${qdrant.api-key:}")
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private String apiKey;
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@Value("${openai.api-key}")
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private String openaiApiKey;
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@Bean
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public EmbeddingStore<TextSegment> embeddingStore() {
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return QdrantEmbeddingStore.builder()
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.collectionName("rag-collection")
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.host(host)
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.port(port)
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.apiKey(apiKey)
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.build();
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}
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@Bean
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public EmbeddingModel embeddingModel() {
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return new AllMiniLmL6V2EmbeddingModel();
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}
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@Bean
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public ChatLanguageModel chatLanguageModel() {
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return OpenAiChatModel.builder()
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.apiKey(openaiApiKey)
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.modelName("gpt-3.5-turbo")
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.build();
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}
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@Bean
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public EmbeddingStoreIngestor embeddingStoreIngestor(
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EmbeddingStore<TextSegment> embeddingStore,
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EmbeddingModel embeddingModel) {
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return EmbeddingStoreIngestor.builder()
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.embeddingStore(embeddingStore)
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.embeddingModel(embeddingModel)
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.build();
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}
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}
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```
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### RAG Service with Assistant
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```java
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package com.example.qdrantdemo.service;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.model.chat.ChatLanguageModel;
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import dev.langchain4j.rag.content.retriever.ContentRetriever;
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import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
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import dev.langchain4j.service.AiServices;
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import dev.langchain4j.store.embedding.EmbeddingStore;
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import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
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import org.springframework.stereotype.Service;
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import java.util.List;
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@Service
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public class RagService {
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// Define the AI assistant interface
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interface Assistant {
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String chat(String userMessage);
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}
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private final EmbeddingStoreIngestor ingestor;
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private final Assistant assistant;
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public RagService(EmbeddingStore<TextSegment> embeddingStore,
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EmbeddingStoreIngestor ingestor,
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ChatLanguageModel chatModel) {
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this.ingestor = ingestor;
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// Create content retriever for RAG
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ContentRetriever contentRetriever = EmbeddingStoreContentRetriever.builder()
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.embeddingStore(embeddingStore)
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.maxResults(3)
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.minScore(0.7)
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.build();
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// Build the AI assistant with RAG capabilities
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this.assistant = AiServices.builder(Assistant.class)
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.chatLanguageModel(chatModel)
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.contentRetriever(contentRetriever)
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.build();
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}
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public void ingestDocument(String text) {
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TextSegment segment = TextSegment.from(text);
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ingestor.ingest(segment);
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}
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public String query(String userQuery) {
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return assistant.chat(userQuery);
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}
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public List<TextSegment> findRelevantDocuments(String query, int maxResults) {
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EmbeddingStore<TextSegment> embeddingStore = ingestor.getEmbeddingStore();
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return embeddingStore.findRelevant(
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ingestor.getEmbeddingModel().embed(query).content(),
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maxResults,
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0.7
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).stream()
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.map(match -> match.embedded())
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.toList();
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}
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}
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```
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### RAG Controller
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```java
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package com.example.qdrantdemo.controller;
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import com.example.qdrantdemo.service.RagService;
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import dev.langchain4j.data.segment.TextSegment;
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import org.springframework.web.bind.annotation.*;
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import java.util.List;
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@RestController
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@RequestMapping("/api/rag")
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public class RagController {
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private final RagService ragService;
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public RagController(RagService ragService) {
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this.ragService = ragService;
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}
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@PostMapping("/ingest")
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public String ingestDocument(@RequestBody String document) {
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ragService.ingestDocument(document);
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return "Document ingested successfully.";
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}
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@PostMapping("/query")
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public String query(@RequestBody QueryRequest request) {
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return ragService.query(request.getQuery());
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}
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@GetMapping("/documents")
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public List<TextSegment> findDocuments(@RequestParam String query,
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@RequestParam(defaultValue = "3") int maxResults) {
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return ragService.findRelevantDocuments(query, maxResults);
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}
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public static class QueryRequest {
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private String query;
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public String getQuery() { return query; }
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public void setQuery(String query) { this.query = query; }
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}
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}
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```
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## 3. Multi-tenant Vector Search Application
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This example demonstrates advanced patterns for multi-tenant applications.
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### Multi-Tenant Vector Service
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```java
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package com.example.qdrantdemo.service;
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import io.qdrant.client.QdrantClient;
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import io.qdrant.client.grpc.Points.PointStruct;
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import io.qdrant.client.grpc.Points.QueryPoints;
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import io.qdrant.client.grpc.Points.ScoredPoint;
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import org.springframework.stereotype.Service;
|
||||
|
||||
import java.util.List;
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import java.util.concurrent.ExecutionException;
|
||||
|
||||
@Service
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public class MultiTenantVectorService {
|
||||
|
||||
private final QdrantClient client;
|
||||
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||||
public MultiTenantVectorService(QdrantClient client) {
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this.client = client;
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||||
}
|
||||
|
||||
// Collection-based multi-tenancy
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public List<ScoredPoint> searchByTenant(String tenantId, List<Float> queryVector, int limit) {
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try {
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String collectionName = "tenant_" + tenantId + "_documents";
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||||
|
||||
return client.queryAsync(
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||||
QueryPoints.newBuilder()
|
||||
.setCollectionName(collectionName)
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.setLimit(limit)
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||||
.addAllVector(queryVector)
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||||
.setWithPayload(true)
|
||||
.build()
|
||||
).get();
|
||||
} catch (InterruptedException | ExecutionException e) {
|
||||
throw new RuntimeException("Multi-tenant search failed", e);
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||||
}
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||||
}
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||||
|
||||
public void upsertForTenant(String tenantId, List<PointStruct> points) {
|
||||
try {
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||||
String collectionName = "tenant_" + tenantId + "_documents";
|
||||
client.upsertAsync(collectionName, points).get();
|
||||
} catch (InterruptedException | ExecutionException e) {
|
||||
throw new RuntimeException("Multi-tenant upsert failed", e);
|
||||
}
|
||||
}
|
||||
|
||||
// Hybrid search with tenant-specific filters
|
||||
public List<ScoredPoint> hybridSearch(String tenantId, List<Float> queryVector,
|
||||
String category, int limit) {
|
||||
try {
|
||||
String collectionName = "tenant_" + tenantId + "_documents";
|
||||
|
||||
QueryPoints.Builder queryBuilder = QueryPoints.newBuilder()
|
||||
.setCollectionName(collectionName)
|
||||
.setLimit(limit)
|
||||
.addAllVector(queryVector);
|
||||
|
||||
// Add category filter if provided
|
||||
if (category != null && !category.isEmpty()) {
|
||||
queryBuilder.setFilter(Filter.newBuilder()
|
||||
.addMust(exactMatch("category", category))
|
||||
.build());
|
||||
}
|
||||
|
||||
return client.queryAsync(queryBuilder.build()).get();
|
||||
} catch (InterruptedException | ExecutionException e) {
|
||||
throw new RuntimeException("Hybrid search failed", e);
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Deployment and Configuration
|
||||
|
||||
### Docker Compose Setup
|
||||
```yaml
|
||||
version: '3.8'
|
||||
services:
|
||||
qdrant:
|
||||
image: qdrant/qdrant:v1.7.0
|
||||
ports:
|
||||
- "6333:6333"
|
||||
- "6334:6334"
|
||||
volumes:
|
||||
- qdrant_storage:/qdrant/storage
|
||||
environment:
|
||||
- QDRANT__SERVICE__HTTP_PORT=6333
|
||||
- QDRANT__SERVICE__GRPC_PORT=6334
|
||||
|
||||
volumes:
|
||||
qdrant_storage:
|
||||
```
|
||||
|
||||
### Production Configuration
|
||||
```properties
|
||||
# application-prod.properties
|
||||
qdrant.host=qdrant-service
|
||||
qdrant.port=6334
|
||||
qdrant.api-key=${QDRANT_API_KEY}
|
||||
|
||||
# Enable HTTPS for production
|
||||
server.ssl.enabled=true
|
||||
server.ssl.key-store=classpath:keystore.p12
|
||||
server.ssl.key-store-password=${SSL_KEYSTORE_PASSWORD}
|
||||
|
||||
# OpenAI Configuration
|
||||
openai.api-key=${OPENAI_API_KEY}
|
||||
|
||||
# Logging
|
||||
logging.level.com.example.qdrantdemo=INFO
|
||||
logging.level.io.qdrant=INFO
|
||||
```
|
||||
|
||||
## Testing Strategy
|
||||
|
||||
### Unit Tests for Vector Service
|
||||
```java
|
||||
import org.junit.jupiter.api.Test;
|
||||
import org.springframework.beans.factory.annotation.Autowired;
|
||||
import org.springframework.boot.test.context.SpringBootTest;
|
||||
import java.util.List;
|
||||
|
||||
import static org.junit.jupiter.api.Assertions.*;
|
||||
|
||||
@SpringBootTest
|
||||
public class VectorSearchServiceTest {
|
||||
|
||||
@Autowired
|
||||
private VectorSearchService vectorSearchService;
|
||||
|
||||
@Test
|
||||
public void testCollectionInitialization() {
|
||||
// Test that collection is created properly
|
||||
// This could involve checking collection metadata
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testDocumentUpsert() {
|
||||
// Test document insertion and retrieval
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testSearchFunctionality() {
|
||||
// Test vector search functionality
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
This comprehensive example provides a complete foundation for building Qdrant-powered applications with Spring Boot and LangChain4j.
|
||||
Reference in New Issue
Block a user