Initial commit
This commit is contained in:
Zhongwei Li
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# LangChain4j RAG Implementation - Practical Examples
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Production-ready examples for implementing Retrieval-Augmented Generation (RAG) systems with LangChain4j.
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## 1. Simple In-Memory RAG
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**Scenario**: Quick RAG setup with documents in memory for development/testing.
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```java
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import dev.langchain4j.data.document.Document;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.model.embedding.EmbeddingModel;
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import dev.langchain4j.model.openai.OpenAiEmbeddingModel;
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import dev.langchain4j.model.openai.OpenAiChatModel;
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import dev.langchain4j.service.AiServices;
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import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
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import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
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import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
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interface DocumentAssistant {
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String answer(String question);
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}
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public class SimpleRagExample {
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public static void main(String[] args) {
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// Setup
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var embeddingStore = new InMemoryEmbeddingStore<TextSegment>();
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var embeddingModel = OpenAiEmbeddingModel.builder()
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.apiKey(System.getenv("OPENAI_API_KEY"))
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.modelName("text-embedding-3-small")
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.build();
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var chatModel = OpenAiChatModel.builder()
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.apiKey(System.getenv("OPENAI_API_KEY"))
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.modelName("gpt-4o-mini")
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.build();
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// Ingest documents
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var ingestor = EmbeddingStoreIngestor.builder()
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.embeddingModel(embeddingModel)
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.embeddingStore(embeddingStore)
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.build();
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ingestor.ingest(Document.from("Spring Boot is a framework for building Java applications with minimal configuration."));
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ingestor.ingest(Document.from("Spring Data JPA provides data access abstraction using repositories."));
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ingestor.ingest(Document.from("Spring Cloud enables building distributed systems and microservices."));
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// Create retriever and AI service
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var contentRetriever = EmbeddingStoreContentRetriever.builder()
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.embeddingStore(embeddingStore)
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.embeddingModel(embeddingModel)
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.maxResults(3)
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.minScore(0.7)
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.build();
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var assistant = AiServices.builder(DocumentAssistant.class)
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.chatModel(chatModel)
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.contentRetriever(contentRetriever)
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.build();
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// Query with RAG
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System.out.println(assistant.answer("What is Spring Boot?"));
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System.out.println(assistant.answer("What does Spring Data JPA do?"));
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}
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}
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```
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## 2. Vector Database RAG (Pinecone)
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**Scenario**: Production RAG with persistent vector database.
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```java
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import dev.langchain4j.store.embedding.pinecone.PineconeEmbeddingStore;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.data.document.Document;
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import dev.langchain4j.data.document.Metadata;
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public class PineconeRagExample {
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public static void main(String[] args) {
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// Production vector store
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var embeddingStore = PineconeEmbeddingStore.builder()
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.apiKey(System.getenv("PINECONE_API_KEY"))
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.index("docs-index")
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.namespace("production")
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.build();
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var embeddingModel = OpenAiEmbeddingModel.builder()
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.apiKey(System.getenv("OPENAI_API_KEY"))
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.build();
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// Ingest with metadata
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var ingestor = EmbeddingStoreIngestor.builder()
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.documentTransformer(doc -> {
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doc.metadata().put("source", "documentation");
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doc.metadata().put("date", LocalDate.now().toString());
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return doc;
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})
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.documentSplitter(DocumentSplitters.recursive(1000, 200))
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.embeddingModel(embeddingModel)
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.embeddingStore(embeddingStore)
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.build();
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ingestor.ingest(Document.from("Your large document..."));
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// Retrieve with filters
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var retriever = EmbeddingStoreContentRetriever.builder()
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.embeddingStore(embeddingStore)
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.embeddingModel(embeddingModel)
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.maxResults(5)
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.dynamicFilter(query ->
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new IsEqualTo("source", "documentation")
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)
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.build();
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}
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}
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```
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## 3. Document Loading and Splitting
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**Scenario**: Load documents from various sources and split intelligently.
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```java
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import dev.langchain4j.data.document.Document;
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import dev.langchain4j.data.document.DocumentSplitter;
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import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
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import dev.langchain4j.data.document.splitter.DocumentSplitters;
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import dev.langchain4j.data.segment.TextSegment;
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import dev.langchain4j.model.openai.OpenAiTokenCountEstimator;
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import java.nio.file.Path;
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import java.nio.file.Paths;
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import java.util.List;
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public class DocumentProcessingExample {
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public static void main(String[] args) {
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// Load from filesystem
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Path docPath = Paths.get("documents");
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List<Document> documents = FileSystemDocumentLoader.load(docPath);
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// Smart recursive splitting with token counting
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DocumentSplitter splitter = DocumentSplitters.recursive(
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500, // Max tokens per segment
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50, // Overlap tokens
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new OpenAiTokenCountEstimator("gpt-4o-mini")
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);
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// Process documents
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for (Document doc : documents) {
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List<TextSegment> segments = splitter.split(doc);
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System.out.println("Document split into " + segments.size() + " segments");
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segments.forEach(segment -> {
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System.out.println("Text: " + segment.text());
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System.out.println("Metadata: " + segment.metadata());
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});
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}
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// Alternative: Character-based splitting
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DocumentSplitter charSplitter = DocumentSplitters.recursive(
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1000, // Max characters
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100 // Overlap characters
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);
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// Alternative: Paragraph-based splitting
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DocumentSplitter paraSplitter = DocumentSplitters.byParagraph(500, 50);
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}
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}
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```
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## 4. Metadata Filtering in RAG
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**Scenario**: Search with complex metadata filters for multi-tenant RAG.
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```java
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import dev.langchain4j.store.embedding.filter.comparison.*;
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import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
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public class MetadataFilteringExample {
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public static void main(String[] args) {
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var retriever = EmbeddingStoreContentRetriever.builder()
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.embeddingStore(embeddingStore)
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.embeddingModel(embeddingModel)
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// Single filter: user isolation
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.filter(new IsEqualTo("userId", "user123"))
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// Complex AND filter
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.filter(new And(
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new IsEqualTo("department", "engineering"),
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new IsEqualTo("status", "active")
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))
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// OR filter: multiple categories
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.filter(new Or(
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new IsEqualTo("category", "tutorial"),
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new IsEqualTo("category", "guide")
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))
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// NOT filter: exclude deprecated
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.filter(new Not(
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new IsEqualTo("deprecated", "true")
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))
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// Numeric filters
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.filter(new IsGreaterThan("relevance", 0.8))
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.filter(new IsLessThanOrEqualTo("createdDaysAgo", 30))
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// Multiple conditions
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.dynamicFilter(query -> {
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String userId = extractUserFromQuery(query);
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return new And(
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new IsEqualTo("userId", userId),
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new IsGreaterThan("score", 0.7)
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);
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})
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.build();
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}
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private static String extractUserFromQuery(Object query) {
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// Extract user context
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return "user123";
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}
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}
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```
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## 5. Document Transformation Pipeline
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**Scenario**: Transform documents with custom metadata before ingestion.
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```java
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import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
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import dev.langchain4j.data.document.Metadata;
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import dev.langchain4j.data.segment.TextSegment;
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import java.time.LocalDate;
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public class DocumentTransformationExample {
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public static void main(String[] args) {
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var ingestor = EmbeddingStoreIngestor.builder()
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// Add metadata to each document
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.documentTransformer(doc -> {
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doc.metadata().put("ingested_date", LocalDate.now().toString());
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doc.metadata().put("source_system", "internal");
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doc.metadata().put("version", "1.0");
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return doc;
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})
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// Split documents intelligently
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.documentSplitter(DocumentSplitters.recursive(500, 50))
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// Transform each segment (e.g., add filename)
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.textSegmentTransformer(segment -> {
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String fileName = segment.metadata().getString("file_name", "unknown");
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String enrichedText = "File: " + fileName + "\n" + segment.text();
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return TextSegment.from(enrichedText, segment.metadata());
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})
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.embeddingModel(embeddingModel)
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.embeddingStore(embeddingStore)
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.build();
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// Ingest with tracking
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IngestionResult result = ingestor.ingest(document);
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System.out.println("Tokens ingested: " + result.tokenUsage().totalTokenCount());
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}
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}
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```
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## 6. Hybrid Search (Vector + Full-Text)
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**Scenario**: Combine semantic search with keyword search for better recall.
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```java
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import dev.langchain4j.store.embedding.neo4j.Neo4jEmbeddingStore;
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public class HybridSearchExample {
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public static void main(String[] args) {
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// Configure Neo4j for hybrid search
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var embeddingStore = Neo4jEmbeddingStore.builder()
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.withBasicAuth("bolt://localhost:7687", "neo4j", "password")
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.dimension(1536)
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// Enable full-text search
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.fullTextIndexName("documents_fulltext")
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.autoCreateFullText(true)
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// Query for full-text context
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.fullTextQuery("Spring OR Boot")
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.build();
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var retriever = EmbeddingStoreContentRetriever.builder()
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.embeddingStore(embeddingStore)
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.embeddingModel(embeddingModel)
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.maxResults(5)
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.build();
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// Search combines both vector similarity and full-text keywords
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}
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}
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```
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## 7. Advanced RAG with Query Transformation
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**Scenario**: Transform user queries before retrieval for better results.
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```java
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import dev.langchain4j.rag.DefaultRetrievalAugmentor;
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import dev.langchain4j.rag.query.transformer.CompressingQueryTransformer;
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import dev.langchain4j.rag.content.aggregator.ReRankingContentAggregator;
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import dev.langchain4j.model.cohere.CohereScoringModel;
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public class AdvancedRagExample {
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public static void main(String[] args) {
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// Scoring model for re-ranking
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var scoringModel = CohereScoringModel.builder()
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.apiKey(System.getenv("COHERE_API_KEY"))
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.build();
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// Advanced retrieval augmentor
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var augmentor = DefaultRetrievalAugmentor.builder()
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// Transform query for better context
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.queryTransformer(new CompressingQueryTransformer(chatModel))
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// Retrieve relevant content
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.contentRetriever(EmbeddingStoreContentRetriever.builder()
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.embeddingStore(embeddingStore)
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.embeddingModel(embeddingModel)
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.maxResults(10)
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.minScore(0.6)
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.build())
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// Re-rank results by relevance
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.contentAggregator(ReRankingContentAggregator.builder()
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.scoringModel(scoringModel)
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.minScore(0.8)
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.build())
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.build();
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// Use with AI Service
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var assistant = AiServices.builder(QuestionAnswering.class)
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.chatModel(chatModel)
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.retrievalAugmentor(augmentor)
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.build();
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}
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}
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```
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## 8. Multi-User RAG with Isolation
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**Scenario**: Per-user vector stores for data isolation.
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```java
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import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
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import java.util.HashMap;
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import java.util.Map;
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public class MultiUserRagExample {
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private final Map<String, EmbeddingStore<TextSegment>> userStores = new HashMap<>();
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public void ingestForUser(String userId, Document document) {
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var store = userStores.computeIfAbsent(userId,
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k -> new InMemoryEmbeddingStore<>());
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var ingestor = EmbeddingStoreIngestor.builder()
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.embeddingModel(embeddingModel)
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.embeddingStore(store)
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.build();
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ingestor.ingest(document);
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}
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public String askQuestion(String userId, String question) {
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var store = userStores.get(userId);
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var retriever = EmbeddingStoreContentRetriever.builder()
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.embeddingStore(store)
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.embeddingModel(embeddingModel)
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.maxResults(3)
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.build();
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var assistant = AiServices.builder(QuestionAnswering.class)
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.chatModel(chatModel)
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.contentRetriever(retriever)
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.build();
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return assistant.answer(question);
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}
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}
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```
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## 9. Streaming RAG with Content Access
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**Scenario**: Stream RAG responses while accessing retrieved content.
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```java
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import dev.langchain4j.service.TokenStream;
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interface StreamingRagAssistant {
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TokenStream streamAnswer(String question);
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}
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public class StreamingRagExample {
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public static void main(String[] args) {
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var assistant = AiServices.builder(StreamingRagAssistant.class)
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.streamingChatModel(streamingModel)
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.contentRetriever(contentRetriever)
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.build();
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assistant.streamAnswer("What is Spring Boot?")
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.onRetrieved(contents -> {
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System.out.println("=== Retrieved Content ===");
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contents.forEach(content ->
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System.out.println("Score: " + content.score() +
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", Text: " + content.textSegment().text()));
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})
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.onNext(token -> System.out.print(token))
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.onCompleteResponse(response ->
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System.out.println("\n=== Complete ==="))
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.onError(error -> System.err.println("Error: " + error))
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.start();
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try {
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Thread.sleep(5000);
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} catch (InterruptedException e) {
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Thread.currentThread().interrupt();
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}
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}
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}
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```
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## 10. Batch Document Ingestion
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**Scenario**: Efficiently ingest large document collections.
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```java
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import dev.langchain4j.data.document.Document;
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import java.util.List;
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import java.util.ArrayList;
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public class BatchIngestionExample {
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public static void main(String[] args) {
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var ingestor = EmbeddingStoreIngestor.builder()
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.embeddingModel(embeddingModel)
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.embeddingStore(embeddingStore)
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.documentSplitter(DocumentSplitters.recursive(500, 50))
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.build();
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// Load batch of documents
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List<Document> documents = new ArrayList<>();
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for (int i = 1; i <= 100; i++) {
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documents.add(Document.from("Content " + i));
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}
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// Ingest all at once
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IngestionResult result = ingestor.ingest(documents);
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System.out.println("Documents ingested: " + documents.size());
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System.out.println("Total tokens: " + result.tokenUsage().totalTokenCount());
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// Track progress
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long tokensPerDoc = result.tokenUsage().totalTokenCount() / documents.size();
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System.out.println("Average tokens per document: " + tokensPerDoc);
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}
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}
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```
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## Performance Considerations
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1. **Batch Processing**: Ingest documents in batches to optimize embedding API calls
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2. **Document Splitting**: Use recursive splitting for better semantic chunks
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3. **Metadata**: Add minimal metadata to reduce embedding overhead
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4. **Vector DB**: Choose appropriate vector DB based on scale (in-memory for dev, Pinecone/Weaviate for prod)
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5. **Similarity Threshold**: Adjust minScore based on use case (0.7-0.85 typical)
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6. **Max Results**: Return top 3-5 results unless specific needs require more
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7. **Caching**: Cache frequently retrieved content to reduce API calls
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8. **Async Ingestion**: Use async ingestion for large datasets
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9. **Monitoring**: Track token usage and retrieval quality metrics
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10. **Testing**: Use in-memory store for unit tests, external DB for integration tests
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