Complete Retrieval-Augmented Generation systems with LangChain4j for knowledge-enhanced AI applications.
Works with
Document ingestion pipelines with configurable chunking, metadata management, and embedding generation using OpenAI or custom embedding models
Vector search and content retrieval with filtering, re-ranking, and configurable similarity thresholds for precise context matching
RAG-enabled AI services that automatically inject retrieved context into chat models, with support for multi
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlangchain4j-rag-implementation-patternsExecute the skills CLI command in your project's root directory to begin installation:
Fetches langchain4j-rag-implementation-patterns from giuseppe-trisciuoglio/developer-kit and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate langchain4j-rag-implementation-patterns. Access via /langchain4j-rag-implementation-patterns in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Implements RAG systems with LangChain4j: document ingestion pipelines, embedding stores, and vector search for chat-with-documents and knowledge-enhanced AI applications.
Create a new Spring Boot project with required dependencies:
pom.xml:
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-spring-boot-starter</artifactId>
<version>1.8.0</version>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai</artifactId>
<version>1.8.0</version>
</dependency>
Configure document loading and processing with validation:
Validation Checkpoint: After ingestion, verify embedding count matches segment count and test retrieval with a sample query.
@Configuration
public class RAGConfiguration {
@Bean
public EmbeddingModel embeddingModel() {
return OpenAiEmbeddingModel.builder()
.apiKey(System.getenv("OPENAI_API_KEY"))
.modelName("text-embedding-3-small")
.build();
}
@Bean
public EmbeddingStore<TextSegment> embeddingStore() {
return new InMemoryEmbeddingStore<>();
}
}
Create document ingestion service:
@Service
@RequiredArgsConstructor
public class DocumentIngestionService {
private final EmbeddingModel embeddingModel;
private final EmbeddingStore<TextSegment> embeddingStore;
public void ingestDocument(String filePath, Map<String, Object> metadata) {
Document document = FileSystemDocumentLoader.loadDocument(filePath);
document.metadata().putAll(metadata);
DocumentSplitter splitter = DocumentSplitters.recursive(
500, 50, new OpenAiTokenCountEstimator("text-embedding-3-small")
);
List<TextSegment> segments = splitter.split(document);
List<Embedding> embeddings = embeddingModel.embedAll(segments).content();
embeddingStore.addAll(embeddings, segments);
// Validation: verify embedding count matches segments
if (embeddings.size() != segments.size()) {
throw new IllegalStateException("Embedding count mismatch: expected " + segments.size() + ", got " + embeddings.size());
}
}
public boolean validateIngestion(String testQuery) {
// Validation: test retrieval with sample query
Embedding queryEmbedding = embeddingModel.embed(testQuery).content();
List<EmbeddingMatch<TextSegment>> results = embeddingStore.search(
EmbeddingSearchRequest.builder()
.queryEmbedding(queryEmbedding)
.maxResults(1)
.build()
).matches();
return !results.isEmpty();
}
}
Setup content retrieval with filtering:
Validation Checkpoint: After configuration, test retrieval with a known query to verify embeddings are searchable.
@Configuration
public class ContentRetrieverConfiguration {
@Bean
public ContentRetriever contentRetriever(
EmbeddingStore<TextSegment> embeddingStore,
EmbeddingModel embeddingModel) {
return EmbeddingStoreContentRetriever.builder()
.embeddingStore(embeddingStore)
.embeddingModel(embeddingModel)
.maxResults(5)
.minScore(0.7)
.build();
}
}
Define AI service with context retrieval:
interface KnowledgeAssistant {
@SystemMessage("""
You are a knowledgeable assistant with access to a comprehensive knowledge base.
When answering questions:
1. Use the provided context from the knowledge base
2. If information is not in the context, clearly state this
3. Provide accurate, helpful responses
4. When possible, reference specific sources
5. If the context is insufficient, ask for clarification
""")
String answerQuestion(String question);
}
@Service
@RequiredArgsConstructor
public class KnowledgeService {
private final KnowledgeAssistant assistant;
public KnowledgeService(ChatModel chatModel,Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
giuseppe-trisciuoglio/developer-kit
giuseppe-trisciuoglio/developer-kit
giuseppe-trisciuoglio/developer-kit
giuseppe-trisciuoglio/developer-kit
wshobson/agents
davila7/claude-code-templates
We added langchain4j-rag-implementation-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
langchain4j-rag-implementation-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.
langchain4j-rag-implementation-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
langchain4j-rag-implementation-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
langchain4j-rag-implementation-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
langchain4j-rag-implementation-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
langchain4j-rag-implementation-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
langchain4j-rag-implementation-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: langchain4j-rag-implementation-patterns is focused, and the summary matches what you get after install.
langchain4j-rag-implementation-patterns is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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