LangChain4J Skills Guide

Quick reference to 9 LangChain4J skills for building AI-powered applications. See individual skill files for complete details.

Skills Overview

Core Integration

langchain4j-spring-boot-integration

File: skills/langchain4j-spring-boot-integration/SKILL.md

Integrate LangChain4J with Spring Boot: beans, configuration, auto-wiring.

When to use:

• Setting up LangChain4J in Spring Boot
• Configuring AI service beans
• Model provider integration

AI Services

langchain4j-ai-services-patterns

File: skills/langchain4j-ai-services-patterns/SKILL.md

AI service design: service patterns, model configuration, error handling.

When to use:

• Designing AI service interfaces
• Model configuration and selection
• Prompt templating and execution

Pattern:

@Service
public class ChatService {
    private final ChatLanguageModel model;
    
    public String answer(String question) {
        return model.generate(question);
    }
}

Retrieval-Augmented Generation (RAG)

langchain4j-rag-implementation-patterns

File: skills/langchain4j-rag-implementation-patterns/SKILL.md

RAG systems: document processing, retriever chains, augmented generation.

When to use:

• Building RAG systems
• Knowledge base integration
• Document retrieval and generation

langchain4j-vector-stores-configuration

File: skills/langchain4j-vector-stores-configuration/SKILL.md

Vector stores: embeddings, similarity search, provider setup.

When to use:

• Configuring embeddings
• Vector database setup
• Similarity search implementation

qdrant

File: skills/qdrant/SKILL.md

Qdrant vector database: Java integration, Spring Boot setup.

When to use:

• Using Qdrant for vector storage
• Embedding storage and retrieval
• Hybrid search patterns

spring-ai-mcp-server-patterns

File: skills/spring-ai-mcp-server-patterns/SKILL.md

Spring AI MCP server: protocol implementation, tool exposure.

When to use:

• Creating MCP servers with Spring AI
• Exposing tools to AI agents
• Spring AI MCP protocol integration

Advanced Patterns

langchain4j-tool-function-calling-patterns

File: skills/langchain4j-tool-function-calling-patterns/SKILL.md

Tool definition: function calling, agent patterns, orchestration.

When to use:

• AI agents with tool access
• Function calling and execution
• Multi-step reasoning

langchain4j-mcp-server-patterns

File: skills/langchain4j-mcp-server-patterns/SKILL.md

MCP servers: tool exposure, data access, integration.

When to use:

• Creating MCP servers
• Exposing tools to AI agents
• Model Context Protocol integration

Testing

langchain4j-testing-strategies

File: skills/langchain4j-testing-strategies/SKILL.md

Testing AI systems: mock models, test containers, integration tests.

When to use:

• Unit testing AI services
• Integration testing RAG
• Mock model behavior

Common Workflows

Building a RAG System

1. langchain4j-spring-boot-integration    → Spring Boot setup
2. langchain4j-vector-stores-configuration → Embeddings + storage
3. langchain4j-rag-implementation-patterns → RAG retriever + generator
4. langchain4j-testing-strategies         → Test RAG pipelines

Creating AI Service

1. langchain4j-spring-boot-integration    → Spring setup
2. langchain4j-ai-services-patterns       → Service interface
3. langchain4j-testing-strategies         → Unit tests

Building AI Agent with Tools

1. langchain4j-spring-boot-integration    → Spring setup
2. langchain4j-ai-services-patterns       → Agent service
3. langchain4j-tool-function-calling-patterns → Tool definition
4. langchain4j-mcp-server-patterns        → MCP exposure
5. langchain4j-testing-strategies         → Agent tests

Full RAG + Agent System

1. langchain4j-spring-boot-integration    → Setup
2. langchain4j-vector-stores-configuration → Vector DB
3. langchain4j-rag-implementation-patterns → RAG system
4. langchain4j-tool-function-calling-patterns → Tool calling
5. langchain4j-mcp-server-patterns        → MCP servers
6. langchain4j-testing-strategies         → Integration tests

Technology Stack

• LangChain4J: Latest stable
• Spring Boot: 3.x/4.x
• Embeddings: OpenAI, Hugging Face
• Vector Stores: Qdrant, Milvus, Weaviate
• LLMs: OpenAI, Anthropic Claude, Ollama
• Testing: Mock models, testcontainers

Key Concepts

AI Services

• Language models and providers
• Prompt templating
• Response parsing
• Error handling

RAG (Retrieval-Augmented Generation)

• Document indexing
• Similarity search
• Retrieved context augmentation
• Multi-step retrieval

Function Calling

• Tool definition and description
• Function execution
• Result integration
• Reasoning loops

MCP (Model Context Protocol)

• Server implementation
• Tool exposure
• Resource management
• Client communication

Note: For complete patterns and examples, see individual skill files in skills/