Can I test LangChain4j applications without calling real LLM APIs?

Yes, this skill emphasizes using mock models for unit tests to ensure fast, cost-effective, and isolated validation of your business logic without external dependencies.

How does this skill help with RAG testing?

It provides implementation patterns for validating the entire RAG pipeline, including document embedding, vector store retrieval, and the final response coherence.

Which Java testing libraries are compatible with this skill?

The strategies are designed to work seamlessly with LangChain4j-test, Mockito, AssertJ, and JUnit 5 for a modern, fluent testing experience.

Does it support Testcontainers?

Absolutely. It includes specific strategies for using Testcontainers with services like Ollama to run integration tests in consistent, containerized environments.

LangChain4j Testing Strategies

Name: LangChain4j Testing Strategies
Author: pur3v4d3r

bypur3v4d3r

•

Security & Testing

Implements comprehensive testing frameworks for LangChain4j applications, covering unit mocks, RAG validation, and containerized integration tests.

About

This skill provides a specialized methodology for testing Java-based AI applications built with LangChain4j, ensuring reliability and performance in AI-driven features. It offers detailed guidance on mocking LLM responses for fast unit tests, setting up Testcontainers for isolated integration environments, and validating complex Retrieval-Augmented Generation (RAG) workflows. By applying a structured testing pyramid, it helps developers verify tool execution, memory management, and streaming responses while maintaining high code quality and reducing reliance on expensive external API calls during development.

Key Features

Mock LLM response generation for fast, isolated unit testing
Validation strategies for Retrieval-Augmented Generation (RAG) systems
Automated guardrail and output sanitization verification
Integration testing patterns using Testcontainers and Ollama
Streaming and asynchronous operation testing patterns
1 GitHub stars

Use Cases

Testing AI-driven business logic without incurring LLM API costs
Setting up CI/CD pipelines for AI services with automated integration tests
Validating document retrieval accuracy and context relevance in RAG pipelines

About

Key Features

Mock LLM response generation for fast, isolated unit testing
Validation strategies for Retrieval-Augmented Generation (RAG) systems
Automated guardrail and output sanitization verification
Integration testing patterns using Testcontainers and Ollama
Streaming and asynchronous operation testing patterns
1 GitHub stars

Use Cases

Testing AI-driven business logic without incurring LLM API costs
Setting up CI/CD pipelines for AI services with automated integration tests
Validating document retrieval accuracy and context relevance in RAG pipelines