langchain4j-testing-strategies
giuseppe-trisciuoglio/developer-kit
Unit, integration, and mock testing patterns for LangChain4j Java AI services and RAG workflows.
What is langchain4j-testing-strategies?
Provides testing strategies for LangChain4j applications including mock LLM responses, Testcontainers-based integration tests, and RAG validation. Use when unit testing AI services, mocking models, or integration testing LangChain4j components with real services.
- Mock ChatModel and EmbeddingModel responses for fast, isolated unit tests
- Integration testing with Testcontainers for real LangChain4j services and RAG pipelines
- Test retrieval chains, embedding stores, and content retrievers
- Validate complete RAG workflows and AI service behavior
- Implement health checks and container readiness verification
- Support streaming, memory, and error handling test patterns
How to install langchain4j-testing-strategies
npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill langchain4j-testing-strategies- Maven or Gradle project with LangChain4j dependencies
- Docker daemon running for Testcontainers-based integration tests
- langchain4j-test, testcontainers, mockito, and assertj libraries configured
How to use langchain4j-testing-strategies
- 1.Add testing dependencies (langchain4j-test, testcontainers, mockito, assertj) to Maven/Gradle
- 2.Create unit tests using mock(ChatModel.class) and when/thenReturn for deterministic responses
- 3.Configure Testcontainers with @Testcontainers and @Container annotations for integration tests
- 4.Add health checks and await() conditions to verify container readiness before assertions
- 5.Implement RAG integration tests with real EmbeddingModel and InMemoryEmbeddingStore
- 6.Follow the testing pyramid: 70% unit tests with mocks, 20% integration tests, 10% end-to-end tests
Use cases
- Unit testing AiServices with mocked ChatModel responses for deterministic results
- Integration testing RAG systems with containerized Ollama or other LLMs
- Validating retrieval chains and embedding store functionality
- Testing tool execution and LLM-based Java applications without external API calls
- End-to-end workflow validation with complete AI service pipelines
- Java developers building LangChain4j applications
- QA engineers testing AI services and RAG systems
- Backend engineers validating LLM integrations
- Teams needing fast, isolated unit tests for AI components
langchain4j-testing-strategies FAQ
Use mocks for unit tests requiring fast, isolated execution without external dependencies. Use Testcontainers for integration tests validating real ChatModel, EmbeddingModel, or RAG pipeline behavior with actual services.
Mock the ChatModel with specific when/thenReturn conditions for unit tests. For integration tests, validate response properties (non-null, contains keywords) rather than exact matches, or use deterministic models like Ollama with fixed seeds.
Verify Docker daemon is running, increase @Timeout duration, add await() health checks before assertions, check container logs for startup errors, and ensure sufficient system resources for the container.
Yes, use InMemoryEmbeddingStore with mocked EmbeddingModel for unit tests. For realistic validation, use Testcontainers with Ollama or another containerized embedding service.
No. Always use mocks in unit tests to avoid costs, rate limiting, and flakiness. Reserve real API calls for integration tests with Testcontainers or separate end-to-end test suites.
Full instructions (SKILL.md)
Source of truth, from giuseppe-trisciuoglio/developer-kit.
name: langchain4j-testing-strategies description: Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java applications. allowed-tools: Read, Write, Edit, Bash, Glob, Grep
LangChain4J Testing Strategies
Overview
Patterns for unit testing with mocks, integration testing with Testcontainers, and end-to-end validation of RAG systems, AI Services, and tool execution.
When to Use
- Unit testing AI services: When you need fast, isolated tests for services using LangChain4j AiServices
- Integration testing LangChain4j components: When testing real ChatModel, EmbeddingModel, or RAG pipelines with Testcontainers
- Mocking AI models: When you need deterministic responses without calling external APIs
- Testing LLM-based Java applications: When validating RAG workflows, tool execution, or retrieval chains
Instructions
1. Unit Testing with Mocks
Use mock models for fast, isolated testing. See references/unit-testing.md.
ChatModel mockModel = mock(ChatModel.class);
when(mockModel.generate(any(String.class)))
.thenReturn(Response.from(AiMessage.from("Mocked response")));
var service = AiServices.builder(AiService.class)
.chatModel(mockModel)
.build();
2. Configure Testing Dependencies
Setup Maven/Gradle dependencies. See references/testing-dependencies.md.
langchain4j-test- Guardrail assertionstestcontainers- Containerized testingmockito- Mock external dependenciesassertj- Fluent assertions
3. Integration Testing with Testcontainers
Test with real services. See references/integration-testing.md.
@Testcontainers
class OllamaIntegrationTest {
@Container
static GenericContainer<?> ollama = new GenericContainer<>(
DockerImageName.parse("ollama/ollama:0.5.4")
).withExposedPorts(11434);
@Test
void shouldGenerateResponse() {
// Verify container is healthy
assertTrue(ollama.isRunning());
await().atMost(30, TimeUnit.SECONDS)
.until(() -> ollama.getLogs().contains("API server listening"));
ChatModel model = OllamaChatModel.builder()
.baseUrl(ollama.getEndpoint())
.build();
// Verify model responds before running tests
assertDoesNotThrow(() -> model.generate("ping"));
String response = model.generate("Test query");
assertNotNull(response);
}
}
4. Advanced Features
Streaming, memory, error handling patterns in references/advanced-testing.md.
5. Testing Workflow
Follow the testing pyramid from references/workflow-patterns.md:
- 70% Unit Tests: Fast, isolated with mocks
- 20% Integration Tests: Real services with health checks
- 10% End-to-End Tests: Complete workflows
70% Unit Tests ─ Mock ChatModel, guardrails, edge cases
20% Integration Tests ─ Testcontainers, vector stores, RAG
10% End-to-End Tests ─ Complete user journeys
Troubleshooting
- Container fails to start: Check Docker daemon is running, verify image exists, increase timeout
- Model not responding: Verify baseUrl is correct, check container logs, ensure model is loaded
- Test timeout: Increase
@Timeoutduration for slow models, check container resource limits - Flaky tests: Add retry logic or health checks before assertions
Examples
Unit Test
@Test
void shouldProcessQueryWithMock() {
ChatModel mockModel = mock(ChatModel.class);
when(mockModel.generate(any(String.class)))
.thenReturn(Response.from(AiMessage.from("Test response")));
var service = AiServices.builder(AiService.class)
.chatModel(mockModel)
.build();
String result = service.chat("What is Java?");
assertEquals("Test response", result);
}
Integration Test with Testcontainers
@Testcontainers
class RAGIntegrationTest {
@Container
static GenericContainer<?> ollama = new GenericContainer<>(
DockerImageName.parse("ollama/ollama:0.5.4")
);
@BeforeAll
static void waitForContainerReady() {
await().atMost(60, TimeUnit.SECONDS)
.until(() -> ollama.getLogs().contains("API server listening"));
}
@Test
void shouldCompleteRAGWorkflow() {
assertTrue(ollama.isRunning());
var chatModel = OllamaChatModel.builder()
.baseUrl(ollama.getEndpoint())
.build();
var embeddingModel = OllamaEmbeddingModel.builder()
.baseUrl(ollama.getEndpoint())
.build();
var store = new InMemoryEmbeddingStore<>();
var retriever = EmbeddingStoreContentRetriever.builder()
.chatModel(chatModel)
.embeddingStore(store)
.embeddingModel(embeddingModel)
.build();
var assistant = AiServices.builder(RagAssistant.class)
.chatLanguageModel(chatModel)
.contentRetriever(retriever)
.build();
String response = assistant.chat("What is Spring Boot?");
assertNotNull(response);
assertTrue(response.contains("Spring"));
}
}
Best Practices
- Use
@BeforeEach/@AfterEachfor test isolation - Never call real APIs in unit tests; use mocks
- Include
@Timeoutfor external service calls - Test both success and error handling scenarios
- Validate response coherence and edge cases
Common Patterns
Mock Strategy
ChatModel mockModel = mock(ChatModel.class);
when(mockModel.generate(anyString())).thenReturn(Response.from(AiMessage.from("Mocked")));
when(mockModel.generate(eq("Hello"))).thenReturn(Response.from(AiMessage.from("Hi")));
when(mockModel.generate(contains("Java"))).thenReturn(Response.from(AiMessage.from("Java")));
Assertion Helpers
assertThat(response).isNotNull().isNotEmpty();
assertThat(response).containsAll(expectedKeywords);
assertThat(response).doesNotContain("error");
Reference Documentation
- Testing Dependencies - Maven/Gradle configuration
- Unit Testing - Mock models, guardrails
- Integration Testing - Testcontainers, real services
- Advanced Testing - Streaming, memory, error handling
- Workflow Patterns - Test pyramid, best practices
Constraints and Warnings
- AI responses are non-deterministic; use mocks for reliable unit tests
- Avoid real API calls in tests to prevent costs and rate limiting
- Integration tests require Docker; use container health checks
- RAG tests need properly seeded embedding stores
- Mock-based tests cannot guarantee actual LLM behavior; supplement with integration tests
- Use test-specific configuration profiles; never affect production data
Related skills
More from giuseppe-trisciuoglio/developer-kit and the wider catalog.

langchain4j-tool-function-calling-patterns
Annotate Java methods as LLM-callable tools, register with AiServices, and handle execution errors in LangChain4j agents.

langchain4j-vector-stores-configuration
Configure LangChain4J vector stores for RAG applications with PostgreSQL, Pinecone, MongoDB, Milvus, and Neo4j.

learn
Provides autonomous project pattern learning by analyzing the codebase to discover development conventions, architectural patterns, and coding standards, then generates project rule files in .claude/rules/. Use when user asks to "learn from project", "extract project rules", "analyze codebase conventions", "discover project patterns", or wants to auto-generate Claude Code rules for the current project.

memory-md-management
Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.

nestjs
NestJS framework patterns with Drizzle ORM for building scalable REST/GraphQL APIs and microservices.

nestjs-best-practices
Provides comprehensive NestJS best practices including modular architecture, dependency injection scoping, exception filters, DTO validation with class-validator, and Drizzle ORM integration. Use when designing NestJS modules, implementing providers, creating exception filters, validating DTOs, or integrating Drizzle ORM within NestJS applications.