langchain4j-testing-strategies
Comprehensive testing strategies for LangChain4j applications with mocks, containers, and RAG validation.
- Provides unit testing patterns with mock models, integration testing via Testcontainers, and end-to-end workflows for RAG systems, AI Services, and tool execution
- Covers testing pyramid approach: 70% unit tests with mocks, 20% integration tests with real services, 10% end-to-end tests
- Includes specialized patterns for streaming responses, memory management, guardrail assertions, and error handling scenarios
- Requires Docker for Testcontainers-based integration tests; emphasizes test isolation, performance targets (unit tests under 50ms), and avoiding real API calls in unit tests
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.
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