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Spring AI Testing: AI Evaluators Example
- Spring AI provides tools for evaluating Large Language Models (LLMs) like Ollama and TestContainers, ensuring accuracy and reliability in AI applications.
- Ollama is a platform for running LLMs locally, offering speed, offline capabilities, and simplified model deployment without cloud dependencies.
- TestContainers is an open-source library for creating disposable instances of services like databases in Docker containers, enhancing test reliability and setup.
- Combining Ollama with TestContainers enables developers to create controlled environments for evaluating AI models efficiently.
- The code example demonstrates setting up a Spring Boot application integrating Ollama in a TestContainer and evaluating LLM responses using Spring AI evaluators.
- Spring dependencies like spring-boot-starter-web and testcontainers are utilized to create and run Ollama instances seamlessly.
- The LlmService communicates with the AI model through OpenAiClient, while evaluators like RelevanceEvaluator and FactCheckingEvaluator assess response quality.
- The LlmController generates LLM responses, evaluates their relevance and accuracy, and returns structured JSON results, enhancing AI response quality.
- Proper application setup is crucial, including defining properties like server port and AI model configurations in application.properties.
- Unit testing using LlmEvaluatorTest class helps validate the functionality of the LlmController, ensuring AI-generated responses meet expected criteria.
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