聊聊Spring AI的Evaluator

序

本文主要研究一下Spring AI的Evaluator

Evaluator

spring-ai-client-chat/src/main/java/org/springframework/ai/evaluation/Evaluator.java

@FunctionalInterface
public interface Evaluator {

	EvaluationResponse evaluate(EvaluationRequest evaluationRequest);

	default String doGetSupportingData(EvaluationRequest evaluationRequest) {
		List<Document> data = evaluationRequest.getDataList();
		return data.stream()
			.map(Document::getText)
			.filter(StringUtils::hasText)
			.collect(Collectors.joining(System.lineSeparator()));
	}

}

Evaluator接口定义了evaluate方法，用于对ai生成的内容进行评估，避免AI没有产生幻觉式的响应，它有两个实现，分别是RelevancyEvaluator、FactCheckingEvaluator

EvaluationRequest

org/springframework/ai/evaluation/EvaluationRequest.java

public class EvaluationRequest {

	private final String userText;

	private final List<Document> dataList;

	private final String responseContent;

	public EvaluationRequest(String userText, String responseContent) {
		this(userText, Collections.emptyList(), responseContent);
	}

	public EvaluationRequest(List<Document> dataList, String responseContent) {
		this("", dataList, responseContent);
	}

	public EvaluationRequest(String userText, List<Document> dataList, String responseContent) {
		this.userText = userText;
		this.dataList = dataList;
		this.responseContent = responseContent;
	}

	//......
}	

EvaluationRequest定义了userText、dataList、responseContent属性，其中userText是用户的输入，dataList是上下文数据，比如RAG追加的内容，responseContent是AI模型的响应

EvaluationResponse

org/springframework/ai/evaluation/EvaluationResponse.java

public class EvaluationResponse {

	private final boolean pass;

	private final float score;

	private final String feedback;

	private final Map<String, Object> metadata;

	@Deprecated
	public EvaluationResponse(boolean pass, float score, String feedback, Map<String, Object> metadata) {
		this.pass = pass;
		this.score = score;
		this.feedback = feedback;
		this.metadata = metadata;
	}

	public EvaluationResponse(boolean pass, String feedback, Map<String, Object> metadata) {
		this.pass = pass;
		this.score = 0;
		this.feedback = feedback;
		this.metadata = metadata;
	}

	//......
}	

EvaluationResponse定义了pass、score、feedback、metadata属性

RelevancyEvaluator

org/springframework/ai/evaluation/RelevancyEvaluator.java

public class RelevancyEvaluator implements Evaluator {

	private static final String DEFAULT_EVALUATION_PROMPT_TEXT = """
				Your task is to evaluate if the response for the query
				is in line with the context information provided.\\n
				You have two options to answer. Either YES/ NO.\\n
				Answer - YES, if the response for the query
				is in line with context information otherwise NO.\\n
				Query: \\n {query}\\n
				Response: \\n {response}\\n
				Context: \\n {context}\\n
				Answer: "
			""";

	private final ChatClient.Builder chatClientBuilder;

	public RelevancyEvaluator(ChatClient.Builder chatClientBuilder) {
		this.chatClientBuilder = chatClientBuilder;
	}

	@Override
	public EvaluationResponse evaluate(EvaluationRequest evaluationRequest) {

		var response = evaluationRequest.getResponseContent();
		var context = doGetSupportingData(evaluationRequest);

		String evaluationResponse = this.chatClientBuilder.build()
			.prompt()
			.user(userSpec -> userSpec.text(DEFAULT_EVALUATION_PROMPT_TEXT)
				.param("query", evaluationRequest.getUserText())
				.param("response", response)
				.param("context", context))
			.call()
			.content();

		boolean passing = false;
		float score = 0;
		if (evaluationResponse.toLowerCase().contains("yes")) {
			passing = true;
			score = 1;
		}

		return new EvaluationResponse(passing, score, "", Collections.emptyMap());
	}

}

RelevancyEvaluator让AI去评估响应是否与上下文信息一致，给出yes或者no的结果，如果是yes则passing为true，score为1，否则默认passing为false，score为0

示例

@Test
void testEvaluation() {

    dataController.delete();
    dataController.load();

    String userText = "What is the purpose of Carina?";

    ChatResponse response = ChatClient.builder(chatModel)
            .build().prompt()
            .advisors(new QuestionAnswerAdvisor(vectorStore))
            .user(userText)
            .call()
            .chatResponse();
    String responseContent = response.getResult().getOutput().getContent();

    var relevancyEvaluator = new RelevancyEvaluator(ChatClient.builder(chatModel));

    EvaluationRequest evaluationRequest = new EvaluationRequest(userText,
            (List<Content>) response.getMetadata().get(QuestionAnswerAdvisor.RETRIEVED_DOCUMENTS), responseContent);

    EvaluationResponse evaluationResponse = relevancyEvaluator.evaluate(evaluationRequest);

    assertTrue(evaluationResponse.isPass(), "Response is not relevant to the question");

}

这里先用userText去问下AI，然后将responseContent、QuestionAnswerAdvisor.RETRIEVED_DOCUMENTS一起丢给relevancyEvaluator，再用AI去评估一下

FactCheckingEvaluator

org/springframework/ai/evaluation/FactCheckingEvaluator.java

public class FactCheckingEvaluator implements Evaluator {

	private static final String DEFAULT_EVALUATION_PROMPT_TEXT = """
				Evaluate whether or not the following claim is supported by the provided document.
				Respond with "yes" if the claim is supported, or "no" if it is not.
				Document: \\n {document}\\n
				Claim: \\n {claim}
			""";

	private static final String BESPOKE_EVALUATION_PROMPT_TEXT = """
				Document: \\n {document}\\n
				Claim: \\n {claim}
			""";

	private final ChatClient.Builder chatClientBuilder;

	private final String evaluationPrompt;

	/**
	 * Constructs a new FactCheckingEvaluator with the provided ChatClient.Builder. Uses
	 * the default evaluation prompt suitable for general purpose LLMs.
	 * @param chatClientBuilder The builder for the ChatClient used to perform the
	 * evaluation
	 */
	public FactCheckingEvaluator(ChatClient.Builder chatClientBuilder) {
		this(chatClientBuilder, DEFAULT_EVALUATION_PROMPT_TEXT);
	}

	/**
	 * Constructs a new FactCheckingEvaluator with the provided ChatClient.Builder and
	 * evaluation prompt.
	 * @param chatClientBuilder The builder for the ChatClient used to perform the
	 * evaluation
	 * @param evaluationPrompt The prompt text to use for evaluation
	 */
	public FactCheckingEvaluator(ChatClient.Builder chatClientBuilder, String evaluationPrompt) {
		this.chatClientBuilder = chatClientBuilder;
		this.evaluationPrompt = evaluationPrompt;
	}

	/**
	 * Creates a FactCheckingEvaluator configured for use with the Bespoke Minicheck
	 * model.
	 * @param chatClientBuilder The builder for the ChatClient used to perform the
	 * evaluation
	 * @return A FactCheckingEvaluator configured for Bespoke Minicheck
	 */
	public static FactCheckingEvaluator forBespokeMinicheck(ChatClient.Builder chatClientBuilder) {
		return new FactCheckingEvaluator(chatClientBuilder, BESPOKE_EVALUATION_PROMPT_TEXT);
	}

	/**
	 * Evaluates whether the response content in the EvaluationRequest is factually
	 * supported by the context provided in the same request.
	 * @param evaluationRequest The request containing the response to be evaluated and
	 * the supporting context
	 * @return An EvaluationResponse indicating whether the claim is supported by the
	 * document
	 */
	@Override
	public EvaluationResponse evaluate(EvaluationRequest evaluationRequest) {
		var response = evaluationRequest.getResponseContent();
		var context = doGetSupportingData(evaluationRequest);

		String evaluationResponse = this.chatClientBuilder.build()
			.prompt()
			.user(userSpec -> userSpec.text(this.evaluationPrompt).param("document", context).param("claim", response))
			.call()
			.content();

		boolean passing = evaluationResponse.equalsIgnoreCase("yes");
		return new EvaluationResponse(passing, "", Collections.emptyMap());
	}

}

FactCheckingEvaluator旨在评估AI生成的响应在给定上下文中的事实准确性。该评估器通过验证给定的声明（claim）是否逻辑上支持提供的上下文（document），帮助检测和减少AI输出中的幻觉现象；在使用FactCheckingEvaluator时，claim和document会被提交给AI模型进行评估。为了更高效地完成这一任务，可以使用更小且更高效的AI模型，例如Bespoke的Minicheck。Minicheck 是一种专门设计用于事实核查的小型高效模型，它通过分析事实信息片段和生成的输出，验证声明是否与文档相符。如果文档能够证实声明的真实性，模型将回答"是"，否则回答"否"。这种模型特别适用于检索增强型生成（RAG）应用，确保生成的答案基于上下文信息。

示例

@Test
void testFactChecking() {
  // Set up the Ollama API
  OllamaApi ollamaApi = new OllamaApi("http://localhost:11434");

  ChatModel chatModel = new OllamaChatModel(ollamaApi,
				OllamaOptions.builder().model(BESPOKE_MINICHECK).numPredict(2).temperature(0.0d).build())


  // Create the FactCheckingEvaluator
  var factCheckingEvaluator = new FactCheckingEvaluator(ChatClient.builder(chatModel));

  // Example context and claim
  String context = "The Earth is the third planet from the Sun and the only astronomical object known to harbor life.";
  String claim = "The Earth is the fourth planet from the Sun.";

  // Create an EvaluationRequest
  EvaluationRequest evaluationRequest = new EvaluationRequest(context, Collections.emptyList(), claim);

  // Perform the evaluation
  EvaluationResponse evaluationResponse = factCheckingEvaluator.evaluate(evaluationRequest);

  assertFalse(evaluationResponse.isPass(), "The claim should not be supported by the context");

}

这里使用ollama调用bespoke-minicheck模型，其temperature设置为0.0，之后把context与claim都传递给factCheckingEvaluator去评估

小结

Spring AI提供了Evaluator接口定义了evaluate方法，用于对ai生成的内容进行评估，避免AI没有产生幻觉式的响应，它有两个实现，分别是RelevancyEvaluator、FactCheckingEvaluator。RelevancyEvaluator用于评估相关性，FactCheckingEvaluator用于评估事实准确性。

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