Fix grammar and minor typos in documentation as well as Javadoc.

Closes #1652

spring-ai-core/src/main/java/org/springframework/ai/chat/messages/MessageType.java

@@ -17,36 +17,36 @@
 package org.springframework.ai.chat.messages;
 
 /**
- * The MessageType enum represents the type of message in a chat application. It can be
- * one of the following: USER, ASSISTANT, SYSTEM, FUNCTION.
+ * Enumeration representing types of {@link Message Messages} in a chat application. It
+ * can be one of the following: USER, ASSISTANT, SYSTEM, FUNCTION.
  */
 public enum MessageType {
 
 	/**
-	 * A message of the type 'user' passed as input Messages with the user role are from
-	 * the end-user or developer.
+	 * A {@link Message} of type {@literal user}, having the user role and originating
+	 * from an end-user or developer.
 	 * @see UserMessage
 	 */
 	USER("user"),
 
 	/**
-	 * A message of the type 'assistant' passed as input Messages with the message is
-	 * generated as a response to the user.
+	 * A {@link Message} of type {@literal assistant} passed in subsequent input
+	 * {@link Message Messages} as the {@link Message} generated in response to the user.
 	 * @see AssistantMessage
 	 */
 	ASSISTANT("assistant"),
 
 	/**
-	 * A message of the type 'system' passed as input Messages with high level
-	 * instructions for the conversation, such as behave like a certain character or
-	 * provide answers in a specific format.
+	 * A {@link Message} of type {@literal system} passed as input {@link Message
+	 * Messages} containing high-level instructions for the conversation, such as behave
+	 * like a certain character or provide answers in a specific format.
 	 * @see SystemMessage
 	 */
 	SYSTEM("system"),
 
 	/**
-	 * A message of the type 'function' passed as input Messages with a function content
-	 * in a chat application.
+	 * A {@link Message} of type {@literal function} passed as input {@link Message
+	 * Messages} with function content in a chat application.
 	 * @see ToolResponseMessage
 	 */
 	TOOL("tool");

spring-ai-core/src/main/java/org/springframework/ai/chat/prompt/ChatOptions.java

@@ -22,7 +22,8 @@ import org.springframework.ai.model.ModelOptions;
 import org.springframework.lang.Nullable;
 
 /**
- * The ChatOptions represent the common options, portable across different chat models.
+ * {@link ModelOptions} representing the common options that are portable across different
+ * chat models.
  */
 public interface ChatOptions extends ModelOptions {
 

spring-ai-core/src/main/java/org/springframework/ai/evaluation/FactCheckingEvaluator.java

@@ -21,21 +21,21 @@ import java.util.Collections;
 import org.springframework.ai.chat.client.ChatClient;
 
 /**
- * The FactCheckingEvaluator class implements a method for evaluating the factual accuracy
- * of Large Language Model (LLM) responses against provided context.
- *
+ * Implementation of {@link Evaluator} used to evaluate the factual accuracy of Large
+ * Language Model (LLM) responses against provided context.
+ * <p/>
  * This evaluator addresses a specific type of potential error in LLM outputs known as
  * "hallucination" in the context of grounded factuality. It verifies whether a given
  * statement (the "claim") is logically supported by a provided context (the "document").
- *
+ * <p/>
  * Key concepts: - Document: The context or grounding information against which the claim
  * is checked. - Claim: The statement to be verified against the document.
- *
+ * <p/>
  * The evaluator uses a prompt-based approach with a separate, typically smaller and more
  * efficient LLM to perform the fact-checking. This design choice allows for
  * cost-effective and rapid verification, which is crucial when evaluating longer LLM
  * outputs that may require multiple verification steps.
- *
+ * <p/>
  * Implementation note: For efficient and accurate fact-checking, consider using
  * specialized models like Bespoke-Minicheck, a grounded factuality checking model
  * developed by Bespoke Labs and available in Ollama. Such models are specifically
@@ -45,12 +45,12 @@ import org.springframework.ai.chat.client.ChatClient;
  * Hallucinations with Bespoke-Minicheck</a> and the research paper:
  * <a href="https://arxiv.org/pdf/2404.10774v1">MiniCheck: An Efficient Method for LLM
  * Hallucination Detection</a>
- *
+ * <p/>
  * Note: This evaluator is specifically designed to fact-check statements against given
  * information. It's not meant for other types of accuracy tests, like quizzing an AI on
  * obscure facts without giving it any reference material to work with (so-called 'closed
  * book' scenarios).
- *
+ * <p/>
  * The evaluation process aims to determine if the claim is supported by the document,
  * returning a boolean result indicating whether the fact-check passed or failed.
  *
@@ -79,7 +79,6 @@ public class FactCheckingEvaluator implements Evaluator {
 		this.chatClientBuilder = chatClientBuilder;
 	}
 
-	@Override
 	/**
 	 * Evaluates whether the response content in the EvaluationRequest is factually
 	 * supported by the context provided in the same request.
@@ -88,6 +87,7 @@ public class FactCheckingEvaluator implements Evaluator {
 	 * @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);

spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallingOptions.java

@@ -53,8 +53,8 @@ public interface FunctionCallingOptions extends ChatOptions {
 	void setFunctionCallbacks(List<FunctionCallback> functionCallbacks);
 
 	/**
-	 * @return List of function names from the ChatModel registry to be used in the next
-	 * chat completion requests.
+	 * @return <@link Set> of function names from the ChatModel registry to be used in the
+	 * next chat completion requests.
 	 */
 	Set<String> getFunctions();
 

spring-ai-core/src/main/java/org/springframework/ai/observation/conventions/AiObservationMetricNames.java

@@ -17,8 +17,9 @@
 package org.springframework.ai.observation.conventions;
 
 /**
- * Collection of metric names used in AI observations. Based on the OpenTelemetry Semantic
- * Conventions for AI Systems.
+ * Enumeration of metric names used in AI observations.
+ * <p/>
+ * Based on OpenTelemetry's Semantic Conventions for AI systems.
  *
  * @author Thomas Vitale
  * @since 1.0.0
@@ -28,10 +29,7 @@ package org.springframework.ai.observation.conventions;
  */
 public enum AiObservationMetricNames {
 
-// @formatter:off
-
-	OPERATION_DURATION("gen_ai.client.operation.duration"),
-	TOKEN_USAGE("gen_ai.client.token.usage");
+	OPERATION_DURATION("gen_ai.client.operation.duration"), TOKEN_USAGE("gen_ai.client.token.usage");
 
 	private final String value;
 
@@ -43,6 +41,4 @@ public enum AiObservationMetricNames {
 		return this.value;
 	}
 
-// @formatter:on
-
 }

spring-ai-core/src/main/java/org/springframework/ai/vectorstore/observation/VectorStoreObservationContext.java

@@ -152,11 +152,11 @@ public class VectorStoreObservationContext extends Observation.Context {
 	public enum Operation {
 
 		/**
-		 * VectorStore delete operation.
+		 * VectorStore add operation.
 		 */
 		ADD("add"),
 		/**
-		 * VectorStore add operation.
+		 * VectorStore delete operation.
 		 */
 		DELETE("delete"),
 		/**

spring-ai-docs/src/main/antora/modules/ROOT/pages/getting-started.adoc

@@ -3,10 +3,11 @@
 
 This section offers jumping off points for how to get started using Spring AI.
 
-You should follow the steps in each of the following section according to your needs.
+You should follow the steps in each of the following sections according to your needs.
 
 NOTE: Spring AI supports Spring Boot 3.2.x and 3.3.x
 
+[[spring-initializr]]
 == Spring Initializr
 
 Head on over to https://start.spring.io/[start.spring.io] and select the AI Models and Vector Stores that you want to use in your new applications.