Fix typos (#3263)

Signed-off-by: Tran Ngoc Nhan <[email protected]>

Author: ngocnhan-tran1996

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/openai-chat.adoc

@@ -302,7 +302,7 @@ TIP: You can pass multiple audio files as well.
 OpenAI models that offer input audio multimodal support include `gpt-4o-audio-preview`.
 Refer to the link:https://platform.openai.com/docs/guides/audio[Audio] guide for more information.
 
-The OpenAI link:https://platform.openai.com/docs/api-reference/chat/create#chat-create-messages[Assystant Message API] can contain a list of base64-encoded audio files with the message.
+The OpenAI link:https://platform.openai.com/docs/api-reference/chat/create#chat-create-messages[Assistant Message API] can contain a list of base64-encoded audio files with the message.
 Spring AI’s link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-model/src/main/java/org/springframework/ai/chat/messages/Message.java[Message] interface facilitates multimodal AI models by introducing the link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-model/src/main/java/org/springframework/ai/chat/messages/Media.java[Media] type.
 This type encompasses data and details regarding media attachments in messages, utilizing Spring’s `org.springframework.util.MimeType` and a `org.springframework.core.io.Resource` for the raw media data.
 Currently, OpenAI support only the following audio types: `audio/mp3` and `audio/wav`.
@@ -325,12 +325,12 @@ String text = response.getResult().getOutput().getContent(); // audio transcript
 byte[] waveAudio = response.getResult().getOutput().getMedia().get(0).getDataAsByteArray(); // audio data
 ----
 
-You have to specify an `audio` modality in the `OpenAiChatOptions` to generate audio output. 
+You have to specify an `audio` modality in the `OpenAiChatOptions` to generate audio output.
 The `AudioParameters` class provides the voice and audio format for the audio output.
 
 == Structured Outputs
 
-OpenAI provides custom https://platform.openai.com/docs/guides/structured-outputs[Structured Outputs] APIs that ensure your model generates responses conforming strictly to your provided `JSON Schema`. 
+OpenAI provides custom https://platform.openai.com/docs/guides/structured-outputs[Structured Outputs] APIs that ensure your model generates responses conforming strictly to your provided `JSON Schema`.
 In addition to the existing Spring AI model-agnostic xref::api/structured-output-converter.adoc[Structured Output Converter], these APIs offer enhanced control and precision.
 
 NOTE: Currently, OpenAI supports a link:https://platform.openai.com/docs/guides/structured-outputs/supported-schemas[subset of the JSON Schema language] format.

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/mcp/mcp-server-boot-starter-docs.adoc

@@ -103,19 +103,19 @@ All properties are prefixed with `spring.ai.mcp.server`:
 |`tool-response-mime-type` |(optional) response MIME type per tool name. For example `spring.ai.mcp.server.tool-response-mime-type.generateImage=image/png` will associate the `image/png` mime type with the `generateImage()` tool name |`-`
 |`sse-message-endpoint` | Custom SSE Message endpoint path for web transport to be used by the client to send messages|`/mcp/message`
 |`sse-endpoint` |Custom SSE endpoint path for web transport |`/sse`
-|`base-url` | Optional URL prefix. For example `base-url=/api/v1` means that the client should access the sse endpont at `/api/v1` + `sse-endpoint` and the message endpoint is `/api/v1` + `sse-message-endpoint` | -
+|`base-url` | Optional URL prefix. For example `base-url=/api/v1` means that the client should access the sse endpoint at `/api/v1` + `sse-endpoint` and the message endpoint is `/api/v1` + `sse-message-endpoint` | -
 |`request-timeout` | Duration to wait for server responses before timing out requests. Applies to all requests made through the client, including tool calls, resource access, and prompt operations. | `20` seconds
 |===
 
 == Sync/Async Server Types
 
-* **Synchronous Server** - The default server type implemented using `McpSyncServer`. 
-It is designed for straightforward request-response patterns in your applications. 
-To enable this server type, set `spring.ai.mcp.server.type=SYNC` in your configuration. 
+* **Synchronous Server** - The default server type implemented using `McpSyncServer`.
+It is designed for straightforward request-response patterns in your applications.
+To enable this server type, set `spring.ai.mcp.server.type=SYNC` in your configuration.
 When activated, it automatically handles the configuration of synchronous tool specifications.
 
-* **Asynchronous Server** - The asynchronous server implementation uses `McpAsyncServer` and is optimized for non-blocking operations. 
-To enable this server type, configure your application with `spring.ai.mcp.server.type=ASYNC`. 
+* **Asynchronous Server** - The asynchronous server implementation uses `McpAsyncServer` and is optimized for non-blocking operations.
+To enable this server type, configure your application with `spring.ai.mcp.server.type=ASYNC`.
 This server type automatically sets up asynchronous tool specifications with built-in Project Reactor support.
 
 == Server Capabilities
@@ -250,7 +250,7 @@ Provides a standardized way for servers to expose completion capabilities to cli
 @Bean
 public List<McpServerFeatures.SyncCompletionSpecification> myCompletions() {
     var completion = new McpServerFeatures.SyncCompletionSpecification(
-        "code-completion", 
+        "code-completion",
         "Provides code completion suggestions",
         (exchange, request) -> {
             // Implementation that returns completion suggestions
@@ -260,7 +260,7 @@ public List<McpServerFeatures.SyncCompletionSpecification> myCompletions() {
             ));
         }
     );
-    
+
     return List.of(completion);
 }
 ----

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/tools-migration.adoc

@@ -109,7 +109,7 @@ class WeatherTools {
 }
 ----
 
-And you can use the same `ChatClient#tools()` API to register method-based tool callbackes:
+And you can use the same `ChatClient#tools()` API to register method-based tool callbacks:
 
 [source,java]
 ----