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Fix typos (#3263)
Signed-off-by: Tran Ngoc Nhan <[email protected]>
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spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/openai-chat.adoc

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@@ -302,7 +302,7 @@ TIP: You can pass multiple audio files as well.
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OpenAI models that offer input audio multimodal support include `gpt-4o-audio-preview`.
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Refer to the link:https://platform.openai.com/docs/guides/audio[Audio] guide for more information.
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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.
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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.
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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.
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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.
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Currently, OpenAI support only the following audio types: `audio/mp3` and `audio/wav`.
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byte[] waveAudio = response.getResult().getOutput().getMedia().get(0).getDataAsByteArray(); // audio data
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----
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You have to specify an `audio` modality in the `OpenAiChatOptions` to generate audio output.
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You have to specify an `audio` modality in the `OpenAiChatOptions` to generate audio output.
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The `AudioParameters` class provides the voice and audio format for the audio output.
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== Structured Outputs
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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`.
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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`.
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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.
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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

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@@ -103,19 +103,19 @@ All properties are prefixed with `spring.ai.mcp.server`:
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|`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 |`-`
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|`sse-message-endpoint` | Custom SSE Message endpoint path for web transport to be used by the client to send messages|`/mcp/message`
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|`sse-endpoint` |Custom SSE endpoint path for web transport |`/sse`
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|`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` | -
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|`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` | -
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|`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
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|===
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== Sync/Async Server Types
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* **Synchronous Server** - The default server type implemented using `McpSyncServer`.
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It is designed for straightforward request-response patterns in your applications.
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To enable this server type, set `spring.ai.mcp.server.type=SYNC` in your configuration.
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* **Synchronous Server** - The default server type implemented using `McpSyncServer`.
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It is designed for straightforward request-response patterns in your applications.
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To enable this server type, set `spring.ai.mcp.server.type=SYNC` in your configuration.
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When activated, it automatically handles the configuration of synchronous tool specifications.
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* **Asynchronous Server** - The asynchronous server implementation uses `McpAsyncServer` and is optimized for non-blocking operations.
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To enable this server type, configure your application with `spring.ai.mcp.server.type=ASYNC`.
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* **Asynchronous Server** - The asynchronous server implementation uses `McpAsyncServer` and is optimized for non-blocking operations.
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To enable this server type, configure your application with `spring.ai.mcp.server.type=ASYNC`.
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This server type automatically sets up asynchronous tool specifications with built-in Project Reactor support.
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== Server Capabilities
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@Bean
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public List<McpServerFeatures.SyncCompletionSpecification> myCompletions() {
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var completion = new McpServerFeatures.SyncCompletionSpecification(
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"code-completion",
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"code-completion",
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"Provides code completion suggestions",
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(exchange, request) -> {
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// Implementation that returns completion suggestions
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));
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}
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);
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return List.of(completion);
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}
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spring-ai-docs/src/main/antora/modules/ROOT/pages/api/tools-migration.adoc

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