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ML Kit puede generar respuestas cortas a mensajes con un modelo en el dispositivo.
Para generar respuestas inteligentes, pasa al Kit de AA un registro de mensajes recientes en un
conversación. Si el Kit de AA determina que la conversación está en inglés y que
la conversación no tiene un tema potencialmente sensible, ML Kit
genera hasta tres respuestas que puedes sugerir al usuario.
Incluye los siguientes pods del ML Kit en tu Podfile:
pod 'GoogleMLKit/SmartReply', '8.0.0'
Después de instalar o actualizar los Pods de tu proyecto, abre el proyecto de Xcode a través de su
.xcworkspace El Kit de AA es compatible con Xcode 12.4 o versiones posteriores.
1. Crea un objeto de historial de conversaciones
Para generar respuestas inteligentes, debes pasar al Kit de AA un array ordenado cronológicamente de
Objetos TextMessage, con la marca de tiempo más antigua primero. Cuando el usuario
envía o recibe un mensaje, agrega el mensaje, su marca de tiempo y el mensaje
ID de usuario del remitente en el historial de conversaciones.
El ID de usuario puede ser cualquier cadena que identifique de forma única al remitente dentro del
conversación. El ID del usuario no tiene que corresponder a ningún dato del usuario.
y el ID del usuario no tiene que ser coherente entre conversaciones o
invocaciones del generador de respuestas inteligentes.
Si el mensaje lo envió el usuario al que quieres sugerirle respuestas, configura
isLocalUser como verdadero.
Swift
varconversation:[TextMessage]=[]// Then, for each message sent and received:letmessage=TextMessage(text:"How are you?",timestamp:Date().timeIntervalSince1970,userID:"userId",isLocalUser:false)conversation.append(message)
Objective-C
NSMutableArray*conversation=[NSMutableArrayarray];// Then, for each message sent and received:MLKTextMessage*message=[[MLKTextMessagealloc]initWithText:@"How are you?"timestamp:[NSDatedate].timeIntervalSince1970userID:userIdisLocalUser:NO];[conversationaddObject:message];
Un objeto de historial de conversaciones se parece al siguiente ejemplo:
Marca de tiempo
userID
isLocalUser
Mensaje
Jue 21 de feb 13:13:39 PST 2019
verdadero
¿vas en camino?
Jue 21 de feb 13:15:03 PST 2019
FRIEND0
falso
Llegaré tarde, lo siento.
El Kit de AA sugiere respuestas al último mensaje de un historial de conversación. Último mensaje
debe ser de un usuario que no sea local. En el ejemplo anterior, el último mensaje de la conversación
es del usuario no local FRIEND0. Cuando usas pasar este registro del ML Kit, te sugiere
respuestas al mensaje de FRIENDO: "¡Llegaré tarde, lo siento!"
2. Recibe respuestas de mensajes
Para generar respuestas inteligentes a un mensaje, obtén una instancia de SmartReply y pasa
el historial de conversaciones a su método suggestReplies(for:completion:):
Swift
SmartReply.smartReply().suggestReplies(for:conversation){result,erroringuarderror==nil,letresult=resultelse{return}if(result.status==.notSupportedLanguage){// The conversation's language isn't supported, so// the result doesn't contain any suggestions.}elseif(result.status==.success){// Successfully suggested smart replies.// ...}}
Objective-C
MLKSmartReply*smartReply=[MLKSmartReplysmartReply];[smartReplysuggestRepliesForMessages:inputTextcompletion:^(MLKSmartReplySuggestionResult*_Nullableresult,NSError*_Nullableerror){if(error||!result){return;}if(result.status==MLKSmartReplyResultStatusNotSupportedLanguage){// The conversation's language isn't supported, so// the result doesn't contain any suggestions.}elseif(result.status==MLKSmartReplyResultStatusSuccess){// Successfully suggested smart replies.// ...}}];
Si la operación se realiza correctamente, se pasará un objeto SmartReplySuggestionResult a
el controlador de finalización. Este objeto contiene una lista de hasta tres
respuestas, que puedes presentar al usuario:
Ten en cuenta que es posible que ML Kit no devuelva resultados si el modelo no está seguro de
la relevancia de las respuestas sugeridas, la conversación ingresada no está
inglés o si el modelo detecta cuestiones sensibles.
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Falta la información que necesito","missingTheInformationINeed","thumb-down"],["Muy complicado o demasiados pasos","tooComplicatedTooManySteps","thumb-down"],["Desactualizado","outOfDate","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Problema con las muestras o los códigos","samplesCodeIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2025-09-04 (UTC)"],[[["\u003cp\u003eML Kit provides an on-device model to generate smart replies for messages in English conversations, enhancing user experience and engagement.\u003c/p\u003e\n"],["\u003cp\u003eBy passing a conversation history to ML Kit, developers can receive up to three suggested replies for the latest message, which can then be displayed to the user.\u003c/p\u003e\n"],["\u003cp\u003eBefore utilizing the API, ensure the device is 64-bit and include the necessary ML Kit pods in your project.\u003c/p\u003e\n"],["\u003cp\u003eThe smart reply feature is optimized for non-sensitive conversations, and may not generate results if the language is unsupported or sensitive topics are detected.\u003c/p\u003e\n"]]],[],null,["ML Kit can generate short replies to messages using an on-device model.\n\nTo generate smart replies, you pass ML Kit a log of recent messages in a\nconversation. If ML Kit determines the conversation is in English, and that\nthe conversation doesn't have potentially sensitive subject matter, ML Kit\ngenerates up to three replies, which you can suggest to your user.\n\n\u003cbr /\u003e\n\n| **Note:** ML Kit iOS APIs only run on 64-bit devices. If you build your app with 32-bit support, check the device's architecture before using this API.\n\nTry it out\n\n- Play around with [the sample app](https://github.com/googlesamples/mlkit/tree/master/ios/quickstarts/smartreply) to see an example usage of this API.\n\nBefore you begin\n\n1. Include the following ML Kit pods in your Podfile: \n\n ```\n pod 'GoogleMLKit/SmartReply', '8.0.0'\n ```\n2. After you install or update your project's Pods, open your Xcode project using its `.xcworkspace`. ML Kit is supported in Xcode version 12.4 or greater.\n\n1. Create a conversation history object\n\nTo generate smart replies, you pass ML Kit a chronologically-ordered array of\n`TextMessage` objects, with the earliest timestamp first. Whenever the user\nsends or receives a message, add the message, its timestamp, and the message\nsender's user ID to the conversation history.\n\nThe user ID can be any string that uniquely identifies the sender within the\nconversation. The user ID doesn't need to correspond to any user data,\nand the user ID doesn't need to be consistent between conversations or\ninvocations of the smart reply generator.\n\nIf the message was sent by the user you want to suggest replies to, set\n`isLocalUser` to true. \n\nSwift \n\n```swift\nvar conversation: [TextMessage] = []\n\n// Then, for each message sent and received:\nlet message = TextMessage(\n text: \"How are you?\",\n timestamp: Date().timeIntervalSince1970,\n userID: \"userId\",\n isLocalUser: false)\nconversation.append(message)\n```\n\nObjective-C \n\n```objective-c\nNSMutableArray *conversation = [NSMutableArray array];\n\n// Then, for each message sent and received:\nMLKTextMessage *message = [[MLKTextMessage alloc]\n initWithText:@\"How are you?\"\n timestamp:[NSDate date].timeIntervalSince1970\n userID:userId\n isLocalUser:NO];\n[conversation addObject:message];\n```\n\nA conversation history object looks like the following example:\n\n| Timestamp | userID | isLocalUser | Message |\n|------------------------------|---------|-------------|----------------------|\n| Thu Feb 21 13:13:39 PST 2019 | | true | are you on your way? |\n| Thu Feb 21 13:15:03 PST 2019 | FRIEND0 | false | Running late, sorry! |\n\nML Kit suggests replies to the last message in a conversation history. The last message\nshould be from a non-local user. In the example above, the last message in the conversation\nis from the non-local user FRIEND0. When you use pass ML Kit this log, it suggests\nreplies to FRIENDO's message: \"Running late, sorry!\"\n\n2. Get message replies\n\nTo generate smart replies to a message, get an instance of `SmartReply` and pass\nthe conversation history to its `suggestReplies(for:completion:)` method: \n\nSwift \n\n```swift\nSmartReply.smartReply().suggestReplies(for: conversation) { result, error in\n guard error == nil, let result = result else {\n return\n }\n if (result.status == .notSupportedLanguage) {\n // The conversation's language isn't supported, so\n // the result doesn't contain any suggestions.\n } else if (result.status == .success) {\n // Successfully suggested smart replies.\n // ...\n }\n}\n```\n\nObjective-C \n\n```objective-c\nMLKSmartReply *smartReply = [MLKSmartReply smartReply];\n[smartReply suggestRepliesForMessages:inputText\n completion:^(MLKSmartReplySuggestionResult * _Nullable result,\n NSError * _Nullable error) {\n if (error || !result) {\n return;\n }\n if (result.status == MLKSmartReplyResultStatusNotSupportedLanguage) {\n // The conversation's language isn't supported, so\n // the result doesn't contain any suggestions.\n } else if (result.status == MLKSmartReplyResultStatusSuccess) {\n // Successfully suggested smart replies.\n // ...\n }\n}];\n```\n\nIf the operation succeeds, a `SmartReplySuggestionResult` object is passed to\nthe completion handler. This object contains a list of up to three suggested\nreplies, which you can present to your user: \n\nSwift \n\n```swift\nfor suggestion in result.suggestions {\n print(\"Suggested reply: \\(suggestion.text)\")\n}\n```\n\nObjective-C \n\n```objective-c\nfor (MLKSmartReplySuggestion *suggestion in result.suggestions) {\n NSLog(@\"Suggested reply: %@\", suggestion.text);\n}\n```\n\nNote that ML Kit might not return results if the model isn't confident in\nthe relevance of the suggested replies, the input conversation isn't in\nEnglish, or if the model detects sensitive subject matter."]]