// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
pub struct PutSessionOutput {
/// <p>Content type as specified in the <code>Accept</code> HTTP header in the request.</p>
#[doc(hidden)]
pub content_type: std::option::Option<std::string::String>,
/// <p>The name of the current intent.</p>
#[doc(hidden)]
pub intent_name: std::option::Option<std::string::String>,
/// <p>Map of zero or more intent slots Amazon Lex detected from the user input during the conversation.</p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code> the default is <code>ORIGINAL_VALUE</code>. </p>
#[doc(hidden)]
pub slots: std::option::Option<std::string::String>,
/// <p>Map of key/value pairs representing session-specific context information.</p>
#[doc(hidden)]
pub session_attributes: std::option::Option<std::string::String>,
/// <p>The next message that should be presented to the user.</p>
/// <p>You can only use this field in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>message</code> field is null. You should use the <code>encodedMessage</code> field instead.</p>
#[deprecated(
note = "The message field is deprecated, use the encodedMessage field instead. The message field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
#[doc(hidden)]
pub message: std::option::Option<std::string::String>,
/// <p>The next message that should be presented to the user.</p>
/// <p>The <code>encodedMessage</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
#[doc(hidden)]
pub encoded_message: std::option::Option<std::string::String>,
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format for the client.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
#[doc(hidden)]
pub message_format: std::option::Option<crate::model::MessageFormatType>,
/// <p></p>
/// <ul>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response to confirm the intent before fulfilling an intent.</p> </li>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit the user's intent.</p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting the value of a slot for the current intent.</p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user has failed. This can happen for various reasons, including the user does not provide an appropriate response to prompts from the service, or if the Lambda function fails to fulfill the intent.</p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function has sucessfully fulfilled the intent.</p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the intent.</p> </li>
/// </ul>
#[doc(hidden)]
pub dialog_state: std::option::Option<crate::model::DialogState>,
/// <p>If the <code>dialogState</code> is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value.</p>
#[doc(hidden)]
pub slot_to_elicit: std::option::Option<std::string::String>,
/// <p>The audio version of the message to convey to the user.</p>
pub audio_stream: aws_smithy_http::byte_stream::ByteStream,
/// <p>A unique identifier for the session.</p>
#[doc(hidden)]
pub session_id: std::option::Option<std::string::String>,
/// <p>A list of active contexts for the session.</p>
#[doc(hidden)]
pub active_contexts: std::option::Option<std::string::String>,
}
impl PutSessionOutput {
/// <p>Content type as specified in the <code>Accept</code> HTTP header in the request.</p>
pub fn content_type(&self) -> std::option::Option<&str> {
self.content_type.as_deref()
}
/// <p>The name of the current intent.</p>
pub fn intent_name(&self) -> std::option::Option<&str> {
self.intent_name.as_deref()
}
/// <p>Map of zero or more intent slots Amazon Lex detected from the user input during the conversation.</p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code> the default is <code>ORIGINAL_VALUE</code>. </p>
pub fn slots(&self) -> std::option::Option<&str> {
self.slots.as_deref()
}
/// <p>Map of key/value pairs representing session-specific context information.</p>
pub fn session_attributes(&self) -> std::option::Option<&str> {
self.session_attributes.as_deref()
}
/// <p>The next message that should be presented to the user.</p>
/// <p>You can only use this field in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>message</code> field is null. You should use the <code>encodedMessage</code> field instead.</p>
#[deprecated(
note = "The message field is deprecated, use the encodedMessage field instead. The message field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
pub fn message(&self) -> std::option::Option<&str> {
self.message.as_deref()
}
/// <p>The next message that should be presented to the user.</p>
/// <p>The <code>encodedMessage</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
pub fn encoded_message(&self) -> std::option::Option<&str> {
self.encoded_message.as_deref()
}
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format for the client.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
pub fn message_format(&self) -> std::option::Option<&crate::model::MessageFormatType> {
self.message_format.as_ref()
}
/// <p></p>
/// <ul>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response to confirm the intent before fulfilling an intent.</p> </li>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit the user's intent.</p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting the value of a slot for the current intent.</p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user has failed. This can happen for various reasons, including the user does not provide an appropriate response to prompts from the service, or if the Lambda function fails to fulfill the intent.</p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function has sucessfully fulfilled the intent.</p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the intent.</p> </li>
/// </ul>
pub fn dialog_state(&self) -> std::option::Option<&crate::model::DialogState> {
self.dialog_state.as_ref()
}
/// <p>If the <code>dialogState</code> is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value.</p>
pub fn slot_to_elicit(&self) -> std::option::Option<&str> {
self.slot_to_elicit.as_deref()
}
/// <p>The audio version of the message to convey to the user.</p>
pub fn audio_stream(&self) -> &aws_smithy_http::byte_stream::ByteStream {
&self.audio_stream
}
/// <p>A unique identifier for the session.</p>
pub fn session_id(&self) -> std::option::Option<&str> {
self.session_id.as_deref()
}
/// <p>A list of active contexts for the session.</p>
pub fn active_contexts(&self) -> std::option::Option<&str> {
self.active_contexts.as_deref()
}
}
impl std::fmt::Debug for PutSessionOutput {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
let mut formatter = f.debug_struct("PutSessionOutput");
formatter.field("content_type", &self.content_type);
formatter.field("intent_name", &self.intent_name);
formatter.field("slots", &self.slots);
formatter.field("session_attributes", &self.session_attributes);
formatter.field("message", &"*** Sensitive Data Redacted ***");
formatter.field("encoded_message", &"*** Sensitive Data Redacted ***");
formatter.field("message_format", &self.message_format);
formatter.field("dialog_state", &self.dialog_state);
formatter.field("slot_to_elicit", &self.slot_to_elicit);
formatter.field("audio_stream", &self.audio_stream);
formatter.field("session_id", &self.session_id);
formatter.field("active_contexts", &"*** Sensitive Data Redacted ***");
formatter.finish()
}
}
/// See [`PutSessionOutput`](crate::output::PutSessionOutput).
pub mod put_session_output {
/// A builder for [`PutSessionOutput`](crate::output::PutSessionOutput).
#[derive(std::default::Default)]
pub struct Builder {
pub(crate) content_type: std::option::Option<std::string::String>,
pub(crate) intent_name: std::option::Option<std::string::String>,
pub(crate) slots: std::option::Option<std::string::String>,
pub(crate) session_attributes: std::option::Option<std::string::String>,
pub(crate) message: std::option::Option<std::string::String>,
pub(crate) encoded_message: std::option::Option<std::string::String>,
pub(crate) message_format: std::option::Option<crate::model::MessageFormatType>,
pub(crate) dialog_state: std::option::Option<crate::model::DialogState>,
pub(crate) slot_to_elicit: std::option::Option<std::string::String>,
pub(crate) audio_stream: std::option::Option<aws_smithy_http::byte_stream::ByteStream>,
pub(crate) session_id: std::option::Option<std::string::String>,
pub(crate) active_contexts: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>Content type as specified in the <code>Accept</code> HTTP header in the request.</p>
pub fn content_type(mut self, input: impl Into<std::string::String>) -> Self {
self.content_type = Some(input.into());
self
}
/// <p>Content type as specified in the <code>Accept</code> HTTP header in the request.</p>
pub fn set_content_type(mut self, input: std::option::Option<std::string::String>) -> Self {
self.content_type = input;
self
}
/// <p>The name of the current intent.</p>
pub fn intent_name(mut self, input: impl Into<std::string::String>) -> Self {
self.intent_name = Some(input.into());
self
}
/// <p>The name of the current intent.</p>
pub fn set_intent_name(mut self, input: std::option::Option<std::string::String>) -> Self {
self.intent_name = input;
self
}
/// <p>Map of zero or more intent slots Amazon Lex detected from the user input during the conversation.</p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code> the default is <code>ORIGINAL_VALUE</code>. </p>
pub fn slots(mut self, input: impl Into<std::string::String>) -> Self {
self.slots = Some(input.into());
self
}
/// <p>Map of zero or more intent slots Amazon Lex detected from the user input during the conversation.</p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code> the default is <code>ORIGINAL_VALUE</code>. </p>
pub fn set_slots(mut self, input: std::option::Option<std::string::String>) -> Self {
self.slots = input;
self
}
/// <p>Map of key/value pairs representing session-specific context information.</p>
pub fn session_attributes(mut self, input: impl Into<std::string::String>) -> Self {
self.session_attributes = Some(input.into());
self
}
/// <p>Map of key/value pairs representing session-specific context information.</p>
pub fn set_session_attributes(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.session_attributes = input;
self
}
/// <p>The next message that should be presented to the user.</p>
/// <p>You can only use this field in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>message</code> field is null. You should use the <code>encodedMessage</code> field instead.</p>
#[deprecated(
note = "The message field is deprecated, use the encodedMessage field instead. The message field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
pub fn message(mut self, input: impl Into<std::string::String>) -> Self {
self.message = Some(input.into());
self
}
/// <p>The next message that should be presented to the user.</p>
/// <p>You can only use this field in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>message</code> field is null. You should use the <code>encodedMessage</code> field instead.</p>
#[deprecated(
note = "The message field is deprecated, use the encodedMessage field instead. The message field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
pub fn set_message(mut self, input: std::option::Option<std::string::String>) -> Self {
self.message = input;
self
}
/// <p>The next message that should be presented to the user.</p>
/// <p>The <code>encodedMessage</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
pub fn encoded_message(mut self, input: impl Into<std::string::String>) -> Self {
self.encoded_message = Some(input.into());
self
}
/// <p>The next message that should be presented to the user.</p>
/// <p>The <code>encodedMessage</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
pub fn set_encoded_message(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.encoded_message = input;
self
}
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format for the client.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
pub fn message_format(mut self, input: crate::model::MessageFormatType) -> Self {
self.message_format = Some(input);
self
}
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format for the client.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
pub fn set_message_format(
mut self,
input: std::option::Option<crate::model::MessageFormatType>,
) -> Self {
self.message_format = input;
self
}
/// <p></p>
/// <ul>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response to confirm the intent before fulfilling an intent.</p> </li>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit the user's intent.</p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting the value of a slot for the current intent.</p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user has failed. This can happen for various reasons, including the user does not provide an appropriate response to prompts from the service, or if the Lambda function fails to fulfill the intent.</p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function has sucessfully fulfilled the intent.</p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the intent.</p> </li>
/// </ul>
pub fn dialog_state(mut self, input: crate::model::DialogState) -> Self {
self.dialog_state = Some(input);
self
}
/// <p></p>
/// <ul>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response to confirm the intent before fulfilling an intent.</p> </li>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit the user's intent.</p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting the value of a slot for the current intent.</p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user has failed. This can happen for various reasons, including the user does not provide an appropriate response to prompts from the service, or if the Lambda function fails to fulfill the intent.</p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function has sucessfully fulfilled the intent.</p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the intent.</p> </li>
/// </ul>
pub fn set_dialog_state(
mut self,
input: std::option::Option<crate::model::DialogState>,
) -> Self {
self.dialog_state = input;
self
}
/// <p>If the <code>dialogState</code> is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value.</p>
pub fn slot_to_elicit(mut self, input: impl Into<std::string::String>) -> Self {
self.slot_to_elicit = Some(input.into());
self
}
/// <p>If the <code>dialogState</code> is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value.</p>
pub fn set_slot_to_elicit(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.slot_to_elicit = input;
self
}
/// <p>The audio version of the message to convey to the user.</p>
pub fn audio_stream(mut self, input: aws_smithy_http::byte_stream::ByteStream) -> Self {
self.audio_stream = Some(input);
self
}
/// <p>The audio version of the message to convey to the user.</p>
pub fn set_audio_stream(
mut self,
input: std::option::Option<aws_smithy_http::byte_stream::ByteStream>,
) -> Self {
self.audio_stream = input;
self
}
/// <p>A unique identifier for the session.</p>
pub fn session_id(mut self, input: impl Into<std::string::String>) -> Self {
self.session_id = Some(input.into());
self
}
/// <p>A unique identifier for the session.</p>
pub fn set_session_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.session_id = input;
self
}
/// <p>A list of active contexts for the session.</p>
pub fn active_contexts(mut self, input: impl Into<std::string::String>) -> Self {
self.active_contexts = Some(input.into());
self
}
/// <p>A list of active contexts for the session.</p>
pub fn set_active_contexts(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.active_contexts = input;
self
}
/// Consumes the builder and constructs a [`PutSessionOutput`](crate::output::PutSessionOutput).
pub fn build(self) -> crate::output::PutSessionOutput {
crate::output::PutSessionOutput {
content_type: self.content_type,
intent_name: self.intent_name,
slots: self.slots,
session_attributes: self.session_attributes,
message: self.message,
encoded_message: self.encoded_message,
message_format: self.message_format,
dialog_state: self.dialog_state,
slot_to_elicit: self.slot_to_elicit,
audio_stream: self.audio_stream.unwrap_or_default(),
session_id: self.session_id,
active_contexts: self.active_contexts,
}
}
}
impl std::fmt::Debug for Builder {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
let mut formatter = f.debug_struct("Builder");
formatter.field("content_type", &self.content_type);
formatter.field("intent_name", &self.intent_name);
formatter.field("slots", &self.slots);
formatter.field("session_attributes", &self.session_attributes);
formatter.field("message", &"*** Sensitive Data Redacted ***");
formatter.field("encoded_message", &"*** Sensitive Data Redacted ***");
formatter.field("message_format", &self.message_format);
formatter.field("dialog_state", &self.dialog_state);
formatter.field("slot_to_elicit", &self.slot_to_elicit);
formatter.field("audio_stream", &self.audio_stream);
formatter.field("session_id", &self.session_id);
formatter.field("active_contexts", &"*** Sensitive Data Redacted ***");
formatter.finish()
}
}
}
impl PutSessionOutput {
/// Creates a new builder-style object to manufacture [`PutSessionOutput`](crate::output::PutSessionOutput).
pub fn builder() -> crate::output::put_session_output::Builder {
crate::output::put_session_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq)]
pub struct PostTextOutput {
/// <p>The current user intent that Amazon Lex is aware of.</p>
#[doc(hidden)]
pub intent_name: std::option::Option<std::string::String>,
/// <p>Provides a score that indicates how confident Amazon Lex is that the returned intent is the one that matches the user's intent. The score is between 0.0 and 1.0. For more information, see <a href="https://docs.aws.amazon.com/lex/latest/dg/confidence-scores.html">Confidence Scores</a>.</p>
/// <p>The score is a relative score, not an absolute score. The score may change based on improvements to Amazon Lex.</p>
#[doc(hidden)]
pub nlu_intent_confidence: std::option::Option<crate::model::IntentConfidence>,
/// <p>One to four alternative intents that may be applicable to the user's intent.</p>
/// <p>Each alternative includes a score that indicates how confident Amazon Lex is that the intent matches the user's intent. The intents are sorted by the confidence score.</p>
#[doc(hidden)]
pub alternative_intents: std::option::Option<std::vec::Vec<crate::model::PredictedIntent>>,
/// <p> The intent slots that Amazon Lex detected from the user input in the conversation. </p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code>, the default is <code>ORIGINAL_VALUE</code>.</p>
#[doc(hidden)]
pub slots:
std::option::Option<std::collections::HashMap<std::string::String, std::string::String>>,
/// <p>A map of key-value pairs representing the session-specific context information.</p>
#[doc(hidden)]
pub session_attributes:
std::option::Option<std::collections::HashMap<std::string::String, std::string::String>>,
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
#[doc(hidden)]
pub message: std::option::Option<std::string::String>,
/// <p>The sentiment expressed in and utterance.</p>
/// <p>When the bot is configured to send utterances to Amazon Comprehend for sentiment analysis, this field contains the result of the analysis.</p>
#[doc(hidden)]
pub sentiment_response: std::option::Option<crate::model::SentimentResponse>,
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format defined by the Lambda function.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
#[doc(hidden)]
pub message_format: std::option::Option<crate::model::MessageFormatType>,
/// <p> Identifies the current state of the user interaction. Amazon Lex returns one of the following values as <code>dialogState</code>. The client can optionally use this information to customize the user interface. </p>
/// <ul>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit user intent. </p> <p>For example, a user might utter an intent ("I want to order a pizza"). If Amazon Lex cannot infer the user intent from this utterance, it will return this dialogState.</p> </li>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response. </p> <p> For example, Amazon Lex wants user confirmation before fulfilling an intent. </p> <p>Instead of a simple "yes" or "no," a user might respond with additional information. For example, "yes, but make it thick crust pizza" or "no, I want to order a drink". Amazon Lex can process such additional information (in these examples, update the crust type slot value, or change intent from OrderPizza to OrderDrink).</p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting a slot value for the current intent. </p> <p>For example, suppose that in the response Amazon Lex sends this message: "What size pizza would you like?". A user might reply with the slot value (e.g., "medium"). The user might also provide additional information in the response (e.g., "medium thick crust pizza"). Amazon Lex can process such additional information appropriately. </p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function configured for the intent has successfully fulfilled the intent. </p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the intent. </p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user failed. </p> <p> This can happen for various reasons including that the user did not provide an appropriate response to prompts from the service (you can configure how many times Amazon Lex can prompt a user for specific information), or the Lambda function failed to fulfill the intent. </p> </li>
/// </ul>
#[doc(hidden)]
pub dialog_state: std::option::Option<crate::model::DialogState>,
/// <p>If the <code>dialogState</code> value is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value. </p>
#[doc(hidden)]
pub slot_to_elicit: std::option::Option<std::string::String>,
/// <p>Represents the options that the user has to respond to the current prompt. Response Card can come from the bot configuration (in the Amazon Lex console, choose the settings button next to a slot) or from a code hook (Lambda function). </p>
#[doc(hidden)]
pub response_card: std::option::Option<crate::model::ResponseCard>,
/// <p>A unique identifier for the session.</p>
#[doc(hidden)]
pub session_id: std::option::Option<std::string::String>,
/// <p>The version of the bot that responded to the conversation. You can use this information to help determine if one version of a bot is performing better than another version.</p>
#[doc(hidden)]
pub bot_version: std::option::Option<std::string::String>,
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
#[doc(hidden)]
pub active_contexts: std::option::Option<std::vec::Vec<crate::model::ActiveContext>>,
}
impl PostTextOutput {
/// <p>The current user intent that Amazon Lex is aware of.</p>
pub fn intent_name(&self) -> std::option::Option<&str> {
self.intent_name.as_deref()
}
/// <p>Provides a score that indicates how confident Amazon Lex is that the returned intent is the one that matches the user's intent. The score is between 0.0 and 1.0. For more information, see <a href="https://docs.aws.amazon.com/lex/latest/dg/confidence-scores.html">Confidence Scores</a>.</p>
/// <p>The score is a relative score, not an absolute score. The score may change based on improvements to Amazon Lex.</p>
pub fn nlu_intent_confidence(&self) -> std::option::Option<&crate::model::IntentConfidence> {
self.nlu_intent_confidence.as_ref()
}
/// <p>One to four alternative intents that may be applicable to the user's intent.</p>
/// <p>Each alternative includes a score that indicates how confident Amazon Lex is that the intent matches the user's intent. The intents are sorted by the confidence score.</p>
pub fn alternative_intents(&self) -> std::option::Option<&[crate::model::PredictedIntent]> {
self.alternative_intents.as_deref()
}
/// <p> The intent slots that Amazon Lex detected from the user input in the conversation. </p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code>, the default is <code>ORIGINAL_VALUE</code>.</p>
pub fn slots(
&self,
) -> std::option::Option<&std::collections::HashMap<std::string::String, std::string::String>>
{
self.slots.as_ref()
}
/// <p>A map of key-value pairs representing the session-specific context information.</p>
pub fn session_attributes(
&self,
) -> std::option::Option<&std::collections::HashMap<std::string::String, std::string::String>>
{
self.session_attributes.as_ref()
}
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
pub fn message(&self) -> std::option::Option<&str> {
self.message.as_deref()
}
/// <p>The sentiment expressed in and utterance.</p>
/// <p>When the bot is configured to send utterances to Amazon Comprehend for sentiment analysis, this field contains the result of the analysis.</p>
pub fn sentiment_response(&self) -> std::option::Option<&crate::model::SentimentResponse> {
self.sentiment_response.as_ref()
}
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format defined by the Lambda function.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
pub fn message_format(&self) -> std::option::Option<&crate::model::MessageFormatType> {
self.message_format.as_ref()
}
/// <p> Identifies the current state of the user interaction. Amazon Lex returns one of the following values as <code>dialogState</code>. The client can optionally use this information to customize the user interface. </p>
/// <ul>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit user intent. </p> <p>For example, a user might utter an intent ("I want to order a pizza"). If Amazon Lex cannot infer the user intent from this utterance, it will return this dialogState.</p> </li>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response. </p> <p> For example, Amazon Lex wants user confirmation before fulfilling an intent. </p> <p>Instead of a simple "yes" or "no," a user might respond with additional information. For example, "yes, but make it thick crust pizza" or "no, I want to order a drink". Amazon Lex can process such additional information (in these examples, update the crust type slot value, or change intent from OrderPizza to OrderDrink).</p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting a slot value for the current intent. </p> <p>For example, suppose that in the response Amazon Lex sends this message: "What size pizza would you like?". A user might reply with the slot value (e.g., "medium"). The user might also provide additional information in the response (e.g., "medium thick crust pizza"). Amazon Lex can process such additional information appropriately. </p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function configured for the intent has successfully fulfilled the intent. </p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the intent. </p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user failed. </p> <p> This can happen for various reasons including that the user did not provide an appropriate response to prompts from the service (you can configure how many times Amazon Lex can prompt a user for specific information), or the Lambda function failed to fulfill the intent. </p> </li>
/// </ul>
pub fn dialog_state(&self) -> std::option::Option<&crate::model::DialogState> {
self.dialog_state.as_ref()
}
/// <p>If the <code>dialogState</code> value is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value. </p>
pub fn slot_to_elicit(&self) -> std::option::Option<&str> {
self.slot_to_elicit.as_deref()
}
/// <p>Represents the options that the user has to respond to the current prompt. Response Card can come from the bot configuration (in the Amazon Lex console, choose the settings button next to a slot) or from a code hook (Lambda function). </p>
pub fn response_card(&self) -> std::option::Option<&crate::model::ResponseCard> {
self.response_card.as_ref()
}
/// <p>A unique identifier for the session.</p>
pub fn session_id(&self) -> std::option::Option<&str> {
self.session_id.as_deref()
}
/// <p>The version of the bot that responded to the conversation. You can use this information to help determine if one version of a bot is performing better than another version.</p>
pub fn bot_version(&self) -> std::option::Option<&str> {
self.bot_version.as_deref()
}
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
pub fn active_contexts(&self) -> std::option::Option<&[crate::model::ActiveContext]> {
self.active_contexts.as_deref()
}
}
impl std::fmt::Debug for PostTextOutput {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
let mut formatter = f.debug_struct("PostTextOutput");
formatter.field("intent_name", &self.intent_name);
formatter.field("nlu_intent_confidence", &self.nlu_intent_confidence);
formatter.field("alternative_intents", &self.alternative_intents);
formatter.field("slots", &"*** Sensitive Data Redacted ***");
formatter.field("session_attributes", &"*** Sensitive Data Redacted ***");
formatter.field("message", &"*** Sensitive Data Redacted ***");
formatter.field("sentiment_response", &self.sentiment_response);
formatter.field("message_format", &self.message_format);
formatter.field("dialog_state", &self.dialog_state);
formatter.field("slot_to_elicit", &self.slot_to_elicit);
formatter.field("response_card", &self.response_card);
formatter.field("session_id", &self.session_id);
formatter.field("bot_version", &self.bot_version);
formatter.field("active_contexts", &"*** Sensitive Data Redacted ***");
formatter.finish()
}
}
/// See [`PostTextOutput`](crate::output::PostTextOutput).
pub mod post_text_output {
/// A builder for [`PostTextOutput`](crate::output::PostTextOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default)]
pub struct Builder {
pub(crate) intent_name: std::option::Option<std::string::String>,
pub(crate) nlu_intent_confidence: std::option::Option<crate::model::IntentConfidence>,
pub(crate) alternative_intents:
std::option::Option<std::vec::Vec<crate::model::PredictedIntent>>,
pub(crate) slots: std::option::Option<
std::collections::HashMap<std::string::String, std::string::String>,
>,
pub(crate) session_attributes: std::option::Option<
std::collections::HashMap<std::string::String, std::string::String>,
>,
pub(crate) message: std::option::Option<std::string::String>,
pub(crate) sentiment_response: std::option::Option<crate::model::SentimentResponse>,
pub(crate) message_format: std::option::Option<crate::model::MessageFormatType>,
pub(crate) dialog_state: std::option::Option<crate::model::DialogState>,
pub(crate) slot_to_elicit: std::option::Option<std::string::String>,
pub(crate) response_card: std::option::Option<crate::model::ResponseCard>,
pub(crate) session_id: std::option::Option<std::string::String>,
pub(crate) bot_version: std::option::Option<std::string::String>,
pub(crate) active_contexts: std::option::Option<std::vec::Vec<crate::model::ActiveContext>>,
}
impl Builder {
/// <p>The current user intent that Amazon Lex is aware of.</p>
pub fn intent_name(mut self, input: impl Into<std::string::String>) -> Self {
self.intent_name = Some(input.into());
self
}
/// <p>The current user intent that Amazon Lex is aware of.</p>
pub fn set_intent_name(mut self, input: std::option::Option<std::string::String>) -> Self {
self.intent_name = input;
self
}
/// <p>Provides a score that indicates how confident Amazon Lex is that the returned intent is the one that matches the user's intent. The score is between 0.0 and 1.0. For more information, see <a href="https://docs.aws.amazon.com/lex/latest/dg/confidence-scores.html">Confidence Scores</a>.</p>
/// <p>The score is a relative score, not an absolute score. The score may change based on improvements to Amazon Lex.</p>
pub fn nlu_intent_confidence(mut self, input: crate::model::IntentConfidence) -> Self {
self.nlu_intent_confidence = Some(input);
self
}
/// <p>Provides a score that indicates how confident Amazon Lex is that the returned intent is the one that matches the user's intent. The score is between 0.0 and 1.0. For more information, see <a href="https://docs.aws.amazon.com/lex/latest/dg/confidence-scores.html">Confidence Scores</a>.</p>
/// <p>The score is a relative score, not an absolute score. The score may change based on improvements to Amazon Lex.</p>
pub fn set_nlu_intent_confidence(
mut self,
input: std::option::Option<crate::model::IntentConfidence>,
) -> Self {
self.nlu_intent_confidence = input;
self
}
/// Appends an item to `alternative_intents`.
///
/// To override the contents of this collection use [`set_alternative_intents`](Self::set_alternative_intents).
///
/// <p>One to four alternative intents that may be applicable to the user's intent.</p>
/// <p>Each alternative includes a score that indicates how confident Amazon Lex is that the intent matches the user's intent. The intents are sorted by the confidence score.</p>
pub fn alternative_intents(mut self, input: crate::model::PredictedIntent) -> Self {
let mut v = self.alternative_intents.unwrap_or_default();
v.push(input);
self.alternative_intents = Some(v);
self
}
/// <p>One to four alternative intents that may be applicable to the user's intent.</p>
/// <p>Each alternative includes a score that indicates how confident Amazon Lex is that the intent matches the user's intent. The intents are sorted by the confidence score.</p>
pub fn set_alternative_intents(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::PredictedIntent>>,
) -> Self {
self.alternative_intents = input;
self
}
/// Adds a key-value pair to `slots`.
///
/// To override the contents of this collection use [`set_slots`](Self::set_slots).
///
/// <p> The intent slots that Amazon Lex detected from the user input in the conversation. </p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code>, the default is <code>ORIGINAL_VALUE</code>.</p>
pub fn slots(
mut self,
k: impl Into<std::string::String>,
v: impl Into<std::string::String>,
) -> Self {
let mut hash_map = self.slots.unwrap_or_default();
hash_map.insert(k.into(), v.into());
self.slots = Some(hash_map);
self
}
/// <p> The intent slots that Amazon Lex detected from the user input in the conversation. </p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code>, the default is <code>ORIGINAL_VALUE</code>.</p>
pub fn set_slots(
mut self,
input: std::option::Option<
std::collections::HashMap<std::string::String, std::string::String>,
>,
) -> Self {
self.slots = input;
self
}
/// Adds a key-value pair to `session_attributes`.
///
/// To override the contents of this collection use [`set_session_attributes`](Self::set_session_attributes).
///
/// <p>A map of key-value pairs representing the session-specific context information.</p>
pub fn session_attributes(
mut self,
k: impl Into<std::string::String>,
v: impl Into<std::string::String>,
) -> Self {
let mut hash_map = self.session_attributes.unwrap_or_default();
hash_map.insert(k.into(), v.into());
self.session_attributes = Some(hash_map);
self
}
/// <p>A map of key-value pairs representing the session-specific context information.</p>
pub fn set_session_attributes(
mut self,
input: std::option::Option<
std::collections::HashMap<std::string::String, std::string::String>,
>,
) -> Self {
self.session_attributes = input;
self
}
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
pub fn message(mut self, input: impl Into<std::string::String>) -> Self {
self.message = Some(input.into());
self
}
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
pub fn set_message(mut self, input: std::option::Option<std::string::String>) -> Self {
self.message = input;
self
}
/// <p>The sentiment expressed in and utterance.</p>
/// <p>When the bot is configured to send utterances to Amazon Comprehend for sentiment analysis, this field contains the result of the analysis.</p>
pub fn sentiment_response(mut self, input: crate::model::SentimentResponse) -> Self {
self.sentiment_response = Some(input);
self
}
/// <p>The sentiment expressed in and utterance.</p>
/// <p>When the bot is configured to send utterances to Amazon Comprehend for sentiment analysis, this field contains the result of the analysis.</p>
pub fn set_sentiment_response(
mut self,
input: std::option::Option<crate::model::SentimentResponse>,
) -> Self {
self.sentiment_response = input;
self
}
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format defined by the Lambda function.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
pub fn message_format(mut self, input: crate::model::MessageFormatType) -> Self {
self.message_format = Some(input);
self
}
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format defined by the Lambda function.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
pub fn set_message_format(
mut self,
input: std::option::Option<crate::model::MessageFormatType>,
) -> Self {
self.message_format = input;
self
}
/// <p> Identifies the current state of the user interaction. Amazon Lex returns one of the following values as <code>dialogState</code>. The client can optionally use this information to customize the user interface. </p>
/// <ul>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit user intent. </p> <p>For example, a user might utter an intent ("I want to order a pizza"). If Amazon Lex cannot infer the user intent from this utterance, it will return this dialogState.</p> </li>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response. </p> <p> For example, Amazon Lex wants user confirmation before fulfilling an intent. </p> <p>Instead of a simple "yes" or "no," a user might respond with additional information. For example, "yes, but make it thick crust pizza" or "no, I want to order a drink". Amazon Lex can process such additional information (in these examples, update the crust type slot value, or change intent from OrderPizza to OrderDrink).</p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting a slot value for the current intent. </p> <p>For example, suppose that in the response Amazon Lex sends this message: "What size pizza would you like?". A user might reply with the slot value (e.g., "medium"). The user might also provide additional information in the response (e.g., "medium thick crust pizza"). Amazon Lex can process such additional information appropriately. </p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function configured for the intent has successfully fulfilled the intent. </p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the intent. </p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user failed. </p> <p> This can happen for various reasons including that the user did not provide an appropriate response to prompts from the service (you can configure how many times Amazon Lex can prompt a user for specific information), or the Lambda function failed to fulfill the intent. </p> </li>
/// </ul>
pub fn dialog_state(mut self, input: crate::model::DialogState) -> Self {
self.dialog_state = Some(input);
self
}
/// <p> Identifies the current state of the user interaction. Amazon Lex returns one of the following values as <code>dialogState</code>. The client can optionally use this information to customize the user interface. </p>
/// <ul>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit user intent. </p> <p>For example, a user might utter an intent ("I want to order a pizza"). If Amazon Lex cannot infer the user intent from this utterance, it will return this dialogState.</p> </li>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response. </p> <p> For example, Amazon Lex wants user confirmation before fulfilling an intent. </p> <p>Instead of a simple "yes" or "no," a user might respond with additional information. For example, "yes, but make it thick crust pizza" or "no, I want to order a drink". Amazon Lex can process such additional information (in these examples, update the crust type slot value, or change intent from OrderPizza to OrderDrink).</p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting a slot value for the current intent. </p> <p>For example, suppose that in the response Amazon Lex sends this message: "What size pizza would you like?". A user might reply with the slot value (e.g., "medium"). The user might also provide additional information in the response (e.g., "medium thick crust pizza"). Amazon Lex can process such additional information appropriately. </p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function configured for the intent has successfully fulfilled the intent. </p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the intent. </p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user failed. </p> <p> This can happen for various reasons including that the user did not provide an appropriate response to prompts from the service (you can configure how many times Amazon Lex can prompt a user for specific information), or the Lambda function failed to fulfill the intent. </p> </li>
/// </ul>
pub fn set_dialog_state(
mut self,
input: std::option::Option<crate::model::DialogState>,
) -> Self {
self.dialog_state = input;
self
}
/// <p>If the <code>dialogState</code> value is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value. </p>
pub fn slot_to_elicit(mut self, input: impl Into<std::string::String>) -> Self {
self.slot_to_elicit = Some(input.into());
self
}
/// <p>If the <code>dialogState</code> value is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value. </p>
pub fn set_slot_to_elicit(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.slot_to_elicit = input;
self
}
/// <p>Represents the options that the user has to respond to the current prompt. Response Card can come from the bot configuration (in the Amazon Lex console, choose the settings button next to a slot) or from a code hook (Lambda function). </p>
pub fn response_card(mut self, input: crate::model::ResponseCard) -> Self {
self.response_card = Some(input);
self
}
/// <p>Represents the options that the user has to respond to the current prompt. Response Card can come from the bot configuration (in the Amazon Lex console, choose the settings button next to a slot) or from a code hook (Lambda function). </p>
pub fn set_response_card(
mut self,
input: std::option::Option<crate::model::ResponseCard>,
) -> Self {
self.response_card = input;
self
}
/// <p>A unique identifier for the session.</p>
pub fn session_id(mut self, input: impl Into<std::string::String>) -> Self {
self.session_id = Some(input.into());
self
}
/// <p>A unique identifier for the session.</p>
pub fn set_session_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.session_id = input;
self
}
/// <p>The version of the bot that responded to the conversation. You can use this information to help determine if one version of a bot is performing better than another version.</p>
pub fn bot_version(mut self, input: impl Into<std::string::String>) -> Self {
self.bot_version = Some(input.into());
self
}
/// <p>The version of the bot that responded to the conversation. You can use this information to help determine if one version of a bot is performing better than another version.</p>
pub fn set_bot_version(mut self, input: std::option::Option<std::string::String>) -> Self {
self.bot_version = input;
self
}
/// Appends an item to `active_contexts`.
///
/// To override the contents of this collection use [`set_active_contexts`](Self::set_active_contexts).
///
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
pub fn active_contexts(mut self, input: crate::model::ActiveContext) -> Self {
let mut v = self.active_contexts.unwrap_or_default();
v.push(input);
self.active_contexts = Some(v);
self
}
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
pub fn set_active_contexts(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::ActiveContext>>,
) -> Self {
self.active_contexts = input;
self
}
/// Consumes the builder and constructs a [`PostTextOutput`](crate::output::PostTextOutput).
pub fn build(self) -> crate::output::PostTextOutput {
crate::output::PostTextOutput {
intent_name: self.intent_name,
nlu_intent_confidence: self.nlu_intent_confidence,
alternative_intents: self.alternative_intents,
slots: self.slots,
session_attributes: self.session_attributes,
message: self.message,
sentiment_response: self.sentiment_response,
message_format: self.message_format,
dialog_state: self.dialog_state,
slot_to_elicit: self.slot_to_elicit,
response_card: self.response_card,
session_id: self.session_id,
bot_version: self.bot_version,
active_contexts: self.active_contexts,
}
}
}
impl std::fmt::Debug for Builder {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
let mut formatter = f.debug_struct("Builder");
formatter.field("intent_name", &self.intent_name);
formatter.field("nlu_intent_confidence", &self.nlu_intent_confidence);
formatter.field("alternative_intents", &self.alternative_intents);
formatter.field("slots", &"*** Sensitive Data Redacted ***");
formatter.field("session_attributes", &"*** Sensitive Data Redacted ***");
formatter.field("message", &"*** Sensitive Data Redacted ***");
formatter.field("sentiment_response", &self.sentiment_response);
formatter.field("message_format", &self.message_format);
formatter.field("dialog_state", &self.dialog_state);
formatter.field("slot_to_elicit", &self.slot_to_elicit);
formatter.field("response_card", &self.response_card);
formatter.field("session_id", &self.session_id);
formatter.field("bot_version", &self.bot_version);
formatter.field("active_contexts", &"*** Sensitive Data Redacted ***");
formatter.finish()
}
}
}
impl PostTextOutput {
/// Creates a new builder-style object to manufacture [`PostTextOutput`](crate::output::PostTextOutput).
pub fn builder() -> crate::output::post_text_output::Builder {
crate::output::post_text_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
pub struct PostContentOutput {
/// <p>Content type as specified in the <code>Accept</code> HTTP header in the request.</p>
#[doc(hidden)]
pub content_type: std::option::Option<std::string::String>,
/// <p>Current user intent that Amazon Lex is aware of.</p>
#[doc(hidden)]
pub intent_name: std::option::Option<std::string::String>,
/// <p>Provides a score that indicates how confident Amazon Lex is that the returned intent is the one that matches the user's intent. The score is between 0.0 and 1.0.</p>
/// <p>The score is a relative score, not an absolute score. The score may change based on improvements to Amazon Lex. </p>
#[doc(hidden)]
pub nlu_intent_confidence: std::option::Option<std::string::String>,
/// <p>One to four alternative intents that may be applicable to the user's intent.</p>
/// <p>Each alternative includes a score that indicates how confident Amazon Lex is that the intent matches the user's intent. The intents are sorted by the confidence score.</p>
#[doc(hidden)]
pub alternative_intents: std::option::Option<std::string::String>,
/// <p>Map of zero or more intent slots (name/value pairs) Amazon Lex detected from the user input during the conversation. The field is base-64 encoded.</p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code>, the default is <code>ORIGINAL_VALUE</code>.</p>
#[doc(hidden)]
pub slots: std::option::Option<std::string::String>,
/// <p> Map of key/value pairs representing the session-specific context information. </p>
#[doc(hidden)]
pub session_attributes: std::option::Option<std::string::String>,
/// <p>The sentiment expressed in an utterance.</p>
/// <p>When the bot is configured to send utterances to Amazon Comprehend for sentiment analysis, this field contains the result of the analysis.</p>
#[doc(hidden)]
pub sentiment_response: std::option::Option<std::string::String>,
/// <p>You can only use this field in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>message</code> field is null. You should use the <code>encodedMessage</code> field instead.</p>
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> in its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
#[deprecated(
note = "The message field is deprecated, use the encodedMessage field instead. The message field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
#[doc(hidden)]
pub message: std::option::Option<std::string::String>,
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> in its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
/// <p>The <code>encodedMessage</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
#[doc(hidden)]
pub encoded_message: std::option::Option<std::string::String>,
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format for the client.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
#[doc(hidden)]
pub message_format: std::option::Option<crate::model::MessageFormatType>,
/// <p>Identifies the current state of the user interaction. Amazon Lex returns one of the following values as <code>dialogState</code>. The client can optionally use this information to customize the user interface. </p>
/// <ul>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit the user's intent. Consider the following examples: </p> <p> For example, a user might utter an intent ("I want to order a pizza"). If Amazon Lex cannot infer the user intent from this utterance, it will return this dialog state. </p> </li>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response. </p> <p>For example, Amazon Lex wants user confirmation before fulfilling an intent. Instead of a simple "yes" or "no" response, a user might respond with additional information. For example, "yes, but make it a thick crust pizza" or "no, I want to order a drink." Amazon Lex can process such additional information (in these examples, update the crust type slot or change the intent from OrderPizza to OrderDrink). </p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting the value of a slot for the current intent. </p> <p> For example, suppose that in the response Amazon Lex sends this message: "What size pizza would you like?". A user might reply with the slot value (e.g., "medium"). The user might also provide additional information in the response (e.g., "medium thick crust pizza"). Amazon Lex can process such additional information appropriately. </p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function has successfully fulfilled the intent. </p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the request. </p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user failed. </p> <p> This can happen for various reasons, including that the user does not provide an appropriate response to prompts from the service (you can configure how many times Amazon Lex can prompt a user for specific information), or if the Lambda function fails to fulfill the intent. </p> </li>
/// </ul>
#[doc(hidden)]
pub dialog_state: std::option::Option<crate::model::DialogState>,
/// <p> If the <code>dialogState</code> value is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value. </p>
#[doc(hidden)]
pub slot_to_elicit: std::option::Option<std::string::String>,
/// <p>The text used to process the request.</p>
/// <p>You can use this field only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>inputTranscript</code> field is null. You should use the <code>encodedInputTranscript</code> field instead.</p>
/// <p>If the input was an audio stream, the <code>inputTranscript</code> field contains the text extracted from the audio stream. This is the text that is actually processed to recognize intents and slot values. You can use this information to determine if Amazon Lex is correctly processing the audio that you send.</p>
#[deprecated(
note = "The inputTranscript field is deprecated, use the encodedInputTranscript field instead. The inputTranscript field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
#[doc(hidden)]
pub input_transcript: std::option::Option<std::string::String>,
/// <p>The text used to process the request.</p>
/// <p>If the input was an audio stream, the <code>encodedInputTranscript</code> field contains the text extracted from the audio stream. This is the text that is actually processed to recognize intents and slot values. You can use this information to determine if Amazon Lex is correctly processing the audio that you send.</p>
/// <p>The <code>encodedInputTranscript</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
#[doc(hidden)]
pub encoded_input_transcript: std::option::Option<std::string::String>,
/// <p>The prompt (or statement) to convey to the user. This is based on the bot configuration and context. For example, if Amazon Lex did not understand the user intent, it sends the <code>clarificationPrompt</code> configured for the bot. If the intent requires confirmation before taking the fulfillment action, it sends the <code>confirmationPrompt</code>. Another example: Suppose that the Lambda function successfully fulfilled the intent, and sent a message to convey to the user. Then Amazon Lex sends that message in the response. </p>
pub audio_stream: aws_smithy_http::byte_stream::ByteStream,
/// <p>The version of the bot that responded to the conversation. You can use this information to help determine if one version of a bot is performing better than another version.</p>
#[doc(hidden)]
pub bot_version: std::option::Option<std::string::String>,
/// <p>The unique identifier for the session.</p>
#[doc(hidden)]
pub session_id: std::option::Option<std::string::String>,
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
#[doc(hidden)]
pub active_contexts: std::option::Option<std::string::String>,
}
impl PostContentOutput {
/// <p>Content type as specified in the <code>Accept</code> HTTP header in the request.</p>
pub fn content_type(&self) -> std::option::Option<&str> {
self.content_type.as_deref()
}
/// <p>Current user intent that Amazon Lex is aware of.</p>
pub fn intent_name(&self) -> std::option::Option<&str> {
self.intent_name.as_deref()
}
/// <p>Provides a score that indicates how confident Amazon Lex is that the returned intent is the one that matches the user's intent. The score is between 0.0 and 1.0.</p>
/// <p>The score is a relative score, not an absolute score. The score may change based on improvements to Amazon Lex. </p>
pub fn nlu_intent_confidence(&self) -> std::option::Option<&str> {
self.nlu_intent_confidence.as_deref()
}
/// <p>One to four alternative intents that may be applicable to the user's intent.</p>
/// <p>Each alternative includes a score that indicates how confident Amazon Lex is that the intent matches the user's intent. The intents are sorted by the confidence score.</p>
pub fn alternative_intents(&self) -> std::option::Option<&str> {
self.alternative_intents.as_deref()
}
/// <p>Map of zero or more intent slots (name/value pairs) Amazon Lex detected from the user input during the conversation. The field is base-64 encoded.</p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code>, the default is <code>ORIGINAL_VALUE</code>.</p>
pub fn slots(&self) -> std::option::Option<&str> {
self.slots.as_deref()
}
/// <p> Map of key/value pairs representing the session-specific context information. </p>
pub fn session_attributes(&self) -> std::option::Option<&str> {
self.session_attributes.as_deref()
}
/// <p>The sentiment expressed in an utterance.</p>
/// <p>When the bot is configured to send utterances to Amazon Comprehend for sentiment analysis, this field contains the result of the analysis.</p>
pub fn sentiment_response(&self) -> std::option::Option<&str> {
self.sentiment_response.as_deref()
}
/// <p>You can only use this field in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>message</code> field is null. You should use the <code>encodedMessage</code> field instead.</p>
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> in its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
#[deprecated(
note = "The message field is deprecated, use the encodedMessage field instead. The message field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
pub fn message(&self) -> std::option::Option<&str> {
self.message.as_deref()
}
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> in its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
/// <p>The <code>encodedMessage</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
pub fn encoded_message(&self) -> std::option::Option<&str> {
self.encoded_message.as_deref()
}
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format for the client.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
pub fn message_format(&self) -> std::option::Option<&crate::model::MessageFormatType> {
self.message_format.as_ref()
}
/// <p>Identifies the current state of the user interaction. Amazon Lex returns one of the following values as <code>dialogState</code>. The client can optionally use this information to customize the user interface. </p>
/// <ul>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit the user's intent. Consider the following examples: </p> <p> For example, a user might utter an intent ("I want to order a pizza"). If Amazon Lex cannot infer the user intent from this utterance, it will return this dialog state. </p> </li>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response. </p> <p>For example, Amazon Lex wants user confirmation before fulfilling an intent. Instead of a simple "yes" or "no" response, a user might respond with additional information. For example, "yes, but make it a thick crust pizza" or "no, I want to order a drink." Amazon Lex can process such additional information (in these examples, update the crust type slot or change the intent from OrderPizza to OrderDrink). </p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting the value of a slot for the current intent. </p> <p> For example, suppose that in the response Amazon Lex sends this message: "What size pizza would you like?". A user might reply with the slot value (e.g., "medium"). The user might also provide additional information in the response (e.g., "medium thick crust pizza"). Amazon Lex can process such additional information appropriately. </p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function has successfully fulfilled the intent. </p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the request. </p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user failed. </p> <p> This can happen for various reasons, including that the user does not provide an appropriate response to prompts from the service (you can configure how many times Amazon Lex can prompt a user for specific information), or if the Lambda function fails to fulfill the intent. </p> </li>
/// </ul>
pub fn dialog_state(&self) -> std::option::Option<&crate::model::DialogState> {
self.dialog_state.as_ref()
}
/// <p> If the <code>dialogState</code> value is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value. </p>
pub fn slot_to_elicit(&self) -> std::option::Option<&str> {
self.slot_to_elicit.as_deref()
}
/// <p>The text used to process the request.</p>
/// <p>You can use this field only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>inputTranscript</code> field is null. You should use the <code>encodedInputTranscript</code> field instead.</p>
/// <p>If the input was an audio stream, the <code>inputTranscript</code> field contains the text extracted from the audio stream. This is the text that is actually processed to recognize intents and slot values. You can use this information to determine if Amazon Lex is correctly processing the audio that you send.</p>
#[deprecated(
note = "The inputTranscript field is deprecated, use the encodedInputTranscript field instead. The inputTranscript field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
pub fn input_transcript(&self) -> std::option::Option<&str> {
self.input_transcript.as_deref()
}
/// <p>The text used to process the request.</p>
/// <p>If the input was an audio stream, the <code>encodedInputTranscript</code> field contains the text extracted from the audio stream. This is the text that is actually processed to recognize intents and slot values. You can use this information to determine if Amazon Lex is correctly processing the audio that you send.</p>
/// <p>The <code>encodedInputTranscript</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
pub fn encoded_input_transcript(&self) -> std::option::Option<&str> {
self.encoded_input_transcript.as_deref()
}
/// <p>The prompt (or statement) to convey to the user. This is based on the bot configuration and context. For example, if Amazon Lex did not understand the user intent, it sends the <code>clarificationPrompt</code> configured for the bot. If the intent requires confirmation before taking the fulfillment action, it sends the <code>confirmationPrompt</code>. Another example: Suppose that the Lambda function successfully fulfilled the intent, and sent a message to convey to the user. Then Amazon Lex sends that message in the response. </p>
pub fn audio_stream(&self) -> &aws_smithy_http::byte_stream::ByteStream {
&self.audio_stream
}
/// <p>The version of the bot that responded to the conversation. You can use this information to help determine if one version of a bot is performing better than another version.</p>
pub fn bot_version(&self) -> std::option::Option<&str> {
self.bot_version.as_deref()
}
/// <p>The unique identifier for the session.</p>
pub fn session_id(&self) -> std::option::Option<&str> {
self.session_id.as_deref()
}
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
pub fn active_contexts(&self) -> std::option::Option<&str> {
self.active_contexts.as_deref()
}
}
impl std::fmt::Debug for PostContentOutput {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
let mut formatter = f.debug_struct("PostContentOutput");
formatter.field("content_type", &self.content_type);
formatter.field("intent_name", &self.intent_name);
formatter.field("nlu_intent_confidence", &self.nlu_intent_confidence);
formatter.field("alternative_intents", &self.alternative_intents);
formatter.field("slots", &self.slots);
formatter.field("session_attributes", &self.session_attributes);
formatter.field("sentiment_response", &self.sentiment_response);
formatter.field("message", &"*** Sensitive Data Redacted ***");
formatter.field("encoded_message", &"*** Sensitive Data Redacted ***");
formatter.field("message_format", &self.message_format);
formatter.field("dialog_state", &self.dialog_state);
formatter.field("slot_to_elicit", &self.slot_to_elicit);
formatter.field("input_transcript", &self.input_transcript);
formatter.field(
"encoded_input_transcript",
&"*** Sensitive Data Redacted ***",
);
formatter.field("audio_stream", &self.audio_stream);
formatter.field("bot_version", &self.bot_version);
formatter.field("session_id", &self.session_id);
formatter.field("active_contexts", &"*** Sensitive Data Redacted ***");
formatter.finish()
}
}
/// See [`PostContentOutput`](crate::output::PostContentOutput).
pub mod post_content_output {
/// A builder for [`PostContentOutput`](crate::output::PostContentOutput).
#[derive(std::default::Default)]
pub struct Builder {
pub(crate) content_type: std::option::Option<std::string::String>,
pub(crate) intent_name: std::option::Option<std::string::String>,
pub(crate) nlu_intent_confidence: std::option::Option<std::string::String>,
pub(crate) alternative_intents: std::option::Option<std::string::String>,
pub(crate) slots: std::option::Option<std::string::String>,
pub(crate) session_attributes: std::option::Option<std::string::String>,
pub(crate) sentiment_response: std::option::Option<std::string::String>,
pub(crate) message: std::option::Option<std::string::String>,
pub(crate) encoded_message: std::option::Option<std::string::String>,
pub(crate) message_format: std::option::Option<crate::model::MessageFormatType>,
pub(crate) dialog_state: std::option::Option<crate::model::DialogState>,
pub(crate) slot_to_elicit: std::option::Option<std::string::String>,
pub(crate) input_transcript: std::option::Option<std::string::String>,
pub(crate) encoded_input_transcript: std::option::Option<std::string::String>,
pub(crate) audio_stream: std::option::Option<aws_smithy_http::byte_stream::ByteStream>,
pub(crate) bot_version: std::option::Option<std::string::String>,
pub(crate) session_id: std::option::Option<std::string::String>,
pub(crate) active_contexts: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>Content type as specified in the <code>Accept</code> HTTP header in the request.</p>
pub fn content_type(mut self, input: impl Into<std::string::String>) -> Self {
self.content_type = Some(input.into());
self
}
/// <p>Content type as specified in the <code>Accept</code> HTTP header in the request.</p>
pub fn set_content_type(mut self, input: std::option::Option<std::string::String>) -> Self {
self.content_type = input;
self
}
/// <p>Current user intent that Amazon Lex is aware of.</p>
pub fn intent_name(mut self, input: impl Into<std::string::String>) -> Self {
self.intent_name = Some(input.into());
self
}
/// <p>Current user intent that Amazon Lex is aware of.</p>
pub fn set_intent_name(mut self, input: std::option::Option<std::string::String>) -> Self {
self.intent_name = input;
self
}
/// <p>Provides a score that indicates how confident Amazon Lex is that the returned intent is the one that matches the user's intent. The score is between 0.0 and 1.0.</p>
/// <p>The score is a relative score, not an absolute score. The score may change based on improvements to Amazon Lex. </p>
pub fn nlu_intent_confidence(mut self, input: impl Into<std::string::String>) -> Self {
self.nlu_intent_confidence = Some(input.into());
self
}
/// <p>Provides a score that indicates how confident Amazon Lex is that the returned intent is the one that matches the user's intent. The score is between 0.0 and 1.0.</p>
/// <p>The score is a relative score, not an absolute score. The score may change based on improvements to Amazon Lex. </p>
pub fn set_nlu_intent_confidence(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.nlu_intent_confidence = input;
self
}
/// <p>One to four alternative intents that may be applicable to the user's intent.</p>
/// <p>Each alternative includes a score that indicates how confident Amazon Lex is that the intent matches the user's intent. The intents are sorted by the confidence score.</p>
pub fn alternative_intents(mut self, input: impl Into<std::string::String>) -> Self {
self.alternative_intents = Some(input.into());
self
}
/// <p>One to four alternative intents that may be applicable to the user's intent.</p>
/// <p>Each alternative includes a score that indicates how confident Amazon Lex is that the intent matches the user's intent. The intents are sorted by the confidence score.</p>
pub fn set_alternative_intents(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.alternative_intents = input;
self
}
/// <p>Map of zero or more intent slots (name/value pairs) Amazon Lex detected from the user input during the conversation. The field is base-64 encoded.</p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code>, the default is <code>ORIGINAL_VALUE</code>.</p>
pub fn slots(mut self, input: impl Into<std::string::String>) -> Self {
self.slots = Some(input.into());
self
}
/// <p>Map of zero or more intent slots (name/value pairs) Amazon Lex detected from the user input during the conversation. The field is base-64 encoded.</p>
/// <p>Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the <code>valueSelectionStrategy</code> selected when the slot type was created or updated. If <code>valueSelectionStrategy</code> is set to <code>ORIGINAL_VALUE</code>, the value provided by the user is returned, if the user value is similar to the slot values. If <code>valueSelectionStrategy</code> is set to <code>TOP_RESOLUTION</code> Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don't specify a <code>valueSelectionStrategy</code>, the default is <code>ORIGINAL_VALUE</code>.</p>
pub fn set_slots(mut self, input: std::option::Option<std::string::String>) -> Self {
self.slots = input;
self
}
/// <p> Map of key/value pairs representing the session-specific context information. </p>
pub fn session_attributes(mut self, input: impl Into<std::string::String>) -> Self {
self.session_attributes = Some(input.into());
self
}
/// <p> Map of key/value pairs representing the session-specific context information. </p>
pub fn set_session_attributes(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.session_attributes = input;
self
}
/// <p>The sentiment expressed in an utterance.</p>
/// <p>When the bot is configured to send utterances to Amazon Comprehend for sentiment analysis, this field contains the result of the analysis.</p>
pub fn sentiment_response(mut self, input: impl Into<std::string::String>) -> Self {
self.sentiment_response = Some(input.into());
self
}
/// <p>The sentiment expressed in an utterance.</p>
/// <p>When the bot is configured to send utterances to Amazon Comprehend for sentiment analysis, this field contains the result of the analysis.</p>
pub fn set_sentiment_response(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.sentiment_response = input;
self
}
/// <p>You can only use this field in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>message</code> field is null. You should use the <code>encodedMessage</code> field instead.</p>
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> in its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
#[deprecated(
note = "The message field is deprecated, use the encodedMessage field instead. The message field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
pub fn message(mut self, input: impl Into<std::string::String>) -> Self {
self.message = Some(input.into());
self
}
/// <p>You can only use this field in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>message</code> field is null. You should use the <code>encodedMessage</code> field instead.</p>
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> in its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
#[deprecated(
note = "The message field is deprecated, use the encodedMessage field instead. The message field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
pub fn set_message(mut self, input: std::option::Option<std::string::String>) -> Self {
self.message = input;
self
}
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> in its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
/// <p>The <code>encodedMessage</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
pub fn encoded_message(mut self, input: impl Into<std::string::String>) -> Self {
self.encoded_message = Some(input.into());
self
}
/// <p>The message to convey to the user. The message can come from the bot's configuration or from a Lambda function.</p>
/// <p>If the intent is not configured with a Lambda function, or if the Lambda function returned <code>Delegate</code> as the <code>dialogAction.type</code> in its response, Amazon Lex decides on the next course of action and selects an appropriate message from the bot's configuration based on the current interaction context. For example, if Amazon Lex isn't able to understand user input, it uses a clarification prompt message.</p>
/// <p>When you create an intent you can assign messages to groups. When messages are assigned to groups Amazon Lex returns one message from each group in the response. The message field is an escaped JSON string containing the messages. For more information about the structure of the JSON string returned, see <code>msg-prompts-formats</code>.</p>
/// <p>If the Lambda function returns a message, Amazon Lex passes it to the client in its response.</p>
/// <p>The <code>encodedMessage</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
pub fn set_encoded_message(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.encoded_message = input;
self
}
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format for the client.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
pub fn message_format(mut self, input: crate::model::MessageFormatType) -> Self {
self.message_format = Some(input);
self
}
/// <p>The format of the response message. One of the following values:</p>
/// <ul>
/// <li> <p> <code>PlainText</code> - The message contains plain UTF-8 text.</p> </li>
/// <li> <p> <code>CustomPayload</code> - The message is a custom format for the client.</p> </li>
/// <li> <p> <code>SSML</code> - The message contains text formatted for voice output.</p> </li>
/// <li> <p> <code>Composite</code> - The message contains an escaped JSON object containing one or more messages from the groups that messages were assigned to when the intent was created.</p> </li>
/// </ul>
pub fn set_message_format(
mut self,
input: std::option::Option<crate::model::MessageFormatType>,
) -> Self {
self.message_format = input;
self
}
/// <p>Identifies the current state of the user interaction. Amazon Lex returns one of the following values as <code>dialogState</code>. The client can optionally use this information to customize the user interface. </p>
/// <ul>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit the user's intent. Consider the following examples: </p> <p> For example, a user might utter an intent ("I want to order a pizza"). If Amazon Lex cannot infer the user intent from this utterance, it will return this dialog state. </p> </li>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response. </p> <p>For example, Amazon Lex wants user confirmation before fulfilling an intent. Instead of a simple "yes" or "no" response, a user might respond with additional information. For example, "yes, but make it a thick crust pizza" or "no, I want to order a drink." Amazon Lex can process such additional information (in these examples, update the crust type slot or change the intent from OrderPizza to OrderDrink). </p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting the value of a slot for the current intent. </p> <p> For example, suppose that in the response Amazon Lex sends this message: "What size pizza would you like?". A user might reply with the slot value (e.g., "medium"). The user might also provide additional information in the response (e.g., "medium thick crust pizza"). Amazon Lex can process such additional information appropriately. </p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function has successfully fulfilled the intent. </p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the request. </p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user failed. </p> <p> This can happen for various reasons, including that the user does not provide an appropriate response to prompts from the service (you can configure how many times Amazon Lex can prompt a user for specific information), or if the Lambda function fails to fulfill the intent. </p> </li>
/// </ul>
pub fn dialog_state(mut self, input: crate::model::DialogState) -> Self {
self.dialog_state = Some(input);
self
}
/// <p>Identifies the current state of the user interaction. Amazon Lex returns one of the following values as <code>dialogState</code>. The client can optionally use this information to customize the user interface. </p>
/// <ul>
/// <li> <p> <code>ElicitIntent</code> - Amazon Lex wants to elicit the user's intent. Consider the following examples: </p> <p> For example, a user might utter an intent ("I want to order a pizza"). If Amazon Lex cannot infer the user intent from this utterance, it will return this dialog state. </p> </li>
/// <li> <p> <code>ConfirmIntent</code> - Amazon Lex is expecting a "yes" or "no" response. </p> <p>For example, Amazon Lex wants user confirmation before fulfilling an intent. Instead of a simple "yes" or "no" response, a user might respond with additional information. For example, "yes, but make it a thick crust pizza" or "no, I want to order a drink." Amazon Lex can process such additional information (in these examples, update the crust type slot or change the intent from OrderPizza to OrderDrink). </p> </li>
/// <li> <p> <code>ElicitSlot</code> - Amazon Lex is expecting the value of a slot for the current intent. </p> <p> For example, suppose that in the response Amazon Lex sends this message: "What size pizza would you like?". A user might reply with the slot value (e.g., "medium"). The user might also provide additional information in the response (e.g., "medium thick crust pizza"). Amazon Lex can process such additional information appropriately. </p> </li>
/// <li> <p> <code>Fulfilled</code> - Conveys that the Lambda function has successfully fulfilled the intent. </p> </li>
/// <li> <p> <code>ReadyForFulfillment</code> - Conveys that the client has to fulfill the request. </p> </li>
/// <li> <p> <code>Failed</code> - Conveys that the conversation with the user failed. </p> <p> This can happen for various reasons, including that the user does not provide an appropriate response to prompts from the service (you can configure how many times Amazon Lex can prompt a user for specific information), or if the Lambda function fails to fulfill the intent. </p> </li>
/// </ul>
pub fn set_dialog_state(
mut self,
input: std::option::Option<crate::model::DialogState>,
) -> Self {
self.dialog_state = input;
self
}
/// <p> If the <code>dialogState</code> value is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value. </p>
pub fn slot_to_elicit(mut self, input: impl Into<std::string::String>) -> Self {
self.slot_to_elicit = Some(input.into());
self
}
/// <p> If the <code>dialogState</code> value is <code>ElicitSlot</code>, returns the name of the slot for which Amazon Lex is eliciting a value. </p>
pub fn set_slot_to_elicit(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.slot_to_elicit = input;
self
}
/// <p>The text used to process the request.</p>
/// <p>You can use this field only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>inputTranscript</code> field is null. You should use the <code>encodedInputTranscript</code> field instead.</p>
/// <p>If the input was an audio stream, the <code>inputTranscript</code> field contains the text extracted from the audio stream. This is the text that is actually processed to recognize intents and slot values. You can use this information to determine if Amazon Lex is correctly processing the audio that you send.</p>
#[deprecated(
note = "The inputTranscript field is deprecated, use the encodedInputTranscript field instead. The inputTranscript field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
pub fn input_transcript(mut self, input: impl Into<std::string::String>) -> Self {
self.input_transcript = Some(input.into());
self
}
/// <p>The text used to process the request.</p>
/// <p>You can use this field only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR, and it-IT locales. In all other locales, the <code>inputTranscript</code> field is null. You should use the <code>encodedInputTranscript</code> field instead.</p>
/// <p>If the input was an audio stream, the <code>inputTranscript</code> field contains the text extracted from the audio stream. This is the text that is actually processed to recognize intents and slot values. You can use this information to determine if Amazon Lex is correctly processing the audio that you send.</p>
#[deprecated(
note = "The inputTranscript field is deprecated, use the encodedInputTranscript field instead. The inputTranscript field is available only in the de-DE, en-AU, en-GB, en-US, es-419, es-ES, es-US, fr-CA, fr-FR and it-IT locales."
)]
pub fn set_input_transcript(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.input_transcript = input;
self
}
/// <p>The text used to process the request.</p>
/// <p>If the input was an audio stream, the <code>encodedInputTranscript</code> field contains the text extracted from the audio stream. This is the text that is actually processed to recognize intents and slot values. You can use this information to determine if Amazon Lex is correctly processing the audio that you send.</p>
/// <p>The <code>encodedInputTranscript</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
pub fn encoded_input_transcript(mut self, input: impl Into<std::string::String>) -> Self {
self.encoded_input_transcript = Some(input.into());
self
}
/// <p>The text used to process the request.</p>
/// <p>If the input was an audio stream, the <code>encodedInputTranscript</code> field contains the text extracted from the audio stream. This is the text that is actually processed to recognize intents and slot values. You can use this information to determine if Amazon Lex is correctly processing the audio that you send.</p>
/// <p>The <code>encodedInputTranscript</code> field is base-64 encoded. You must decode the field before you can use the value.</p>
pub fn set_encoded_input_transcript(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.encoded_input_transcript = input;
self
}
/// <p>The prompt (or statement) to convey to the user. This is based on the bot configuration and context. For example, if Amazon Lex did not understand the user intent, it sends the <code>clarificationPrompt</code> configured for the bot. If the intent requires confirmation before taking the fulfillment action, it sends the <code>confirmationPrompt</code>. Another example: Suppose that the Lambda function successfully fulfilled the intent, and sent a message to convey to the user. Then Amazon Lex sends that message in the response. </p>
pub fn audio_stream(mut self, input: aws_smithy_http::byte_stream::ByteStream) -> Self {
self.audio_stream = Some(input);
self
}
/// <p>The prompt (or statement) to convey to the user. This is based on the bot configuration and context. For example, if Amazon Lex did not understand the user intent, it sends the <code>clarificationPrompt</code> configured for the bot. If the intent requires confirmation before taking the fulfillment action, it sends the <code>confirmationPrompt</code>. Another example: Suppose that the Lambda function successfully fulfilled the intent, and sent a message to convey to the user. Then Amazon Lex sends that message in the response. </p>
pub fn set_audio_stream(
mut self,
input: std::option::Option<aws_smithy_http::byte_stream::ByteStream>,
) -> Self {
self.audio_stream = input;
self
}
/// <p>The version of the bot that responded to the conversation. You can use this information to help determine if one version of a bot is performing better than another version.</p>
pub fn bot_version(mut self, input: impl Into<std::string::String>) -> Self {
self.bot_version = Some(input.into());
self
}
/// <p>The version of the bot that responded to the conversation. You can use this information to help determine if one version of a bot is performing better than another version.</p>
pub fn set_bot_version(mut self, input: std::option::Option<std::string::String>) -> Self {
self.bot_version = input;
self
}
/// <p>The unique identifier for the session.</p>
pub fn session_id(mut self, input: impl Into<std::string::String>) -> Self {
self.session_id = Some(input.into());
self
}
/// <p>The unique identifier for the session.</p>
pub fn set_session_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.session_id = input;
self
}
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
pub fn active_contexts(mut self, input: impl Into<std::string::String>) -> Self {
self.active_contexts = Some(input.into());
self
}
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
pub fn set_active_contexts(
mut self,
input: std::option::Option<std::string::String>,
) -> Self {
self.active_contexts = input;
self
}
/// Consumes the builder and constructs a [`PostContentOutput`](crate::output::PostContentOutput).
pub fn build(self) -> crate::output::PostContentOutput {
crate::output::PostContentOutput {
content_type: self.content_type,
intent_name: self.intent_name,
nlu_intent_confidence: self.nlu_intent_confidence,
alternative_intents: self.alternative_intents,
slots: self.slots,
session_attributes: self.session_attributes,
sentiment_response: self.sentiment_response,
message: self.message,
encoded_message: self.encoded_message,
message_format: self.message_format,
dialog_state: self.dialog_state,
slot_to_elicit: self.slot_to_elicit,
input_transcript: self.input_transcript,
encoded_input_transcript: self.encoded_input_transcript,
audio_stream: self.audio_stream.unwrap_or_default(),
bot_version: self.bot_version,
session_id: self.session_id,
active_contexts: self.active_contexts,
}
}
}
impl std::fmt::Debug for Builder {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
let mut formatter = f.debug_struct("Builder");
formatter.field("content_type", &self.content_type);
formatter.field("intent_name", &self.intent_name);
formatter.field("nlu_intent_confidence", &self.nlu_intent_confidence);
formatter.field("alternative_intents", &self.alternative_intents);
formatter.field("slots", &self.slots);
formatter.field("session_attributes", &self.session_attributes);
formatter.field("sentiment_response", &self.sentiment_response);
formatter.field("message", &"*** Sensitive Data Redacted ***");
formatter.field("encoded_message", &"*** Sensitive Data Redacted ***");
formatter.field("message_format", &self.message_format);
formatter.field("dialog_state", &self.dialog_state);
formatter.field("slot_to_elicit", &self.slot_to_elicit);
formatter.field("input_transcript", &self.input_transcript);
formatter.field(
"encoded_input_transcript",
&"*** Sensitive Data Redacted ***",
);
formatter.field("audio_stream", &self.audio_stream);
formatter.field("bot_version", &self.bot_version);
formatter.field("session_id", &self.session_id);
formatter.field("active_contexts", &"*** Sensitive Data Redacted ***");
formatter.finish()
}
}
}
impl PostContentOutput {
/// Creates a new builder-style object to manufacture [`PostContentOutput`](crate::output::PostContentOutput).
pub fn builder() -> crate::output::post_content_output::Builder {
crate::output::post_content_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq)]
pub struct GetSessionOutput {
/// <p>An array of information about the intents used in the session. The array can contain a maximum of three summaries. If more than three intents are used in the session, the <code>recentIntentSummaryView</code> operation contains information about the last three intents used.</p>
/// <p>If you set the <code>checkpointLabelFilter</code> parameter in the request, the array contains only the intents with the specified label.</p>
#[doc(hidden)]
pub recent_intent_summary_view: std::option::Option<std::vec::Vec<crate::model::IntentSummary>>,
/// <p>Map of key/value pairs representing the session-specific context information. It contains application information passed between Amazon Lex and a client application.</p>
#[doc(hidden)]
pub session_attributes:
std::option::Option<std::collections::HashMap<std::string::String, std::string::String>>,
/// <p>A unique identifier for the session.</p>
#[doc(hidden)]
pub session_id: std::option::Option<std::string::String>,
/// <p>Describes the current state of the bot.</p>
#[doc(hidden)]
pub dialog_action: std::option::Option<crate::model::DialogAction>,
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
#[doc(hidden)]
pub active_contexts: std::option::Option<std::vec::Vec<crate::model::ActiveContext>>,
}
impl GetSessionOutput {
/// <p>An array of information about the intents used in the session. The array can contain a maximum of three summaries. If more than three intents are used in the session, the <code>recentIntentSummaryView</code> operation contains information about the last three intents used.</p>
/// <p>If you set the <code>checkpointLabelFilter</code> parameter in the request, the array contains only the intents with the specified label.</p>
pub fn recent_intent_summary_view(
&self,
) -> std::option::Option<&[crate::model::IntentSummary]> {
self.recent_intent_summary_view.as_deref()
}
/// <p>Map of key/value pairs representing the session-specific context information. It contains application information passed between Amazon Lex and a client application.</p>
pub fn session_attributes(
&self,
) -> std::option::Option<&std::collections::HashMap<std::string::String, std::string::String>>
{
self.session_attributes.as_ref()
}
/// <p>A unique identifier for the session.</p>
pub fn session_id(&self) -> std::option::Option<&str> {
self.session_id.as_deref()
}
/// <p>Describes the current state of the bot.</p>
pub fn dialog_action(&self) -> std::option::Option<&crate::model::DialogAction> {
self.dialog_action.as_ref()
}
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
pub fn active_contexts(&self) -> std::option::Option<&[crate::model::ActiveContext]> {
self.active_contexts.as_deref()
}
}
impl std::fmt::Debug for GetSessionOutput {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
let mut formatter = f.debug_struct("GetSessionOutput");
formatter.field(
"recent_intent_summary_view",
&self.recent_intent_summary_view,
);
formatter.field("session_attributes", &"*** Sensitive Data Redacted ***");
formatter.field("session_id", &self.session_id);
formatter.field("dialog_action", &self.dialog_action);
formatter.field("active_contexts", &"*** Sensitive Data Redacted ***");
formatter.finish()
}
}
/// See [`GetSessionOutput`](crate::output::GetSessionOutput).
pub mod get_session_output {
/// A builder for [`GetSessionOutput`](crate::output::GetSessionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default)]
pub struct Builder {
pub(crate) recent_intent_summary_view:
std::option::Option<std::vec::Vec<crate::model::IntentSummary>>,
pub(crate) session_attributes: std::option::Option<
std::collections::HashMap<std::string::String, std::string::String>,
>,
pub(crate) session_id: std::option::Option<std::string::String>,
pub(crate) dialog_action: std::option::Option<crate::model::DialogAction>,
pub(crate) active_contexts: std::option::Option<std::vec::Vec<crate::model::ActiveContext>>,
}
impl Builder {
/// Appends an item to `recent_intent_summary_view`.
///
/// To override the contents of this collection use [`set_recent_intent_summary_view`](Self::set_recent_intent_summary_view).
///
/// <p>An array of information about the intents used in the session. The array can contain a maximum of three summaries. If more than three intents are used in the session, the <code>recentIntentSummaryView</code> operation contains information about the last three intents used.</p>
/// <p>If you set the <code>checkpointLabelFilter</code> parameter in the request, the array contains only the intents with the specified label.</p>
pub fn recent_intent_summary_view(mut self, input: crate::model::IntentSummary) -> Self {
let mut v = self.recent_intent_summary_view.unwrap_or_default();
v.push(input);
self.recent_intent_summary_view = Some(v);
self
}
/// <p>An array of information about the intents used in the session. The array can contain a maximum of three summaries. If more than three intents are used in the session, the <code>recentIntentSummaryView</code> operation contains information about the last three intents used.</p>
/// <p>If you set the <code>checkpointLabelFilter</code> parameter in the request, the array contains only the intents with the specified label.</p>
pub fn set_recent_intent_summary_view(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::IntentSummary>>,
) -> Self {
self.recent_intent_summary_view = input;
self
}
/// Adds a key-value pair to `session_attributes`.
///
/// To override the contents of this collection use [`set_session_attributes`](Self::set_session_attributes).
///
/// <p>Map of key/value pairs representing the session-specific context information. It contains application information passed between Amazon Lex and a client application.</p>
pub fn session_attributes(
mut self,
k: impl Into<std::string::String>,
v: impl Into<std::string::String>,
) -> Self {
let mut hash_map = self.session_attributes.unwrap_or_default();
hash_map.insert(k.into(), v.into());
self.session_attributes = Some(hash_map);
self
}
/// <p>Map of key/value pairs representing the session-specific context information. It contains application information passed between Amazon Lex and a client application.</p>
pub fn set_session_attributes(
mut self,
input: std::option::Option<
std::collections::HashMap<std::string::String, std::string::String>,
>,
) -> Self {
self.session_attributes = input;
self
}
/// <p>A unique identifier for the session.</p>
pub fn session_id(mut self, input: impl Into<std::string::String>) -> Self {
self.session_id = Some(input.into());
self
}
/// <p>A unique identifier for the session.</p>
pub fn set_session_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.session_id = input;
self
}
/// <p>Describes the current state of the bot.</p>
pub fn dialog_action(mut self, input: crate::model::DialogAction) -> Self {
self.dialog_action = Some(input);
self
}
/// <p>Describes the current state of the bot.</p>
pub fn set_dialog_action(
mut self,
input: std::option::Option<crate::model::DialogAction>,
) -> Self {
self.dialog_action = input;
self
}
/// Appends an item to `active_contexts`.
///
/// To override the contents of this collection use [`set_active_contexts`](Self::set_active_contexts).
///
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
pub fn active_contexts(mut self, input: crate::model::ActiveContext) -> Self {
let mut v = self.active_contexts.unwrap_or_default();
v.push(input);
self.active_contexts = Some(v);
self
}
/// <p>A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the <code>PostContent</code>, <code>PostText</code>, or <code>PutSession</code> operation.</p>
/// <p>You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.</p>
pub fn set_active_contexts(
mut self,
input: std::option::Option<std::vec::Vec<crate::model::ActiveContext>>,
) -> Self {
self.active_contexts = input;
self
}
/// Consumes the builder and constructs a [`GetSessionOutput`](crate::output::GetSessionOutput).
pub fn build(self) -> crate::output::GetSessionOutput {
crate::output::GetSessionOutput {
recent_intent_summary_view: self.recent_intent_summary_view,
session_attributes: self.session_attributes,
session_id: self.session_id,
dialog_action: self.dialog_action,
active_contexts: self.active_contexts,
}
}
}
impl std::fmt::Debug for Builder {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
let mut formatter = f.debug_struct("Builder");
formatter.field(
"recent_intent_summary_view",
&self.recent_intent_summary_view,
);
formatter.field("session_attributes", &"*** Sensitive Data Redacted ***");
formatter.field("session_id", &self.session_id);
formatter.field("dialog_action", &self.dialog_action);
formatter.field("active_contexts", &"*** Sensitive Data Redacted ***");
formatter.finish()
}
}
}
impl GetSessionOutput {
/// Creates a new builder-style object to manufacture [`GetSessionOutput`](crate::output::GetSessionOutput).
pub fn builder() -> crate::output::get_session_output::Builder {
crate::output::get_session_output::Builder::default()
}
}
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(std::clone::Clone, std::cmp::PartialEq, std::fmt::Debug)]
pub struct DeleteSessionOutput {
/// <p>The name of the bot associated with the session data.</p>
#[doc(hidden)]
pub bot_name: std::option::Option<std::string::String>,
/// <p>The alias in use for the bot associated with the session data.</p>
#[doc(hidden)]
pub bot_alias: std::option::Option<std::string::String>,
/// <p>The ID of the client application user.</p>
#[doc(hidden)]
pub user_id: std::option::Option<std::string::String>,
/// <p>The unique identifier for the session.</p>
#[doc(hidden)]
pub session_id: std::option::Option<std::string::String>,
}
impl DeleteSessionOutput {
/// <p>The name of the bot associated with the session data.</p>
pub fn bot_name(&self) -> std::option::Option<&str> {
self.bot_name.as_deref()
}
/// <p>The alias in use for the bot associated with the session data.</p>
pub fn bot_alias(&self) -> std::option::Option<&str> {
self.bot_alias.as_deref()
}
/// <p>The ID of the client application user.</p>
pub fn user_id(&self) -> std::option::Option<&str> {
self.user_id.as_deref()
}
/// <p>The unique identifier for the session.</p>
pub fn session_id(&self) -> std::option::Option<&str> {
self.session_id.as_deref()
}
}
/// See [`DeleteSessionOutput`](crate::output::DeleteSessionOutput).
pub mod delete_session_output {
/// A builder for [`DeleteSessionOutput`](crate::output::DeleteSessionOutput).
#[derive(std::clone::Clone, std::cmp::PartialEq, std::default::Default, std::fmt::Debug)]
pub struct Builder {
pub(crate) bot_name: std::option::Option<std::string::String>,
pub(crate) bot_alias: std::option::Option<std::string::String>,
pub(crate) user_id: std::option::Option<std::string::String>,
pub(crate) session_id: std::option::Option<std::string::String>,
}
impl Builder {
/// <p>The name of the bot associated with the session data.</p>
pub fn bot_name(mut self, input: impl Into<std::string::String>) -> Self {
self.bot_name = Some(input.into());
self
}
/// <p>The name of the bot associated with the session data.</p>
pub fn set_bot_name(mut self, input: std::option::Option<std::string::String>) -> Self {
self.bot_name = input;
self
}
/// <p>The alias in use for the bot associated with the session data.</p>
pub fn bot_alias(mut self, input: impl Into<std::string::String>) -> Self {
self.bot_alias = Some(input.into());
self
}
/// <p>The alias in use for the bot associated with the session data.</p>
pub fn set_bot_alias(mut self, input: std::option::Option<std::string::String>) -> Self {
self.bot_alias = input;
self
}
/// <p>The ID of the client application user.</p>
pub fn user_id(mut self, input: impl Into<std::string::String>) -> Self {
self.user_id = Some(input.into());
self
}
/// <p>The ID of the client application user.</p>
pub fn set_user_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.user_id = input;
self
}
/// <p>The unique identifier for the session.</p>
pub fn session_id(mut self, input: impl Into<std::string::String>) -> Self {
self.session_id = Some(input.into());
self
}
/// <p>The unique identifier for the session.</p>
pub fn set_session_id(mut self, input: std::option::Option<std::string::String>) -> Self {
self.session_id = input;
self
}
/// Consumes the builder and constructs a [`DeleteSessionOutput`](crate::output::DeleteSessionOutput).
pub fn build(self) -> crate::output::DeleteSessionOutput {
crate::output::DeleteSessionOutput {
bot_name: self.bot_name,
bot_alias: self.bot_alias,
user_id: self.user_id,
session_id: self.session_id,
}
}
}
}
impl DeleteSessionOutput {
/// Creates a new builder-style object to manufacture [`DeleteSessionOutput`](crate::output::DeleteSessionOutput).
pub fn builder() -> crate::output::delete_session_output::Builder {
crate::output::delete_session_output::Builder::default()
}
}