Struct aws_sdk_lexruntime::Client

pub struct Client { /* private fields */ }

Client for Amazon Lex Runtime Service

Client for invoking operations on Amazon Lex Runtime Service. Each operation on Amazon Lex Runtime Service is a method on this this struct. .send() MUST be invoked on the generated operations to dispatch the request to the service.

Constructing a Client

A Config is required to construct a client. For most use cases, the aws-config crate should be used to automatically resolve this config using aws_config::load_from_env(), since this will resolve an SdkConfig which can be shared across multiple different AWS SDK clients. This config resolution process can be customized by calling aws_config::from_env() instead, which returns a ConfigLoader that uses the builder pattern to customize the default config.

In the simplest case, creating a client looks as follows:

let config = aws_config::load_from_env().await;
let client = aws_sdk_lexruntime::Client::new(&config);

Occasionally, SDKs may have additional service-specific that can be set on the Config that is absent from SdkConfig, or slightly different settings for a specific client may be desired. The Config struct implements From<&SdkConfig>, so setting these specific settings can be done as follows:

let sdk_config = ::aws_config::load_from_env().await;
let config = aws_sdk_lexruntime::config::Builder::from(&sdk_config)
    .some_service_specific_setting("value")
    .build();

See the aws-config docs and Config for more information on customizing configuration.

Note: Client construction is expensive due to connection thread pool initialization, and should be done once at application start-up.

Using the Client

A client has a function for every operation that can be performed by the service. For example, the DeleteSession operation has a Client::delete_session, function which returns a builder for that operation. The fluent builder ultimately has a send() function that returns an async future that returns a result, as illustrated below:

let result = client.delete_session()
    .bot_name("example")
    .send()
    .await;

The underlying HTTP requests that get made by this can be modified with the customize_operation function on the fluent builder. See the customize module for more information.

Implementations

impl Client

pub fn delete_session(&self) -> DeleteSessionFluentBuilder

Constructs a fluent builder for the DeleteSession operation.

• The fluent builder is configurable:
  • bot_name(impl ::std::convert::Into<String>) / set_bot_name(Option<String>):

    The name of the bot that contains the session data.

  • bot_alias(impl ::std::convert::Into<String>) / set_bot_alias(Option<String>):

    The alias in use for the bot that contains the session data.

  • user_id(impl ::std::convert::Into<String>) / set_user_id(Option<String>):

    The identifier of the user associated with the session data.

• On success, responds with DeleteSessionOutput with field(s):
  • bot_name(Option<String>):

    The name of the bot associated with the session data.

  • bot_alias(Option<String>):

    The alias in use for the bot associated with the session data.

  • user_id(Option<String>):

    The ID of the client application user.

  • session_id(Option<String>):

    The unique identifier for the session.

• On failure, responds with SdkError<DeleteSessionError>

impl Client

pub fn get_session(&self) -> GetSessionFluentBuilder

Constructs a fluent builder for the GetSession operation.

• The fluent builder is configurable:
  • bot_name(impl ::std::convert::Into<String>) / set_bot_name(Option<String>):

    The name of the bot that contains the session data.

  • bot_alias(impl ::std::convert::Into<String>) / set_bot_alias(Option<String>):

    The alias in use for the bot that contains the session data.

  • user_id(impl ::std::convert::Into<String>) / set_user_id(Option<String>):

    The ID of the client application user. Amazon Lex uses this to identify a user’s conversation with your bot.

  • checkpoint_label_filter(impl ::std::convert::Into<String>) / set_checkpoint_label_filter(Option<String>):

    A string used to filter the intents returned in the recentIntentSummaryView structure.

    When you specify a filter, only intents with their checkpointLabel field set to that string are returned.

• On success, responds with GetSessionOutput with field(s):
  • recent_intent_summary_view(Option<Vec<IntentSummary>>):

    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 recentIntentSummaryView operation contains information about the last three intents used.

    If you set the checkpointLabelFilter parameter in the request, the array contains only the intents with the specified label.

  • session_attributes(Option<HashMap<String, String>>):

    Map of key/value pairs representing the session-specific context information. It contains application information passed between Amazon Lex and a client application.

  • session_id(Option<String>):

    A unique identifier for the session.

  • dialog_action(Option<DialogAction>):

    Describes the current state of the bot.

  • active_contexts(Option<Vec<ActiveContext>>):

    A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the PostContent, PostText, or PutSession operation.

    You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.

• On failure, responds with SdkError<GetSessionError>

impl Client

pub fn post_content(&self) -> PostContentFluentBuilder

Constructs a fluent builder for the PostContent operation.

• The fluent builder is configurable:
  • bot_name(impl ::std::convert::Into<String>) / set_bot_name(Option<String>):

    Name of the Amazon Lex bot.

  • bot_alias(impl ::std::convert::Into<String>) / set_bot_alias(Option<String>):

    Alias of the Amazon Lex bot.

  • user_id(impl ::std::convert::Into<String>) / set_user_id(Option<String>):

    The ID of the client application user. Amazon Lex uses this to identify a user’s conversation with your bot. At runtime, each request must contain the userID field.

    To decide the user ID to use for your application, consider the following factors.

    • The userID field must not contain any personally identifiable information of the user, for example, name, personal identification numbers, or other end user personal information.

    • If you want a user to start a conversation on one device and continue on another device, use a user-specific identifier.

    • If you want the same user to be able to have two independent conversations on two different devices, choose a device-specific identifier.

    • A user can’t have two independent conversations with two different versions of the same bot. For example, a user can’t have a conversation with the PROD and BETA versions of the same bot. If you anticipate that a user will need to have conversation with two different versions, for example, while testing, include the bot alias in the user ID to separate the two conversations.

  • session_attributes(impl ::std::convert::Into<String>) / set_session_attributes(Option<String>):

    You pass this value as the x-amz-lex-session-attributes HTTP header.

    Application-specific information passed between Amazon Lex and a client application. The value must be a JSON serialized and base64 encoded map with string keys and values. The total size of the sessionAttributes and requestAttributes headers is limited to 12 KB.

    For more information, see Setting Session Attributes.

  • request_attributes(impl ::std::convert::Into<String>) / set_request_attributes(Option<String>):

    You pass this value as the x-amz-lex-request-attributes HTTP header.

    Request-specific information passed between Amazon Lex and a client application. The value must be a JSON serialized and base64 encoded map with string keys and values. The total size of the requestAttributes and sessionAttributes headers is limited to 12 KB.

    The namespace x-amz-lex: is reserved for special attributes. Don’t create any request attributes with the prefix x-amz-lex:.

    For more information, see Setting Request Attributes.

  • content_type(impl ::std::convert::Into<String>) / set_content_type(Option<String>):

    You pass this value as the Content-Type HTTP header.

    Indicates the audio format or text. The header value must start with one of the following prefixes:

    • PCM format, audio data must be in little-endian byte order.

      • audio/l16; rate=16000; channels=1

      • audio/x-l16; sample-rate=16000; channel-count=1

      • audio/lpcm; sample-rate=8000; sample-size-bits=16; channel-count=1; is-big-endian=false

    • Opus format

      • audio/x-cbr-opus-with-preamble; preamble-size=0; bit-rate=256000; frame-size-milliseconds=4

    • Text format

      • text/plain; charset=utf-8

  • accept(impl ::std::convert::Into<String>) / set_accept(Option<String>):

    You pass this value as the Accept HTTP header.

    The message Amazon Lex returns in the response can be either text or speech based on the Accept HTTP header value in the request.

    • If the value is text/plain; charset=utf-8, Amazon Lex returns text in the response.

    • If the value begins with audio/, Amazon Lex returns speech in the response. Amazon Lex uses Amazon Polly to generate the speech (using the configuration you specified in the Accept header). For example, if you specify audio/mpeg as the value, Amazon Lex returns speech in the MPEG format.

    • If the value is audio/pcm, the speech returned is audio/pcm in 16-bit, little endian format.

    • The following are the accepted values:

      • audio/mpeg

      • audio/ogg

      • audio/pcm

      • text/plain; charset=utf-8

      • audio/* (defaults to mpeg)

  • input_stream(ByteStream) / set_input_stream(ByteStream):

    User input in PCM or Opus audio format or text format as described in the Content-Type HTTP header.

    You can stream audio data to Amazon Lex or you can create a local buffer that captures all of the audio data before sending. In general, you get better performance if you stream audio data rather than buffering the data locally.

  • active_contexts(impl ::std::convert::Into<String>) / set_active_contexts(Option<String>):

    A list of contexts active for the request. A context can be activated when a previous intent is fulfilled, or by including the context in the request,

    If you don’t specify a list of contexts, Amazon Lex will use the current list of contexts for the session. If you specify an empty list, all contexts for the session are cleared.

• On success, responds with PostContentOutput with field(s):
  • content_type(Option<String>):

    Content type as specified in the Accept HTTP header in the request.

  • intent_name(Option<String>):

    Current user intent that Amazon Lex is aware of.

  • nlu_intent_confidence(Option<String>):

    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.

    The score is a relative score, not an absolute score. The score may change based on improvements to Amazon Lex.

  • alternative_intents(Option<String>):

    One to four alternative intents that may be applicable to the user’s intent.

    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.

  • slots(Option<String>):

    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.

    Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the valueSelectionStrategy selected when the slot type was created or updated. If valueSelectionStrategy is set to ORIGINAL_VALUE, the value provided by the user is returned, if the user value is similar to the slot values. If valueSelectionStrategy is set to TOP_RESOLUTION Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don’t specify a valueSelectionStrategy, the default is ORIGINAL_VALUE.

  • session_attributes(Option<String>):

    Map of key/value pairs representing the session-specific context information.

  • sentiment_response(Option<String>):

    The sentiment expressed in an utterance.

    When the bot is configured to send utterances to Amazon Comprehend for sentiment analysis, this field contains the result of the analysis.

  • message(Option<String>):

    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 message field is null. You should use the encodedMessage field instead.

    The message to convey to the user. The message can come from the bot’s configuration or from a Lambda function.

    If the intent is not configured with a Lambda function, or if the Lambda function returned Delegate as the dialogAction.type 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.

    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 msg-prompts-formats.

    If the Lambda function returns a message, Amazon Lex passes it to the client in its response.

  • encoded_message(Option<String>):

    The message to convey to the user. The message can come from the bot’s configuration or from a Lambda function.

    If the intent is not configured with a Lambda function, or if the Lambda function returned Delegate as the dialogAction.type 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.

    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 msg-prompts-formats.

    If the Lambda function returns a message, Amazon Lex passes it to the client in its response.

    The encodedMessage field is base-64 encoded. You must decode the field before you can use the value.

  • message_format(Option<MessageFormatType>):

    The format of the response message. One of the following values:

    • PlainText - The message contains plain UTF-8 text.

    • CustomPayload - The message is a custom format for the client.

    • SSML - The message contains text formatted for voice output.

    • Composite - 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.

  • dialog_state(Option<DialogState>):

    Identifies the current state of the user interaction. Amazon Lex returns one of the following values as dialogState. The client can optionally use this information to customize the user interface.

    • ElicitIntent - Amazon Lex wants to elicit the user’s intent. Consider the following examples:

      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.

    • ConfirmIntent - Amazon Lex is expecting a “yes” or “no” response.

      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).

    • ElicitSlot - Amazon Lex is expecting the value of a slot for the current intent.

      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.

    • Fulfilled - Conveys that the Lambda function has successfully fulfilled the intent.

    • ReadyForFulfillment - Conveys that the client has to fulfill the request.

    • Failed - Conveys that the conversation with the user failed.

      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.

  • slot_to_elicit(Option<String>):

    If the dialogState value is ElicitSlot, returns the name of the slot for which Amazon Lex is eliciting a value.

  • input_transcript(Option<String>):

    The text used to process the request.

    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 inputTranscript field is null. You should use the encodedInputTranscript field instead.

    If the input was an audio stream, the inputTranscript 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.

  • encoded_input_transcript(Option<String>):

    The text used to process the request.

    If the input was an audio stream, the encodedInputTranscript 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.

    The encodedInputTranscript field is base-64 encoded. You must decode the field before you can use the value.

  • audio_stream(ByteStream):

    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 clarificationPrompt configured for the bot. If the intent requires confirmation before taking the fulfillment action, it sends the confirmationPrompt. 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.

  • bot_version(Option<String>):

    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.

  • session_id(Option<String>):

    The unique identifier for the session.

  • active_contexts(Option<String>):

    A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the PostContent, PostText, or PutSession operation.

    You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.

• On failure, responds with SdkError<PostContentError>

impl Client

pub fn post_text(&self) -> PostTextFluentBuilder

Constructs a fluent builder for the PostText operation.

• The fluent builder is configurable:
  • bot_name(impl ::std::convert::Into<String>) / set_bot_name(Option<String>):

    The name of the Amazon Lex bot.

  • bot_alias(impl ::std::convert::Into<String>) / set_bot_alias(Option<String>):

    The alias of the Amazon Lex bot.

  • user_id(impl ::std::convert::Into<String>) / set_user_id(Option<String>):

    The ID of the client application user. Amazon Lex uses this to identify a user’s conversation with your bot. At runtime, each request must contain the userID field.

    To decide the user ID to use for your application, consider the following factors.

    • The userID field must not contain any personally identifiable information of the user, for example, name, personal identification numbers, or other end user personal information.

    • If you want a user to start a conversation on one device and continue on another device, use a user-specific identifier.

    • If you want the same user to be able to have two independent conversations on two different devices, choose a device-specific identifier.

    • A user can’t have two independent conversations with two different versions of the same bot. For example, a user can’t have a conversation with the PROD and BETA versions of the same bot. If you anticipate that a user will need to have conversation with two different versions, for example, while testing, include the bot alias in the user ID to separate the two conversations.

  • session_attributes(HashMap<String, String>) / set_session_attributes(Option<HashMap<String, String>>):

    Application-specific information passed between Amazon Lex and a client application.

    For more information, see Setting Session Attributes.

  • request_attributes(HashMap<String, String>) / set_request_attributes(Option<HashMap<String, String>>):

    Request-specific information passed between Amazon Lex and a client application.

    The namespace x-amz-lex: is reserved for special attributes. Don’t create any request attributes with the prefix x-amz-lex:.

    For more information, see Setting Request Attributes.

  • input_text(impl ::std::convert::Into<String>) / set_input_text(Option<String>):

    The text that the user entered (Amazon Lex interprets this text).

  • active_contexts(Vec<ActiveContext>) / set_active_contexts(Option<Vec<ActiveContext>>):

    A list of contexts active for the request. A context can be activated when a previous intent is fulfilled, or by including the context in the request,

    If you don’t specify a list of contexts, Amazon Lex will use the current list of contexts for the session. If you specify an empty list, all contexts for the session are cleared.

• On success, responds with PostTextOutput with field(s):
  • intent_name(Option<String>):

    The current user intent that Amazon Lex is aware of.

  • nlu_intent_confidence(Option<IntentConfidence>):

    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 Confidence Scores.

    The score is a relative score, not an absolute score. The score may change based on improvements to Amazon Lex.

  • alternative_intents(Option<Vec<PredictedIntent>>):

    One to four alternative intents that may be applicable to the user’s intent.

    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.

  • slots(Option<HashMap<String, String>>):

    The intent slots that Amazon Lex detected from the user input in the conversation.

    Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the valueSelectionStrategy selected when the slot type was created or updated. If valueSelectionStrategy is set to ORIGINAL_VALUE, the value provided by the user is returned, if the user value is similar to the slot values. If valueSelectionStrategy is set to TOP_RESOLUTION Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don’t specify a valueSelectionStrategy, the default is ORIGINAL_VALUE.

  • session_attributes(Option<HashMap<String, String>>):

    A map of key-value pairs representing the session-specific context information.

  • message(Option<String>):

    The message to convey to the user. The message can come from the bot’s configuration or from a Lambda function.

    If the intent is not configured with a Lambda function, or if the Lambda function returned Delegate as the dialogAction.type 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.

    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 msg-prompts-formats.

    If the Lambda function returns a message, Amazon Lex passes it to the client in its response.

  • sentiment_response(Option<SentimentResponse>):

    The sentiment expressed in and utterance.

    When the bot is configured to send utterances to Amazon Comprehend for sentiment analysis, this field contains the result of the analysis.

  • message_format(Option<MessageFormatType>):

    The format of the response message. One of the following values:

    • PlainText - The message contains plain UTF-8 text.

    • CustomPayload - The message is a custom format defined by the Lambda function.

    • SSML - The message contains text formatted for voice output.

    • Composite - 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.

  • dialog_state(Option<DialogState>):

    Identifies the current state of the user interaction. Amazon Lex returns one of the following values as dialogState. The client can optionally use this information to customize the user interface.

    • ElicitIntent - Amazon Lex wants to elicit user intent.

      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.

    • ConfirmIntent - Amazon Lex is expecting a “yes” or “no” response.

      For example, Amazon Lex wants user confirmation before fulfilling an intent.

      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).

    • ElicitSlot - Amazon Lex is expecting a slot value for the current intent.

      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.

    • Fulfilled - Conveys that the Lambda function configured for the intent has successfully fulfilled the intent.

    • ReadyForFulfillment - Conveys that the client has to fulfill the intent.

    • Failed - Conveys that the conversation with the user failed.

      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.

  • slot_to_elicit(Option<String>):

    If the dialogState value is ElicitSlot, returns the name of the slot for which Amazon Lex is eliciting a value.

  • response_card(Option<ResponseCard>):

    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).

  • session_id(Option<String>):

    A unique identifier for the session.

  • bot_version(Option<String>):

    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.

  • active_contexts(Option<Vec<ActiveContext>>):

    A list of active contexts for the session. A context can be set when an intent is fulfilled or by calling the PostContent, PostText, or PutSession operation.

    You can use a context to control the intents that can follow up an intent, or to modify the operation of your application.

• On failure, responds with SdkError<PostTextError>

impl Client

pub fn put_session(&self) -> PutSessionFluentBuilder

Constructs a fluent builder for the PutSession operation.

• The fluent builder is configurable:
  • bot_name(impl ::std::convert::Into<String>) / set_bot_name(Option<String>):

    The name of the bot that contains the session data.

  • bot_alias(impl ::std::convert::Into<String>) / set_bot_alias(Option<String>):

    The alias in use for the bot that contains the session data.

  • user_id(impl ::std::convert::Into<String>) / set_user_id(Option<String>):

    The ID of the client application user. Amazon Lex uses this to identify a user’s conversation with your bot.

  • session_attributes(HashMap<String, String>) / set_session_attributes(Option<HashMap<String, String>>):

    Map of key/value pairs representing the session-specific context information. It contains application information passed between Amazon Lex and a client application.

  • dialog_action(DialogAction) / set_dialog_action(Option<DialogAction>):

    Sets the next action that the bot should take to fulfill the conversation.

  • recent_intent_summary_view(Vec<IntentSummary>) / set_recent_intent_summary_view(Option<Vec<IntentSummary>>):

    A summary of the recent intents for the bot. You can use the intent summary view to set a checkpoint label on an intent and modify attributes of intents. You can also use it to remove or add intent summary objects to the list.

    An intent that you modify or add to the list must make sense for the bot. For example, the intent name must be valid for the bot. You must provide valid values for:

    • intentName

    • slot names

    • slotToElict

    If you send the recentIntentSummaryView parameter in a PutSession request, the contents of the new summary view replaces the old summary view. For example, if a GetSession request returns three intents in the summary view and you call PutSession with one intent in the summary view, the next call to GetSession will only return one intent.

  • accept(impl ::std::convert::Into<String>) / set_accept(Option<String>):

    The message that Amazon Lex returns in the response can be either text or speech based depending on the value of this field.

    • If the value is text/plain; charset=utf-8, Amazon Lex returns text in the response.

    • If the value begins with audio/, Amazon Lex returns speech in the response. Amazon Lex uses Amazon Polly to generate the speech in the configuration that you specify. For example, if you specify audio/mpeg as the value, Amazon Lex returns speech in the MPEG format.

    • If the value is audio/pcm, the speech is returned as audio/pcm in 16-bit, little endian format.

    • The following are the accepted values:

      • audio/mpeg

      • audio/ogg

      • audio/pcm

      • audio/* (defaults to mpeg)

      • text/plain; charset=utf-8

  • active_contexts(Vec<ActiveContext>) / set_active_contexts(Option<Vec<ActiveContext>>):

    A list of contexts active for the request. A context can be activated when a previous intent is fulfilled, or by including the context in the request,

    If you don’t specify a list of contexts, Amazon Lex will use the current list of contexts for the session. If you specify an empty list, all contexts for the session are cleared.

• On success, responds with PutSessionOutput with field(s):
  • content_type(Option<String>):

    Content type as specified in the Accept HTTP header in the request.

  • intent_name(Option<String>):

    The name of the current intent.

  • slots(Option<String>):

    Map of zero or more intent slots Amazon Lex detected from the user input during the conversation.

    Amazon Lex creates a resolution list containing likely values for a slot. The value that it returns is determined by the valueSelectionStrategy selected when the slot type was created or updated. If valueSelectionStrategy is set to ORIGINAL_VALUE, the value provided by the user is returned, if the user value is similar to the slot values. If valueSelectionStrategy is set to TOP_RESOLUTION Amazon Lex returns the first value in the resolution list or, if there is no resolution list, null. If you don’t specify a valueSelectionStrategy the default is ORIGINAL_VALUE.

  • session_attributes(Option<String>):

    Map of key/value pairs representing session-specific context information.

  • message(Option<String>):

    The next message that should be presented to the user.

    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 message field is null. You should use the encodedMessage field instead.

  • encoded_message(Option<String>):

    The next message that should be presented to the user.

    The encodedMessage field is base-64 encoded. You must decode the field before you can use the value.

  • message_format(Option<MessageFormatType>):

    The format of the response message. One of the following values:

    • PlainText - The message contains plain UTF-8 text.

    • CustomPayload - The message is a custom format for the client.

    • SSML - The message contains text formatted for voice output.

    • Composite - 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.

  • dialog_state(Option<DialogState>):

    • ConfirmIntent - Amazon Lex is expecting a “yes” or “no” response to confirm the intent before fulfilling an intent.

    • ElicitIntent - Amazon Lex wants to elicit the user’s intent.

    • ElicitSlot - Amazon Lex is expecting the value of a slot for the current intent.

    • Failed - 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.

    • Fulfilled - Conveys that the Lambda function has sucessfully fulfilled the intent.

    • ReadyForFulfillment - Conveys that the client has to fulfill the intent.

  • slot_to_elicit(Option<String>):

    If the dialogState is ElicitSlot, returns the name of the slot for which Amazon Lex is eliciting a value.

  • audio_stream(ByteStream):

    The audio version of the message to convey to the user.

  • session_id(Option<String>):

    A unique identifier for the session.

  • active_contexts(Option<String>):

    A list of active contexts for the session.

• On failure, responds with SdkError<PutSessionError>

impl Client

pub fn from_conf(conf: Config) -> Self

Creates a new client from the service Config.

Panics

• This method will panic if the conf is missing an async sleep implementation. If you experience this panic, set the sleep_impl on the Config passed into this function to fix it.
• This method will panic if the conf is missing an HTTP connector. If you experience this panic, set the http_connector on the Config passed into this function to fix it.

pub fn config(&self) -> &Config

Returns the client’s configuration.

impl Client

pub fn new(sdk_config: &SdkConfig) -> Self

Creates a new client from an SDK Config.

Panics

• This method will panic if the sdk_config is missing an async sleep implementation. If you experience this panic, set the sleep_impl on the Config passed into this function to fix it.
• This method will panic if the sdk_config is missing an HTTP connector. If you experience this panic, set the http_connector on the Config passed into this function to fix it.

Trait Implementations

impl Clone for Client

fn clone(&self) -> Client

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Client

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.