PNG  IHDR;IDATxܻn0K )(pA 7LeG{ §㻢|ذaÆ 6lذaÆ 6lذaÆ 6lom$^yذag5bÆ 6lذaÆ 6lذa{ 6lذaÆ `}HFkm,mӪôô! x|'ܢ˟;E:9&ᶒ}{v]n&6 h_tڠ͵-ҫZ;Z$.Pkž)!o>}leQfJTu іچ\X=8Rن4`Vwl>nG^is"ms$ui?wbs[m6K4O.4%/bC%t Mז -lG6mrz2s%9s@-k9=)kB5\+͂Zsٲ Rn~GRC wIcIn7jJhۛNCS|j08yiHKֶۛkɈ+;SzL/F*\Ԕ#"5m2[S=gnaPeғL lذaÆ 6l^ḵaÆ 6lذaÆ 6lذa; _ذaÆ 6lذaÆ 6lذaÆ RIENDB` package Paws::LexRuntime; use Moose; sub service { 'runtime.lex' } sub signing_name { 'lex' } sub version { '2016-11-28' } sub flattened_arrays { 0 } has max_attempts => (is => 'ro', isa => 'Int', default => 5); has retry => (is => 'ro', isa => 'HashRef', default => sub { { base => 'rand', type => 'exponential', growth_factor => 2 } }); has retriables => (is => 'ro', isa => 'ArrayRef', default => sub { [ ] }); with 'Paws::API::Caller', 'Paws::API::EndpointResolver', 'Paws::Net::V4Signature', 'Paws::Net::RestJsonCaller'; sub DeleteSession { my $self = shift; my $call_object = $self->new_with_coercions('Paws::LexRuntime::DeleteSession', @_); return $self->caller->do_call($self, $call_object); } sub GetSession { my $self = shift; my $call_object = $self->new_with_coercions('Paws::LexRuntime::GetSession', @_); return $self->caller->do_call($self, $call_object); } sub PostContent { my $self = shift; my $call_object = $self->new_with_coercions('Paws::LexRuntime::PostContent', @_); return $self->caller->do_call($self, $call_object); } sub PostText { my $self = shift; my $call_object = $self->new_with_coercions('Paws::LexRuntime::PostText', @_); return $self->caller->do_call($self, $call_object); } sub PutSession { my $self = shift; my $call_object = $self->new_with_coercions('Paws::LexRuntime::PutSession', @_); return $self->caller->do_call($self, $call_object); } sub operations { qw/DeleteSession GetSession PostContent PostText PutSession / } 1; ### main pod documentation begin ### =head1 NAME Paws::LexRuntime - Perl Interface to AWS Amazon Lex Runtime Service =head1 SYNOPSIS use Paws; my $obj = Paws->service('LexRuntime'); my $res = $obj->Method( Arg1 => $val1, Arg2 => [ 'V1', 'V2' ], # if Arg3 is an object, the HashRef will be used as arguments to the constructor # of the arguments type Arg3 => { Att1 => 'Val1' }, # if Arg4 is an array of objects, the HashRefs will be passed as arguments to # the constructor of the arguments type Arg4 => [ { Att1 => 'Val1' }, { Att1 => 'Val2' } ], ); =head1 DESCRIPTION Amazon Lex provides both build and runtime endpoints. Each endpoint provides a set of operations (API). Your conversational bot uses the runtime API to understand user utterances (user input text or voice). For example, suppose a user says "I want pizza", your bot sends this input to Amazon Lex using the runtime API. Amazon Lex recognizes that the user request is for the OrderPizza intent (one of the intents defined in the bot). Then Amazon Lex engages in user conversation on behalf of the bot to elicit required information (slot values, such as pizza size and crust type), and then performs fulfillment activity (that you configured when you created the bot). You use the build-time API to create and manage your Amazon Lex bot. For a list of build-time operations, see the build-time API, . For the AWS API documentation, see L =head1 METHODS =head2 DeleteSession =over =item BotAlias => Str =item BotName => Str =item UserId => Str =back Each argument is described in detail in: L Returns: a L instance Removes session information for a specified bot, alias, and user ID. =head2 GetSession =over =item BotAlias => Str =item BotName => Str =item UserId => Str =item [CheckpointLabelFilter => Str] =back Each argument is described in detail in: L Returns: a L instance Returns session information for a specified bot, alias, and user ID. =head2 PostContent =over =item BotAlias => Str =item BotName => Str =item ContentType => Str =item InputStream => Str =item UserId => Str =item [Accept => Str] =item [ActiveContexts => Str] =item [RequestAttributes => Str] =item [SessionAttributes => Str] =back Each argument is described in detail in: L Returns: a L instance Sends user input (text or speech) to Amazon Lex. Clients use this API to send text and audio requests to Amazon Lex at runtime. Amazon Lex interprets the user input using the machine learning model that it built for the bot. The C operation supports audio input at 8kHz and 16kHz. You can use 8kHz audio to achieve higher speech recognition accuracy in telephone audio applications. In response, Amazon Lex returns the next message to convey to the user. Consider the following example messages: =over =item * For a user input "I would like a pizza," Amazon Lex might return a response with a message eliciting slot data (for example, C): "What size pizza would you like?". =item * After the user provides all of the pizza order information, Amazon Lex might return a response with a message to get user confirmation: "Order the pizza?". =item * After the user replies "Yes" to the confirmation prompt, Amazon Lex might return a conclusion statement: "Thank you, your cheese pizza has been ordered.". =back Not all Amazon Lex messages require a response from the user. For example, conclusion statements do not require a response. Some messages require only a yes or no response. In addition to the C, Amazon Lex provides additional context about the message in the response that you can use to enhance client behavior, such as displaying the appropriate client user interface. Consider the following examples: =over =item * If the message is to elicit slot data, Amazon Lex returns the following context information: =over =item * C header set to C =item * C header set to the intent name in the current context =item * C header set to the slot name for which the C is eliciting information =item * C header set to a map of slots configured for the intent with their current values =back =item * If the message is a confirmation prompt, the C header is set to C and the C header is omitted. =item * If the message is a clarification prompt configured for the intent, indicating that the user intent is not understood, the C header is set to C and the C header is omitted. =back In addition, Amazon Lex also returns your application-specific C. For more information, see Managing Conversation Context (https://docs.aws.amazon.com/lex/latest/dg/context-mgmt.html). =head2 PostText =over =item BotAlias => Str =item BotName => Str =item InputText => Str =item UserId => Str =item [ActiveContexts => ArrayRef[L]] =item [RequestAttributes => L] =item [SessionAttributes => L] =back Each argument is described in detail in: L Returns: a L instance Sends user input to Amazon Lex. Client applications can use this API to send requests to Amazon Lex at runtime. Amazon Lex then interprets the user input using the machine learning model it built for the bot. In response, Amazon Lex returns the next C to convey to the user an optional C to display. Consider the following example messages: =over =item * For a user input "I would like a pizza", Amazon Lex might return a response with a message eliciting slot data (for example, PizzaSize): "What size pizza would you like?" =item * After the user provides all of the pizza order information, Amazon Lex might return a response with a message to obtain user confirmation "Proceed with the pizza order?". =item * After the user replies to a confirmation prompt with a "yes", Amazon Lex might return a conclusion statement: "Thank you, your cheese pizza has been ordered.". =back Not all Amazon Lex messages require a user response. For example, a conclusion statement does not require a response. Some messages require only a "yes" or "no" user response. In addition to the C, Amazon Lex provides additional context about the message in the response that you might use to enhance client behavior, for example, to display the appropriate client user interface. These are the C, C, C, and C fields in the response. Consider the following examples: =over =item * If the message is to elicit slot data, Amazon Lex returns the following context information: =over =item * C set to ElicitSlot =item * C set to the intent name in the current context =item * C set to the slot name for which the C is eliciting information =item * C set to a map of slots, configured for the intent, with currently known values =back =item * If the message is a confirmation prompt, the C is set to ConfirmIntent and C is set to null. =item * If the message is a clarification prompt (configured for the intent) that indicates that user intent is not understood, the C is set to ElicitIntent and C is set to null. =back In addition, Amazon Lex also returns your application-specific C. For more information, see Managing Conversation Context (https://docs.aws.amazon.com/lex/latest/dg/context-mgmt.html). =head2 PutSession =over =item BotAlias => Str =item BotName => Str =item UserId => Str =item [Accept => Str] =item [ActiveContexts => ArrayRef[L]] =item [DialogAction => L] =item [RecentIntentSummaryView => ArrayRef[L]] =item [SessionAttributes => L] =back Each argument is described in detail in: L Returns: a L instance Creates a new session or modifies an existing session with an Amazon Lex bot. Use this operation to enable your application to set the state of the bot. For more information, see Managing Sessions (https://docs.aws.amazon.com/lex/latest/dg/how-session-api.html). =head1 PAGINATORS Paginator methods are helpers that repetively call methods that return partial results =head1 SEE ALSO This service class forms part of L =head1 BUGS and CONTRIBUTIONS The source code is located here: L Please report bugs to: L =cut