AI-Ollama-Client
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lib/AI/Ollama/GenerateCompletionResponse.pm view on Meta::CPAN
AI::Ollama::GenerateCompletionResponse -
=head1 SYNOPSIS
my $obj = AI::Ollama::GenerateCompletionResponse->new();
...
=cut
sub as_hash( $self ) {
return { $self->%* }
}
=head1 PROPERTIES
=head2 C<< context >>
An encoding of the conversation used in this response, this can be sent in the next request to keep a conversational memory.
=cut
has 'context' => (
is => 'ro',
isa => ArrayRef[Int],
);
=head2 C<< created_at >>
Date on which a model was created.
=cut
has 'created_at' => (
is => 'ro',
isa => Str,
);
=head2 C<< done >>
Whether the response has completed.
=cut
has 'done' => (
is => 'ro',
);
=head2 C<< eval_count >>
Number of tokens the response.
=cut
has 'eval_count' => (
is => 'ro',
isa => Int,
);
=head2 C<< eval_duration >>
Time in nanoseconds spent generating the response.
=cut
has 'eval_duration' => (
is => 'ro',
isa => Int,
);
=head2 C<< load_duration >>
Time spent in nanoseconds loading the model.
=cut
has 'load_duration' => (
is => 'ro',
isa => Int,
);
=head2 C<< model >>
The model name.
Model names follow a `model:tag` format. Some examples are `orca-mini:3b-q4_1` and `llama2:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
=cut
has 'model' => (
is => 'ro',
isa => Str,
);
=head2 C<< prompt_eval_count >>
Number of tokens in the prompt.
=cut
has 'prompt_eval_count' => (
is => 'ro',
isa => Int,
);
=head2 C<< prompt_eval_duration >>
Time spent in nanoseconds evaluating the prompt.
=cut
has 'prompt_eval_duration' => (
is => 'ro',
isa => Int,
);
=head2 C<< response >>
The response for a given prompt with a provided model.
=cut
has 'response' => (
is => 'ro',
isa => Str,
);
=head2 C<< total_duration >>
Time spent generating the response.
=cut
has 'total_duration' => (
is => 'ro',
isa => Int,
);
1;
( run in 1.883 second using v1.01-cache-2.11-cpan-39bf76dae61 )