view release on metacpan or  search on metacpan

lib/AI/Embedding.pm  view on Meta::CPAN

    my %vector;
    my @embed = split /,/, $embed_string;
    for (my $i = 0; $i < @embed; $i++) {
       $vector{'feature' . $i} = $embed[$i];
   }
   return \%vector;
}

# Return a comparator to compare to a set vector
sub comparator {
    my($self, $embed) = @_;
    $self->{'error'} = '';

    my $vector1 = $self->_make_vector($embed);
    return sub {
        my($embed2) = @_;
        my $vector2 = $self->_make_vector($embed2);
        return $self->_compare_vector($vector1, $vector2);
    };
}

# Compare 2 Embeddings
sub compare {
    my ($self, $embed1, $embed2) = @_;

    my $vector1 = $self->_make_vector($embed1);
    my $vector2;
    if (defined $embed2) {
        $vector2 = $self->_make_vector($embed2);
    } else {
        $vector2 = $self->{'comparator'};
    }

    if (!defined $vector2) {
        $self->{'error'} = 'Nothing to compare!';
        return;
    }

    if (scalar keys %$vector1 != scalar keys %$vector2) {
        $self->{'error'} = 'Embeds are unequal length';
        return;
    }

    return $self->_compare_vector($vector1, $vector2);
}

# Compare 2 Vectors
sub _compare_vector {
    my ($self, $vector1, $vector2) = @_;
    my $cs = Data::CosineSimilarity->new;
    $cs->add( label1 => $vector1 );
    $cs->add( label2 => $vector2 );
    return $cs->similarity('label1', 'label2')->cosine;
}

1;

__END__

=encoding utf8

=head1 NAME

AI::Embedding - Perl module for working with text embeddings using various APIs

=head1 VERSION

Version 1.11

=head1 SYNOPSIS

    use AI::Embedding;

    my $embedding = AI::Embedding->new(
        api => 'OpenAI',
        key => 'your-api-key'
    );

    my $csv_embedding  = $embedding->embedding('Some sample text');
    my $test_embedding = $embedding->test_embedding('Some sample text');
    my @raw_embedding  = $embedding->raw_embedding('Some sample text');

    my $cmp = $embedding->comparator($csv_embedding2);

    my $similarity = $cmp->($csv_embedding1);
    my $similarity_with_other_embedding = $embedding->compare($csv_embedding1, $csv_embedding2);

=head1 DESCRIPTION

The L<AI::Embedding> module provides an interface for working with text embeddings using various APIs. It currently supports the L<OpenAI|https://www.openai.com> L<Embeddings API|https://platform.openai.com/docs/guides/embeddings/what-are-embeddings>...

Embeddings allow the meaning of passages of text to be compared for similarity.  This is more natural and useful to humans than using traditional keyword based comparisons.

An Embedding is a multi-dimensional vector representing the meaning of a piece of text.  The Embedding vector is created by an AI Model.  The default model (OpenAI's C<text-embedding-ada-002>) produces a 1536 dimensional vector.  The resulting vector...

=head2 Comparator

Embeddings are used to compare similarity of meaning between two passages of text.  A typical work case is to store a number of pieces of text (e.g. articles or blogs) in a database and compare each one to some user supplied search text.  L<AI::Embed...

Alternatively, the C<comparator> method can be called with one Embedding.  The C<comparator> returns a reference to a method that takes a single Embedding to be compared to the Embedding from which the Comparator was created.

When comparing multiple Embeddings to the same Embedding (such as search text) it is faster to use a C<comparator>.

=head1 CONSTRUCTOR

=head2 new

    my $embedding = AI::Embedding->new(
        api         => 'OpenAI',
        key         => 'your-api-key',
        model       => 'text-embedding-ada-002',
    );

Creates a new AI::Embedding object. It requires the 'key' parameter. The 'key' parameter is the API key provided by the service provider and is required.

Parameters:

=over

=item *