AI-Embedding

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MANIFEST  view on Meta::CPAN

Makefile.PL
MANIFEST			This list of files
README
t/00-load.t
t/01-openai.t
t/02-test.t
t/manifest.t
t/pod-coverage.t
t/pod.t
t/version.t
META.yml                                 Module YAML meta-data (added by MakeMaker)
META.json                                Module JSON meta-data (added by MakeMaker)

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

     return $header{$self->{'api'}};
 }

 # Return Embedding as a CSV string
 sub embedding {
     my ($self, $text, $verbose) = @_;

     my $response = $self->_get_embedding($text);
     if ($response->{'success'}) {
         my $embedding = decode_json($response->{'content'});
         return join (',', @{$embedding->{'data'}[0]->{'embedding'}});
     }
     $self->{'error'} = 'HTTP Error - ' . $response->{'reason'};
     return $response if defined $verbose;
     return undef;
 }

 # Return Embedding as an array
 sub raw_embedding {
     my ($self, $text, $verbose) = @_;

     my $response = $self->_get_embedding($text);
     if ($response->{'success'}) {
         my $embedding = decode_json($response->{'content'});
         return @{$embedding->{'data'}[0]->{'embedding'}};
     }
     $self->{'error'} = 'HTTP Error - ' . $response->{'reason'};
     return $response if defined $verbose;
     return undef;
 }

 # Return Test Embedding
 sub test_embedding {
     my ($self, $text, $dimension) = @_;
     $self->{'error'} = '';

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

    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(

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

=head2 error

Returns the last error message or an empty string if B<success> returned true

=head2 embedding

    my $csv_embedding = $embedding->embedding('Some text passage', [$verbose]);

Generates an embedding for the given text and returns it as a comma-separated string. The C<embedding> method takes a single parameter, the text to generate the embedding for.

Returns a (rather long) string that can be stored in a C<TEXT> database field.

If the method call fails it sets the L</"error"> message and returns C<undef>.  If the optional C<verbose> parameter is true, the complete L<HTTP::Tiny> response object is also returned to aid with debugging issues when using this module.

=head2 raw_embedding

    my @raw_embedding = $embedding->raw_embedding('Some text passage', [$verbose]);

Generates an embedding for the given text and returns it as an array. The C<raw_embedding> method takes a single parameter, the text to generate the embedding for.

It is not normally necessary to use this method as the Embedding will almost always be used as a single homogeneous unit.

If the method call fails it sets the L</"error"> message and returns C<undef>.  If the optional C<verbose> parameter is true, the complete L<HTTP::Tiny> response object is also returned to aid with debugging issues when using this module.

=head2 test_embedding

    my $test_embedding = $embedding->test_embedding('Some text passage', $dimensions);

Used for testing code without making a chargeable call to the API.

Provides a CSV string of the same size and format as L<embedding> but with meaningless random data.

Returns a random embedding.  Both parameters are optional.  If a text string is provided, the returned embedding will always be the same random embedding otherwise it will be random and different every time.  The C<dimension> parameter controls the n...

=head2 comparator

    $embedding->comparator($csv_embedding2);

Sets a vector as a C<comparator> for future comparisons and returns a reference to a method for using the C<comparator>.

The B<comparator> method takes a single parameter, the comma-separated Embedding string to use as the comparator.