<?php
/**
* Azure OpenAI Embeddings integration
*/
namespace Classifai\Providers\Azure;
use Classifai\Providers\OpenAI\EmbeddingCalculations;
use Classifai\Providers\OpenAI\Tokenizer;
use Classifai\Normalizer;
use Classifai\Features\Classification;
use Classifai\Features\Feature;
use Classifai\EmbeddingsScheduler;
use WP_Error;
class Embeddings extends OpenAI {
const ID = 'azure_openai_embeddings';
/**
* Embeddings URL fragment.
*
* @var string
*/
protected $embeddings_url = 'openai/deployments/{deployment-id}/embeddings';
/**
* Embeddings API version.
*
* @var string
*/
protected $api_version = '2024-02-01';
/**
* Maximum number of tokens our model supports.
*
* @var int
*/
protected $max_tokens = 8191;
/**
* Number of dimensions for the embeddings.
*
* @var int
*/
protected $dimensions = 512;
/**
* Maximum number of terms we process.
*
* @var int
*/
protected $max_terms = 5000;
/**
* NLU features that are supported by this provider.
*
* @var array
*/
public $nlu_features = [];
/**
* Scheduler instance.
*
* @var EmbeddingsScheduler|null
*/
private static $scheduler_instance = null;
/**
* OpenAI Embeddings constructor.
*
* @param Feature $feature_instance The feature instance.
*/
public function __construct( $feature_instance = null ) {
$this->feature_instance = $feature_instance;
if (
$this->feature_instance &&
method_exists( $this->feature_instance, 'get_supported_taxonomies' )
) {
$settings = get_option( $this->feature_instance->get_option_name(), [] );
$post_types = isset( $settings['post_types'] ) ? $settings['post_types'] : [ 'post' => 1 ];
foreach ( $this->feature_instance->get_supported_taxonomies( $post_types ) as $tax => $label ) {
$this->nlu_features[ $tax ] = [
'feature' => $label,
'threshold' => __( 'Threshold (%)', 'classifai' ),
'threshold_default' => 75,
'taxonomy' => __( 'Taxonomy', 'classifai' ),
'taxonomy_default' => $tax,
];
}
}
}
/**
* Get the number of dimensions for the embeddings.
*
* @return int
*/
public function get_dimensions(): int {
/**
* Filter the dimensions we want for each embedding.
*
* Useful if you want to increase or decrease the length
* of each embedding.
*
* @since 3.1.0
* @hook classifai_azure_openai_embeddings_dimensions
*
* @param {int} $dimensions The default dimensions.
*
* @return {int} The dimensions.
*/
return apply_filters( 'classifai_azure_openai_embeddings_dimensions', $this->dimensions );
}
/**
* Get the maximum number of tokens.
*
* @return int
*/
public function get_max_tokens(): int {
/**
* Filter the max number of tokens.
*
* Useful if you want to change to a different model
* that uses a different number of tokens, or be more
* strict on the amount of tokens that can be used.
*
* @since 3.1.0
* @hook classifai_azure_openai_embeddings_max_tokens
*
* @param {int} $model The default maximum tokens.
*
* @return {int} The maximum tokens.
*/
return apply_filters( 'classifai_azure_openai_embeddings_max_tokens', $this->max_tokens );
}
/**
* Get the maximum number of terms we process.
*
* @return int
*/
public function get_max_terms(): int {
/**
* Filter the max number of terms.
*
* Default for this is 5000 but this filter can be used to change
* this, either decreasing to help with performance or increasing
* to ensure we consider more terms.
*
* @since 3.1.0
* @hook classifai_azure_openai_embeddings_max_terms
*
* @param {int} $terms The default maximum terms.
*
* @return {int} The maximum terms.
*/
return apply_filters( 'classifai_azure_openai_embeddings_max_terms', $this->max_terms );
}
/**
* Register what we need for the plugin.
*
* This only fires if can_register returns true.
*/
public function register() {
add_filter( 'classifai_feature_classification_get_default_settings', [ $this, 'modify_default_feature_settings' ], 10, 2 );
$feature = new Classification();
self::$scheduler_instance = new EmbeddingsScheduler(
'classifai_schedule_generate_azure_embedding_job',
__( 'Azure OpenAI Embeddings', 'classifai' )
);
self::$scheduler_instance->init();
add_action( 'classifai_schedule_generate_azure_embedding_job', [ $this, 'generate_embedding_job' ], 10, 4 );
if (
! $feature->is_feature_enabled() ||
$feature->get_feature_provider_instance()::ID !== static::ID
) {
return;
}
add_action( 'created_term', [ $this, 'generate_embeddings_for_term' ] );
add_action( 'edited_terms', [ $this, 'generate_embeddings_for_term' ] );
add_action( 'wp_ajax_get_post_classifier_embeddings_preview_data', array( $this, 'get_post_classifier_embeddings_preview_data' ) );
}
/**
* Modify the default settings for the classification feature.
*
* @param array $settings Current settings.
* @param Feature $feature_instance The feature instance.
* @return array
*/
public function modify_default_feature_settings( array $settings, $feature_instance ): array {
remove_filter( 'classifai_feature_classification_get_default_settings', [ $this, 'modify_default_feature_settings' ], 10, 2 );
if ( $feature_instance->get_settings( 'provider' ) !== static::ID ) {
return $settings;
}
add_filter( 'classifai_feature_classification_get_default_settings', [ $this, 'modify_default_feature_settings' ], 10, 2 );
$defaults = [];
foreach ( array_keys( $feature_instance->get_supported_taxonomies() ) as $tax ) {
$enabled = 'category' === $tax ? true : false;
$defaults[ $tax ] = $enabled;
$defaults[ $tax . '_threshold' ] = 75;
$defaults[ $tax . '_taxonomy' ] = $tax;
}
return array_merge( $settings, $defaults );
}
/**
* Sanitization for the options being saved.
*
* @param array $new_settings Array of settings about to be saved.
* @return array The sanitized settings to be saved.
*/
public function sanitize_settings( array $new_settings ): array {
$new_settings = parent::sanitize_settings( $new_settings );
// Trigger embedding generation for all terms in enabled taxonomies if the feature is on.
if ( isset( $new_settings['status'] ) && 1 === (int) $new_settings['status'] ) {
foreach ( array_keys( $this->nlu_features ) as $feature_name ) {
if ( isset( $new_settings[ $feature_name ] ) && 1 === (int) $new_settings[ $feature_name ] ) {
$this->trigger_taxonomy_update( $feature_name );
}
}
}
return $new_settings;
}
/**
* Build and return the API endpoint based on settings.
*
* @param Feature $feature Feature instance
* @return string
*/
protected function prep_api_url( Feature $feature = null ): string {
$settings = $feature->get_settings( static::ID );
$endpoint = $settings['endpoint_url'] ?? '';
$deployment = $settings['deployment'] ?? '';
if ( ! $endpoint ) {
return '';
}
if ( $deployment ) {
$endpoint = trailingslashit( $endpoint ) . str_replace( '{deployment-id}', $deployment, $this->embeddings_url );
$endpoint = add_query_arg( 'api-version', $this->api_version, $endpoint );
}
return $endpoint;
}
/**
* Authenticates our credentials.
*
* @param string $url Endpoint URL.
* @param string $api_key Api Key.
* @param string $deployment Deployment name.
* @return bool|WP_Error
*/
protected function authenticate_credentials( string $url, string $api_key, string $deployment ) {
$rtn = false;
// This does basically the same thing that prep_api_url does but when running authentication,
// we don't have settings saved yet, which prep_api_url needs.
$endpoint = trailingslashit( $url ) . str_replace( '{deployment-id}', $deployment, $this->embeddings_url );
$endpoint = add_query_arg( 'api-version', $this->api_version, $endpoint );
$request = wp_remote_post(
$endpoint,
[
'headers' => [
'api-key' => $api_key,
'Content-Type' => 'application/json',
],
'body' => wp_json_encode(
[
'input' => 'This is a test',
'dimensions' => $this->get_dimensions(),
]
),
]
);
if ( ! is_wp_error( $request ) ) {
$response = json_decode( wp_remote_retrieve_body( $request ) );
if ( ! empty( $response->error ) ) {
$rtn = new WP_Error( 'auth', $response->error->message );
} else {
$rtn = true;
}
}
return $rtn;
}
/**
* Get the threshold for the similarity calculation.
*
* @param string $taxonomy Taxonomy slug.
* @return float
*/
public function get_threshold( string $taxonomy = '' ): float {
$settings = ( new Classification() )->get_settings();
$threshold = 1;
if ( ! empty( $taxonomy ) ) {
$threshold = isset( $settings[ $taxonomy . '_threshold' ] ) ? $settings[ $taxonomy . '_threshold' ] : 75;
}
// Convert $threshold (%) to decimal.
$threshold = 1 - ( (float) $threshold / 100 );
/**
* Filter the threshold for the similarity calculation.
*
* @since 2.5.0
* @hook classifai_threshold
*
* @param {float} $threshold The threshold to use.
* @param {string} $taxonomy The taxonomy to get the threshold for.
*
* @return {float} The threshold to use.
*/
return apply_filters( 'classifai_threshold', $threshold, $taxonomy );
}
/**
* Get the data to preview terms.
*
* @return array
*/
public function get_post_classifier_embeddings_preview_data(): array {
$nonce = isset( $_POST['nonce'] ) ? sanitize_text_field( wp_unslash( $_POST['nonce'] ) ) : false;
if ( ! $nonce || ! wp_verify_nonce( $nonce, 'classifai-previewer-action' ) ) {
wp_send_json_error( esc_html__( 'Failed nonce check.', 'classifai' ) );
}
$post_id = filter_input( INPUT_POST, 'post_id', FILTER_SANITIZE_NUMBER_INT );
$embeddings = $this->generate_embeddings_for_post( $post_id, true );
$embeddings_terms = [];
// Add terms to this item based on embedding data.
if ( $embeddings && ! is_wp_error( $embeddings ) ) {
$embeddings_terms = $this->get_terms( $embeddings );
}
return wp_send_json_success( $embeddings_terms );
}
/**
* Trigger embedding generation for content being saved.
*
* @param int $post_id ID of post being saved.
* @param bool $force Whether to force generation of embeddings even if they already exist. Default false.
* @return array|WP_Error
*/
public function generate_embeddings_for_post( int $post_id, bool $force = false ) {
// Don't run on autosaves.
if ( defined( 'DOING_AUTOSAVE' ) && DOING_AUTOSAVE ) {
return new WP_Error( 'invalid', esc_html__( 'Classification will not work during an autosave.', 'classifai' ) );
}
// Ensure the user has permissions to edit.
if ( ! current_user_can( 'edit_post', $post_id ) && ( ! defined( 'WP_CLI' ) || ! WP_CLI ) ) {
return new WP_Error( 'invalid', esc_html__( 'User does not have permission to classify this item.', 'classifai' ) );
}
/**
* Filter whether ClassifAI should classify an item.
*
* Default is true, return false to skip classifying.
*
* @since 3.1.0
* @hook classifai_azure_openai_embeddings_should_classify
*
* @param {bool} $should_classify Whether the item should be classified. Default `true`, return `false` to skip.
* @param {int} $id The ID of the item to be considered for classification.
* @param {string} $type The type of item to be considered for classification.
*
* @return {bool} Whether the item should be classified.
*/
if ( ! apply_filters( 'classifai_azure_openai_embeddings_should_classify', true, $post_id, 'post' ) ) {
return new WP_Error( 'invalid', esc_html__( 'Classification is disabled for this item.', 'classifai' ) );
}
// Try to use the stored embeddings first.
if ( ! $force ) {
$embeddings = get_post_meta( $post_id, 'classifai_azure_openai_embeddings', true );
if ( ! empty( $embeddings ) ) {
return $embeddings;
}
}
// Chunk the post content down.
$embeddings = [];
$content = $this->get_normalized_content( $post_id, 'post' );
$content_chunks = $this->chunk_content( $content );
// Get the embeddings for each chunk.
if ( ! empty( $content_chunks ) ) {
$tokenizer = new Tokenizer( $this->get_max_tokens() );
$total_tokens = $tokenizer->tokens_in_content( $content );
// If we have a lot of tokens, we need to get embeddings for each chunk individually.
if ( $this->max_tokens < $total_tokens ) {
foreach ( $content_chunks as $chunk ) {
$embedding = $this->generate_embedding( $chunk );
if ( $embedding && ! is_wp_error( $embedding ) ) {
$embeddings[] = array_map( 'floatval', $embedding );
}
}
} else {
// Otherwise let's get all embeddings in a single request.
$all_embeddings = $this->generate_embeddings( $content_chunks );
if ( $all_embeddings && ! is_wp_error( $all_embeddings ) ) {
$embeddings = array_map(
function ( $embedding ) {
return array_map( 'floatval', $embedding );
},
$all_embeddings
);
}
}
}
// Store the embeddings for future use.
if ( ! empty( $embeddings ) ) {
update_post_meta( $post_id, 'classifai_azure_openai_embeddings', $embeddings );
}
return $embeddings;
}
/**
* Add terms to a post based on embeddings.
*
* @param int $post_id ID of post to set terms on.
* @param array $embeddings Embeddings data.
* @param bool $link Whether to link the terms or not.
* @return array|WP_Error
*/
public function set_terms( int $post_id = 0, array $embeddings = [], bool $link = true ) {
if ( ! $post_id || ! get_post( $post_id ) ) {
return new WP_Error( 'post_id_required', esc_html__( 'A valid post ID is required to set terms.', 'classifai' ) );
}
if ( empty( $embeddings ) ) {
return new WP_Error( 'data_required', esc_html__( 'Valid embedding data is required to set terms.', 'classifai' ) );
}
$embeddings_similarity = [];
// Iterate through all of our embedding chunks and run our similarity calculations.
foreach ( $embeddings as $embedding ) {
$embeddings_similarity = array_merge( $embeddings_similarity, $this->get_embeddings_similarity( $embedding ) );
}
// Ensure we have some results.
if ( empty( $embeddings_similarity ) ) {
return new WP_Error( 'invalid', esc_html__( 'No matching terms found.', 'classifai' ) );
}
// Sort the results by similarity.
usort(
$embeddings_similarity,
function ( $a, $b ) {
return $a['similarity'] <=> $b['similarity'];
}
);
// Remove duplicates based on the term_id field.
$uniques = array_unique( array_column( $embeddings_similarity, 'term_id' ) );
$embeddings_similarity = array_intersect_key( $embeddings_similarity, $uniques );
$sorted_results = [];
// Sort the results into taxonomy buckets.
foreach ( $embeddings_similarity as $item ) {
$sorted_results[ $item['taxonomy'] ][] = $item;
}
$return = [];
/**
* If $link is true, immediately link all the terms
* to the item.
*
* If it is false, build an array of term data that
* can be used to display the terms in the UI.
*/
foreach ( $sorted_results as $tax => $terms ) {
if ( $link ) {
wp_set_object_terms( $post_id, array_map( 'absint', array_column( $terms, 'term_id' ) ), $tax, false );
} else {
$terms_to_link = [];
foreach ( $terms as $term ) {
$found_term = get_term( $term['term_id'] );
if ( $found_term && ! is_wp_error( $found_term ) ) {
$terms_to_link[ $found_term->name ] = $term['term_id'];
}
}
$return[ $tax ] = $terms_to_link;
}
}
return empty( $return ) ? $embeddings_similarity : $return;
}
/**
* Determine which terms best match a post based on embeddings.
*
* @param array $embeddings An array of embeddings data.
* @return array|WP_Error
*/
public function get_terms( array $embeddings = [] ) {
if ( empty( $embeddings ) ) {
return new WP_Error( 'data_required', esc_html__( 'Valid embedding data is required to get terms.', 'classifai' ) );
}
$embeddings_similarity = [];
// Iterate through all of our embedding chunks and run our similarity calculations.
foreach ( $embeddings as $embedding ) {
$embeddings_similarity = array_merge( $embeddings_similarity, $this->get_embeddings_similarity( $embedding, false ) );
}
// Ensure we have some results.
if ( empty( $embeddings_similarity ) ) {
return new WP_Error( 'invalid', esc_html__( 'No matching terms found.', 'classifai' ) );
}
// Sort the results by similarity.
usort(
$embeddings_similarity,
function ( $a, $b ) {
return $a['similarity'] <=> $b['similarity'];
}
);
// Remove duplicates based on the term_id field.
$uniques = array_unique( array_column( $embeddings_similarity, 'term_id' ) );
$embeddings_similarity = array_intersect_key( $embeddings_similarity, $uniques );
$sorted_results = [];
// Sort the results into taxonomy buckets.
foreach ( $embeddings_similarity as $item ) {
$sorted_results[ $item['taxonomy'] ][] = $item;
}
// Prepare the results.
$index = 0;
$results = [];
foreach ( $sorted_results as $tax => $terms ) {
// Get the taxonomy name.
$taxonomy = get_taxonomy( $tax );
$tax_name = $taxonomy->labels->singular_name;
// Setup our taxonomy object.
$results[] = new \stdClass();
$results[ $index ]->{$tax_name} = [];
foreach ( $terms as $term ) {
// Convert $similarity to percentage.
$similarity = round( ( 1 - $term['similarity'] ), 10 );
// Store the results.
$results[ $index ]->{$tax_name}[] = [ // phpcs:ignore Squiz.PHP.DisallowMultipleAssignments.Found
'label' => get_term( $term['term_id'] )->name,
'score' => $similarity,
];
}
++$index;
}
return $results;
}
/**
* Get the similarity between an embedding and all terms.
*
* @param array $embedding Embedding data.
* @param bool $consider_threshold Whether to consider the threshold setting.
* @return array
*/
private function get_embeddings_similarity( array $embedding, bool $consider_threshold = true ): array {
$feature = new Classification();
$embedding_similarity = [];
$taxonomies = $feature->get_all_feature_taxonomies();
$calculations = new EmbeddingCalculations();
foreach ( $taxonomies as $tax ) {
$exclude = [];
if ( is_numeric( $tax ) ) {
continue;
}
if ( 'tags' === $tax ) {
$tax = 'post_tag';
}
if ( 'categories' === $tax ) {
$tax = 'category';
// Exclude the uncategorized term.
$uncat_term = get_term_by( 'name', 'Uncategorized', 'category' );
if ( $uncat_term ) {
$exclude = [ $uncat_term->term_id ];
}
}
$terms = get_terms(
[
'taxonomy' => $tax,
'orderby' => 'count',
'order' => 'DESC',
'hide_empty' => false,
'fields' => 'ids',
'meta_key' => 'classifai_azure_openai_embeddings', // phpcs:ignore WordPress.DB.SlowDBQuery.slow_db_query_meta_key
'number' => $this->get_max_terms(),
'exclude' => $exclude, // phpcs:ignore WordPressVIPMinimum.Performance.WPQueryParams.PostNotIn_exclude
]
);
if ( is_wp_error( $terms ) || empty( $terms ) ) {
continue;
}
// Get threshold setting for this taxonomy.
$threshold = $this->get_threshold( $tax );
// Get embedding similarity for each term.
foreach ( $terms as $term_id ) {
if ( ! current_user_can( 'assign_term', $term_id ) && ( ! defined( 'WP_CLI' ) || ! WP_CLI ) ) {
continue;
}
$term_embedding = get_term_meta( $term_id, 'classifai_azure_openai_embeddings', true );
if ( ! empty( $term_embedding ) ) {
// Loop through the chunks and run a similarity calculation on each.
foreach ( $term_embedding as $chunk ) {
$similarity = $calculations->cosine_similarity( $embedding, $chunk );
if ( false !== $similarity && ( ! $consider_threshold || $similarity <= $threshold ) ) {
$embedding_similarity[] = [
'taxonomy' => $tax,
'term_id' => $term_id,
'similarity' => $similarity,
];
}
}
}
}
}
return $embedding_similarity;
}
/**
* Schedules the job to generate embedding data for all terms within a taxonomy.
*
* @param string $taxonomy Taxonomy slug.
* @param bool $all Whether to generate embeddings for all terms or just those without embeddings.
* @param array $args Overrideable query args for get_terms()
* @param int $user_id The user ID to run this as.
*/
private function trigger_taxonomy_update( string $taxonomy = '', bool $all = false, array $args = [], int $user_id = 0 ) {
$feature = new Classification();
if (
! $feature->is_feature_enabled() ||
$feature->get_feature_provider_instance()::ID !== static::ID
) {
return;
}
$exclude = [];
// Exclude the uncategorized term.
if ( 'category' === $taxonomy ) {
$uncat_term = get_term_by( 'name', 'Uncategorized', 'category' );
if ( $uncat_term ) {
$exclude = [ $uncat_term->term_id ];
}
}
/**
* Filter the number of terms to process in a batch.
*
* @since 3.1.0
* @hook classifai_azure_openai_embeddings_terms_per_job
*
* @param {int} $number Number of terms to process per job.
*
* @return {int} Filtered number of terms to process per job.
*/
$number = apply_filters( 'classifai_azure_openai_embeddings_terms_per_job', 100 );
$default_args = [
'taxonomy' => $taxonomy,
'orderby' => 'count',
'order' => 'DESC',
'hide_empty' => false,
'fields' => 'ids',
'meta_key' => 'classifai_azure_openai_embeddings', // phpcs:ignore WordPress.DB.SlowDBQuery.slow_db_query_meta_key
'meta_compare' => 'NOT EXISTS',
'number' => $number,
'offset' => 0,
'exclude' => $exclude, // phpcs:ignore WordPressVIPMinimum.Performance.WPQueryParams.PostNotIn_exclude
];
$default_args = array_merge( $default_args, $args );
// If we want all terms, remove our meta query.
if ( $all ) {
unset( $default_args['meta_key'], $default_args['meta_compare'] );
} else {
unset( $default_args['offset'] );
}
if ( 0 === $user_id ) {
$user_id = get_current_user_id();
}
$job_args = [
'taxonomy' => $taxonomy,
'all' => $all,
'args' => $default_args,
'user_id' => $user_id,
];
// We return early and don't schedule the job if there are no terms.
if ( function_exists( 'as_has_scheduled_action' ) && ! \as_has_scheduled_action( 'classifai_schedule_generate_azure_embedding_job', $job_args ) ) {
$terms = get_terms( $default_args );
if ( is_wp_error( $terms ) || empty( $terms ) ) {
return;
}
}
if ( function_exists( 'as_enqueue_async_action' ) ) {
\as_enqueue_async_action( 'classifai_schedule_generate_azure_embedding_job', $job_args );
}
}
/**
* Job to generate embedding data for all terms within a taxonomy.
*
* @param string $taxonomy Taxonomy slug.
* @param bool $all Whether to generate embeddings for all terms or just those without embeddings.
* @param array $args Overrideable query args for get_terms()
* @param int $user_id The user ID to run this as.
*/
public function generate_embedding_job( string $taxonomy = '', bool $all = false, array $args = [], int $user_id = 0 ) {
if ( $user_id > 0 ) {
// We set this as current_user_can() fails when this function runs
// under the context of Action Scheduler.
wp_set_current_user( $user_id );
}
$terms = get_terms( $args );
if ( is_wp_error( $terms ) || empty( $terms ) ) {
return;
}
// Re-orders the keys.
$terms = array_values( $terms );
$exclude = [];
// Generate embedding data for each term.
foreach ( $terms as $term_id ) {
/** @var int $term_id */
$has_generated = $this->generate_embeddings_for_term( $term_id, $all );
if ( is_wp_error( $has_generated ) ) {
$exclude[] = $term_id;
}
}
if ( $all && isset( $args['offset'] ) && isset( $args['number'] ) ) {
$args['offset'] = $args['offset'] + $args['number'];
}
if ( ! empty( $exclude ) ) {
$args['exclude'] = array_merge( $args['exclude'], $exclude ); // phpcs:ignore WordPressVIPMinimum.Performance.WPQueryParams.PostNotIn_exclude
}
$this->trigger_taxonomy_update( $taxonomy, $all, $args, $user_id );
}
/**
* Trigger embedding generation for term being saved.
*
* @param int $term_id ID of term being saved.
* @param bool $force Whether to force generation of embeddings even if they already exist. Default false.
* @param Feature $feature The feature instance.
* @return array|WP_Error
*/
public function generate_embeddings_for_term( int $term_id, bool $force = false, Feature $feature = null ) {
// Ensure the user has permissions to edit.
if ( ! current_user_can( 'edit_term', $term_id ) ) {
return new WP_Error( 'invalid', esc_html__( 'User does not have valid permissions to edit this term.', 'classifai' ) );
}
$term = get_term( $term_id );
if ( ! is_a( $term, '\WP_Term' ) ) {
return new WP_Error( 'invalid', esc_html__( 'This is not a valid term.', 'classifai' ) );
}
if ( ! $feature ) {
$feature = new Classification();
}
$taxonomies = $feature->get_all_feature_taxonomies();
if ( in_array( 'tags', $taxonomies, true ) ) {
$taxonomies[] = 'post_tag';
}
if ( in_array( 'categories', $taxonomies, true ) ) {
$taxonomies[] = 'category';
}
// Ensure this term is part of a taxonomy we support.
if ( ! in_array( $term->taxonomy, $taxonomies, true ) ) {
return new WP_Error( 'invalid', esc_html__( 'This taxonomy is not supported.', 'classifai' ) );
}
/**
* Filter whether ClassifAI should classify an item.
*
* Default is true, return false to skip classifying.
*
* @since 3.1.0
* @hook classifai_azure_openai_embeddings_should_classify
*
* @param {bool} $should_classify Whether the item should be classified. Default `true`, return `false` to skip.
* @param {int} $id The ID of the item to be considered for classification.
* @param {string} $type The type of item to be considered for classification.
*
* @return {bool} Whether the item should be classified.
*/
if ( ! apply_filters( 'classifai_azure_openai_embeddings_should_classify', true, $term_id, 'term' ) ) {
return new WP_Error( 'invalid', esc_html__( 'Classification is disabled for this item.', 'classifai' ) );
}
// Try to use the stored embeddings first.
$embeddings = get_term_meta( $term_id, 'classifai_azure_openai_embeddings', true );
if ( ! empty( $embeddings ) && ! $force ) {
return $embeddings;
}
// Chunk the term content down.
$embeddings = [];
$content = $this->get_normalized_content( $term_id, 'term' );
$content_chunks = $this->chunk_content( $content );
// Get the embeddings for each chunk.
if ( ! empty( $content_chunks ) ) {
foreach ( $content_chunks as $chunk ) {
$embedding = $this->generate_embedding( $chunk, $feature );
if ( $embedding && ! is_wp_error( $embedding ) ) {
$embeddings[] = array_map( 'floatval', $embedding );
}
}
}
// Store the embeddings for future use.
if ( ! empty( $embeddings ) ) {
update_term_meta( $term_id, 'classifai_azure_openai_embeddings', $embeddings );
}
return $embeddings;
}
/**
* Generate an embedding for a particular piece of text.
*
* @param string $text Text to generate the embedding for.
* @param Feature $feature Feature instance.
* @return array|boolean|WP_Error
*/
public function generate_embedding( string $text = '', Feature $feature = null ) {
if ( ! $feature ) {
$feature = new Classification();
}
$settings = $feature->get_settings();
// Ensure the feature is enabled.
if ( ! $feature->is_feature_enabled() ) {
return new WP_Error( 'not_enabled', esc_html__( 'Classification is disabled or OpenAI authentication failed. Please check your settings.', 'classifai' ) );
}
/**
* Filter the request body before sending to OpenAI.
*
* @since 3.1.0
* @hook classifai_azure_openai_embeddings_request_body
*
* @param {array} $body Request body that will be sent to OpenAI.
* @param {string} $text Text we are getting embeddings for.
*
* @return {array} Request body.
*/
$body = apply_filters(
'classifai_azure_openai_embeddings_request_body',
[
'input' => $text,
'dimensions' => $this->get_dimensions(),
],
$text
);
// Make our API request.
$response = wp_remote_post(
$this->prep_api_url( $feature ),
[
'headers' => [
'api-key' => $settings[ static::ID ]['api_key'],
'Content-Type' => 'application/json',
],
'body' => wp_json_encode( $body ),
'timeout' => 60, // phpcs:ignore WordPressVIPMinimum.Performance.RemoteRequestTimeout.timeout_timeout
]
);
$response = $this->get_result( $response );
set_transient( 'classifai_azure_openai_embeddings_latest_response', $response, DAY_IN_SECONDS * 30 );
if ( is_wp_error( $response ) ) {
return $response;
}
if ( empty( $response['data'] ) ) {
return new WP_Error( 'no_data', esc_html__( 'No data returned from Azure OpenAI.', 'classifai' ) );
}
$return = [];
// Parse out the embeddings response.
foreach ( $response['data'] as $data ) {
if ( ! isset( $data['embedding'] ) || ! is_array( $data['embedding'] ) ) {
continue;
}
$return = $data['embedding'];
break;
}
return $return;
}
/**
* Generate embeddings for an array of text.
*
* @param array $strings Array of text to generate embeddings for.
* @param Feature|null $feature Feature instance.
* @return array|boolean|WP_Error
*/
public function generate_embeddings( array $strings = [], $feature = null ) {
if ( ! $feature ) {
$feature = new Classification();
}
$settings = $feature->get_settings();
// Ensure the feature is enabled.
if ( ! $feature->is_feature_enabled() ) {
return new WP_Error( 'not_enabled', esc_html__( 'Classification is disabled or OpenAI authentication failed. Please check your settings.', 'classifai' ) );
}
/**
* Filter the request body before sending to OpenAI.
*
* @since 3.1.0
* @hook classifai_azure_openai_embeddings_request_body
*
* @param {array} $body Request body that will be sent to OpenAI.
* @param {array} $strings Array of text we are getting embeddings for.
*
* @return {array} Request body.
*/
$body = apply_filters(
'classifai_azure_openai_embeddings_request_body',
[
'input' => $strings,
'dimensions' => $this->get_dimensions(),
],
$strings
);
// Make our API request.
$response = wp_remote_post(
$this->prep_api_url( $feature ),
[
'headers' => [
'api-key' => $settings[ static::ID ]['api_key'],
'Content-Type' => 'application/json',
],
'body' => wp_json_encode( $body ),
'timeout' => 60, // phpcs:ignore WordPressVIPMinimum.Performance.RemoteRequestTimeout.timeout_timeout
]
);
$response = $this->get_result( $response );
if ( is_wp_error( $response ) ) {
return $response;
}
if ( empty( $response['data'] ) ) {
return new WP_Error( 'no_data', esc_html__( 'No data returned from OpenAI.', 'classifai' ) );
}
$return = [];
// Parse out the embeddings response.
foreach ( $response['data'] as $data ) {
if ( ! isset( $data['embedding'] ) || ! is_array( $data['embedding'] ) ) {
continue;
}
$return[] = $data['embedding'];
}
return $return;
}
/**
* Chunk content into smaller pieces with an overlap.
*
* @param string $content Content to chunk.
* @param int $chunk_size Size of each chunk, in words.
* @param int $overlap_size Overlap size for each chunk, in words.
* @return array
*/
public function chunk_content( string $content = '', int $chunk_size = 150, $overlap_size = 25 ): array {
// Remove multiple whitespaces.
$content = preg_replace( '/\s+/', ' ', $content );
// Split text by single whitespace.
$words = explode( ' ', $content );
$chunks = [];
$text_count = count( $words );
// Iterate through and chunk data with an overlap.
for ( $i = 0; $i < $text_count; $i += $chunk_size ) {
// Join a set of words into a string.
$chunk = implode(
' ',
array_slice(
$words,
max( $i - $overlap_size, 0 ),
$chunk_size + $overlap_size
)
);
array_push( $chunks, $chunk );
}
return $chunks;
}
/**
* Get our content, ensuring it is normalized.
*
* @param int $id ID of item to get content from.
* @param string $type Type of content. Default 'post'.
* @return string
*/
public function get_normalized_content( int $id = 0, string $type = 'post' ): string {
$normalizer = new Normalizer();
// Get the content depending on the type.
switch ( $type ) {
case 'post':
// This will include the post_title and post_content.
$content = $normalizer->normalize( $id );
break;
case 'term':
$content = '';
$term = get_term( $id );
if ( is_a( $term, '\WP_Term' ) ) {
$content = $term->name . ' ' . $term->slug . ' ' . $term->description;
}
break;
}
/**
* Filter content that will get sent to OpenAI.
*
* @since 3.1.0
* @hook classifai_azure_openai_embeddings_content
*
* @param {string} $content Content that will be sent to OpenAI.
* @param {int} $post_id ID of post we are submitting.
* @param {string} $type Type of content.
*
* @return {string} Content.
*/
return apply_filters( 'classifai_azure_openai_embeddings_content', $content, $id, $type );
}
/**
* Common entry point for all REST endpoints for this provider.
*
* @param int $post_id The Post Id we're processing.
* @param string $route_to_call The route we are processing.
* @param array $args Optional arguments to pass to the route.
* @return string|WP_Error
*/
public function rest_endpoint_callback( $post_id = 0, string $route_to_call = '', array $args = [] ) {
if ( ! $post_id || ! get_post( $post_id ) ) {
return new WP_Error( 'post_id_required', esc_html__( 'A valid post ID is required to run classification.', 'classifai' ) );
}
$route_to_call = strtolower( $route_to_call );
$return = '';
// Handle all of our routes.
switch ( $route_to_call ) {
case 'classify':
$return = $this->generate_embeddings_for_post( $post_id, true );
break;
}
return $return;
}
/**
* Returns the debug information for the provider settings.
*
* @return array
*/
public function get_debug_information(): array {
$settings = $this->feature_instance->get_settings();
$debug_info = [];
if ( $this->feature_instance instanceof Classification ) {
foreach ( array_keys( $this->feature_instance->get_supported_taxonomies() ) as $tax ) {
$debug_info[ "Taxonomy ($tax)" ] = Feature::get_debug_value_text( $settings[ $tax ], 1 );
$debug_info[ "Taxonomy ($tax threshold)" ] = absint( $settings[ $tax . '_threshold' ] );
}
$debug_info[ __( 'Latest response', 'classifai' ) ] = $this->get_formatted_latest_response( get_transient( 'classifai_azure_openai_embeddings_latest_response' ) );
}
return apply_filters(
'classifai_' . self::ID . '_debug_information',
$debug_info,
$settings,
$this->feature_instance
);
}
}