Expanding Search Queries¶

In this example, we will demonstrate how to leverage the enums and typed arrays to segment a complex search prompt into multiple, better structured queries that can be executed separately against specialized APIs or search engines.

Extracting a list of tasks from text is a common use case for leveraging language models. This pattern can be applied to various applications, such as virtual assistants like Siri or Alexa, where understanding user intent and breaking down requests into actionable tasks is crucial. In this example, we will demonstrate how to use Instructor to segment search queries, so you can execute them separately against specialized APIs or search engines.

Structure of the Data¶

The SearchQuery class is a PHP class that defines the structure of an individual search query. It has three fields: title, query, and type. The title field is the title of the request, the query field is the query to search for relevant content, and the type field is the type of search. The execute method is used to execute the search query.

<?php
$loader = require 'vendor/autoload.php';
$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');

use Cognesy\Instructor\Instructor;

enum SearchType : string {
    case TEXT = "text";
    case IMAGE = "image";
    case VIDEO = "video";
}

class Search
{
    /** @var SearchQuery[] */
    public array $queries = [];
}

class SearchQuery
{
    public string $title;
    /**  Rewrite query for a search engine */
    public string $query;
    /** Type of search - image, video or text */
    public SearchType $type;

    public function execute() {
        // ... write actual search code here
        print("Searching for `{$this->title}` with query `{$this->query}` using `{$this->type->value}`\n");
    }
}
?>

Segmenting the Search Prompt¶

The segment function takes a string data and segments it into multiple search queries. It uses the Instructor::respond method to send a prompt and extract the data into the target object. The responseModel parameter specifies Search::class as the model to use for extraction.

<?php
function segment(string $data) : Search {
    return (new Instructor)->respond(
        messages: [[
            "role" => "user",
            "content" => "Consider the data below: '\n$data' and segment it into multiple search queries",
        ]],
        responseModel: Search::class,
    );
}

$search = segment("Find a picture of a cat and a video of a dog");
foreach ($search->queries as $query) {
    $query->execute();
}
// Results:
// Searching with query `picture of a cat` using `image`
// Searching with query `video of a dog` using `video`

assert(count($search->queries) === 2);
?>