Cookbook
Cookbook \ Instructor \ Basics
- Basic use
- Basic use via mixin
- Handling errors with `Maybe` helper class
- Modes
- Making some fields optional
- Private vs public object field
- Automatic correction based on validation results
- Using attributes
- Using LLM API connections from config file
- Validation
- Custom validation using Symfony Validator
- Validation across multiple fields
- Validation with LLM
Cookbook \ Instructor \ Advanced
- Context caching (structured output)
- Customize parameters of LLM driver
- Custom prompts
- Customize parameters via DSN
- Using structured data as an input
- Extracting arguments of function or method
- Streaming partial updates during inference
- Providing example inputs and outputs
- Extracting scalar values
- Extracting sequences of objects
- Streaming
- Structures
Cookbook \ Instructor \ Troubleshooting
Cookbook \ Instructor \ LLM API Support
Cookbook \ Instructor \ Extras
- Extraction of complex objects
- Extraction of complex objects (Anthropic)
- Extraction of complex objects (Cohere)
- Extraction of complex objects (Gemini)
- Image processing - car damage detection
- Image to data (OpenAI)
- Image to data (Anthropic)
- Image to data (Gemini)
- Generating JSON Schema from PHP classes
- Generating JSON Schema from PHP classes
- Generating JSON Schema dynamically
- Create tasks from meeting transcription
- Translating UI text fields
- Web page to PHP objects
Cookbook \ Polyglot \ LLM Basics
- Working directly with LLMs
- Working directly with LLMs and JSON - JSON mode
- Working directly with LLMs and JSON - JSON Schema mode
- Working directly with LLMs and JSON - MdJSON mode
- Working directly with LLMs and JSON - Tools mode
- Generating JSON Schema from PHP classes
- Generating JSON Schema from PHP classes
Cookbook \ Polyglot \ LLM Advanced
Cookbook \ Polyglot \ LLM Troubleshooting
Cookbook \ Polyglot \ LLM API Support
Cookbook \ Polyglot \ LLM Extras
Cookbook \ Prompting \ Zero-Shot Prompting
Cookbook \ Prompting \ Few-Shot Prompting
Cookbook \ Prompting \ Thought Generation
Cookbook \ Prompting \ Miscellaneous
- Arbitrary properties
- Consistent values of arbitrary properties
- Chain of Summaries
- Chain of Thought
- Single label classification
- Multiclass classification
- Entity relationship extraction
- Handling errors
- Limiting the length of lists
- Reflection Prompting
- Restating instructions
- Ask LLM to rewrite instructions
- Expanding search queries
- Summary with Keywords
- Reusing components
- Using CoT to improve interpretation of component data
Cookbook \ Instructor \ Extras
Image to data (Gemini)
Overview
This is an example of how to extract structured data from an image using Instructor. The image is loaded from a file and converted to base64 format before sending it to OpenAI API.
The response model is a PHP class that represents the structured receipt information with data of vendor, items, subtotal, tax, tip, and total.
Scanned image
Here’s the image we’re going to extract data from.
Example
<?php
require 'examples/boot.php';
use Cognesy\Addons\Image\Image;
use Cognesy\Instructor\Instructor;
use Cognesy\Polyglot\LLM\Enums\OutputMode;
class Vendor {
public ?string $name = '';
public ?string $address = '';
public ?string $phone = '';
}
class ReceiptItem {
public string $name;
public ?int $quantity = 1;
public float $price;
}
class Receipt {
public Vendor $vendor;
/** @var ReceiptItem[] */
public array $items = [];
public ?float $subtotal;
public ?float $tax;
public ?float $tip;
public float $total;
}
$receipt = (new Instructor)->withConnection('gemini')->respond(
input: Image::fromFile(__DIR__ . '/receipt.png'),
responseModel: Receipt::class,
prompt: 'Extract structured data from the receipt. Return result as JSON following this schema: <|json_schema|>',
mode: OutputMode::Json,
options: ['max_tokens' => 4096]
);
dump($receipt);
assert($receipt->total === 169.82);
?>
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Responses are generated using AI and may contain mistakes.