Extras
Generating JSON Schema from PHP classes
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
Advanced
- Context caching
- Context caching (Anthropic)
- Customize parameters of OpenAI client
- Custom prompts
- 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
Troubleshooting
LLM API Support
Extras
- Extraction of complex objects
- Extraction of complex objects (Anthropic)
- Extraction of complex objects (Cohere)
- Extraction of complex objects (Gemini)
- Embeddings
- Image processing - car damage detection
- Image to data (OpenAI)
- Image to data (Anthropic)
- Image to data (Gemini)
- 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
- Prompts
- Generating JSON Schema from PHP classes
- Generating JSON Schema dynamically
- Simple content summary
- Create tasks from meeting transcription
- Translating UI text fields
- Web page to PHP objects
Extras
Generating JSON Schema from PHP classes
Overview
Instructor has a built-in support for generating JSON Schema from the classes or objects. This is useful as it helps you avoid writing the JSON Schema manually, which can be error-prone and time-consuming.
Example
<?php
$loader = require 'vendor/autoload.php';
$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');
use Cognesy\Instructor\Enums\Mode;
use Cognesy\Instructor\Features\LLM\Inference;
use Cognesy\Instructor\Features\Schema\Factories\SchemaFactory;
class City {
public string $name;
public int $population;
public int $founded;
}
$schema = (new SchemaFactory)->schema(City::class);
$data = (new Inference)
->withConnection('openai')
->create(
messages: [['role' => 'user', 'content' => 'What is capital of France? \
Respond with JSON data.']],
responseFormat: [
'type' => 'json_schema',
'description' => 'City data',
'json_schema' => [
'name' => 'city_data',
'schema' => $schema->toJsonSchema(),
'strict' => true,
],
],
options: ['max_tokens' => 64],
mode: Mode::JsonSchema,
)
->toJson();
echo "USER: What is capital of France\n";
echo "ASSISTANT:\n";
dump($data);
?>