Extras
Generating JSON Schema dynamically
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 dynamically
Overview
Instructor has a built-in support for generating JSON Schema from
dynamic objects with Structure
class.
This is useful when the data model is built during runtime or defined by your app users.
Structure
helps you flexibly design and modify data models that
can change with every request or user input and allows you to generate
JSON Schema for them.
Example
<?php
$loader = require 'vendor/autoload.php';
$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');
use Cognesy\Instructor\Enums\Mode;
use Cognesy\Instructor\Extras\Structure\Field;
use Cognesy\Instructor\Extras\Structure\Structure;
use Cognesy\Instructor\Features\LLM\Inference;
$city = Structure::define('city', [
Field::string('name', 'City name')->required(),
Field::int('population', 'City population')->required(),
Field::int('founded', 'Founding year')->required(),
]);
$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' => $city->toJsonSchema(),
'strict' => true,
],
],
options: ['max_tokens' => 64],
mode: Mode::JsonSchema,
)
->toJson();
echo "USER: What is capital of France\n";
echo "ASSISTANT:\n";
dump($data);
?>