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
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
require 'examples/boot.php';
use Cognesy\Instructor\Extras\Structure\Field;
use Cognesy\Instructor\Extras\Structure\Structure;
use Cognesy\Polyglot\LLM\Enums\OutputMode;
use Cognesy\Polyglot\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' => $city->toJsonSchema(),
'strict' => true,
],
],
options: ['max_tokens' => 64],
mode: OutputMode::JsonSchema,
)
->toJson();
echo "USER: What is capital of France\n";
echo "ASSISTANT:\n";
dump($data);
?>
Assistant
Responses are generated using AI and may contain mistakes.