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 \ Basics
Using attributes
Overview
Instructor supports Description
and Instructions
attributes to provide more
context to the language model or to provide additional instructions to the model.
Example
<?php
require 'examples/boot.php';
use Cognesy\Instructor\Features\Schema\Attributes\Description;
use Cognesy\Instructor\Features\Schema\Attributes\Instructions;
use Cognesy\Instructor\Instructor;
// Step 1: Define a class that represents the structure and semantics
// of the data you want to extract
#[Description("Information about user")]
class User {
#[Description("User's age")]
public int $age;
#[Instructions("Make user name ALL CAPS")]
public string $name;
#[Description("User's job")]
#[Instructions("Ignore hobbies, identify profession")]
#[Instructions("Make the profession name lowercase")]
public string $job;
}
// Step 2: Get the text (or chat messages) you want to extract data from
$text = "Jason is 25 years old, 10K runner, speaker and an engineer.";
print("Input text:\n");
print($text . "\n\n");
// Step 3: Extract structured data using default language model API (OpenAI)
print("Extracting structured data using LLM...\n\n");
$user = (new Instructor)->respond(
messages: $text,
responseModel: User::class,
);
// Step 4: Now you can use the extracted data in your application
print("Extracted data:\n");
dump($user);
assert(isset($user->name));
assert($user->name === "JASON");
assert(isset($user->age));
assert($user->age === 25);
assert(isset($user->job));
assert($user->job === "engineer");
?>
Assistant
Responses are generated using AI and may contain mistakes.