Cookbook
Cookbook \ Instructor \ Basics
- Basic use
- Specifying required and optional parameters via constructor
- Getters and setters
- Private vs public object field
- Basic use via mixin
- Fluent API
- Handling errors with `Maybe` helper class
- Mixed Type Property
- Modes
- Making some fields optional
- Automatic correction based on validation results
- Using attributes
- Using LLM API connection presets from config file
- Validation
- Custom validation using Symfony Validator
- Validation across multiple fields
- Validation with LLM
Cookbook \ Instructor \ Advanced
- Use custom configuration providers
- Context caching (structured output)
- Customize parameters of LLM driver
- Custom prompts
- Customize parameters via DSN
- Extracting arguments of function or method
- Logging monolog
- Logging psr
- 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)
- Using structured data as an input
- 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 \ Advanced
Extracting arguments of function or method
Overview
Instructor offers FunctionCall class to extract arguments of a function or method from content.
This is useful when you want to build tool use capability, e.g. for AI chatbots or agents.
Example
Copy
<?php
require 'examples/boot.php';
use Cognesy\Addons\FunctionCall\FunctionCall;
use Cognesy\Instructor\StructuredOutput;
class DataStore
{
/** Save user data to storage */
public function saveUser(string $name, int $age, string $country) : void {
// Save user to database
echo "Saving user ... saveUser('$name', $age, '$country')\n";
}
}
$text = "His name is Jason, he is 28 years old and he lives in Germany.";
$args = (new StructuredOutput)->with(
messages: $text,
responseModel: FunctionCall::fromMethodName(DataStore::class, 'saveUser'),
)->get();
echo "\nCalling the function with the extracted arguments:\n";
(new DataStore)->saveUser(...$args);
echo "\nExtracted arguments:\n";
dump($args);
assert(count($args) == 3);
expect($args['name'] === 'Jason');
expect($args['age'] == 28);
expect($args['country'] === 'Germany');
?>
Assistant
Responses are generated using AI and may contain mistakes.