Advanced
Providing example inputs and outputs
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
Advanced
Providing example inputs and outputs
Overview
To improve the results of LLM inference you can provide examples of the expected output. This will help LLM to understand the context and the expected structure of the output.
It is typically useful in the Mode::Json
and Mode::MdJson
modes, where the output
is expected to be a JSON object.
Example
<?php
$loader = require 'vendor/autoload.php';
$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');
use Cognesy\Instructor\Enums\Mode;
use Cognesy\Instructor\Events\HttpClient\RequestSentToLLM;
use Cognesy\Instructor\Features\Core\Data\Example;
use Cognesy\Instructor\Instructor;
class User {
public int $age;
public string $name;
}
echo "\nREQUEST:\n";
$user = (new Instructor)
->onEvent(RequestSentToLLM::class, fn($event)=>dump($event->request->toMessages()))
->request(
messages: "Our user Jason is 25 years old.",
responseModel: User::class,
examples: [
new Example(
input: "John is 50 and works as a teacher.",
output: ['name' => 'John', 'age' => 50]
),
new Example(
input: "We have recently hired Ian, who is 27 years old.",
output: ['name' => 'Ian', 'age' => 27],
template: "example input:\n<|input|>\noutput:\n```json\n<|output|>\n```\n",
),
],
mode: Mode::Json)
->get();
echo "\nOUTPUT:\n";
dump($user);
assert($user->name === 'Jason');
assert($user->age === 25);
?>