LLM API Support
Local / Ollama
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
LLM API Support
Local / Ollama
Overview
You can use Instructor with local Ollama instance.
Please note that, at least currently, OS models do not perform on par with OpenAI (GPT-3.5 or GPT-4) model for complex data schemas.
Supported modes:
- Mode::MdJson - fallback mode, works with any capable model
- Mode::Json - recommended
- Mode::Tools - supported (for selected models - check Ollama docs)
Example
<?php
$loader = require 'vendor/autoload.php';
$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');
use Cognesy\Instructor\Enums\Mode;
use Cognesy\Instructor\Instructor;
enum UserType : string {
case Guest = 'guest';
case User = 'user';
case Admin = 'admin';
}
class User {
public int $age;
public string $name;
public string $username;
public UserType $role;
/** @var string[] */
public array $hobbies;
}
// Get Instructor with specified LLM client connection
// See: /config/llm.php to check or change LLM client connection configuration details
$instructor = (new Instructor)->withConnection('ollama');
$user = $instructor->respond(
messages: "Jason (@jxnlco) is 25 years old and is the admin of this project. He likes playing football and reading books.",
responseModel: User::class,
examples: [[
'input' => 'Ive got email Frank - their developer. Asked to connect via Twitter @frankch. Btw, he plays on drums!',
'output' => ['name' => 'Frank', 'role' => 'developer', 'hobbies' => ['playing drums'], 'username' => 'frankch', 'age' => null],
],[
'input' => 'We have a meeting with John, our new user. He is 30 years old - check his profile: @j90.',
'output' => ['name' => 'John', 'role' => 'admin', 'hobbies' => [], 'username' => 'j90', 'age' => 30],
]],
mode: Mode::Json,
);
print("Completed response model:\n\n");
dump($user);
assert(isset($user->name));
assert(isset($user->role));
assert(isset($user->age));
assert(isset($user->hobbies));
assert(isset($user->username));
assert(is_array($user->hobbies));
assert(count($user->hobbies) > 0);
assert($user->role === UserType::Admin);
assert($user->age === 25);
assert($user->name === 'Jason');
assert(in_array($user->username, ['jxnlco', '@jxnlco']));
?>