LLM API Support
Fireworks.ai
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
Fireworks.ai
Overview
Please note that the larger Mistral models support Mode::Json, which is much more reliable than Mode::MdJson.
Mode compatibility:
- Mode::Tools - selected models
- Mode::Json - selected models
- Mode::MdJson
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('fireworks');
$user = $instructor
->respond(
messages: "Jason (@jxnlco) is 25 years old and is the admin of this project. He likes playing football and reading books.",
responseModel: User::class,
examples: [[
'input' => 'Ive got email Frank - their developer, who\'s 30. He asked to come back to him frank@hk.ch. Btw, he plays on drums!',
'output' => ['age' => 30, 'name' => 'Frank', 'username' => 'frank@hk.ch', 'role' => 'developer', 'hobbies' => ['playing drums'],],
]],
model: 'accounts/fireworks/models/mixtral-8x7b-instruct',
mode: Mode::Json,
);
print("Completed response model:\n\n");
dump($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']));
?>