Extras
Working directly with LLMs
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
- Inference and tool use
- 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
Extras
Working directly with LLMs
Overview
Inference
class offers access to LLM APIs and convenient methods to execute
model inference, incl. chat completions, tool calling or JSON output
generation.
LLM providers access details can be found and modified via
/config/llm.php
.
Example
<?php
$loader = require 'vendor/autoload.php';
$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');
use Cognesy\Instructor\Features\LLM\Inference;
use Cognesy\Instructor\Utils\Str;
// EXAMPLE 1: simplified API, default connection for convenient ad-hoc calls
$answer = Inference::text('What is capital of Germany');
echo "USER: What is capital of Germany\n";
echo "ASSISTANT: $answer\n";
assert(Str::contains($answer, 'Berlin'));
// EXAMPLE 2: regular API, allows to customize inference options
$answer = (new Inference)
->withConnection('openai') // optional, default is set in /config/llm.php
->create(
messages: [['role' => 'user', 'content' => 'What is capital of France']],
options: ['max_tokens' => 64]
)
->toText();
echo "USER: What is capital of France\n";
echo "ASSISTANT: $answer\n";
assert(Str::contains($answer, 'Paris'));
// EXAMPLE 3: streaming response
$stream = (new Inference)
->create(
messages: [['role' => 'user', 'content' => 'Describe capital of Brasil']],
options: ['max_tokens' => 128, 'stream' => true]
)
->stream()
->responses();
echo "USER: Describe capital of Brasil\n";
echo "ASSISTANT: ";
foreach ($stream as $partial) {
echo $partial->contentDelta;
}
echo "\n";
?>