Cookbook
Cookbook \ Instructor \ 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
- Validation with LLM
Cookbook \ Instructor \ Advanced
- Context caching (structured output)
- Customize parameters of LLM driver
- Custom prompts
- Customize parameters via DSN
- 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
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)
- 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 \ Troubleshooting
Tracking token usage via events
Overview
Some use cases require tracking the token usage of the API responses.
This can be done by getting Usage
object from Instructor LLM response
object.
Code below demonstrates how it can be retrieved for both sync and streamed requests.
Example
<?php
require 'examples/boot.php';
use Cognesy\Instructor\Instructor;
use Cognesy\Polyglot\LLM\Data\Usage;
class User {
public int $age;
public string $name;
}
function printUsage(Usage $usage) : void {
echo "Input tokens: $usage->inputTokens\n";
echo "Output tokens: $usage->outputTokens\n";
echo "Cache creation tokens: $usage->cacheWriteTokens\n";
echo "Cache read tokens: $usage->cacheReadTokens\n";
echo "Reasoning tokens: $usage->reasoningTokens\n";
}
echo "COUNTING TOKENS FOR SYNC RESPONSE\n";
$text = "Jason is 25 years old and works as an engineer.";
$response = (new Instructor)
->request(
messages: $text,
responseModel: User::class,
)->response();
echo "\nTEXT: $text\n";
assert($response->usage()->total() > 0);
printUsage($response->usage());
echo "\n\nCOUNTING TOKENS FOR STREAMED RESPONSE\n";
$text = "Anna is 19 years old.";
$stream = (new Instructor)
->request(
messages: $text,
responseModel: User::class,
options: ['stream' => true],
)
->stream();
$response = $stream->final();
echo "\nTEXT: $text\n";
assert($stream->usage()->total() > 0);
printUsage($stream->usage());
?>
Assistant
Responses are generated using AI and may contain mistakes.