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 \ Prompting \ Miscellaneous
Single label classification
Overview
For single-label classification, we first define an enum
for possible labels
and a PHP class for the output.
Example
Let’s start by defining the data structures.
Copy
<?php
require 'examples/boot.php';
use Cognesy\Instructor\Instructor;
// Enumeration for single-label text classification.
enum Label : string {
case SPAM = "spam";
case NOT_SPAM = "not_spam";
}
// Class for a single class label prediction.
class SinglePrediction {
public Label $classLabel;
}
?>
Classifying Text
The function classify will perform the single-label classification.
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<?php
// Perform single-label classification on the input text.
function classify(string $data) : SinglePrediction {
return (new Instructor())->respond(
messages: [[
"role" => "user",
"content" => "Classify the following text: $data",
]],
responseModel: SinglePrediction::class,
);
}
?>
Testing and Evaluation
Let’s run an example to see if it correctly identifies a spam message.
Copy
<?php
// Test single-label classification
$prediction = classify("Hello there I'm a Nigerian prince and I want to give you money");
dump($prediction);
assert($prediction->classLabel == Label::SPAM);
?>
Assistant
Responses are generated using AI and may contain mistakes.