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 \ Polyglot \ LLM Advanced
Context caching (text inference)
Overview
Instructor offers a simplified way to work with LLM providers’ APIs supporting caching (currently only Anthropic API), so you can focus on your business logic while still being able to take advantage of lower latency and costs.
Note 1: Instructor supports context caching for Anthropic API and OpenAI API.
Note 2: Context caching is automatic for all OpenAI API calls. Read more in the OpenAI API documentation.
Example
When you need to process multiple requests with the same context, you can use context caching to improve performance and reduce costs.
In our example we will be analyzing the README.md file of this Github project and generating its summary for 2 target audiences.
Copy
<?php
require 'examples/boot.php';
use Cognesy\Polyglot\LLM\Inference;
use Cognesy\Utils\Str;
$data = file_get_contents(__DIR__ . '/../../../README.md');
$inference = (new Inference)->withConnection('anthropic')->withCachedContext(
messages: [
['role' => 'user', 'content' => 'Here is content of README.md file'],
['role' => 'user', 'content' => $data],
['role' => 'user', 'content' => 'Generate short, very domain specific pitch of the project described in README.md'],
['role' => 'assistant', 'content' => 'For whom do you want to generate the pitch?'],
],
);
$response = $inference->create(
messages: [['role' => 'user', 'content' => 'CTO of lead gen software vendor']],
options: ['max_tokens' => 256],
)->response();
print("----------------------------------------\n");
print("\n# Summary for CTO of lead gen vendor\n");
print(" ({$response->usage()->cacheReadTokens} tokens read from cache)\n\n");
print("----------------------------------------\n");
print($response->content() . "\n");
assert(!empty($response->content()));
assert(Str::contains($response->content(), 'Instructor'));
assert(Str::contains($response->content(), 'lead', false));
$response2 = $inference->create(
messages: [['role' => 'user', 'content' => 'CIO of insurance company']],
options: ['max_tokens' => 256],
)->response();
print("----------------------------------------\n");
print("\n# Summary for CIO of insurance company\n");
print(" ({$response2->usage()->cacheReadTokens} tokens read from cache)\n\n");
print("----------------------------------------\n");
print($response2->content() . "\n");
assert(!empty($response2->content()));
assert(Str::contains($response2->content(), 'Instructor'));
assert(Str::contains($response2->content(), 'insurance', false));
//assert($response2->cacheReadTokens > 0);
?>
Assistant
Responses are generated using AI and may contain mistakes.