Polyglot - LLM Advanced
Reasoning Content Access
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
Instructor - Advanced
- Context caching (structured output)
- Customize parameters of LLM driver
- 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
Instructor - Troubleshooting
Instructor - LLM API Support
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 dynamically
- Create tasks from meeting transcription
- Translating UI text fields
- Web page to PHP objects
Polyglot - LLM Basics
Polyglot - LLM Advanced
Polyglot - LLM Troubleshooting
Polyglot - LLM API Support
Polyglot - LLM Extras
Prompting - Zero-Shot Prompting
Prompting - Few-Shot Prompting
Prompting - Thought Generation
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
Polyglot - LLM Advanced
Reasoning Content Access
Overview
Deepseek API allows to access reasoning content, which is a detailed explanation of how the response was generated. This feature is useful for debugging and understanding the reasoning behind the response.
Example
<?php
require 'examples/boot.php';
use Cognesy\Polyglot\LLM\Inference;
use Cognesy\Utils\Debug\Debug;
use Cognesy\Utils\Str;
//Debug::setEnabled();
// EXAMPLE 1: regular API, allows to customize inference options
$response = (new Inference)
->withConnection('deepseek-r') // optional, default is set in /config/llm.php
->create(
messages: [['role' => 'user', 'content' => 'What is the capital of France. Answer with just a name.']],
options: ['max_tokens' => 64]
)
->response();
echo "\nCASE #1: Sync response\n";
echo "USER: What is capital of France\n";
echo "ASSISTANT: {$response->content()}\n";
echo "REASONING: {$response->reasoningContent()}\n";
assert($response->content() !== '');
assert(Str::contains($response->content(), 'Paris'));
assert($response->reasoningContent() !== '');
// EXAMPLE 2: streaming response
$stream = (new Inference)
->withConnection('deepseek-r') // optional, default is set in /config/llm.php
->create(
messages: [['role' => 'user', 'content' => 'What is capital of Brasil. Answer with just a name.']],
options: ['max_tokens' => 128, 'stream' => true]
)
->stream();
echo "\nCASE #2: Streamed response\n";
echo "USER: What is capital of Brasil\n";
echo "ASSISTANT: ";
foreach ($stream->responses() as $partial) {
echo $partial->contentDelta;
}
echo "\n";
echo "REASONING: {$stream->final()->reasoningContent()}\n";
assert($stream->final()->reasoningContent() !== '');
assert($stream->final()->content() !== '');
assert(Str::contains($stream->final()->content(), 'BrasÃlia'));
?>