PHP code example of inspector-apm / neuron-ai

    
        
<?php
require_once('vendor/autoload.php');

/* Start to develop here. Best regards https://php-download.com/ */

    

inspector-apm / neuron-ai example snippets

use NeuronAI\Agent;
use NeuronAI\SystemPrompt;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;

class YouTubeAgent extends Agent
{
    public function provider(): AIProviderInterface
    {
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
        );
    }

    public function instructions(): string
    {
        return new SystemPrompt(
            background: ["You are an AI Agent specialized in writing YouTube video summaries."],
            steps: [
                "Get the url of a YouTube video, or ask the user to provide one.",
                "Use the tools you have available to retrieve the transcription of the video.",
                "Write the summary.",
            ],
            output: [
                "Write a summary in a paragraph without using lists. Use just fluent text.",
                "After the summary add a list of three sentences as the three most important take away from the video.",
            ]
        );
    }
}

$agent = YouTubeAgent::make();

$response = $agent->run(new UserMessage("Hi, I'm Valerio. Who are you?"));
echo $response->getContent();
// I'm a friendly YouTube assistant to help you summarize videos.


$response = $agent->run(
    new UserMessage("Do you know my name?")
);
echo $response->getContent();
// Your name is Valerio, as you said in your introduction.

use NeuronAI\Agent;
use NeuronAI\SystemPrompt;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;
use NeuronAI\Tools\Tool;
use NeuronAI\Tools\ToolProperty;

class YouTubeAgent extends Agent
{
    public function provider(): AIProviderInterface
    {
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
        );
    }

    public function instructions(): string
    {
        return new SystemPrompt(
            background: ["You are an AI Agent specialized in writing YouTube video summaries."],
            steps: [
                "Get the url of a YouTube video, or ask the user to provide one.",
                "Use the tools you have available to retrieve the transcription of the video.",
                "Write the summary.",
            ],
            output: [
                "Write a summary in a paragraph without using lists. Use just fluent text.",
                "After the summary add a list of three sentences as the three most important take away from the video.",
            ]
        );
    }

    public function tools(): array
    {
        return [
            Tool::make(
                'get_transcription',
                'Retrieve the transcription of a youtube video.',
            )->addProperty(
                new ToolProperty(
                    name: 'video_url',
                    type: 'string',
                    description: 'The URL of the YouTube video.',
                    

use NeuronAI\Agent;
use NeuronAI\MCP\McpConnector;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;
use NeuronAI\Tools\Tool;
use NeuronAI\Tools\ToolProperty;

class SEOAgent extends Agent
{
    public function provider(): AIProviderInterface
    {
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
        );
    }

    public function instructions(): string
    {
        return new SystemPrompt(
            background: ["Act as an expert of SEO (Search Engine Optimization)."]
            steps: [
                "Analyze a text of an article.",
                "Provide suggestions on how the content can be improved to get a better rank on Google search."
            ],
            output: ["Structure your analysis in sections. One for each suggestion."]
        );
    }

    public function tools(): array
    {
        return [
            // Connect an MCP server
            ...McpConnector::make([
                'command' => 'npx',
                'args' => ['-y', '@modelcontextprotocol/server-everything'],
            ])->tools(),

            // Implement your custom tools
            Tool::make(
                'get_transcription',
                'Retrieve the transcription of a youtube video.',
            )->addProperty(
                new ToolProperty(
                    name: 'video_url',
                    type: 'string',
                    description: 'The URL of the YouTube video.',
                    

use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;
use NeuronAI\RAG\Embeddings\EmbeddingsProviderInterface;
use NeuronAI\RAG\Embeddings\VoyageEmbeddingProvider;
use NeuronAI\RAG\RAG;
use NeuronAI\RAG\VectorStore\PineconeVectoreStore;
use NeuronAI\RAG\VectorStore\VectorStoreInterface;

class MyChatBot extends RAG
{
    public function provider(): AIProviderInterface
    {
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
        );
    }

    public function embeddings(): EmbeddingsProviderInterface
    {
        return new VoyageEmbeddingProvider(
            key: 'VOYAGE_API_KEY',
            model: 'VOYAGE_MODEL'
        );
    }

    public function vectorStore(): VectorStoreInterface
    {
        return new PineconeVectoreStore(
            key: 'PINECONE_API_KEY',
            indexUrl: 'PINECONE_INDEX_URL'
        );
    }
}

use NeuronAI\StructuredOutput\Property;

// Define the output structure with a PHP class, including validation constraints.
class Person
{
    #[Property(description: 'The user name')]
    public string $name;

    #[Property(description: 'What the user love to eat')]
    public string $preference;
}


// Talk to the agent requiring the structured output
$person = MyAgent::make()->structured(
    new UserMessage("I'm John and I like pizza!"),
    Person::class
);

echo $person->name ' like '.$person->preference;
// John like pizza