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Requirements

• PHP: ^8.0

Official documentation

Go to the official documentation

Install

Install the latest version of the package:

composer require inspector-apm/neuron-ai

Create an Agent

Neuron provides you with the Agent class you can extend to inherit the main features of the framework, and create fully functional agents. This class automatically manages some advanced mechanisms for you such as memory, tools and function calls, up to the RAG systems. You can go deeper into these aspects in the documentation. In the meantime, let's create the first agent, extending the NeuronAI\Agent class:

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

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

    public function instructions()
    {
        return "Act as an expert of SEO (Search Engine Optimization). ".
            "Your role is to analyze a text of an article and provide suggestions ".
            "on how the content can be improved to get a better rank on Google search.";
    }
}

Talk to the Agent

Send a prompt to the agent to get a response from the underlying LLM:

$seoAgent = SEOAgent::make();

$response = $seoAgent->run(new UserMessage("Who are you?"));
echo $response->getContent();
// I'm a SEO expert, how can I help you today?


$response = $seoAgent->run(
    new UserMessage("What do you think about the following article? --- ".file_get_contents('./README.md'))
);
echo $response->getContent();
// It's well done! Anyway, let me give you some advice to get a better rank on Google...

Tools & Function Calls

You can easily add the ability to perform concrete tasks to your Agent with an array of Tool:

use NeuronAI\Agent;
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()
    {
        return "Act as an expert of SEO (Search Engine Optimization). ".
            "Your role is to analyze a text of an article and provide suggestions ".
            "on how the content can be improved to get a better rank on Google search.";
    }

    public function tools(): array
    {
        return [
            Tool::make(
                "get_article_content",
                "Use the ID of the article to get its content."
            )->addProperty(
                new ToolProperty(
                    name: 'article_id',
                    type: 'integer',
                    description: 'The ID  of the article you want to analyze.',
                    required: true
                )
            )->setCallable(function (string $article_id) {
                // You should use your DB layer here...
                $stm = $pdo->prepare("SELECT * FROM articles WHERE id=? LIMIT 1");
                $stm->execute([$article_id]);
                return json_encode(
                    $stmt->fetch(PDO::FETCH_ASSOC)
                );
            })
        ];
    }
}

Learn more about Tools on the documentation.

MCP server connector

Instead of implementing tools manually, you can connect tools exposed by an MCP server with the McpConnector component:

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()
    {
        return "Act as an expert of SEO (Search Engine Optimization). ".
            "Your role is to analyze a text of an article and provide suggestions ".
            "on how the content can be improved to get a better rank on Google search.";
    }

    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_article_content",
                "Use the ID of the article to get its content."
            )->addProperty(
                new ToolProperty(
                    name: 'article_id',
                    type: 'integer',
                    description: 'The ID  of the article you want to analyze.',
                    required: true
                )
            )->setCallable(function (string $article_id) {
                // You should use your DB layer here...
                $stm = $pdo->prepare("SELECT * FROM articles WHERE id=? LIMIT 1");
                $stm->execute([$article_id]);
                return json_encode(
                    $stmt->fetch(PDO::FETCH_ASSOC)
                );
            })
        ];
    }
}

Learn more about MCP connector on the documentation.

Implement RAG systems

For RAG use case, you must extend the NeuronAI\RAG\RAG class instead of the default Agent class.

To create a RAG you need to attach some additional components other than the AI provider, such as a vector store, and an embeddings provider.

Here is an example of a RAG implementation:

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'
        );
    }
}

Learn more about RAG on the documentation.

Official documentation

Go to the official documentation

Contributing

We encourage you to contribute to the development of Neuron AI Framework! Please check out the Contribution Guidelines about how to proceed. Join us!

LICENSE

This bundle is licensed under the MIT license.