Laravel的MCP
](https://packagist.org/packages/settled/mcp-laravel) 
Laravel的MCP（模型上下文协议）-用于构建智能应用程序的强大AI框架。基于 检测器apm/神经元ai
需求
- PHP:^8.0
- Laravel：^11.0
正式文件
安装
安装最新版本的软件包：
composer require settled/mcp-laravel创建代理
Laravel的MCP为您提供了Agent类，您可以扩展该类以继承框架的主要功能， 并创建功能齐全的代理。这个类会自动为你管理一些高级机制，比如内存， 工具和函数调用，直至RAG系统。让我们创建第一个代理，扩展 Settled\MCP\Agent 类别：
use Settled\MCP\Agent;
use Settled\MCP\SystemPrompt;
use Settled\MCP\Providers\AIProviderInterface;
use Settled\MCP\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.",
]
);
}
}这 SystemPrompt 类旨在接受您的基本指令，并为底层模型构建一致的提示 减少了快速工程的工作量。
与代理人交谈
向代理发送提示，以获取底层LLM的响应：
$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.正如您在上面的示例中看到的，Agent会自动记住正在进行的对话。在中了解有关内存的更多信息 文档.
支持的LLM提供商
使用Neuron，您只需一行代码就可以在LLM提供者之间切换，而不会对您的代理实现产生任何影响。 支持的提供商：
- Anthropic
- Ollama（也可作为 嵌入提供者)
- OpenAI
- 米斯特拉尔
- 深度求索
工具和函数调用
您可以使用以下数组为代理添加执行具体任务的能力 Tool:
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.',
required: true
)
)->setCallable(function (string $video_url) {
// ... retrieve the video transcription
})
];
}
}了解有关工具的更多信息，请访问 文档.
MCP服务器连接器
您可以将MCP服务器公开的工具与 McpConnector 组件：
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.',
required: true
)
)->setCallable(function (string $video_url) {
// ... retrieve the video transcription
})
];
}
}了解有关MCP连接器的更多信息，请访问 文档.
实施RAG系统
对于RAG用例，您必须扩展 NeuronAI\RAG\RAG 类，而不是默认的Agent类。
要创建RAG，您需要附加除AI提供者之外的一些其他组件，例如 vector store, 和a embeddings provider.
以下是RAG实现的示例：
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'
);
}
}了解更多关于RAG的信息 文档.
正式文件
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许可证
此捆绑包根据 麻省理工学院 许可证。