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Example code of inspector-apm/neuron-ai

Informations about the package neuron-ai

Latest Stable Version

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Video Tutorial

Requirements

Official documentation

Go to the official documentation

Forum

You can post questions and feedback on the Inspector Forum.

Neuron AI Examples

Install

Install the latest version of the package:

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:

The SystemPrompt class is designed to take your base instructions and build a consistent prompt for the underlying model reducing the effort for prompt engineering.

Talk to the Agent

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

As you can see in the example above, the Agent automatically has memory of the ongoing conversation. Learn more about memory in the documentation.

Supported LLM Providers

With Neuron you can switch between LLM providers with just one line of code, without any impact on your agent implementation. Supported providers:

Tools & Function Calls

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

Learn more about Tools in the documentation.

MCP server connector

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

Learn more about MCP connector in 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:

Learn more about RAG in the documentation.

Structured Output

For many applications, such as chatbots, Agents need to respond to users directly in natural language. However, there are scenarios where we need Agents to understand natural language, but output in a structured format.

One common use-case is extracting data from text to insert into a database or use with some other downstream system. This guide covers a few strategies for getting structured outputs from the agent.

Learn more about Structured Output on the documentation.

Observability

Neuron offers a built-in integration with Inspector.dev to monitor the performance of your agents and detect unexpected errors in real time.

You have to install the Inspector package based on your development environment. We provide integration packages for PHP, Laravel, Symfony, CodeIgniter, Drupal.

Attach the AgentMonitoring component to the agent to monitor the internal execution timeline in the Inspector dashboard. If the agent fires an error, you will be alerted in real-time. You can connect several notification channels like email, slack, discord, telegram, and more. Here is a code example in a legacy PHP script:

If you use a framework like Laravel, Symfony, or CodeIgniter, the connection is even easier, since you already have the Inspector instance in the container.

Learn more about Observability in the documentation.

Official documentation

Go to the official documentation

All versions of neuron-ai with dependencies

PHP Build Version
Package Version
Requires php Version ^8.1
guzzlehttp/guzzle Version ^7.0
psr/log Version ^1.0|^2.0|^3.0
psr/http-message Version ^1.0|^2.0
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