Build Retrieval‑Augmented Generation (RAG) Agents in PHP with Neuron AI

RAG Agent Overview

When I first encountered the term "RAG" (Retrieval‑Augmented Generation), it felt like a wall of jargon filled with buzzwords such as vector databases, embeddings, and retrieval‑enhanced generation. The real challenge is not just implementing a system but understanding what it does and why it matters.

Understanding the Basics: What RAG Actually Means

RAG combines three key components to solve a fundamental AI problem: how can a language model access specific, up‑to‑date, or proprietary information that is absent from its original training data?

The "G" stands for a generative model (e.g., GPT, Claude, Gemini) that can produce human‑like text. These models are powerful but limited to knowledge captured at training time and cannot query internal documents, personal notes, or live databases.

The "retrieval" part augments the model by pulling relevant information from external sources at generation time, effectively giving the AI a research assistant that fetches context before answering.

Below is a visual illustration of the process:

The Magic Behind Embeddings and Vector Spaces

Embeddings convert text, images, or any data into numeric vectors. Similar concepts produce similar vectors, creating a mathematical space where related ideas cluster together.

With thousands of documents—support tickets, manuals, wikis, research papers—traditional keyword search requires exact matches. Embeddings let you ask natural questions like "How to troubleshoot a connection issue?" and retrieve relevant documents even if they never used that exact phrase.

The system transforms both the query and all documents into vectors, then measures proximity in the high‑dimensional space, acting like a knowledgeable librarian rather than a literal keyword matcher.

Vector Databases: The Memory System for Your Agent

Vector databases store embedding vectors and perform fast similarity searches across millions of documents, something traditional relational databases struggle with.

Using Neuron ADK, the agent can retrieve and synthesize information from a private knowledge base, answering questions about specific products, policies, or customer histories rather than giving generic responses.

Challenges of Implementing RAG

Building a working RAG system involves several dynamic steps: chunking documents, generating embeddings, storing and indexing them, implementing semantic search, and orchestrating retrieval with generation.

Key technical decisions affect performance: how to split long documents, which embedding model to use, how to keep the knowledge base up‑to‑date, and how to balance retrieval accuracy with response latency.

Installing Neuron AI

composer require neuron-core/neuron-ai

Implementing Your First RAG Agent

Extend the NeuronAI\RAG\RAG class and attach a vector store, an embedding provider, and an AI provider.

Example RAG class:

<?php

namespace App\Neuron;

use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;
use NeuronAI\RAG\Embeddings\EmbeddingsProviderInterface;
use NeuronAI\RAG\Embeddings\OpenAIEmbeddingProvider;
use NeuronAI\RAG\RAG;
use NeuronAI\RAG\VectorStore\FileVectoreStore;
use NeuronAI\RAG\VectorStore\VectorStoreInterface;

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

    protected function embeddings(): EmbeddingsProviderInterface
    {
        new OpenAIEmbeddingProvider(
            key: 'OPENAI_API_KEY',
            model: 'OPENAI_MODEL',
        );
    }

    protected function vectorStore(): VectorStoreInterface
    {
        new FileVectoreStore(
            directory: __DIR__,
            key: 'demo'
        );
    }
}

The three required components are:

LLM (e.g., Anthropic)

Embedding provider – converts text to vectors

Vector store – persists embeddings and enables similarity search

Explore the component documentation for alternatives: https://docs.neuron-ai.dev/components/ai-provider

Populating Your RAG Knowledge Base

Initially the vector store is empty, so the agent can only rely on the LLM’s training data.

use NeuronAI\Chat\Messages\UserMessage;

$response = MyChatBot::make()
    ->chat(new UserMessage('What size is the door handle on our top car model?'));

echo $response->getContent();
// Output: I don't really know specifically about your top car model. Do you want to provide me with additional information?

Load documents into the vector store using a data loader:

use NeuronAI\RAG\DataLoader\FileDataLoader;

MyChatBot::make()->addDocuments(
    FileDataLoader::for(__DIR__.'/README.md')->getDocuments()
);

After loading, the agent can answer domain‑specific questions:

use NeuronAI\Chat\Messages\UserMessage;

$response = MyChatBot::make()
    ->chat(new UserMessage('How can I create an agent with Neuron AI ADK?'));

echo $response->getContent();
// Output: Neuron is an ADK that simplifies the development of AI Agents in PHP…

Monitoring and Debugging

Programming an AI agent means dealing with probabilistic outputs; the same input may produce different results. Therefore observability, version control, and debugging become critical.

NeuronAI includes an Inspector that lets you monitor running agents, helping you maintain production‑grade implementations.

Moving Forward: From Theory to Practice

Coordinating embeddings, vector databases, and language models can be intimidating, but every expert started as a beginner.

The next step is to dive deeper into concrete implementations with Neuron AI ADK for PHP, which bridges RAG theory and production‑ready agents while offering flexibility for custom use cases.