Retrieval

Implement custom retrieval strategies

Introduction

The RAG module has a separate retrieval component that allows you to implement different strategies to accomplish context retrieval from external data sources. By default, RAG uses SimilarityRetrieval that simply queries the vector store to retrieve documents:

namespace App\Neuron;

use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\RAG\Embeddings\EmbeddingsProviderInterface;
use NeuronAI\RAG\RAG;
use NeuronAI\RAG\RAG\Retrieval\RetrievalInterface;
use NeuronAI\RAG\RAG\Retrieval\SimilarityRetrieval;
use NeuronAI\RAG\VectorStore\VectorStoreInterface;

class WorkoutTipsAgent extends RAG
{
    protected function retrieval(): RetrievalInterface
    {
        return new SimilarityRetrieval(
            $this->resolveVectorStore(),
            $this->resolveEmbeddingsProvider()
        );
    }
    
    protected function provider(): AIProviderInterface
    {
        // Return an instance of an AI provider...
    }
    
    protected function embeddings(): EmbeddingsProviderInterface
    {
        // Return an embeddings provider instance...
    }
    
    protected function vectorStore(): VectorStoreInterface
    {
        // Return a vector store instance...
    }
}

Implementing RetrievalInterface you are free to create any custom retrieval behaviour for your RAG.

interface RetrievalInterface
{
    /**
     * Retrieve relevant documents for the given query.
     *
     * @return Document[]
     */
    public function retrieve(Message $query): array;
}

If you are implementing custom workflow you can use retrieval as a standalone component to dynamically retrieve context data for use in your agentic systems.

RAPTOR Retrieval Module

Most retrieval-augmented models work by breaking down documents into small chunks and retrieving only the most relevant ones. However, this approach has some limitations:

• Loss of Context: Retrieving only small, isolated chunks may miss the bigger picture especially for documents with long contexts.

• Difficulty in Multi-Step Reasoning: Some questions require information from multiple sections of a document.

Use RAPTOR when:

• Users ask open-ended questions that require comprehensive coverage

• Your domain involves complex topics where context matters as much as facts

• You need to handle queries about themes, trends, or relationships across documents

Stick with traditional RAG when:

• Users primarily need quick, specific fact retrieval

• Processing speed and token efficiency are critical constraints

Learn more about RAPTOR in the dedicated repository:

GitHub - neuron-core/raptor-retrieval: Recursive Abstractive Processing for Tree-Organized Retrieval - Neuron PHP FrameworkGitHub