> For the complete documentation index, see [llms.txt](https://docs.neuron-ai.dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.neuron-ai.dev/v2/rag/retrieval.md).

# Retrieval

### 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 query the vector store to retrieve documents similar to the input message:

```php
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\FileVectorStore;
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.

```php
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:

{% embed url="<https://github.com/neuron-core/raptor-retrieval>" %}