# 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>" %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.neuron-ai.dev/v2/rag/retrieval.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.