neuron-rag-specialist

name

neuron-rag-specialist

description

Implement RAG (Retrieval-Augmented Generation) with Neuron AI including vector stores, embeddings providers, document loaders, and retrieval strategies. Use this skill whenever the user mentions RAG, retrieval, vector search, document retrieval, semantic search, knowledge bases, chat with documents, or wants to build AI systems that can query and understand external documents. Also trigger for tasks involving vector databases, embeddings, document chunking, or retrieval strategies.

Neuron AI RAG Specialist

This skill helps you implement Retrieval-Augmented Generation (RAG) in Neuron AI. RAG extends the Agent class with document retrieval capabilities.

Core RAG Architecture

RAG systems in Neuron AI consist of three main components:

1. Vector Store - Stores document embeddings for semantic search
2. Embeddings Provider - Converts text to vector embeddings
3. Retrieval Strategy - Determines how to search and rank documents

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

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

    protected function embeddings(): EmbeddingsProviderInterface
    {
        return new OpenAIEmbeddingProvider(
            key: $_ENV['OPENAI_API_KEY'],
            model: 'OPENAI_MODEL',
        );
    }

    protected function vectorStore(): VectorStoreInterface
    {
        return new PineconeVectorStore(
            key: $_ENV['PINECONE_API_KEY'],
            indexUrl: $_ENV['PINECONE_INDEX_URL']
        );
    }
}

Vector Stores

Pinecone

use NeuronAI\RAG\VectorStore\PineconeVectorStore;

new PineconeVectorStore(
    key: $_ENV['PINECONE_API_KEY'],
    indexUrl: $_ENV['PINECONE_INDEX_URL'],
    environment: 'us-east-1-aws'
);

Chroma

use NeuronAI\RAG\VectorStore\ChromaVectorStore;

new ChromaVectorStore(
    host: 'localhost',
    port: 8000,
    collection: 'my_collection'
);

Qdrant

use NeuronAI\RAG\VectorStore\QdrantVectorStore;

new QdrantVectorStore(
    apiKey: $_ENV['QDRANT_API_KEY'],
    url: $_ENV['QDRANT_URL'],
    collection: 'my_collection'
);

Elasticsearch

use NeuronAI\RAG\VectorStore\ElasticsearchVectorStore;

new ElasticsearchVectorStore(
    hosts: ['http://localhost:9200'],
    index: 'documents'
);

Typesense

use NeuronAI\RAG\VectorStore\TypesenseVectorStore;

new TypesenseVectorStore(
    apiKey: $_ENV['TYPESENSE_API_KEY'],
    nodes: [['host' => 'localhost', 'port' => 8108]],
    collection: 'documents'
);

Other Vector Stores

• MemoryVectorStore - In-memory for testing
• MilvusVectorStore - Milvus database
• RedisVectorStore - Redis with RediSearch
• WeaviateVectorStore - Weaviate database
• PgVectorStore - PostgreSQL with pgvector extension

Embeddings Providers

OpenAI

use NeuronAI\RAG\Embeddings\OpenAIEmbeddingProvider;

new OpenAIEmbeddingProvider(
    key: $_ENV['OPENAI_API_KEY'],
    model: 'OPENAI_EMBEDDING_MODEL'  // or 'text-embedding-3-large'
);

Ollama

use NeuronAI\RAG\Embeddings\OllamaEmbeddingProvider;

new OllamaEmbeddingProvider(
    baseUrl: 'http://localhost:11434',
    model: 'OLLAMA_EMBEDDING_MODEL'
);

Gemini

use NeuronAI\RAG\Embeddings\GeminiEmbeddingProvider;

new GeminiEmbeddingProvider(
    key: $_ENV['GEMINI_API_KEY'],
    model: 'GEMINI_EMBEDDING_MODEL'
);

Voyage

use NeuronAI\RAG\Embeddings\VoyageEmbeddingProvider;

new VoyageEmbeddingProvider(
    key: $_ENV['VOYAGE_API_KEY'],
    model: 'VOYAGE_EMBEDDING_MODEL'
);

Document Loading and Chunking

Text Documents

use NeuronAI\RAG\DocumentLoader\TextLoader;
use NeuronAI\RAG\Chunker\RecursiveCharacterTextSplitter;

$loader = new TextLoader('/path/to/document.txt');
$documents = $loader->load();

// Chunk documents
$chunker = new RecursiveCharacterTextSplitter(
    chunkSize: 1000,
    chunkOverlap: 200
);
$chunks = $chunker->chunk($documents);

PDF Documents

use NeuronAI\RAG\DocumentLoader\PDFLoader;

$loader = new PDFLoader('/path/to/document.pdf');
$documents = $loader->load();

HTML Documents

use NeuronAI\RAG\DocumentLoader\HtmlLoader;

$loader = new HtmlLoader('https://example.com/page');
$documents = $loader->load();

Loading Multiple Files

use NeuronAI\RAG\DocumentLoader\DirectoryLoader;

$loader = new DirectoryLoader('/path/to/documents');
$documents = $loader->load();

Ingesting Documents

$rag = MyChatBot::make();

// Load and chunk documents
$loader = new DirectoryLoader('./docs');
$documents = $loader->load();

$chunker = new RecursiveCharacterTextSplitter(
    chunkSize: 1000,
    chunkOverlap: 200
);
$chunks = $chunker->chunk($documents);

// Add to vector store
$rag->vectorStore()->addDocuments($chunks);

Retrieval Strategies

Basic Similarity Search

use NeuronAI\RAG\Retrieval\SimilaritySearch;

$rag->setRetrieval(new SimilaritySearch(
    k: 5  // Return top 5 documents
));

Hybrid Search (Keyword + Semantic)

use NeuronAI\RAG\Retrieval\HybridSearch;

$rag->setRetrieval(new HybridSearch(
    k: 5,
    alpha: 0.5  // Balance between semantic (1.0) and keyword (0.0)
));

Max Marginal Relevance (MMR)

use NeuronAI\RAG\Retrieval\MMRSearch;

$rag->setRetrieval(new MMRSearch(
    k: 5,
    fetchK: 20,  // Fetch 20, return diverse 5
    lambdaMult: 0.5  // Diversity parameter
));

Pre and Post Processors

Pre-Processors (Query Transformation)

use NeuronAI\RAG\Processor\QueryExpansionProcessor;

$rag->addPreProcessor(new QueryExpansionProcessor(
    numQueries: 3  // Generate 3 query variations
));

use NeuronAI\RAG\Processor\HydeProcessor;

$rag->addPreProcessor(new HydeProcessor(
    model: $rag->provider()  // Generate hypothetical document
));

Post-Processors (Result Enhancement)

use NeuronAI\RAG\Processor\RerankProcessor;
use NeuronAI\RAG\Processor\JinaReranker;

$rag->addPostProcessor(new RerankProcessor(
    reranker: new JinaReranker(
        apiKey: $_ENV['JINA_API_KEY']
    ),
    topK: 5
));

use NeuronAI\RAG\Processor\CompressorProcessor;

$rag->addPostProcessor(new CompressorProcessor(
    maxTokens: 2000
));

Using the RAG

Basic Query

$rag = MyChatBot::make();

$response = $rag->chat(
    new UserMessage("What are the main features of our product?")
)->getMessage();

echo $response->getContent();
// Response includes retrieved documents as context

Streaming

use NeuronAI\Chat\Messages\Stream\Chunks\TextChunk;

foreach ($rag->stream(new UserMessage("Explain the architecture"))->events() as $event) {
    if ($event instanceof TextChunk) {
        echo $event->content;
    }
}

Structured Output with RAG

$summary = $rag->structured(
    new UserMessage("Summarize the pricing information"),
    PricingSummary::class
);

CLI Generation

vendor/bin/neuron make:rag MyKnowledgeBot

Advanced Configuration

Custom Retrieval

use NeuronAI\RAG\Retrieval\RetrievalInterface;

class CustomRetrieval implements RetrievalInterface
{
    public function retrieve(string $query, VectorStoreInterface $vectorStore): array
    {
        // Custom retrieval logic
        return $vectorStore->similaritySearch($query, k: 3);
    }
}

$rag->setRetrieval(new CustomRetrieval());

Filtering Results

$rag->chat(
    new UserMessage("Find documents about pricing")
)->withMetadataFilter([
    'category' => 'pricing',
    'year' => 2024
]);

Common Patterns

Company Knowledge Base

class CompanyKnowledgeBot extends RAG
{
    protected function embeddings(): EmbeddingsProviderInterface
    {
        return new OpenAIEmbeddingProvider(
            key: $_ENV['OPENAI_API_KEY'],
            model: 'OPENAI_EMBEDDING_MODEL'
        );
    }

    protected function vectorStore(): VectorStoreInterface
    {
        return new PineconeVectorStore(
            key: $_ENV['PINECONE_API_KEY'],
            indexUrl: $_ENV['PINECONE_INDEX_URL']
        );
    }

    protected function retrieval(): RetrievalInterface
    {
        return new HybridSearch(k: 5, alpha: 0.7);
    }

    protected function instructions(): string
    {
        return (string) new SystemPrompt(
            background: [
                "You are a helpful assistant that answers questions",
                "about our company using the provided context.",
            ],
            constraints: [
                "Only use the provided context to answer.",
                "If the answer is not in the context, say you don't know.",
            ]
        );
    }
}

Document Q&A with Reranking

$rag = MyChatBot::make();

$rag->addPostProcessor(new CohereRerankerPostProcessor(
    key: $_ENV['COHERE_API_KEY'],
    model: $_ENV['COHERE_RERANK_MODEL'],
    topN: 5
);

$rag->chat(new UserMessage("Your question here"));

Performance Considerations

Chunk Size Selection

• Smaller chunks (500-1000 tokens): More precise retrieval, more documents to process
• Larger chunks (1500-2000 tokens): More context per document, less precise
• Chunk overlap: 10-20% helps maintain context across chunk boundaries

Top-K Selection

• 3-5 documents: Good for focused queries, faster responses
• 10+ documents: Better for comprehensive answers

Testing RAG

use PHPUnit\Framework\TestCase;

class MyChatBotTest extends TestCase
{
    public function testRAGRetrieval(): void
    {
        $rag = MyChatBot::make();

        // Add test document
        $rag->vectorStore()->addDocument(
            new Document('test', 'The product costs $99.')
        );

        $response = $rag->chat(
            new UserMessage("How much does it cost?")
        )->getMessage();

        $this->assertStringContainsString('99', $response->getContent());
    }
}