neuron-rag-specialist

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'
);