# RAG System Architecture

## Main Components of RAG Systems

A RAG system consists of three main components that work together to provide intelligent responses.

### Component 1: Retrieval System

The retrieval system is the first component of a RAG system. A retrieval system finds relevant documents from large document collections. Vector databases serve as the primary storage for the retrieval system. Search engines can also function as retrieval systems in RAG architectures.

### Component 2: Embedding Model

The embedding model is the second component of a RAG system. An embedding model converts text into vector representations for similarity search. The embedding model transforms documents and queries into numerical vectors. These vector representations enable semantic similarity matching between queries and documents.

### Component 3: Large Language Model

The large language model is the third component of a RAG system. An LLM generates responses based on retrieved context from documents. The large language model synthesizes information from multiple sources into coherent answers. LLMs provide natural language generation capabilities for the RAG system.

## How Components Work Together

The retrieval system fetches relevant documents for a user query. The embedding model enables similarity matching between query and documents. The LLM generates the final response using retrieved context. These three components collaborate to provide accurate, contextual responses.
