Retrieval Augmented Generation (RAG) Architecture

Retrieval Augmented Generation (RAG) Architecture

RAG Architecture: Retrieval and Generation

RAG systems have a two-step pipeline to generate responses - retrieval and generation. First, in the retrieval phase, the model searches through databases or document collections to find the most relevant facts and passages for the given prompt or user question. For open domains like general web searches, this could leverage indexed webpages. In closed domains like customer support, it may retrieve from a controlled set of manuals and articles.
These snippets of external knowledge are appended to the original user input to augment the context. Next, in the generation phase, the language model analyzes this expanded prompt to produce a response. It references both the retrieved information and its internally trained patterns to formulate an informative and natural answer.
The final response can optionally include links back to the originating sources, enabling explainability and transparency. This output is then passed on to downstream applications like chatbots to provide the end user with a reliable and engaging interaction.
The RAG Framework Core Components
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Core Components for Building a RAG System

Implementing an effective retrieval-augmented generation (RAG) system requires several key components working in harmony:
  1. The Language Model: The foundation of a RAG architecture is a pretrained language model that handles text generation. Models like GPT-3 possess strong language comprehension and synthesis abilities to engage in conversational dialog.
  1. Vector Store: At the heart of the retrieval functionality is a vector store database persisting document embeddings for similarity search. This allows rapid identification of relevant contextual information.
  1. Retriever: The retriever module leverages the vector store to efficiently find pertinent documents and passages to augment prompts. Neural retrieval approaches excel at semantic matching.
  1. Embedder: To populate the vector store, an embedder encodes source documents into vector representations consumable by the retriever. Models like BERT are ideal for this text-to-vector abstraction.
  1. Document Ingestion - Robust pipelines ingest and preprocess source documents, chunking them into manageable passages for embedding and efficient lookup.
By orchestrating these core components, RAG systems empower language models to access vast knowledge for grounded generation.