Beginner Explanation
Imagine you’re writing a story, but instead of making everything up from scratch, you have a library of books right next to you. When you get stuck or need a fact, you can quickly grab a book, find the information you need, and then continue your story. Retrieval-Augmented Generation (RAG) works in a similar way. It uses a system that can look up information from a database (like your library) and then uses that information to create better responses or predictions (like finishing your story). This makes the answers more accurate and relevant, just like how using a book can help you write a better story.Technical Explanation
Retrieval-Augmented Generation (RAG) is a hybrid approach that integrates a retrieval mechanism with a generative model. The process involves two main steps: retrieval and generation. In the retrieval step, relevant documents or information are fetched from a knowledge base using techniques such as BM25 or dense vector similarity. These retrieved documents are then provided as context to a generative model, like GPT or BART, which produces a final output by conditioning on both the input query and the retrieved information. The architecture can be implemented using libraries like Hugging Face Transformers. Here’s a simple code example: “`python from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration tokenizer = RagTokenizer.from_pretrained(‘facebook/rag-sequence-base’) retriever = RagRetriever.from_pretrained(‘facebook/rag-sequence-base’) model = RagSequenceForGeneration.from_pretrained(‘facebook/rag-sequence-base’) input_text = ‘What is the capital of France?’ tokens = tokenizer(input_text, return_tensors=’pt’) output = model.generate(**tokens) response = tokenizer.decode(output[0], skip_special_tokens=True) print(response) “`Academic Context
Retrieval-Augmented Generation (RAG) combines the strengths of information retrieval and generative modeling. The concept was introduced in the paper ‘Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks’ by Lewis et al. (2020), where the authors demonstrated that augmenting generative models with retrieved documents significantly improves performance on tasks requiring external knowledge. The mathematical foundation lies in the combination of probabilistic retrieval models and neural generative models, where the retrieval component can be viewed as a prior distribution that informs the generative process. RAG leverages techniques from both NLP and information retrieval, making it a powerful tool for knowledge-intensive tasks.Code Examples
Example 1:
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
tokenizer = RagTokenizer.from_pretrained('facebook/rag-sequence-base')
retriever = RagRetriever.from_pretrained('facebook/rag-sequence-base')
model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-base')
input_text = 'What is the capital of France?'
tokens = tokenizer(input_text, return_tensors='pt')
output = model.generate(**tokens)
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)Example 2:
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
tokenizer = RagTokenizer.from_pretrained('facebook/rag-sequence-base')
retriever = RagRetriever.from_pretrained('facebook/rag-sequence-base')
model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-base')View Source: https://arxiv.org/abs/2511.16654v1