Retrieval-Augmented Generation (RAG)

Overview

Retrieval-Augmented Generation (RAG) is a hybrid model that combines retrieval-based and generation-based techniques to improve the performance of language models. It leverages a retrieval mechanism to fetch relevant documents from a large corpus and then uses a generative model to produce contextually accurate responses or outputs.

Components

  1. Retriever

    • Fetches relevant documents from a knowledge base.
    • Typically uses models like BM25, DPR (Dense Passage Retrieval).

  2. Generator

    • Generates text based on the retrieved documents.
    • Commonly uses models like GPT-3, BERT, T5.

Workflow

  1. Query Input: The user provides a query.
  2. Document Retrieval: The retriever fetches relevant documents based on the query.
  3. Contextual Generation: The generator uses the retrieved documents to generate a response.

Benefits

  • Improved Accuracy: Leverages external knowledge sources to provide more accurate responses.
  • Contextual Relevance: Ensures the generated text is relevant to the provided context.
  • Scalability: Can handle large knowledge bases efficiently.

Implementation Example

Retriever Example using DPR

from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer, DPRContextEncoder, DPRContextEncoderTokenizer

# Initialize the retriever components
question_encoder = DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
question_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
context_encoder = DPRContextEncoder.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base')
context_tokenizer = DPRContextEncoderTokenizer.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base')

# Encode the query
question = "What is Retrieval-Augmented Generation?"
question_inputs = question_tokenizer(question, return_tensors='pt')
question_embeddings = question_encoder(**question_inputs).pooler_output

# Encode a context
context = "Retrieval-Augmented Generation (RAG) is a hybrid model combining retrieval-based and generation-based techniques."
context_inputs = context_tokenizer(context, return_tensors='pt')
context_embeddings = context_encoder(**context_inputs).pooler_output

Generator Example using T5

from transformers import T5ForConditionalGeneration, T5Tokenizer

# Initialize the generator
model = T5ForConditionalGeneration.from_pretrained('t5-small')
tokenizer = T5Tokenizer.from_pretrained('t5-small')

# Generate a response
input_text = "summarize: Retrieval-Augmented Generation (RAG) is a hybrid model combining..."
input_ids = tokenizer(input_text, return_tensors='pt').input_ids
outputs = model.generate(input_ids)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)

RAG in Practice

Setup Environment

# Install required libraries
pip install transformers
pip install torch

Example Usage

  1. Initialize Retriever and Generator Models:
    • Load pretrained models for both retriever and generator.
  2. Process Input Query:
    • Encode the input query using the retriever.
  3. Retrieve Relevant Documents:
    • Use the retriever to fetch documents related to the query.
  4. Generate Response:
    • Use the generator to produce a response based on the retrieved documents.