RAG: Retrieval-Augmented Generation.

RAG typically stands for Retrieval-Augmented Generation. It’s a technique used in natural language processing and artificial intelligence that combines information retrieval with text generation.

What RAG

Here’s a brief overview:

  1. Retrieval: The system searches a large database or knowledge base to find relevant information related to a given query or prompt.
  2. Augmentation: The retrieved information is then used to supplement or “augment” the input to a language model.
  3. Generation: Finally, the language model generates a response based on both the original query and the retrieved information.

Why RAG

RAG (Retrieval-Augmented Generation) was developed to address several key challenges in AI and natural language processing:

  1. Knowledge limitations: Large language models have vast knowledge, but it’s static and limited to their training data. RAG allows access to external, updatable knowledge sources.
  2. Factual accuracy: By retrieving relevant information, RAG can improve the factual accuracy of responses, reducing hallucinations or outdated information.
  3. Contextual relevance: RAG helps provide more contextually appropriate responses by pulling in specific, relevant information for each query.
  4. Transparency: The retrieval step can make it easier to trace the sources of information used in generating responses.
  5. Efficiency: It’s more efficient to retrieve specific information than to encode all possible knowledge into a model’s parameters.
  6. Customization: RAG allows for easier customization of AI systems for specific domains or use cases by changing the knowledge base.
  7. Up-to-date information: The knowledge base can be updated without retraining the entire model, keeping responses current.

RAG is particularly useful because it allows AI systems to access and utilize external knowledge that isn’t part of their original training data. This can lead to more accurate, up-to-date, and contextually relevant responses (less hallucinations).

Would you like me to elaborate on any specific aspect of RAG or its applications?

Step 1: Set Up Your Environment

Ensure you have the necessary libraries installed. You will need libraries such as transformers, datasets, faiss, and torch. You can install these using pip:

pip install transformers datasets faiss-cpu torch

Step 2: Load Pre-trained Models and Tokenizers

Load the pre-trained models and tokenizers for both the retriever and the generator. For this example, we’ll use HuggingFace Transformers.

from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
from transformers import BartTokenizer, BartForConditionalGeneration

# Load the retriever model and tokenizer
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')

# Load the generator model and tokenizer
generator_tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
generator = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')

Step 3: Prepare Your Data

Prepare your encyclopedia data. You need a collection of context documents. For simplicity, let’s assume you have a list of documents.

documents = [
    {"title": "Document 1", "text": "This is the text of document 1."},
    {"title": "Document 2", "text": "This is the text of document 2."},
    # Add more documents
]

Step 4: Index the Documents

Use FAISS to index the document embeddings.

import faiss
import numpy as np

# Encode the documents
context_embeddings = []
for doc in documents:
    inputs = context_tokenizer(doc['text'], return_tensors='pt')
    embeddings = context_encoder(**inputs).pooler_output.detach().numpy()
    context_embeddings.append(embeddings[0])

# Convert to a numpy array
context_embeddings = np.array(context_embeddings)

# Create a FAISS index
index = faiss.IndexFlatL2(context_embeddings.shape[1])
index.add(context_embeddings)

Step 5: Retrieve Relevant Documents

For a given query, encode it using the question encoder and retrieve the most relevant documents from the index.

def retrieve_documents(query, top_k=5):
    inputs = question_tokenizer(query, return_tensors='pt')
    question_embedding = question_encoder(**inputs).pooler_output.detach().numpy()
    _, indices = index.search(question_embedding, top_k)
    return [documents[idx] for idx in indices[0]]

query = "What is the text of document 1?"
retrieved_docs = retrieve_documents(query)

Step 6: Generate a Response

Concatenate the retrieved documents and use the generator to produce a response.

def generate_response(query, retrieved_docs):
    context = " ".join([doc['text'] for doc in retrieved_docs])
    inputs = generator_tokenizer(query + " " + context, return_tensors='pt', max_length=1024, truncation=True)
    summary_ids = generator.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True)
    return generator_tokenizer.decode(summary_ids[0], skip_special_tokens=True)

response = generate_response(query, retrieved_docs)
print(response)

Full source code – git

Vijay Gokarn

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