Retrieval-Augmented Generation (RAG) with Document Chunks, Embeddings, and GPT-4

Introduction

In the age of information overload, efficiently retrieving and utilizing information from numerous documents is essential. Retrieval-Augmented Generation (RAG) is a powerful technique that enhances the performance of language models by combining document retrieval with generation capabilities. This article explores how to implement RAG using document chunking, embedding models for creating embeddings, computing cosine similarity, and utilizing the GPT-4 model to generate precise responses to queries.

Understanding RAG

Before diving into the implementation, let’s briefly understand the key components of RAG:

1. Document Chunking: Dividing large documents into smaller, manageable chunks to improve retrieval and processing efficiency.
2. Embeddings: Representing text data as high-dimensional vectors using an embeddings model.
3. Cosine Similarity: Measuring the similarity between embeddings to find the most relevant chunks.
4. GPT-4: Leveraging GPT-4 to generate accurate responses based on the retrieved chunks.

Steps to Implement RAG

1. Document Chunking

Large documents are divided into smaller chunks to enhance retrieval efficiency. This can be done by splitting the text based on paragraphs, sentences, or a fixed number of tokens.

def chunk_document(text, chunk_size=200):
    words = text.split()
    chunks = [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
    return chunks        

2. Creating Embeddings

Using an embeddings model, such as Sentence-BERT, to convert text chunks into high-dimensional vectors.

from sentence_transformers import SentenceTransformer

# Load the embeddings model
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

# Function to create embeddings for each chunk
def create_embeddings(chunks):
    embeddings = model.encode(chunks, convert_to_tensor=True)
    return embeddings        

3. Computing Cosine Similarity

Calculate cosine similarity between the query embedding and document chunk embeddings to identify the most relevant chunks.

import torch
from sklearn.metrics.pairwise import cosine_similarity

def find_most_similar_chunks(query, embeddings, chunks, top_k=5):
    query_embedding = model.encode(query, convert_to_tensor=True)
    similarities = cosine_similarity(query_embedding, embeddings)
    top_k_indices = similarities.argsort()[-top_k:][::-1]
    return [chunks[i] for i in top_k_indices]        

4. Generating Responses with GPT-4

Feed the most relevant chunks to GPT-4 to generate a precise response to the query.

import openai

# Function to get response from GPT-4
def get_response_from_gpt4(query, relevant_chunks):
    context = ' '.join(relevant_chunks)
    response = openai.ChatCompletion.create(
        model="gpt-4",  # Use GPT-4 model
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": f"Context: {context}\n\nQuery: {query}\n\nResponse:"}
        ]
    )
    return response.choices[0].message['content'].strip()

# Main function to implement RAG
def retrieve_and_generate_response(documents, query, chunk_size=200, top_k=5):
    all_chunks = [chunk for doc in documents for chunk in chunk_document(doc, chunk_size)]
    embeddings = create_embeddings(all_chunks)
    relevant_chunks = find_most_similar_chunks(query, embeddings, all_chunks, top_k)
    response = get_response_from_gpt4(query, relevant_chunks)
    return response        

Hands-on Implementation

Assuming we have a list of documents and a query, we can implement the RAG pipeline as follows:

documents = [
    "Document 1 text...",
    "Document 2 text...",
    "Document 3 text...",
    # Add more documents as needed
]

query = "What are the benefits of using Retrieval-Augmented Generation?"

response = retrieve_and_generate_response(documents, query)
print(response)        

Conclusion

Retrieval-Augmented Generation is a powerful technique that combines the strengths of document retrieval and text generation to provide accurate and contextually relevant responses. By chunking documents, creating embeddings, computing cosine similarity, and utilizing GPT-4, we can efficiently handle and query large collections of documents. This approach is highly valuable in various applications, including customer support, research, and information retrieval, enabling users to extract meaningful information quickly and accurately.

Llm

Gpt

Retrieval Augmented

Machine Learning

Data Science