Local Retrieval Augmented Generation (RAG) from Scratch (step by step tutorial)

Introduction

This tutorial will guide you through building a Retrieval Augmented Generation (RAG) pipeline from scratch, specifically focusing on a project called NutriChat. The aim is to enable users to ask questions about a 1200-page Nutrition Textbook PDF. By the end of this guide, you will understand the components of the RAG pipeline and how to implement them locally.

Step 1: Understanding Retrieval Augmented Generation

• What is RAG?
  RAG is a technique that combines retrieval and generation. It retrieves relevant information to support text generation, making responses more accurate and context-aware.

• Why use RAG?
  RAG enhances the quality of generated text by grounding it in specific information, which is particularly useful for complex topics like nutrition.

• Benefits of running RAG locally

  • Greater control over data privacy.
  • Customizable to specific needs or datasets.
  • Potentially faster processing without network latency.

Step 2: Setting Up the Environment

• Install Required Libraries
  Ensure you have Python installed and then set up the necessary libraries:

  pip install PyPDF2 numpy sentence-transformers
  

• Clone the GitHub Repository
  Access the code and resources by cloning the repository:

  git clone https://github.com/mrdbourke/simple-local-rag
  cd simple-local-rag
  

Step 3: Importing and Processing the PDF Document

• Extract PDF Text
  Use the PyPDF2 library to read and extract text from your Nutrition Textbook PDF:

  import PyPDF2
  
  pdf_file = open('nutrition_textbook.pdf', 'rb')
  pdf_reader = PyPDF2.PdfReader(pdf_file)
  text = ''
  
  for page in pdf_reader.pages:
      text += page.extract_text()
  pdf_file.close()
  

• Make the text readable
  Clean the extracted text as necessary to ensure it’s usable.

Step 4: Preprocessing Text into Chunks

• Text Splitting
  Break the text into manageable chunks for processing. This helps in embedding creation and retrieval later on.

  def split_text(text, max_length=1000):
      return [text[i:i + max_length] for i in range(0, len(text), max_length)]
  
  chunks = split_text(text)
  

Step 5: Creating Embeddings

• Understanding Embeddings
  Embeddings are numerical representations of text that capture semantic meaning. They allow for effective similarity searches.

• Create Embedding Model
  Use a pre-trained model from the sentence-transformers library to generate embeddings:

  from sentence_transformers import SentenceTransformer
  
  model = SentenceTransformer('all-MiniLM-L6-v2')
  embeddings = model.encode(chunks)
  

Step 6: Implementing Semantic Search

• Set Up Retrieval Logic
  Create a function to perform semantic searches among the embeddings:

  import numpy as np
  
  def semantic_search(query, embeddings):
      query_embedding = model.encode([query])
      similarities = np.dot(embeddings, query_embedding.T).flatten()
      return np.argsort(similarities)[::-1]
  

Step 7: Running a Local Language Model

• Choosing a Language Model
  Decide which large language model (LLM) to use (e.g., GPT-2, GPT-3). Ensure it can run locally and is compatible with your setup.

• Loading the LLM
  Load your chosen LLM into your environment. This might involve downloading the model weights and installing additional libraries.

Step 8: Text Generation with Context

• Generate Contextual Responses
  Use the retrieved information to augment prompts for text generation. Combine the results from the semantic search with user queries to formulate your prompt.

Step 9: Functionizing the Pipeline

• Integrate Components
  Wrap the entire process into functions for better organization and usability. This includes functions for text extraction, preprocessing, embedding creation, retrieval, and generation.

Conclusion

In this tutorial, you have learned how to build a RAG pipeline from scratch to query a Nutrition Textbook PDF. Key steps included text extraction, chunking, embedding creation, semantic search implementation, and text generation.

For further exploration, consider:

• Experimenting with different embedding models.
• Fine-tuning the performance of your semantic search.
• Integrating additional data sources for richer responses.

You can find the complete code and additional resources in the provided GitHub repository. Happy coding!