Building a Simple Python RAG Pipeline Using Local Files

In this tutorial, we will guide you through the process of building a simple Retrieval-Augmented Generation (RAG) pipeline using Python. This pipeline will utilize your local files as context, allowing you to generate responses based on the information contained within those files. Whether you’re a beginner or looking to enhance your skills, this guide will provide you with a clear and structured approach.

Prerequisites

Before we dive into the tutorial, make sure you have the following prerequisites:

• Basic understanding of Python programming.
• Python installed on your local machine (preferably version 3.6 or higher).
• Familiarity with command line or terminal usage.
• Some local text files that you want to use as context for the RAG pipeline.

Step-by-Step Guide

Step 1: Setting Up Your Environment

First, we need to set up our Python environment. You can use virtual environments to keep your project dependencies organized. Here’s how to create a virtual environment:

python -m venv rag_pipeline_env
source rag_pipeline_env/bin/activate  # On Windows use `rag_pipeline_env\Scripts\activate`

Step 2: Installing Required Libraries

Next, we need to install the necessary libraries for our RAG pipeline. We will be using the transformers library from Hugging Face and torch for our model. Install them using pip:

pip install transformers torch

Step 3: Loading Your Local Files

Now, let’s load the local files that will serve as our context. You can use Python’s built-in functions to read text files. Here’s an example of how to read a file:

def load_file(file_path):
    with open(file_path, 'r') as file:
        return file.read()

Make sure to replace file_path with the path to your local file.

Step 4: Implementing the RAG Pipeline

With our files loaded, we can now implement the RAG pipeline. This involves using a pre-trained model to generate responses based on the context provided by our local files. Here’s a simple implementation:

from transformers import pipeline

# Load the model
rag_pipeline = pipeline('text2text-generation', model='facebook/rag-token-base')

# Generate a response
context = load_file('your_local_file.txt')
response = rag_pipeline(f'Context: {context} Question: What is the main idea?')
print(response)

In this code, replace your_local_file.txt with the name of your actual file.

Explanation of the RAG Pipeline

The Retrieval-Augmented Generation (RAG) pipeline combines retrieval and generation techniques. It retrieves relevant information from a set of documents (in our case, local files) and uses that information to generate coherent and contextually relevant responses. This approach is particularly useful for applications like chatbots, question-answering systems, and more.

Conclusion

Congratulations! You have successfully built a simple Python RAG pipeline using your local files as context. This tutorial has provided you with the foundational steps to create a more complex system tailored to your needs. Feel free to experiment with different models and datasets to enhance your understanding and capabilities.

For further reading and resources, check out the original post Hitchhiker’s Guide to RAG with ChatGPT API and LangChain”>here and explore more on this topic Towards Data Science”>here.