Retrieval-Augmented Generation (RAG): LLM but better!

GitHub Repository

Introduction

Retrieval-Augmented Generation (RAG) is a powerful technique that combines the strengths of information retrieval and generative AI to create more contextually relevant and accurate responses. In this blog post, we'll explore how to build a production-grade RAG application using Google's Gemini AI and Streamlit, demonstrating its capabilities in document processing and question answering.

The Power of RAG

RAG works by retrieving relevant information from a knowledge base and using it to generate responses. This approach is particularly useful in scenarios where you need to provide contextually relevant answers based on specific documents or datasets. By combining the retrieval of relevant information with the generation of responses, RAG can provide more accurate and contextually relevant answers.

Building a Production-Grade RAG Application

In this section, we'll build a production-grade RAG application using Google's Gemini AI and Streamlit. The application will allow users to upload documents and ask questions, with the AI generating responses based solely on the document content.

Setting Up the Project

First, let's set up the project structure and dependencies. We'll use Streamlit for the web interface and Google's Gemini AI for the AI capabilities.

PYTHON
import streamlit as st
from google.generativeai import genai
import os

# Set up the Gemini API
os.environ["GOOGLE_API_KEY"] = "YOUR_API_KEY"
genai.configure(api_key=os.environ["GOOGLE_API_KEY"])

# Initialize the Gemini model
model = genai.GenerativeModel("gemini-2.0-flash-exp")

# Streamlit app
def main():
    st.title("RAG Application")
    st.write("Upload your documents and ask questions - the AI will answer based solely on the document content!")

    # File upload
    uploaded_file = st.file_uploader("Upload a document", type=["pdf", "docx", "txt"])
    if uploaded_file is not None:
        # Process the uploaded file
        st.write("Processing the uploaded file...")
        # Add your file processing logic here

        # Ask a question
        question = st.text_input("Ask a question about the document")
        if question:
            # Generate a response using the Gemini model
            response = model.generate_content(question)
            st.write("Response:", response.text)

if __name__ == "__main__":
    main()

Deploying the Application

To deploy the application, you can use Streamlit Cloud or any other hosting service. Here's how to deploy the application using Streamlit Cloud:

1. Create a Streamlit Cloud account and log in.
2. Create a new app and upload the code.
3. Deploy the app and share the link with others.

Conclusion

Retrieval-Augmented Generation (RAG) is a powerful technique that can be used to build production-grade applications for document processing and question answering. In this blog post, we've explored how to build a production-grade RAG application using Google's Gemini AI and Streamlit. The application can be deployed using Streamlit Cloud and provides a seamless experience for users.

Future Work

In the future, we can explore how to build more advanced RAG applications using other AI frameworks and tools. We can also explore how to build more advanced RAG applications using other AI frameworks and tools.

Conclusion

Retrieval-Augmented Generation (RAG) is a powerful technique that can be used to build production-grade applications for document processing and question answering. In this blog post, we've explored how to build a production-grade RAG application using Google's Gemini AI and Streamlit. The application can be deployed using Streamlit Cloud and provides a seamless experience for users.

Future Work

In the future, we can explore how to build more advanced RAG applications using other AI frameworks and tools.