Create a Multi PDF RAG Chatbot with Gradio UI, Langchain,ChromaDB and Gemini

Introduction

In this article, we’ll explore how to create a Multi-PDF Retrieval-Augmented Generation (RAG) chatbot using Gradio UI, Langchain, ChromaDB, and Gemini. Last time, we focused on creating a single PDF chatbot; this tutorial will expand on that by introducing a user-friendly interface capable of handling multiple PDFs.

Overview

Creating a chatbot that can interact with multiple PDF files presents a useful application for sharing documents in a more interactive way. Gradio facilitates easy deployment of Python models with user-friendly interfaces. Here, we will detail the required steps to get your chatbot up and running.

Prerequisites

1. Python Environment: Make sure you have Python installed along with Gradio, Langchain, and necessary libraries.
2. Virtual Environment: It’s recommended to create a virtual environment to prevent conflicts with other packages.

Step-by-Step Guide

Step 1: Setup the Codebase

In this project, create three new files in your codebase:

• gradio_gemini.py: For a basic Gradio UI setup.
• gradio_streaming.py: To demonstrate streaming capabilities in responses.
• gradio_rag.py: To handle multiple PDF uploads and allow users to query the documents.

You can pull this code directly from your repository. Here’s an outline of the gradio_rag.py file.

Step 2: Create the Basic Gradio UI

In the gradio_gemini.py, start by importing Gradio and the model you wish to use. Follow these snippets:

import gradio as gr

## Introduction
def generate_response(prompt):
    response = model.generate(prompt)
    return response

## Introduction
iface = gr.Interface(fn=generate_response, 
                     inputs=gr.inputs.Textbox(label="Enter your prompt"), 
                     outputs=gr.outputs.Textbox(label="Response"),
                     title="PDF Chatbot",
                     description="A chatbot for PDF interaction")
iface.launch(share=True)

This code creates a simple UI where users can input their queries, and the model returns responses.

Step 3: Implement Streaming Responses

Next, we adapt our approach in gradio_streaming.py to handle streaming responses. The use of the yield function will allow us to return parts of the response as they are generated instead of waiting for the complete response:

def stream_response(prompt):
    response = model.generate(prompt)
    for chunk in response:
        yield chunk  # Yielding chunks as they become available

Note that, in the Gradio interface, the output will reflect streaming updates.

Step 4: Create Multi-PDF Capability

For multi-PDF functionality in gradio_rag.py, we will modify our loading function to accept multiple files and process them accordingly:

def load_pdfs(files):
    all_chunks = []
    for file in files:
        chunks = process_pdf(file)  # Assuming a function exists to convert PDFs into chunks
        all_chunks.extend(chunks)
    return all_chunks

Here’s how the full Gradio app might look:

iface_rag = gr.Interface(fn=load_pdfs, 
                         inputs=gr.inputs.File(label="Upload PDF(s)", type="file", file_count="multiple"),
                         outputs="text",
                         title="Multi PDF Chatbot",
                         description="Submit multiple PDFs for querying.")
iface_rag.launch()

This code sets up the interface for uploading files, handily allowing for multiple documents to be processed.

Conclusion

You now have a multi-PDF chatbot capable of processing user queries with an elegant Gradio interface. This example emphasizes the user experience by allowing PDF uploads and streaming responses for real-time interaction.

Feel free to experiment with the code and adjust it for your local use cases. If you face any challenges while setting up the environment or running the code, don’t hesitate to ask in the comments.

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Keywords

Keywords: Multi-PDF, RAG, Chatbot, Gradio, Langchain, ChromaDB, Gemini, User Interface, Streaming Responses, PDF Processing

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FAQ

Q1: What is a Multi-PDF RAG Chatbot?
A1: A Multi-PDF RAG Chatbot is a conversational agent that can retrieve and generate responses from multiple uploaded PDF documents simultaneously.

Q2: What libraries are needed to create this chatbot?
A2: You will need Gradio, Langchain, ChromaDB, and a model like Gemini for natural language processing tasks.

Q3: Can I create a chatbot without using a virtual environment?
A3: Yes, but it’s highly recommended to use a virtual environment to avoid potential package conflicts.

Q4: How does streaming response work in this chatbot?
A4: Streaming responses allow the chatbot to send parts of the output as they are generated, improving the interactivity and user experience.

Q5: Where can I find the code for this project?
A5: You can pull the complete code from your designated repository or GitHub link provided in your development setup.