LLM & RAG -Powered Document & Text Q&A

This is a Streamlit application that allows users to upload a PDF document or Text and ask questions about its content using LLM and RAG-powered. The app uses Langchain, Google gemini model, and HuggingFaceEmbeddings for document retrieval and question answering.

Requirements

The following libraries are required for this project:

• langchain-community >= 0.3.18
• langchain-huggingface >= 0.1.2
• langchain-openai >= 0.3.7
• langchain >= 0.3.19
• pypdf >= 5.3.0
• sentence-transformers >= 3.4.1
• streamlit >= 1.42.2
• scripts.secret (for storing your OpenAI API key)
• scripts.document_loader (for loading and splitting PDF documents)

You can install the necessary dependencies using pip:

pip install streamlit langchain faiss

Overview

A powerful open-source Streamlit web app that allows you to:

• Upload PDF or TXT files
• Paste large text manually
• Ask questions about the content
• Get AI-generated answers using Google Gemini (fast, efficient, context-aware)

Demonstration

Demo Video

https://drive.google.com/file/d/1d2l1iX5cZu4Zyf7Q6N3Gtz0uGp0mYEJu/view

🚀 Features

• Supports both documents and text input
• Uses FAISS vector search for efficient retrieval
• Integrated with Google Gemini 1.5 Flash / 2.5 Flash
• Clean, minimal Streamlit UI
• Ready for cloud deployment (Streamlit, Render, or Google Cloud)

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🧠 Tech Stack

• Frontend/UI: Streamlit
• LLM: Google Gemini (via langchain-google-genai)
• Vector Store: FAISS
• Embeddings: Sentence Transformers
• Document Parsing: LangChain + PyPDF
• Language: Python 3.10+

How it Works

1. Document Upload The user can upload a PDF document/text via the Streamlit interface. The file is saved temporarily on the server, and the document is then processed into text chunks for easier retrieval.

2. Text Splitting and Vector Database Creation Once the document is uploaded:

• The document is loaded and split into smaller chunks to optimize retrieval performance.
• Embeddings (numerical representations of the document's contents) are generated using the OpenAIEmbeddings model.
• A FAISS vector store is created from these embeddings, allowing efficient similarity search.

3. Question-Answering System The system utilizes a retrieval-based approach (RAG - Retrieval-Augmented Generation) to answer user queries. When a user asks a question, the system retrieves the most relevant chunks from the document using FAISS and processes them with GPT-4 to generate a meaningful answer.

4. User Interface The application presents:

• An input field to upload the PDF file or text input for entering the text.
• A text box to ask questions about the document.
• Responses generated by the AI based on the content of the document.

Code Walkthrough

Imports

The required libraries are imported to handle document processing, vector database creation, and AI-powered responses:

from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain.chains import create_retrieval_chain
from langchain_openai import ChatOpenAI
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from scripts.document_loader import load_document
import streamlit as st

Streamlit App Setup

The Streamlit app is initialized, with the title displayed and a file uploader for PDFs.

Document Processing

When a file is uploaded, the following steps are performed:

• The document is saved temporarily.
• The document is split into chunks using the load_document function.

Embeddings and Vector Store

Embeddings are generated using OpenAI's model (text-embedding-ada-002), and a FAISS vector store is created to hold these embeddings.

Question Answering Chain

The question-answering chain is created by combining the retriever (FAISS) and the document processing chain (StuffDocumentsChain). The model used for answering questions is GPT-4.

User Interaction

The user is prompted to input a question, and the system responds with an answer based on the document's content.

Running the App

To run the app, execute the following command in your terminal:

streamlit run app.py

This will start the Streamlit server and open the app in your browser.

🛠️ Local Setup

Clone the Repository

git clone https://github.com/yourusername/QueryBot.git
cd QueryBot

Install Dependencies

pip install -r requirements.txt

Set Up Environment Variables

GOOGLE_API_KEY=your_google_gemini_api_key_here

You can get your Gemini API key here: https://makersuite.google.com/app/apikey

Run the App

python -m streamlit run app.py

Then open the URL shown in your terminal (usually http://localhost:8501).

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Connect With Me

If you liked this project or found it useful, feel free to connect with me on LinkedIn

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