ridzzzzzzzzzzzzzz/RAG-Based-Document-Chatbot

RAG-Based Document Chatbot

A retrieval-augmented generation (RAG) chatbot for querying your own documents. This project allows you to upload documents (PDF, DOCX, TXT), prepare them, and interact with them. Works great for research and study session ;)

Features

• —Supports multiple document formats: PDF, DOCX, TXT.

• —Documents are chunked and stored in a vector store (FAISS).

• —Uses all-MiniLM-L6-v2 embeddings for document chunks.

• —Retrieves relevant chunks based on queries via similarity search.

• —Uses Groq for LLM integration (llama-3.3-70b-versatile)

• —App metrics and monitoring logs to keep track of performance.

• —Demo deployed on Hugging Face spaces

How It Works

Document Preparation

• —Upload your documents in DOCX, PDF, or TXT format.

• —Documents are chunked along with source information and IDs.

Embedding Creation

• —Each chunk is converted into vector embeddings using the all-MiniLM-L6-v2 model.

• —Embeddings are stored in a FAISS vector store for efficient retrieval.

Query Retrieval

• —When a query is entered, a similarity search retrieves the most relevant chunks from the vector store.

Answer Generation

• —Retrieved chunks are passed as context to a prompt.

• —The prompt is sent to the via an API call to llama-3.3-70b-versatile, which generates the final answer.

Requirements

All Python dependencies are listed in requirements.txt.

Key packages include:

• —gradio for the UI

• —faiss for vector store

• —sentence-transformers for embeddings

• —Groq for LLM integration

• —HuggingFace spaces for deployement

Setup Instructions

Clone the repository:

git clone https://github.com/Ridzz110/RAG-Based-Document-Chatbot.git
cd RAG-Based-Document-Chatbot

Set up Python virtual environment:

python -m venv venv
# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate

Install Python dependencies:

pip install -r requirements.txt

Run the Gradio interface:

python src/app.py

This will start the RAG chatbot UI.

Future Improvements.

• —Implement docker.
• —User Sessions persistance.

Author

Rida Batool AI Undergraduate | Aspiring ML Engineer

GitHub: https://github.com/Ridzz110 LinkedIn: https://www.linkedin.com/in/ridabatool110