Abstract

This project implements a RAG-based (Retrieval-Augmented Generation)
AI assistant that answers questions based on Ready Tensor publications.
The system combines vector search with large language models to provide
accurate, context-aware responses grounded in real documents.

Overview

Traditional AI chatbots generate responses purely from training data,
which can lead to hallucinations or outdated information. This RAG-based
assistant solves that by first retrieving relevant content from a
document corpus, then using an LLM to generate answers based on that
retrieved context.

System Architecture

The pipeline works in the following steps:

1. Document Loading - Text files are loaded from the data directory
2. Text Chunking - Documents are split into 500-character chunks
3. Embedding - Chunks are converted to vectors using
   sentence-transformers (all-MiniLM-L6-v2)
4. Storage - Vectors are stored in ChromaDB
5. Retrieval - User queries are matched against stored vectors
6. Generation - Retrieved context + query is sent to an LLM
   for response generation

Tech Stack

• LLM: Groq (Llama 3.1)
• Vector Database: ChromaDB
• Embeddings: HuggingFace sentence-transformers
• Framework: LangChain
• Language: Python 3.12

Setup Instructions

1. Clone the repository
2. Install dependencies:

8. Setup Instructions

Prerequisites

• Python 3.12+
• Free Groq API key from console.groq.com

Steps

# 1. Clone the repository
git clone https://github.com/Nakul-Narang/rt-aaidc-project1-template
cd rt-aaidc-project1-template

# 2. Install dependencies
pip install -r requirements.txt

# 3. Set up environment variables
cp .env.example .env
# Add your GROQ_API_KEY to the .env file

# 4. Add your documents
# Place .txt files in the data/ folder

# 5. Run the assistant
python src/app.py
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### After pasting this:

1. Upload the two diagrams where marked `[Upload ... here]`
2. Take a terminal screenshot of your app running and upload it in Section 7
3. Click **Save** then **Publish**

Let me know when done! 🚀