📚 RAG Tutor: Document-Grounded Mentoring System

RAG Tutor is a Streamlit-based Retrieval-Augmented Generation (RAG) application designed to provide personalized tutoring and mentoring based on your uploaded documents. This system processes documents like PDFs and DOCX files, retrieves relevant content, and uses a Large Language Model (LLM) to generate accurate, contextual answers and explanations.

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✨ Features

• Multi-Document Support: Easily upload and process .pdf, .docx, and .txt files.
• Contextual Q&A: The RAG pipeline grounds all answers strictly in the content of your documents, minimizing LLM hallucination.
• Intuitive UI: Built with Streamlit for a simple, interactive, and fast chat interface.
• Vector Database: Uses ChromaDB to efficiently store and retrieve document embeddings (vector representations) for rapid similarity search.
• LangChain Orchestration: Utilizes the LangChain framework to manage the entire RAG workflow: document loading, chunking, embedding, retrieval, and response generation.

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🛠️ Technology Stack

Component	Technology	Role
Frontend/UI	Streamlit	Interactive web application interface.
Orchestration	LangChain	Framework connecting the LLM, documents, and vector store.
Vector DB	ChromaDB	Stores document embeddings for efficient retrieval.
Document Parsing	pypdf, python-docx	Libraries to read and extract text from various file formats.
Backend	Python 3.x	Core programming language.

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🚀 Setup and Installation

Prerequisites

1. Python: Ensure you have Python 3.8+ installed.
2. API Key: An API key for your chosen Large Language Model (In our case it is Google Gemini API key.).

Installation Steps

1. Clone the Repository:

   git clone [YOUR-REPO-URL]
   cd rag-tutor-system

2. Create and Activate a Virtual Environment (Recommended):

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

3. Install Dependencies: You'll need the requirements.txt file (see the next section) to install all necessary packages.

   pip install -r requirements.txt

4. Set API Key: Set your LLM API key as an environment variable in the ./rag_tutor/generator.py (GEMINI_API_KEY).

   os.environ["GOOGLE_API_KEY"] = "********YOUR-API-KEY********"

5. Run the Application:

   streamlit run app.py # Replace app.py with your main Streamlit file

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