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Developed an intelligent AI chatbot utilizing the DeepSeek LLM, designed for efficient interaction with large documents such as textbooks and study materials. Integrated Docling for parsing and processing large files, and implemented a Retrieval-Augmented Generation (RAG) pipeline using FAISS and Sentence Transformers to optimize context retrieval

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🌌 Impactoverse AI Mentor

An AI-powered chat mentor that helps users interact with educational PDFs and documents. Built using DeepSeek API, FAISS, and Docling for contextual PDF understanding. Combines two separate backends: one for open-source AI models and another for MongoDB-based data management.


🔐 Environment Setup

Create a .env file in the Node.js backend and add the following:

DEEPSEEK_API_KEY=your_deepseek_api_key MONGO_URI=your_mongodb_connection_string

🚀 Tech Stack

Frontend:

  • React

Backend:

  • Node.js (API & MongoDB)
  • FastAPI (Open-source models)
  • Docling, FAISS, SentenceTransformers

AI & NLP:

  • DeepSeek API
  • Sentence Transformers
  • FAISS (Facebook AI Similarity Search)
  • Docling for parsing PDFs

🛠️ Installation

🔧 FastAPI Backend for AI Models

  1. Navigate to the backend folder:

    cd backend

  2. Create a virtual environment:

    python3.11 -m venv parse

  3. Activate the virtual enviornment

    On Windows parse\Scripts\activate

    On macOS/Linux source parse/bin/activate

  4. Install dependencies:

    pip install fastapi uvicorn faiss-cpu sentence-transformers docling python-multipart

  5. Run the FastAPI server:

    uvicorn app.app:app --reload

    This will start the backend server on http://localhost:8000

🗃️ Node.js + MongoDB Backend

  1. Navigate to the server directory:

    cd backend npm install

  2. Run MongoDB server

    node server.js

⚠️ Ensure MongoDB is installed and running locally or provide a cloud URI.

💻 React Frontend

  1. Navigate to the frontend directory: cd frontend

  2. Install dependencies npm install

  3. Start the frontend server: npm run dev

🌟 Features

  • AI Mentor using DeepSeek API

  • Intelligent PDF parsing with Docling

  • Vector embeddings via Sentence Transformers

  • Semantic search using FAISS

  • MongoDB integration for chat and user history

  • Modular backend architecture (FastAPI + Node.js)

About

Developed an intelligent AI chatbot utilizing the DeepSeek LLM, designed for efficient interaction with large documents such as textbooks and study materials. Integrated Docling for parsing and processing large files, and implemented a Retrieval-Augmented Generation (RAG) pipeline using FAISS and Sentence Transformers to optimize context retrieval

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