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📄 AskMyDocs

Chat with Your Documents using AI (RAG)

Welcome to AskMyDocs — an AI-powered document chatbot that allows you to upload documents and interact with them conversationally. Built using Retrieval-Augmented Generation (RAG), it combines document retrieval, embeddings, and large language models to deliver contextual answers directly from your files.


🎥 Project Demo

Watch Demo

👉 Click the image above to watch the full demo video.


✨ Overview

AskMyDocs simplifies document analysis using AI:

  • Upload multiple files
  • Ask questions naturally
  • Receive contextual AI-generated answers
  • Quickly extract insights without manual searching

This project demonstrates how modern AI pipelines can transform document interaction and knowledge retrieval.


🚀 Features

📂 Document Processing

  • Supports PDF, TXT, CSV, DOCX files
  • Automatic text extraction
  • Efficient document chunking and embedding

🤖 AI Chatbot

  • Conversational Q&A from documents
  • Context-aware responses
  • Retrieval-Augmented Generation pipeline

🧠 Smart Enhancements

  • Optional prompt refinement
  • Document summarization
  • Fast inference powered by Groq LLM

💻 User Interface

  • Streamlit interactive dashboard
  • Simple upload & chat workflow
  • Clean, responsive design

🗂️ Project Structure

.
├── icons/
│   └── ico.png
├── src/
│   ├── analytics.py
│   ├── app.py
│   ├── conversation.py
│   ├── document_utils.py
│   ├── prompt_refiner.py
│   └── text_processing.py
├── README.md
├── pyproject.toml
├── setup.sh
└── uv.lock

📋 Requirements

  • Python 3.9+
  • UV package manager recommended

Install uv:

pip install uv

🔑 Environment Setup

Create .env file in root:

GROQ_API_KEY=your_api_key_here

Required for AI inference.


💻 Installation

Clone Repository

git clone https://github.com/hindav/AskMyDocs.git
cd AskMyDocs

Run Application

Using uv:

uv run streamlit run src/app.py

Or:

bash setup.sh

▶️ Access the App

Open browser:

http://localhost:8501

📝 Usage

  1. Upload documents
  2. System converts them into embeddings
  3. Ask questions naturally
  4. Get contextual AI responses

Optional:

  • Enable prompt refinement
  • Generate document summaries

🧰 Tech Stack

  • Python
  • Streamlit
  • LangChain
  • FAISS Vector Database
  • HuggingFace Embeddings
  • Groq LLM (Llama models)

🎯 Use Cases

  • Research document analysis
  • Study material Q&A
  • Knowledge base assistant
  • Business document exploration
  • Personal document AI assistant

👨‍💻 Maintained By

Hindav Deshmukh AI • Data Engineering • Machine Learning

GitHub: https://github.com/hindav LinkedIn: https://www.linkedin.com/in/hindav/


📜 License

MIT License — free to use, modify, and distribute with attribution.


⭐ If you find this project useful, consider starring the repository!

About

A RAG-powered chatbot built with Streamlit and LangChain that lets you chat with your documents (PDF, TXT, CSV, DOCX). Features ultra-fast inference using Groq (Llama 3.3), local embeddings, conversation memory, and prompt refinement.

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