DocuMind - RAG-Based FAQ Assistant

DocuMind is a full-stack, document-grounded FAQ assistant. Upload a UTF-8 .txt document, ask a question, and receive an answer generated from the most relevant parts of that document -- along with the source chunks used to create it.

View the project on GitHub

Features

• Upload and index a .txt knowledge document
• Generate grounded answers with a Groq-hosted LLM
• Retrieve relevant document chunks with semantic search
• Show the source chunks used for every answer
• Save conversation history and uploaded-document metadata in Supabase/PostgreSQL
• View recent questions, documents, and the active knowledge source in the UI
• Use FAISS for vector search, with a NumPy fallback for Windows development

How it works

TXT document -> chunking -> embeddings -> vector search -> relevant chunks -> Groq LLM -> answer + sources

1. A .txt document is uploaded through the Next.js interface.
2. The backend splits it into overlapping chunks and generates embeddings with sentence-transformers.
3. FAISS (or the Windows NumPy fallback) identifies the chunks most relevant to the question.
4. Those chunks are sent to the configured Groq model with the user's question.
5. The answer and its sources are returned to the UI and saved to PostgreSQL.

Each new upload replaces the currently active knowledge document, keeping answers tied to the latest file.

Tech stack

Layer	Tools
Frontend	Next.js, React, Tailwind CSS
Backend	FastAPI, Uvicorn
RAG	Sentence Transformers, FAISS, NumPy
LLM	Groq API (configurable model)
Database	Supabase PostgreSQL

Project structure

.
|- faq-bot/          # FastAPI backend, RAG pipeline, database schema
|- frontend/         # Next.js chat interface
|- backend_faq-bot.md
`- README.md

Prerequisites

• Python 3.10+
• Node.js 18+
• A Groq API key
• A Supabase PostgreSQL project and connection string

Local setup

1. Clone the repository

git clone https://github.com/Uv0503/DocuMind-RAG-Based-FAQ-Assistant.git
cd DocuMind-RAG-Based-FAQ-Assistant

2. Configure the backend

Create faq-bot/.env:

DATABASE_URL=your_supabase_postgres_connection_string
GROQ_API_KEY=your_groq_api_key
# Optional; defaults to openai/gpt-oss-20b
GROQ_MODEL=openai/gpt-oss-20b

Install and start the API:

cd faq-bot
python -m venv venv

Windows PowerShell:

.\venv\Scripts\Activate.ps1
pip install -r requirements.txt
python -m uvicorn main:app --host 0.0.0.0 --port 8888

The API will be available at http://localhost:8888, and interactive API documentation is available at http://localhost:8888/docs.

3. Configure the database

In the Supabase SQL Editor, run the contents of faq-bot/schema.sql.

4. Configure and start the frontend

Create frontend/.env.local:

NEXT_PUBLIC_API_URL=http://localhost:8888

In a second terminal:

cd frontend
npm install
npm run dev

Open http://localhost:3000, upload a .txt file, and start asking questions.

API endpoints

Method	Endpoint	Description
GET	/	API welcome message
GET	/health/db	Database health check
GET	/knowledge	Active source and indexed chunk count
POST	/upload	Upload and index a .txt document
POST	/ask	Ask a question about the active document
GET	/conversations	Saved question-and-answer history
GET	/documents	Uploaded document history

Example question request:

curl -X POST http://localhost:8888/ask \
  -H "Content-Type: application/json" \
  -d "{\"question\":\"What does this document say about pricing?\"}"

Environment and security

• Keep GROQ_API_KEY and DATABASE_URL in local .env files only.
• Do not commit .env, .env.local, virtual environments, node_modules, or uploaded documents.
• The repository .gitignore already excludes these local and sensitive files.

Future improvements

• Support PDF, DOCX, and multiple-document uploads
• Add user authentication and per-user document libraries
• Store embeddings in a persistent vector database
• Add citations that include document names and page/section information

Created by

Aman Singh Chauhan GitHub: @Uv0503