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RAG & Knowledge Systems

Edumate Rag

Edumate Rag is a FastAPI question-answering service that indexes university course PDFs, retrieves relevant passages, and returns answers grounded in that material with source-document attribution.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Edumate Rag — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when building or locally demonstrating a backend that answers questions from university course PDFs, especially when answers should identify their source documents.

Who it is for

  • Developers integrating course-material question answering into an educational app.
  • Developers preparing a standalone RAG demo or local development service.

Documented uses

  • Index course PDFs for retrieval.
  • Ask questions against indexed course materials and return grounded answers with source attribution.
  • Connect an application backend to a RAG query service.
Before you choose

Requirements & limits

  • The service requires a Groq API key and course PDFs, along with a Python environment and at least 4 GB of RAM.
  • The repository documents Qdrant or ChromaDB as vector-store options.
  • In app integration mode, the .NET backend owns authentication and conversation history; standalone mode has its own query endpoint and local conversation memory.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and index course PDFs

Prepare the service and index course PDFs before querying them.

Before you start

  • Python 3.9 or later
  • Git
  • At least 4 GB of RAM
  • A Groq API key
  • Course PDFs

Steps

  1. Clone the repository and enter its directory.

  2. Create and activate a Python virtual environment, then install dependencies from requirements.txt.

  3. Copy .env.example to .env, add the Groq API key, and place course PDFs in assets/course_pdfs/.

  4. Start the service with python main.py and request indexing with POST /api/index.

Expected outcome

The service is set up and the course PDFs have been submitted for indexing.

Guide limitations

  • This guide covers setup and indexing, not verification of a successful indexing run.
  • The standalone query workflow is separate from the EduMate app integration flow.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
GitHub - MoTahaAboHeiba/EduMate-RAG: FastAPI RAG microservice powering the AI layer of EduMate, indexes 50+ CS textbooks into ChromaDB for Dev and Qdrant Cloud for development, retrieves via cosine similarity, and generates grounded answers · Official homepage title · retained in its source language
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