Practical overview
When it fits
Use Mini Rag when learning RAG application development through a code repository and its step-by-step Arabic video lessons, or when studying the documented Python setup and service architecture.
Who it is for
- Developers learning how to build a RAG question-answering application.
- Python learners who want to follow an implementation through API, file-processing, data-indexing, and deployment topics.
Documented uses
- Study a minimal RAG application for question answering.
- Follow code and lessons through stages such as FastAPI routes, file upload and processing, semantic search, and augmented answers.
- Explore documented service setup involving Docker Compose, database migrations, and Celery workers.
Requirements & limits
The project repository is public and identifies its license as Apache-2.0.
- The accompanying course videos are in Arabic.
- The README specifies Python 3.10 and documents both Python development setup and Docker Compose services.
- The course spans multiple implementation stages, including MongoDB in earlier lessons and PostgreSQL with PgVector in later ones.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare the documented Python development environment
Install dependencies, configure environment values, and apply the database migration.
Before you start
- Python 3.10
- The project repository and its requirements file
- Environment values for the application; the README gives OPENAI_API_KEY as an example
Steps
Install the required packages with pip install -r requirements.txt.
Copy .env.example to .env and set the required environment values.
Run alembic upgrade head.
Expected outcome
The documented Python dependencies are installed, environment configuration is in place, and Alembic migrations have been applied.
Guide limitations
- This covers preparation only; it does not start the application or Docker Compose services.
- The README also lists system packages for Ubuntu and an optional MiniConda environment setup.
Start the documented Docker Compose services
Configure and start the project’s Docker Compose services.
Before you start
- Docker and Docker Compose
- The project repository
- Credentials and other environment values required by the Docker services
Steps
Change to the docker directory.
Copy .env.example to .env in that directory and update it with your credentials.
From the docker directory, run sudo docker compose up -d.
Expected outcome
The Docker Compose services are started in detached mode; the README lists local access addresses for FastAPI, Flower, Grafana, and Prometheus.
Guide limitations
- The README does not enumerate all required credentials or provide service-specific troubleshooting.
- The listed local addresses are documentation, not a compatibility or successful-run guarantee.
Compare options for Retrieval-augmented applications
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Mini Rag | Study a minimal RAG application for question answering. | Docker, Server | The project repository is public and identifies its license as Apache-2.0. |
| Rag Powered AI Assistant Transforming Data Retrieval And Analysis Across The Web And Pdfs | Retrieve relevant passages from web and PDF content to answer a question. | Web | |
| LangChain in Supabase Edge Functions | Initialize and start a Supabase project locally. | Cloud, Server |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- bakrianoo/mini-rag · Official homepage title · retained in its source language