mini-rag
Mini-RAG is an open-source educational project that teaches RAG (Retrieval-Augmented Generation) application development through step-by-step tutorials. It demonstrates production-ready patterns using FastAPI, PostgreSQL with pgvector, MongoDB, Celery workers, and LLM integration (OpenAI or local Ollama).
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | bakrianoo/mini-rag |
| Owner | bakrianoo |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 653 |
| Forks | 280 |
| Open issues | 11 |
| Latest release | minirag-mongodv-v1 (2024-12-02) |
| Last updated | 2025-08-15 |
| Source | https://github.com/bakrianoo/mini-rag |
What mini-rag is
A Python-based RAG framework built on FastAPI with pluggable LLM and vector database factories, supporting both MongoDB and PostgreSQL backends, async file processing via Celery, semantic search, and deployment patterns. Includes monitoring via Prometheus/Grafana and task scheduling with Celery Beat.
Get the mini-rag source
Clone the repository and explore it locally.
git clone https://github.com/bakrianoo/mini-rag.gitcd mini-rag# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires Python 3.10+, PostgreSQL 13+, and Redis for Celery. Docker Compose provided but assumes Docker/Linux environment familiarity.
- Vector DB factory supports multiple backends (MongoDB, PostgreSQL pgvector); verify dependency versions and compatibility before production use.
- LLM factory abstracts OpenAI and Ollama; adding new LLM providers requires implementing the factory interface.
- Alembic migrations included; database schema evolves across tutorial branches, so migration path must be validated for your target state.
- Celery workers, beat scheduler, and flower dashboard require separate process/container management; horizontally scaling workers requires Redis or RabbitMQ tuning.
When to avoid it — and what to weigh
- Requires Commercial LLM Support Without Setup — Integration assumes you provide own API keys (OpenAI, etc.) and configure endpoints. Not a managed service; requires infrastructure management.
- High-Volume Production with SLA Requirements — Project is educational in scope. Lacks explicit benchmarks, security audit documentation, and enterprise-grade operational tooling (observability, failover, cost controls).
- Non-English Deployment Teams — Core documentation and videos are in Arabic, which is valuable for that audience but may limit adoption or troubleshooting for teams without Arabic speakers.
- Proprietary/Non-Disclosure Requirements — Educational nature and public GitHub presence mean modifications or customizations may not align with confidentiality constraints in some enterprises.
License & commercial use
Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license. Allows commercial use, modification, and distribution with attribution and liability/warranty disclaimers.
Apache-2.0 permits commercial use, including proprietary derivatives, subject to license notice retention and no warranty/liability claims against licensor. Recommended to include LICENSE file in any distribution. No patent indemnity clause; review Apache-2.0 terms for patent-sensitive contexts.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | High |
| DEV.co fit | Good |
| Assessment confidence | High |
Project is educational and does not claim security audit. Key areas requiring review: API key/credential management (env vars in Docker), database access control, Flower dashboard authentication (admin/password), CORS policies, input validation on file uploads, and rate limiting. No mention of secrets encryption at rest or in transit hardening.
Alternatives to consider
LangChain or LlamaIndex
Production-grade libraries with broader LLM ecosystem, chain abstractions, and community integrations. Less educational but more feature-complete for commercial RAG.
Haystack by Deepset
Open-source RAG framework with built-in orchestration, modular pipelines, and enterprise docs. Steeper learning curve but battle-tested for production.
Verba (Weaviate-based RAG)
Lightweight, open-source RAG starter with vector DB bundled. Simpler than mini-rag for quick proof-of-concepts but less tutorial structure.
Build on mini-rag with DEV.co software developers
Use Mini-RAG as your learning blueprint or reference architecture. Review the code branches, run Docker Compose locally, and adapt the patterns to your use case. For production deployments, consult Devco's AI development team.
Talk to DEV.coRelated open-source tools
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mini-rag FAQ
Can I use this in production?
Do I need to watch the Arabic videos to use the code?
What LLMs are supported?
How do I scale this for higher throughput?
Work with a software development agency
Need help beyond evaluating mini-rag? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and rag frameworks integrations — and maintain them long-term.
Ready to Build a RAG Application?
Use Mini-RAG as your learning blueprint or reference architecture. Review the code branches, run Docker Compose locally, and adapt the patterns to your use case. For production deployments, consult Devco's AI development team.