🚀 create-rag-app
A CLI tool that lets you generate production-ready RAG (Retrieval-Augmented Generation) applications in seconds.
⚙️ Choose your stack. 📦 Get a complete Dockerized project. 🧠 Power your app with your data + LLMs.
🌟 Why create-rag-app?
Building RAG apps is complex—you need to juggle LLMs, embeddings, chunking, retrieval, APIs, and more. With create-rag-app, all of this becomes plug-and-play:
✅ Standardized architecture
✅ Developer-first CLI
✅ Open-source & extensible
Whether you're building an AI chatbot over your docs or a powerful knowledge assistant, this CLI sets you up with a solid, scalable foundation.
🔧 What It Will Do
You run:
npx create-rag-app
And answer a few questions:
- LLM Provider? (OpenAI, Claude, Local)
- Vector DB? (Qdrant)
- Frontend? (None, Streamlit, Next.js)
- Document Loader? (Local, Web, Notion, YouTube)
- Chunking Strategy? (Fixed, Recursive, Metadata-aware)
- Embedding Model? (OpenAI, HuggingFace, Local)
- RAG Framework? (LangChain, LlamaIndex, None)
- Prompt Type? (Basic, Conversational, Agentic)
- Auth? (None, JWT, Basic)
- Extras? (Monitoring, Eval Setup, Dataset preload)
💥 Boom. Your RAG app scaffold is ready inside a Docker container.
📁 Example Output Structure
my-rag-app/
├── backend/
│ ├── api/
│ ├── loaders/
│ ├── retriever/
│ ├── llm/
│ └── main.py
├── frontend/ (optional)
│ └── pages/
├── data/
├── .env.template
├── docker-compose.yml
└── README.md
🚧 Features To Be Implemented
Here's what's on the roadmap for the first MVP:
✅ Core CLI
- Interactive CLI with
inquirer - Dynamic template scaffolding
- Backend stack choices (FastAPI, Express)
🧠 RAG Configuration
- LLM selection (OpenAI, Anthropic, local models)
- Embedder selection (OpenAI, BGE, Cohere, etc.)
- Vector DB support (Qdrant)
- Data loader types (PDF, YouTube, Notion, Web)
🧱 Frontend (optional)
- Streamlit or Next.js integration
- Basic chat UI with source highlighting
🧪 Extras
- Prompt customization options
- Eval flow scaffold (Precision@K, feedback loop)
- Monitoring/logging (basic + OpenTelemetry)
- Auth layer (JWT / basic auth)
🐳 DevOps
- Dockerized full-stack output
- Git auto-init and install
.envtemplating and secrets handling
🤝 Contributing
Got an idea? Want to add a new integration (e.g., Qdrant or Supabase)? We'd love to have you onboard.
git clone https://github.com/yourname/create-rag-app
cd create-rag-app
npm install
npm link
create-rag-app
🛠 Tech Stack (Planned)
- CLI: Node.js + TypeScript
- CLI UI: Inquirer.js, Chalk
- Templates: EJS-based folders
- Backend: FastAPI (Python) or Express (Node)
- Frontend: Next.js or Streamlit
- DevOps: Docker, Docker Compose
Metadata
Release files for create-rag-app 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| create_rag_app-0.1.2.tar.gz | 21.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| create_rag_app-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 54.2 kB
Release files / create_rag_app-0.1.2.tar.gz
| Download URL | create_rag_app-0.1.2.tar.gz |
|---|---|
| Size | 21.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
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Release files / create_rag_app-0.1.2-py3-none-any.whl
| Download URL | create_rag_app-0.1.2-py3-none-any.whl |
|---|---|
| Size | 33.0 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/2.1.3 CPython/3.13.3 Darwin/24.4.0
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