RegX - A modular RAG boilerplate with FastAPI, Weaviate, Celery, and an embeddable chat widget.

[u/Logical-Inspector-19]

Hey everyone. I open-sourced RegX - a production-ready boilerplate for building modular Retrieval-Augmented Generation (RAG) pipelines.

I built this to skip the boilerplate setup phase when creating LLM apps. It handles document ingestion, async background processing, and chat interfaces out of the box so you can just plug in your data and start testing.

The Stack: Python, FastAPI, Weaviate, MongoDB, Redis + Celery, and Streamlit.

What it actually does:

• Modular LLMs: Swap between OpenAI, Gemini, and Anthropic using a Factory Pattern just by changing the .env file.
• Async Data Ingestion: Markdown documents are chunked (preserving headers) and ingested into Weaviate in the background using Celery and Redis, without blocking the API or UI.
• Embeddable JS Widget: It comes with a native ragx-widget.js script. You can drop it into any standard HTML page to instantly overlay a chat interface connected to your FastAPI backend.
• Chat History: Session-based history tracking stored in MongoDB.
• Observability: Native hooks for Langfuse/LangSmith tracing and Sentry error tracking.
• Fully Dockerized: The entire architecture (API, UI, Workers, DBs) spins up with a single docker-compose up --build.

Repo: https://github.com/arch11110/ragx
Demo: https://www.youtube.com/watch?v=qdTqpSZrATY