Hi there! I built a custom RAG system using Python with Langchain and Chroma database. Now I need to create a user interface for it. The tricky part is that I want to keep everything running locally on my machine without deploying to any cloud service.
I came across OpenWebUI and wondering if anyone has experience connecting it to a custom Python RAG setup. My main concern is whether I can use my existing system without having to migrate all my data into OpenWebUI’s knowledge base format.
The interface needs to support multiple users accessing it at the same time, all running from my local server. Has anyone worked with similar setups before? What frontend solutions would you recommend for this kind of project?
Any advice or suggestions would be really helpful. Thanks!
Been there. Built plenty of RAG interfaces and everyone overcomplicates this stuff.
Your Langchain + Chroma setup works fine. The issue isn’t your backend - it’s connecting everything without breaking what you’ve got.
Skip learning new frameworks or writing API wrappers. Just automate the interface layer. Connect your Python functions straight to a web UI without touching your code.
Did this for a project where multiple data scientists wanted to test different RAG configs. Set up automated workflows that grabbed their Python functions and generated complete interfaces - user auth, session management, concurrent users, the works.
No FastAPI routes to maintain. No Streamlit sessions to debug. No React components to build. Just automation bridging your RAG system to a production interface.
Your Chroma database stays put. Your Langchain pipeline doesn’t change. The automation handles everything between your Python code and users.
Runs locally like you want, scales when needed. Got our team from prototype to production interface in one afternoon.
Skip the web dev headaches entirely. I’ve built RAG systems before and creating frontends from scratch is a massive time drain.
Don’t mess with FastAPI routes or Streamlit sessions - just automate the interface creation. You can get a complete web UI talking to your Langchain setup without changing any existing code.
The automation handles API connections, user management, and scales for multiple users automatically. Your Chroma database stays put. Your Python functions remain untouched.
Did this exact thing last month for a client. Got their RAG system running with a clean interface in 2 hours instead of 2 weeks. No migration, no rewriting, just automation connecting everything.
Best part? Runs locally like you want but handles enterprise-level users without the complexity.
openwebui’s probably overkill here. i just used flask with basic html/js for my langchain setup - worked great. got it running locally in 30 minutes, didn’t have to touch my chroma db at all. handles multiple users fine on my machine.
FastAPI + React combo was perfect for my LangChain RAG project. I kept my entire Python backend as-is and just exposed it through API endpoints. Users hit the React frontend, and it handles multiple requests without issues. Didn’t touch my Chroma DB or migrate anything - just added routes to serve my existing functions.
I built something similar last year with Streamlit instead of OpenWebUI - worked great with my Langchain and Chroma setup. Barely had to change my existing RAG pipeline, just wrapped the query functions with Streamlit components. It handles multiple users without issues since each person gets their own session state. You can run it locally and open it up to your network by tweaking the server settings. Best part? No data migration needed - your Python functions stay exactly the same, and performance held up fine with several users hitting it at once.
Check out Gradio for your local setup. I just integrated it with my Langchain RAG pipeline and it was surprisingly easy. You can wrap your existing query functions with Gradio interfaces without changing anything else. It handles multiple users automatically with built-in queuing, which fixed my multi-user worries instantly. Best part? I only needed about 15 lines of code to get a working web interface. Your Chroma database stays untouched, and you keep full control over local deployment. The interface is pretty flexible too - you can customize styling down the road if you want.