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Self-Hosted RAG with Llama 3
100% offline RAG for engineers who can't use cloud AI
A Dockerized RAG system built with Ollama, ChromaDB, and Streamlit — for engineers handling sensitive documents (datasheets, specs, NDAs) that can't be pasted into ChatGPT. No cloud dependency. No API fees. No data leaving your machine. Ask questions about your technical docs in plain English, get answers grounded in your own files. Built for controls engineers, automation teams, and anyone who needs a private alternative to cloud AI tools for document Q&A.
I'm Phil, a Senior Automation Engineer working in industrial automation. I built this because I kept running into the same problem: engineering docs, datasheets, and specs that I couldn't paste into ChatGPT due to NDA restrictions — but I still wanted AI-assisted search over them.
So I built a fully offline RAG stack: Docker + Ollama (running Llama 3) + ChromaDB for vector search + Streamlit for the UI. No API calls, no data leaving your machine, no monthly fees.
It's built for engineers, technical writers, or anyone who works with sensitive documents and wants local AI search without the cloud dependency.
Happy to answer any technical questions — how it handles PDFs, context window limits, deployment on different hardware, whatever you're curious about. And if you've tried something similar, I'd love to hear what worked (or didn't) for you.
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About Self-Hosted RAG with Llama 3 on Product Hunt
“100% offline RAG for engineers who can't use cloud AI”
Self-Hosted RAG with Llama 3 was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #9 on the daily leaderboard. A Dockerized RAG system built with Ollama, ChromaDB, and Streamlit — for engineers handling sensitive documents (datasheets, specs, NDAs) that can't be pasted into ChatGPT. No cloud dependency. No API fees. No data leaving your machine. Ask questions about your technical docs in plain English, get answers grounded in your own files. Built for controls engineers, automation teams, and anyone who needs a private alternative to cloud AI tools for document Q&A.
Self-Hosted RAG with Llama 3 was featured in Privacy (11.3k followers), Developer Tools (518.8k followers) and Artificial Intelligence (477.8k followers) on Product Hunt. Together, these topics include over 210.2k products, making this a competitive space to launch in.
Who hunted Self-Hosted RAG with Llama 3?
Self-Hosted RAG with Llama 3 was hunted by Phil Yeh. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Self-Hosted RAG with Llama 3 stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Phil Yeh
Hey Product Hunt 👋
I'm Phil, a Senior Automation Engineer working in industrial automation. I built this because I kept running into the same problem: engineering docs, datasheets, and specs that I couldn't paste into ChatGPT due to NDA restrictions — but I still wanted AI-assisted search over them.
So I built a fully offline RAG stack: Docker + Ollama (running Llama 3) + ChromaDB for vector search + Streamlit for the UI. No API calls, no data leaving your machine, no monthly fees.
It's built for engineers, technical writers, or anyone who works with sensitive documents and wants local AI search without the cloud dependency.
Happy to answer any technical questions — how it handles PDFs, context window limits, deployment on different hardware, whatever you're curious about. And if you've tried something similar, I'd love to hear what worked (or didn't) for you.