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100% local AI document assistant — RAG on your files, no cloud, no data leak.
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PrivateGPT is an open-source RAG (Retrieval-Augmented Generation) platform that lets you chat with your own documents — PDFs, DOCX, spreadsheets and emails — using a local large language model. Unlike cloud-based AI assistants, PrivateGPT never sends your data outside the server: ingestion, vectorization, storage and inference all happen on your VPS. Powered by an embedded vector store and connected to any OpenAI-compatible inference server (Ollama, llama.cpp, vLLM), it gives you a full RAG pipeline under your control.
Deployed on a ServOrbit VPS alongside Ollama, PrivateGPT becomes a self-contained private AI workspace: upload your contracts, technical documentation or meeting notes, and query them in natural language. Each answer cites the source passage and file name, so you know exactly where it came from. The OpenAI-compatible API at port 8080 lets you integrate the same document intelligence into your internal tools or build custom agents on top of the same indexed corpus.
Upload contracts, employment agreements and HR files to PrivateGPT. Ask 'What are the termination clauses in contract X?' and get a cited answer without any data leaving the VPS — fully compliant with GDPR Article 28.
Index your team's technical documentation, runbooks and meeting notes. Every team member gets a natural-language interface to the corpus, with answers pointing to the exact source passage so they can verify and go deeper.
Feed proprietary source code or security audit reports into PrivateGPT. Ask questions about architecture, find undocumented functions, or cross-reference CVE descriptions against your own codebase — without a single line of source code leaving the server.
Guide optimized for ServOrbit Cloud VPS.
Allocate at least 8 GB of RAM: Ollama needs 4–6 GB for a 7B model (Q4_K_M), PrivateGPT itself takes around 500 MB, and you need headroom for document ingestion. A 16 GB VPS is the comfortable choice. The machine ships with Ubuntu 24.04 and Docker pre-installed.
In your ServOrbit client area: Marketplace → Artificial Intelligence → PrivateGPT → Deploy. The stack starts two containers: PrivateGPT (web UI + API on port 8080, bound to 127.0.0.1) and an Ollama sidecar (internal only, not reachable from the internet). Both share a private Docker network — only port 8080 is exposed on the loopback.
Once the stack is up, pull a language model into Ollama via SSH: docker exec ollama ollama pull qwen2.5:7b. For a lightweight start on 8 GB RAM, qwen2.5:3b (2.3 GB) is a good entry point. For richer answers on long documents, mistral:7b or llama3.1:8b are recommended. For embeddings, nomic-embed-text improves retrieval quality.
PrivateGPT's interface is at http://127.0.0.1:8080/ui (loopback only). Open an SSH tunnel from your workstation: ssh -L 8080:127.0.0.1:<port> root@<vps-ip> (replace <port> with the port shown in your client area), then go to http://localhost:8080/ui in your browser.
Use the Upload button in the UI or the /v1/ingest/file API endpoint to upload your documents. PrivateGPT splits each file into chunks, computes embeddings via Ollama, and stores them in the local vector store. Ingestion of a 50-page PDF typically takes 30–90 seconds on CPU.
Type your question in the chat. PrivateGPT retrieves the most relevant chunks from the vector store, passes them as context to the LLM, and returns an answer with source passages cited below. The /v1/chat/completions endpoint is OpenAI-compatible for API clients.
Open-source AI workspace with built-in RAG, AI agents, and multi-model chat. Connect your own documents, use any LLM — hosted privately on your VPS.
Artificial IntelligenceWeb interface to interact with your local or remote LLMs. Your data stays on your infrastructure — no third party involved.
Artificial IntelligenceBuild LLM applications visually: chatbots, RAG agents, pipelines — without writing any backend code.
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