Deploying a Retrieval-Augmented Generation (RAG) pipeline is the standard approach for allowing LLMs to securely interact with proprietary data. However, relying on public APIs introduces latency and data sovereignty risks. By self-hosting your inference architecture, you retain absolute data sovereignty. This guide demonstrates how to architect a high-performance, fully private RAG pipeline using vLLM , LangChain , and Qdrant . 🛠️ Prerequisites OS: Ubuntu 22.04 LTS GPU: Minimum 24GB VRAM (NVIDIA RTX 3090/4090). 70B+ models require A100/H100 clusters. Drivers: NVIDIA Drivers (v535+) & CUDA 12.1+ Environment: Python 3.10+, Docker & Docker Compose 🚀 Step 1: Prepare the GPU Environment Verify your GPU availability and setup a virtual environment: nvidia-smi python3 -m venv rag_env source rag_env/bin/activate pip install vllm langchain langchain-openai langchain-community sentence-transformers qdrant-client pypdf Enter fullscreen mode Exit fullscreen mode 🤖 Step 2: Deploy vLLM API Server vLLM is an…