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

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🤖 Step 2: Deploy vLLM API Server

vLLM is…