Setting up a Private Retrieval Augmented Generation (RAG) System with Local Llama 2 model and Vector Database

Unstructured is a specialized ETL pipeline designed to streamline and cleanse data for language models, transforming scattered and varied data formats into actionable insights. The tool plays a crucial role in setting up Retrieval Augmented Generation (RAG) systems by ensuring data privacy, reducing latency, and managing costs through local implementations. The text provides a detailed guide on constructing a local RAG system using Unstructured, which includes environment setup, document ingestion, data processing, and indexing with Weaviate. Additionally, the guide highlights the benefits of local RAG systems, including enhanced data security and the potential for future enhancements like Role-Based Access Control (RBAC). The blog post also encourages community engagement through a Slack group for further discussion and support.