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Ragna is an open source RAG orchestration framework.
With an intuitive API for quick experimentation and built-in tools for creating production-ready application, you can quickly leverage Large Language Models (LLMs) for your work.
In this article, Dr. Leon Eversberg explains how to build an advanced Local Language Model (LLM) Retrieval-Augmented Generation (RAG) pipeline using open-source bi-encoders and cross-encoders for better chatbot performance.
In this notebook, we will explore a typical RAG solution where we will utilize an open-source model and the vector database Chroma DB. However, we will integrate a semantic cache system that will store various user queries and decide whether to generate the prompt enriched with information from the vector database or the cache.
This is a local LLM chatbot project with RAG for processing PDF input files