Build a legal RAG app that won't be held in contempt

This tutorial provides a step-by-step guide for building a Legal Retrieval Augmented Generation (RAG) application using Python, aimed at beginners familiar with Python and Large Language Models (LLMs). The process involves using tools such as semchunk for semantic chunking, Kanon 2 Embedder and Reranker for embedding and reranking tasks, LangChain for the RAG framework, and Gemini for generative tasks. The goal is to address the limitation of LLMs in accessing updated information by retrieving relevant context to feed into an LLM, thus reducing hallucinations. The tutorial walks through various stages including dataset preparation, semantic splitting, embedding, storing vectors, retrieval, reranking, and generating answers with the context provided. The example uses Australian legal cases to demonstrate how updated and relevant information can be efficiently retrieved and utilized to answer legal queries effectively, emphasizing the importance of an updated information source and efficient retrieval to enhance the accuracy and reliability of LLM outputs.