Building RAG with Mistral v0.3

In this video, Mosleh Mahamud demonstrates how to build a Retrieval-Augmented Generation (RAG) pipeline using the Mistral v3 model with Ollama. He begins by introducing the Mistral v3.0 model, highlighting its advanced features such as Sliding Window Attention, Grouped Query Attention (GQA), and Flash Attention 2, which enhance long-sequence processing and speed up inference. The model also supports quantization to reduce memory usage, making it efficient and scalable, particularly when used with Microsoft Azure. Mosleh emphasizes the model’s suitability for automated AI agents, embeddings, and other machine learning tasks. The tutorial is designed to be accessible for beginners while also offering valuable insights for more advanced users. Mosleh starts by guiding viewers through the process of downloading and setting up the Mistral v3 model using Ollama. He then walks through the steps to implement a simple RAG pipeline, including loading data from a web-based source using LangChain, converting the data into a vector database, and defining the Mistral v3 model as the language model (LLM) for the pipeline. He demonstrates how to create a prompt template and use it with the LLM and vector store to build a QA chain. Mosleh tests the pipeline by asking a question about a table showing statistics from various basketball games, and the model provides a descriptive analysis of the data. Despite the process taking some time, the results are impressive, showcasing the model’s capabilities in handling complex data structures. The video concludes with Mosleh encouraging viewers to subscribe for more content on LLMs, machine learning, and data science tools.