Building a RAG System with Azure OpenAI, Hugging Face, and MongoDB Atlas
Building a Retrieval-Augmented Generation (RAG) system with Azure OpenAI, Hugging Face, and MongoDB involves creating an advanced AI model that enhances natural language processing tasks by integrating external knowledge sources. This system leverages Azure OpenAI for its powerful language models, Hugging Face for an extensive library of pre-trained models and NLP tools, and MongoDB Atlas for storing and managing the large datasets used as the knowledge base. The RAG approach combines the strengths of generative models (like those from OpenAI) with the ability to query and retrieve relevant information from a database (stored in MongoDB) during the generation process, leading to more informed and contextually accurate outputs. This setup is handy for applications requiring deep understanding and generation of human-like text, such as chatbots, search engines, and content creation tools.
