In this comprehensive course, we learn how to implement Vector search and embeddings to power AI applications. We start by understanding the concepts and then proceed to develop three projects. The first project focuses on building a semantic search feature for movie recommendations using Python machine learning models and Atlas Vector search. The second project involves creating a question-answering app using the RAG architecture and the Atlas Vector search to answer questions using custom data. Finally, we modify a chat GPT clone to answer questions about contributing to the freecodecamp.org curriculum based on the official documentation. We leverage the power of hugging face and open AI APIs to create embeddings, perform semantic search, and develop a question-answering app. The course also covers the limitations of large language models and how the RAG architecture addresses these issues. We explore the Lang chain framework and gradio to build a web interface for machine learning applications, providing a complete understanding of how to utilize AI models in real-world projects.
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