Building a RAG Pipeline with MongoDB: Vector Search for Personalized Picks

Explore building a personalized movie recommendation system using a Retrieval-Augmented Generation (RAG) pipeline and MongoDB’s vector search capabilities. By integrating large language models (LLMs) and structured data retrieval, this project showcases how to handle user queries and generate accurate recommendations. Steps covered include setting up the environment, data modeling with Pydantic, embedding generation via OpenAI's API, data ingestion into MongoDB, and performing vector search operations.