mongodb-atlas-vector-search and mongodb-ai-vector-search

About mongodb-atlas-vector-search

sujee/mongodb-atlas-vector-search

Using MongDB Atlas with embedding models and LLMs to do vector search and RAG applications

This helps developers build applications that can understand and answer questions about large collections of documents. It takes raw text or PDF documents, processes them to create numerical representations (embeddings), and stores them in MongoDB Atlas. The output is a system that can intelligently retrieve relevant information and generate answers using large language models, useful for creating smart search or Q&A features.

About mongodb-ai-vector-search

irfan-iiitr/mongodb-ai-vector-search

Building a movie recommender app backend using MongoDB Atlas Search and local embeddings. Features include efficient searches, user interaction, and logging for debugging.