Getting Started with MongoDB Atlas for Semantic Search

On June 22nd, MongoDB launched <a href="https://www.mongodb.com/blog/post/introducing-atlas-vector-search-build-intelligent-applications-semantic-search-ai" rel="noopener ugc nofollow" target="_blank">Atlas Vector Search</a> in preview mode. I tried this new feature for you! The idea is to store a small dataset of common English proverbs on MongoDB and ask something like: <blockquote> Question: Things that look good outwardly may not be as valuable or good. Answer: All that glitters is not gold. </blockquote> The inspiration for this post was taken from the <a href="https://www.mongodb.com/developer/products/atlas/semantic-search-mongodb-atlas-vector-search/" rel="noopener ugc nofollow" target="_blank">official MongoDB Atlas Vector Search tutorial</a>. <h1>Introduction to MongoDB Atlas Vector Search</h1> Vector search is an advanced technique used to perform semantic searches, where data is searched based on its meaning rather than the data itself. This search method utilizes Machine Learning models to effectively search unstructured data, including text, audio, video, and images. It allows finding items that are similar or related to the search item. It is used for several use cases, like recommendation systems, chatbots, or search engines. When dealing with text data, vector search makes finding words or phrases of similar meaning possible, even if the exact query words are not in the searched sentences. Vector search is based on the concept of embedding. <h2>Embeddings</h2> Vector Search employs sophisticated Machine Learning models, known as encoders, to produce vector embeddings that provide a numerical representation of unstructured input data. <a href="https://blog.det.life/getting-started-with-mongodb-atlas-for-semantic-search-7ac77ed3d195">Click Here</a>