Vector Search: The Future of Data Querying Explained | Semantic Searching
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** Support for the '$vectorSearch' aggregation pipeline stage is available with MongoDB Atlas 6.0.11 and 7.0.2. **
Welcome to this comprehensive guide on Vector Search, a revolutionary technique that allows you to search data based on meaning rather than just keywords. If you've ever struggled to find exactly what you're looking for in a database, this video is for you. We'll explore how Vector Search works, its benefits, and how to implement it in MongoDB for a seamless and powerful search experience.
🛠 What You'll Learn
What is Vector Search?: Understand the machine learning models behind Vector Search and how they transform text, audio, images, or other types of data into high-dimensional vectors.
Benefits of Vector Search: Discover why semantic understanding, scalability, and flexibility make Vector Search a must-have feature for modern databases.
MongoDB & Vector Search: Learn how to set up Vector Search in MongoDB Atlas, create triggers with OpenAI API, and perform vector search queries. We'll walk you through each step, from setting up your MongoDB Atlas account to writing JavaScript functions for querying.
📚 Resources 📚
✅ Written article & code → https://mdb.link/semantic-search-mongodb-atlas-vector-search-1ZIYVNvRVsY
✅ Vector Search Documentation→ https://mdb.link/vector-search-1ZIYVNvRVsY
✅ OpenAI → https://platform.openai.com/overview
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