Building a Semantic Search Application with MongoDB and Quarkus using Vector Search
30:49
Building a Semantic Search Application with MongoDB and Quarkus using Vector Search
901 viewsJan 22, 2025
YouTubeMongoDB
Let’s talk about the future of search. What if you could combine the precision of keyword search with the semantic power of vector search, in a single query? 🚀 Introducing native hybrid search in MongoDB Atlas, now in public preview. With the new $rankFusion stage in Atlas Search, you can merge full-text and vector search results into a single ranked list using reciprocal rank fusion (RRF). That means smarter, more relevant results—without bolting on a separate vector database or search engine.
1:26
Let’s talk about the future of search. What if you could combine the precision of keyword search with the semantic power of vector search, in a single query? 🚀 Introducing native hybrid search in MongoDB Atlas, now in public preview. With the new $rankFusion stage in Atlas Search, you can merge full-text and vector search results into a single ranked list using reciprocal rank fusion (RRF). That means smarter, more relevant results—without bolting on a separate vector database or search engine.
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FacebookMongoDB
MongoDB Atlas Vector Search Fundamentals Skill Badge | MongoDB University
MongoDB Atlas Vector Search Fundamentals Skill Badge | MongoDB University
3 weeks ago
mongodb.com
Scaling vector search is one of the biggest challenges for AI-driven applications. We tested MongoDB Atlas Vector Search with a 15.3 million vector dataset and found it retains 90-95% accuracy with less than 50ms of query latency. In our new blog post, we break down the methodology, key performance factors like quantization and dimensionality, and what the results mean for your own large-scale workloads. Learn how to optimize your deployment. Read the full analysis: https://mongodb.social/6186fQ
0:05
Scaling vector search is one of the biggest challenges for AI-driven applications. We tested MongoDB Atlas Vector Search with a 15.3 million vector dataset and found it retains 90-95% accuracy with less than 50ms of query latency. In our new blog post, we break down the methodology, key performance factors like quantization and dimensionality, and what the results mean for your own large-scale workloads. Learn how to optimize your deployment. Read the full analysis: https://mongodb.social/6186fQ
303 views9 months ago
FacebookMongoDB
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