Vectors and Embeddings Fundamentals | gen AI Semantic Search Made Easy
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Vectors and Embeddings Fundamentals | gen AI Semantic Search Made Easy
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Testing MongoDB's AI Vector Search: A Deep Dive
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[GHW Open Source] MongoDB Vector Search 101
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[GHW Open Source] MongoDB Vector Search 101
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YouTubeMajor League Hacking
[GHW Data Week] MongoDB Vector Search 101
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[GHW Data Week] MongoDB Vector Search 101
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YouTubeMajor League Hacking
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
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[GHW Beginners Week] MongoDB Vector Search 101
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YouTubeMajor League Hacking
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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 views8 months ago
FacebookMongoDB
MongoDB Atlas Vector Search Fundamentals Skill Badge | MongoDB University
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mongodb.com
2:21
New in MongoDB Atlas: Lexical Prefilters for Vector Search. You can now use full analyzed text filtering (fuzzy, phrase, queryString, geo) as prefilters for $search.vectorSearch, with vector fields supported inside Lucene $search indexes. With this new capability, you'll get ✅ Advanced filtering vector search in one flow (no more knnBeta / knnVector) ⚡️ More efficient retrieval (fewer candidates = less compute) 🎯 Better precision vs post-filtering 🧠 Complex boolean logic with queryString Learn
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FacebookMongoDB
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YouTubeMongoDB
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Designing Intelligent Systems with MongoDB and Vector Search | ContentCatalyst
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YouTubeContent Catalyst
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MongoDB Vector Search Tutorial
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MongoDB Atlas combines vector and full-text search with a database that’s built to run apps anywhere. | MongoDB
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Using Atlas Vector Search and PyMongoArrow to Semantically Search Through Luxury Fashion Items
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