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MongoDB Text Search Tutorial: $text Operator & Search Scores (Guide)
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We’ll search through different data types, including text, numbers, dates, and geoJSON while exploring a variety of search capabilities | MongoDB
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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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The engine behind MongoDB Search and Vector Search, mongot, is now available in Public Preview under the Server Side Public License (SSPL). By making mongot source available, we’re bringing a key MongoDB technology closer to our developer community. Now you can: • Run the same search and vector search capabilities across any environment, whether MongoDB Atlas, on-premise, or hybrid. • For sensitive workloads, inspect the source code to fully understand how queries are executed. • Build mongot fr
64 views3 months ago
FacebookMongoDB
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In recent years search capabilities in databases have evolved tremendously. Modern applications require a more intuitive approach that understands user intent, not just about matching keywords to the best results. That's where semantic and vector search come in. - Semantic Search focuses on the meaning and context behind a query. If a user searches for "shoes for the beach," it knows to return sandals and flip-flops, understanding the contextual relationship. - Vector Search is the powerful engi
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