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
1:33
FacebookMongoDB
In recent years search capabilities in databases have evolved tremendously. Modern applications require a more intuitive approach that understands user intent, not just about
MongoDB. . 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 ...
421 views8 months ago
MongoDB Tutorial
MongoDB Tutorial - Learn MongoDB from Scratch
MongoDB Tutorial - Learn MongoDB from Scratch
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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
0:05
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
FacebookMongoDB
64 views3 months ago
How to Integrate Elasticsearch and MongoDB: Easy Steps
How to Integrate Elasticsearch and MongoDB: Easy Steps
hevodata.com
Feb 3, 2025
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
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
FacebookMongoDB
975 views3 months ago
MongoDB Vs SQL
MongoDB vs SQL
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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
0:05
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
How to Integrate Elasticsearch and MongoDB: Easy Steps
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hevodata.com
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
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
975 views3 months ago
FacebookMongoDB
New in our native hybrid search experience: the $scoreFusion (Public Preview) 🔎 With $scoreFusion, you can: • Normalize scores across pipelines (text, vector, geo, and more) • Combine them with weighted averages or custom expressions • Fine-tune relevance for complex search AI use cases Together with $rankFusion, you have even more control to deliver accurate, semantically rich results — whether you’re building smarter search, powering RAG, or enabling next-gen AI agents. Watch the demo and exp
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869 views8 months ago
FacebookMongoDB
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.
1.4K views10 months ago
FacebookMongoDB
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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FacebookMongoDB
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