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Quarkus is an open-source, Kubernetes-native Java framework designed for developing cloud-native applications. It optimizes Java for serverless and microservices architectures, offering fast startup times and low memory footprint. Quarkus supports various programming models, including reactive and imperative, and integrates seamlessly with popular Java libraries and frameworks.- Latest
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Build Quarkus Applications with MongoDB and Panache!
See how to use Panache with Quarkus to simplify CRUD operations and perform aggregation queries seamlessly within a MongoDB database. By leveraging Panache's built-in functionalities, we reduced boilerplate code, enhancing development speed and efficiency. Ideal for cloud-native and serverless applications, Panache's integration with Hibernate ORM and JPA allows developers to focus on business logic while maintaining clean and efficient code. Learn more by reading the full article on MongoDB.com!
Apr 22, 2025
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Building a Semantic Search Application with MongoDB and Quarkus using Vector Search
✅ Try MongoDB 8.0 → https://mdb.link/91SzYGDmFoI ✅ Sign-up for a free cluster → https://mdb.link/91SzYGDmFoI-try ✅ Article link → https://mdb.link/91SzYGDmFoI-read - Discover how to harness the power of MongoDB's vector search capability to build a semantic search application using the Quarkus framework. In this comprehensive tutorial, we'll guide you step-by-step from understanding vector search fundamentals to implementing a functional Java application. Learn how to use Gemini AI for vector embeddings, create optimized queries, and set up your MongoDB Atlas cluster for seamless integration. Whether you're new to vector search or looking to enhance your generative AI applications, this video provides all the tools you need to get started. - 📚 Git repo: https://github.com/mongodb-developer/mongodb-vector-search-with-quarkus Resources: 📚 Vector Embeddings: https://mdb.link/91SzYGDmFoI-models 📚 Gemini AI: https://ai.google.dev/api?lang=python https://ai.google.dev/gemini-api/docs/api-key Similarity values: 📚 Euclidean: https://en.wikipedia.org/wiki/Euclidean_distance 📚 Cosine: https://en.wikipedia.org/wiki/Cosine_similarity 📚 Dot Product: https://en.wikipedia.org/wiki/Dot_product
Jan 21, 2025