Building a Semantic Search Application with MongoDB and Quarkus using Vector Search

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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.
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📚 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

Видео Building a Semantic Search Application with MongoDB and Quarkus using Vector Search канала MongoDB