beginner~2h
Embeddings & Vector Databases
Module 01 introduced embeddings conceptually. This module makes them operational: how similarity is actually computed, how to store millions of vectors, and how to pick from the eleven store integrations Spring AI supports.
Learning objectives
- Beginner: Explain what an embedding is and why 'closeness' between two embeddings maps to semantic similarity.
- Intermediate: Use Spring AI's VectorStore abstraction to store and query embeddings without depending on one specific vector database's API.
- Advanced: Choose an appropriate vector store integration and metadata filtering strategy for a given retrieval use case's scale and query pattern.
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Embeddings & Vector Databases