Vector Databases and Similarity Search

• Visualize data as points in a multi-dimensional coordinate system

• Explain how machine learning models convert text into numerical vectors

• Define the limitations of traditional keyword-based search systems

• Calculate similarity between vectors using geometric distance formulas

• Interpret vector direction as a measure of semantic relevance

• Describe the curse of dimensionality in vector storage

• Categorize common indexing methods for vector retrieval

• Explain Hierarchical Navigable Small World graphs

• Describe the IVF partitioning process for vector clusters

• Summarize product quantization for memory optimization

• Apply vector search to build personalized recommendation engines

• Implement visual search using image feature vectors

• Explain Retrieval-Augmented Generation for large language models

• Evaluate trade-offs between speed and search accuracy

• Predict emerging trends in vector database technology