
Storage backends and indexing for vector databases in Elixir. Use when choosing persistence, implementing exact vs approximate k-NN, or integrating pgvector, file, or ETS for vector storage.
When publishing a new elix-db version or making version changes, run versioned sample use cases, collect memory/time/latency, compare to the previous version report, and update reports. Remember this workflow for releases.
Debug, verify, and compare elix-db to industry after each plan step. Use after implementing any plan step or changing vector/store/API logic; run tests, IEx checks, and document efficiency vs Qdrant/Milvus/pgvector.
Design and implement a vector database in Elixir. Use when building embedding storage, similarity search, k-NN retrieval, or when the user mentions vector DB, embeddings, or semantic search in Elixir.
Elixir patterns for vector storage and similarity search. Use when implementing GenServer-based vector store, ETS index, Nx/Scholar distance math, or supervision for a vector DB in Elixir.