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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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.
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.
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.
| name | vector-db-elixir |
| description | 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. |
A vector DB stores high-dimensional vectors (embeddings) and supports similarity search: find the k nearest vectors to a query vector by distance (cosine, Euclidean, dot product).
| Operation | Description |
|---|---|
| insert | Add vector(s) with optional metadata (id, payload) |
| search | Query vector → return k nearest vectors (by similarity) |
| delete | Remove by id or filter |
| get | Fetch by id (optional) |
Choose storage backend
Choose distance metric
Implement or wire
Expose API
insert/2, search/2, delete/1