القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.
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.
| name | vector-db-storage |
| description | 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. |
| Backend | Use case | Persistence | Scale |
|---|---|---|---|
| ETS | Prototype, small index, low latency | No | < ~100k–500k vectors in memory |
| File | Simple persistence, no DB | Yes (binary/term) | Same as ETS, plus disk |
| PostgreSQL + pgvector | Production, SQL + vectors | Yes | Large; use IVFFlat/HNSW indexes |
| Qdrant (client) | Dedicated vector engine | Yes (external) | Very large |
{id, vector, metadata}; vector as list or binary.terminate/2 or :gen_server.cast(pid, :persist) → write ETS contents to file (e.g. :erlang.term_to_binary(entries) or custom binary format).Use pgvector and Ecto for persistent, indexed storage.
id, embedding (pgvector type), metadata (jsonb/map).embedding for approximate k-NN.<=>, <->) in raw SQL or Ecto fragments; limit k.# Example: nearest neighbors (cosine distance)
# fragment("embedding <=> ?::vector", ^Nx.to_flat_list(query))
| Method | Accuracy | Speed | When |
|---|---|---|---|
| Exact (scan all) | Exact | O(n) per query | Small n (e.g. < 100k), correctness critical |
| IVFFlat (pgvector) | Approximate | Fast | Medium/large, good recall with tuning |
| HNSW (pgvector) | Approximate | Very fast | Large, high recall |
For in-memory ETS, start with exact k-NN; move to pgvector (or Qdrant) when n grows or persistence is required.