8dazo/vector-db-storage
.cursor/skills/vector-db-storage/SKILL.md
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.
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skillsauthnpx skillsauth add 8dazo/elix-db vector-db-storageInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- 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.
Vector DB Storage and Indexing
Storage Backends
| 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 |
In-Memory (ETS)
- :set table, key = vector id.
- Value:
{id, vector, metadata}; vector as list or binary. - No persistence: load from file on GenServer init if needed (see File below).
- Exact k-NN: iterate all rows, compute distance, sort, take k. O(n) per query.
File Persistence
- On shutdown: GenServer
terminate/2or:gen_server.cast(pid, :persist)→ write ETS contents to file (e.g.:erlang.term_to_binary(entries)or custom binary format). - On start: read file, recreate ETS and insert all entries.
- Keep dimension and metadata format in header or config so reload is consistent.
PostgreSQL + pgvector
Use pgvector and Ecto for persistent, indexed storage.
- Schema: table with
id,embedding(pgvector type),metadata(jsonb/map). - Index: create HNSW or IVFFlat index on
embeddingfor approximate k-NN. - Queries: use pgvector’s distance operators (
<=>,<->) in raw SQL or Ecto fragments; limit k.
# Example: nearest neighbors (cosine distance)
# fragment("embedding <=> ?::vector", ^Nx.to_flat_list(query))
- Handles large datasets; persistence and backups via Postgres.
Exact vs Approximate k-NN
| 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.
Design Checklist
- [ ] Persistence requirement clear: none, file, or Postgres
- [ ] If file: define format (term vs binary) and load/save on start/stop
- [ ] If pgvector: add migration for vector column and chosen index (HNSW/IVFFlat)
- [ ] Dimension and distance metric match across insert and search
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