A. Storage & Query Cost Calculator

Estimate how much you can save moving from FP32 / INT8 to SEMQ for embeddings at scale.

B. Replace / Complement Vector DBs

SEMQ codes can be stored in traditional databases (Postgres, Mongo, SQLite) and queried with Hamming-like distance or symbolic indexes, reducing dependency on dedicated vector DBs.

-- Store SEMQ codes in Postgres
CREATE TABLE semq_embeddings (
  id TEXT PRIMARY KEY,
  code BYTEA,      -- packed SEMQ symbols
  payload JSONB
);

-- Example: Hamming-like search using extension / custom op
SELECT id, payload
FROM semq_embeddings
ORDER BY semq_hamming_distance(code, :query_code)
LIMIT 10;

Real implementations would use native extensions or UDFs for SEMQ distance, but the idea is simple: store symbolic codes and sort by a fast symbolic distance.

C. Pattern Mining on Symbolic Codes

Once embeddings live in a symbolic domain, you can search for recurring patterns in the SEMQ codes, like motifs in DNA sequences or time-series.