Web search is for text. Subsets is for numbers.
Search and SQL over 1 up‑to‑date datasets from official statistical sources, cleaned into plain relational tables. Every number traces back to the open-source run that produced it.
pip install subsetsiosubsets sync --bundle small # or medium, or allsubsets serve # search + SQL at http://localhost:8080Free and open — the data syncs straight from the public bucket, and search + SQL run entirely on your machine. See docs →
Web search vs. Subsets
Ask both: “How high did Australian inflation spike during the Korean War wool boom?”
“Inflation surged in the early 1950s — retrospectives suggest prices rose by somewhere around 20% at the peak of the boom.”
Paraphrased from a retrospective article · no primary source, no exact figure
SELECT obs_date, value_text FROM "reserve-bank-of-australia-g1-data" WHERE series_id = 'GCPIAGYP' AND obs_date BETWEEN '1951-01-01' AND '1951-12-31'
Q4 1951 · +25.6% year on year
Source
Reserve Bank of Australia · G1 — Consumer price inflation · view the dataset →
Where every number comes from
Official source
rba.gov.au · raw releaseStatistical agencies and central banks like the ONS, RBA, and Bundesbank publish the raw data.
Open-source connector
subsetsio/connectorsThe exact ingestion code is public. Read it, fork it, file an issue.
Public CI run
public run logsEvery table is built in a logged workflow run, one click from its dataset page.
Versioned table
rba g1-data · pinnedRevisions are tracked and reviewed. Your agent queries a pinned version.