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bench/ — runnable demo

A self-contained walkthrough of Lang2SQL V1's headline value, with no Discord token and no live database. It runs the real shipped modules against the canned Postgres stub and the offline FakeLLM.

Run

.venv/bin/python bench/ecommerce_demo.py

No environment variables are needed. (If OPENAI_API_KEY happens to be set, Section 0 will route through OpenAI instead of the FakeLLM — everything else is offline.)

What each section proves

Section What it shows Maps to
0 — assembled harness ContextConcierge wires the FakeLLM + canned PostgresExplorer + six tools into a HarnessContext, and agent_loop drives one full LLM→tool→LLM cycle. A wiring proof, not an intelligence proof. harness + concierge
1 — define metrics Three e-commerce metrics (total_revenue, aov, paid_orders) are written to the current channel's scope and read back from the effective layer. ★① context learning
2 — federation (the money shot) active_user is defined as "30-day login" in #marketing and "paid subscriber" in #finance. Each channel resolves its own definition by walking its scope chain — same term, two live meanings, zero conflict. ★④ semantic federation
3 — safety gate DROP, INSERT, and a CTE-hidden INSERT are all blocked fail-closed; a plain SELECT ... GROUP BY passes. The read-only guarantee is enforced before any SQL would reach a database. ★① safety pipeline

The two headline pillars demonstrated are ★① safety (Sections 0 + 3) and ★④ federation (Sections 1 + 2).

Honesty notes

  • The PostgresExplorer is a V1 stub: it returns canned orders/users schema and sample rows and does not connect to a real database. Real psycopg execution lands in v1.5.
  • The FakeLLM is a deterministic stub for offline runs; it does not reason. It blindly calls the first available tool, which is why Section 0's turn ends at the safety gate rather than counting orders. With a real key it would call gpt-4.1-mini.
  • Federation state in Sections 1–2 lives in an in-memory SemanticStore; the SQLite-backed persistent store is wired through the concierge for the bot.