Ship a starter model rate table at src/infospace_bench/model_rates.yaml
(prompt_per_1k / completion_per_1k for the OpenRouter models we have
actually touched: gpt-4o, gpt-4o-mini, gpt-4-turbo, claude 3.5 sonnet
and haiku, claude 3 opus, gemini 1.5 flash/pro, llama 3.1 70b) and a
load_rate_table() / estimate_cost_usd() pair that overlays an optional
<workspace>/model-rates.yaml on top of the bundled defaults.
generate run now passes a workspace-aware cost_resolver into
record_run_usage, so cost_usd_estimated lands on every usage bucket
whose model matches the table. Adapter-returned cost still wins
(cost_status="known"); rate-table cost is reported under
cost_status="estimated"; unmatched models are recorded as
cost_status="unknown" rather than silently zeroed. Rate-table file is
listed in pyproject.toml package-data so pip-installed users keep the
defaults.
106 tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Every completed generate run now aggregates per-call adapter usage from
the workflow-engine run records into output/budget/usage.yaml. Per-call
data is bucketed by (workflow_id, stage_id, provider, model) with
running totals for calls, prompt_tokens, completion_tokens,
total_tokens, and cost_usd_known (sum of adapter-reported cost when the
provider returns it; usually zero today). A run-level entry captures
run_index, started_at, completed_at, duration_seconds, the executing
plan snapshot_id (resolved from the latest plans.yaml entry), and the
workflow-level run_id / stage_count summaries.
cost_usd_estimated is left as None for this task; T03 wires the
rate-table resolver so the same bucket gets a model-priced fallback
when the adapter does not return cost directly.
Fixture-mode runs are recorded with provider='fixture', zero tokens,
and cost_status='unknown' rather than silently skipped, so the rollup
honestly reflects which stages actually ran.
102 tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Every generate plan invocation now appends its compact summary to
output/budget/plans.yaml with a deterministic 12-char snapshot_id
hashed over the selection filters and the estimated call/token/cost
totals. Identical-fingerprint plans refresh the most recent entry's
recorded_at instead of stacking duplicates. Retention defaults to the
last 50 snapshots; older entries are pruned and counted on a top-level
pruned_count field.
The summary now echoes its input filters (chapter_filter, chunk_filter,
from_chapter, to_chapter) so reviewers can read the snapshot without
cross-referencing the CLI invocation.
New module src/infospace_bench/budget.py owns layer 1 (per-infospace
recording) of the IB-WP-0019 three-layer design; layer 2 still belongs
in llm-connect LLM-WP-0004 and layer 3 in state-hub.
99 tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Open a separate workplan for the budget/usage recording layer surfaced
by the T03 conversation. Three-layer design: layer 1 (per-infospace
budget log) and layer 3 (state-hub emission) live here; layer 2
(cross-application quality observations for adaptive routing) stays in
llm-connect LLM-WP-0004.
Seven tasks cover plan snapshot persistence, run usage rollup,
rate-table cost computation, plan-vs-actual variance, state-hub token
events with hub-down isolation, a workspace-level rollup CLI, and
archive integration so IB-WP-0014 packages carry their budget shape.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
