examples/routing/trading-literature.yaml is the checked-in starting
config for a Lefevre-style run. It applies the IB-WP-0018 task-type
taxonomy: cheap candidates for summary + evaluation, smart candidates
for entity + relation extraction, and a separate baseline rule wiring
claude_code for a follow-on T05 ShadowingAdapter step. Workspace-
relative ledger_path keeps adaptive observations with the workspace.
tests/test_routing_config.py gains a regression test that asserts the
shipped example parses cleanly, every stage in stage_to_task_type maps
to a declared task type, and the baseline candidate uses the
claude_code provider — so the example will not bit-rot silently.
tests/test_openrouter_live.py gains test_provider_routing_one_chapter_live_smoke
gated on the same INFOSPACE_BENCH_ENABLE_LIVE_OPENROUTER + OPENROUTER_API_KEY
opt-in as the existing static smoke. It builds a one-candidate routing
config, runs a single chapter through --provider routing, and asserts
the per-stage adapter-choices report section names the routed model
and the routed artifacts carry adapter_id provenance.
docs/generic-source-generator.md gains a "Live runs with --provider
routing" subsection that walks through the one-command routed run,
explains the --quality-floor override, and points at the parallel
live smoke test.
174 tests pass, 2 skipped (both live smokes, correctly gated).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Add --chapter / --from-chapter / --to-chapter / --chunk selection flags
to generate init and generate from-source, plumb them into
init_generation_infospace via a new _filter_chunks_by_chapter helper,
and refuse to create an infospace when the filters reject every chunk
(InfospaceError "empty_chapter_selection"). The flags use the same
T03/T02 plumbing (chapter labels, roman numerals, chunk ids) so a
single-chapter selection is a one-flag command.
OpenRouter run-record metadata (model, request_id, usage tokens,
retry_count, duration_seconds) already lands in
output/workflows/runs/*.yaml; this task just adds the smoke test that
proves it stays there, plus the parallel guarantee that the same
provider metadata reaches generated artifact provenance via
provenance.provider_metadata.
tests/test_openrouter_live.py covers:
- chapter-filter, from/to-chapter range, and empty-selection failure on
init (non-live, deterministic)
- CLI smoke through generate from-source with --chapter
- a pytest-skipped live OpenRouter one-chapter end-to-end gated by
OPENROUTER_API_KEY + INFOSPACE_BENCH_ENABLE_LIVE_OPENROUTER, with
INFOSPACE_BENCH_LIVE_MODEL override (default openai/gpt-4o-mini)
docs/generic-source-generator.md gains a "Live OpenRouter runs (handle
with care)" section that walks plan-before-run, single-chapter live
run, the budget/usage artifacts, and the checks a reviewer should run
before scaling to the full book.
129 tests pass, 1 skipped (the live smoke, correctly gated).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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>
Ship a specialized profile for trading memoirs and market-structure
texts. The profile names eight entity categories (trader, market,
strategy, error, psychological_pattern, institution, instrument,
evidence_bearing_claim), five relation types (cause_effect,
lesson_evidence, risk_mitigation, actor_venue, strategy_outcome), and
four evaluation criteria (groundedness, lesson_clarity,
historical_context, overgeneralization_risk). Each is reflected in the
prompts and contracts so the LLM is steered toward operator-level
findings rather than biographical detail or moralising.
The generic profile remains the default. A 2-chapter Lefevre smoke run
with --profile trading-literature completes end-to-end with viable
metrics; 93 tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Replace generate plan's full-prompt dump with a compact summary that
reports selected-chunk counts, selected chapter numbers, per-workflow
call counts, prompt-word and token estimates, and a rough USD cost when
--cost-per-1k is supplied. Selection filters --chapter (label or number,
repeatable), --from-chapter / --to-chapter (numeric range), and --chunk
(repeatable id) shape the estimate. Budget caps --max-calls and
--cost-cap are reported as exceeds_* booleans so callers can fail fast
before run.
The old full per-workflow plan with prompts remains available behind
--full so deep inspection is opt-in instead of the default.
Whole-Lefevre estimate at default max_words=800: 146 chunks, 730 calls,
~518k prompt tokens, ~$155 at $0.30/1k. Chapters 3-5 only: 19 chunks,
95 calls, ~64k tokens. 87 tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
