After every generate run, compute variance between the executing plan
snapshot and the just-recorded usage rollup, persist it to
output/budget/summary.yaml (overwrite-on-run), and surface it both in
the generate status JSON (new budget_summary field) and as a "Plan
variance" line in reports/generation-summary.md.
Variance fields: calls / prompt_tokens / total_tokens each carry
{estimated, actual, delta, ratio}; cost_usd carries {estimated,
actual_known, actual_estimated_from_rates, actual_total, delta, ratio};
per_workflow rolls the per-bucket usage up to the same workflow_id grain
the plan reports. Runs whose snapshot_id cannot be resolved (no prior
plan, or pruned from the retention window) still record a variance row
with null comparison fields and snapshot_resolved=false, so the
consumer always sees a current summary.
Reordered run_generation so usage and variance are written before the
generation report, allowing the report to embed the variance line on
the same pass.
110 tests pass.
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>
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>
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>
Resolve chapter labels from EPUB nav entries (when present) and from the
first in-document h1/h2/h3 heading, parse roman-numeral and "Chapter N"
labels into numeric chapter indices, and generate stable IDs of the form
chapter-NN with -part-NNN suffix when a chapter exceeds max_words. The
chunker now operates on cleaned body text, distributes id="Page_*" page
anchors per part via inline markers extracted before splitting, and
supports a configurable overlap_words evidence window between adjacent
parts of the same chapter. Reclassify body sections whose chapter label
matches contents/transcriber-notes/license/colophon tokens so they leave
the body stream by default. Strip <head>...</head> from HTML body
extraction to stop the <title> tag from duplicating heading text in the
chunk markdown.
Real Lefevre EPUB now detects all 24 roman-numeral chapters with stable
chapter-NN IDs, distributes Page_N anchors across multi-part chapters,
and reclassifies Contents and Transcriber's Notes out of body
(role histogram body=67, cover=1, header=1, toc=1, notes=1, footer=2).
82 tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Parse META-INF/container.xml and the OPF package document, then iterate
documents in spine reading order instead of archive-name sort. Classify
each spine item (body, cover, nav, toc, header, footer, notes, license,
auxiliary) and exclude non-body sections by default; include_non_body=True
opts them back in for inspection. Capture OPF book metadata (title,
creator, language, subjects, rights, identifier, source_url, modified)
onto every chunk and propagate it through source artifact provenance.
Preserve the legacy zip-without-OPF fallback for malformed EPUBs.
Real Lefevre EPUB now yields 148 body chunks in spine order (was 155
mixed, archive-sorted) with cover=1, header=1, footer=4 detected and
dropped. 78 tests pass.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
