docs(examples): add AWS Comprehend to the redact example and price LLMs at real-time list

[svonava]

What changes

examples/redact gains a sixth measured arm, AWS Comprehend DetectPiiEntities, recorded on the same 660 documents and 1,792 in-scope spans as the other arms.

• Evidence. fetch.py pins superlinked/sie-task-evidence at 06b6b5cb7f6593bcbd1badd07dee1a78508533d1, which adds redact/rows/comprehend.jsonl, the Comprehend arm in results/gretel-main_results.json (every other arm unchanged) and the arm settings in manifest.json. rows/comprehend.jsonl is now a required file.
• Scoring. score.py scores Comprehend like Presidio and Privacy Filter: every returned entity is masked, whatever its type or score. Settings: LanguageCode="en", us-east-1, one request per document.
• Prices. All six arms answer in real time, so the LLM arms now use standard real-time list prices: GPT-6 Luna $0.10/$0.50 and Claude Haiku 4.5 $1/$5 per 1M input/output tokens, which comes to $113 and $1,450 a month for 1M documents. The README still mentions the Batch API, but only as a fact. Comprehend stays at $351, its cheapest tier.
• README. The results table has a Comprehend row, a "Comprehend now measured" paragraph replaces the statement that it was not measured, and the arms and price inputs are updated.

Results

Arm	Coverage recall	95% interval	States and countries excused	$ a month, 1M documents
SIE, two models composed	88.8%	86.7% to 90.9%	90.5%	$32
AWS Comprehend	83.2%	80.3% to 85.8%	84.2%	$351

SIE minus Comprehend, paired: +2.7 to +8.6 points on coverage recall and +3.5 to +9.3 with states and countries excused. The interval stays above zero on both figures.

Verification

python3 fetch.py   # every file checked against the dataset and manifest.json
python3 score.py   # exit 0: "Every figure matches the recorded results and the published page."

ruff check and ruff format --check pass on the example.

🤖 Generated with Claude Code

Summary by CodeRabbit

• Documentation
  • Expanded the redaction study to include measured AWS Comprehend results and updated its scores, paired comparisons, and study limitations.
  • Clarified that Comprehend was tested on English documents without a score threshold, and that the results do not establish detection performance against services that were not tested.
  • Updated LLM cost estimates to standard real-time list prices, with Batch API pricing noted as a lower-cost alternative. Clarified Comprehend’s monthly cost is a lower-bound estimate.

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📝 Walkthrough

Walkthrough

The redaction example adds AWS Comprehend as a measured comparison arm. The fetch and scoring scripts include its recorded data, coverage, and cost. The READMEs report the six-arm results and use standard real-time LLM list prices for monthly estimates.

Changes

Redaction study

Layer / File(s)	Summary
Add Comprehend to data and scoring examples/redact/fetch.py, examples/redact/score.py	The fetch script pins the evidence bundle and requires rows/comprehend.jsonl. The scoring script adds Comprehend to its arms, uses the published covered-span count, and reports coverage for each arm.
Update monthly cost estimates examples/redact/score.py	Monthly LLM estimates use standard real-time list prices. The cost checks compare recorded costs with the monthly estimates.
Publish the six-arm results examples/README.md, examples/redact/README.md	The documentation describes Comprehend’s setup and results, updates the paired comparisons and qualifications, and explains the pricing basis and evidence bundle.

Suggested reviewers: fm1320

Priority: ⬇️ Low

Merge Risk: ⚪ Minimal · up to 57cab

This change adds an AWS Comprehend comparison arm and updates the pricing notes in the redaction example. No concrete merge-blocking risk was identified in the supplied material.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 50.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 2 functions across 2 files. (1 skipped: 1…	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely summarizes the two main changes: adding AWS Comprehend to the redact example and pricing LLMs at real-time list rates.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

Full details: Docstring Coverage

Explanation

Docstring coverage is 50.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 2 functions across 2 files. (1 skipped: 1 unsupported.)

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Inline comments:
Review comments at @examples/redact/README.md:
- Line 48: Update the AWS Comprehend row and its price comparison to use the
tiered rates, 3-unit minimum, and per-request rounding described in the review,
based on the request volumes used by score.py; alternatively, label the existing
$351 figure explicitly as an optimistic lower-bound estimate.
- Around line 59-60: Clarify the first-figure comparison in the README by
completing the sentence to say that SIE’s lead comes from address spans where
the LLM prompts leave the state readable. Preserve the point that the label list
has no `state`.

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