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Stirling-PDF/engine/tests/contradiction/test_page_traceability.py
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ConnorYohandGitHub 017c8d59fa feat(ai): add Contradiction Agent on a new ChunkedMapper primitive (#6369)
## Summary

Adds a new AI specialist that finds **textual contradictions** across
one or more PDFs — conflicting claims, recommendations, points of view,
contested facts — built entirely in Python on top of the new
`DocumentService` + `ChunkedReasoner` stack from #6314. Replaces the
closed #6304, which was started before #6314 landed and therefore
over-engineered (Java orchestrator, two-round handshake, resume
artifact, discriminated-union lift).

Two commits:
1. **`refactor(engine): extract ChunkedMapper[T] from ChunkedReasoner`**
— pure refactor, public API of ChunkedReasoner unchanged. New
`ChunkedMapper[T: BaseModel]` is a generic parallel-chunk primitive
(slicing, semaphore, time-bounded extraction, cancellation drain,
progress events) that's now a peer to ChunkedReasoner rather than locked
inside it. The compression loop stays on ChunkedReasoner where it
belongs.
2. **`feat(ai): add Contradiction Agent on ChunkedMapper`** — the agent
itself, plus integrations into `PdfReviewAgent` and `PdfQuestionAgent`.

## Architecture

- **Python-only.** No Java code. No `AgentToolId.CONTRADICTION_AGENT`.
No dedicated HTTP endpoint. No resume artifact, no discriminated-union
lift in `contracts/common.py`. Detector runs inside the Python engine
and the Python engine alone.
- **Review path** (`PdfReviewAgent`): a new
`ContradictionIntentClassifier` fires on contradiction-flavoured
prompts; agent runs detection synchronously and emits a single
`EditPlanResponse(steps=[ADD_COMMENTS])`. Single-turn flow — no resume.
- **Question path** (`PdfQuestionAgent`): a new
`ContradictionCapability` joins `RagCapability` and
`WholeDocReaderCapability` in the smart-model toolset, exposing
`find_contradictions(query)`. The smart model picks it from the toolset
alongside `search_knowledge` and `read_full_document`.

## Inside `ContradictionDetector.detect()`

1. `DocumentService.read_pages(file_id)` → ordered `list[Page]`.
2. `ChunkedMapper[_ExtractedClaims].map_pages(...)` — char-budgeted
multi-page slicing; each slice runs the claim-extractor LLM in parallel
under a semaphore.
3. Page-traceability: the extractor returns `_ExtractedClaim.page`
(which `[Page N]` marker the claim came from). The wrapper validates
`page ∈ chunk.pages`; if not, mechanical fallback searches the chunk's
page text for the verbatim quote and reassigns. If still no match, drop
the claim.
4. `Claim.anchor_quality: Literal[\"verbatim\", \"paraphrased\"]` is set
by a substring check against the declared page's text. Verbatim quotes
feed `anchor_text` for snap-to-quote add-comments placement; paraphrased
ones fall back to margin geometry.
5. Subject canonicalisation: ONE fast-model LLM call collapses synonyms
across the document. Fails open to lexical bucketing.
6. Pre-filters: drop identical-quote pairs; drop same-page same-polarity
paraphrases.
7. Per-bucket pair detection in parallel (separate semaphore, cap 5).
Buckets > 12 claims chunk into windows of 12 with overlap 2; pairs
deduped across overlapping windows by frozen `(i, j)` index pair.
8. Summary fast-model call with fallback string on error.

## Prompt-injection hardening

Every prompt that interpolates user-supplied or PDF-extracted text wraps
content in `<user_message>` / `<verdict>` / `<content>` tags with an
explicit SECURITY preamble instructing the model to treat tagged content
as data only.

## Limitations

- **Combined math + contradiction intent**: when both intent classifiers
fire on the same prompt, contradiction takes precedence and the math
intent is silently dropped. Documented in the Review module docstring
and pinned by
`test_review_integration.py::test_contradiction_precedence_over_math`.
- **Cross-window contradiction reach**: within a subject bucket, pairs
more than ~10 claim indices apart in the same chunked window may be
missed by the overlap-2 strategy. Documented in `test_detector.py`.
Acceptable for v1.

## Settings (engine/src/stirling/config/settings.py)

```python
contradiction_detect_concurrency = 5     # per-bucket detector semaphore
contradiction_bucket_chunk_size = 12     # max claims per detector call
contradiction_bucket_chunk_overlap = 2   # overlap for >threshold buckets
```

`chars_per_slice` and extraction concurrency are reused from the
existing `chunked_reasoner_*` settings.

## Test plan

- [x] `uv run pytest tests/ -v` — **245/245 pass** (210 pre-existing +
35 new)
- [x] `uv run ruff check src/ tests/` — clean
- [x] `uv run pyright src/stirling/agents/contradiction/
src/stirling/contracts/contradiction.py` — 0 errors
- [x] `./gradlew :proprietary:test` — green; no Java was touched, but
verified untouched
- [x] Page-traceability tests cover: valid page kept, hallucinated page
dropped, mechanical-reassign on misattribution, anchor-quality verbatim
vs paraphrased
- [x] Review integration: ADD_COMMENTS plan with two paired CommentSpecs
per contradiction; NeedIngestResponse precheck; precedence vs math
intent pinned
- [x] Question integration: all three capabilities wired into
smart-model toolset; `find_contradictions` returns formatted report text
- [x] ChunkedMapper standalone: slicing, multi-chunk ordering, worker
failures, timeouts, cancellation drain, semaphore saturation
- [x] ChunkedReasoner regression: all pre-existing tests pass unchanged
after the internal split

## Relationship to closed #6304

#6304 was closed in favour of this PR. The closed PR predated #6314 and
modelled the agent as a Java-orchestrated two-round examine/deliberate
flow with its own HTTP endpoint and a discriminated-union resume
artifact. With #6314 making full ordered page text available to the
engine via `DocumentService.read_pages`, none of that is needed. Net
effect: drop ~600 lines of Java, drop the two-round handshake, drop the
`ToolReportArtifact` lift, while ending up with a more scalable agent
(chunk-based instead of page-based extraction; tested to
ChunkedReasoner-equivalent scale).
2026-05-22 13:23:52 +00:00

151 lines
5.1 KiB
Python

"""Page-traceability validation for extracted claims.
Covers the wrapper logic that maps an LLM-emitted ``_ExtractedClaim`` to
the public ``Claim`` after sanity-checking its declared page against
the chunk's covered pages, and assigns ``anchor_quality`` based on
whether the quote is a verbatim substring of the page's text.
"""
from __future__ import annotations
from stirling.agents.contradiction.detector import (
ContradictionDetector,
_ExtractedClaim,
_ExtractedClaims,
)
from stirling.agents.shared.chunked_mapper import ChunkOutput
from stirling.contracts.contradiction import ClaimPolarity
from stirling.contracts.documents import Page
def _page(n: int, text: str) -> Page:
return Page(page_number=n, text=text, char_count=len(text))
def _chunk_output(pages: list[Page]) -> ChunkOutput[_ExtractedClaims]:
page_nums = [p.page_number for p in pages]
label = f"pages={page_nums[0]}" if len(page_nums) == 1 else f"pages={page_nums[0]}-{page_nums[-1]}"
return ChunkOutput(pages=page_nums, output=_ExtractedClaims(claims=[]), label=label)
def _raw(
*,
page: int,
quote: str,
subject: str = "deadline",
polarity: ClaimPolarity = "assert",
text: str = "Claim about the deadline.",
) -> _ExtractedClaim:
return _ExtractedClaim(
page=page,
subject=subject,
polarity=polarity,
text=text,
quote=quote,
)
# Valid page → kept
def test_valid_page_in_chunk_is_kept_verbatim() -> None:
pages = [_page(1, "The deadline is March 5."), _page(2, "Other content.")]
chunk = _chunk_output(pages)
pages_by_num = {p.page_number: p for p in pages}
raw = _raw(page=1, quote="The deadline is March 5.")
claim = ContradictionDetector._validate_extracted_claim(raw, chunk, pages_by_num)
assert claim is not None
assert claim.page == 1
assert claim.anchor_quality == "verbatim"
def test_quote_present_in_page_text_yields_verbatim_anchor() -> None:
pages = [_page(1, "Sentence A. The deadline is March 5. Sentence C.")]
chunk = _chunk_output(pages)
pages_by_num = {p.page_number: p for p in pages}
raw = _raw(page=1, quote="The deadline is March 5.")
claim = ContradictionDetector._validate_extracted_claim(raw, chunk, pages_by_num)
assert claim is not None
assert claim.anchor_quality == "verbatim"
def test_quote_absent_from_page_text_yields_paraphrased_anchor() -> None:
"""A claim whose quote isn't a substring of the declared page must
still survive (the LLM may have paraphrased), but it's marked
paraphrased so the comment placer falls back to margin geometry."""
pages = [_page(1, "March 5 was named as the deadline.")]
chunk = _chunk_output(pages)
pages_by_num = {p.page_number: p for p in pages}
raw = _raw(page=1, quote="The deadline is March 5.")
claim = ContradictionDetector._validate_extracted_claim(raw, chunk, pages_by_num)
assert claim is not None
assert claim.page == 1
assert claim.anchor_quality == "paraphrased"
# Page outside chunk + mechanical fallback
def test_page_outside_chunk_but_quote_uniquely_in_another_page_is_reassigned() -> None:
"""LLM declared page 3, but the quote literally appears on page 2 (which
is in the chunk). The wrapper reassigns and keeps the claim."""
pages = [
_page(1, "Nothing relevant here."),
_page(2, "The deadline is March 5."),
]
chunk = _chunk_output(pages)
pages_by_num = {p.page_number: p for p in pages}
raw = _raw(page=3, quote="The deadline is March 5.")
claim = ContradictionDetector._validate_extracted_claim(raw, chunk, pages_by_num)
assert claim is not None
assert claim.page == 2 # reassigned mechanically
assert claim.anchor_quality == "verbatim"
def test_page_outside_chunk_and_quote_not_in_any_chunk_page_is_dropped() -> None:
pages = [_page(1, "Unrelated."), _page(2, "Also unrelated.")]
chunk = _chunk_output(pages)
pages_by_num = {p.page_number: p for p in pages}
raw = _raw(page=3, quote="The deadline is March 5.")
claim = ContradictionDetector._validate_extracted_claim(raw, chunk, pages_by_num)
assert claim is None
def test_quote_matching_multiple_chunk_pages_is_dropped() -> None:
"""Ambiguous reassignment: if more than one chunk page contains the quote,
we have no way to pick — drop with a warning instead of guessing."""
pages = [
_page(1, "The deadline is March 5."),
_page(2, "The deadline is March 5."),
]
chunk = _chunk_output(pages)
pages_by_num = {p.page_number: p for p in pages}
raw = _raw(page=99, quote="The deadline is March 5.")
claim = ContradictionDetector._validate_extracted_claim(raw, chunk, pages_by_num)
assert claim is None
# Defensive drops
def test_empty_subject_drops_claim() -> None:
pages = [_page(1, "anything")]
chunk = _chunk_output(pages)
pages_by_num = {p.page_number: p for p in pages}
raw = _ExtractedClaim(page=1, subject=" ", polarity="assert", text="real text", quote="real quote")
claim = ContradictionDetector._validate_extracted_claim(raw, chunk, pages_by_num)
assert claim is None