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Stirling-PDF/engine/tests/contradiction/test_question_integration.py
T
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

123 lines
4.3 KiB
Python

"""PdfQuestionAgent — contradiction capability wiring.
The smart-model agent picks the right tool based on the question; here
we don't drive the smart model — we directly verify that the agent
wires the contradiction capability into its toolset alongside RAG and
the whole-document reader, and that the capability dispatches to the
detector when invoked.
"""
from __future__ import annotations
from dataclasses import replace
import pytest
from pydantic_ai.toolsets import FunctionToolset
from stirling.agents.pdf_questions import PdfQuestionAgent
from stirling.contracts import (
AiFile,
PageText,
PdfQuestionRequest,
)
from stirling.contracts.contradiction import Claim
from stirling.documents import DocumentService, SqliteVecStore
from stirling.models import FileId
from stirling.services.runtime import AppRuntime
from tests.test_pdf_question_agent import StubEmbedder
def _file(file_id: str, name: str) -> AiFile:
return AiFile(id=FileId(file_id), name=name)
def _claim(page: int, quote: str) -> Claim:
return Claim(
page=page,
subject="deadline",
polarity="assert",
text=f"paraphrase {page}",
quote=quote,
)
@pytest.fixture
def runtime_with_stub_docs(runtime: AppRuntime) -> AppRuntime:
stub = DocumentService(
embedder=StubEmbedder(), # type: ignore[arg-type]
store=SqliteVecStore.ephemeral(),
default_top_k=runtime.settings.rag_default_top_k,
)
return replace(runtime, documents=stub)
@pytest.mark.anyio
async def test_run_answer_agent_builds_agent_with_three_toolsets(
runtime_with_stub_docs: AppRuntime,
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""``_run_answer_agent`` constructs an ``Agent`` with all three retrieval
toolsets (rag, whole-doc, contradiction). We intercept the Agent
constructor and inspect what was wired.
Uses pytest's ``monkeypatch`` fixture rather than direct attribute
assignment so pyright sees the swap as a typed test-only operation
and restoration is automatic if the test raises.
"""
file = _file("doc-a", "a.pdf")
await runtime_with_stub_docs.documents.ingest(
file.id,
[PageText(page_number=1, text="content")],
source=file.name,
)
agent = PdfQuestionAgent(runtime_with_stub_docs)
captured: dict[str, object] = {}
import pydantic_ai
real_agent_init = pydantic_ai.Agent.__init__
# The Agent class is generic on deps/output types — its __init__ accepts
# arbitrary positional+keyword arguments depending on those parameters.
# We're monkey-patching the class itself for one test, so the bound
# method's signature is intentionally opaque here. Typing through Any
# is honest about that boundary ("we can't statically describe it")
# and avoids wallpapering the body with type-ignore directives.
from typing import Any
def _capture_init(self: Any, *args: Any, **kwargs: Any) -> None:
captured["toolsets"] = kwargs.get("toolsets")
captured["instructions"] = kwargs.get("instructions")
# Call the real init for safety.
real_agent_init(self, *args, **kwargs)
# Stub the agent's `.run` so we don't reach a real model.
async def _stub_run(self: Any, *args: Any, **kwargs: Any) -> object:
class _Result:
output = "stubbed"
return _Result()
monkeypatch.setattr(pydantic_ai.Agent, "__init__", _capture_init)
monkeypatch.setattr(pydantic_ai.Agent, "run", _stub_run)
await agent._run_answer_agent(PdfQuestionRequest(question="any conflicts?", files=[file]))
toolsets = captured.get("toolsets")
assert isinstance(toolsets, list)
assert len(toolsets) == 3
# Inspect the registered tool names. A regression that double-wired
# one capability (e.g. two ``rag.toolset`` and dropping
# ``contradiction.toolset``) would still satisfy ``len == 3`` but
# the union of tool names would not include ``find_contradictions``.
tool_names: set[str] = set()
for ts in toolsets:
assert isinstance(ts, FunctionToolset), f"expected FunctionToolset, got {type(ts).__name__}"
tool_names.update(ts.tools.keys())
assert tool_names == {"search_knowledge", "read_full_document", "find_contradictions"}, (
f"unexpected toolset wiring; tool names = {sorted(tool_names)}"
)