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## 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).
357 lines
13 KiB
Python
357 lines
13 KiB
Python
"""Tests for the generic ``ChunkedMapper`` primitive.
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The mapper is the per-chunk fan-out machinery extracted from
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``ChunkedReasoner``: char-budgeted slicing, parallel scheduling under a
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semaphore, time-bounded extraction with cancellation, progress events, and
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worker-failure tolerance. These tests drive it with a stubbed
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``Agent[None, T]`` so the model boundary stays patched out.
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"""
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from __future__ import annotations
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import asyncio
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from dataclasses import dataclass
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from unittest.mock import AsyncMock, patch
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import pytest
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from pydantic import BaseModel
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from pydantic_ai import Agent
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from stirling.agents.shared.chunked_mapper import ChunkedMapper
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from stirling.contracts.documents import Page
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from stirling.services.runtime import AppRuntime
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@dataclass
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class _StubAgentResult[T]:
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output: T
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class _Extracted(BaseModel):
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"""Tiny per-chunk extractor payload used by these tests."""
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label: str
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def _page(n: int, text: str) -> Page:
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return Page(page_number=n, text=text, char_count=len(text))
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def _build_mapper(
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runtime: AppRuntime,
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*,
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chars_per_slice: int | None = None,
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concurrency: int | None = None,
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worker_timeout_seconds: float | None = None,
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) -> ChunkedMapper[_Extracted]:
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"""Build a mapper wrapping a real ``Agent`` whose ``.run`` is patched per test."""
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extractor: Agent[None, _Extracted] = Agent(
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model=runtime.fast_model,
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output_type=_Extracted,
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model_settings=runtime.fast_model_settings,
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)
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return ChunkedMapper(
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runtime,
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extractor=extractor,
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chars_per_slice=chars_per_slice,
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concurrency=concurrency,
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worker_timeout_seconds=worker_timeout_seconds,
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)
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class TestSlicePages:
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"""The static helper is pure: no I/O, no scheduling."""
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def test_single_slice_when_under_budget(self) -> None:
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pages = [_page(1, "abc"), _page(2, "def"), _page(3, "gh")]
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slices = ChunkedMapper.slice_pages(pages, chars_per_slice=20)
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assert [[p.page_number for p in s] for s in slices] == [[1, 2, 3]]
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def test_starts_new_slice_when_budget_exceeded(self) -> None:
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pages = [_page(1, "a" * 6), _page(2, "b" * 6), _page(3, "c" * 6)]
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slices = ChunkedMapper.slice_pages(pages, chars_per_slice=10)
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# 6 + 6 > 10 → break after each page
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assert [[p.page_number for p in s] for s in slices] == [[1], [2], [3]]
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def test_oversized_page_is_its_own_slice(self) -> None:
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"""Page boundaries are never broken: an oversize page becomes its own slice."""
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pages = [_page(1, "small"), _page(2, "x" * 100), _page(3, "tiny")]
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slices = ChunkedMapper.slice_pages(pages, chars_per_slice=10)
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assert [[p.page_number for p in s] for s in slices] == [[1], [2], [3]]
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def test_rejects_non_positive_budget(self) -> None:
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with pytest.raises(ValueError, match="chars_per_slice"):
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ChunkedMapper.slice_pages([_page(1, "x")], chars_per_slice=0)
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class TestFormatChunkContent:
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def test_renders_page_markers(self) -> None:
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rendered = ChunkedMapper.format_chunk_content([_page(2, "two"), _page(3, "three")])
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assert "[Page 2]\ntwo" in rendered
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assert "[Page 3]\nthree" in rendered
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# Blank-line separator between pages
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assert "two\n\n[Page 3]" in rendered
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class TestMapPages:
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@pytest.mark.anyio
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async def test_single_chunk_returns_single_output(self, runtime: AppRuntime) -> None:
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mapper = _build_mapper(runtime, chars_per_slice=1000)
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pages = [_page(1, "alpha"), _page(2, "beta"), _page(3, "gamma")]
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canned = _Extracted(label="one")
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with patch.object(
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mapper._extractor,
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"run",
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AsyncMock(return_value=_StubAgentResult(output=canned)),
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) as run_mock:
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outputs = await mapper.map_pages(pages, "what")
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assert run_mock.await_count == 1
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assert len(outputs) == 1
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assert outputs[0].pages == [1, 2, 3]
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assert outputs[0].output == canned
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assert outputs[0].label == "pages=1-3"
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@pytest.mark.anyio
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async def test_multi_chunk_outputs_are_in_document_order(self, runtime: AppRuntime) -> None:
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"""Outputs are sorted by first covered page regardless of completion order."""
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mapper = _build_mapper(runtime, chars_per_slice=10, concurrency=3)
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pages = [_page(i, "x" * 8) for i in range(1, 4)]
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# Each chunk's worker awaits a release event; we release in reverse
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# order so completion order is the inverse of slice order.
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release = [asyncio.Event() for _ in pages]
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call_index = 0
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async def _gated(*_args: object, **_kwargs: object) -> _StubAgentResult[_Extracted]:
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nonlocal call_index
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mine = call_index
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call_index += 1
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await release[mine].wait()
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return _StubAgentResult(output=_Extracted(label=f"slice-{mine + 1}"))
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async def _release_in_reverse() -> None:
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await asyncio.sleep(0)
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for ev in reversed(release):
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ev.set()
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await asyncio.sleep(0)
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await asyncio.sleep(0)
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with patch.object(mapper._extractor, "run", AsyncMock(side_effect=_gated)):
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task = asyncio.create_task(mapper.map_pages(pages, "anything"))
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await _release_in_reverse()
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outputs = await task
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assert [o.pages for o in outputs] == [[1], [2], [3]]
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@pytest.mark.anyio
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async def test_worker_failure_drops_only_that_chunk(self, runtime: AppRuntime) -> None:
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mapper = _build_mapper(runtime, chars_per_slice=10)
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pages = [_page(i, "x" * 8) for i in range(1, 4)]
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results: list[_Extracted | BaseException] = [
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_Extracted(label="a"),
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RuntimeError("boom"),
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_Extracted(label="c"),
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]
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async def _stub(*_args: object, **_kwargs: object) -> _StubAgentResult[_Extracted]:
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value = results.pop(0)
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if isinstance(value, BaseException):
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raise value
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return _StubAgentResult(output=value)
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with patch.object(mapper._extractor, "run", AsyncMock(side_effect=_stub)):
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outputs = await mapper.map_pages(pages, "anything")
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assert len(outputs) == 2
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assert {o.output.label for o in outputs} == {"a", "c"}
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@pytest.mark.anyio
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async def test_worker_timeout_drops_only_that_chunk(self, runtime: AppRuntime) -> None:
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mapper = _build_mapper(runtime, chars_per_slice=10, worker_timeout_seconds=0.05)
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pages = [_page(i, "x" * 8) for i in range(1, 4)]
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async def _stub(*_args: object, **_kwargs: object) -> _StubAgentResult[_Extracted]:
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# Page 2 hangs forever; pages 1 and 3 return immediately.
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prompt = _args[0]
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assert isinstance(prompt, str)
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if "[Page 2]" in prompt:
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await asyncio.sleep(10)
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return _StubAgentResult(output=_Extracted(label="ok"))
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with patch.object(mapper._extractor, "run", AsyncMock(side_effect=_stub)):
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outputs = await mapper.map_pages(pages, "anything")
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covered = sorted({p for o in outputs for p in o.pages})
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assert covered == [1, 3]
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@pytest.mark.anyio
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async def test_outer_cancellation_drains_pending_tasks(self, runtime: AppRuntime) -> None:
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"""Cancellation propagating in from upstream cancels per-chunk model
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calls rather than letting them keep billing tokens."""
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mapper = _build_mapper(runtime, chars_per_slice=10, concurrency=5)
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pages = [_page(i, "x" * 8) for i in range(1, 5)]
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cancellations = 0
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async def _hang(*_args: object, **_kwargs: object) -> _StubAgentResult[_Extracted]:
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nonlocal cancellations
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try:
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await asyncio.sleep(60)
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except asyncio.CancelledError:
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cancellations += 1
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raise
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return _StubAgentResult(output=_Extracted(label="never"))
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with patch.object(mapper._extractor, "run", AsyncMock(side_effect=_hang)):
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task = asyncio.create_task(mapper.map_pages(pages, "anything"))
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# Yield once so all four workers are blocked on their sleep.
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await asyncio.sleep(0)
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await asyncio.sleep(0)
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task.cancel()
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with pytest.raises(asyncio.CancelledError):
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await task
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assert cancellations == len(pages)
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@pytest.mark.anyio
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async def test_semaphore_caps_concurrency(self, runtime: AppRuntime) -> None:
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"""At most ``concurrency`` workers run at once; with strictly more work
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items than slots the observed max is exactly the configured cap."""
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concurrency = 2
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mapper = _build_mapper(runtime, chars_per_slice=10, concurrency=concurrency)
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pages = [_page(i, "x" * 8) for i in range(1, 6)] # 5 items > 2 slots
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active = 0
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peak = 0
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async def _track(*_args: object, **_kwargs: object) -> _StubAgentResult[_Extracted]:
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nonlocal active, peak
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active += 1
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peak = max(peak, active)
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# Yield enough times that other waiters get a chance to enter.
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for _ in range(5):
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await asyncio.sleep(0)
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active -= 1
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return _StubAgentResult(output=_Extracted(label="ok"))
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with patch.object(mapper._extractor, "run", AsyncMock(side_effect=_track)):
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outputs = await mapper.map_pages(pages, "anything")
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assert len(outputs) == 5
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assert peak == concurrency
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@pytest.mark.anyio
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async def test_rejects_empty_pages(self, runtime: AppRuntime) -> None:
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mapper = _build_mapper(runtime)
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with pytest.raises(ValueError, match="at least one page"):
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await mapper.map_pages([], "anything")
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class TestSummaryCounts:
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"""``summary_counts`` callback feeds the WholeDocSliceDone event's
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excerpts/facts counters from the consumer's extractor output shape
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without the mapper itself duck-typing fields on ``T``."""
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@pytest.mark.anyio
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async def test_default_callback_emits_zero_counts(self, runtime: AppRuntime) -> None:
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"""No callback supplied → events emit ``excerpts=0 facts=0``."""
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from stirling.contracts import WholeDocSliceDone
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from stirling.services import reset_progress_emitter, set_progress_emitter
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mapper = _build_mapper(runtime, chars_per_slice=1000)
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pages = [_page(1, "small")]
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canned = _Extracted(label="ok")
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emitted: list[WholeDocSliceDone] = []
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async def _emit(event: object) -> None:
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if isinstance(event, WholeDocSliceDone):
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emitted.append(event)
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token = set_progress_emitter(_emit)
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try:
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with patch.object(
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mapper._extractor,
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"run",
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AsyncMock(return_value=_StubAgentResult(output=canned)),
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):
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await mapper.map_pages(pages, "q")
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finally:
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reset_progress_emitter(token)
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assert len(emitted) == 1
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assert emitted[0].excerpts == 0
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assert emitted[0].facts == 0
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@pytest.mark.anyio
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async def test_user_callback_drives_counts(self, runtime: AppRuntime) -> None:
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"""A supplied callback receives each chunk's typed output and its
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returned tuple is what the event carries."""
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from stirling.contracts import WholeDocSliceDone
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from stirling.services import reset_progress_emitter, set_progress_emitter
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captured: list[_Extracted] = []
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def _counts(output: _Extracted) -> tuple[int, int]:
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captured.append(output)
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return (3, 7)
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extractor: Agent[None, _Extracted] = Agent(
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model=runtime.fast_model,
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output_type=_Extracted,
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model_settings=runtime.fast_model_settings,
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)
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mapper: ChunkedMapper[_Extracted] = ChunkedMapper(
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runtime,
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extractor=extractor,
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chars_per_slice=1000,
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summary_counts=_counts,
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)
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canned = _Extracted(label="ok")
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emitted: list[WholeDocSliceDone] = []
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async def _emit(event: object) -> None:
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if isinstance(event, WholeDocSliceDone):
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emitted.append(event)
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token = set_progress_emitter(_emit)
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try:
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with patch.object(
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mapper._extractor,
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"run",
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AsyncMock(return_value=_StubAgentResult(output=canned)),
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):
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await mapper.map_pages([_page(1, "small")], "q")
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finally:
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reset_progress_emitter(token)
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assert len(captured) == 1
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assert captured[0].label == "ok"
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assert emitted[0].excerpts == 3
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assert emitted[0].facts == 7
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class TestChunkOutputShape:
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@pytest.mark.anyio
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async def test_single_page_label(self, runtime: AppRuntime) -> None:
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mapper = _build_mapper(runtime, chars_per_slice=5)
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pages = [_page(7, "x" * 6)] # one oversize page → one slice
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canned = _Extracted(label="solo")
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with patch.object(
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mapper._extractor,
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"run",
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AsyncMock(return_value=_StubAgentResult(output=canned)),
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):
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outputs = await mapper.map_pages(pages, "q")
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assert outputs[0].label == "pages=7"
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