Initial curated RAGcore source from c16667f0c9397e2bfc8dd0255b114fba23c8d05e

tests/evaluation/framework/test_runner.py

@@ -0,0 +1,119 @@
+"""EvaluationRunner: offline fixture-corpus runs with reproducible results."""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime
+from pathlib import Path
+from uuid import uuid4
+
+import pytest
+
+from ragcore.evaluation import (
+    AnswerabilityStatus,
+    EvaluationCase,
+    EvaluationDataset,
+    EvaluationDatasetError,
+    EvaluationRunner,
+    FixtureCorpusRetriever,
+    RetrievalRunProfile,
+)
+
+REPOSITORY_ROOT = Path(__file__).resolve().parents[3]
+
+
+def make_dataset() -> tuple[EvaluationDataset, dict]:
+    matched_chunk, missing_chunk, unexpected_chunk = uuid4(), uuid4(), uuid4()
+    case = EvaluationCase(
+        id="case-1",
+        question="What is the timeout?",
+        language="en",
+        expected_answerability=AnswerabilityStatus.ANSWERABLE,
+        expected_document_ids=(uuid4(),),
+        expected_chunk_ids=(matched_chunk, missing_chunk),
+        required_facts=("the timeout is bounded",),
+    )
+    dataset = EvaluationDataset(id="offline", version="v1", cases=(case,))
+    corpus = {"case-1": (matched_chunk, unexpected_chunk)}
+    return dataset, {
+        "corpus": corpus,
+        "matched": matched_chunk,
+        "missing": missing_chunk,
+        "unexpected": unexpected_chunk,
+    }
+
+
+def make_profile() -> RetrievalRunProfile:
+    return RetrievalRunProfile(
+        embedding_profile_digest="a" * 64,
+        sparse_profile_version="lexical-v1",
+        index_alias="rag_dense_hybrid_active",
+        index_collection_version="v1",
+    )
+
+
+async def test_runner_works_offline_with_a_fixture_corpus() -> None:
+    dataset, ids = make_dataset()
+    retriever = FixtureCorpusRetriever(ids["corpus"])
+    runner = EvaluationRunner(retriever, clock=lambda: datetime(2026, 8, 2, tzinfo=UTC))
+
+    run = await runner.run(dataset, run_profile=make_profile())
+
+    assert run.dataset_id == "offline"
+    assert run.dataset_version == "v1"
+    assert len(run.case_results) == 1
+    result = run.case_results[0]
+    assert result.matched_expected_ids == (ids["matched"],)
+    assert result.missing_expected_ids == (ids["missing"],)
+    assert result.unexpected_ids == (ids["unexpected"],)
+
+
+async def test_run_records_profile_model_and_index_revisions() -> None:
+    dataset, ids = make_dataset()
+    runner = EvaluationRunner(FixtureCorpusRetriever(ids["corpus"]))
+    profile = RetrievalRunProfile(
+        embedding_profile_digest="b" * 64,
+        sparse_profile_version="lexical-v2",
+        index_alias="rag_dense_hybrid_active",
+        index_collection_version="v3",
+        reranker_profile_digest="c" * 64,
+    )
+
+    run = await runner.run(dataset, run_profile=profile)
+
+    assert run.run_profile.embedding_profile_digest == "b" * 64
+    assert run.run_profile.sparse_profile_version == "lexical-v2"
+    assert run.run_profile.index_collection_version == "v3"
+    assert run.run_profile.reranker_profile_digest == "c" * 64
+
+
+async def test_results_are_reproducible_given_identical_inputs() -> None:
+    dataset, ids = make_dataset()
+    run_id = uuid4()
+    fixed_time = datetime(2026, 8, 2, 9, tzinfo=UTC)
+    runner_a = EvaluationRunner(FixtureCorpusRetriever(ids["corpus"]), clock=lambda: fixed_time)
+    runner_b = EvaluationRunner(FixtureCorpusRetriever(ids["corpus"]), clock=lambda: fixed_time)
+
+    run_a = await runner_a.run(dataset, run_profile=make_profile(), run_id=run_id)
+    run_b = await runner_b.run(dataset, run_profile=make_profile(), run_id=run_id)
+
+    assert run_a == run_b
+
+
+async def test_runner_rejects_a_retriever_exceeding_the_bounded_limit() -> None:
+    dataset, _ids = make_dataset()
+    oversized = {"case-1": tuple(uuid4() for _ in range(201))}
+    # FixtureCorpusRetriever itself would reject this at construction time; a
+    # raw retriever proves the runner independently enforces the limit too,
+    # regardless of which Reranker-style implementation is plugged in.
+    runner = EvaluationRunner(_RawRetriever(oversized))
+
+    with pytest.raises(EvaluationDatasetError, match="bounded limit"):
+        await runner.run(dataset, run_profile=make_profile())
+
+
+class _RawRetriever:
+    def __init__(self, corpus: dict) -> None:
+        self._corpus = corpus
+
+    async def retrieve(self, case: EvaluationCase) -> tuple:
+        return self._corpus.get(case.id, ())