YoVinchen
f4ba5d5342
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The Stage 1 foundation now proves provider contracts with mocks and validates PostgreSQL/pgvector ingestion, approval binding, retrieval, reranking, and idempotency using only synthetic data. Live Bailian validation remains gated on rotating the exposed key. Constraint: The key shown in chat is compromised and cannot be used or committed Rejected: Mark Stage 1 complete from mock and offline results | real three-model smoke is still required Confidence: high Scope-risk: moderate Reversibility: clean Directive: Do not enable real-data ingestion until Stage 3 cloud approval and outbound manifest controls are enforced end to end Tested: make verify; 41 pytest tests; strict mypy; Ruff; Compose config; pinned image build; empty-volume migration; role denial; two idempotent 20-vector seeds; database restart persistence Not-tested: Live Bailian calls require a newly rotated key; React product UI is not implemented
48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
import math
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import pytest
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from app.adapters.fake import FakeEmbeddingProvider, FakeReranker
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from app.ports.model_providers import ModelProviderError
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@pytest.mark.asyncio
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async def test_fake_embedding_is_deterministic_normalized_and_1024_dimensional() -> None:
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provider = FakeEmbeddingProvider()
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first = await provider.embed_documents(["斑岩铜矿 钾化 绢英岩化"])
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second = await provider.embed_query("斑岩铜矿 钾化 绢英岩化")
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assert first.vectors[0] == second.vectors[0]
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assert len(first.vectors[0]) == 1024
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assert math.isclose(sum(value * value for value in first.vectors[0]), 1.0)
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@pytest.mark.asyncio
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async def test_fake_reranker_preserves_original_candidate_index() -> None:
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reranker = FakeReranker()
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documents = ["煤层测井对比", "斑岩铜矿钾化带", "铝土矿含矿层"]
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result = await reranker.rerank("斑岩铜矿的钾化带", documents, top_n=2)
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assert result.items[0].index == 1
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assert result.items[0].document == documents[1]
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assert result.items[0].relevance_score >= result.items[1].relevance_score
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@pytest.mark.asyncio
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async def test_fake_providers_reject_inputs_the_live_contract_rejects() -> None:
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embedder = FakeEmbeddingProvider()
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reranker = FakeReranker()
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with pytest.raises(ModelProviderError):
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await embedder.embed_documents([])
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with pytest.raises(ModelProviderError):
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await embedder.embed_documents(["x"] * 11)
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with pytest.raises(ModelProviderError):
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await reranker.rerank("", ["document"], top_n=1)
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with pytest.raises(ModelProviderError):
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await reranker.rerank("query", [], top_n=1)
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with pytest.raises(ModelProviderError):
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await reranker.rerank("query", ["document"], top_n=0)
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