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49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
from app.core.retrieval import retriever as r
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from app.models.dto import QueryRequest
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import app.core.database.qdrant as qdr
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import app.core.database.qdrant_points as qp
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import app.services.embeddings_client as ec
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class _DummyClient:
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pass
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def _fake_get_client(cfg):
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return _DummyClient()
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def _fake_embed_text(text: str):
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# Dimension egal, da wir search_chunks_by_vector faken
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return [0.0] * 384
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def _fake_search_chunks_by_vector_weights(client, prefix, vector, top=10, filters=None):
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# Zwei Treffer mit unterschiedlichem retriever_weight
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return [
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("chunk:low", 0.9, {"note_id": "note:low", "path": "low.md", "section_title": "S1", "retriever_weight": 0.2}),
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("chunk:high", 0.8, {"note_id": "note:high", "path": "high.md", "section_title": "S2", "retriever_weight": 1.5}),
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]
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def test_retriever_weight_influences_ranking(monkeypatch):
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# Konfiguration: Qdrant & Embedding faken
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monkeypatch.setattr(qdr, "get_client", _fake_get_client)
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monkeypatch.setattr(ec, "embed_text", _fake_embed_text)
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monkeypatch.setattr(qp, "search_chunks_by_vector", _fake_search_chunks_by_vector_weights)
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req = QueryRequest(mode="semantic", query="karate", top_k=2)
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resp = r.semantic_retrieve(req)
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assert len(resp.results) == 2
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first = resp.results[0]
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second = resp.results[1]
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# Semantic Scores: 0.9 (low), 0.8 (high)
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# Total Scores: 0.9 * 0.2 = 0.18 vs. 0.8 * 1.5 = 1.2 -> high sollte oben liegen
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assert first.note_id == "note:high"
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assert second.note_id == "note:low"
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assert first.total_score > second.total_score
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