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75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
from app.core import retriever as r
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from app.models.dto import QueryRequest
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import app.core.qdrant as qdr
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import app.core.qdrant_points as qp
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import app.core.graph_adapter as ga
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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 ist 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(client, prefix, vector, top=10, filters=None):
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# Drei Treffer mit identischem semantic_score und retriever_weight,
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# aber unterschiedlichen Graph-Boni
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return [
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("chunk:1", 0.8, {"note_id": "note:1", "chunk_id": "c1", "path": "a.md", "section_title": "S1"}),
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("chunk:2", 0.8, {"note_id": "note:2", "chunk_id": "c2", "path": "b.md", "section_title": "S2"}),
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("chunk:3", 0.8, {"note_id": "note:3", "chunk_id": "c3", "path": "c.md", "section_title": "S3"}),
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]
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class _DummySubgraph:
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def __init__(self, edge_scores, cent_scores):
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self._edge = edge_scores
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self._cent = cent_scores
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def edge_bonus(self, node_id: str) -> float:
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return self._edge.get(node_id, 0.0)
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def centrality_bonus(self, node_id: str) -> float:
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return self._cent.get(node_id, 0.0)
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def _fake_expand(client, prefix, seeds, depth=1, edge_types=None):
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# Wir gewichten c2 > c1 > c3
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edge_scores = {"c1": 0.1, "c2": 0.5, "c3": 0.0}
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cent_scores = {"c1": 0.05, "c2": 0.2, "c3": 0.0}
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return _DummySubgraph(edge_scores, cent_scores)
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def test_hybrid_retrieve_uses_edge_scores(monkeypatch):
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# Qdrant-Client, Embeddings, Chunk-Suche und Graph-Expansion 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)
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monkeypatch.setattr(ga, "expand", _fake_expand)
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# expand.depth > 0 triggern, edge_types sind hier egal
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req = QueryRequest(mode="hybrid", query="karate", top_k=3, expand={"depth": 1, "edge_types": ["references"]})
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resp = r.hybrid_retrieve(req)
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assert len(resp.results) == 3
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# Da alle semantic_scores und retriever_weight gleich sind,
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# bestimmt die Summe aus edge_bonus + centrality_bonus das Ranking.
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first = resp.results[0]
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second = resp.results[1]
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third = resp.results[2]
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assert first.note_id == "note:2" # c2 hat höchste Graph-Boni
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assert second.note_id == "note:1"
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assert third.note_id == "note:3"
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assert first.total_score > second.total_score > third.total_score
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