from app.core import retriever as r
from app.models.dto import QueryRequest
import app.core.qdrant as qdr
import app.core.qdrant_points as qp
import app.core.graph_adapter as ga
import app.services.embeddings_client as ec


class _DummyClient:
    pass


def _fake_get_client(cfg):
    return _DummyClient()


def _fake_embed_text(text: str):
    # Dimension ist egal, da wir search_chunks_by_vector faken
    return [0.0] * 384


def _fake_search_chunks_by_vector(client, prefix, vector, top=10, filters=None):
    # Drei Treffer mit identischem semantic_score und retriever_weight,
    # aber unterschiedlichen Graph-Boni
    return [
        ("chunk:1", 0.8, {"note_id": "note:1", "chunk_id": "c1", "path": "a.md", "section_title": "S1"}),
        ("chunk:2", 0.8, {"note_id": "note:2", "chunk_id": "c2", "path": "b.md", "section_title": "S2"}),
        ("chunk:3", 0.8, {"note_id": "note:3", "chunk_id": "c3", "path": "c.md", "section_title": "S3"}),
    ]


class _DummySubgraph:
    def __init__(self, edge_scores, cent_scores):
        self._edge = edge_scores
        self._cent = cent_scores

    def edge_bonus(self, node_id: str) -> float:
        return self._edge.get(node_id, 0.0)

    def centrality_bonus(self, node_id: str) -> float:
        return self._cent.get(node_id, 0.0)


def _fake_expand(client, prefix, seeds, depth=1, edge_types=None):
    # Wir gewichten c2 > c1 > c3
    edge_scores = {"c1": 0.1, "c2": 0.5, "c3": 0.0}
    cent_scores = {"c1": 0.05, "c2": 0.2, "c3": 0.0}
    return _DummySubgraph(edge_scores, cent_scores)


def test_hybrid_retrieve_uses_edge_scores(monkeypatch):
    # Qdrant-Client, Embeddings, Chunk-Suche und Graph-Expansion faken
    monkeypatch.setattr(qdr, "get_client", _fake_get_client)
    monkeypatch.setattr(ec, "embed_text", _fake_embed_text)
    monkeypatch.setattr(qp, "search_chunks_by_vector", _fake_search_chunks_by_vector)
    monkeypatch.setattr(ga, "expand", _fake_expand)

    # expand.depth > 0 triggern, edge_types sind hier egal
    req = QueryRequest(mode="hybrid", query="karate", top_k=3, expand={"depth": 1, "edge_types": ["references"]})
    resp = r.hybrid_retrieve(req)

    assert len(resp.results) == 3

    # Da alle semantic_scores und retriever_weight gleich sind,
    # bestimmt die Summe aus edge_bonus + centrality_bonus das Ranking.
    first = resp.results[0]
    second = resp.results[1]
    third = resp.results[2]

    assert first.note_id == "note:2"  # c2 hat höchste Graph-Boni
    assert second.note_id == "note:1"
    assert third.note_id == "note:3"

    assert first.total_score > second.total_score > third.total_score