from fastapi.testclient import TestClient
from app.main import create_app
import app.services.embeddings_client as ec
import app.core.database.qdrant_points as qp
import app.core.graph.graph_subgraph as ga

def _fake_embed_text(text: str):
    # Liefert stabilen 384-d Vektor ohne echtes Modell
    return [0.01] * 384

def _fake_search_chunks_by_vector(client, prefix, vector, top=10, filters=None):
    # einfache Hitliste
    return [
        ("chunk:1", 0.9, {"note_id":"note:1","path":"a.md","section_title":"S1"}),
        ("chunk:2", 0.7, {"note_id":"note:2","path":"b.md","section_title":"S2"}),
        ("chunk:3", 0.5, {"note_id":"note:3","path":"c.md","section_title":"S3"}),
    ]

def _fake_get_edges_for_sources(client, prefix, source_ids, edge_types=None, limit=2048):
    out=[]
    for sid in source_ids:
        out.append({"source_id":sid,"target_id":f"{sid}-x","kind":"references","weight":0.2})
    return out

def test_query_with_text(monkeypatch):
    # Patch Embedding + Qdrant-Aufrufe
    monkeypatch.setattr(ec, "embed_text", _fake_embed_text)
    monkeypatch.setattr(qp, "search_chunks_by_vector", _fake_search_chunks_by_vector)
    monkeypatch.setattr(ga, "get_edges_for_sources", _fake_get_edges_for_sources, raising=False)

    app = create_app()
    with TestClient(app) as c:
        payload = {"mode":"hybrid","query":"karate trainingsplan", "top_k":2,
                   "expand":{"depth":1,"edge_types":["references"]}}
        r = c.post("/query", json=payload)
        assert r.status_code == 200, r.text
        body = r.json()
        assert body["used_mode"] == "hybrid"
        assert len(body["results"]) == 2