"""Explicit opt-in: actual pinned model -> HTTP -> PostgreSQL -> HTTP process restart."""

import os

import pytest
from sqlalchemy import text
from test_runtime import server

from app.services.embedding import SMALL_ZH


@pytest.mark.skipif(
    os.environ.get("RUN_LOCAL_EMBEDDING_TEST") != "1",
    reason="requires optional CPU runtime and offline pinned model cache",
)
def test_real_local_model_vectors_retrieval_and_http_restart(migrated_database):
    options = {
        "EMBEDDING_ENABLED": "true",
        "EMBEDDING_MODEL": "small",
        "EMBEDDING_CACHE_DIR": os.environ["EMBEDDING_CACHE_DIR"],
        "HF_HUB_OFFLINE": "1",
        "HF_HUB_DISABLE_TELEMETRY": "1",
        "HF_HUB_DISABLE_IMPLICIT_TOKEN": "1",
        "TOKENIZERS_PARALLELISM": "false",
    }
    query = {"query": "合成测试中交通安排应查阅哪份资料？"}
    with server(migrated_database, environment_overrides=options) as client:
        ids = []
        for topic, body in [
            ("交通", "【合成测试】占位交通资料介绍测试公交线路与班次的查询入口。"),
            ("住宿", "【合成测试】占位住宿资料介绍测试房型与入住须知。"),
            ("审核", "【合成测试】占位审核资料说明测试出处核验流程。"),
        ]:
            item = client.post(
                "/api/knowledge",
                json={
                    "title": f"{topic}合成测试",
                    "content": body,
                    "source_url": "https://example.com/",
                    "collected_at": "2026-10-16T00:00:00Z",
                },
            ).json()
            response = client.post(
                f"/api/knowledge/{item['id']}/review",
                json={
                    "expected_version": item["version"],
                    "verification_status": "verified",
                    "reviewer": "test-reviewer",
                    "verification_notes": "仅为模型/HTTP/数据库持久化测试，不是事实资料",
                },
            )
            assert response.status_code == 200
            ids.append(item["id"])
        text_ = client.post("/api/indexes", json={"chunk_size": 200, "overlap": 20}).json()
        assert (
            client.post(
                f"/api/indexes/{text_['id']}/publish", json={"expected_generation": 0}
            ).status_code
            == 200
        )
        built = client.post(
            "/api/vector-indexes", json={"text_index_version_id": text_["id"]}, timeout=60
        )
        assert built.status_code == 201, built.text
        version = built.json()
        assert version["space_id"] == SMALL_ZH.space_id and version["dimension"] == 512
        published = client.post(
            f"/api/vector-indexes/{version['id']}/publish", json={"expected_generation": 0}
        )
        assert published.status_code == 200
        active = published.json()
        response = client.post("/api/retrieval/vector", json=query, timeout=60)
        assert response.status_code == 200, response.text
        first = response.json()
        assert {h["knowledge_id"] for h in first["results"]} == set(ids)
        assert all(-1 <= h["score"] <= 1 for h in first["results"])
        assert first["config"]["space"]["spec"]["revision"] == SMALL_ZH.revision
    with migrated_database.connect() as c:
        assert (
            c.scalar(
                text("SELECT count(*) FROM chunk_vectors WHERE public.vector_dims(embedding)=512")
            )
            == 3
        )
        assert c.scalar(text("SELECT count(*) FROM vector_publications")) == 1
    with server(migrated_database, environment_overrides=options) as client:
        assert client.get("/api/vector-indexes/active").json() == active
        assert client.get(f"/api/retrieval/runs/{first['run_id']}").json() == first
        response = client.post("/api/retrieval/vector", json=query, timeout=60)
        assert response.status_code == 200, response.text
        assert response.json()["results"] == first["results"]