from __future__ import annotations

import sys
from pathlib import Path


ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(ROOT / "scripts"))

import search_1c_semantic_cache as semantic_search  # noqa: E402


def test_search_semantic_cache_sends_query_embedding_and_validation(monkeypatch) -> None:
    seen: dict = {}

    def fake_adapter_call(adapter_url: str, method: str, payload: dict, *, timeout_seconds: int = 180) -> dict:
        seen["adapter_url"] = adapter_url
        seen["method"] = method
        seen["payload"] = payload
        return {
            "status": "ok",
            "matches": [
                {
                    "document_id": "doc-1",
                    "score": 0.9,
                    "match_by": "vector_embedding",
                    "object": {"kind": "Template", "name": "ПФ_MXL"},
                    "cache": {"embedding_model": "local-hashing-v1", "vector_status": "embedded"},
                }
            ],
        }

    monkeypatch.setattr(semantic_search, "adapter_call", fake_adapter_call)

    result = semantic_search.search_semantic_cache(
        adapter_url="http://adapter/rpc",
        base_id="upo_test",
        query="ОбластьШапка",
        kind="Template",
        limit=3,
        dimensions=12,
        validate_candidates=True,
        validation_limit=2,
    )

    assert result["status"] == "ok"
    assert result["client_embedding"]["dimensions"] == 12
    assert seen["method"] == "semantic.cache.search"
    assert seen["payload"]["base_id"] == "upo_test"
    assert seen["payload"]["query"] == "ОбластьШапка"
    assert seen["payload"]["kind"] == "Template"
    assert seen["payload"]["limit"] == 3
    assert seen["payload"]["validate_candidates"] is True
    assert seen["payload"]["validation_limit"] == 2
    assert isinstance(seen["payload"]["query_embedding"], list)
    assert len(seen["payload"]["query_embedding"]) == 12


def test_search_semantic_cache_can_embed_pending_before_search(monkeypatch) -> None:
    calls: list[str] = []

    def fake_embed_pending_semantic_cache(**kwargs) -> dict:
        calls.append("embed")
        assert kwargs["base_id"] == "upo_test"
        assert kwargs["kind"] == "Template"
        assert kwargs["limit"] == 7
        assert kwargs["batch_size"] == 3
        return {"status": "ok", "counts": {"pending": 1, "processed": 1, "stored": 1}, "embedding": {"model": "local-hashing-v1"}}

    def fake_adapter_call(adapter_url: str, method: str, payload: dict, *, timeout_seconds: int = 180) -> dict:
        calls.append(method)
        return {"status": "ok", "matches": []}

    monkeypatch.setattr(semantic_search, "embed_pending_semantic_cache", fake_embed_pending_semantic_cache)
    monkeypatch.setattr(semantic_search, "adapter_call", fake_adapter_call)

    result = semantic_search.search_semantic_cache(
        adapter_url="http://adapter/rpc",
        base_id="upo_test",
        query="ОбластьШапка",
        kind="Template",
        dimensions=8,
        embed_pending=True,
        embed_limit=7,
        embed_batch_size=3,
    )

    assert calls == ["embed", "semantic.cache.search"]
    assert result["embedding_refresh"]["counts"]["stored"] == 1