"""Tests for EPUB search HTTP model adapters."""

from __future__ import annotations

from typing import TYPE_CHECKING

import httpx
import pytest

from python.ebook_search.answer import answer_query
from python.ebook_search.config import EbookSearchConfig, RerankConfig
from python.ebook_search.embeddings import embed_texts
from python.ebook_search.search import SearchResult

if TYPE_CHECKING:
    from pytest_mock import MockerFixture


def make_async_client(mocker: MockerFixture, fake_post) -> httpx.AsyncClient:
    """Build a mock async client whose post call is served by fake_post."""
    client = mocker.MagicMock(spec=httpx.AsyncClient)
    client.post = mocker.AsyncMock(side_effect=fake_post)
    return client


async def test_answer_query_uses_httpx_chat_completions(mocker: MockerFixture) -> None:
    captured: dict[str, object] = {}

    def fake_post(url: str, **kwargs: object) -> httpx.Response:
        captured["url"] = url
        captured["kwargs"] = kwargs
        return httpx.Response(
            200,
            json={"choices": [{"message": {"content": "grounded answer"}}]},
            request=httpx.Request("POST", url),
        )

    client = make_async_client(mocker, fake_post)
    config = EbookSearchConfig(
        rerank=RerankConfig(enabled=False),
        vllm_base_url="https://ollama.com/v1",
        vllm_api_key="secret",
        chat_model="deepseek-v4-flash",
    )

    results = [SearchResult(chunk_id=1, text="source", source_title="Book")]
    answer = await answer_query(client, "question", results, config)

    assert answer == "grounded answer"
    assert captured["url"] == "https://ollama.com/v1/chat/completions"
    kwargs = captured["kwargs"]
    assert isinstance(kwargs, dict)
    assert kwargs["headers"] == {"Authorization": "Bearer secret"}
    payload = kwargs["json"]
    assert isinstance(payload, dict)
    assert payload["model"] == "deepseek-v4-flash"


async def test_embed_texts_uses_httpx_embeddings(mocker: MockerFixture) -> None:
    captured: dict[str, object] = {}
    vector = [0.0] * 1024

    def fake_post(url: str, **kwargs: object) -> httpx.Response:
        captured["url"] = url
        captured["kwargs"] = kwargs
        return httpx.Response(
            200,
            json={"data": [{"embedding": vector}]},
            request=httpx.Request("POST", url),
        )

    client = make_async_client(mocker, fake_post)
    config = EbookSearchConfig(
        rerank=RerankConfig(enabled=False),
        embedding_base_url="http://bob:8000/v1",
        embedding_model="qwen3-embedding-0.6b",
    )

    embeddings = await embed_texts(client, ["hello"], config)

    assert embeddings == [vector]
    assert captured["url"] == "http://bob:8000/v1/embeddings"
    kwargs = captured["kwargs"]
    assert isinstance(kwargs, dict)
    assert kwargs["headers"] == {}
    assert kwargs["json"] == {"model": "qwen3-embedding-0.6b", "input": ["hello"]}


async def test_embed_texts_rejects_bad_response_shape(mocker: MockerFixture) -> None:
    def fake_post(url: str, **_kwargs: object) -> httpx.Response:
        return httpx.Response(200, json={"data": [{}]}, request=httpx.Request("POST", url))

    client = make_async_client(mocker, fake_post)
    config = EbookSearchConfig(rerank=RerankConfig(enabled=False))

    with pytest.raises(RuntimeError, match="Embedding request failed"):
        await embed_texts(client, ["hello"], config)