"""检索模块测试."""
import tempfile

import pytest

from src.core.config import EmbedConfig
from src.core.db import VectorDB
from src.core.embedder import create_embedder
from src.core.ingest import DocumentIngestor
from src.core.search import Searcher


@pytest.fixture
def searcher():
    """创建带测试数据的 Searcher."""
    tmpdir = tempfile.mkdtemp()
    db = VectorDB(persist_dir=tmpdir)
    embedder = create_embedder(EmbedConfig(mode="local"))
    ingestor = DocumentIngestor(db, embedder, "test_search")

    # 入库一些测试文档
    content = """# Python 入门
Python 是一种解释型编程语言。

## 安装 Python
从 python.org 下载安装包。

# 向量数据库
ChromaDB 是一个轻量级向量数据库。

## ChromaDB 安装
使用 pip install chromadb 安装。"""
    ingestor.ingest_content(content, "guide.md")

    return Searcher(db, embedder, "test_search")


class TestSearcher:
    """检索器测试."""

    def test_search_returns_results(self, searcher):
        """搜索返回至少一条结果."""
        results = searcher.search("Python 编程", top_k=3)
        assert len(results) > 0
        for r in results:
            assert r["content"]
            assert r["source_file"]
            assert "score" in r

    def test_search_scores_are_descending(self, searcher):
        """搜索结果按相似度降序排列."""
        results = searcher.search("向量数据库", top_k=5)
        scores = [r["score"] for r in results]
        assert scores == sorted(scores, reverse=True)

    def test_search_respects_top_k(self, searcher):
        """top_k 参数限制返回数量."""
        results = searcher.search("安装", top_k=2)
        assert len(results) <= 2

    def test_search_returns_all_fields(self, searcher):
        """搜索结果包含完整字段."""
        results = searcher.search("ChromaDB", top_k=1)
        if results:
            r = results[0]
            assert "content" in r
            assert "source_file" in r
            assert "section_title" in r
            assert "heading_level" in r
            assert "chunk_index" in r
            assert "score" in r

    def test_search_no_results(self, searcher):
        """无语义匹配时不崩溃."""
        results = searcher.search("xyzxyz不存在的内容abcabc", top_k=3)
        assert isinstance(results, list)

    def test_list_sources(self, searcher):
        """list_sources 返回已入库的源文件列表."""
        sources = searcher.list_sources()
        assert isinstance(sources, list)

    def test_get_collection_info(self, searcher):
        """get_collection_info 返回 collection 信息."""
        info = searcher.get_collection_info()
        assert info["name"] == "test_search"
        assert info["count"] > 0

    def test_delete_by_source(self, searcher):
        """delete_by_source 删除源文件的所有 chunks."""
        sources_before = searcher.list_sources()
        if sources_before:
            target = sources_before[0]
            result = searcher.delete_by_source(target)
            assert result is True
            sources_after = searcher.list_sources()
            assert target not in sources_after

    def test_search_with_source_filter(self, searcher):
        """带 source_file 过滤的搜索."""
        sources = searcher.list_sources()
        if sources:
            results = searcher.search("测试", top_k=3, source_file=sources[0])
            assert isinstance(results, list)
            for r in results:
                assert r["source_file"] == sources[0]

    def test_delete_by_source_nonexistent(self, searcher):
        """删除不存在的源文件返回 False."""
        result = searcher.delete_by_source("nonexistent_file_xyz.md")
        assert result is False

    def test_list_sources_empty_collection(self, tmp_path):
        """空 collection 的 list_sources 返回空列表."""
        from src.core.config import EmbedConfig
        from src.core.db import VectorDB
        from src.core.embedder import create_embedder
        from src.core.search import Searcher

        db = VectorDB(persist_dir=str(tmp_path))
        embedder = create_embedder(EmbedConfig(mode="local"))
        searcher = Searcher(db, embedder, "empty_coll")
        sources = searcher.list_sources()
        assert sources == []


def test_list_sources_empty():
    """空 collection 的 list_sources 返回空列表."""
    from src.core.search import Searcher

    class EmptyColl:
        def count(self): return 0
        def get(self, **kwargs):
            return {"ids": [], "documents": [], "metadatas": []}

    class EmptyDB:
        def get_or_create_collection(self, name): return EmptyColl()
        def list_collections(self): return []

    class FakeEmb:
        @property
        def dimension(self): return 4
        def embed(self, texts): return [[0.0] * 4]

    s = Searcher(EmptyDB(), FakeEmb(), "empty")
    assert s.list_sources() == []