Implement corpus indexing and MCP search

tests/test_search.py

@@ -0,0 +1,106 @@
+from pathlib import Path
+
+import pytest
+
+from corpus_mcp.database import (
+    CorpusError,
+    build_database,
+    read_document_excerpt,
+    search_database,
+)
+
+
+def test_plain_search_returns_highlighted_excerpt(corpus: tuple[Path, Path]) -> None:
+    _, database = corpus
+
+    result = search_database(database, "quick search")
+
+    assert result["syntax"] == "plain"
+    assert [match["path"] for match in result["matches"]] == ["guide.md"]
+    excerpt = result["matches"][0]["excerpts"][0]
+    assert "<match>quick</match>" in excerpt["text"]
+    assert "<match>search</match>" in excerpt["text"]
+
+
+def test_fts5_search_supports_phrases_and_rejects_invalid_syntax(
+    corpus: tuple[Path, Path],
+) -> None:
+    _, database = corpus
+
+    result = search_database(database, '"full text"', syntax="fts5")
+    assert result["matches"][0]["path"] == "guide.md"
+
+    with pytest.raises(CorpusError, match="Invalid FTS5 query"):
+        search_database(database, '"unterminated', syntax="fts5")
+
+
+def test_search_returns_separate_excerpts_from_a_large_document(tmp_path: Path) -> None:
+    source = tmp_path / "source"
+    source.mkdir()
+    content = "needle first\n\n" + ("filler " * 800) + "\n\nneedle second"
+    (source / "large.md").write_text(content, encoding="utf-8")
+    database = tmp_path / "corpus.sqlite"
+    build_database(source, database)
+
+    result = search_database(database, "needle", excerpts_per_document=2)
+
+    excerpts = result["matches"][0]["excerpts"]
+    assert len(excerpts) == 2
+    assert excerpts[0]["chunk_end_char"] <= len(content)
+    assert excerpts[1]["chunk_end_char"] <= len(content)
+
+
+def test_large_document_does_not_hide_other_matching_documents(
+    tmp_path: Path,
+) -> None:
+    source = tmp_path / "source"
+    source.mkdir()
+    (source / "large.txt").write_text(("common term " * 200_000), encoding="utf-8")
+    (source / "small.txt").write_text("common term", encoding="utf-8")
+    database = tmp_path / "corpus.sqlite"
+    build_database(source, database)
+
+    result = search_database(database, "common", limit=2)
+
+    assert {match["path"] for match in result["matches"]} == {
+        "large.txt",
+        "small.txt",
+    }
+
+
+def test_read_document_excerpt_is_bounded_and_navigable(
+    corpus: tuple[Path, Path],
+) -> None:
+    _, database = corpus
+
+    first = read_document_excerpt(database, "guide.md", max_chars=12)
+    assert first["next_offset"] is not None
+    second = read_document_excerpt(
+        database, "guide.md", offset=first["next_offset"], max_chars=12
+    )
+
+    assert len(first["text"]) == 12
+    assert first["previous_offset"] is None
+    assert second["start_char"] == 12
+    assert second["previous_offset"] == 0
+
+
+@pytest.mark.parametrize(
+    ("kwargs", "message"),
+    [
+        ({"offset": -1}, "offset must not be negative"),
+        ({"max_chars": 20_001}, "max_chars must be between"),
+    ],
+)
+def test_read_document_excerpt_validates_bounds(
+    corpus: tuple[Path, Path], kwargs: dict, message: str
+) -> None:
+    _, database = corpus
+    with pytest.raises(CorpusError, match=message):
+        read_document_excerpt(database, "guide.md", **kwargs)
+
+
+def test_search_validates_empty_query(corpus: tuple[Path, Path]) -> None:
+    _, database = corpus
+    with pytest.raises(CorpusError, match="must not be empty"):
+        search_database(database, "  ")