381 lines
14 KiB
Python
381 lines
14 KiB
Python
"""Tests for EPUB search core helpers."""
|
|
|
|
from __future__ import annotations
|
|
|
|
from dataclasses import replace
|
|
from datetime import UTC, datetime
|
|
from os import environ
|
|
from pathlib import Path
|
|
from threading import Event
|
|
from types import ModuleType
|
|
|
|
import pytest
|
|
from sqlalchemy import create_engine, select
|
|
from sqlalchemy.orm import sessionmaker
|
|
|
|
from python.ebook_search.answer import answer_query
|
|
from python.ebook_search.bm25_corpus import (
|
|
BM25Corpus,
|
|
BM25CorpusUnavailableError,
|
|
BM25Manifest,
|
|
ensure_bm25_corpus,
|
|
load_bm25_corpus,
|
|
)
|
|
from python.ebook_search.config import EbookSearchConfig, RerankConfig, load_config, normalize_embedding_model
|
|
from python.ebook_search.embeddings import MODEL_DIMENSIONS, ensure_embedding_models
|
|
from python.ebook_search.ingest import chunk_text, find_existing_source
|
|
from python.ebook_search.search import (
|
|
SearchResponse,
|
|
SearchResult,
|
|
bm25_candidates,
|
|
reciprocal_rank_fusion,
|
|
retrieval_query_from_text,
|
|
search_ebooks,
|
|
)
|
|
from python.ebook_search.timing import RuntimeStep
|
|
from python.orm.richie import EbookEmbeddingModel, EbookSource, RichieBase
|
|
|
|
|
|
def test_chunk_text_uses_overlap() -> None:
|
|
chunks = chunk_text(" ".join(str(index) for index in range(100)), chunk_tokens=20, overlap_tokens=5)
|
|
|
|
assert len(chunks) > 1
|
|
assert chunks[0].token_start == 0
|
|
assert chunks[1].token_start == 15
|
|
assert all(chunk.token_count <= 20 for chunk in chunks)
|
|
|
|
|
|
def test_reciprocal_rank_fusion_combines_vector_and_bm25_rankings() -> None:
|
|
vector_results = [
|
|
SearchResult(chunk_id=1, text="a", source_title="A", score=0.9, vector_score=0.9),
|
|
SearchResult(chunk_id=2, text="b", source_title="B", score=0.8, vector_score=0.8),
|
|
]
|
|
lexical_results = [
|
|
SearchResult(chunk_id=2, text="b", source_title="B", score=4.2, bm25_score=4.2),
|
|
SearchResult(chunk_id=3, text="c", source_title="C", score=2.1, bm25_score=2.1),
|
|
]
|
|
|
|
fused = reciprocal_rank_fusion(vector_results, lexical_results)
|
|
|
|
assert [result.chunk_id for result in fused] == [2, 1, 3]
|
|
assert fused[0].rank_source == "Hybrid"
|
|
assert fused[0].vector_score == 0.8
|
|
assert fused[0].bm25_score == 4.2
|
|
assert fused[0].fused_score == fused[0].score
|
|
|
|
|
|
def test_find_existing_source_matches_path_or_hash() -> None:
|
|
engine = create_engine("sqlite+pysqlite:///:memory:", future=True)
|
|
RichieBase.metadata.create_all(engine)
|
|
with sessionmaker(bind=engine, expire_on_commit=False, future=True)() as session:
|
|
source = EbookSource(
|
|
title="Book",
|
|
author=None,
|
|
language=None,
|
|
publisher=None,
|
|
identifier=None,
|
|
file_path="/old/book.epub",
|
|
file_sha256="a" * 64,
|
|
file_mtime=datetime.now(tz=UTC),
|
|
file_size=10,
|
|
)
|
|
session.add(source)
|
|
session.commit()
|
|
|
|
assert find_existing_source(session, Path("/old/book.epub"), "b" * 64) == source
|
|
assert find_existing_source(session, Path("/new/book.epub"), "a" * 64) == source
|
|
|
|
|
|
def test_reciprocal_rank_fusion_marks_hybrid_source() -> None:
|
|
vector_results = [SearchResult(chunk_id=1, text="a", source_title="A")]
|
|
lexical_results = [SearchResult(chunk_id=2, text="b", source_title="B")]
|
|
|
|
fused = reciprocal_rank_fusion(vector_results, lexical_results)
|
|
|
|
assert {result.rank_source for result in fused} == {"Hybrid"}
|
|
|
|
|
|
def test_search_response_sums_runtime_steps() -> None:
|
|
response = SearchResponse(
|
|
query="query",
|
|
results=[],
|
|
rank_label="Hybrid",
|
|
timings=(
|
|
RuntimeStep(name="A", duration_ms=1.25),
|
|
RuntimeStep(name="B", duration_ms=2.75),
|
|
RuntimeStep(name="Parallel detail", duration_ms=10.0, counts_toward_total=False),
|
|
),
|
|
)
|
|
|
|
assert response.total_runtime_ms == 4.0
|
|
|
|
|
|
def test_search_ebooks_runs_vector_and_bm25_in_parallel(monkeypatch) -> None:
|
|
engine = create_engine("sqlite+pysqlite:///:memory:", future=True)
|
|
vector_started = Event()
|
|
bm25_started = Event()
|
|
received_engines: list[object] = []
|
|
|
|
def fake_vector_candidates(received_engine, query, _config):
|
|
"""Return vector candidates after confirming BM25 has started."""
|
|
received_engines.append(received_engine)
|
|
assert query == "parallel"
|
|
vector_started.set()
|
|
assert bm25_started.wait(timeout=2)
|
|
return [SearchResult(chunk_id=1, text="vector", source_title="Vector", vector_score=0.9)]
|
|
|
|
def fake_bm25_candidates(query, _config):
|
|
"""Return BM25 candidates after confirming vector search has started."""
|
|
assert query == "parallel"
|
|
bm25_started.set()
|
|
assert vector_started.wait(timeout=2)
|
|
return [SearchResult(chunk_id=2, text="bm25", source_title="BM25", bm25_score=2.0)]
|
|
|
|
monkeypatch.setattr("python.ebook_search.search.vector_candidates", fake_vector_candidates)
|
|
monkeypatch.setattr("python.ebook_search.search.bm25_candidates", fake_bm25_candidates)
|
|
config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
|
|
|
|
response = search_ebooks(engine, "parallel", config)
|
|
|
|
timings = {step.name: step for step in response.timings}
|
|
assert [result.chunk_id for result in response.results] == [1, 2]
|
|
assert timings["Embedding + vector search"].counts_toward_total is False
|
|
assert timings["BM25 search"].counts_toward_total is False
|
|
assert timings["Hybrid retrieval"].counts_toward_total is True
|
|
assert received_engines == [engine]
|
|
|
|
|
|
def test_retrieval_query_keeps_entity_and_series_terms() -> None:
|
|
assert retrieval_query_from_text("what does Damien Montgomery stand for in starship mage") == (
|
|
"damien montgomery stand starship mage"
|
|
)
|
|
|
|
|
|
def test_bm25_candidates_scores_whole_corpus(monkeypatch) -> None:
|
|
record = {
|
|
"chunk_id": 2,
|
|
"text": "high",
|
|
"source_title": "B",
|
|
"source_author": None,
|
|
"chapter_title": None,
|
|
"page_label": None,
|
|
"bm25_text": "high",
|
|
}
|
|
manifest = BM25Manifest(created_at=datetime.now(tz=UTC), db_updated_at=None, chunk_count=1)
|
|
corpus = BM25Corpus(retriever=object(), records=(record,), manifest=manifest)
|
|
captured: dict[str, object] = {}
|
|
|
|
def fake_score_bm25_corpus(query, saved_corpus, *, limit):
|
|
captured["query"] = query
|
|
captured["corpus"] = saved_corpus
|
|
captured["limit"] = limit
|
|
return [(record, 1.5)]
|
|
|
|
monkeypatch.setattr("python.ebook_search.search.load_bm25_corpus", lambda _config: corpus)
|
|
monkeypatch.setattr("python.ebook_search.search.score_bm25_corpus", fake_score_bm25_corpus)
|
|
config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
|
|
|
|
results = bm25_candidates("high", config)
|
|
|
|
assert captured["query"] == "high"
|
|
assert captured["corpus"] == corpus
|
|
assert captured["limit"] == 120
|
|
assert [result.chunk_id for result in results] == [2]
|
|
assert [result.bm25_score for result in results] == [1.5]
|
|
|
|
|
|
def test_bm25_candidates_raises_when_corpus_is_unavailable(monkeypatch) -> None:
|
|
def fake_load_bm25_corpus(_config):
|
|
raise BM25CorpusUnavailableError
|
|
|
|
monkeypatch.setattr("python.ebook_search.search.load_bm25_corpus", fake_load_bm25_corpus)
|
|
config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
|
|
|
|
with pytest.raises(BM25CorpusUnavailableError):
|
|
bm25_candidates("high", config)
|
|
|
|
|
|
def test_load_bm25_corpus_caches_disk_load(monkeypatch, tmp_path) -> None:
|
|
load_bm25_corpus.cache_clear()
|
|
manifest = BM25Manifest(created_at=datetime.now(tz=UTC), db_updated_at=None, chunk_count=1)
|
|
record = {
|
|
"chunk_id": 2,
|
|
"text": "cached",
|
|
"source_title": "B",
|
|
"source_author": None,
|
|
"chapter_title": None,
|
|
"page_label": None,
|
|
"bm25_text": "cached",
|
|
}
|
|
load_count = 0
|
|
|
|
class FakeRetriever:
|
|
"""Fake persisted BM25 retriever."""
|
|
|
|
corpus = (record,)
|
|
|
|
class FakeBM25:
|
|
"""Fake BM25 class with observable load count."""
|
|
|
|
@staticmethod
|
|
def load(index_path, *, load_corpus, mmap):
|
|
nonlocal load_count
|
|
load_count += 1
|
|
assert index_path == tmp_path
|
|
assert load_corpus is True
|
|
assert mmap is True
|
|
return FakeRetriever()
|
|
|
|
fake_bm25s = ModuleType("bm25s")
|
|
fake_bm25s.BM25 = FakeBM25
|
|
monkeypatch.setattr("python.ebook_search.bm25_corpus.read_bm25_manifest", lambda _path: manifest)
|
|
monkeypatch.setattr("python.ebook_search.bm25_corpus.bm25_index_exists", lambda _path, _manifest: True)
|
|
monkeypatch.setattr("python.ebook_search.bm25_corpus.bm25s", fake_bm25s)
|
|
config = EbookSearchConfig(rerank=RerankConfig(enabled=False), bm25_index_dir=str(tmp_path))
|
|
|
|
try:
|
|
first = load_bm25_corpus(config)
|
|
second = load_bm25_corpus(config)
|
|
finally:
|
|
load_bm25_corpus.cache_clear()
|
|
|
|
assert first is second
|
|
assert first is not None
|
|
assert first.records == (record,)
|
|
assert load_count == 1
|
|
|
|
|
|
def test_load_bm25_corpus_raises_when_index_is_missing(monkeypatch, tmp_path) -> None:
|
|
load_bm25_corpus.cache_clear()
|
|
monkeypatch.setattr("python.ebook_search.bm25_corpus.read_bm25_manifest", lambda _path: None)
|
|
monkeypatch.setattr("python.ebook_search.bm25_corpus.bm25_index_exists", lambda _path, _manifest: False)
|
|
config = EbookSearchConfig(rerank=RerankConfig(enabled=False), bm25_index_dir=str(tmp_path))
|
|
|
|
try:
|
|
with pytest.raises(BM25CorpusUnavailableError, match="BM25 corpus is not available"):
|
|
load_bm25_corpus(config)
|
|
finally:
|
|
load_bm25_corpus.cache_clear()
|
|
|
|
|
|
def test_ensure_bm25_corpus_refreshes_missing_index(monkeypatch) -> None:
|
|
refreshed: list[object] = []
|
|
db_updated_at = datetime.now(tz=UTC)
|
|
|
|
monkeypatch.setattr("python.ebook_search.bm25_corpus.read_bm25_manifest", lambda _path: None)
|
|
monkeypatch.setattr("python.ebook_search.bm25_corpus.bm25_index_exists", lambda _path, _manifest: False)
|
|
monkeypatch.setattr("python.ebook_search.bm25_corpus.corpus_last_updated_at", lambda _session: db_updated_at)
|
|
monkeypatch.setattr(
|
|
"python.ebook_search.bm25_corpus.refresh_bm25_corpus",
|
|
lambda session, config, *, db_updated_at: refreshed.append((session, config, db_updated_at)),
|
|
)
|
|
|
|
config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
|
|
session = object()
|
|
|
|
ensure_bm25_corpus(session, config)
|
|
|
|
assert refreshed == [(session, config, db_updated_at)]
|
|
|
|
|
|
def test_ensure_bm25_corpus_refreshes_stale_index(monkeypatch) -> None:
|
|
refreshed: list[object] = []
|
|
created_at = datetime(2026, 1, 1, tzinfo=UTC)
|
|
db_updated_at = datetime(2026, 1, 2, tzinfo=UTC)
|
|
manifest = BM25Manifest(created_at=created_at, db_updated_at=created_at, chunk_count=10)
|
|
|
|
monkeypatch.setattr("python.ebook_search.bm25_corpus.read_bm25_manifest", lambda _path: manifest)
|
|
monkeypatch.setattr("python.ebook_search.bm25_corpus.bm25_index_exists", lambda _path, _manifest: True)
|
|
monkeypatch.setattr("python.ebook_search.bm25_corpus.corpus_last_updated_at", lambda _session: db_updated_at)
|
|
monkeypatch.setattr(
|
|
"python.ebook_search.bm25_corpus.refresh_bm25_corpus",
|
|
lambda session, config, *, db_updated_at: refreshed.append((session, config, db_updated_at)),
|
|
)
|
|
|
|
config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
|
|
session = object()
|
|
|
|
ensure_bm25_corpus(session, config)
|
|
|
|
assert refreshed == [(session, config, db_updated_at)]
|
|
|
|
|
|
def test_supported_embedding_models_match_service_names() -> None:
|
|
assert MODEL_DIMENSIONS == {
|
|
"qwen3-embedding-0.6b": 1024,
|
|
"qwen3-embedding-4b": 2560,
|
|
"qwen3-embedding-8b": 4096,
|
|
}
|
|
|
|
|
|
def test_ensure_embedding_models_registers_service_names() -> None:
|
|
engine = create_engine("sqlite+pysqlite:///:memory:", future=True)
|
|
RichieBase.metadata.create_all(engine)
|
|
with sessionmaker(bind=engine, expire_on_commit=False, future=True)() as session:
|
|
ensure_embedding_models(session)
|
|
session.commit()
|
|
|
|
models = list(session.scalars(select(EbookEmbeddingModel).order_by(EbookEmbeddingModel.name)))
|
|
|
|
assert [(model.name, model.dimension) for model in models] == [
|
|
("qwen3-embedding-0.6b", 1024),
|
|
("qwen3-embedding-4b", 2560),
|
|
("qwen3-embedding-8b", 4096),
|
|
]
|
|
|
|
|
|
def test_embedding_model_aliases_normalize_to_provider_names() -> None:
|
|
assert normalize_embedding_model() == "qwen3-embedding-0.6b"
|
|
|
|
environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "qwen3-embedding-0.6b"
|
|
assert normalize_embedding_model() == "qwen3-embedding-0.6b"
|
|
|
|
environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "Qwen3-Embedding-0.6B"
|
|
assert normalize_embedding_model() == "qwen3-embedding-0.6b"
|
|
|
|
environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "Qwen/Qwen3-Embedding-4B"
|
|
|
|
assert normalize_embedding_model() == "qwen3-embedding-4b"
|
|
|
|
environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "qwen3-embedding:8b"
|
|
assert normalize_embedding_model() == "qwen3-embedding-8b"
|
|
|
|
environ["EBOOK_SEARCH_EMBEDDING_MODEL"] = "qwen3-embedding-8b"
|
|
assert normalize_embedding_model() == "qwen3-embedding-8b"
|
|
|
|
|
|
def test_answer_generation_is_enabled_by_default(monkeypatch) -> None:
|
|
monkeypatch.delenv("EBOOK_SEARCH_ANSWER_ENABLED", raising=False)
|
|
|
|
config = load_config()
|
|
|
|
assert config.answer_enabled is True
|
|
|
|
|
|
def test_chat_defaults_use_ollama_cloud(monkeypatch) -> None:
|
|
monkeypatch.delenv("EBOOK_SEARCH_VLLM_BASE_URL", raising=False)
|
|
monkeypatch.delenv("EBOOK_SEARCH_CHAT_MODEL", raising=False)
|
|
|
|
config = load_config()
|
|
|
|
assert config.vllm_base_url == "https://ollama.com/v1"
|
|
assert config.chat_model == "deepseek-v4-flash"
|
|
|
|
|
|
def test_chat_api_key_falls_back_to_ollama_api_key(monkeypatch) -> None:
|
|
monkeypatch.delenv("EBOOK_SEARCH_VLLM_API_KEY", raising=False)
|
|
monkeypatch.setenv("OLLAMA_API_KEY", "ollama-key")
|
|
|
|
config = load_config()
|
|
|
|
assert config.vllm_api_key == "ollama-key"
|
|
|
|
|
|
def test_answer_query_does_not_call_model_when_disabled() -> None:
|
|
config = replace(load_config(), answer_enabled=False)
|
|
result = SearchResult(chunk_id=1, text="source text", source_title="Book")
|
|
|
|
answer = answer_query("question", [result], config)
|
|
|
|
assert "Answer generation is disabled" in answer
|