"""End-to-end tests against real academic papers."""
from pathlib import Path
from zotero_chunk_rag.config import Config
from zotero_chunk_rag.embedder import create_embedder
from zotero_chunk_rag.vector_store import VectorStore
from zotero_chunk_rag.retriever import Retriever
def _create_test_config(tmp_path: Path) -> Config:
zotero_dir = tmp_path / "zotero"
zotero_dir.mkdir(exist_ok=True)
(zotero_dir / "zotero.sqlite").touch()
return Config(
zotero_data_dir=zotero_dir,
chroma_db_path=tmp_path / "chroma",
embedding_model="all-MiniLM-L6-v2",
embedding_dimensions=384,
chunk_size=400,
chunk_overlap=100,
gemini_api_key=None,
embedding_provider="local",
embedding_timeout=120.0,
embedding_max_retries=3,
rerank_alpha=0.7,
rerank_section_weights=None,
rerank_journal_weights=None,
rerank_enabled=True,
oversample_multiplier=3,
oversample_topic_factor=5,
stats_sample_limit=10000,
ocr_language="eng",
openalex_email=None,
)
def test_all_papers_produce_multiple_sections(chunked_papers):
for pdf_name in ["noname1.pdf", "noname2.pdf", "noname3.pdf"]:
chunks = chunked_papers[pdf_name]
sections_found = set(c.section for c in chunks)
assert len(sections_found) >= 3, f"{pdf_name}: only {len(sections_found)} sections in chunks: {sections_found}"
def test_all_papers_produce_enough_chunks(chunked_papers):
for pdf_name in ["noname1.pdf", "noname2.pdf", "noname3.pdf"]:
chunks = chunked_papers[pdf_name]
assert len(chunks) > 20, f"{pdf_name}: only {len(chunks)} chunks"
def test_full_pipeline_retriever(tmp_path, extracted_papers, chunked_papers):
"""Full pipeline: extract -> chunk -> embed -> store -> retriever.search() returns results."""
config = _create_test_config(tmp_path)
ex = extracted_papers["noname1.pdf"]
chunks = chunked_papers["noname1.pdf"]
embedder = create_embedder(config)
store = VectorStore(config.chroma_db_path, embedder)
doc_meta = {
"title": "Test Paper", "authors": "Test Author", "year": 2020,
"citation_key": "test2020", "publication": "Test Journal",
"journal_quartile": "", "doi": "", "tags": "", "collections": "",
"pdf_hash": "test", "quality_grade": ex.quality_grade,
}
store.add_chunks("test_noname1", doc_meta, chunks)
retriever = Retriever(store)
results = retriever.search("ECG modeling", top_k=5)
assert len(results) > 0
