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5959a78 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | """Real ChromaDB, fake (deterministic) embeddings: index -> search -> isolate -> delete."""
import hashlib
import fitz
import numpy as np
import pytest
from backend.core import settings
from backend.services.pdf_processor import PDFProcessor
from backend.services.vector_store import VectorStoreService
class FakeEmbedder:
"""Bag-of-words hashing into 64 dims, L2-normalised (like the real model's output)."""
DIM = 64
def _vec(self, text):
v = np.zeros(self.DIM, dtype="float32")
for word in text.lower().split():
v[int(hashlib.md5(word.encode()).hexdigest(), 16) % self.DIM] += 1.0
norm = np.linalg.norm(v)
return v / norm if norm else v
def embed_texts(self, texts):
return np.stack([self._vec(t) for t in texts])
def embed_query(self, text):
return self._vec(text)
def make_doc(tmp_path, name, text):
path = tmp_path / name
pdf = fitz.open(); pdf.new_page().insert_text((72, 72), text); pdf.save(path); pdf.close()
return PDFProcessor().process(str(path))
@pytest.fixture()
def store(tmp_path, monkeypatch):
monkeypatch.setattr(settings, "vectorstore_path", str(tmp_path / "chroma"))
service = VectorStoreService()
service.embedding_service = FakeEmbedder()
return service
def test_index_search_filter_isolate_and_delete(store, tmp_path):
finance = make_doc(tmp_path, "finance.pdf", "Quarterly revenue increased twelve percent due to subscription growth")
biology = make_doc(tmp_path, "biology.pdf", "Mitochondria produce cellular energy through oxidative phosphorylation")
assert store.index_document(finance, "alice") and store.index_document(biology, "bob")
# exact text -> cosine similarity ~ 1, and the right document wins
hits = store.search("Quarterly revenue increased twelve percent due to subscription growth", top_k=3)
assert hits[0]["doc_name"] == "finance.pdf"
assert hits[0]["similarity"] == pytest.approx(1.0, abs=1e-3)
assert hits[0]["page_number"] == 1
# owner isolation
assert {d["doc_id"] for d in store.list_documents("alice")} == {finance.doc_id}
only_alice = store.search("mitochondria energy", top_k=5, owner_id="alice")
assert all(h["doc_name"] == "finance.pdf" for h in only_alice)
# doc_id filter
only_bio = store.search("revenue growth", doc_ids=[biology.doc_id], top_k=5)
assert only_bio and all(h["doc_name"] == "biology.pdf" for h in only_bio)
# existence + delete are owner-aware
assert store.document_exists(finance.doc_id, "alice")
assert not store.document_exists(finance.doc_id, "bob")
assert not store.delete_document(finance.doc_id, "bob")
assert store.delete_document(finance.doc_id, "alice")
assert not store.document_exists(finance.doc_id)
assert all(h["doc_name"] != "finance.pdf" for h in store.search("revenue", top_k=5))
def test_search_on_empty_store_returns_empty_list(store):
assert store.search("anything at all") == []
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