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https://github.com/lllin000/PaperForge.git
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49f84a1aad
Rewrite embedding provider to use openai.OpenAI client instead of raw requests.post. Old code preserved in requests_fallback.py, selectable via VECTOR_DB_PROVIDER_TYPE env/setting. Changes: - paperforge/embedding/providers/openai_compatible.py: rewrite to SDK - paperforge/embedding/providers/requests_fallback.py: NEW old impl - paperforge/embedding/_config.py: add get_provider_type() - tests/test_openai_compatible.py: 6 new tests (encode, timeout, provider_type switch, no-key error, custom base url) feat(#33): E2E test fixtures — 3 synthetic PDFs + expected outputs 3 synthetic papers generated with PyMuPDF, each 3 pages: - paper_a: The Effect of Machine Learning on Clinical Outcomes - paper_b: A Randomized Trial of Remimazolam vs Propofol (with tables) - paper_c: Deep Learning for Medical Image Segmentation (with figures) Changes: - tests/fixtures/papers/: 3 PDFs - tests/fixtures/expected_outputs/: expected blocks + body units - tests/conftest.py: e2e_fixture_dir + synthetic_paper_paths fixtures feat(#26): sqlite-vec schema — vec0 virtual tables + build_state Schema version bumped 4→5. Adds: - 3 vec0 virtual tables: vec_fulltext, vec_body, vec_objects (1536-dim) - 3 companion metadata tables: vec_*_meta (paper_id, chunk_index, text) - build_state table (key/value/updated_at) - ensure_vec_extension() in db.py for extension loading Changes: - paperforge/memory/schema.py: schema v5, new tables in ensure_schema() - paperforge/memory/db.py: ensure_vec_extension() helper - tests/test_vector_schema.py: 5 new tests (tables, virtual, idempotent, v5)
95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
"""Unit tests for OpenAICompatibleProvider (openai SDK path)."""
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from __future__ import annotations
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from pathlib import Path
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from unittest.mock import MagicMock, patch
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import pytest
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from paperforge.embedding.providers.openai_compatible import OpenAICompatibleProvider
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@pytest.fixture
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def mock_client():
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"""Patch openai.OpenAI and return the mock client instance."""
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with patch("paperforge.embedding.providers.openai_compatible.openai.OpenAI") as mock_cls:
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client = MagicMock()
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mock_cls.return_value = client
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embedding_response = MagicMock()
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emb1 = MagicMock()
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emb1.embedding = [0.1, 0.2, 0.3]
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emb2 = MagicMock()
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emb2.embedding = [0.4, 0.5, 0.6]
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embedding_response.data = [emb1, emb2]
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client.embeddings.create.return_value = embedding_response
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yield client
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@pytest.fixture(autouse=True)
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def _env(monkeypatch):
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monkeypatch.setenv("VECTOR_DB_API_KEY", "test-key-123")
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class TestOpenAICompatibleProvider:
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def test_encode_calls_client_embeddings_create(
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self, mock_client, tmp_path: Path,
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):
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provider = OpenAICompatibleProvider(tmp_path)
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result = provider.encode(["hello", "world"])
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mock_client.embeddings.create.assert_called_once_with(
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model="text-embedding-3-small",
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input=["hello", "world"],
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)
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assert result == [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
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def test_encode_single_returns_one_embedding(self, mock_client, tmp_path: Path):
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provider = OpenAICompatibleProvider(tmp_path)
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result = provider.encode_single("hello")
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assert result == [0.1, 0.2, 0.3]
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def test_timeout_and_retries_applied(self, tmp_path: Path):
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with patch("paperforge.embedding.providers.openai_compatible.openai.OpenAI") as mock_cls:
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OpenAICompatibleProvider(tmp_path)
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mock_cls.assert_called_once()
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_args, kwargs = mock_cls.call_args
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assert kwargs["timeout"] == 30.0
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assert kwargs["max_retries"] == 2
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def test_provider_type_requests_delegates_to_fallback(self, monkeypatch, tmp_path: Path):
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monkeypatch.setenv("VECTOR_DB_PROVIDER_TYPE", "requests")
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with patch(
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"paperforge.embedding.providers.requests_fallback.requests.post",
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) as mock_post:
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mock_post.return_value.json.return_value = {
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"data": [{"embedding": [0.7, 0.8, 0.9]}],
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}
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mock_post.return_value.raise_for_status = lambda: None
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provider = OpenAICompatibleProvider(tmp_path)
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result = provider.encode(["test"])
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mock_post.assert_called_once()
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assert result == [[0.7, 0.8, 0.9]]
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def test_raises_when_no_api_key(self, monkeypatch, tmp_path: Path):
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monkeypatch.delenv("VECTOR_DB_API_KEY", raising=False)
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monkeypatch.delenv("OPENAI_API_KEY", raising=False)
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with pytest.raises(ValueError, match="No API key configured"):
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OpenAICompatibleProvider(tmp_path)
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def test_accepts_custom_base_url(self, mock_client, monkeypatch, tmp_path: Path):
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monkeypatch.setenv("VECTOR_DB_API_BASE", "https://custom.api.com/v1")
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provider = OpenAICompatibleProvider(tmp_path)
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provider.encode(["test"])
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_args, kwargs = mock_client.embeddings.create.call_args
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assert kwargs["model"] == "text-embedding-3-small"
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