"""Pytest fixtures for obsidian-rag-mcp tests."""
import tempfile
from pathlib import Path
from unittest.mock import Mock, patch
import pytest
@pytest.fixture
def temp_vault():
"""Create a temporary vault directory with sample markdown files."""
with tempfile.TemporaryDirectory() as tmpdir:
vault_path = Path(tmpdir)
# Create some sample markdown files
(vault_path / "note1.md").write_text("""---
title: First Note
tags: [important, test]
---
# First Note
This is the first test note. It contains important information about testing.
## Section One
Details about section one.
## Section Two
Details about section two.
""")
(vault_path / "note2.md").write_text("""# Second Note
This note discusses #python and #programming topics.
Some code example:
```python
def hello():
return "world"
```
""")
# Create a subdirectory with more notes
subdir = vault_path / "subfolder"
subdir.mkdir()
(subdir / "nested-note.md").write_text("""# Nested Note
This is a note in a subfolder. It talks about #nested #organization.
""")
# Create .obsidian directory (should be ignored)
obsidian_dir = vault_path / ".obsidian"
obsidian_dir.mkdir()
(obsidian_dir / "config.json").write_text('{"theme": "dark"}')
yield vault_path
@pytest.fixture
def temp_persist_dir():
"""Create a temporary directory for ChromaDB persistence."""
with tempfile.TemporaryDirectory() as tmpdir:
yield Path(tmpdir)
@pytest.fixture
def mock_openai():
"""Mock OpenAI client for embedding tests."""
with patch("src.rag.embedder.OpenAI") as mock_class:
mock_client = Mock()
mock_class.return_value = mock_client
mock_client.api_key = "test-key"
# Setup default embedding response
def create_embeddings(**kwargs):
texts = kwargs["input"]
response = Mock()
# Return deterministic embeddings based on text hash
response.data = [
Mock(index=i, embedding=[hash(t) % 1000 / 1000 for _ in range(1536)])
for i, t in enumerate(texts)
]
return response
mock_client.embeddings.create.side_effect = create_embeddings
yield mock_client
@pytest.fixture
def sample_chunks():
"""Create sample chunks for testing."""
from src.rag.chunker import Chunk
return [
Chunk(
content="Python is a great programming language for AI.",
source_path="python.md",
chunk_index=0,
title="Python Guide",
tags=["python", "programming"],
),
Chunk(
content="Machine learning uses data to train models.",
source_path="ml.md",
chunk_index=0,
title="ML Basics",
tags=["ml", "ai"],
),
Chunk(
content="Obsidian is a note-taking app for knowledge management.",
source_path="tools/obsidian.md",
chunk_index=0,
title="Obsidian Guide",
tags=["tools", "notes"],
),
]
