"""Pytest configuration and fixtures for RAGBrain MCP tests."""
import pytest
from ragbrain_mcp.client import RAGBrainClient
from ragbrain_mcp.config import Settings
from ragbrain_mcp.tools import ToolHandler
@pytest.fixture
def settings() -> Settings:
"""Create test settings."""
return Settings(
url="http://localhost:8000",
timeout=10.0,
log_level="DEBUG",
max_results=20,
max_document_length=10000,
)
@pytest.fixture
def client(settings: Settings) -> RAGBrainClient:
"""Create a test RAGBrain client."""
return RAGBrainClient(settings)
@pytest.fixture
def tool_handler(client: RAGBrainClient, settings: Settings) -> ToolHandler:
"""Create a test tool handler."""
return ToolHandler(client, settings)
# Sample API responses for mocking
@pytest.fixture
def sample_namespaces() -> dict:
"""Sample namespace list response."""
return {
"namespaces": [
{
"id": "personal",
"name": "Personal",
"description": "Personal notes and documents",
"doc_count": 15,
"chunk_count": 150,
},
{
"id": "work",
"name": "Work",
"description": "Work-related documents",
"doc_count": 25,
"chunk_count": 300,
},
]
}
@pytest.fixture
def sample_namespace_tree() -> dict:
"""Sample namespace tree response."""
return {
"tree": [
{
"id": "personal",
"name": "Personal",
"doc_count": 15,
"children": [
{"id": "personal/notes", "name": "Notes", "doc_count": 10, "children": []},
],
},
{
"id": "work",
"name": "Work",
"doc_count": 25,
"children": [],
},
]
}
@pytest.fixture
def sample_search_results() -> dict:
"""Sample search results response."""
return {
"question": "machine learning",
"sources": [
{
"content": "This is a sample search result about machine learning.",
"score": 0.95,
"metadata": {
"filename": "ml-notes.md",
"namespace": "personal",
"doc_id": "abc-123",
},
},
{
"content": "Another result about deep learning techniques.",
"score": 0.87,
"metadata": {
"filename": "dl-guide.pdf",
"namespace": "work",
"doc_id": "def-456",
},
},
],
"namespace": "default",
"reranked": False,
}
@pytest.fixture
def sample_search_results_with_answer() -> dict:
"""Sample search results with synthesized answer."""
return {
"question": "what is machine learning",
"sources": [
{
"content": "Machine learning is a subset of AI.",
"score": 0.92,
"metadata": {
"filename": "ai-intro.md",
"namespace": "personal",
"doc_id": "ghi-789",
},
},
],
"answer": "Machine learning is a field of artificial intelligence that enables systems to learn from data.",
"namespace": "default",
"reranked": False,
}
@pytest.fixture
def sample_documents() -> dict:
"""Sample document list response."""
return {
"namespace": "personal",
"total": 2,
"documents": [
{
"doc_id": "abc-123",
"filename": "notes.md",
"total_chunks": 5,
"created_at": "2024-01-15T10:30:00Z",
},
{
"doc_id": "def-456",
"filename": "guide.pdf",
"total_chunks": 12,
"created_at": "2024-01-20T14:00:00Z",
},
],
}
@pytest.fixture
def sample_document() -> dict:
"""Sample full document response."""
return {
"doc_id": "abc-123",
"filename": "notes.md",
"namespace": "personal",
"total_chunks": 5,
"reconstructed_text": "# My Notes\n\nThis is the content of my notes document.\n\n## Section 1\n\nSome important information here.",
}
