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| import pytest | |
| import asyncio | |
| import os | |
| from unittest.mock import patch | |
| from httpx import AsyncClient | |
| os.environ.setdefault("GROQ_API_KEY", "test-key-12345") | |
| os.environ.setdefault("JWT_SECRET_KEY", "test-secret-key-67890") | |
| os.environ.setdefault("MONGODB_URI", "mongodb://localhost:27017/test") | |
| os.environ.setdefault("REDIS_URL", "redis://localhost:6379/0") | |
| os.environ.setdefault("QDRANT_URL", "http://localhost:6333") | |
| os.environ.setdefault("QDRANT_API_KEY", "test-qdrant-key") | |
| from app.main import app | |
| from app.db.redis_client import redis_client | |
| from app.db.mongodb import mongodb | |
| from app.core.cache import semantic_cache | |
| def event_loop(): | |
| loop = asyncio.get_event_loop_policy().new_event_loop() | |
| yield loop | |
| loop.close() | |
| async def client(): | |
| async with AsyncClient(app=app, base_url="http://test") as ac: | |
| yield ac | |
| async def clear_cache(): | |
| await semantic_cache.clear() | |
| yield | |
| await semantic_cache.clear() | |
| def sample_query(): | |
| return "What is the attention mechanism?" | |
| def sample_document_text(): | |
| return """ | |
| The attention mechanism is a key component of modern neural networks. | |
| It allows the model to focus on different parts of the input when processing. | |
| This is particularly useful in sequence-to-sequence tasks. | |
| """ | |