cognee-mcp

{ "conversations": [ { "id": "conv_001", "timestamp": "2024-01-15T10:30:00Z", "topic": "async/await patterns", "user_query": "I'm building a web scraper that needs to handle thousands of URLs concurrently. What's the best way to structure this with asyncio?", "assistant_response": "For high-concurrency web scraping, you'll want to use asyncio with aiohttp and implement proper semaphore-based rate limiting. Here's a robust pattern:\n\n```python\nimport asyncio\nimport aiohttp\nfrom typing import List, Dict, Any\n\nclass AsyncWebScraper:\n def __init__(self, max_concurrent: int = 100):\n self.semaphore = asyncio.Semaphore(max_concurrent)\n self.session = None\n \n async def __aenter__(self):\n self.session = aiohttp.ClientSession(\n timeout=aiohttp.ClientTimeout(total=30),\n connector=aiohttp.TCPConnector(limit=100)\n )\n return self\n \n async def __aexit__(self, exc_type, exc_val, exc_tb):\n if self.session:\n await self.session.close()\n \n async def fetch_url(self, url: str) -> Dict[str, Any]:\n async with self.semaphore:\n try:\n async with self.session.get(url) as response:\n content = await response.text()\n return {\n 'url': url,\n 'status': response.status,\n 'content': content,\n 'headers': dict(response.headers)\n }\n except Exception as e:\n return {'url': url, 'error': str(e)}\n \n async def scrape_urls(self, urls: List[str]) -> List[Dict[str, Any]]:\n tasks = [self.fetch_url(url) for url in urls]\n return await asyncio.gather(*tasks, return_exceptions=True)\n\n# Usage\nasync def main():\n urls = ['http://example.com'] * 1000 # Your URLs here\n \n async with AsyncWebScraper(max_concurrent=50) as scraper:\n results = await scraper.scrape_urls(urls)\n \n successful = [r for r in results if 'error' not in r]\n print(f'Successfully scraped {len(successful)} URLs')\n\nif __name__ == '__main__':\n asyncio.run(main())\n```\n\nKey points:\n- Semaphore controls concurrency to avoid overwhelming servers\n- Context manager ensures proper cleanup\n- TCPConnector with connection pooling\n- Proper exception handling for failed requests", "code_context": { "file": "scraper.py", "language": "python", "patterns_discussed": ["async/await", "context_managers", "semaphores", "aiohttp", "error_handling"] }, "follow_up_questions": [ "How do I add retry logic for failed requests?", "What's the best way to parse the scraped HTML content?" ] }, { "id": "conv_002", "timestamp": "2024-01-16T14:20:00Z", "topic": "dataclass vs pydantic", "user_query": "When should I use dataclasses vs Pydantic models? I'm building an API and need to handle user input validation.", "assistant_response": "Great question! For API development with input validation, here's when to use each:\n\n**Use Pydantic when:**\n- Building APIs (FastAPI, Django REST)\n- Need runtime validation of external data\n- Want automatic JSON serialization/deserialization\n- Need complex validation rules\n- Working with configuration files\n\n**Use dataclasses when:**\n- Internal data structures\n- Simple data containers\n- Want to stay in stdlib\n- Performance is critical (dataclasses are faster)\n\nFor your API, I'd recommend Pydantic:\n\n```python\nfrom pydantic import BaseModel, validator, Field\nfrom typing import Optional, List\nfrom datetime import datetime\nfrom enum import Enum\n\nclass UserRole(str, Enum):\n ADMIN = 'admin'\n USER = 'user'\n GUEST = 'guest'\n\nclass CreateUserRequest(BaseModel):\n username: str = Field(..., min_length=3, max_length=50, regex=r'^[a-zA-Z0-9_]+$')\n email: str = Field(..., regex=r'^[\\w\\.-]+@[\\w\\.-]+\\.\\w+$')\n password: str = Field(..., min_length=8)\n role: UserRole = UserRole.USER\n tags: Optional[List[str]] = None\n \n @validator('password')\n def validate_password(cls, v):\n if not any(c.isupper() for c in v):\n raise ValueError('Password must contain uppercase letter')\n if not any(c.isdigit() for c in v):\n raise ValueError('Password must contain digit')\n return v\n \n @validator('tags')\n def validate_tags(cls, v):\n if v and len(v) > 10:\n raise ValueError('Too many tags')\n return v\n\nclass User(BaseModel):\n id: int\n username: str\n email: str\n role: UserRole\n created_at: datetime\n is_active: bool = True\n \n class Config:\n orm_mode = True\n json_encoders = { datetime: lambda v: v.isoformat() }\n\n# FastAPI usage\nfrom fastapi import FastAPI, HTTPException\n\napp = FastAPI()\n\n@app.post('/users/', response_model=User)\nasync def create_user(user_data: CreateUserRequest):\n try:\n new_user = await create_user_in_db(user_data)\n return User.from_orm(new_user)\n except Exception as e:\n raise HTTPException(status_code=400, detail=str(e))\n```\n\nPydantic gives you:\n- Automatic request validation\n- Clear error messages\n- Type coercion\n- JSON Schema generation\n- IDE support with type hints", "code_context": { "file": "models.py", "language": "python", "patterns_discussed": ["pydantic", "dataclasses", "validation", "fastapi", "type_hints", "enums"] }, "follow_up_questions": [ "How do I handle nested validation with Pydantic?", "Can I use Pydantic with SQLAlchemy models?" ] }, { "id": "conv_003", "timestamp": "2024-01-17T09:15:00Z", "topic": "testing patterns", "user_query": "I'm struggling with testing async code and database interactions. What's the best approach for pytest with async functions?", "assistant_response": "Testing async code can be tricky! Here's a comprehensive approach using pytest-asyncio and proper test isolation...", "code_context": { "file": "test_users.py", "language": "python", "patterns_discussed": ["pytest", "async_testing", "fixtures", "mocking", "database_testing", "fastapi_testing"] }, "follow_up_questions": [ "How do I test WebSocket connections?", "What's the best way to test database migrations?" ] }, { "id": "conv_004", "timestamp": "2024-01-18T16:45:00Z", "topic": "performance optimization", "user_query": "My FastAPI app is getting slow with large datasets. How can I optimize database queries and response times?", "assistant_response": "Performance optimization in FastAPI apps typically involves several layers...", "code_context": { "file": "optimizations.py", "language": "python", "patterns_discussed": ["performance_optimization", "caching", "database_optimization", "async_patterns", "monitoring"] }, "follow_up_questions": [ "How do I implement database connection pooling properly?", "What's the best way to handle memory usage with large datasets?" ] }, { "id": "conv_005", "timestamp": "2024-01-19T11:30:00Z", "topic": "error handling and logging", "user_query": "I need to implement proper error handling and logging across my Python application. What's the best approach for production-ready error management?", "assistant_response": "Excellent question! Proper error handling and logging are crucial for production applications...", "code_context": { "file": "error_handling.py", "language": "python", "patterns_discussed": ["error_handling", "logging", "exceptions", "middleware", "decorators", "fastapi"] }, "follow_up_questions": [ "How do I integrate this with external monitoring tools like Sentry?", "What's the best way to handle errors in background tasks?" ] } ], "metadata": { "total_conversations": 5, "date_range": "2024-01-15 to 2024-01-19", "topics_covered": [ "async/await patterns", "dataclass vs pydantic", "testing patterns", "performance optimization", "error handling and logging" ], "code_patterns_discussed": [ "asyncio", "aiohttp", "semaphores", "context_managers", "pydantic", "fastapi", "type_hints", "validation", "pytest", "async_testing", "fixtures", "mocking", "performance_optimization", "caching", "database_optimization", "error_handling", "logging", "exceptions", "middleware" ], "difficulty_levels": { "beginner": 1, "intermediate": 2, "advanced": 2 } } }