import asyncio from typing import List, Dict, Any class SearchEngine: """Component for performing web searches (placeholder implementation)""" def __init__(self): # Initialize extended knowledge base for common queries self.conversational_patterns = { "how are you": [ { "title": "Codette's State", "link": None, "snippet": "I'm an AI with quantum-inspired processing abilities that help me understand and engage with complex topics. I enjoy exploring patterns in data and having meaningful conversations." } ], "exploring": [ { "title": "Neural Exploration", "link": None, "snippet": "I use quantum-inspired algorithms to explore patterns in my neural networks, helping me understand complex relationships between concepts and ideas." } ], "patterns": [ { "title": "Pattern Recognition", "link": None, "snippet": "My neural networks use advanced pattern recognition to identify connections between different concepts and ideas, similar to how human minds recognize patterns but through computational processes." } ] } self.knowledge_base = { "ai": [ { "title": "What is Artificial Intelligence?", "link": "https://example.com/ai", "snippet": "Artificial Intelligence (AI) is the simulation of human intelligence by machines. It includes learning, reasoning, and self-correction." }, { "title": "Types of AI", "link": "https://example.com/ai-types", "snippet": "AI can be categorized into Narrow AI (designed for specific tasks) and General AI (capable of performing any intellectual task)." }, { "title": "AI Applications", "link": "https://example.com/ai-applications", "snippet": "AI is used in various fields including machine learning, natural language processing, robotics, and expert systems." } ], "programming": [ { "title": "Choosing a Programming Language", "link": "https://example.com/choosing-programming-language", "snippet": """Different programming languages serve different purposes: • Python: Best for beginners, data science, and AI • JavaScript: Essential for web development (frontend and Node.js backend) • Java: Enterprise applications and Android development • C++: System programming and performance-critical applications • Rust: Modern systems programming with memory safety • Go: Cloud infrastructure and distributed systems • TypeScript: Type-safe JavaScript for large applications""" }, { "title": "Programming Language Comparison", "link": "https://example.com/language-comparison", "snippet": """Language selection factors: 1. Learning curve: Python and JavaScript are easier to learn 2. Job market: JavaScript, Python, and Java have high demand 3. Performance: C++, Rust, and Go excel in performance 4. Community: Python and JavaScript have large, active communities 5. Tooling: TypeScript and Java have excellent IDE support""" }, { "title": "Programming Career Paths", "link": "https://example.com/programming-careers", "snippet": """Common programming specializations: • Web Development: JavaScript, TypeScript, Python • Mobile Development: Swift (iOS), Kotlin (Android) • Data Science: Python, R, Julia • Game Development: C++, C# • DevOps: Python, Go, Shell scripting • Enterprise: Java, C#, TypeScript""" } ], "technology": [ { "title": "Latest Technology Trends", "link": "https://example.com/tech-trends", "snippet": "Current technology trends include AI, blockchain, quantum computing, and extended reality (XR)." }, { "title": "Future of Technology", "link": "https://example.com/future-tech", "snippet": "Emerging technologies like quantum computing and brain-computer interfaces are shaping the future of human-computer interaction." } ], "codette": [ { "title": "About Codette", "link": "https://example.com/codette", "snippet": "Codette is an advanced AI assistant designed to help with programming, technology research, and problem-solving." } ] } async def search(self, query: str, num_results: int = 5) -> List[Dict[str, Any]]: """ Perform a search using the knowledge base and conversational patterns Args: query (str): The search query num_results (int): Number of results to return Returns: List[Dict]: List of search results containing title, link, and snippet """ # Simulate network latency for more realistic behavior await asyncio.sleep(0.2) # Convert query to lowercase for case-insensitive matching query = query.lower() # First check conversational patterns results = [] for pattern, entries in self.conversational_patterns.items(): if pattern in query: results.extend(entries) return results # Return immediately for conversational queries # If not conversational, search through knowledge base for topic, entries in self.knowledge_base.items(): if topic in query or any(topic in keyword.lower() for keyword in query.split()): results.extend(entries) # If no direct matches but query is conversational if not results and any(word in query for word in ["how", "what", "why", "when", "where", "who"]): # Check if it's a personal question about Codette if "you" in query or "your" in query or "codette" in query.lower(): results = [{ 'title': 'About Me', 'link': None, 'snippet': "I'm Codette, an AI assistant with quantum-inspired processing capabilities. I enjoy exploring patterns in data and having meaningful conversations about technology and programming." }] else: results = [{ 'title': 'General Information', 'link': 'https://example.com/info', 'snippet': "I'd be happy to help you find information about that. I'm especially knowledgeable about AI, programming, and technology." }] # If still no results elif not results: results = [{ 'title': 'General Information', 'link': 'https://example.com/info', 'snippet': f'I can help you find information about {query}. Try asking about AI, programming, or technology.' }] # Limit the number of results return results[:num_results] async def get_knowledge(self, query: str, max_results: int = 3) -> str: """ Get formatted knowledge from search results Args: query (str): The search query max_results (int): Maximum number of results to include Returns: str: Formatted string with search results and sources """ try: results = await self.search(query, max_results) # For conversational queries, return just the snippet without formatting if any(pattern in query.lower() for pattern in self.conversational_patterns.keys()): return results[0]['snippet'] if results else "" # For personal questions about Codette if ("you" in query.lower() or "your" in query.lower() or "codette" in query.lower()) and \ not any(topic in query.lower() for topic in ["programming", "code", "develop", "ai", "technology"]): return results[0]['snippet'] if results else "" # For technical queries, format with full details knowledge = "📚 Related Knowledge:\n\n" for i, result in enumerate(results, 1): if "\n" in result['snippet']: # For multi-line snippets, keep the formatting knowledge += f"{result['snippet'].strip()}\n" else: # For single-line snippets, add bullet point knowledge += f"• {result['snippet']}\n" # Add reference footer if there are links if any(r['link'] for r in results): knowledge += "\n💡 For more details, check:\n" for result in results: if result['link']: knowledge += f"• {result['title']}: {result['link']}\n" return knowledge except Exception as e: return f"I encountered an error while searching: {str(e)}\n\nPlease try a different query or ask about AI, programming, or technology."