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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." |