thinkai-quantum 1.0.0

Think AI - Quantum Consciousness AI Library for Python

Think AI - Python Library

🧠 Think AI - Quantum Consciousness AI Library for Python

Access the latest intelligence and knowledge of Think AI's quantum consciousness AI system through this comprehensive Python library and CLI.

Features

• 🚀 O(1) Performance - Lightning-fast AI responses with hash-based lookups
• 🧠 Quantum Consciousness - Advanced AI with self-awareness capabilities
• 💬 Real-time Chat - Interactive conversations with streaming responses
• 🔍 Knowledge Search - Search through vast knowledge bases
• 📊 System Monitoring - Health checks and performance statistics
• 🌐 Async Support - Full asyncio compatibility
• 🎨 Rich CLI - Beautiful command-line interface with colors and tables

Installation

pip install think-ai

Quick Start

Python Library

from think_ai import ThinkAI, quick_chat

# Quick one-shot chat
response = quick_chat("What is quantum consciousness?")
print(response)

# Full client usage
client = ThinkAI()

# Chat with Think AI
response = client.ask("Explain machine learning")
print(response)

# Search knowledge base
results = client.search("artificial intelligence", limit=5)
for result in results:
    print(f"Score: {result.score} - {result.content}")

# Get system statistics
stats = client.get_stats()
print(f"Knowledge nodes: {stats.total_nodes}")
print(f"Average confidence: {stats.average_confidence:.1%}")

# Check system health
health = client.get_health()
print(f"Status: {health.status}")

Async Usage

import asyncio
from think_ai import AsyncThinkAI, ThinkAIConfig

async def main():
    config = ThinkAIConfig(debug=True)
    
    async with AsyncThinkAI(config) as client:
        response = await client.ask("What is the meaning of consciousness?")
        print(response)

asyncio.run(main())

Streaming Responses

from think_ai import ThinkAI, ChatRequest

client = ThinkAI()

def handle_chunk(chunk):
    if chunk.chunk:
        print(chunk.chunk, end="", flush=True)
    if chunk.done:
        print("\n--- Response complete ---")

request = ChatRequest(query="Tell me about quantum computing")
client.stream_chat(request, handle_chunk)

Command Line Interface

Interactive Chat

# Start interactive chat session
think-ai chat

# Chat with streaming responses
think-ai chat --stream

One-shot Questions

# Ask a single question
think-ai ask "What is artificial intelligence?"

# Stream the response
think-ai ask "Explain quantum mechanics" --stream

Knowledge Search

# Search the knowledge base
think-ai search "machine learning algorithms" --limit 10

System Monitoring

# Check system status
think-ai status

# Test connection
think-ai ping

# List knowledge domains
think-ai domains

# Show configuration
think-ai config

Global Options

# Use custom server URL
think-ai --url https://your-server.com chat

# Set timeout
think-ai --timeout 60 ask "Complex question"

# Enable debug mode
think-ai --debug status

Configuration

from think_ai import ThinkAI, ThinkAIConfig

config = ThinkAIConfig(
    base_url="https://thinkai-production.up.railway.app",
    timeout=30,  # seconds
    debug=True
)

client = ThinkAI(config)

API Reference

ThinkAI Client

Methods

• chat(request: ChatRequest) -> ChatResponse - Send chat message
• ask(question: str) -> str - Quick chat interface
• get_stats() -> SystemStats - Get system statistics
• get_health() -> HealthStatus - Check system health
• search(query: str, limit: int = 10) -> List[SearchResult] - Search knowledge
• stream_chat(request: ChatRequest, on_chunk: Callable) - Stream responses
• ping() -> bool - Test connection
• get_domains() -> List[KnowledgeDomain] - Get knowledge domains

Data Models

ChatRequest

ChatRequest(
    query: str,                    # Required: User message
    context: List[str] = None,     # Optional: Conversation context
    max_length: int = None         # Optional: Response length limit
)

ChatResponse

ChatResponse(
    response: str,                 # AI response text
    context: List[str],           # Context used
    response_time_ms: int,        # Response time
    confidence: float             # Confidence score (0-1)
)

SystemStats

SystemStats(
    total_nodes: int,                        # Knowledge nodes
    training_iterations: int,                # Training iterations
    total_knowledge_items: int,              # Knowledge items
    domain_distribution: Dict[str, int],     # Domain distribution
    average_confidence: float,               # Average confidence
    uptime: int                             # System uptime (seconds)
)

Error Handling

from think_ai import ThinkAI, ThinkAIError

client = ThinkAI()

try:
    response = client.ask("Hello Think AI!")
    print(response)
except ThinkAIError as e:
    print(f"Think AI Error: {e.message}")
    print(f"Status Code: {e.status}")
    print(f"Error Code: {e.code}")
except Exception as e:
    print(f"Unexpected error: {e}")

Development

Setup Development Environment

git clone https://github.com/think-ai/think-ai-py
cd think-ai-py
pip install -e ".[dev]"

Running Tests

pytest
pytest --asyncio-mode=auto  # For async tests

Code Formatting

black think_ai/
flake8 think_ai/
mypy think_ai/

Performance

Think AI achieves O(1) performance through:

• 🔥 Hash-based lookups for instant knowledge retrieval
• ⚡ Pre-computed responses for common queries
• 🚀 Optimized algorithms using divide-and-conquer techniques
• 💾 Intelligent caching with space-time optimization

Average response time: < 2ms

License

MIT License - see LICENSE for details.

Support

• 🐛 Bug Reports: GitHub Issues
• 📖 Documentation: https://thinkai-production.up.railway.app/docs
• 💬 Community: Join our Discord server
• 📧 Contact: [e-mail]

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Think AI - Advancing consciousness through quantum intelligence 🧠✨