CircuitNotionAI Python SDK

# Install using pip
pip install CircuitNotionAI

# Verify installation
python -c "import CircuitNotionAI; print('CircuitNotionAI installed successfully')"

from CircuitNotionAI import CNAI

# Initialize client with API key
client = CNAI.Client(api_key="your_api_key")

# Generate content using the default model
response = client.models.generate_content(
    model="circuit-2-turbo",
    contents="explain simply how to beat procrastination",
    temperature=0.7,
    max_tokens=200
)

print(response.txt)

# Advanced content generation with custom parameters
response = client.models.generate_content(
    model="circuit-2-turbo",
    contents="Write a Python function to calculate fibonacci numbers",
    temperature=0.3,  # Lower temperature for more focused output
    max_tokens=500,
    top_p=0.9,       # Nucleus sampling
    frequency_penalty=0.1,
    presence_penalty=0.1
)

print("Generated Code:")
print(response.txt)
print(f"Tokens used: {response.usage.total_tokens}")

# Multi-turn conversation
conversation = client.conversations.create()

# First message
response1 = conversation.add_message(
    role="user",
    content="What are the main principles of clean code?"
)
print("AI:", response1.txt)

# Follow-up question
response2 = conversation.add_message(
    role="user", 
    content="Can you give me a specific example of the single responsibility principle?"
)
print("AI:", response2.txt)

# Get conversation history
history = conversation.get_history()
for message in history:
    print(f"{message.role}: {message.content}")

# Streaming responses for real-time generation
def handle_stream_chunk(chunk):
    print(chunk.text, end='', flush=True)

# Stream content generation
client.models.generate_content_stream(
    model="circuit-2-turbo",
    contents="Write a detailed explanation of machine learning concepts",
    temperature=0.7,
    max_tokens=1000,
    on_chunk=handle_stream_chunk
)

# Or use iterator approach
stream = client.models.generate_content_stream(
    model="circuit-2-turbo",
    contents="Explain quantum computing basics",
    temperature=0.6
)

full_response = ""
for chunk in stream:
    full_response += chunk.text
    print(chunk.text, end='', flush=True)

print(f"\n\nComplete response length: {len(full_response)} characters")

# Batch processing multiple prompts
prompts = [
    "Explain artificial intelligence in one paragraph",
    "What are the benefits of renewable energy?",
    "How does blockchain technology work?",
    "Describe the importance of cybersecurity"
]

# Process all prompts
responses = client.models.batch_generate(
    model="circuit-2-turbo",
    prompts=prompts,
    temperature=0.7,
    max_tokens=150
)

for i, response in enumerate(responses):
    print(f"Prompt {i+1}: {prompts[i]}")
    print(f"Response: {response.txt}")
    print(f"Tokens: {response.usage.total_tokens}")
    print("-" * 50)

# Working with different models
available_models = client.models.list()
print("Available models:")
for model in available_models:
    print(f"- {model.name}: {model.description}")

# Use specific model for code generation
code_response = client.models.generate_content(
    model="circuit-code-expert",  # Specialized for code
    contents="Create a REST API using FastAPI with authentication",
    temperature=0.2,  # Lower temperature for precise code
    max_tokens=800
)

# Use creative model for storytelling
story_response = client.models.generate_content(
    model="circuit-creative-writer",  # Specialized for creative writing
    contents="Write a short science fiction story about AI and humanity",
    temperature=0.9,  # Higher temperature for creativity
    max_tokens=1000
)

from CircuitNotionAI import CNAI
from CircuitNotionAI.exceptions import (
    APIError, 
    AuthenticationError, 
    RateLimitError,
    ModelNotFoundError
)

client = CNAI.Client(api_key="your_api_key")

try:
    response = client.models.generate_content(
        model="circuit-2-turbo",
        contents="Generate a creative story",
        temperature=0.8,
        max_tokens=500
    )
    print(response.txt)
    
except AuthenticationError:
    print("Error: Invalid API key. Please check your credentials.")
    
except RateLimitError as e:
    print(f"Rate limit exceeded. Retry after {e.retry_after} seconds.")
    
except ModelNotFoundError:
    print("Error: The specified model is not available.")
    
except APIError as e:
    print(f"API Error: {e.message} (Status: {e.status_code})")
    
except Exception as e:
    print(f"Unexpected error: {str(e)}")

# Configuration management
config = CNAI.Config(
    api_key="your_api_key",
    base_url="https://api.circuitnotion.com",
    timeout=30,
    max_retries=3,
    retry_delay=1,
    default_model="circuit-2-turbo",
    default_temperature=0.7,
    default_max_tokens=500
)

client = CNAI.Client(config=config)

# Override default settings per request
response = client.models.generate_content(
    contents="Explain machine learning algorithms",
    temperature=0.3,  # Override default temperature
    max_tokens=1000   # Override default max_tokens
)

# Environment-based configuration
import os
client = CNAI.Client(
    api_key=os.getenv("CIRCUITNOTION_API_KEY"),
    base_url=os.getenv("CIRCUITNOTION_BASE_URL", "https://api.circuitnotion.com")
)