Quickstart

This guide will help you get started with ClientAI quickly. We'll cover the basic setup and usage for each supported AI provider.

Minimal Example

Here's a minimal example to get you started with ClientAI:

quickstart.py

from clientai import ClientAI

# Initialize the client (example with OpenAI)
client = ClientAI('openai', api_key="your-openai-api-key")

# Generate text
response = client.generate_text(
    "Tell me a joke",
    model="gpt-3.5-turbo",
)
print(response)

# Use chat functionality
messages = [
    {"role": "user", "content": "What is the capital of France?"},
    {"role": "assistant", "content": "The capital of France is Paris."},
    {"role": "user", "content": "What's its population?"}
]
response = client.chat(messages, model="gpt-3.5-turbo")
print(response)

Setup for Different Providers

OpenAI

openai_setup.py

from clientai import ClientAI

# Initialize the OpenAI client
client = ClientAI('openai', api_key="your-openai-api-key")

# Now you can use the client for text generation or chat

Replicate

replicate_setup.py

from clientai import ClientAI

# Initialize the Replicate client
client = ClientAI('replicate', api_key="your-replicate-api-key")

# Now you can use the client for text generation or chat

Ollama

ollama_setup.py

from clientai import ClientAI

# Initialize the Ollama client
client = ClientAI('ollama', host="your-ollama-host")

# Now you can use the client for text generation or chat

Basic Usage

Once you have initialized the client, you can use it for text generation and chat functionality:

Text Generation

text_generation.py

from clientai import ClientAI

client = ClientAI('openai', api_key="your-openai-api-key")

# Generate text
response = client.generate_text(
    "Explain the concept of quantum computing",
    model="gpt-3.5-turbo",
    max_tokens=100
)
print(response)

Chat

chat.py

from clientai import ClientAI

client = ClientAI('openai', api_key="your-openai-api-key")

# Use chat functionality
messages = [
    {"role": "user", "content": "What is machine learning?"},
    {"role": "assistant", "content": "Machine learning is a branch of artificial intelligence..."},
    {"role": "user", "content": "Can you give an example of its application?"}
]
response = client.chat(
    messages,
    model="gpt-3.5-turbo",
    max_tokens=150
)
print(response)

Ollama Server Management

If you're running Ollama locally, ClientAI provides a convenient way to manage the Ollama server:

ollama_manager.py

from clientai.ollama import OllamaManager

# Start and automatically stop the server using a context manager
with OllamaManager() as manager:
    # Server is now running
    client = ClientAI('ollama')
    response = client.generate_text("Hello, world!", model="llama2")
    print(response)

You can also configure basic server settings:

from clientai.ollama import OllamaManager, OllamaServerConfig

config = OllamaServerConfig(
    host="127.0.0.1",
    port=11434,
    gpu_layers=35  # Optional: Number of layers to run on GPU
)

with OllamaManager(config) as manager:
    # Your code here
    pass

Next Steps

Now that you've seen the basics of ClientAI, you can:

1. Explore more advanced features like streaming responses and handling full response objects.
2. Check out the Usage Guide for detailed information on all available methods and options.
3. See the API Reference for a complete list of ClientAI's classes and methods.

Remember to handle API keys securely and never expose them in your code or version control systems.