aixnet-beta 0.2.3

The official package of AI-X-Net.

AIXNet Beta

Overview

AIXNet Beta is a Python package that provides an interface to interact with the OpenAI API. It simplifies the process by requiring only the API key and the payload to get started.

Installation

To install the package, use:

pip install aixnet-beta

Usage

Step 1: Import the OpenAIClient class

from aixnet_beta.fm import OpenAIClient

Step 2: Initialize the client

Initialize the client with your OpenAI API key.

# Replace 'YOUR_API_KEY' with your actual API key
api_key = "YOUR_API_KEY"

# Initialize the OpenAI client
client = OpenAIClient(api_key)

Step 3: Define the payload

Define the payload for your API request. The payload must be a dictionary that represents the request you want to send to the OpenAI API.

payload = {
    "model": "gpt-4o-mini",
    "messages": [
        {"role": "user", "content": "What is 1+1?"}
    ],
    "temperature": 0.7
}

Step 4: Invoke the API and get the response

Use the invoke_api method of the OpenAIClient class to send the request and receive a response.

# Invoke the API and get the response
response = client.invoke_api(payload)

# Print the response from the API
print(json.dumps(response, indent=4))

Example: GPT

Here is a complete example of using the OpenAIClient:

from aixnet_beta.fm import OpenAIClient
import json

# Initialize the OpenAI client with the API key
api_key = "alex123"  # Replace with your actual API key
client = OpenAIClient(api_key)

# Define the payload for the request
payload = {
    "model": "gpt-3.5-turbo",
    "messages": [
        {"role": "user", "content": "Say this is a test!"}
    ],
    "temperature": 0.7
}

# Invoke the API and get the response
response = client.invoke_api(payload)

# Print the response from the API
print(json.dumps(response, indent=4))

Alternatively, you can run the code and chat with the foundation model in terminal.

# Initialize the OpenAIClient with the API key
api_key = "alex123"  # Replace with your actual API key
chatbot = OpenAIClient(api_key)

# Start the chat session
chatbot.start_chatting()

Example: Meta LLama

We have implemented the most latest foundation model from Meta. For example, Meta's Meta-Llama-3-405B-Instruct is included in this package. You can chat to it directly using the .start_chat() method.

AZURE_INFERENCE_ENDPOINT = "ENDPOINT_URL_HERE"
AZURE_INFERENCE_CREDENTIAL = "API_KEY

from aixnet_beta.meta_llama import MetaLlama

bot = MetaLlama(
    AZURE_INFERENCE_ENDPOINT,
    AZURE_INFERENCE_CREDENTIAL,
    "You are a coding assistant. You always use type hints, docstring, and comments in py code.")
bot.start_chat()

Here's an expected output:

🤖 Start chatting with us! Enter 'EXIT' to quit. 🛑
🧑 Human: write a hello world py func
🤖 Bot: Here is a "Hello World" Python function with type hints, docstring, and comments:
    ```python
    def hello_world(name: str) -> None:
        """
        Prints a personalized "Hello World" message.

        Args:
            name (str): The name to include in the greeting.

        Returns:
            None
        """
        # Print the greeting message
        print(f"Hello, {name}!")

    # Example usage:
    hello_world("Alice")  # Output: Hello, Alice!
    ```
Let me know if you'd like me to explain any part of this code!
🧑 Human: EXIT
🙌 Thank you for using the chatbot! Have a great day! 🌟

License

This project is licensed under the MIT License.