Declarai is an innovative framework that transforms your Python functions into LLM (Large Language Model) tasks, making it seamless to embed AI functionalities into your existing code. This article will guide you through the steps to get started with Declarai, and we’ll even throw in some troubleshooting tips for good measure.

What is Declarai?

Declarai simplifies the integration of LLMs into your Python workflow. Imagine it as a friendly robot assistant that helps clarify your coding tasks without needing you to learn a new language or system. By annotating your Python functions with docstrings and type hints, you can provide precise instructions for the AI model quickly.

Getting Started with Declarai

Installation

• To install Declarai, run the following command in your terminal:

bash
pip install declarai

Setup

• Set up your OpenAI API key by executing:

bash
export OPENAI_API_KEY=your_openai_token

Alternatively, you can pass the API token when initializing the Declarai object:

python
import declarai

gpt_35 = declarai.openai(model='gpt-3.5-turbo', openai_token='your-openai-key')

Creating Your First Task

Let’s say your task is to rank the urgency of a message. This showcases how Declarai can transform a simple function into an AI-powered task:

python
@gpt_35.task
def rank_by_severity(message: str) -> int:
    """Rank the severity of the provided message by its urgency.
    Urgency is ranked on a scale of 1-5, with 5 being the most urgent.
    :param message: The message to rank
    :return: The urgency of the message
    """
    # AI magic happens here!
    return rank_by_severity(message="The server is down!")

Think of this function as an automated traffic light system: it assesses the urgency of messages (green to red lights) and responds accordingly!

Advanced Features

Declarai offers a plethora of functionalities:

• Intelligent Prompts: Generate prompts automatically based on your code.
• Conversational API: Engage in iterative conversations with memory management.
• Real-time Streaming: Stream long LLM responses to your applications.
• Parsing with Pydantic: Effortlessly parse LLM responses into defined models.
• Native Python Support: No need to stray from your familiar coding practices.

Troubleshooting Tips

If you run into issues while using Declarai, here are some steps to consider:

• Verify Installation: Ensure you have installed Declarai correctly. Re-run the installation command if necessary.
• Check API Key: Double-check that your OpenAI API key is correct and active.
• Code Validation: Ensure your docstrings and type hints are correctly formatted, as these are crucial for the framework’s operation.
• Console Logs: Look out for error messages in your console which can provide insights into what’s going wrong.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.