intura-ai 0.0.5.11

Intelligent Research and Experimentation AI for LLM experimentation production. ⚠️ This package is in BETA and under active development.

Intura-AI: Intelligent Research and Experimentation AI

intura-ai is a Python package designed to streamline LLM experimentation and production. It provides tools for logging LLM usage and managing experiment predictions, with seamless LangChain compatibility.

⚠️ Beta Status

IMPORTANT: Intura AI is currently in BETA and under active development. While we're working hard to ensure stability, you may encounter:

• API changes without prior notice
• Incomplete features
• Bugs and performance issues

We welcome your feedback and contributions to help improve the library!

Getting Started with Intura

Dive into the world of Intura and begin experimenting with Large Language Models (LLMs) in under 5 minutes. This guide will walk you through the essential steps to set up your first experiment, either through our SDK or directly via the Intura Dashboard.

Quick Start Options

• SDK-Based Experiment Creation:
  • Utilize the Intura AI SDK for programmatic experiment creation and management, offering flexibility and integration into your existing workflows.
• Intura Dashboard:
  • Jump straight into experimentation with our user-friendly dashboard, accessible at intura.dashboard. This option is perfect for quickly exploring Intura's capabilities.

Prerequisites

Before you begin, ensure you have the following:

• Python 3.10 or Later:
  • Download and install Python from python.org/downloads. During installation, select the option to add Python to your system's PATH and install all necessary dependencies. This ensures seamless SDK functionality.

SDK Initialization and Setup

To leverage the Intura AI SDK, you'll need to install it and configure your environment.

1. Install the Intura AI SDK:

   • Open your terminal or command prompt and execute the following command:

   pip install intura-ai
   

2. Obtain Your API Key:

   • Your API key is essential for authenticating with the Intura platform. You can find it within the Intura Dashboard or by contacting [e-mail]. Store this key securely, as it grants access to your Intura resources.

Creating Your First Experiment

With the SDK installed and your API key ready, you can now define your experiment.

1. Experiment Definition:

   • Use the DashboardPlatform functions from the intura_ai.platform module to operate your experiment. You'll also need to import and use ExperimentModel and ExperimentTreatmentModel from intura_ai.platform.domain to define your experiment and its variations.

   from intura_ai.platform import DashboardPlatform
   from intura_ai.platform.domain import ExperimentModel, ExperimentTreatmentModel
   
   client = DashboardPlatform(intura_api_key=os.environ.get("INTURA_API_KEY", "<INTURA_API_KEY>"))
   experiment_id = client.create_experiment(ExperimentModel(
       experiment_name="Example Experiment",
       treatment_list=[
           ExperimentTreatmentModel(
               treatment_model_name="gemini-1.5-flash",
               treatment_model_provider="Google",
               prompt="Act as personal assistant"
           ),
           ExperimentTreatmentModel(
               treatment_model_name="claude-3-5-sonnet-20240620",
               treatment_model_provider="Anthropic",
               prompt="Act as personal assistant"
           ),
       ]
   ))
   

Running Your Experiment

After defining your experiment, you can run it and analyze the results.

1. Initialize the Experiment Client:

   • Use the ChatModelExperiment class to create a client that interacts with your experiment.

   import os
   from intura_ai.experiments import ChatModelExperiment
   
   client = ChatModelExperiment(
       intura_api_key=os.environ.get("INTURA_API_KEY", "<INTURA_API_KEY>")
   )
   
   choiced_model, model_config, chat_prompts = client.build(
       experiment_id=experiment_id,
       features={
           "user_id": "Rama12345",
           "membership": "FREE",
           "employment_type": "FULL_TIME",
           "feature_x": "your custom features"
       }
   )
   

2. Craft the Final Prompt:

   • Add your final user prompt to the chat prompts list.

   chat_prompts.append(('human', 'give me motivation for today'))
   

3. Set Up and Invoke the Model:

   • Initialize the chosen LLM model with its configuration and API key, then invoke it with the crafted prompts.

   import os
   
   # Set your LLM API keys as environment variables
   os.environ["GOOGLE_API_KEY"] = "xxx"
   os.environ["ANTHROPIC_API_KEY"] = "xxx"
   os.environ["DEEPSEEK_API_KEY"] = "xxx"
   os.environ["OPENAI_API_KEY"] = "xxx"
   os.environ["ANOTHER_LLM_KEY"] = "xxx"
   
   model = choiced_model(**model_config)
   
   # Or set LLM API keys as parameters
   # model = choiced_model(**model_config, api_key="<YOUR_LLM_API_KEY>")
   
   response = model.invoke(chat_prompts)
   print(response)
   

By following these steps, you can quickly set up and run your first experiment with Intura, exploring the power of LLMs and optimizing their performance for your specific needs.

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for bug reports or feature requests.

License

This project is licensed under the MIT License.