Building a Single User Generative AI application with Python

To experiment with generative AI orchestrator architecture patterns, I implemented a simplified single user orchestrator application with Python. As described in the article, I studied common orchestrator components and selected a basic stack of OpenAI API models, Elasticsearch, Python, and Streamlit for the UI. With this modular architecture, I could show case core orchestration functions like managing AI model APIs, tracking user context, and delivering personalized responses. Although basic, building this application gave hands-on experience with an end-to-end generative AI workflow. I leveraged accelerators like ChatGPT and GitHub Copilot to help assemble the components more efficiently. Overall, this project allowed me to learn orchestration design principles and demonstrate the capabilities of a generative AI assistant for individual users and developers.

The Power of Generative AI

I wrote about Unleashing the Power of Generative AI in Your Career: It’s Not Just About Knowledge, It’s About Language, initially published on August 27, 2023, at datatunnel.io. I delve into the transformative potential of generative AI in augmenting human skills across various professional domains—from software engineering and human resources to finance and sales.

However, the article argues that the real power of these AI models is not merely their extensive knowledge base, but in the user’s ability to effectively communicate with them. In AI parlance, knowing how to craft the perfect “prompts” can unlock highly tailored and context-specific solutions, supercharging productivity. While highlighting the golden use cases where generative AI excels, the article also emphasizes the importance of understanding the associated risks and ethical considerations. The key takeaway is that using generative AI responsibly and to your maximum benefit is not just about what the AI knows; it is about knowing how to ask questions.

Ai-explains – A Single User AI Orchestrator Application

As you continue reading this article, we take a deeper dive into the mechanics of the Single User AI Orchestrator Application—a simplified yet potent thin-layer application in Python that mimics core functionalities of a multi-tenant generative AI orchestrator.

A multi-tenant orchestrator is an advanced system designed to manage the intricacies of deploying generative AI applications across multiple users or tenant groups. Its architecture is modular, with each component serving a specific function in the AI application workflow. Simon Attard introduced some of these concepts on June 20, 2023 in an article called Leveraging Large Language Models in your Software Applications.

Our open source thin-layer application is called AI-explain and is available on GitHub.  Tailored for individual users and AI developers, this application serves as a bridge between you and an array of advanced AI models, making your interactions more personalized and efficient.

Our single user app manages the interaction between a user and an AI system. It manages OpenAI API requests and provides personalized responses based on the user’s prompt context and history. The implementation of this single user application includes:

1. User Interface: This component provides the user with a way to interact with the system, such as a web or mobile application.
2. Basic Orchestrator: This component manages the interaction between the user and the AI system. It receives requests from the user, selects the appropriate API model to use, and generates a response based on the user’s prompt.
3. API Models: These are pre-trained language models to generate responses to user requests. In this example, the OpenAI API provides access API models, including gpt-3.5-turbo, gpt-3.5-turbo-16k, gpt-4, and gpt-4-32k.
4. Elasticsearch Database: This component stores and retrieves data related to the user’s context and history. It provides fast and efficient search capabilities and stores a wide range of data types, including text, numerical data, and geospatial data.
5. Python: This is a programming language for AI development. I use Python to write the code that implements the various components of the single user orchestrator.
6. Streamlit UI: This is a Python library that creates interactive web applications. It provides a simple and intuitive way to create user interfaces for AI systems.

Python and Streamlit UI come together to create a basic interactive web application that not only makes it easy for you to engage with AI but also enriches the quality of those engagements.

The Single User AI Orchestrator Application offers a personalized and efficient experience with AI systems. By utilizing pre-trained language models and state-of-the-art search functionalities, this basic application aims to furnish you with the basics about collections, prompt design templates and search memory, all within a user-friendly interface.

If you are an AI developer or end-user, feel free to explore how this application can be extended to become your personalized AI assistant, augmenting your productivity. The code is open sourced and available on fedenolasco/ai-explains (github.com).