/AI-Agent-Lab

Primary LanguagePythonMIT LicenseMIT

AI Agent Lab

The AI Agent Lab is a module-based environment for working with the GPT-3.5 architecture, designed to facilitate rapid experimentation and testing of language models. The AI Agent Lab includes a docker-compose configuration with QuestDB, Grafana, Code-Server, Nginx and an AI Agent, providing a seamless interface for managing and querying data, visualizing results, and coding in real-time. With the AI Agent Lab, users can quickly set up a notebook environment and start experimenting with GPT-3.5 models, without the need for complex setup or configuration

The AI Agent Lab is also the basis for the AI Agent Farm, a modular system for developing and deploying AI agents. By using the AI Agent Lab as a module in the AI Agent Farm, users can easily connect their agents to real-time data streams and other sources of information, allowing for more sophisticated and accurate decision-making. With its flexible and modular design, AI-Agent-Lab is a powerful tool for anyone working with GPT-3.5 models and data streams in their AI applications.

To use AI Agent Lab with a remote JupyterHub environment, follow these steps:

  • Set up or use an existing remote JupyterHub that includes the necessary dependencies for working with GPT-3.5 models and data streams.

  • Launch the AI Agent Lab using the provided docker-compose file.

  • Connect to the remote JupyterHub environment from within the Code-Server interface provided by AI-Agent-Lab.

Start working with GPT-3.5 models and data streams, using the pre-installed tools and libraries that are included in your remote environment.

Features

  1. QuestDB: QuestDB is a high-performance, open-source time-series database. It allows for efficient storage and querying of time-series data, making it ideal for working with real-time data streams.

  2. Grafana: Grafana is a popular open-source platform for data visualization and monitoring. It provides a rich set of features for creating interactive dashboards and visualizing data from various sources.

  3. Code-Server: Code-Server is a web-based IDE based on Visual Studio Code. It provides a familiar coding environment with features such as code completion, syntax highlighting, and debugging capabilities.

  4. Nginx: Nginx is a widely-used web server and reverse proxy server. It enhances the AI Agent Lab by providing additional functionality for routing and load balancing, improving performance and security

  5. AI Agent: The AI Agent is the core backend service in the AI Agent Lab, handling AI processing, data retrieval, and related operations.

  6. AI Agent UI: The AI Agent UI provides an intuitive, web-based interface for interacting with the AI agent.

Getting Started

To use the AI Agent Lab, follow these steps:

  1. Set up or use an existing environment with Docker installed.

  2. Clone the AI Agent Lab repository and navigate to the docker directory.

git clone https://github.com/quantiota/AI-Agent-Lab.git
cd AI-Agent-Lab/docker

  1. Follow all prerequisite steps that should be completed before bringing the Docker Stack. Refer to the Docker Readme file for guidance

  2. Launch the AI Agent Lab using the provided docker-compose configuration.

docker compose up --build -d
  1. Once the services are up and running, you can access the AI Agent Lab interfaces:
  1. To connect the AI Agent Lab to a remote JupyterHub environment from Code-Server:

  • Set up or use an existing remote JupyterHub that includes the necessary dependencies for working with your notebooks and data.

  • Connect to the remote JupyterHub environment from within the Code-Server interface provided by the AI Agent Lab

Start working with your notebooks and data, using the pre-installed tools and libraries that are included in your remote environment.

AI Agent Lab Architecture Diagram

AI Agent Lab diagram

Hardware Requirements

For optimal performance, the AI Agent Lab requires the following hardware setup:

  • Server: HP Microserver Gen8
  • Processor: Quad-core CPU
  • Primary Storage: 250GB SSD
  • Memory: 16GB of RAM
  • Controler: HP Smart Array P410
  • Additional Storage: 4x1TB RAID data storage
  • Operating System: Ubuntu 22.04 Server

References