AI-Powered Task Management System with Python, OpenAI, and Vector Databases

Introduction to AI-Powered Task Management System

An AI-powered task management system is a powerful tool that leverages Python, OpenAI, and vector databases like Chroma or Weaviate to create, prioritize, and execute tasks. The system generates tasks based on the outcomes of previous tasks and predefined objectives. OpenAI’s natural language processing (NLP) capabilities are used to create new tasks, while Chroma/Weaviate stores and retrieves task results for context.

Read more on Github page of project: https://github.com/yoheinakajima/babyagi

How the AI-Powered Task Management System Works

The system operates through an infinite loop that performs the following steps:

1. Retrieves the first task from the task list.
2. Sends the task to the execution agent, which utilizes OpenAI’s API to complete the task based on the context.
3. Enriches the result and stores it in Chroma/Weaviate.
4. Generates new tasks and reprioritizes the task list according to the objective and the outcome of the previous task.

Key Functions of the AI-Powered Task Management System

The

execution_agent()

function uses the OpenAI API and takes two parameters: the objective and the task. It sends a prompt to OpenAI’s API, which returns the task result. The prompt includes a description of the AI system’s task, the objective, and the task itself. The result is returned as a string.

The

task_creation_agent()

function employs OpenAI’s API to create new tasks based on the objective and the result of the previous task. It takes four parameters: the objective, the result of the previous task, the task description, and the current task list. It sends a prompt to OpenAI’s API, which returns a list of new tasks as strings. The function then returns the new tasks as a list of dictionaries, where each dictionary contains the task name.

The

prioritization_agent()

function uses OpenAI’s API to reprioritize the task list. It takes one parameter, the ID of the current task. It sends a prompt to OpenAI’s API, which returns the reprioritized task list as a numbered list.

Lastly, the script uses Chroma/Weaviate to store and retrieve task results for context. The script creates a Chroma/Weaviate collection based on the table name specified in the TABLE_NAME variable. Chroma/Weaviate is then used to store the task results in the collection, along with the task name and any additional metadata.

How to Use the AI-Powered Task Management System

To use the script, follow these steps:

1. Clone the repository via

   git clone https://github.com/yoheinakajima/babyagi.git

   and cd into the cloned repository.

2. Install the required packages:

   pip install -r requirements.txt

3. Copy the .env.example file to .env:

   cp .env.example .env

   . This is where you will set the following variables.

4. Set your OpenAI API key in the OPENAI_API_KEY and OPENAPI_API_MODEL variables.
5. Set the name of the table where the task results will be stored in the TABLE_NAME variable.
6. (Optional) Set the name of the BabyAGI instance in the BABY_NAME variable.
7. (Optional) Set the objective of the task management system in the OBJECTIVE variable.
8. (Optional) Set the first task of the system in the INITIAL_TASK variable.
9. Run the script:

   python babyagi.py

All optional values above can also be specified on the command line.