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Divide-and-Conquer Example

In computer science, divide and conquer is an algorithm design paradigm. A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly. The solutions to the sub-problems are then combined to give a solution to the original problem.

This example demonstrates how to use the framework for divide-and-conquer tasks. The example code can be found in the examples/general_dnc directory.

   cd examples/general_dnc

Overview

This example implements a general divide-and-conquer workflow that consists of following components:

  1. DnC Input Interface

    • Handles user input containing questions and(or) images
    • Construct data structure for workflow running

  2. Init Set Variable Task

    • Initialize global workflow variables that is needed in the entire workflow

  3. Conqueror Task

    • Conqueror task executes and manages complex task trees: direct agent answer, conquer current task, use tool call for current task or break current task into several subtasks
    • It takes a hierarchical task tree and processes each task node, maintaining context and state between task executions

  4. Conqueror Update Set Variable Task

    • Update global workflow variables changed after conqueror task excution for better reading experience in conductor UI

  5. Divider Task

    • Break down complex task into multiple smaller subtasks
    • Generate and match milestones to each subtask

  6. Divider Update Set Variable Task

    • Update global workflow variables changed after divider task excution for better reading experience in conductor UI

  7. Rescue Task

    • Rescue failed tool call task, attempt to fix the issue by retrying with corrected parameters

  8. Conclude Task

    • Solid end of the workflow, conclude the original root task based on all related information

  9. Switch Task

    • After conqueror task, based on it's dicision, switch to specific next worker.
    • Default case is the next conqueror task
    • If too complex, switch to divider task
    • If failed, switch to rescue task

  10. Task Exit Monitor Task

    • Monitor whether the exit condition of the DnC loop task is met
    • Based on the conqueror and divider task(s), the task tree is dynamicly generated and continuesly updated in the whole workflow

  11. Post Set Variable Task

    • Update global workflow variables changed after task exit monitor task execution for better reading experience in conductor UI

  12. DnC Loop Task

    • The core of the DnC workflow, takes a hierarchical task tree and processes each task node, maintaining context and state between task executions
    • It contains three main tasks: conqueror task, divider task and rescue task, and other supporting tasks mentioned above

This whole workflow is looked like the following diagram:

DnC Workflow

Prerequisites

  • Python 3.10+
  • Required packages installed (see requirements.txt)
  • Access to OpenAI API or compatible endpoint (see configs/llms/*.yml)
  • [Optional] Access to Bing API for WebSearch tool (see configs/tools/*.yml)
  • Redis server running locally or remotely
  • Conductor server running locally or remotely

Configuration

The container.yaml file is a configuration file that manages dependencies and settings for different components of the system, including Conductor connections, Redis connections, and other service configurations. To set up your configuration:

  1. Generate the container.yaml file:

    python compile_container.py

    This will create a container.yaml file with default settings under examples/general_dnc.

  2. Configure your LLM and tool settings in configs/llms/*.yml and configs/tools/*.yml:

    • Set your OpenAI API key or compatible endpoint through environment variable or by directly modifying the yml file
    export custom_openai_key="your_openai_api_key"
export custom_openai_endpoint="your_openai_endpoint"
    • [Optional] Set your Bing API key or compatible endpoint through environment variable or by directly modifying the yml file
    export bing_api_key="your_bing_api_key"

    Note: It isn't mandatory to set the Bing API key, as the WebSearch tool will rollback to use duckduckgo search. But it is recommended to set it for better search quality.

    • Configure other model settings like temperature as needed through environment variable or by directly modifying the yml file

  3. Update settings in the generated container.yaml:

    • Modify Redis connection settings:
      • Set the host, port and credentials for your Redis instance
      • Configure both redis_stream_client and redis_stm_client sections
    • Update the Conductor server URL under conductor_config section
    • Adjust any other component settings as needed

Running the Example

  1. Run the general DnC example:

    For terminal/CLI usage:

    python run_cli.py

    For app/GUI usage:

    python run_app.py

Troubleshooting

If you encounter issues:

  • Verify Redis is running and accessible
  • Check your OpenAI API key is valid
  • Check your Bing API key is valid if search results are not as expected
  • Ensure all dependencies are installed correctly
  • Review logs for any error messages
  • Open an issue on GitHub if you can't find a solution, we will do our best to help you out!

Building the Example

Coming soon! This section will provide detailed instructions for building and packaging the general_dnc example step by step.