general_dnc

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. Input Interface

   • Handles user input containing questions and(or) images

2. DnC Workflow

   • Decompose the original task into multiple sub-tasks
   • Conquer each sub-task to complete the original task

3. Conclude Task

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

This whole workflow is looked like the following diagram:

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

3. 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.