SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures

Self-Discover

This repository is an extension of the SELF-DISCOVER repository by Kailash Prasannakumar, which implements the ideas from the following original paper:

Original Paper

SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures Link to the paper

The original paper was written by Pei Zhou with Google DeepMind, while the SELF-DISCOVER repository is an implementation of that paper by Kailash Prasannakumar.

Installation

Clone this repository:

git clone https://github.com/ashokchhetri7/self-discover.git

Install the required libraries:
```
pip install -r requirements.txt
```
create a .env file
Open the .env file in a text editor.
Add the following line to the .env file:
```
GOOGLE_API_KEY= add_your_api_key_here_no_quotation_needed
```
Replace your_google_api_key_here with your actual Google API key obtained from google makersuite. Your can also use OPENAI_API_KEY as well

Customization

Modify the reasoning_modules variable in prompts.py to add, remove, or modify reasoning modules.
Adjust the prompts in prompts.py to customize the user interaction flow.

How to use the reasoning JSON structure

As mentioned in the paper

For Stage 2, where we use the self-discovered structure to solve the task instances, we start with the prompt: “Follow the
step-by-step reasoning plan in JSON to correctly solve the task. Fill in the values following the keys by reasoning specifically 
about the task given. Do not simply rephrase the keys.”, followed by the reasoning structure, and finally the task instance.

Final Output

Based on the above reasoning structure and step I have given the following structure to get the final output. No need to run this. (It's not runnable)

Follow the step-by-step reasoning plan in {reasoning_structure} to correctly solve the task. 
Fill in the values following the keys by reasoning specifically about the task given. 
Do not simply rephrase the keys. And finally provide the "final_answer" of the given question.

For the given task; 
<Task>
{Task}
</Task>

Given reasoning steps;
{reasoning_structure}

Expected Output:
{
  "final_answer": {
    ...
    }
}

Usage

Simply run the command below:

python self_discover.py

You can also see that in the Streamlit

python streamlit run app.py

You can see the ouptut of the select, adapt, implement as well as final ouptut in the web using this command.

ashokchhetri7 / self-discover