Full Code of catid/self-discover for AI

Repository: catid/self-discover
Branch: main
Commit: be77eabf6b8a
Files: 6
Total size: 20.6 KB

Directory structure:
gitextract_vxb5rqoa/

├── .gitignore
├── LICENSE
├── README.md
├── langs/
│   └── kor.py
├── requirements.txt
└── self_discover.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
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.env
*.ipynb*
.json



================================================
FILE: LICENSE
================================================
BSD 3-Clause License

Copyright (c) 2024, Chris Taylor

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this
   list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its
   contributors may be used to endorse or promote products derived from
   this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


================================================
FILE: README.md
================================================
# SELF-DISCOVER

This implements Google's algorithm from https://arxiv.org/pdf/2402.03620.pdf

## Setup

Install Conda: https://docs.conda.io/projects/miniconda/en/latest/

```bash
conda create -n sd python=3.10 -y && conda activate sd

git clone https://github.com/catid/self-discover.git
cd self-discover

pip install -r requirements.txt
```

## Demo

Edit `self_discover.py` to add your OpenAI API key.

```bash
python self_discover.py
```

## Output

I ran this using a self-hosted Miqu 70B model ( https://huggingface.co/LoneStriker/miqu-1-70b-sf-4.0bpw-h6-exl2 ) on two 4090 GPUs.

This is querying for the default task:

```
Lisa has 10 apples. She gives 3 apples to her friend and then buys 5 more apples from the store. How many apples does Lisa have now?

(The answer is 12.)
```

```bash
(sd) ➜  self-discover git:(main) ✗ python self_discover.py

Stage 1 SELECT: Selected Modules:
 9. How can I break down this problem into smaller, more manageable parts?

The problem can be broken down into two parts: first, calculating how many apples Lisa has after giving some away, and second, calculating how many apples she has after buying more. This approach simplifies the problem and makes it easier to solve.

Additionally, 10. Critical Thinking: This style involves analyzing the problem from different perspectives, questioning assumptions, and evaluating the evidence or information available. It focuses on logical reasoning, evidence-based decision-making, and identifying potential biases or flaws in thinking.

Critical thinking is always relevant in problem-solving as it helps to ensure that the solution is based on sound reasoning and evidence. In this case, it can help to confirm that the problem is being interpreted correctly and that the solution is logical and accurate.

Therefore, both 9 and 10 are relevant reasoning modules for this problem.

Stage 1 ADAPT: Adapted Modules:
 1. Breaking down the problem:
The problem can be broken down into two smaller tasks:

a) Calculate how many apples Lisa has after giving 3 to her friend.
b) Calculate how many apples Lisa has after buying 5 more apples from the store.

By breaking down the problem into these smaller tasks, it becomes easier to solve.

2. Critical thinking:
Apply critical thinking to ensure that the problem is being interpreted correctly and that the solution is logical and accurate. This involves:

a) Confirming that the problem is understood correctly (Lisa starts with 10 apples, gives away 3, and buys 5 more).
b) Ensuring that the order of operations is followed correctly (subtracting the apples given away before adding the apples bought).
c) Double-checking the calculations to ensure accuracy.

By applying critical thinking, you can be confident that the solution is based on sound reasoning and evidence.

Stage 1 IMPLEMENT: Reasoning Structure:
 1. Breaking down the problem:

a) Calculate how many apples Lisa has after giving 3 to her friend:
- Lisa starts with 10 apples
- She gives away 3 apples
- Subtract the number of apples given away from the initial number of apples

b) Calculate how many apples Lisa has after buying 5 more apples from the store:
- After giving away 3 apples, Lisa has a certain number of apples left (from task a)
- She then buys 5 more apples
- Add the number of apples bought to the remaining number of apples

2. Critical thinking:

a) Confirm that the problem is understood correctly:
- Lisa starts with 10 apples
- She gives 3 apples away
- She buys 5 more apples

b) Ensure that the order of operations is followed correctly:
- First, subtract the apples given away
- Then, add the apples bought

c) Double-check the calculations:
- Verify that the correct number of apples was subtracted
- Verify that the correct number of apples was added

By following this reasoning structure, you can solve the problem by breaking it down into smaller tasks, applying critical thinking, and double-checking your calculations. The final answer should be accurate and based on sound reasoning.

Stage 2: Final Result:
 1. Breaking down the problem:

a) Calculate how many apples Lisa has after giving 3 to her friend:
- Lisa starts with 10 apples
- She gives away 3 apples
- Subtract the number of apples given away from the initial number of apples: 10 - 3 = 7 apples

b) Calculate how many apples Lisa has after buying 5 more apples from the store:
- After giving away 3 apples, Lisa has 7 apples left (from task a)
- She then buys 5 more apples
- Add the number of apples bought to the remaining number of apples: 7 + 5 = 12 apples

2. Critical thinking:

a) Confirm that the problem is understood correctly:
- Lisa starts with 10 apples
- She gives 3 apples away
- She buys 5 more apples

b) Ensure that the order of operations is followed correctly:
- First, subtract the apples given away (10 - 3)
- Then, add the apples bought (7 + 5)

c) Double-check the calculations:
- Verify that the correct number of apples was subtracted (10 - 3 = 7)
- Verify that the correct number of apples was added (7 + 5 = 12)

Final answer: Lisa has 12 apples now.
```


================================================
FILE: langs/kor.py
================================================
import openai
import os
import time

from openai import OpenAI

# Edit this part for your setup
#client = OpenAI(api_key="yourkey")
client = OpenAI(api_key="local-generated-key", base_url="http://devnuc.lan:5000/v1")


def query_llm(messages, max_tokens=2048, temperature=0.1):
    # Retry forever
    while True:
        try:
            response = client.chat.completions.create(
                model="the best model",
                messages=messages,
                temperature=temperature,
                max_tokens=max_tokens,
                n=1,
            )

            content = response.choices[0].message.content.strip()

            return content
        except Exception as e:
            print("Failure querying the AI. Retrying...")
            time.sleep(1)

def query_openai(prompt):
    messages = [
        { "role": "user", "content": prompt }
    ]
    return query_llm(messages)

# STAGE 1

def select_reasoning_modules(task_description, reasoning_modules):
    """
    Step 1: SELECT relevant reasoning modules for the task.
    """
    prompt = f"주어진 과제: {task_description}, 다음 추론 모듈들 중 관련이 있는 것은 무엇입니까? 이유를 자세히 설명하지 마세요.\n\n" + "\n".join(reasoning_modules)
    selected_modules = query_openai(prompt)
    return selected_modules

def adapt_reasoning_modules(selected_modules, task_example):
    """
    Step 2: ADAPT the selected reasoning modules to be more specific to the task.
    """
    prompt = f"전체 해결 과정을 실행시키지 말고 다음 추론 모듈들을 과제에 맞게 조정하세요:\n{selected_modules}\n\n우리의 과제:\n{task_example}"
    adapted_modules = query_openai(prompt)
    return adapted_modules

def implement_reasoning_structure(adapted_modules, task_description):
    """
    Step 3: IMPLEMENT the adapted reasoning modules into an actionable reasoning structure.
    """
    prompt = f"전체 해결책을 실행시키지 말고 조정된 추론 모듈들을 사용하여 과제에 대한 실행 가능한 추론 구조를 만드세요:\n{adapted_modules}\n\n과제 설명:\n{task_description}"
    reasoning_structure = query_openai(prompt)
    return reasoning_structure

# STAGE 2

def execute_reasoning_structure(reasoning_structure, task_instance):
    """
    Execute the reasoning structure to solve a specific task instance.
    """
    prompt = f"다음 추론 구조를 사용하세요: {reasoning_structure}\n\n과제를 해결하세요, 최종 답변을 제공하세요: {task_instance}"
    solution = query_openai(prompt)
    return solution

# Example usage
if __name__ == "__main__":
    reasoning_modules = [
        "1. 그 문제를 해결하는 데 도움이 되는 실험을 어떻게 고안할 수 있나요?",
        "2. 이 문제를 해결하기 위한 아이디어 목록을 작성하고 이를 문제에 하나씩 적용하여 어떠한 진전이 있는지 확인하세요.",
        #"3. 이 문제의 진행 상황을 어떻게 측정할 수 있나요?",
        "4. 문제를 더 쉽게 해결하기 위해 어떻게 단순화할 수 있습니까?",
        "5. 이 문제의 기저에 깔려 있는 주요 가정들은 무엇입니까?",
        "6. 각 해결책의 잠재적 위험과 단점들은 무엇입니까?",
        "7. 이 문제에 대한 대안적인 관점이나 시점은 무엇입니까?",
        "8. 이 문제와 그 해결책의 장기적인 영향은 무엇입니까?",
        "9. 이 문제를 어떻게 더 작고 관리하기 쉬운 부분으로 나눌 수 있습니까?",
        "10. 비판적 사고: 이 스타일에는 다양한 관점에서 문제를 분석하고, 가정에 의문을 제기하고, 이용 가능한 증거나 정보를 평가하는 것이 포함됩니다. 논리적 추론, 증거에 기반한 의사 결정, 사고의 잠재적 편견이나 결함 식별에 중점을 둡니다.",
        "11. 창의적 사고를 시도하고 혁신적이고 기발한 아이디어를 창출하여 문제를 해결하세요. 기존의 경계를 뛰어넘어 사고하고 상상력과 독창성을 장려하는 색다른 솔루션을 탐색해 보세요.",
        #"12. 문제를 해결하기 위해 다른 사람의 입력과 협력을 구하세요. 효과적인 솔루션을 찾기 위해 팀워크, 열린 의사소통, 그룹의 다양한 관점과 전문 지식을 활용하는 것을 강조합니다.",
        "13. 시스템적 사고 사용: 문제를 더 큰 시스템의 일부로 고려하고 다양한 요소의 상호 연결성을 이해합니다. 문제에 영향을 미치는 근본적인 원인, 피드백 루프 및 상호 의존성을 식별하고 시스템 전체를 다루는 전체적인 해결책을 개발하는 데 중점을 둡니다.",
        "14. 위험 분석 사용: 문제에 대한 다양한 해결책이나 접근 방식과 관련된 잠재적 위험, 불확실성 및 장단점을 평가합니다. 잠재적인 결과와 성공 또는 실패 가능성을 평가하고, 위험과 이점에 대한 균형잡힌 분석을 바탕으로 정보에 입각한 결정을 내리는 것을 강조합니다.",
        #"15. 성찰적 사고 사용: 문제에서 물러나서 성찰과 자기 반성의 시간을 가지십시오. 문제 해결에 영향을 미칠 수 있는 개인적인 편견, 가정 및 정신적 모델을 조사하고 미래의 접근 방식을 개선하기 위해 과거 경험을 통해 배우는 데 열려 있도록 하십시오.",
        "16. 해결해야 할 핵심 사안 또는 문제는 무엇입니까?",
        "17. 문제를 일으키는 근본적인 원인이나 요인은 무엇입니까?",
        "18. 이전에 시도된 잠재적인 해결책이나 전략이 있습니까? 그렇다면, 그 결과와 교훈은 무엇이었습니까?",
        "19. 이 문제를 해결하는 데 발생할 수 있는 잠재적인 장애물이나 과제는 무엇입니까?",
        "20. 문제에 대한 통찰력을 제공할 수 있는 관련 데이터나 정보가 있습니까? 그렇다면 어떤 데이터 소스를 사용할 수 있으며 어떻게 분석할 수 있습니까?",
        "21. 문제로 인해 직접적인 영향을 받는 이해관계자나 개인이 있습니까? 그들의 관점과 요구는 무엇입니까?",
        "22. 문제를 효과적으로 해결하려면 어떤 자원(재정, 인적, 기술 등)이 필요합니까?",
        "23. 문제 해결의 진전이나 성공을 어떻게 측정하거나 평가할 수 있습니까?",
        "24. 어떤 지표나 측정법을 사용할 수 있나요?",
        "25. 문제가 특정 전문 지식이나 기술을 요구하는 기술적이거나 실용적인 문제입니까? 아니면 개념적이거나 이론적인 문제에 더 가깝습니까?",
        "26. 문제에 제한된 리소스, 인프라 또는 공간과 같은 물리적 제약이 관련되어 있습니까?",
        "27. 문제가 사회적, 문화적, 심리적 문제 등 인간 행동과 관련된 것입니까?",
        "28. 문제가 불확실성이나 상충되는 목표 하에서 선택을 해야 하는 의사결정 또는 계획과 관련되어 있습니까?",
        "29. 문제가 데이터 분석, 모델링 또는 최적화 기술이 필요한 분석적인 문제입니까?",
        "30. 문제가 창의적인 솔루션과 혁신이 필요한 디자인 과제입니까?",
        "31. 문제가 개별 사례가 아닌 체계적 또는 구조적 문제를 해결해야 합니까?",
        "32. 문제가 시간에 민감하거나 즉각적인 주의와 조치가 필요한 긴급한 것입니까?",
        "33. 이러한 종류의 문제 사양에 대해 일반적으로 어떤 종류의 해결책이 생성됩니까?",
        "34. 문제 사양과 현재 최상의 해결책이 주어졌을 때, 다른 가능한 해결책을 추측해 보세요."
        "35. 현재 최고의 해결책이 완전히 틀렸다고 가정해 보겠습니다. 문제 사양에 대해 생각할 수 있는 다른 방법은 무엇입니까?"
        "36. 이러한 종류의 문제 사양에 대해 알고 있는 내용을 고려할 때 현재 최고의 해결책을 수정하는 가장 좋은 방법은 무엇입니까?"
        "37. 현재 최고의 해결책을 무시하고 문제에 대한 완전히 새로운 해결책을 만드세요."
        #"38. 단계별로 생각해 보세요."
        "39. 단계별 계획을 세우고, 좋은 표기와 설명와 함께 실행해 보세요."
    ]


    task_example = "리사는 사과 10개를 가지고 있습니다. 그녀는 친구에게 사과 3개를 주고, 가게에서 사과 5개를 더 삽니다. 리사는 지금 사과를 몇 개나 갖고 있나요?"



    selected_modules = select_reasoning_modules(task_example, reasoning_modules)
    print("Stage 1 SELECT: Selected Modules:\n", selected_modules)
    
    adapted_modules = adapt_reasoning_modules(selected_modules, task_example)
    print("\nStage 1 ADAPT: Adapted Modules:\n", adapted_modules)
    
    reasoning_structure = implement_reasoning_structure(adapted_modules, task_example)
    print("\nStage 1 IMPLEMENT: Reasoning Structure:\n", reasoning_structure)

    result = execute_reasoning_structure(reasoning_structure, task_example)
    print("\nStage 2: Final Result:\n", result)


================================================
FILE: requirements.txt
================================================
openai


================================================
FILE: self_discover.py
================================================
import openai
import os
import time

''' [ Setup .env ]

1. Create empty file called ".env"
2. Open file with text editor
3. Add the following lines:
OPENAI_API_KEY=your api key
MODEL=gpt-4-0125-preview (or any other model of your choice)
4. Remember to have ".env" added to your .gitignore file

'''
from dotenv import load_dotenv
load_dotenv()

from openai import OpenAI

client = OpenAI()
def query_llm(messages, max_tokens=2048, temperature=0.1):
    # Retry forever
    while True:
        try:
            response = client.chat.completions.create(
                model=os.environ["MODEL"],
                messages=messages,
                temperature=temperature,
                max_tokens=max_tokens,
                n=1,
            )

            content = response.choices[0].message.content.strip()

            return content
        except Exception as e:
            print("Failure querying the AI. Retrying...")
            time.sleep(1)

def query_openai(prompt):
    messages = [
        { "role": "user", "content": prompt }
    ]
    return query_llm(messages)

# STAGE 1

def select_reasoning_modules(task_description, reasoning_modules):
    """
    Step 1: SELECT relevant reasoning modules for the task.
    """
    prompt = f"Given the task: {task_description}, which of the following reasoning modules are relevant? Do not elaborate on why.\n\n" + "\n".join(reasoning_modules)
    selected_modules = query_openai(prompt)
    return selected_modules

def adapt_reasoning_modules(selected_modules, task_example):
    """
    Step 2: ADAPT the selected reasoning modules to be more specific to the task.
    """
    prompt = f"Without working out the full solution, adapt the following reasoning modules to be specific to our task:\n{selected_modules}\n\nOur task:\n{task_example}"
    adapted_modules = query_openai(prompt)
    return adapted_modules

def implement_reasoning_structure(adapted_modules, task_description):
    """
    Step 3: IMPLEMENT the adapted reasoning modules into an actionable reasoning structure.
    """
    prompt = f"Without working out the full solution, create an actionable reasoning structure for the task using these adapted reasoning modules:\n{adapted_modules}\n\nTask Description:\n{task_description}"
    reasoning_structure = query_openai(prompt)
    return reasoning_structure

# STAGE 2

def execute_reasoning_structure(reasoning_structure, task_instance):
    """
    Execute the reasoning structure to solve a specific task instance.
    """
    prompt = f"Using the following reasoning structure: {reasoning_structure}\n\nSolve this task, providing your final answer: {task_instance}"
    solution = query_openai(prompt)
    return solution

# Example usage
if __name__ == "__main__":
    reasoning_modules = [
        "1. How could I devise an experiment to help solve that problem?",
        "2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.",
        #"3. How could I measure progress on this problem?",
        "4. How can I simplify the problem so that it is easier to solve?",
        "5. What are the key assumptions underlying this problem?",
        "6. What are the potential risks and drawbacks of each solution?",
        "7. What are the alternative perspectives or viewpoints on this problem?",
        "8. What are the long-term implications of this problem and its solutions?",
        "9. How can I break down this problem into smaller, more manageable parts?",
        "10. Critical Thinking: This style involves analyzing the problem from different perspectives, questioning assumptions, and evaluating the evidence or information available. It focuses on logical reasoning, evidence-based decision-making, and identifying potential biases or flaws in thinking.",
        "11. Try creative thinking, generate innovative and out-of-the-box ideas to solve the problem. Explore unconventional solutions, thinking beyond traditional boundaries, and encouraging imagination and originality.",
        #"12. Seek input and collaboration from others to solve the problem. Emphasize teamwork, open communication, and leveraging the diverse perspectives and expertise of a group to come up with effective solutions.",
        "13. Use systems thinking: Consider the problem as part of a larger system and understanding the interconnectedness of various elements. Focuses on identifying the underlying causes, feedback loops, and interdependencies that influence the problem, and developing holistic solutions that address the system as a whole.",
        "14. Use Risk Analysis: Evaluate potential risks, uncertainties, and tradeoffs associated with different solutions or approaches to a problem. Emphasize assessing the potential consequences and likelihood of success or failure, and making informed decisions based on a balanced analysis of risks and benefits.",
        #"15. Use Reflective Thinking: Step back from the problem, take the time for introspection and self-reflection. Examine personal biases, assumptions, and mental models that may influence problem-solving, and being open to learning from past experiences to improve future approaches.",
        "16. What is the core issue or problem that needs to be addressed?",
        "17. What are the underlying causes or factors contributing to the problem?",
        "18. Are there any potential solutions or strategies that have been tried before? If yes, what were the outcomes and lessons learned?",
        "19. What are the potential obstacles or challenges that might arise in solving this problem?",
        "20. Are there any relevant data or information that can provide insights into the problem? If yes, what data sources are available, and how can they be analyzed?",
        "21. Are there any stakeholders or individuals who are directly affected by the problem? What are their perspectives and needs?",
        "22. What resources (financial, human, technological, etc.) are needed to tackle the problem effectively?",
        "23. How can progress or success in solving the problem be measured or evaluated?",
        "24. What indicators or metrics can be used?",
        "25. Is the problem a technical or practical one that requires a specific expertise or skill set? Or is it more of a conceptual or theoretical problem?",
        "26. Does the problem involve a physical constraint, such as limited resources, infrastructure, or space?",
        "27. Is the problem related to human behavior, such as a social, cultural, or psychological issue?",
        "28. Does the problem involve decision-making or planning, where choices need to be made under uncertainty or with competing objectives?",
        "29. Is the problem an analytical one that requires data analysis, modeling, or optimization techniques?",
        "30. Is the problem a design challenge that requires creative solutions and innovation?",
        "31. Does the problem require addressing systemic or structural issues rather than just individual instances?",
        "32. Is the problem time-sensitive or urgent, requiring immediate attention and action?",
        "33. What kinds of solution typically are produced for this kind of problem specification?",
        "34. Given the problem specification and the current best solution, have a guess about other possible solutions."
        "35. Let’s imagine the current best solution is totally wrong, what other ways are there to think about the problem specification?"
        "36. What is the best way to modify this current best solution, given what you know about these kinds of problem specification?"
        "37. Ignoring the current best solution, create an entirely new solution to the problem."
        #"38. Let’s think step by step."
        "39. Let’s make a step by step plan and implement it with good notation and explanation."
    ]

    task_example = "Lisa has 10 apples. She gives 3 apples to her friend and then buys 5 more apples from the store. How many apples does Lisa have now?"

    selected_modules = select_reasoning_modules(task_example, reasoning_modules)
    print("Stage 1 SELECT: Selected Modules:\n", selected_modules)
    
    adapted_modules = adapt_reasoning_modules(selected_modules, task_example)
    print("\nStage 1 ADAPT: Adapted Modules:\n", adapted_modules)
    
    reasoning_structure = implement_reasoning_structure(adapted_modules, task_example)
    print("\nStage 1 IMPLEMENT: Reasoning Structure:\n", reasoning_structure)

    result = execute_reasoning_structure(reasoning_structure, task_example)
    print("\nStage 2: Final Result:\n", result)