What’s Skeleton of Ideas and its Python Implementation?

Introduction 

Think about having the total potential of your favourite AI assistant, remodeling it from a useful software right into a artistic powerhouse. Image a world the place your interactions with Massive Language Fashions(LLMs) are environment friendly, profoundly intuitive, and impactful. Enter the Skeleton of Ideas (SoT) – a groundbreaking framework poised to revolutionize synthetic intelligence and pure language processing. Let’s study this intriguing concept in additional element and see the way it would possibly enhance your AI-powered artistic and problem-solving processes.

Overview

1. The Skeleton of Ideas (SoT) framework transforms AI right into a artistic powerhouse with structured but adaptable outlines for complicated duties.
2. SoT permits AI to handle challenges with readability and depth by offering a versatile conceptual framework.
3. Core parts of SoT embody structured outlines, versatile enlargement, hierarchical pondering, iterative growth, and cross-linking.
4. Implementing SoT entails making a skeletal define, increasing factors, and connecting elements utilizing OpenAI’s GPT mannequin.
5. SoT presents readability, scalability, flexibility, holistic understanding, and environment friendly exploration of complicated subjects.
6. Actual-world purposes of SoT embody educational analysis, product growth, and strategic planning for structured exploration of intricate topics.

Revealing the Thought Skeleton

What when you might assemble an AI conceptual framework that permits flexibility and creativity whereas guiding the mannequin’s thought course of? That’s exactly the purpose of the Skeleton of Ideas. SoT permits AI fashions to sort out challenges with unprecedented readability, coherence, and depth by offering a structured but adaptable define for complicated duties.

The Core Idea of SoT

1. Structured Define: A high-level construction outlining the principle parts of the task.
2. Versatile Enlargement: The entire skeletal elements might be improved and expanded upon as required.
3. Hierarchical Considering: Concepts are organized in a logical, nested construction.
4. Iterative Growth: The skeleton adjustments because the AI investigates and refines ideas.
5. Cross-Linking: Dynamically created connections between varied skeleton elements are attainable.

Implementing the Skeleton of Ideas

Let’s carry this idea to life with a Python implementation that leverages OpenAI’s GPT mannequin:

Pre-Requisite and Setup

!pip set up openai --upgrade

Importing Libraries

from openai importOpenAI
import openai 
import time 
import re
from IPython.show import Markdown, show

Setting API Key Configuration 

os.environ["OPENAI_API_KEY"]= “Your openAPIKey”

from openai import OpenAI
from openai import OpenAI
import time
import re

class SkeletonOfThoughts:
    """
    A category to create and manipulate a skeletal define utilizing OpenAI's API.
    """


    def __init__(self, api_key, mannequin="gpt-3.5-turbo"):
        """
        Initialize the SkeletonOfThoughts object.


        Args:
            api_key (str): OpenAI API key
            mannequin (str): OpenAI mannequin to make use of (default is "gpt-3.5-turbo")
        """
        self.consumer = OpenAI(api_key=api_key)
        self.mannequin = mannequin
        self.skeleton = {}


    def create_skeleton(self, matter):
        """
        Create a skeletal define for the given matter.


        matter (str): The subject to create a skeleton for


        Returns:
            dict: The created skeleton define
        """
        immediate = f"""Create an in depth skeletal define for the subject: '{matter}'.
        Use the next format:
        1. Principal level
        1.1. Subpoint
        1.2. Subpoint
        2. Principal level
        2.1. Subpoint
        2.2. Subpoint
        Present a minimum of 3 details with 2-3 subpoints every."""
        attempt:
            response = self.execute_prompt(immediate, max_tokens=500)
            print(f"Uncooked API Response:n{response}")  # Debug print
            self.skeleton = self.parse_skeleton(response)
            print(f"Parsed Skeleton:n{self.skeleton}")  # Debug print
            return self.skeleton
        besides Exception as e:
            print(f"Error creating skeleton: {str(e)}")
            return {}


    def expand_point(self, point_number):
        """
        Increase a particular level within the skeleton.


   
            point_number (int): The variety of the purpose to develop


        Returns:
            str: The expanded level content material
        """
        point_key = self.find_point_key(point_number)
        if point_key is None:
            return f"Level {point_number} not discovered within the skeleton."
        current_content = self.skeleton[point_key]
        immediate = f"Increase on the next level: {current_content}"
        enlargement = self.execute_prompt(immediate)
        self.skeleton[point_key] = f"{current_content}n{enlargement}"
        return self.skeleton[point_key]


    def add_cross_link(self, point1, point2):
        """
        Add a cross-link between two factors within the skeleton.


  
         point1 (int): The variety of the primary level
         point2 (int): The variety of the second level


        Returns:
            str: The created cross-link rationalization
        """
        key1 = self.find_point_key(point1)
        key2 = self.find_point_key(point2)
        if key1 is None or key2 is None:
            return "One or each factors not discovered within the skeleton."
        immediate = f"Clarify how these two factors are associated: 1) {self.skeleton[key1]} 2) {self.skeleton[key2]}"
        hyperlink = self.execute_prompt(immediate)
        link_key = f"Hyperlink: {key1} - {key2}"
        self.skeleton[link_key] = hyperlink
        return hyperlink


    def execute_prompt(self, immediate, max_tokens=500):
        """
        Execute a immediate utilizing the OpenAI API.


        
            immediate (str): The immediate to ship to the API
            max_tokens (int): Most variety of tokens within the response


        Returns:
            str: The API response content material
        """
        attempt:
            response = self.consumer.chat.completions.create(
                mannequin=self.mannequin,
                messages=[
                    {"role": "system", "content": "You are an AI assistant creating a structured outline."},
                    {"role": "user", "content": prompt}
                ],
                max_tokens=max_tokens
            )
            content material = response.decisions[0].message.content material.strip()
            print(f"API Response Content material:n{content material}")  # Debug print
            return content material
        besides Exception as e:
            print(f"Error executing immediate: {str(e)}")
            return ""


    def parse_skeleton(self, textual content):
        """
        Parse the skeleton textual content right into a structured dictionary.


       
            textual content (str): The uncooked skeleton textual content to parse


        Returns:
            dict: The parsed skeleton construction
        """
        print("Parsing textual content:", textual content)  # Debug print
        traces = textual content.break up('n')
        skeleton = {}
        current_main_point = ""
        for line in traces:
            line = line.strip()
            if not line:
                proceed


            # Match details (1., 2., and many others.)
            main_point_match = re.match(r'^(d+.)s*(.*)', line)
            if main_point_match:
                current_main_point = main_point_match.group(1)
                skeleton[current_main_point] = main_point_match.group(2)


            # Match subpoints (1.1., 1.2., 2.1., and many others.)
            subpoint_match = re.match(r'^(d+.d+.)s*(.*)', line)
            if subpoint_match:
                subpoint_key = subpoint_match.group(1)
                skeleton[subpoint_key] = subpoint_match.group(2)


            # If it isn't a numbered level, add it to the present principal level
            elif current_main_point and line:
                skeleton[current_main_point] += " " + line


        return skeleton


    def find_point_key(self, point_number):
        """
        Discover the important thing within the skeleton dictionary for a given level quantity.


         point_number (int): The purpose quantity to seek out


        Returns:
            str: The important thing within the skeleton dictionary, or None if not discovered
        """
        for key in self.skeleton.keys():
            if key.startswith(str(point_number) + '.'):
                return key
        return None


    def display_skeleton(self):
        """
        Show the present skeleton construction.
        """
        if not self.skeleton:
            print("The skeleton is empty.")
        else:
            for key, worth in self.skeleton.gadgets():
                print(f"{key} {worth}n")


def principal():
    """
    Principal operate to reveal the utilization of SkeletonOfThoughts class.
    """
    api_key = key  # Change together with your precise OpenAI API key
    if api_key == "your-api-key-here":
        print("Please exchange 'your-api-key-here' together with your precise OpenAI API key.")
        return


    attempt:
        sot = SkeletonOfThoughts(api_key)


        matter = "The Affect of Synthetic Intelligence on Future Job Markets"
        print(f"Creating skeleton for matter: {matter}")
        initial_skeleton = sot.create_skeleton(matter)


        if not initial_skeleton:
            print("Did not create preliminary skeleton. Please test the error messages above.")
            return


        print("nInitial Skeleton:")
        sot.display_skeleton()


        # Increase some extent
        print("nExpanding Level 1:")
        expanded_point = sot.expand_point(1)
        print(expanded_point)


        # Add a cross-link
        print("nAdding Cross-link between factors 1 and a couple of:")
        hyperlink = sot.add_cross_link(1, 2)
        print(hyperlink)


        print("nFinal Skeleton:")
        sot.display_skeleton()


    besides Exception as e:
        print(f"An error occurred: {str(e)}")


if __name__ == "__main__":
    principal()

Output With Rationalization

Creating skeleton: The system created an in depth define for the subject “The Affect of Synthetic Intelligence on Future Job Markets.” This define contains details and subpoints masking varied facets of AI’s affect on job markets.

Preliminary Skeleton: The parsed skeleton is displayed, exhibiting the define’s construction with details and subpoints. Nevertheless, there appear to be some parsing points, as some subpoints are incorrectly related to details.

Increasing Level 1: The system expanded on the primary level, offering an in depth rationalization of the definition of Synthetic Intelligence. This enlargement contains what AI is, its elements, potential impacts, and moral concerns.

Including Cross-link between factors 1 and a couple of: The system related the primary level (Definition of AI) and the second level (Dialogue on upskilling and retraining). It explains how these factors are associated by way of their shared deal with AI’s affect on jobs and the workforce.

Ultimate Skeleton: The ultimate skeleton combines all of the earlier steps. It contains the preliminary define, the expanded first level, and the cross-link between factors 1 and a couple of. This supplies a complete construction for discussing AI’s affect on future job markets.

This implementation brings the Skeleton of Ideas to life:

1. We’ve created a Skeleton Of Ideas class that encapsulates our strategy, initialized with an OpenAI API key and a mannequin selection (defaulting to “gpt3.5-turbo”).
2. The create_skeleton methodology generates an preliminary define for a given matter:
   • It makes use of a particular immediate format to make sure a structured response from the AI.
   • The tactic handles API interactions and parses the response right into a dictionary construction.
3. expand_point permits for deeper exploration of particular factors within the skeleton:
   • It identifies the purpose to develop based mostly on its quantity.
   • The tactic then generates further content material for that time utilizing the AI.
4. add_cross_link establishes connections between completely different elements of the skeleton:
   • It takes two-point numbers as enter and explains their relationship.
   • The hyperlink is then added to the skeleton as a brand new entry.
5. The skeleton is saved as a dictionary, with keys representing level numbers (e.g., “1.”, “1.1.”) and values containing the content material.
6. Further helper strategies improve performance:
   • execute_prompt handles API calls with error administration and debugging output.
   • parse_skeleton converts the AI’s textual content response right into a structured dictionary.
   • find_point_key locates particular factors within the skeleton.
   • display_skeleton prints the present state of the skeleton.

The Magic of SoT in Motion

Let’s break down what occurs after we run this code:

1. Preliminary Skeleton Creation
   • The AI generates a high-level define for the given matter (e.g., “The Affect of AI on Future Job Markets”).
   • This define contains details and subpoints, formatted as a numbered record.
2. Level Enlargement
   • We are able to dive deeper into particular factors (e.g., level 1) utilizing the expand_point methodology.
   • The AI supplies extra detailed details about the chosen level.
3. CrossLinking
   • Connections between completely different elements of the skeleton are established utilizing add_cross_link.
   • For instance, we are able to hyperlink factors 1 and a couple of, producing an evidence of their relationship.
4. Iterative Growth
   • The skeleton might be repeatedly refined by increasing factors or including crosslinks.
   • Every operation updates the skeleton dictionary, permitting for cumulative data constructing.
5. Sturdy Error Dealing with
   • The implementation contains in depth error checking and debugging output.
   • This helps determine and tackle points with API calls or parsing.

This structured but versatile strategy permits for a extra complete and interconnected exploration of complicated subjects. Utilizing AI to generate content material and the programmatic construction for organizing and linking concepts creates a strong software for thought growth and evaluation.

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Benefits of the Skeleton of Ideas

Listed here are some great benefits of the skeleton of ideas:

1. Readability and Group: Concepts are structured in a logical, easy-to-follow method.
2. Scalability: The skeleton might be as easy or as complicated as the duty requires.
3. Flexibility: Straightforward to adapt and refine as new insights emerge.
4. Holistic Understanding: Crosslinking promotes a extra complete grasp of the subject.
5. Environment friendly Exploration: Supplies a roadmap for thorough investigation with out getting misplaced in particulars.

Actual World Functions

Listed here are the real-world purposes:

1. Educational Analysis: Think about utilizing SoT to stipulate a posh analysis paper. The skeleton might present the principle sections and subsections, with the power to develop on key factors and set up connections between completely different elements of the argument.
2. Product Growth: Within the tech trade, SoT could possibly be used to map out the options and elements of a brand new product, permitting groups to visualise the general construction whereas diving into particular particulars as wanted.
3. Strategic Planning: Companies might use SoT to develop complete methods, outlining principal goals, potential challenges, and motion plans, with the pliability to develop and join completely different facets of the technique.

Challenges and Issues

Whereas the Skeleton of Ideas presents thrilling prospects, it’s vital to contemplate:

1. Balancing Construction and Flexibility: Discovering the proper stability between offering a transparent construction and permitting for artistic exploration.
2. Managing Complexity: Managing the skeleton’s progress and interconnections can change into difficult for very giant or intricate subjects.
3. AI Limitations: The skeleton’s high quality and expansions are nonetheless sure by the capabilities of the underlying language mannequin.

The Way forward for Immediate Engineering

As AI advances, strategies just like the Skeleton of Ideas will improve our means to sort out complicated issues and discover intricate subjects. Offering a structured but versatile framework for AI-assisted pondering opens up new prospects for data group, problem-solving, and inventive exploration.

Conclusion

The Skeleton of Ideas represents a big step in structuring and guiding AI-assisted thought processes. By offering a versatile scaffolding for concepts, we are able to discover complicated subjects with unprecedented readability and depth whereas sustaining the power to adapt and refine our strategy as new insights emerge.

Whether or not you’re a researcher, a enterprise strategist, or somebody who loves exploring concepts, the Skeleton of Ideas strategy presents a strong new software for organizing and creating your ideas. So why not give it a attempt? You would possibly simply uncover a brand new mind-set that transforms the way you strategy complicated subjects and challenges!

Regularly Requested Questions