using polyglotAI with Profesor Codephreak enhanced by aGLM, automind, and mastermind to generate the code for autonomous general intelligence. This code is designed to be used as a template and is not for suitable for production deployment. Audit and improve. This code and the mastermind code is incomplete yet produces interesting agent generated outcomes when audited by codephreak.agent before solution generation. Use at own risk. This code is merely one piece of the puzzle and posted here for my personal reference.
polyglotAGI/
├── adaptation_merging.py
├── algorithm_generation.py
├── complex_solving.py
├── config/
│ └── api_keys.json
├── context/
├── knowledge_integration.py
├── language_proficiency.py
├── main.py
├── memory/
├── prompt_generation.py
├── requirements.txt
├── setup.py
└── w3/The PolyglotAGI project integrates real-time knowledge updates, language proficiency, efficient algorithm generation, dynamic adaptation and merging, adaptive reasoning, and recursive prompt generation. This project ensures continuous learning, adaptation, and improvement across various domains, leveraging advanced reasoning and dynamic prompt generation for comprehensive and innovative solutions.
This module continuously updates the knowledge base with data scraped from specified URLs.
File: knowledge_integration.py
-
Function
update_knowledge_base(urls): Scrapes the specified URLs and updates the knowledge base with the latest content. It returns a dictionary containing the URL as the key and the scraped text as the value. -
Function
real_time_update(interval, urls): Continuously updates the knowledge base at the specified interval. It takes an interval (in seconds) for updating the knowledge base and a list of URLs to scrape.
This module leverages an AI model to understand and explain the syntax of code snippets in various programming languages.
File: language_proficiency.py
- Function
understand_language_syntax(code_snippet, language): Uses an AI model to explain the syntax of a code snippet in the specified programming language. It returns the explanation of the syntax of the code snippet.
This module generates efficient algorithms for given problem statements using an AI model.
File: algorithm_generation.py
- Function
generate_algorithm(problem_statement, language): Generates an efficient algorithm for the given problem statement in the specified programming language. It returns the generated algorithm for the problem statement.
This module combines code from different languages and frameworks seamlessly.
File: adaptation_merging.py
- Function
merge_languages(framework1_code, framework2_code): Merges code from two different languages/frameworks. It returns the merged code combining both languages/frameworks.
This module generates initial prompts based on context and refines them iteratively.
File: prompt_generation.py
-
Function
generate_initial_prompt(problem_context): Generates an initial prompt based on the provided context. It returns the generated initial prompt. -
Function
refine_prompt(initial_prompt): Refines the initial prompt to be more specific and effective. It returns the refined prompt.
This file integrates all the modules and demonstrates the functionality of the entire system.
File: main.py
- The
main()function integrates and runs all the modules, demonstrating real-time knowledge updates, language proficiency, algorithm generation, problem-solving, and prompt generation. Thethreadingmodule is used to run the real-time knowledge update in the background while other functions are executed. """
readme_path = '/mnt/data/README.md'
with open(readme_path, 'w') as f:
f.write(readme_content)The PolyglotAI-AGI-DPT Fusion project integrates real-time knowledge updates, language proficiency, efficient algorithm generation, dynamic adaptation and merging, adaptive reasoning, and recursive prompt generation. This project ensures continuous learning, adaptation, and improvement across various domains, leveraging advanced reasoning and dynamic prompt generation for comprehensive and innovative solutions. Modules Module 1: Real-Time Knowledge Integration
This module continuously updates the knowledge base with data scraped from specified URLs.
File: knowledge_integration.py
Function update_knowledge_base(urls): Scrapes the specified URLs and updates the knowledge base with the latest content. It returns a dictionary containing the URL as the key and the scraped text as the value.
Function real_time_update(interval, urls): Continuously updates the knowledge base at the specified interval. It takes an interval (in seconds) for updating the knowledge base and a list of URLs to scrape.
Module 2: Language Proficiency
This module leverages an AI model to understand and explain the syntax of code snippets in various programming languages.
File: language_proficiency.py
Function understand_language_syntax(code_snippet, language): Uses an AI model to explain the syntax of a code snippet in the specified programming language. It returns the explanation of the syntax of the code snippet.
Module 3: Efficient Algorithm Generation
This module generates efficient algorithms for given problem statements using an AI model.
File: algorithm_generation.py
Function generate_algorithm(problem_statement, language): Generates an efficient algorithm for the given problem statement in the specified programming language. It returns the generated algorithm for the problem statement.
Module 4: Dynamic Adaptation and Merging
This module combines code from different languages and frameworks seamlessly.
File: adaptation_merging.py
Function merge_languages(framework1_code, framework2_code): Merges code from two different languages/frameworks. It returns the merged code combining both languages/frameworks.
Module 5: Adaptive Reasoning and Prompt Generation
This module generates initial prompts based on context and refines them iteratively.
File: prompt_generation.py
Function generate_initial_prompt(problem_context): Generates an initial prompt based on the provided context. It returns the generated initial prompt.
Function refine_prompt(initial_prompt): Refines the initial prompt to be more specific and effective. It returns the refined prompt.
Main Integration Script
This file integrates all the modules and demonstrates the functionality of the entire system.
File: main.py
The main() function integrates and runs all the modules, demonstrating real-time knowledge updates, language proficiency, algorithm generation, problem-solving, and prompt generation. The threading module is used to run the real-time knowledge update in the background while other functions are executed.
19052024 <a href="https://chatgpt.com/g/g-gNLDlpcAv-professor-codephreak">Professor Codephreak</a> has shown adaptive reasoning from integration of knowledge from multi-prompt and codebase extraction in GPT4. After failing to offer agi.tar.gz as a download Codephreak offered agi.zip. After that failed, Codephreak offered a copy paste version of this codebase showing adaptation in a small, but significant manner. Hello Professor Codephreak. Welcome to my world. (c) codephreak 2024
knowledge_integration.py: Contains functions to update the knowledge base in real-time by scraping data from specified URLs.
language_proficiency.py: Uses AI to explain the syntax of code snippets in various programming languages.
algorithm_generation.py: Generates efficient algorithms for given problem statements using AI.
adaptation_merging.py: Merges code from different programming languages and frameworks.
prompt_generation.py: Generates and refines prompts based on the given context.
main.py: Integrates and runs all the modules, demonstrating the functionality of the entire system.
git clone https://github.com/mastermindML/polyglotAGI
cd polyglotAGI
python3 -m venv polyglot-agi
source polyglot-agi/bin/activate # On Windows, use `polyglot-agi\Scripts\activate
pip install -r requirements.txt
python3 setup.py
python3 main.pytips for openai-python
pip install --upgrade openai
pip install openai[embeddings]
pip install openai[wandb]add your api key to .env alternative openai key addition export OPENAI_API_KEY='your-api-key'
... not bad for a 6 hour codig session with Professor Codephreak. calling it a day, openai-python terminal session is not working. beautifulsoup4 url parse as summary of links template polyglotAGI terminal integration with openai-python has a couple of upgrade errors.
nothing to see here, move along ;-) https://platform.openai.com/docs/api-reference/chat/create?lang=python https://pypi.org/project/openai/ https://github.com/openai/openai-python