To effectively communicate with different sections of the community, share resources, and utilize processing power for staying competitive amidst the influx of investments in AI, it is essential to explore innovative solutions.

One of the key features we are considering is implementing a Peer-to-Peer (P2P) system. While we initially contemplated utilizing a protocol like JXTA, we now believe that integrating a Discord server could be a more intelligent approach. We recommend investigating Kord, an embeddable Discord server, as it could potentially streamline communication and collaboration within our community.

https://github.com/kordlib/kord

This issue should be visible now on the discord server

We need a check that runs test, etc. so we can later use it in code review

One approver needed for merge into main.
Green Checks needed for merge into main

KGS-1 As a user, I want to generate high-quality text and code with GPT-4

Description:
The application should provide an easy-to-use interface for users to input their text or code prompts and get generated outputs using the GPT-4 model. The users should be able to specify the output length, temperature, and other relevant parameters to control the creativity and quality of the generated text or code. The system should ensure that the GPT-4 model is utilized efficiently and safely, with appropriate API usage and error handling.

To accommodate both GPT-4 and GPT-3.5, a modular architecture with a common class for language models should be implemented. This will enable the application to easily switch between the two models or support additional models in the future, minimizing code duplication.

Acceptance Criteria:

1. User can input text or code prompts and receive generated outputs from the GPT-4 model.
2. User can specify output length, temperature, and other relevant parameters.
3. The system utilizes the GPT-4 model efficiently and safely.
4. Appropriate error handling and API usage are implemented.
5. Generated outputs are of high quality and meet user expectations.
6. The application's architecture supports both GPT-4 and GPT-3.5, allowing for easy switching between models and extensibility to other models.

Key Classes:

1. LanguageModel: A class that handles the interactions with both GPT-4 and GPT-3.5 APIs, including methods for setting parameters, generating text or code, and managing authentication.
2. LanguageModelHandler: A class responsible for handling user interactions, receiving input from the user, invoking the LanguageModel to generate text or code, and displaying generated outputs. This class will manage any error messages or status updates.
3. LanguageModelConfiguration: A class managing the global configuration settings for language models, such as API keys, endpoint URLs, and other settings.

By implementing these classes and fulfilling the acceptance criteria, KGS-1 can be successfully completed, allowing users to generate high-quality text and code with GPT-4 and providing support for GPT-3.5 as well.

Alternative to #22

In order to achieve this user story, the KogniSwarm application should be able to fetch web content, process the content, and generate summaries using GPT-4 or other LLM models. The following key classes and components can be considered for this user story:

WebContentFetcher: A class responsible for fetching web content from given URLs or search queries.
ContentProcessor: A class for preprocessing the fetched web content, extracting relevant information, and preparing it for summarization.
LLMModelManager: A class for managing the interaction with the LLM models, including selecting and loading the appropriate model for summarization.
SummaryGenerator: A class that uses the LLMModelManager to generate summaries from the processed web content.
OutputManager: A class that handles the output of the generated summaries, including formatting and displaying the summaries to the user or exporting them to different formats.
To ensure the acceptance criteria for KGS-2 are fulfilled, various tests should be designed to cover different scenarios and edge cases. Some example tests include:

Test that WebContentFetcher can fetch content from a valid URL.
Test that WebContentFetcher handles invalid URLs gracefully.
Test that ContentProcessor can extract relevant information from web content.
Test that ContentProcessor handles different types of web content (e.g., news articles, blog posts, etc.).
Test that LLMModelManager can load the appropriate LLM model for summarization.
Test that SummaryGenerator can generate summaries from processed web content.
Test that SummaryGenerator handles different lengths and complexities of web content.
Test that OutputManager can format and display summaries properly.
Test that OutputManager can export summaries to different formats (e.g., PDF, text file, etc.).
Test that the entire workflow from fetching web content to generating summaries works as expected.
By addressing these classes, components, and tests, the KogniSwarm application can effectively provide the desired functionality of accessing and summarizing web content using LLM models.