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.