As a user, I want to access and summarize web content using LLM models

[florentine-doemges]

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.

[florentine-doemges]

As an experienced development team working on the KogniSwarm application, team members may have various questions regarding the project. Here are some possible questions they might ask:

1. What are the specific requirements and constraints for each key class and component in the KogniSwarm application?
2. Are there any specific LLM models we should focus on for summarization, or should we support multiple models?
3. How do we handle the selection of the best summarization model based on the input content?
4. How do we ensure the scalability of the application as the number of users and requests increase?
5. Are there any particular coding standards or conventions we should follow while developing the application?
6. How will we handle authentication and authorization for accessing external services, such as Google Search, Twitter, and Hugging Face?
7. What kind of performance metrics should we monitor and optimize for in the application?
8. How do we ensure the security and privacy of user data and the generated content?
9. Are there any specific accessibility requirements or guidelines we should consider while designing the user interface?
10. How will we manage version control, continuous integration, and continuous deployment for the project?
11. What is the plan for testing and quality assurance, including unit testing, integration testing, and end-to-end testing?
12. How do we handle edge cases, such as low-quality or non-standard web content, for summarization?
13. What kind of documentation is expected for the project, and what is the process for maintaining and updating it?
14. What is the expected timeline and milestones for the project, and how will progress be tracked and communicated?
15. How will we handle feature requests, bug reports, and support for users of the KogniSwarm application?

These questions can help the development team clarify expectations, requirements, and constraints, ensuring a smooth and efficient development process.

[florentine-doemges]

1. tbd
2. At start we are just aiming at GPT-3.5 and GPT-4
3. There should be a simple mode and an advanced mode
4. Concurrency with coroutines is the way to go
5. tbd
6. not applicable for this story
7. Number of calls, Timing of calls, cache hits, cache misses
8. tbd
9. tbd
10. github
11. we do all 3
12. we create test for those edge cases
13. we want to keep the docs small. Good code should be readable enough so we do not need too much docs
14. tbd
15. github

[florentine-doemges]

he development team while working on the KogniSwarm application:

Specific requirements and constraints for each key class and component will be determined later (TBD).
Initially, the application will focus on GPT-3.5 and GPT-4 LLM models for text summarization.
There will be a simple mode and an advanced mode for selecting the best summarization model.
Concurrency with coroutines will be used to ensure scalability and efficient handling of multiple requests.
Coding standards and conventions will be determined later (TBD).
Authentication and authorization for accessing external services are not applicable for this user story.
Performance metrics to monitor include the number of calls, timing of calls, cache hits, and cache misses.
Security and privacy of user data and generated content will be determined later (TBD).
Accessibility requirements and guidelines will be determined later (TBD).
Version control, continuous integration, and continuous deployment will be managed using GitHub.
The testing plan includes unit testing, integration testing, and end-to-end testing.
Edge cases, such as low-quality or non-standard web content, will be addressed by creating tests for those scenarios.
Documentation will be kept minimal, with a focus on writing readable and self-explanatory code.
The project timeline and milestones will be determined later (TBD).
Feature requests, bug reports, and support will be managed using GitHub.
By following these guidelines and addressing any outstanding issues, the development team can work effectively on the KogniSwarm application, ensuring its success in fulfilling the desired user stories.

[florentine-doemges]

done