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Hyperledger Labs AIFAQ is an LLM ChatBot testable as a Proof-of-Concept. It replies to questions about Hyperledger standard documentation. The current version is a Google Colab Notebook which uses Gradio as GUI. This project proposes an implementation of a standard ChatBot GUI and the deployment of a prototype. Our end goal is to have a more usable system installed on a Cloud Server.

Official Repository: https://github.com/hyperledger-labs/aifaq

Mentor and Mentee

Mentors: Barbara (Bobbi) Muscara, Gianluca Capuzzi, Tripur Joshi, Swapnil Tripathi, Arunima Chaudhuri

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  •  All deliverables (documentation, code, performance report, cost evaluation)

Timeline

June 3 - June 23 (Completed)

Onboarding

Understand the project scope and learn about LLM (Large Language Models)

  • review the existing Hyperledger Labs AIFAQ documentation

June 24 - July 5 (Completed)

Understand the project scope and learn about LLM (Large Language Models)

  • research and document the basics of LLM, focusing on how they can be applied to create intelligent ChatBots
  • setup and familiarize with existing codespace

July 8 - July 19

Develop the front-end component of the ChatBot

  • design a user-friendly interface for the ChatBot using JavaScript
  • implement the interface as a functional prototype that can later be integrated with the backend LLM

July 22 - July 261st Quarter Evaluation
July 29 - August 9

Understand Cloud Architecture and Deployment

  • research cloud service providers (e.g., AWS, Google Cloud, Azure) and their offerings
  • document the pros and cons of each service with respect to the project's needs and select one for deployment.
  • begin the deployment of the front-end component as a test.

August 12 - August 23

Containerization

  • research and document the basics of container technology (e.g., Docker).
  • create a simple container for the ChatBot's front-end.

August 26 - August 31

Buffer Time & Documentation

  • catch up on documenting the current progress

September 2 - September 6Midterm Evaluation
September 9 - September 20

Integration & Testing

  • integrate the front-end component with the LLM backend, ensuring they communicate effectively.
  • conduct initial testing and document any issues or bugs.

September 23 - October 4

Cloud Server Deployment

  • finalize the deployment of the ChatBot on the chosen cloud service
  • perform comprehensive testing to ensure functionality and performance standards are met

October 7 - October 11

Buffer Time & Documentation



October 14 - October 183rd Quarter Evaluation
October 21 - November 1

Project Documentation & Quality Assurance

  • document the project extensively, including setup instructions, user guides, and technical details
  • perform final rounds of testing, focusing on user experience and bug fixing

November 4 - November 8Buffer Time
November 11 - November 29

Project Wrap-up, Review & Feedback

  • organize a project demonstration for stakeholders to gather feedback
  • reflect on the project process, documenting lessons learned and potential improvements
  • finalize all project documentation and ensure all code and resources are well-organized and accessible
  • outline potential future enhancements and areas for further development

Final Evaluation


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