Project Theme

Virtual BRC Helpdesk

Project Summary

Many researchers find bioinformatics resources challenging to navigate, especially when learning new tools or trying unfamiliar analyses. This project aims to build an intelligent virtual helpdesk powered by AI and large language models that can assist users with navigating BRC resources, answering bioinformatics questions in natural language, and providing step-by-step guidance through complex analysis workflows. By lowering the barrier to entry, we can make BRC tools accessible to a broader community of infectious disease researchers.

Goals and Objectives

1. Develop a conversational AI interface that understands user queries about BRC tools, data, and workflows
2. Integrate BRC documentation using RAG (Retrieval-Augmented Generation) to provide accurate, context-aware responses
3. Generate executable code snippets for common bioinformatics tasks on BRC platforms
4. Create a prototype web interface for users to interact with the virtual helpdesk

Approach

Methods and AI/ML Approaches:

• Large Language Models (GPT-4, Claude, or open-source alternatives like Llama)
• Retrieval-Augmented Generation (RAG) for BRC-specific knowledge
• Vector embeddings of BRC documentation, tutorials, and user guides
• Fine-tuning on BRC FAQs and support ticket history

Implementation Steps:

1. Collect and preprocess BRC documentation, tutorials, and help resources
2. Build a vector database of BRC knowledge for RAG
3. Design conversational prompts and system instructions
4. Develop a chat interface (web-based or API)
5. Test with common user queries and refine responses
6. Evaluate accuracy and usefulness with test users

Data and Resources Required

Resource Type	Source / Link	Description / Purpose
Data	BV-BRC tutorials, documentation, FAQs	Training corpus for RAG system
Data	User forum questions/answers	Real-world query examples
Tools / Services	BV-BRC API	For demonstrating tool usage
LLMs / AI Models	GPT-4, Claude 3, or Llama 3	Core conversational AI
Tools / Services	LangChain, LlamaIndex	RAG framework
Compute / Storage	Vector database (Pinecone, Chroma, or Weaviate)	Store document embeddings

Expected Outcomes / Deliverables

• Functional prototype of AI-powered helpdesk with chat interface
• RAG pipeline for BRC documentation retrieval
• Evaluation metrics for response quality, accuracy, and helpfulness
• Code repository on GitHub with documentation and examples
• Integration roadmap for deploying on BRC platforms
• Demo presentation showing real user interactions

Potential Impact and Next Steps

Impact on:

• Infectious disease research: Lowers barrier for researchers to access powerful bioinformatics tools
• AI/ML automation: Demonstrates practical application of LLMs for scientific support
• Public health preparedness: Enables rapid onboarding of new users during outbreaks

Next Steps After Codeathon:

• Expand knowledge base to include all BRC resources
• Add multi-turn conversation capabilities
• Integrate with BRC user authentication for personalized help
• Deploy as a widget on BRC websites
• Collect user feedback and continuously improve

Technical Support Needed

• BRC documentation in machine-readable format
• LLM API access (GPT-4 or Claude)
• Sample user query logs (anonymized)
• Mentor support from BRC team
• Testing accounts for BRC platforms

Team Information

Teams will be formed during the Codeathon. Ideal team composition:

• AI/ML Developer: LLM integration and RAG implementation
• Bioinformatician: BRC domain expertise and use case validation
• Frontend Developer: Chat interface design
• UX Designer: User experience and interaction design
• Documentation Specialist: Content curation and knowledge base