-
Notifications
You must be signed in to change notification settings - Fork 44
Description
Project Title: RAG-Based Chat Bot for Enhanced Challenge Support
Description: This project aims to enhance the user experience for both challenge hosts and participants by developing an intelligent, RAG (Retrieval Augmented Generation) based chatbot. The chatbot will efficiently address queries related to challenge hosting, guidelines, troubleshooting, and FAQs. By integrating state-of-the-art NLP techniques with robust retrieval mechanisms, the solution will ensure prompt, accurate, and context-aware responses that reduce support overhead and streamline communication.
Using the RAG approach, the chatbot will retrieve relevant information from challenge documentation and combine it with generative models to create coherent and helpful answers. This will empower hosts to manage challenges more effectively and assist participants in resolving queries, ultimately contributing to a smoother and more interactive challenge experience.
Deliverable:
-
RAG Framework Implementation:
- Develop a framework that combines document retrieval with generative models to accurately interpret and respond to both host and participant queries.
- Dynamic Information Retrieval: Integrate with existing challenge databases and documentation sources to fetch up-to-date content for accurate responses.
- Contextual Understanding: Enhance the model’s ability to understand query context, ensuring responses are relevant to the specific stage or requirements of the challenge.
-
Enhanced UI Experience:
- Interactive Chat UI: Design a web-based chat interface that caters to both hosts and participants.
- Responsive Design: Ensure the interface is mobile-friendly and accessible across various devices.
- Fallback and Escalation Mechanisms: Implement fallback protocols to escalate unresolved queries to human support, ensuring a seamless user experience.
Backend Infrastructure and Integration:
-
Comprehensive Documentation:
- Technical Documentation: Create a detailed documentation on the chat bot’s architecture, setup, and maintenance procedures.
-
Testing, Feedback, and Continuous Improvement:
- Robust Testing Suite: Evaluate the chat bot’s accuracy, response time, and resilience under various query scenarios.
- User Feedback Integration: Establish feedback channels to gather insights from hosts and participants, using this data to refine and improve the chat bot’s performance.
Mentors: @gautamjajoo, @RishabhJain2018
Skills: Python, Natural Language Processing (NLP), Machine Learning, Retrieval Augmented Generation (RAG), Django, SQL
Skill Level: Medium
Get Started:
Begin by reviewing existing challenge documentation and query logs to identify common patterns. Prototype the retrieval module and generative response system using Python and your chosen web framework. This initial phase will help in understanding the requirements and iterating on the RAG model to deliver accurate, context-aware responses.
Important Links:
EvalAI Website: eval.ai
EvalAI Github repository: Cloud-CV/EvalAI
EvalAI Docs: http://evalai.readthedocs.io/en/latest
GSoC Proposal Template: Cloud-CV/GSoC-Ideas/wiki/GSOC-2020-Proposal-Template
Slack Channel: Cloud-CV
Mailing list: groups.google.com/forum/#!forum/cloudcv