By Fenil Nasriwala | 21012021052 | U.V. Patel College of Engineering | BEE Robokids Pvt Ltd
RAG AI Agent is a smart assistant that combines document retrieval with AI response generation for developer support.
Addresses inefficiencies in navigating technical documentation and reduces cognitive load for developers.
Automatically indexes and retrieves relevant technical documents.
Provides context-aware answers using conversational AI.
Modular design separating core functionalities like document retrieval and response generation.
Built using Next.js, ElizaOS, OpenAI, Groq, and React.js.
Interprets natural language queries and matches them with relevant documents.
Responsive interface for developer queries with multi-turn memory support.
Built with ElizaOS and Node.js, featuring plugin-based architecture.
Created using React.js and Next.js for a dynamic user experience.
Used Vitest for unit testing and functional validation.
Performed integration testing, performance monitoring, and user acceptance testing.
Features real-time interaction with syntax highlighting and example prompts.
Intuitive navigation and contextually accurate responses enhance UX.
Improved documentation access, enhanced developer productivity, and reduced troubleshooting time.
Hands-on experience with RAG pipelines, AI integration, and full-stack development.
Includes code snippet generation, IDE integration, and personalized documentation suggestions.
Potential for broader framework support and multilingual documentation handling.
Special thanks to Prof. Ritesh Upadhyay and Prof. Ravi Raval for their guidance and encouragement.
Grateful to BEE Robokids Pvt Ltd and Ganpat University for the opportunity and support.
Internship Project Report Presentation
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