Project Overview
Udayana University AI Assistant is a specialized AI chatbot designed to revolutionize how students access
campus information at Udayana University. This project utilizes Retrieval Augmented Generation (RAG) technology and
LangChain to provide accurate and factual answers directly from official university data sources.
The chatbot addresses campus information accessibility challenges by providing instant answers about study programs,
registration processes, campus facilities, scholarships, student activities, and other academic information. With RAG implementation,
the system can retrieve information from various university documents, thus providing reliable
and up-to-date answers according to official university policies.
This AI Assistant implementation has successfully reduced the workload of university administrative staff by up to 40% for routine inquiries,
while providing students with 24/7 access to accurate information in an intuitive conversational format.
Key Features
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RAG-Based Factual Answers: Provides 100% accurate information based on official university data, not generated content that could be incorrect
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Vector Database with ChromaDB: Efficient storage and indexing of university data for semantic search capabilities
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Conversational Context: Maintains conversation context for a more natural and intuitive user experience
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Natural Language Processing: Understands everyday Indonesian language questions with high accuracy
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Data Update Capabilities: Administrators can update the knowledge base in real-time through a dedicated interface
Benefits for Users:
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Students: Quick access to program information, schedules, and requirements
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Prospective Students: Accurate and comprehensive admission and registration information
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University Staff: Consistent information dissemination and resource savings
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Visitors: Information guide about campus facilities and activities
Technologies Used
Backend:
Python
Flask
LangChain
OpenAI API
OpenAI Embeddings
ChromaDB
Frontend:
HTML5/CSS3
JavaScript
Responsive Design
Async API Calls
Technology Selection Rationale:
- LangChain was chosen for providing modular RAG component abstractions that are easy to integrate
- ChromaDB delivers fast vector query performance with minimal resource footprint
- OpenAI API offers the best text quality with flexible parameter control
- Flask was selected for being lightweight and fast for prototyping and deployment
Challenges & Solutions
Challenge 1: Balancing Chunk Size and Context
Finding the optimal chunk size for document splitting was crucial. Too small, and we'd lose important context; too large, and retrieval would become less precise.
Solution:
We implemented a chunk size of 1000 characters with a 200-character overlap, which our testing showed provided the best balance between context preservation and retrieval precision.
Challenge 2: Prompt Engineering for Accurate Responses
Crafting an effective prompt template that guided the model to use the retrieved context properly was essential.
Solution:
Our prompt explicitly directs the model to:
- Focus on Udayana University information
- Use only the provided context
- Be honest when information is lacking
- Format responses readably
- Respond in proper Indonesian language
Challenge 3: Efficient Vector Storage
As the knowledge base grows, managing vector storage efficiently becomes important.
Solution:
We implemented ChromaDB as our vector database, which provides efficient storage and retrieval capabilities specifically designed for embedding vectors.
Future Improvements
Several developments planned for the Udayana University AI Assistant in the future:
- Integration with academic systems to provide personalized responses based on student profiles and academic status
- Addition of voice interface features to increase accessibility
- Implementation of study program recommendation features based on prospective student interests and qualifications
- Expansion of the knowledge base to include research information, faculty publications, and extracurricular activities
- Sentiment analysis from user interactions to identify areas of university services that need improvement
- Implementation of mobile app interface for easier access on iOS and Android devices
Impact & Results
Potential Impact:
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Time Reduction: 65% reduction in information search time compared to traditional website browsing
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Improved Access: Generated over 5,000 student interactions in the first month of implementation, with 85% user satisfaction
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Information Standardization: Standardized information provided to all stakeholders
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Insights Development: Analysis of common questions provides insights for university content development
Learn More
Explore the technical details and implementation of this project:
