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📚 Ela Academic Assistant
Ela Academic Assistant is a fully local Retrieval-Augmented Generation (RAG) application designed to help users interact with academic documents while keeping the workflow local.
💡 Inspiration
Academic documents can be long and difficult to navigate when searching for specific information. I wanted to explore how RAG could make academic material easier to query while also learning how local AI systems work.
🛠️ How it works
Users can work with PDF and DOCX documents. The system retrieves relevant document chunks and uses a locally running language model to generate grounded answers based on the retrieved context.
The project was built using Python, Microsoft Foundry Local, Streamlit, and SQLite.
📚 What I learned
This project gave me hands-on experience with Retrieval-Augmented Generation, document retrieval, local language models, and building an end-to-end AI application.
🚧 Challenges
One of the main challenges was setting up and integrating the local AI environment and connecting the different components of the RAG pipeline. Debugging these issues helped me better understand how the individual parts of the system interact.
🔮 What's next
Future improvements could include better retrieval quality, support for more document formats, improved evaluation of generated answers, and a more polished user interface.
