ScholarSAGE: LLM-powered guide to research queries

Why ScholarSAGE?

• Understanding and navigating through the extensive body of work authored by a researcher poses a significant challenge for individuals interested in their contributions.
• Accessing relevant information efficiently is often hindered by the sheer volume of research paper.
• There exists a need for an intelligent solution that streamlines this process, offering personalized assistance and insights.
• Current methods lack the adaptability and comprehensiveness required to address the unique nature of scholarly inquiries, necessitating the development of a specialized chatbot to bridge this gap.

Objectives

• To develop a sophisticated chatbot, by fine-tuning pre-trained Large Language Models (LLM) on the extensive research papers authored by a specific researcher.
• Improve on performance metrics in terms of cosine similarity.

Workflow

Getting started

• Clone the repository. Create a virtual env using the command python -m venv <path_to_new_virtual_env>.
• Install the requirements using the command pip install -r requirements.txt.
• Replace the HUGGINGFACEHUB_API_TOKEN and PINECONE_API_KEY in initializations.py with your respective keys.
• To run the application run streamlit run chat.py.