ClarifAI is an AI-powered research summarizer that converts academic papers into interactive, easy-to-consume summaries enriched with visuals, keyword extraction, and a RAG-powered conversational interface. The tool is designed for researchers, students, and professionals who need fast, meaningful understanding of dense research content.
- Enhanced summaries — concise, intelligible summaries with extended facts and keyword extraction.
- Visual augmentation — relevant images, diagrams and generated graphics to clarify concepts.
- Interactive chatbot (RAG) — conversational Q&A over the paper content and external context.
- Upload flexible input — accept full papers or snippets (PDF / text).
- Fast inference — local/remote LLMs + vector store for low-latency responses.
- Backend: Python + Flask / FastAPI
- LLMs: Llama 3.1 (or hosted LLM) for generation
- Retrieval: Vector DB / graph DB (for semantic search & relations)
- RAG & orchestration: qdrant / milvus / custom graph + retriever
- Image generation: Stable Diffusion (or similar) for visual augmentation
- Frontend: React (web) with an embedded chat UI
- Deployment: Docker, GPU server for model endpoints
- Upload — user uploads a paper (PDF / text).
- Preprocess & extract — parse text, extract sections and keywords.
- Index & store — store embeddings in a graph / vector DB for retrieval.
- Generate visuals — extract keywords and optionally generate images/diagrams (stable-diffusion style).
- RAG inference — LLM (e.g., Llama 3.1) + retrieval layer answers queries and produces interactive summaries.
- Present — display summarized cards, visuals, and a chat interface for follow-up Q&A.
This section showcases sample outputs generated by ClarifAI during testing and evaluation.
ClarifAI simplifies the process of understanding research papers by converting complex academic content into clear and interactive summaries. By combining large language models with retrieval-based techniques and visual support, the system helps users save time and improve comprehension. This project demonstrates the practical application of AI in academic research and knowledge management.