Prepend any arxiv.org link with 'talk2' to load the paper into a responsive RAG chat application (e.g. www.arxiv.org/pdf/1706.03762.pdf -> www.talk2arxiv.org/pdf/1706.03762.pdf).

Talk2Arxiv is an open-source RAG (Retrieval-Augmented Generation) system specially built for academic paper PDFs. Powered by talk2arxiv-server

Screenshot

Installation

Just run yarn and then yarn run dev.

PDF Parsing: Utilizes GROBID for efficient text extraction from PDFs.
Chunking Algorithm: Custom-built algorithm for optimal text chunking. Chunks by logical section (intro, abstract, authors, etc.) and also utilizes recursive subdivision chunking (chunk at 512 characters, then 256, then 128...)
Text Embedding: Uses Cohere's EmbedV3 model for accurate text embeddings.
Vector Database Integration: Uses Qdrant for storing and querying embeddings. This also functions to cache research papers so a paper only ever needs to be embedded once.
Contextual Relevance: Employs a reranking process to select the most relevant content based on user input.

Frontend: Developed using Typescript, ReactJS, TailwindCSS, and NextJS. Backend: Powered by talk2arxiv-server, which uses Flask, Gunicorn, and Nginx.

Improved chunking strategy
Switch to extracting source LaTeX code to increase retrieval effectiveness for symbolic math formulas and non standard text elements
Use visual understanding LLM models as well
Account based personalization

The backend is not built to handle any level of scale, with lots of concurrent requests it will stall as it single threadedly handles them