ChaiBookLM is an AI research assistant that turns PDFs, websites, and videos into a notebook you can actually talk to — with traceable, source-backed answers.
RAG combines retrieval with generation. Documents are chunked and embedded into a vector store, then the question is embedded and the nearest chunks are retrieved by similarity before the model answers. 1
Production-grade architecture with the features that matter for grounded, citation-first answers.
Each notebook is its own knowledge base. Conversations never leak across your projects.
Responses point back to the exact page, section, or timestamp they came from.
Watch grounded responses generate in real time with markdown and code rendering.
A lightweight model rewrites vague questions into retrieval-friendly queries first.
Dense embeddings in Qdrant surface the most relevant chunks for your question.
If the context doesn't contain an answer, the assistant says so — no fabrication.
A clean, modular RAG pipeline you can follow end to end.
Drop in PDFs, paste a website, or link a YouTube video. Each source is parsed and chunked.
Chunks are embedded and stored in a vector database, ready for semantic retrieval.
Chat with your notebook and receive streaming answers with clickable citations.
Five source types, one retrieval pipeline.
Create a notebook, add a source, and ask your first grounded question in under a minute.
Get started