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Search, manage, and enrich academic PDFs with local-first RAG tools.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "rag-paper" yet — see the docs or source repo.
Search my local paper library for papers about "applications of retrieval-augmented generation in academic question answering," rank the top 10 by relevance, and return title, authors, year, and abstract.
A ranked paper list with key metadata and abstracts for quick review and selection.
Scan my imported PDFs, fill in missing title, authors, publication year, DOI, and journal information, and flag any fields that cannot be verified.
Updated paper records showing which fields were auto-filled and which still need manual verification.
Build a citation graph centered on this paper, showing key papers it cites and later papers that cite it, organized chronologically.
A clear citation timeline and graph that reveals research evolution and relationships among core papers.
Let AI securely query private local documents with persistent memory.
Search local documents with vector similarity for RAG answers.
Convert research PDFs to Markdown and search them with grep plus semantics.
Set up a local RAG server for private knowledge search and QA.
Search, retrieve, and answer questions from PDF documents with RAG.
Connect local code and docs for fast AI vector-based retrieval.