내 PDF를 검색하세요 브라우저에서 바로.
클라이언트 사이드 PDF RAG. 추출, 임베딩, 검색 — 모두 온디바이스, 서버 없음.
시맨틱 검색. 업로드 제로.
PDF를 업로드하고 질문하면 인용이 포함된 답변을 — 모두 로컬에서.
100% 프라이빗
문서는 절대 기기를 떠나지 않습니다. 모든 처리는 로컬에서 이루어집니다.
온디바이스 임베딩
Transformers.js로 로컬에서 텍스트 임베딩을 생성합니다. API 호출 없음.
청크 분할 및 검색
자동 텍스트 청킹과 벡터 유사도 검색으로 정확한 검색을 제공합니다.
인용이 포함된 답변
답변에 출처 인용이 포함되어 맥락을 검증할 수 있습니다.
Three steps. Zero servers.
- 01
Upload a PDF
Drop in any text-based PDF. Text is extracted page by page in your browser via pdf.js.
- 02
AI builds the index
Text is chunked and embedded on-device with all-MiniLM-L6-v2 via WebGPU, stored in an in-memory vector store.
- 03
Ask & retrieve
Type a question. The top matching passages are ranked by cosine similarity and shown instantly.
About document RAG search
A free “chat with your PDF” tool that builds a semantic search index over your documents entirely in the browser. Instead of keyword matching, it finds passages by meaning — ideal for research papers, contracts, manuals, and long reports where the answer exists but you do not know the exact words to search for.
How it works
pdf.js extracts the text layer of your PDF on-device. The document is split into chunks and embedded with all-MiniLM-L6-v2, a compact sentence transformer running via Transformers.js with WebGPU acceleration; the embeddings live in an in-memory vector store. When you ask a question, your query is embedded with the same model and ranked against every chunk by cosine similarity, returning the top matching passages with page numbers and scores in seconds.
Limits & requirements
The tool reads PDFs with a selectable text layer — scanned, image-only PDFs need OCR first. There is no hard file-size limit, but very large documents scale with your device’s available RAM. Chrome or Edge with WebGPU is recommended for fast indexing; other browsers work via WebAssembly.
Privacy
The model downloads once from the open-source model hub, then indexing and search are 100% local. Confidential documents never touch a server, and nothing about their content is collected.