Durchsuche deine PDFs direkt im Browser.
Client-seitiges PDF-RAG. Extrahieren, einbetten und abrufen — alles on-device, keine Server.
Semantische Suche. Keine Uploads.
PDFs hochladen, Fragen stellen und Antworten mit Quellenangaben erhalten — alles lokal.
100 % privat
Deine Dokumente verlassen dein Gerät nie. Die gesamte Verarbeitung erfolgt lokal.
On-Device-Embeddings
Text-Embeddings werden lokal via Transformers.js erzeugt. Keine API-Aufrufe.
Chunking & Abruf
Automatisches Text-Chunking und Vektor-Ähnlichkeitssuche für präzisen Abruf.
Antworten mit Quellen
Antworten enthalten Quellenangaben, damit du den Kontext überprüfen kannst.
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.