Upload PDFs and ask questions about specific documents. Get page-by-page summaries, compare documents side-by-side, and search with AI-powered Q&A.
This tool runs 100% in your browser. All computation happens locally on your device — your input is never uploaded to any server. Results are for reference only.
Reading a document is a retrieval-augmented pipeline. Your PDF is parsed with PDF.js, the text is split into chunks, and each chunk is indexed into a TF-IDF vector space. When you ask a question, the query is vectorized the same way and matched to chunks by cosine similarity, so the most relevant passages surface even if your wording differs from the text. In Q&A mode, the top matches are then handed to the local LLM as context, which writes a sourced answer — without re-reading the whole document.
Search mode returns ranked passages with similarity scores; Q&A mode adds the local LLM, which synthesizes those passages into a cited answer.
Omar uploads a 14-page service agreement and switches to Q&A mode. Instead of scrolling, he asks a targeted question and lets retrieval pull the relevant clause.
To chat with a PDF: upload your document, then ask questions about its content — the AI extracts and analyzes text locally, providing answers with page references.
FreeToolHub AI PDF Reader is a free browser-based tool that lets you chat with PDF documents using AI, no signup, no upload.
Upload PDFs and ask questions in plain English. TF-IDF vector search finds relevant passages. 100% local, no upload, no signup, free.
The AI PDF Reader lets you ask plain-English questions about your own documents and get answers grounded in them. Upload one PDF or a whole stack; each file is parsed in your browser and split into page-numbered chunks that are indexed for vector search. Two modes cover different jobs. Search returns the five most relevant passages with their document name, page number, and similarity score, while Q&A loads a local language model that reads those passages and writes a direct answer, citing sources as [1], [2]. If the documents do not contain the answer, it says so instead of improvising. You can also generate page-by-page summaries and compare two documents side by side.
Researchers juggling dozens of papers use it to find which study mentioned a method or result without skimming every PDF again. Students query textbooks and lecture notes before exams, jumping straight to the page that answers the question. Legal and procurement teams search contracts and agreements for specific clauses, obligations, or dates across multiple versions at once. Consultants preparing for client meetings pull facts from lengthy reports on the spot. Because every uploaded file stays on the reader's own machine, it also suits anyone under confidentiality obligations, such as HR files, medical records, or unreleased financials, where uploading to a cloud chat service is simply not allowed.
(1) Upload your PDFs. The reader accepts multiple files and merges them into one searchable library, or you can load a bundled sample to try the flow first. Text extraction, chunking, and indexing all happen locally. (2) Switch between Search and Q&A mode. Search matches your question against every chunk using TF-IDF vectorization and cosine similarity, ranking passages by relevance. Q&A optionally loads a local model, with a progress timer while it runs, and answers from the top excerpts. (3) Go deeper: generate a summary for each page of the selected document with live progress, or, with two or more documents loaded, run a comparison that highlights what differs between them.
Nothing leaves your device, and that is a design constraint here rather than a toggle. PDF parsing runs in the browser with a JavaScript library, and the search index, meaning the TF-IDF vectors built from your pages, lives only in memory for the current tab. When you clear the library or close the browser, documents, chunks, and vectors are discarded together. The optional question-answering model downloads once into cache and executes locally as well; your questions and the retrieved passages are never transmitted. There is no upload endpoint at any point, no account, and no analytics on document content, which is exactly what makes the tool viable for sensitive material.
No. The PDF is parsed and indexed entirely in your browser. The AI model builds a local vector index of the document's text chunks, then answers your questions by searching this index and generating responses on-device. A 200-page PDF indexes in 30-60 seconds on first load, with subsequent questions answered in 2-5 seconds.
It handles PDFs up to several hundred pages (tested reliably to 300+ pages). The limiting factor is your device RAM—indexing a 500-page document uses approximately 500MB-1GB of memory. For best performance, use PDFs with a text layer (not scanned images). Larger documents may take 2-3 minutes for initial indexing.
Yes. Every answer includes references to the specific page numbers and paragraphs used to generate the response. You can click a citation to jump to that section in the PDF. This makes it suitable for legal research, academic review, and compliance auditing where source verification is mandatory.
This tool is also known by these tasks — each link opens the same tool with a focused guide:
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