Upload any document and ask questions in plain English. DocuMind uses Retrieval-Augmented Generation (RAG) to find relevant passages and generate accurate, source-cited answers.
Overview
A production-ready RAG system that turns your documents into an intelligent knowledge base.
DocuMind is an AI-powered document assistant built on Retrieval-Augmented Generation (RAG). Instead of sending your entire document to an LLM, it intelligently chunks, embeds, and indexes your content in a vector database. When you ask a question, it retrieves only the most relevant passages and feeds them to the language model — producing accurate, grounded answers with source citations.
This approach solves two critical problems with vanilla LLMs: hallucination (making up facts) and context-window limits (documents too large to fit in a single prompt). DocuMind handles PDFs, Word docs, plain text, Markdown, and CSV files.
The app comes pre-loaded with three sample documents so you can start asking questions immediately — no upload required.
Architecture
Two pipelines power DocuMind — one for ingestion, one for querying.
Document Ingestion Pipeline
Query Pipeline
Testing Walkthrough
Follow these 6 steps to explore every feature of DocuMind.
DocuMind comes with 3 seed documents already indexed and ready to query. You'll see them in the document sidebar when you open the app:
Type a natural-language question in the chat input. DocuMind will search the indexed documents, retrieve relevant chunks, and generate an answer with source citations.
Click the debug toggle to open the RAG transparency panel. This shows exactly what happened behind the scenes — which chunks were retrieved, their similarity scores, embedding details, token usage, and cost breakdown.
Click the upload button or drag & drop a file into the upload area. Supported formats: PDF, DOCX, TXT, MD, and CSV. The document will be chunked, embedded, and indexed in real time.
Open the settings panel to fine-tune how DocuMind processes and answers queries. You can change the language model, temperature, number of chunks retrieved (top-K), similarity threshold, and response style.
Upload multiple documents and ask questions that require information from more than one source. DocuMind retrieves chunks from all indexed documents and cites each source separately in the answer.
Features
Everything built into DocuMind.
Upload PDF, DOCX, TXT, Markdown, and CSV files. Text is extracted, chunked, and embedded automatically.
Ask questions in plain English. Get accurate, contextual answers generated from your document content.
Every answer includes citations pointing to the exact document and passage the information came from.
Full transparency into the RAG pipeline — see retrieved chunks, similarity scores, token usage, and cost per query.
Tune the model, temperature, top-K, similarity threshold, chunking strategy, and response style to your needs.
Index multiple documents and ask cross-document questions. Answers pull from all sources with per-document citations.