AtlasRAG
Answers that come with their sources.
Answers that come with their sources.
A document workspace that turns uploaded material into searchable passages and returns answers with traceable source references.
Full-Stack AI Engineer Needed — Node.js, Python & RAG.
Build an AI-driven document intelligence system allowing users to upload documents, retrieve relevant information, and ask questions with traceable source citations.
The problem to solve
Useful information gets buried across documents. A plain search result still leaves someone reading everything to find a dependable answer.
How it works
- 01Upload a document to a knowledge workspace.
- 02Process its text and organize searchable passages.
- 03Ask a question and inspect the supporting sources.
Core capabilities
- Document upload
- Document parsing
- Chunking and indexing
- Semantic retrieval
- Document search
- AI-assisted question answering
- Source citations
- Conversation history
Implemented technology
The engineering details
Document processing and retrieval are presented as a connected user workflow.
Citations keep answers tied to uploaded material rather than an unsupported response.
Scope & implementation notes
The inspected deployment is a personal browser workspace. Documents and conversations are stored on the device; no Python service or paid hosted model connection is claimed.
An independent application inspired by a real client job brief. The capabilities and technical notes describe the built implementation; they do not imply a paid commission, real customer outcomes or connections to services that have not been verified.