Mateusz OzimkiewiczMateuszAI Automation · Full-Stack

CONCEPT INTERFACERAG system

RAG Document Assistant

RAG-powered document search with source-grounded answers.

RAG Document Assistant — concept interface render.

Swipe or use arrow keys to inspect the full interface.

Interface concept. Figures shown — document and chunk counts, confidence and match scores, timings — are placeholder values; no live data source or production deployment is connected.

01 · Product premise

Ask a question in plain language and get an answer written from your own documents — with the sources shown next to it, so the answer can be checked rather than trusted.

The class of business problem it addresses: internal knowledge search, questions about procedures, offers or policies, and support documentation — anywhere staff or customers need a checkable answer from a company's own documents. These are the kinds of application this pattern fits, not deployments claimed here.

02 · Document ingestion

Documents of common types are added to named collections. Each is prepared for search on the way in, and the interface keeps the document list one glance away.

03 · Chunking + embeddings

Long documents are split into passages that keep their metadata, then turned into embeddings so meaning — not just keywords — can be searched.

04 · Storage / indexing

Embeddings are stored in a semantic index alongside the source reference of each passage, which is what lets an answer point back to where it came from.

05 · Retrieval

A question is matched against the index, and the most relevant passages are retrieved and reranked before any answer is written.

06 · Answer with sources

The answer is composed from the retrieved passages only, with numbered citations inline. The point of the design is that an answer is never shown without the material it was built from.

07 · Source inspection / citations

Every citation resolves to a source entry: the document, the page, and a relevance indicator. A reader can check the claim against the source in one step.

08 · Architecture pipeline

Six stages, one direction — the same pipeline the interface concept presents. The highlighted stage is the one the reader actually sees.

  1. IngestionUpload documents
  2. ChunkingSplit with metadata
  3. EmbeddingGenerate embeddings
  4. Storage / indexingSemantic index
  5. RetrievalSearch and rerank
  6. Answer with sources — key stageCited answer

09 · Current status

CONCEPT INTERFACE · NOT A DEPLOYMENT

Presented as a portfolio case-study visual. It is not a claim of a live client deployment, production scale, or measured retrieval quality, and no specific embedding or storage technology is claimed.