VIOMX Academy | Learn. Build. Intern.

Project overview

What you'll
actually build.

Students build a retrieval-augmented generation pipeline from scratch: ingesting documents, chunking and embedding them, storing vectors, and grounding LLM responses in real, cited sources instead of guesses.

✓Document ingestion and chunking pipeline
✓Embeddings stored in a vector database
✓Retrieval-augmented generation for accurate answers
✓Source citations in every response
✓Chat interface with streaming responses

PythonFastAPILangChainVector databaseLLM APIsReact frontend

Project structure

rag-knowledge-assistant/
├── backend/
│   ├── app/
│   │   ├── ingestion/
│   │   │   ├── loader.py
│   │   │   └── chunker.py
│   │   ├── retrieval/
│   │   │   ├── embeddings.py
│   │   │   └── vector_store.py
│   │   ├── routes/
│   │   │   └── chat.py
│   │   └── main.py
│   └── requirements.txt
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   │   ├── ChatWindow.jsx
│   │   │   └── SourceCard.jsx
│   │   └── App.jsx
│   └── package.json
└── README.md

Ready to build it?

This is what real work looks like.

Every module ends with a project like this one — a real structure, real tools, and something you can walk into an interview and explain line by line.

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RAG knowledge assistant

What you'llactually build.

This is what real work looks like.

What you'll
actually build.