Embed a corpus and index it in a vector database

Turn a corpus into embeddings, measure similarity, and index it in a vector database such as FAISS or Chroma for semantic search.

Start lesson 1 The Applied AI domain

6 lessons
~4 h 30 of reading
6 quizzes
0 proof lab

The path

Where it comes from, where it leads

What you need to know first, where you stand on this skill, and what it opens next. Your status is computed in your browser, from what you have read and passed.

Before · 2 prerequisites

Recommended before · 1

Prepare a corpus for RAG: extract, clean, chunk

This skill, for you

Embed a corpus and index it in a vector database

Computing

It unlocks · 1 skill

Build an end-to-end RAG, make its retrieval reliable and take it to production

Lessons

How to learn it

6 lessons, about ~4 h 30, in program order. Each lesson ends with its quiz.

The program

6 lessons · ~4 h 30

Part 1 · Inférence (in French) 1 lesson · ~1 h 10

1Embeddings et recherche sémantique (in French)· 70 min

Part 2 · RAG (in French) 5 lessons · ~3 h 20

2Embeddings & similarité (in French)· 40 min
3FAISS (in French)· 40 min
4Chroma (in French)· 40 min
5Qdrant (in French)· 40 min
6pgvector (in French)· 40 min

Beyond the lessons

Assess, practise, pass the proof

Reading a lesson confirms nothing. A skill is confirmed by a passed quiz, practised in a guided lab, and put to the test by a challenge or a mission; an imported lab result is not verified by the site.

Assess yourself

6 quizzes

At the end of the lessons: passed at 70%, they confirm the skill.

Practice

No guided lab

No guided lab practices it yet.

Proof

No proof

Neither a challenge nor a mission with a proof yet.

Around it

Who requires it, who teaches it

Required by

No published job requires it yet.

Taught by

Python IA (in French)

Same block · Build a RAG