Lesson Complete!

What you did in this lesson

• Learned that embeddings are vectors where similar text points in the same direction
• Wrote configure_gemini() — authenticates with the Gemini API
• Wrote embed_text() — converts one chunk into a 768-float vector
• Wrote embed_all_chunks() — produces a vector for every chunk

What comes next

You have a list of chunks and a matching list of vectors. In Lesson 4, you will write the search function that finds which chunks are most relevant to a given question.