Hey there, Eden here.
And we are done with the data indexing.
And in the next couple of videos we are going to dive deep and we're going to be discussing the retrieval
part of our pipeline.
So in the retrieval part we're going to be taking the user's query.
We're going to embed it into a vector.
And then we're going to ask from the vector database to bring us the top k chunks which are the most
similar to our question vector.
So those relevant chunks with the original user query, that's eventually what's going to go to the
LLM.
And this is what's going to generate the answer, which is going to be grounded in the information we
wanted in just a quick heads up, because LinkedIn has changed a lot since I made this course.
The next couple of videos you're about to watch were refilmed.
So I deleted the old recordings and I rerecorded everything to be up to date with the latest version.
That is why the IDE I'm going to be using now is going to be cursor, and the package manager is going
to be UV and not Pipenv.
And I just want to clarify that all the code we wrote so far is still the best practice when it comes
to the latest version, so it didn't change at all.
All the ingestion part.
What significantly changed was the retrieval part, and this is why I rerecorded the following videos.
And by the way, I do plan to rerecord the first videos with cursor and with the UV package manager,
so everything is going to be smoother.
So please forgive me for this minor inconvenience.
At the moment I'm really trying to do my best to keep this course up to date.