WEBVTT

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Hey there, Eden here.

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And we are done with the data indexing.

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And in the next couple of videos we are going to dive deep and we're going to be discussing the retrieval

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part of our pipeline.

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So in the retrieval part we're going to be taking the user's query.

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We're going to embed it into a vector.

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And then we're going to ask from the vector database to bring us the top k chunks which are the most

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similar to our question vector.

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So those relevant chunks with the original user query, that's eventually what's going to go to the

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LLM.

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And this is what's going to generate the answer, which is going to be grounded in the information we

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wanted in just a quick heads up, because LinkedIn has changed a lot since I made this course.

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The next couple of videos you're about to watch were refilmed.

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So I deleted the old recordings and I rerecorded everything to be up to date with the latest version.

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That is why the IDE I'm going to be using now is going to be cursor, and the package manager is going

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to be UV and not Pipenv.

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And I just want to clarify that all the code we wrote so far is still the best practice when it comes

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to the latest version, so it didn't change at all.

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All the ingestion part.

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What significantly changed was the retrieval part, and this is why I rerecorded the following videos.

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And by the way, I do plan to rerecord the first videos with cursor and with the UV package manager,

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so everything is going to be smoother.

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So please forgive me for this minor inconvenience.

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At the moment I'm really trying to do my best to keep this course up to date.