From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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58 lines
1.7 KiB
58 lines
1.7 KiB
WEBVTT |
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And would you believe at this point you're 55% of the way along the journey? |
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Uh, it's been a while since I've thrown that stat out there, but congratulations. |
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That's really awesome. |
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Uh, so we have made progress with rag. |
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We're taking it slowly because it's about to get real as we get into vector databases. |
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But at this point, uh, not only do you have the sort of foundational understanding of why we're going |
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to be using vectors, but you can now use Lang chain to load in documents, to split them, to add in |
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metadata. |
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And hopefully you've played around with that and you've satisfied yourself about how it's working and |
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you're ready for the real deal. |
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We next time are going to take these chunks and convert them into vectors using OpenAI's OpenAI embeddings, |
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which was one of the encoding llms that we talked about. |
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We're then going to store the vectors in an open source vector data store called chroma, which is an |
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extremely popular open source vector database. |
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And it's terrific. |
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And we're going to enjoy putting our vectors in there, because we're then going to visualize them and |
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see them and get a sense of what does it even mean to have a vector in a database. |
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So lots to be done. |
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I will see you next time.
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