WEBVTT 00:01.160 --> 00:05.570 And would you believe at this point you're 55% of the way along the journey? 00:05.780 --> 00:09.890 Uh, it's been a while since I've thrown that stat out there, but congratulations. 00:09.890 --> 00:11.120 That's really awesome. 00:11.210 --> 00:14.270 Uh, so we have made progress with rag. 00:14.270 --> 00:18.560 We're taking it slowly because it's about to get real as we get into vector databases. 00:18.560 --> 00:24.920 But at this point, uh, not only do you have the sort of foundational understanding of why we're going 00:24.920 --> 00:31.310 to be using vectors, but you can now use Lang chain to load in documents, to split them, to add in 00:31.310 --> 00:32.120 metadata. 00:32.120 --> 00:36.620 And hopefully you've played around with that and you've satisfied yourself about how it's working and 00:36.620 --> 00:39.020 you're ready for the real deal. 00:39.020 --> 00:46.070 We next time are going to take these chunks and convert them into vectors using OpenAI's OpenAI embeddings, 00:46.070 --> 00:49.280 which was one of the encoding llms that we talked about. 00:49.310 --> 00:55.640 We're then going to store the vectors in an open source vector data store called chroma, which is an 00:55.640 --> 00:59.090 extremely popular open source vector database. 00:59.090 --> 01:00.080 And it's terrific. 01:00.080 --> 01:04.790 And we're going to enjoy putting our vectors in there, because we're then going to visualize them and 01:04.790 --> 01:10.010 see them and get a sense of what does it even mean to have a vector in a database. 01:10.010 --> 01:11.930 So lots to be done. 01:11.930 --> 01:13.790 I will see you next time.