hi, this is Lance from Lang chain. We're starting a new series called rag from scratch that's going to walk through some of the basic principles for Rag and kind of build up to Advanced topics. Um, so one of the main motivations for rag is simply that llms haven't seen all of the data that you may care about. So like private data or very recent data would not be included in the pre-training Run for these LMS. And you can see here on the graph on the x-axis that the number of tokens that they're pre-trained on, which is of course very large. Um, but of course it's still always going to be limited relative to private data that you care about or for example recent data. But there's another interesting consideration is that llms have context windows that are actually getting increasingly large. So you know coming going from like thousands of tokens to many thousands of tokens, which represents you know dozens of Pages up to hundreds of pages. We can fit information into them from external sources. And a way to think about this is

llms are kind of a kernel of a new kind of operating system, and connecting them to external data is kind of a very central capability in the development of this kind new emergent operating system. So retrieval, alignment to generation, or rag, is a very popular kind of general paradigm for doing this, which typically involves three stages. So the first stage is indexing some external documents such that they can be easily retrieved based on an input query. So for example, we ask a question, we retrieve documents that are relevant to that question, we feed those documents into an llm in the final generation stage to produce an answer that's grounded in those retrieved documents. Now we're starting from scratch, but we're going to kind of build up to this broader view of rag. You can see here there's a lot of interesting methods and tricks that kind of fan out from those three basic components of indexing, retrieval, and generation, and future videos are actually going to walk through those in detail. We're going to try to keep each

video pretty short, like five minutes, but we're going to spend a lot of time on some of those more advanced topics. First, over the next three videos, I'll just be laying out the very basic kind of ideas behind indexing, retrieval, and generation. And then we'll kind of build beyond that into those more advanced themes. And now I want to show just a quick code walkthrough because we want to make these videos also a little bit interactive. So right here in this repo will be shared. It's public. I have a notebook open and I've just basically installed a few packages and I've set a few environment variables for my LSmith keys, which I personally do recommend. It's really useful for tracing observability, particularly when you're building RAG pipelines. So what I'm going to show here is the code for a RAG quick start, which is linked here. And I'm going to run this, but I'm then going to kind of walk through everything that's going on. So actually, if we think back to our diagram.

all we're doing here is we're loading documents. In this case, I'm loading a blog post. We're then splitting them, and we'll talk about that in future like short videos on why splitting is important. But just for now, recognize we're splitting them or setting a chunk size of, you know, a thousand characters. So we're splitting up our documents. Every split is embedded and indexed into this Vector store. So we say we picked open eye embeddings. We're using chromas as our Vector storage runs locally. And now we find this retriever. We then have defined a prompt for rag. We've defined our llm. We've done some minor document processing. We set up this chain, which will basically take our input question, run our retriever to fetch relevant documents, put the retrieved documents and our question into our prompt, pass it to the LM, format the output as a string. And we can see here's our output. Now we can open up lsmith and we can actually see how this ran. So here was our question, and here's our output, and we can

actually look here's our retriever here is our retrieve documents so that's pretty nice and ultimately here was the prompt that we actually passed into the LM you're an assistant for QA task use the following pieces of retrieve content to answer the question here's our question and then here's all the content this we retrieved and that DRS answer so this just gives a very general overview of how rag works and in future uh short videos we're going to like break down each of these pieces and I in in a lot more detail uh thanks

RAG From Scratch: Part 1 (Overview)

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RAG From Scratch: Part 1 (Overview)

RAG From Scratch: Part 1 (Overview)

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