Llama Index (aka GPT Index) Overview

hey fam so today I want to talk to you about llama index llama index what is it it is a tool that allows you to connect your data to an llm such as chat GPT so this is really a great tool because it moves you beyond that Q window or the sorry the query window right you can input much more than you can fit in that little text box and you can interact with large bodies of data so when I say data there's really a lot of things I'm talking about apis documents existing databases spreadsheets really the options are infinite they got a bunch of data connectors already and it's moving fast so I expect a lot a lot more to be available soon and so how does it work first your data whatever that is is broken down into nodes these nodes are then indexed they can be indexed with the help of the large language model itself which kind of learns about your documents or they can be indexed three third-party embeddings system and then stored in a database which the llm then interacts with this diagram really kind of sums it up the best these are the different types of indexes they have so this is a list index the first one where what happens is when you interact with your data the tool Loma index essentially searches through each node nodes are just chunks of document so let's say you upload a PDF right the PDF gets broken down into smaller chunks which can be interacted with by the llm to avoid token limits it takes the index down breaks it into small nodes and then matches kind of a keyword search against a node if a node matches it passes it to the llm and then extracts from there this is the more important one this the second one is a vector index so essentially it grabs embeddings and organizes it in a way that's more natural to llms this is much faster and then there's also a tree index that I forgot what they call this fourth one but yeah a little more advanced and these two down here are more costly so I would kind of start off with these two top and then let's see if um two makes sense for you and then go from there here the kind of breakdown of this that's what I'm saying see the the first ones indices with no llm calls these don't require calls to a large language models so these can be operated locally are these ones with llm calls they have to go and interact with the large language model to figure out what the text is and then sort it categorize it and sort of thing so this is kind of a one-time fee as long as you say the index but depending on your data store size it can get quite costly which is why I recommend starting with the first ones and this guy is looking you know looking for the keywords in your data so here's kind of how it works so your documents are passed into an index when a query is received this is what you ask about your data let's say you ask Chad GPT you know what did the author do during his time in art school it goes through the index figures out through each of like depending on which index you use it figures out what nodes it needs it takes the most relevant notes that passes those in as context to llama index to the tool sorry not to the tool but it's a large language model okay so it passes those this context and then the larger language model answers based on your content so it it doesn't kind of just come up with an answer it actually uses whatever you fed into it so it's really powerful you got to understand the power is huge of this because it unlocks a ton of possible use cases okay think about this research papers this is an actual website here they you could take a scientific paper document parse it into the system and then ask questions about the document so you can ask it to summarize the document you can ask it for some key takeaways a bullet point summary whatever you want what happened here what happened what were the methods anything right you can interact with the actual research paper instead of just asking chat GPT open-ended knowledge and this is another one video cues so this person what they did you take a video transcribe the video and then interact with the transcript via large language model OKAY Bing Bing is not actually run by llama index but the concept behind Bing is pretty much the same right so they take search results user searches for something they take those search results pass it into the large language model as context and then the large language model is able to actually kind of give you more factual responses than just hallucinating things out of the blue to get started they have a pip install um so this is a python project it's open source on GitHub uh the name is llama Dash index it used to be called I believe GPT index that's original name but they switched to the Llama index to make it more kind of open to different models right you can use llama llm the one that's leaked from Facebook you can use other llms it can be local Source times when I ran in a python 310 I got errors so 311 up I was fine dual content environment and you're good to go uh welcome to alarm index is the documentation page it's got uh yeah basically it's pretty good the thing is it's removing quite quickly and there's references to GPT index so some of the things already outdated here um especially with the embeddings documentation so you might have to dig around in the GitHub issue queue as well for kind of more up-to-date uh details there's questions being asked about this so you'll find a lot of the stuff there uh there's some tutorials here I would recommend maybe doing the SEC 10K analysis that's a good one you can see it at the bottom here apart from that I mean yeah there's really good documentation so far especially considering the age of the project and it's moving quite fast and the use cases are really abundant so I think you'll find lots more documentation available lots more use cases being shown one other thing I want to bring to your attention is this website here I believe this is their website I'm not 100 sure maybe it's third party but it's again open source free these are just connectors so these are data connectors remember if we go back to um what you can do with the PDF stocks SQL these are all sorts of data connectors which feed into the large language model and um yeah if anybody's interested drop a comment below I've been working along my index for a few weeks now and it there's some nuances which I found with um what I did was I hooked it up to a conda database and then generated my own embeddings locally with a hugging face model and this led to a much better cost thing first of all and I don't know I just kind of felt better doing part of the stuff locally my cost to set up a huge huge database was basically my GPU time for half an hour and then yeah my query is too open AI I don't know a thousand tokens or something on jet chat GPT turbo model so yeah I mean it's it's pretty cost effective um if anybody wants to see that tutorial or watch me actually go through the details there drop a link in the comments below thanks for watching and see you on the next

Channel: aidoks

Views: 20,893

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Id: bQw92baScME

Length: 7min 30sec (450 seconds)

Published: Thu Apr 20 2023