navarre.aiClarisTalk AI · the FileMaker AI podcast
Semantic Search is Native in FileMaker 2024! Special Guest: Ronnie Rios
Show Notes: New FileMaker 2024 AI release: https://support.claris.com/s/answerview?anum=000041889&language=en_US Links: Claris Community Live: Learn about AI prompting basics: https://www.youtube.com/watch?v=AQlOu-BIs5U See CRIS at Summerfest-Tech “A.I Deployed: Real World Examples” on WED June 26th in Milwaukee: https://www.summerfest-tech.com/summerfest-tech-2024 Join the conversation on the Cla
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Transcript
Transcribed by machine (whisper, large model), 37,103 words. Names and product terms are sometimes misheard; the audio is the record.
Claris Talk AI. It's another episode of Claris Talk AI. Hello, Chris. Hello. We have a special guest today. It's Ronnie Rios from Claris. Welcome. Hello, everyone. Hey, Ronnie. I'm really excited to be here, man. This is really great. I'm really glad you're here. Yeah, me too. I get to talk about my two favorite subjects, FileMaker and AI. This is going to be awesome. It's going to be a lot of fun. For sure. Thanks so much for inviting me. Anything big happen three days ago? I don't know. I can't think of anything. New episodes of my favorite show? I don't know. Let's not keep people in suspense. FileMaker 2024. I think I added a 20 in there. Shipped three days ago. Exciting.
A bunch of new AI features. I'm here in Berlin at the .FMP conference seeing a bunch of cool sessions about it. But I would really love it, Ronnie, if you could take over and show us some cool stuff. Talk about what you've got to talk about with a new version. And I think the reason Ronnie's here, and Ronnie, you want to talk a little bit about your role in bringing these things to market, I think will kind of frame things up nicely for everybody. Sure. That would be fantastic. So Chris knows I've been working with AI. It's been kind of my little passion on the side for quite some time. And we've been, you know, everything from Coromel to just about anything that will come my way.
But, you know, obviously with generative AI, a couple of years. So I've been playing around with some of the things that it really enables you to do. So I've been playing around and trying really to figure out how to bring it into FileMaker. What's the best way of enabling, bringing value to the end user, right, as a developer, bringing generative AI. And some of the things I've been playing around with for quite some time, I was able to show off some of those things at Engage. Yeah, you did actually an excellent job. And actually, you were a representative. You were sending Claris there as a product manager, if I'm not mistaken, on the AI stuff. I know your role was influential.
I just don't know what they call that over at Claris these days. And you did the hit of the Claris Engage conference. You were at the time, though, not talking about FileMaker 2024 stuff, were you? You were doing what was at the time in market. Is that fair to say? Yeah. Yeah, that's right. So my session at Engage was really about showing how to bring AI and kind of more focusing. On generative AI, because that's what everybody's really looking at right now, is how do you bring that? How do you power features for your end users with generative AI? So you're talking about like writing poems and stuff like that and question-answer machine stuff, right? Yeah, question-answer.
Anything that's zero-shot is good. Yeah, anything that's zero-shot, right? Yeah, Chris and I have always been joking. Yeah, these bots are great for generating. Poems and stuff like that. But really, what can I do for my end users? How do I bring AI-powered features to my end users? How do I bring values in through AI? And so I've been trying to do that, playing around with it for a while. And so my session at Engage was really about showing some of those use cases, some of the things that I've been playing around with, some of the things that I think that people can start using, again, with the in-market version of FileMaker, what was in market at the time, right, in FileMaker 2023, and how to enable those features using AI.
And so we had a whole session where we go through some of the concepts and showing some of the use cases there. And I think people liked it. I think it was a lot of fun for sure. Huge hit. Huge hit. I love it. You want to show us some stuff? What are we starting with? Yeah. Sure, I can show something from it. Yeah, so we'll go into the Wayback Machine to look at some stuff that Ronnie was talking about, which will help frame up why the stuff that came out this week is so important and compelling. Yeah. Ooh, Sprint Dashboard. Yeah, I think just one of the features, I showed about four or five different use cases, and one of the ones that really kind of got people excited, because I also teased it during the keynote, was about called Smart Dashboard.
It's really how do you do, how do you generate charts in FileMaker using AI? You know, I was explaining during the keynote that one of the things that I've almost always had to do is build some sort of chart in every application that I've built. There's some sort of report. There's usually some sort of charts, some sort of dashboard there is. And the problem with charts is that it's almost everything that we build as a developer is that it's going to be fixed in place for a while, right? Whatever we build, we kind of have to predict what the user is going to be asking, how they're going to use it. And that's almost impossible, especially when it comes to reports and dashboards, because they're used by decision makers.
And I don't know how they're going to want to manipulate the data in order to answer whatever challenges they're having that day. And so to me, charts was just kind of like that. Really good use case where the idea here was to use AI to generate charts. So behind the scenes, really what it does, it's the scripts and with the APIs, it's really generating SQL statements behind the scenes. So you provide a description of what it is that you want. You pass that along with the schema of the entire database. And so the LLM just generates some SQL. In my particular case, there's no data exchange. There is no, none of your data gets sent to the LLM. And I'm not saying that's just a bad thing in of itself, but it does make it a lot faster.
If I don't have to pass along my entire table or the results of the SQL, it's going to make it a lot faster. Well, actually, Ronnie, double click on that a little bit. Like, I don't mean literally, but like, that's actually kind of a huge deal, especially if people are concerned about data privacy, right? And that was one of the things I really was impressed with on your demo. So first of all, you're using SQL as your retrieval mechanism. We've talked before how you can do vector database searches, or you can even do API calls. API calls, yeah. You chose to do SQL over DAPI, for example, which we've had some conversations about. And I thought it was really interesting, your decision-making process there.
And then did that also help you facilitate not actually sending the data over to the language model or those independent? So the reason why I chose SQL, and I've heard some people say, well, why did you choose SQL? Because it's not that efficient, especially in FileMaker. But there is just one, the easy answer is almost every large language model knows SQL. SQL has been around for so long. There's so much data. Part of the training data is SQL that almost every large language model knows SQL. So I didn't have to train it. I didn't have to provide it. It's fairly straightforward to generate. And it's really, really good at it. Most models are fairly good at doing that.
Some are better than the others. But it's just a way out. You could have done API. And as a matter of fact, I had Mike Wallace walk up to me at Engage and showed me how he had a large language model generating calls to DAPI, which is fantastic, right? Again, this is just one way of doing it. It was not meant to be showing what's the right way, but showing that it is possible to do these things, which I think was important to do. Yeah, I think the thing with DAPI, because that's what we use in our intuition platform when we're communicating with FileMaker, is in, you know, I've talked about this before, but it's the context. Part of it, right? You're communicating then through an API in the same manner as you would have done with CWP or something like that in the past.
It's through a layout to provide context into the data. So rather than, and then so, and actually the TOGs might come into play as well, too, as far as context and related data. Yeah. What's this do? I want to see what this does. I'm dying to see what's going on. Well, okay. So, but the point is he's using SQL here. Right, yeah. And the really important part of it is that. There's no data exchange. So you want to talk about why that is and why that's important. Because I see the terms in the Claris messaging, by the way, all over the website about responsible. And that's actually a big thing to bite off if you're going to say that your AI is responsible. So I think this is maybe one of the ways that Claris is indicating that using these tools is a responsible way to do it.
So if you want to expound upon that a little. Yeah. So, again, this example sends no data whatsoever. All I'm getting back. From the large language model is the configuration of the chart, getting the SQL itself and how the chart is going to be configured. What the data is, as far as a large language model is concerned, really is irrelevant. And so I can separate the two things, right? That's the construction of the chart and the data. And so no data really needs to be sent over. This gives me a great advantage, right? Of obviously privacy and security. So I do not have to send over that. The only thing I'm really sending that might be considered for some people a little sensitive would be the schema.
Right. But that's that's a decision, but nothing else is being sent over. The other reason about that also is it makes the makes the inference a lot faster. The less data I'm sending, especially the less data I'm generating is is going to make it a lot faster and less expensive, too, because you're not sure. Absolutely. Absolutely. So just by separating those two things, it just makes a huge, huge difference. So I tried to do that, make it faster, make it private by design. So because of those things like it's really fast, like it's almost magically fast. So I'll show this really quickly. This is kind of what I showed during the session. So I have this little dashboard that I created for my users, and there's already a list of widgets or I'm calling them widgets.
These are charts and indicators that the user can use and place them in which are some of these one of these five places that I have here. But if they want to generate one, they click on the plus sign, they choose what type. Of which they want, maybe an indicator and they type what is that they want to really describe what it is. So in this case, you know, cell cycle length, they click on the button and that information, right, this description, along with the schema and some other things get sent over. And we get back is the SQL query that's going to be required to get the data that I need and just a JSON object that describes how this data is going to be manipulated, how it's going to be presented.
And so I can make changes over here. I can say, hey, this is going to be a number. And then I can do all that processing, all the formatting here locally in FileMaker. And once that's there, then, right, the sky's the limit, what I can do with this. All of this is all native FileMaker stuff. There's no, there's no plugins, no JavaScript. There's no crazy stuff over here. This is all FileMaker stuff. - So for those that are listening, we are looking at FileMaker charts. Yes? - Yeah, this is all FileMaker charts. - So, and just to kind of, you know, I think that's a really important point here because those are probably one of the more difficult things to configure and use inside the FileMaker UI.
And you have managed to use Gen AI to make that more approachable. So approachable that users who may not even work for the company that's going to be using the system that you're building this for, they get onboarded years to come. They will just be able to interact with this and get exactly what they need, right? Like that's, I think the really interesting part of this is how dynamic this interface is. - Yeah, it's extremely dynamic. Again, once they configure these, you know, these widgets, which again, they're just records. Here in the database, you can just present them in whichever way you want. There's nothing fancy about this. Again, these are all native FileMaker tools and objects.
We've got some slide panels and some stuff in here, but still native FileMaker charts and objects over here, just used in a particular way so that it makes it very easy and very flexible. And the last part here I'm showing here is that because I'm using SQL, which is another advantage of SQL, is that it is not dependent on my relationship graph. - Mm-hmm. - It is dependent on the constraints of context that is brought to by the relationship graph then, which does happen with DAPI, for example. - Yeah. - And so this flexibility allows the user to ask for anything that they want, because they might see connections later on that we didn't foresee, or that maybe we didn't represent it in the most efficient way in the graph.
And so, you know, I brought this up here in a show to here. What I'm showing here is the relationship graph. You can see that the data tables are actually not even part of it. There's no relationship between them in FileMaker. They're just sending LLM tables right now. But because it's SQL, right, all those relationships get created on the fly, this still works, which is really cool. - It's a huge advantage. And just a little bit of commentary on how you're not sending the data to LLMs. And the reason I keep bringing that up is because the traditional RAG or retrieval augmented generation is literally about sending your data to the LLMs. And I think the interesting thing about this approach is that you're not.
- Yeah. There's nothing here, again, no data other than the schemas being sent. Because I think this is one of the concepts that, as a developer, I'm always looking... I've been looking at generative AI is as a...kind of as a logic engine. So here, it is really...all it is doing is helping bridge that gap. It's here as doing a function. It's not manipulating the data. It's not looking at the data. It is something as a programming language, if you will, because when I'm programming it, when I'm asking it, it's in plain English. But it's sending back a result in a fixed way that I want. So it's really there as a logic engine, right? Something that would normally require a human being to write out, like, it is doing for me.
- Right, yeah, which would take a lot of effort, right? I mean, but the other thing that occurs to me is this could apply to other than just, like, built-in FileMaker charts, but I work a lot with Google Charts. And I don't know if that would work, like, with the SQL query, but you can just give it a variable data. You can give it, like, a...you can have it build HTML with CSS or something. And have it build that in your application as well. Then you get all the other chart types. - Absolutely. I actually have a version of this, similar to this, using Chart.js, which I love. It is really easy. And it does the exact same thing. And by the way, it's not really writing JSON.
It's not writing JavaScript. It's not writing HTML. It is just giving a JSON object with the configuration. My JavaScript takes that, interprets it, and creates the chart on the fly. - Yeah, that would be local, too, right? - It's just a logic engine. - Yeah, like, if you use Google Charts, you're still sending your data out. But, yeah, with the...if you're running it your way. So, yeah, let's take a look. Can we see some more examples of this? I guess we saw a couple of them a minute ago. - So, this here is just showing kind of the scripts. I can see the scripts behind the scenes. It's really, really easy. It's really, really small. There's a main script here, generates the SQL and the settings, and then everything gets run locally.
So, the entire query gets run online. You're a machine, and that's why it's really that fast. It is very, very fast, which is really nice. - And just for some clarity, this was the before-this-week release of scripts. We're going to talk in a moment about some of the new tools that are at your disposal. But just to really drive the point home, all of this was possible even before the big announcement this week. And I think the real takeaway there is that there's no reason to be waiting to try to integrate these features into your solutions. - Absolutely. Absolutely. You can start using those things today. And this and the other files that I used during the session are available on Community.
So, you can go into community.claris.com, look for the session, be able to download the slides as well that I used during the session. And just take these files apart, look inside, see what makes it tick, modify it, learn from it. I really, really would love people to look at this and iterate on it, make it better. We've had already some people do that. We had Nicholas the other day who took the defined example and made it into an add-on. This was fantastic. I just love when people take stuff and… - Yeah, you're talking about the Claris Community Live live stream, episode number one, where the feature was taking one of those files, like you said, and they did a great discussion on talking about that.
By the way, the next one was one that we'll put at the link in the show notes, but it was one that… That I did with Rosemary on basic prompting principles, which I would encourage people to go check out. And then, by the way, there is one coming after that too, right, at some point over the next couple of weeks? - Yeah, we're looking to have it just… We'll have the link out soon. We'll have another live stream. We're looking at July 11th. It'll be just about AI, so we'll have some people over and we'll have a conversation about some of the new features, some of the things that we're going to talk about here today, but more from a developer's perspective, right?
We want to hear from the community. How they're using it. How are you guys using it? What do you see? What makes it… What's exciting about it for you? It should be great. Really, really good. - Fantastic. Great resource. Looking forward to that. And so, you're showing us now how you've integrated into a dashboard. And by the way, Matt loves dashboards. He's a huge fan of dashboards. And so, we're actually seeing FileMaker charts interactively and mixed maybe with some other charts. Is that fair to say? - Yeah. So, here… Yeah. So, here, you can see… You can see all the different… Again, it's the dashboard. You can see all the different widgets when you create a new widget in here or just another record.
You can choose a type. What I showed earlier before was an indicator, but here's a chart. This is a pie chart. I'm asking you to create a pie chart that shows the count of opportunities for each deal on the stage. And you click on the button and you see the chart appears in the back. And that's literally… This is real time. This is not… No fast forwarding, no editing. That's how long it really took to generate that chart. But once again, it's not sending all the data. It's not sending any of the data over. It's just sending some configuration and all you're getting back is that little JSON object. And then all the query gets run locally. The cool thing about that is that then these charts can always run.
It's not static data. And so, because it's a query, right, these charts will always be live, which is just great. - And technically, different users can get different experiences as far as the visualization of their data as well. Potentially based on job roles or the data they have access to. And it's something that developers don't have to think about deterministically in advance, right? So, I think the era of deterministic coding meaning we have to, you know, try to capture all the possible use cases weeks, potentially years in advance of actual users engaging with their applications might be behind us. Ronny, philosophically, your thoughts on that?
- I hope that a lot of that can move away. Again, I see especially generative AI as… As a developer, right, I see it as an amazing tool to be used as a kind of a logic engine, right? Where it can do things that would normally require some human reasoning. And I can shift those things away and build that into the application and take away some of the things that I would normally have to think ahead way too much, try to predict how the user is going to use it, just give them the tools to be able to be completely flexible to do that, right? This is an example of that. I have no idea what type of charts they're going to want to create, what type of… Which data, what type of relationships they're going to want to create.
So, this allows me to take that away, just build in that logic and let them go with it. And I think that's extremely powerful. - That probably applies to the Ask interface too, right? So, if we look into the FileMaker 2024 release, so, there's a… It's not an add-on, it's a… Or is it an add-on, the Ask, the chat interface that has also been made available? So, it's available to the developer community as part of the release this week? - Yeah. So, it's about asking about your data. You know, I've seen so many iterations of things like that, and I think it's just not uncommon, right, for people nowadays to want to be able to chat with their data. Like, they're seeing all these little chatbots out there.
And so, those things can very much well be built into FileMaker. So, if you think about how this is built, right, a chart could just be a chat interface. So, you know, being able to generate, retrieve the data, generate that, and generate an answer, it's just fairly easy to afford. - And if you don't want to build that from scratch, there is a… Is it an add-on or a file or something as part of the 2024 deliverable is what I'm angling towards here that actually allows for the chat interface in your data? - We're testing those things. We're trying to bring in some of those things. And 2024 is not officially there. So, we'll keep you posted. - Scoop. Okay, got you.
Loud and clear. Yeah, so now, I think the biggest thing that we see if we're looking at a lot of the clearest messaging, it really is about semantic search. Can you talk a little bit from a product management standpoint how that decision was made and what that means? And frankly, you know, in the context of FileMaker searching, how is that different than maybe what we're used to? - So, I love semantic search. I was talking about, you know, a few moments on stage during the keynote in Gage, I was talking about, you know, some of the things that we're working on behind the scenes. We're playing with a lot of AI technology at Clarus, right, trying to see what makes sense for us to bring in how.
And semantic search just keeps coming up, right? Christian and I have talked so much about this as well. But part of, you know, what a lot of people are doing is, you know, is retrieval, augmented generation that reduce the amount of hallucinations in your interactions with large… with large language models, and I'd say maybe the preferred way of doing the retrieval is using semantic search. And semantic search isn't new, right? But it definitely has seen a resurgence now with regenerative AI, and especially with large language models. So, I thought that, and especially when we're talking with some of the early testers, that this came up over and over again.
Like, this is the feature that really is kind of the building block for a lot of the use cases that… - Like the chat we just talked about. People may not know that that is the linchpin of chat, chatting against your data is the semantic search, the retrieval aspect of it. - Exactly. - As far as vectors are concerned, we should say. So, how do vectors, embeddings, and semantic search all fit in to FileMaker 2024 in that experience? - And I thought, and I think you guys have talked about it before in the, here on the podcast, that semantic search, you know, one of the things that excites me, what was recently something that really excites me is, you know, FileMaker already has some really good search capabilities, right?
We've had, searching FileMaker is really easy, it's fairly easy to use, right? It's a search by example, you don't have to learn, you know, any query language and anything like that. But semantic search really is about finding things by how similar they are to other things, right? Instead of being specifically by lexical, like the exact keywords and things like that. So, for instance, in semantic search, if you were to search for improving customer service, you know, it might yield results that include enhancing client support or boosting shopper satisfaction, right? Because semantically, the meaning are very similar, even though that the word... - Yeah, or even opposite, it's pretty fascinating, right?
So, things that are... - And you can do the opposite as well. That's one of the other use cases, right? Defining outliers in a dataset, which is really great. And so, you can imagine that if you take semantic search and you use it with the FileMaker traditional keyword-based search and combine those, you can make some really powerful search capabilities. And, you know, it really builds some really cool things. But like you said, there are other things that you can use it for, like finding outliers in a dataset. You can use it for recommendation systems and things like that. It just really is really great. But where really I think it shines is when you really combine it with large language models and FileMaker.
I think just magic happens. It's really, really amazing. There's some really cool things that happen when you take all these things and bring it together. And so, at Engage, I was actually showing one example. During the keynote of using a pre-release version of FileMaker that has semantic search and can do some really cool stuff. And I'm really happy to say that that version of that application that worked only in a pre-release version in our current release works beautifully. Nice. I will try to make this file available to everybody, but I don't know if you guys want to see it. Yeah. Yes, please. Scoot. Awesome. All right. So, this file is fairly simple.
It's fairly straightforward. What it does, it really is. It allows you to ask questions about your documents. And when you say documents, just to get it into FileMaker speak here, we're talking about documents stored in a container, yes? Yes. Documents stored in a container. Have they been transcribed into their text representation? Or, I mean, there's got to be some sort of overhead, right? Or just storage? These are just regular files. So, I'm going to take a regular file over here. I'm going to take, just to show you how this works for the end user. I'm going to add here. Nothing up your sleeve. Nothing up my sleeve. I'm just going to add a list of documents already in here.
I'm going to click here to add a brand new document. And is this to require a special type of document? Because, you know, obviously, we have a lot of flexibility from a binary file storage. Any constraints there? Right now, the way I've built this right now is limited to text files, but it can absolutely be expanded to other types of files. It's just really about how to get the data out of it. Since FileMaker has really easy built-in functions to take text files and just get access to the text. So, there's no reason for me to start building those things, but no reason why you couldn't say add PDFs or something like that. So, I'm going to just drag this file over here to this container field, and it kind of just kind of sucks it in, right?
So, now I've got 24 documents instead of 23. And what happened behind the scenes is that that document got split up into the text was extracted from the file. It got split up into a lot of little chunks, and for each one of those chunks, a vector embedding was created. So, there's a lot of variations of the meaning of that text. And then what that allows me to do now is that when I ask it a question, let's say... And by the way, can I point to the chunk part, I think is actually a really important part here too. The reason that you're not just saying put the whole document as a vector, which you can do, by the way. And there are some use cases possibly where vectorizing an entire document as one series of vectors may be meaningful.
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So finding the sweet spot at maybe 300 is actually very beneficial from a decision-making standpoint for a developer. So finding the sweet spot at maybe 300 is actually very beneficial from a decision-making standpoint for a developer. So what all this means, by the way, at the end of the day means once those things are there, then we can search by those meaning. So what all this means, by the way, at the end of the day means once those things are there, then we can search by those meaning. So what all this means, by the way, at the end of the day means once those things are there, then we can search by those meaning. We can search what things are more relevant to something that we're looking for.
We can search what things are more relevant to something that we're looking for. So say in this case, if I'm asking for, you know, what are the new AI functions in FileMaker 2024, So say in this case, if I'm asking for, you know, what are the new AI functions in FileMaker 2024, So say in this case, if I'm asking for, you know, what are the new AI functions in FileMaker 2024, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know,
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and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know,
and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know,
and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know,
and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know,
and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know, and I've got here a found set of all these different documents that include, you know,