Welcome to the Deep Dive. Today, we are jumping right into a really core problem
for modern AI
applications. It's all about getting AI to actually understand the documents you
give it,
especially the tricky ones, you know, visual stuff, complex PDFs, multimodal things,
the technical specs, manuals, diagrams, maybe even training videos.
Exactly. If you're trying to build something reliable, something that gives
accurate answers,
well, you know, the moment you throw in a chart or like a complex PDF, accuracy
just tanks.
We're looking at some sources today that really diagnose this, and they introduce a
toolset called
Morphic. They claim it provides the most accurate foundation for these kinds of
document-based AI
apps. Right. So our mission here, for you listening, is to get a clear picture of
why the standard
approach of retrieval, augmented generation, or RAG, why it often fails when things
get serious,
when you try to scale it up, and then how this newer AI native toolset, Morphic,
how it aims to
fix that, the scaling, the cost, and especially the accuracy, making it easier for
beginners too.
Definitely. We'll get into those fragile pipelines in a moment. But first, just a
quick word from our
supporter who helps keep these deep dives going. Safe Server ensures the robust
hosting of cutting
edge software, like the tools we're discussing. They support you in your digital
transformation.
You can find more info at www.safeserver.de. Okay, so let's start with that
baseline. Retrieval,
augmented generation, R. It's pretty much the standard way for grounding large
language models,
the big AI brains in real world data, like your company Docs, so they don't just
make stuff up.
Yeah. Arga is great for a proof of concept, a quick demo. But the sources we have
are really
clear on this, those POCs. They often fail spectacularly in production. And the
reason
is actually pretty straightforward. It feels like your whole system is held
together with
digital duct tape. Duct taping. I saw that mentioned. Like a dozen different tools
cobbled
together. You've got text extraction over here, OCR doing its thing there, embedding
models,
vector databases. Each one is a potential breaking point, right? Creating these
really fragile
pipelines that just will break under actual real world pressure. Absolutely. And
that fragility.
It hurts most when you hit those visually rich documents. The fundamental issue
with
these traditional pipelines is they basically treat everything as if it's just
plain text,
even when it's obviously not. So, okay, if the pipeline just strips out all the
visual context,
what happens to something like, say, a wiring diagram? Does it just become a random
list of
labels? Pretty much, yes. The crucial visual information, gone. That detailed
diagram loses
its vital spatial relationships. You know, the fact that component A is connected
to component B,
that meaning is lost. Or a critical bar chart, maybe showing performance dropping
off.
It just becomes meaningless text fragments to the AI. Tables, oh, tables get mangled
into
unreadable strings. The system totally misses the headers, the columns, the
structure.
Wow. And the result then is pretty bad because the AI app might be confidently
returning wrong
answers. Yeah. It thinks it knows because it's awesome text, but it missed the
crucial bit in
the image or misunderstood the layout. That sounds like a huge business risk.
It is. And don't forget the cost side. Think about an application trying to answer
questions
about some massive 500-page equipment manual. The old RAGRA forces the LLM to
process and
reprocess that huge document over and over for almost every single question.
That gets incredibly slow and really, really expensive when you scale it up.
Okay. So if that's the reality of traditional RAG, inaccurate, fragile, expensive,
RAGRAs,
how do we actually build systems that can see the charts properly? This is where
Morphic comes in,
described as an AI-native toolset. The sources say it provides the most accurate
document search and
store for building AI apps. Right. This is the big shift. It's designed end-to-end
specifically
to store, represent, and search unstructured data. It treats those complex things,
PDFs, videos,
diagrams, as, well, first-class citizens right from the start. It doesn't try to cram
visual
data into a text-only box. Let's get into the features then. How does it actually
achieve that
accuracy? Multi-modal data handling sounds like step one. It offers first-class
support for
unstructured data. That seems key because, yeah, most existing systems kind of
choke when you give
them a video or a really complex PDF. And the search itself is smarter. It uses
specialized
techniques. The sources mentioned something called cold poly to build search that
actually
understands the visual content. So you can search across images, PDFs, videos, all
sorts of things
using just one single endpoint because the system gets the meaning of the visuals,
not just the text nearby. Okay. And what about that cost and scaling nightmare you
mentioned,
the constant reprocessing of giant manuals? They have something called cache
augmented generation.
Sounds technical. Can you break down what that actually means for like my server
costs? Yeah,
absolutely. It lets you create what they call persistent KV caches of your
documents. Think of
it like this. The LLM reads that whole 500 page manual properly just once. Morphic
then takes a
perfect index snapshot of the LLM's understanding of that document. It essentially
freezes that
understanding and saves it like a super smart sticky note. So the AI doesn't have
to reread
the entire thing from scratch every single time someone asks a related question. Ah,
so you're pre-processing the intelligence, not just the raw text. That sounds like
it would
massively speed things up and cut down compute costs drastically, avoiding all that
repetitive
heavy lifting. That's a big deal for a production system. Huge deal. And for
developers who need
more control, Morphic helps bring structure back to this unstructured mess.
Remember we talked about
diagrams losing their spatial meaning? Knowledge graphs are the answer there. Right.
So letting
users build the main specific knowledge graphs and doing it with just a single line
of code,
that means you're putting the logic back in, turning those mangled strings back
into a
connected map of how things relate. Precisely. The AI can then follow those
connections logically,
much more reliable. And alongside that, there's the natural language rules engine.
Think of it like defining rules for your unstructured data, but using plain English,
not complicated code. You can just tell it how data should be ingested, categorized,
queried.
It's like using common sense to structure chaos. And it also handles metadata
extraction,
pulling out specifics like bounding boxes from images or classifying parts of a
document
quickly and scalably. Yes. Extracting that crucial low-level detail without adding
more
fragility to the pipeline. Okay. It sounds really powerful, but maybe complex. If
Morphic is doing
all this heavy lifting visual analysis, knowledge graphs, caching, does that just
shift the cost
from compute to developer complexity, especially for a beginner? That's a really
fair point. But
the aim here is unification. Instead of you juggling, say, 12 different tools, you're
managing
one integrated system. And they've tried to make getting started pretty easy. If
you're a beginner
and you just want your AI app to stop being confidently wrong, the hosted option is
probably
the simplest path. Okay. Tell us more. How does someone actually get started with
this? Easiest
way. Sign up for the free tier directly on Morphic's site. They say it's a generous
free tier,
enough to actually build and test things properly before you hit any paywalls.
After that, the
pricing is transparent based on your actual compute usage. No complex licenses.
Just pay
for what you use. And for developers, people who want to code against it. There's a
Python SDK
and also a REST API. The examples in the sources look pretty simple, like ingesting
a file,
basically one line of code, and asking a question is straightforward, too. You can
ask something
specific like, what's the height of screw 14A in the chair assembly instructions?
And the system
does the hard work of finding the diagram, reading it, and pulling out that exact
measurement.
What about someone who isn't a coder, maybe yet, but still needs this kind of
accurate understanding
from their documents? There's the Morfit console. It's a web interface. You can
just upload files
there, connect to other data sources, and basically chat with your data all within
the same place.
So you get the power of the backend without needing to write code right away.
Good to have options. Now, for folks thinking about self-hosting or larger deployments,
we should touch on licensing. That's always critical. Right. The core product is
source
available. It uses the business source license 1.1. What that means is it's
completely free
for personal use or for indie developers. If you're building a commercial product
with it,
it's still free as long as that deployment makes less than $2,000 per month in
gross revenue.
Okay. Good to know. And there was one crucial note about updates,
something about a migration script. Ah, yes. Very important detail.
If you happen to install Morphic before June 22nd, 2025, you absolutely need to run
a specific
migration script they provide. It's not just a routine update, it optimizes the
authentication
system. They're claiming a 70, 80% faster query performance after running it.
Wow. 70, 80%. Okay. Yeah. You definitely want that speed boost.
Definitely capture that. So yeah, overall, Morphic seems to tackle that core RREG
problem head on.
It unifies the tools, treats visual and unstructured data properly from the start,
and delivers better accuracy and scaling using smart caching within a single system.
So to recap for everyone listening, we've seen why just duct taping standard tools
together leads to
inaccurate, fragile, and expensive AI pipelines, especially with visual data, and
how Morphic uses
this AI native database approach to properly ingest, actually understand, and
reliably retrieve
info from complex multimodal documents. And before we wrap up, let's give one
final thank you to SafeServer. Again, that's www.safeserver.de. They provide the
kind of
robust infrastructure that makes hosting advanced software like this possible,
really supporting
digital transformation. Okay, so this whole conversation leaves me with a final
thought,
something for you to mull over. If developers don't have to fight with stitching
together a dozen
tools anymore for complex documents, and AI can finally truly understand charts and
diagrams
accurately, what's the next really complex multimodal data source that's going to
become
critical for AI to master? Is it maybe like highly detailed satellite imagery or
analyzing
real-time video feeds from a busy factory floor? Hmm, something to think about.
Definitely food
for thought. Until next time, keep digging deep.