You know, if you've ever tried to build a rag
agent for like a real world application, you
know the pain. It's just this massive technical
headache that can stop a project coal. Oh, it's
brutal. The infrastructure alone. You're trying
to figure out document chunking, getting the
right vector embeddings, and then you have to
stand up and manage a whole vector database.
Pinecone, Milvus, whatever. It's a full time
engineering job. Exactly. And the costs just
start climbing right away. Yeah. But what if
there was a way to just... Skip 90 % of that.
Well, that's what we're talking about. Here's
the headline that should make you just stop.
We're talking about indexing 121 -page PDF, a
huge knowledge base, for less than two cents.
Welcome back to the Deep Dive. Today, our whole
mission is to unpack Google's new Gemini File
Search API because it looks like it completely
automates the hardest parts of retrieval augmented
generation. And you can do it with a simple no
-code tool. Yeah. And let's just make sure we're
on the same page with ARAC. Retrieval augmented
generation just means using your own documents
to ground an LLM to make sure its answers are
based on some kind of truth, not just its training
data. That grounding is absolutely everything.
So here's what we're going to do. We'll walk
through the insane cost savings. Yeah. Then the
super simple four -step workflow. And then we
have to talk about the limitations. Because,
you know, if it sounds this good, there's got
to be some catches. Okay, let's get into it.
Let's see just how simple this really is. So
the traditional way of doing our edge, it really
was a gauntlet. It wasn't just, you know, pointing
an LLM at a file. You had like... A dozen different
things you had to build and then maintain. Absolutely.
You had to worry about all the different file
types, how to ingest them, adding metadata, and
then the chunking. Oh, the chunking. Recursive
character splitting, running every single one
of those little chunks through an embeddings
model. And only then, after all that, could you
even put it in the database. Right. It was so
intense. That's just the definition of high friction.
So how does this Gemini file search solution...
get around all that. What's Google actually doing
under the hood? It just simplifies the whole
thing from the developer side. You just upload
the file. That's it. Google takes care of the
chunking. They generate their own embeddings
and they handle the storage. That entire pipeline
is managed for you. So the big idea is you don't
have to build your own search system. You don't
have to set up a vector database or worry about
keeping things in sync. You're just using their
pipeline. Exactly. And what's really interesting
is that their chunking is probably way better
than what most of us would build. It's not just
split every 500 characters. It understands the
document's flow and structure. That's a great
point. And just to be clear for everyone, when
we say embeddings, we're just talking about turning
words into numbers, right? Into a mathematical
format so a computer can search them incredibly
fast. That's it. It's just turning language into
math for quick comparisons. So if you had to
pick just one thing. What's the single biggest
piece of complexity that this new Gemini method
just gets rid of? It's that whole chain of manual
document splitting, generating the embeddings,
and then managing all the separate search infrastructure
to glue it all together. Okay, let's talk about
the money, because this is where the story gets
kind of wild. You said you could index a huge
document for pennies. How does that pricing actually
work? The main thing is that you really only
get charged for that first step, the upload,
the indexing, and the cost is just... Tiny. It's
15 cents per 1 million tokens. Let's put that
in real terms again. That 121 -page PDF was,
what, about 95 ,000 tokens? Yeah. So the math
on that really is less than 2 cents. It costs
basically nothing to load your knowledge. It's
incredibly cheap to get the data in. And here's
maybe the biggest deal right now. Storage is
free. Totally free. You are not paying by the
gigabyte for all those vectors to just sit there
on Google servers. Okay, but what about querying?
I have this agent running all day. Am I going
to get slammed with retrieval fees? No, not for
the retrieval itself. You pay the normal rate
for using the LLM, you know, for Gemini 2 .5
flash to generate the answer. But the actual
cost of pulling the data from your store is,
for now, absorbed. I saw the cost comparison
table. Yeah. And it's a little bit shocking.
Right. Let's say you have 100 gigs of data and
you run a million queries in a month. Right.
For a traditional setup with something like Pinecone
plus all the compute you'd need, you're looking
at hundreds, maybe even thousands of dollars
a month easily. Whoa. Yeah. And with Gemini File
Search, that whole first month, including the
one -time indexing fee for all that data, is
about $47. Wait, $47? Yeah. For that kind of
volume? Yeah. That's the moment of wonder right
there. I mean, imagine. Imagine what you could
build, what you could experiment with if your
entire knowledge -based infrastructure costs
less than a pizza. It just opens a powerful RAG
to everyone. It's a huge democratization. It's
moving away from spending big capital on infrastructure
to just a simple operational cost. So beyond
that initial tiny fee, what's the main takeaway
on cost for someone just starting out? The fact
that storage is currently free. that removes
the single biggest recurring cost that you always
have with traditional vector databases okay so
the money part is a no -brainer let's get practical
let's talk about building this thing in nat which
is basically a tool for visualizing api calls
right and you only need four of them four simple
http request nodes it's like stacking lego blocks
it's really that linear okay walk us through
them what are those four steps doing step one
is create store Think of this as just making
a folder. You're creating a permanent named index
on Google's side where your documents are going
to live. Got it. Then you have to get the actual
file up there. Exactly. Step two is upload file.
But this is just a temporary step. The file is
in the Google Cloud environment, but it's not
connected to your store yet. It's just sitting
there. So you have to link the file to the folder.
That is step three. Move file to store. This
is the magic step. This is what actually kicks
off the indexing and makes the file a permanent
part of your knowledge base. And then finally,
you can ask it a question. Step four, query the
store. This is the request you send to the Gemini
model, and you tell it, hey, use this specific
knowledge base to help you answer the user's
question. Okay, a quick but important detour
on setup. Getting the security right. The original
notes mention some confusion in Google's documentation.
So what's the right way to do authentication
in ADEM? The best way is to not paste your API
key in every single node. That's messy and insecure.
Instead, you use ADEM's generic credential type.
Right. And you use the query auth option specifically.
Correct. You just tell it the parameter is called
key and you paste your Gemini API key in there
once. Then all four of your nodes can just reference
that saved credential. It keeps everything clean
and secure. And why is getting that authentication
right so important, even if you're just building
a quick prototype? Because we should always be
building securely from the start. It just prevents
you from accidentally exposing your key in a
bunch of different places. All right, let's talk
about actually running this. You said step three,
moving the file into the store, is the critical
one. What happens if you forget to do that? The
file just stays temporary, just floating out
there in the cloud, and it'll get deleted after
a little while. It never gets indexed, so your
agent can't see it. You have to make that link
to the store. Okay, so once it's linked... and
we're ready to query it in step four what does
that api call look like so we're using the gemini
2 .5 flash model and the most important part
is in the json you send you have to include a
search config parameter and paste in the unique
store name id that you got from step one that's
how you tell the llm exactly where to look so
if you get that id wrong The agent just defaults
to its general knowledge. It doesn't use your
documents at all. Precisely. It's the whole key.
And then, of course, you need good prompt engineering.
We used a clear instruction. You are a helpful
ag agent. Use your knowledge -based tool for
truth. Cite your sources. And there was that
weirdly specific rule you found. No punctuation,
quotation marks, or new lines. Why that? Huh,
yeah, that's just a practical little hack. It's
to stop the underlying API from throwing a JSON
error when it tries to pass data back. It just
ensures the data transfer is clean. A strange
quirk, but it's necessary for stability right
now. So let's get to the results. You threw three
really different documents at it. The official
rules of golf, an NVIDIA press release, and an
Apple 10K filing. How did it do? It did extremely
well. For instance, I asked the golf PDF, what
happens if your club breaks during the middle
of the round? It came back with a perfect cited
answer about how you can continue using it or
have it repaired legally. And what about across
multiple documents? Could it find a specific
number from the NVIDIA file? Yep. Asked for the
Q1 2025 fiscal summary. It correctly pulled out
the $26 billion in total revenue and the $22
billion in data center revenue. And it cited
the press release correctly. And the overall
score. after 10 really tough questions across
almost 200 pages of documents, was a 4 .5 out
of 5 for correctness. That's amazing for a setup
that took minutes. What's so impressive about
that 4 .5 out of 5 score is that it got there
with basically zero complexity, zero fine -tuning,
and zero maintenance from the person building
it. So we know it's simple, we know it's cheap,
we know it's accurate. But now we have to be
realistic. This isn't magic. Where does it fall
short? What are the limitations? Okay. Limitation
number one is a big one for any real application,
data management. Right now, Google doesn't have
any kind of version control for the files in
your store. So if I have my Q1 report in there
and then I upload the Q2 report, now I just have
two of them. You have two of them. And the store
gets cluttered with old conflicting data. Your
agent might pull from the wrong one. So the only
solution right now is manual. You have to track
your own versions and remember to delete the
old file before you upload the new one. I appreciate
you sharing that vulnerability. It feels like
no matter how advanced the tools get, we're always
stuck with data hygiene problems. Oh, yeah. I
still wrestle with prompt drift and messy data
pipelines in my own complex projects. It's just
a constant frustrating challenge in this field.
And what about the quality of the documents themselves?
Garbage in, garbage out. That rule absolutely
still applies. Gemini has OCR, which is great,
but it's not a miracle worker. If you upload
a blurry, poorly formatted PDF, you're going
to get bad answers. You still have to do the
cleanup. Okay, and limitation number three. This
one's about what it's actually good at. Right.
This system is fantastic at finding a needle
in a haystack. A specific fact, a number, a rule.
It fails completely when you ask it for a holistic
summary or to understand the whole document.
So you couldn't ask it to, say, summarize a 500
-page book. Exactly. We saw this in our tests.
I asked it, how many total rules are in the Gulf
PDF? And it answered five. It couldn't see the
whole document to count them all. It just found
the five nearest chunks that mentioned the word
rule. And the last one, which is maybe the most
important for businesses. You have to remember
your documents are on Google servers, so you
have to be really careful about what you upload.
No sensitive PII, personally identifiable information,
and no top secret company data. And you need
to think about compliance. Absolutely. GDPR,
IAPA, if you have really strict data sovereignty
or security needs, you might still need to build
your own on -premise solution. This might not
be for you. So just to be crystal clear. If your
goal is to summarize a huge entire document,
should you use this? No. Its architecture is
built for finding facts inside chunks, which
limits the kind of holistic understanding you
need for a good summary. OK, so let's pull all
this together. The big takeaway here seems to
be that Gemini File Search just massively lowers
the barrier to entry for RAG. It's a huge leap
in simplicity and cost effectiveness. The verdict
is pretty clear. You can set it up in 30 minutes.
It's basically free for most normal use cases,
and it delivers really high accuracy. That 4
.5 out of 5 is no joke. Four simple API calls
are automating what used to be weeks of painful
infrastructure work. It's pretty incredible.
So who should be using this right now? I'd say
developers who are prototyping org ideas, small
businesses that need a simple internal Q &A bot,
or maybe content creators trying to organize
a huge library of their own work. And who should
maybe hold off for now? Big companies with really
strict compliance rules, anyone who needs total
control over their data, or use cases that are
all about deep, full document summarization instead
of fact -finding. It really lets you change your
focus. You can stop worrying about the RRAG infrastructure
and just start building a valuable agent. Which
leads to a really interesting thought. If this
is the trend... If R just becomes a cheap built
-in feature of these models, what does that mean
for the future of, say, a specialized vector
database engineer? Is that entire job going to
change? That is a fascinating question to think
about. It really is. All right. Go out and build
value, not infrastructure. We hope this deep
dive gave you the clarity you were looking for.
Until next time.