Deploy Scalable RAG in Three Steps
Key Points
- Retrieval‑augmented generation (RAG) delivers the highest ROI for enterprise LLM use, but scaling it requires managing vector stores, embeddings, authentication, and high‑volume data pipelines beyond simple notebooks.
- The speaker demonstrates a three‑step setup using IBM watsonx Flows: install the CLI, authenticate with domain and API keys, then ingest and chunk data to create a deployable RAG flow.
- watsonx Flows automates core tasks—tokenization, vector retrieval, guardrails, and even hallucination‑metric calculation—so developers can build or modify a full RAG pipeline simply by editing flow steps.
- After deploying the flow, an API endpoint is generated that can be integrated into applications, returning query completions along with groundedness and hallucination warnings for enterprise‑grade reliability.
Full Transcript
# Deploy Scalable RAG in Three Steps **Source:** [https://www.youtube.com/watch?v=LpKGm1jJXv4](https://www.youtube.com/watch?v=LpKGm1jJXv4) **Duration:** 00:02:38 ## Summary - Retrieval‑augmented generation (RAG) delivers the highest ROI for enterprise LLM use, but scaling it requires managing vector stores, embeddings, authentication, and high‑volume data pipelines beyond simple notebooks. - The speaker demonstrates a three‑step setup using IBM watsonx Flows: install the CLI, authenticate with domain and API keys, then ingest and chunk data to create a deployable RAG flow. - watsonx Flows automates core tasks—tokenization, vector retrieval, guardrails, and even hallucination‑metric calculation—so developers can build or modify a full RAG pipeline simply by editing flow steps. - After deploying the flow, an API endpoint is generated that can be integrated into applications, returning query completions along with groundedness and hallucination warnings for enterprise‑grade reliability. ## Sections - [00:00:00](https://www.youtube.com/watch?v=LpKGm1jJXv4&t=0s) **Scaling RAG with Watsonx Flows** - The speaker outlines the complexities of deploying Retrieval‑Augmented Generation at scale and demonstrates a three‑step setup using Watsonx Flows to provision vector databases, manage embeddings, create authenticated APIs, and enforce guardrails automatically. ## Full Transcript
You're into LLM so you probably heard about RAG, right?
Well, I'm going to throw it out there.
It's the best way to get bang for buck when using a LLMs for a business.
But when you're doing it at scale, there's more to think about
than just a Jupyter notebook.
And unlike when you're debugging with the service desk, works on my laptop.
Won't work here.
Standing up vector databases, managing embeddings, creating
authenticated APIs, and hooking your LLM takes work.
And even more so when you're dealing with big data volumes or tons of users.
So what if I told you you could get rig up and running for business in three steps?
What if it handled all the hard stuff like tokenization, retrieval,
but also guardrails?
And what if it calculated hallucination metrics for you automatically?
I'm going to show you how to do it in three steps.
And it begins with every developer's favorite bit installing stuff.
I'm going to be using the watsonx flows engine.
The first goal is to be able to run workflows dash dash version on my MacBook.
If I get back a version number.
We're going to do this. I just need to download
the install from here and install it using this command.
Don't let the commands get you, just do it the same way I use Excel every day.
Copy, paste and pray.
Now if I run workflows dash dash version, I get a version number back.
Side note I can also run workflows.
Dash dash help to see all of the commands available.
Now right watsonx flows I like strangers,
like meeting your coworker on the weekend.
So go to I need to authenticate.
I need to get wxflows to recognize me when I run the "whoami" command.
Run watsonx flows login an explode to kick off the authentication process,
it prompts with the environment domain and admin key.
These are all available from this link.
Once done, if I run watsonx flows, who am I? Again?
I get back the domain environment admin key and API key.
I'm in final goal.
Upload the data and deploy a float.
Once I'm done with this step, I'll have an API endpoint that I can use
first run wxflows init dash dash interactive.
This is going to take me through a wizard to chunk up my data.
It prompts for the data location.
In this case, I've got IBM's annual report in markdown format
as well as some chunking parameters.
Once that's done, I get back three new files.
This is the kicker with watsonx Flows,
I can build an entire RAG or LLM flow just by changing the steps in the flow.
Need a prompt template?
Easy! one hallucination metrics calculated?
Add in the hallucination score step.
Need distance metrics raginfo has that.
I can load the data into the vectors store by writing
wxflows collection deploy, choose the RAG flow by and commenting the flow I want in the terminal file,
and deploy it by running wxflows deploy.
This will return an API endpoint.
I can plug the environment details into my application,
and I've now got an enterprise RAG application up and running.
When a query, we can see the completion and groundedness warnings,
as well as the hallucination metrics and source documents.