How do you roll out embeddings across your product catalog?
episode
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
9 min
1 speaker
3 chapters
transcribed 1 month ago
Transcript
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What is the main challenge enterprises face with product catalog search?
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Last question I'm gonna throw at you is game plan. So you're the SEO director at a big enterprise e-commerce site and you want to take what we've been talking about and you want to utilize embeddings across the entire product catalog. What would be your kind of like initial steps to implement that?
Yeah. The first steps anybody should take in figuring out their problem is really get some clarity around it. So, you know, we've been talk we've been talking about SEO. Plenty of ways you can implement this for SEO, but e-commerce can benefit from turning your your assets into vector embeddings in many different ways. There's site search I mentioned, uh product recommendations. We see examples of these already today. So figuring out exactly what it is you're trying to do. Uh that's where you need to get set up. And then you need to get into the database as well. So not every database can support embeddings. Um you need a specialized database. So like uh Pinecone or uh setting things up on like a knowledge graph, that's what's going to help you.
And then you'll need to ensure that all those product details are uh from the catalog are in there, like the title, uh the SQU data, obviously, the the sales data. So folks Focusing on things that facilitate these uh objectives, uh, descriptions, um, the categories, obviously. That's what that's what you need to get set up. So once you have that in place, uh, you want to start small. So you need to build a proof of concept. So uh the best way I would approach this is you know, think of the 80-20 rule. So what's driving real revenue? Or uh what has the most robust amount of content that you can actually work with. Um so there's l lot of different applications that you can apply here, but let's focus on things that are actually gonna have an impactful, because you don't wanna test on things that
aren't getting any attention anyway. Then pick a model. So I had mentioned the the mini the mini LM model, um, the all mini LM from send uh sentence transformer. It's a good place to start. That's where you build your prototype uh in your IDE and you can start querying the database or or interacting with that database to to try to figure out like how you can come up with solutions for your exact problem. But for e-commerce, you're probably gonna need something a little more specific, like something like uh Marco e-commerce is is specific to e-commerce. That's you know down the line once you've built some proof of concept. Then you're gonna build a stack. So, you know. The first part of getting clarity around what you're doing is you really should be seeking the buy-in.
And this is where that buy-in is essential.
How do you determine the first steps for implementing vector embeddings?
So if you're trying to build like a new database, you know, you're gonna need buy-in from the teams who are able to do these things. You can talk to ChatGPT and figure out how to set up Pine Cone. It's it's not crazy difficult. But you also need to integrate like What's changing in your catalog on a regular basis? So nightly, this should be indexing so that you can, you know, have some have some support for the things as they evolve, as you're producing them. And ultimately, you want to demonstrate some real world impact. So I mentioned site search. Most site produ uh most site search products aren't that great. Um, even if you integrate like uh the Google search product, if you're querying against a vector database, you're gonna find things that are
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