Hi. I’m an AI engineer of an emerging retailer. We’re continuously pushing the boundaries of our user search experience. We’ve got a massive inventory, hence a lot of data to be managed. This got me thinking about the untapped potential of neural search.
I've had my hands on OpenAI's GPT and Deepset's Haystack lately. Both tools are great in specific scenarios, but integrating them seamlessly at an enterprise scale is challenging, especially when we're talking about real-time user interactions. The primary challenge remains in managing multimodal data efficiently without sacrificing speed.
To add context, my goal in leveraging something like GPT for e-commerce is to create a more intuitive, conversational, and responsive search function. Imagine a user typing in a vague description or query, and the system providing product suggestions like a seasoned salesperson would. Given the vast product range, the neural search could bridge the gap between user intent and the most relevant product offerings.
If anyone has experience with this I’d like to hear your thoughts, and if you have any other tool recommendations for this pls do share. I’d be grateful for any help
thanks for sharing! good thing you found success with an API backend implemented with fastapi and opensearch. I'm also impressed by how well you handled the hybrid versions of opensearch but tbh, am curious what challenges did you encounter while integrating the hybrid versions of opensearch?🤔