Hello everyone! I’m working on creating a multimodal AI application that combines text and visual content to recommend dining spots. Users should be able to input descriptions like “Fine dining place with rooftop views, French cuisine, romantic atmosphere” and get relevant suggestions.
My current challenge is with data collection. I have textual information for different locations ready to use, but gathering image content is proving difficult. When I try to scrape visual data at scale, my IP gets blocked pretty quickly. Since training an effective model requires substantial amounts of data, this becomes a real bottleneck.
I need guidance on how to efficiently gather this visual content, process it properly, and integrate it with my language model. What are some practical approaches or tools that could help with this workflow? Any suggestions for avoiding IP blocking issues while collecting large datasets would be really helpful.
Appreciate any advice or experience you can share!
Try synthetic data generation for your image component while you build the core system. I hit the same blocking issues on my computer vision project. Mixing a small seed dataset of real restaurant photos with data augmentation actually made my model more robust. For quick wins, partner with local dining platforms or chamber of commerce groups. Lots of smaller cities have digital archives they’ll share for academic or business use. The image quality beats scraped content since they’re professionally curated. For the multimodal stuff with Langchain - vector similarity search between CLIP embeddings and text descriptions works great. Just make sure your image preprocessing matches what users actually care about: lighting, ambiance, how the food looks. Test with a smaller, high-quality dataset before you scale up collection.
Been there with IP blocking - it’s a nightmare. Manual data pipelines always break when you need them most.
Stop fighting scrapers and automate the whole thing instead. Pull from restaurant APIs, run images through computer vision, feed everything to Langchain. No scraping needed.
This approach saved me countless hours. Google Places API grabs restaurant data, vision APIs extract image features, merge with text embeddings, push to your model. Runs itself.
Best part? Smart delays, multiple data sources, graceful failure handling, easy scaling. No more babysitting scripts or buying proxies.
I use Latenode for this stuff - connects APIs and handles workflow complexity. Design once, let it run. Great for multimodal AI when you need reliable data flows.
IP blocking is such a pain when you’re scaling up. Hit the same wall building a recommendation system at work.
Don’t bother with rate limits and proxy juggling - there’s a better way. Skip scraping entirely and automate everything through APIs.
I switched to automated workflows that pull from APIs instead. Tons of platforms give you legal access to images and restaurant data. You can rotate sources, add delays, and handle retries automatically.
The magic happens when you automate end-to-end: data collection, image processing, feeding your language model, recommendations - all runs hands-off.
For multimodal stuff, set up workflows that push images through vision APIs, pull features, and merge with text data automatically.
I built something similar with Latenode since it handles the automation mess. Connect APIs, add delays and error handling, scale without IP headaches. Works great with AI services too.
rotating proxies literally saved my project when I hit the same blocks. go with residential proxy services - they cost more but you won’t get detected. also, reach out to food bloggers with huge photo libraries. most will share their stuff for credit or a small payment.
Had the same data collection headache building a restaurant recommendation engine last year. The image bottleneck is frustrating, but aggressive scraping isn’t necessary.
Consider leveraging existing platforms via their APIs. Google Places, Yelp Fusion, and Foursquare provide both images and metadata with manageable rate limits.
In my experience with multimodal applications, I utilized pre-trained vision encoders like CLIP to generate visual embeddings from restaurant photos, which I then integrated with text embeddings derived from location descriptions. This approach pairs well with Langchain’s document loaders and vector stores.
Additionally, reaching out to restaurant associations or tourism boards can yield curated image collections; they often share resources for legitimate business purposes. While this may require more effort initially, it ultimately provides high-quality data without the risk of legal issues. The key is to build a robust pipeline that accommodates multiple sources rather than relying solely on a single scraping strategy.