Hi all, We are currently running a Solr cloud cluster for text-based product searches, and overall the setup is working well. However, we’ve observed a few cases where the *product images shown alongside the text search results are not relevant*. This is due to some internal mapping issues, which in turn affects user experience.
To improve this, we are exploring a *hybrid search approach* — where we want to combine both text and image similarity. The idea is: - Run the primary query based on text (as we currently do). - Validate the results using image similarity. - If the text matches but the product image is not relevant, we would filter it out before displaying it to the user on the front end. We would like to know: 1. B*est practices* for handling hybrid text + image search in Solr? 2. Anyone implemented something similar (using Solr’s vector search or external embeddings for images)? 3. Any guidance on *indexing image embeddings* in Solr (since Solr 9+ supports dense vectors) and combining them with text search efficiently? *Few details fyi:* Current Solr Version on our Production: v9.6.1 Index Size: ~250 GB Number of documents: ~180M Number of Shards: 63 Number of Nodes: 10 Average response time: ~80-100ms *Thanks & Regards,* *Uday Kumar*
