[cipherself]

I am working on search but rather for text-to-image retrieval, nevertheless, I am curious if by that's all baked into Azure AI search you also meant synthetic query generation from the grandparent comment. If so, what's your latency for this? And do you extract structured data from the query? If so, do you use LLMs for that?

Moreover I am curious why you guys use bm25 over SPLADE?

[pamelafox]

Yes, AI Search has a new agentic retrieval feature that includes synthetic query generation: https://techcommunity.microsoft.com/blog/azure-ai-foundry-bl... You can customize the model used and the max # of queries to generate, so latency depends on those factors, plus the length of the conversation history passed in. The model is usually gpt-4o or gpt-4.1 or the -mini of those, so it's the standard latency for those. A more recent version of that feature also uses the LLM to dynamically decide which of several indices to query, and executes the searches in parallel.

That query generation approach does not extract structured data. I do maintain another RAG template for PostgreSQL that uses function calling to turn the query into a structured query, such that I can construct SQL filters dynamically. Docs here: https://github.com/Azure-Samples/rag-postgres-openai-python/...

I'll ask the search about SPLADE, not sure.

[cipherself]

Got it, I think this might make sense for a "conversation" type of search not for an instant search feature because lowest latency is gonna be too high IMO.

[pmc00]

Fair point on latency, we (Azure AI Search) target both scenarios with different features. For instant search you can just do the usual hybrid + rerank combo, or if you want query rewriting to improve user queries, you can enable QR at a moderate latency hit. We evaluated this approach at length here: https://techcommunity.microsoft.com/blog/azure-ai-foundry-bl...

Of course, agentic retrieval is just better quality-wise for a broader set of scenarios, usual quality-latency trade-off.

We don't do SPLADE today. We've explored it and may get back to it at some point, but we ended up investing more on reranking to boost precision, we've found we have fewer challenges on the recall side.