pmc00
The project has some of the properties discussed above such as not having a typical main() (or winmain), because there’s no CRT to call it.
I wrote this little systemwide mute utility for Windows that way, annoying to be missing some parts of the CRT but not bad, code here: https://github.com/pablocastro/minimute
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.
Using an LLM for query planning in RAG –> 40% better answer relevance
Nearly 100x Compression for Vector Search with Minimal Quality Loss
VoiceRAG: A pattern for RAG and voice with the GPT-4o Realtime API for audio
We also included supporting data in that write up showing you can improve significantly on top of Hybrid/RRF using a reranking stage (assuming you have a good reranker model), so we shipped one as an optional step as part of our search engine.
More details, including old vs new limits: https://techcommunity.microsoft.com/t5/ai-azure-ai-services-...
Up to 11x vector database capacity increase in Azure AI Search, no price change
If you look at the table in the section "3. Hybrid Retrieval brings out the best of Keyword and Vector Search" of that article, we shared there the significant variability of metrics as a function of query types.
https://techcommunity.microsoft.com/t5/ai-azure-ai-services-...
(disclaimer: I work in that team)
Vector search and state of the art retrieval for Generative AI (Ignite talk) [video]
Vector search and hybrid retrieval, intro and evaluations on Azure Search
We recently did a bunch of evaluation work to quantify the differences between keyword search, vector search, hybrid, reranking, etc. across a few datasets. We shared the results here: https://techcommunity.microsoft.com/t5/azure-ai-services-blo...
Disclosure - I work in the Azure Search team.
Outperforming vector search with hybrid retrieval and ranking
Native vector search support in Microsoft Azure
A simple intro to how to create ChatGPT-like apps with your own data in Azure
Got ChatGPT to run (interpret? guess?) x86 assembly for the Fibonacci sequence
The Cognitive Search team continues to grow. We're looking for engineering managers for multiple locations. Join us to work at the intersection of cloud services, information retrieval and machine learning.
To apply: Seattle: https://careers.microsoft.com/us/en/job/1167446/Principal-So... Atlanta: https://careers.microsoft.com/us/en/job/1018927/Principal-So... Bangalore: https://careers.microsoft.com/us/en/job/1188511/Principal-So... Hyderabad: https://careers.microsoft.com/us/en/job/1157546/Principal-So...
For any questions reply here or ping me at pablo DOT castro AT company name.
Show HN: Game of Life projected on a torus, where it belongs:)
We're growing our teams in a number of different areas. If you have expertise in database systems internals (query processing, optimization, distributed query execution, storage engines), search (lucene, elasticsearch, infrastructure) or cloud systems (any cloud platform), we probably have a role that could be interesting for you.
Our current positions are all for folks with at least 2-3 years of experience and go up to fairly senior. We don't have entry-level positions in these teams right now (I'm sure other parts of Microsoft most likely have some though).
If interested apply on the links above, or email me and I'll route to the right hiring manager. pablo <dot> castro <at> company's domain.