vespa.ai (@vespaengine) 's Twitter Profile
vespa.ai

@vespaengine

Vespa.ai - the open source platform for combining data and AI, online.

Vectors/tensors, full-text, structured data; ML model inference at scale.

ID: 905446316754522112

linkhttps://vespa.ai calendar_today06-09-2017 15:03:19

496 Tweet

3,3K Followers

6 Following

The New Stack (@thenewstack) 's Twitter Profile Photo

To support complex RAG workloads at scale, an AI search platform must do far more than basic keyword or vector matching. Thanks to vespa.ai thenewstack.io/a-blueprint-fo…

Radu Gheorghe (@radu0gheorghe) 's Twitter Profile Photo

We just did a podcast about the process of migration (trade-offs included) from #Elasticsearch to vespa.ai With Dainius Jocas and Kevin Petrie 🙌 em360tech.com/podcasts/vinte…

Radu Gheorghe (@radu0gheorghe) 's Twitter Profile Photo

ACORN-1 and Adaptive Beam Search have been in vespa.ai for a while, but now we have a detailed post about how it works: blog.vespa.ai/additions-to-h…

ACORN-1 and Adaptive Beam Search have been in <a href="/vespaengine/">vespa.ai</a> for a while, but now we have a detailed post about how it works: blog.vespa.ai/additions-to-h…
The New Stack (@thenewstack) 's Twitter Profile Photo

Vector search alone isn’t enough. Production-grade AI search needs more: combining semantic, keyword and metadata retrieval, applying machine-learned ranking and handling constantly changing structured and unstructured data, all at scale. Thanks to vespa.ai

Liana (@lianapatel_) 's Twitter Profile Photo

Filtered vector search is a massively important and overlooked problem for RAG and vector DBs. Very excited to see this new blog post from vespa.ai detailing its implementation of ACORN, along with many clever extensions to deliver huge speedups for search with filters.

vespa.ai (@vespaengine) 's Twitter Profile Photo

If you want to create your own RAG application with this level of quality, clone the open source RAG Blueprint. github.com/vespa-engine/s…