All Stories
Photo by Nicole Avagliano on Unsplash
Announcing Maximum Inner Product Search
Vespa can now solve Maximum Inner Product Search problems using an internal transformation to a Nearest Neighbor search. This is enabled by the new dotproduct distance metric.
Photo by Marc Sendra Martorell on Unsplash
Announcing vector streaming search: AI assistants at scale without breaking the bank
With personal data, you need complete results at low cost, something vector databases cannot provide. Vespa's new vector streaming search delivers complete results at a fraction of the cost.
Vespa at Berlin Buzzwords 2023
Summarizing Berlin Buzzwords 2023, Germany’s most exciting conference on storing, processing, streaming and searching large amounts of digital data.
Photo by vnwayne fan on Unsplash
Enhancing Vespa’s Embedding Management Capabilities
We are thrilled to announce significant updates to Vespa’s support for inference with text embedding models that maps texts into vector representations.
Photo by Scott Graham on Unsplash
Vespa Newsletter, May 2023
Advances in Vespa features and performance include multi-vector HNSW Indexing, global-phase re-ranking, LangChain support, improved bfloat16 throughput, and new document feed/export features in the Vespa CLI.
Photo by Shiro hatori on Unsplash
High performance feeding with Vespa CLI
Vespa CLI can now feed large sets of documents to Vespa efficiently.
Vespa support in langchain
Langchain now comes with a Vespa retriever.
Photo by Will van Wingerden on Unsplash
Minimizing LLM Distraction with Cross-Encoder Re-Ranking
Announcing global-phase re-ranking support in Vespa, unlocking efficient re-ranking with precise cross-encoder models. Cross-encoder models minimize distraction in retrieval-augmented completions generated by Large Language Models.
Customizing Reusable Frozen ML-Embeddings with Vespa
Deep-learned embeddings are popular for search and recommendation use cases. This post introduces the concept of using reusable frozen embeddings and tailoring them with Vespa.
Photo by Peter Herrmann on Unsplash
Revolutionizing Semantic Search with Multi-Vector HNSW Indexing in Vespa
Announcing multi-vector indexing support in Vespa, which allows you to index multiple vectors per document and retrieve documents by the closest vector in each document.
Photo by Taylor Vick on Unsplash
Private regional endpoints in Vespa Cloud
Set up private endpoint services on your Vespa Cloud application, and access them from your own VPC, in the same region, through the cloud provider's private network.
Photo by Ilya Pavlov on Unsplash