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Leveraging frozen embeddings in Vespa with SentenceTransformers

How to implement frozen embeddings approach in Vespa using SentenceTransformers library and optimize your search application at the same time.

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.

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.

Enhancing Vespa’s Embedding Management Capabilities

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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.

Vespa Newsletter, May 2023

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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.

High performance feeding with Vespa CLI

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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.

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

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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.

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.

Private regional endpoints in Vespa Cloud

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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.