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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.
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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.
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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.
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Vespa Newsletter, March 2023
Advances in Vespa features and performance include GPU support, advanced BCP autoscaling, GCP Private Service Connect, and a great update to the e-commerce sample app.
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GPU-accelerated ML inference in Vespa Cloud
Today we're introducing support for GPU-accelerated ONNX model inference in Vespa, together with support for GPU instances in Vespa Cloud!
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Improving Search Ranking with Few-Shot Prompting of LLMs
Distilling the knowledge and power of generative Large Language Models (LLMs) with billions of parameters to ranking models with a few million parameters.
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Vespa Newsletter, January 2023
Advances in Vespa features and performance include Better Tensor formats, AWS PrivateLink, Autoscaling, Data Plane Access Control and Container and Content Node Performance.
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Improving Zero-Shot Ranking with Vespa Hybrid Search - part two
Where should you begin if you plan to implement search functionality but have not yet collected data from user interactions to train ranking models?
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Improving Zero-Shot Ranking with Vespa Hybrid Search
If you are planning to implement search functionality but have not yet collected data from user interactions to train ranking models, where should you begin?
Photo by Niels Weiss on Unsplash
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