Tag vespaengine
Vespa Meetup in Sunnyvale
WHAT: Vespa meetup with various presentations from the Vespa team.
Tag database
Photo by Scott Graham on Unsplash
Vespa Newsletter, December 2023
Advances in Vespa features and performance include rank score normalization in the global ranking phase, improved feeding using PyVespa, token authentication, and query match debugging!
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, October 2023
Advances in Vespa features and performance include Vespa Cloud Enclave, Lucene Linguistics integration, much faster fuzzy query term matching, and performance and usability improvements.
Photo by Scott Graham on Unsplash
Vespa Newsletter, August 2023
Advances in Vespa features and performance include multilingual models, more control over ANN queries, mapped tensors in queries, and multiple new features in pyvespa and the Vespa CLI.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, July 2023
Advances in Vespa features and performance include Vector Streaming Search, GPU accelerated embeddings, Huggingface models and a solution to MIPS using a nearest neighbor search.
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 Ilya Pavlov on Unsplash
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.
Photo by Scott Graham on Unsplash
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.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, November 2022
Vespa features and performance advances include ANN Pre-Filter Performance, Parent Field Hit-Estimates, Model Training Notebooks, and GCP Support.
Photo by Scott Graham on Unsplash
Vespa Newsletter, October 2022
Advances in Vespa features and performance include a BertBase Embedder / model hub, improved query performance, paged attributes, ARM64 support and term bolding in string arrays.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, September 2022
Advances in Vespa features and performance include rank-phase statistics, detailed rank performance analysis, new query- and trace-applications and a new training video!
Photo by Scott Graham on Unsplash
Vespa Newsletter, June 2022
Advances in Vespa features and performance include ANN with configurable filtering, fuzzy matching, and native embedding support. Also see pyvespa’s new experimental ranking module!
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, April 2022
Advances in Vespa features and performance include tensor and ranking configuration improvements, pyvespa usability features and grouping configuration. Also find new guides for performance and ANN. And a podcast!
Photo by Scott Graham on Unsplash
Vespa Newsletter, January 2022
Advances in Vespa features, performance and operability improvements include: Improved synonym support, faster node recovery, re-balancing and re-indexing, WeakAnd query type and new pyvespa features and sample applications.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, December 2021
Advances in Vespa features and performance include tensor performance improvements, match-features and a Vespa IntelliJ plugin.
Photo by Scott Graham on Unsplash
Vespa Newsletter, November 2021
Advances in Vespa features and performance include improved schema inheritance, Intellij plugin for schemas, Hamming distance in ranking, and performance gains in data dump and application deployment.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, September 2021
Advances in Vespa features and performance include Vespa CLI, nearest neighbor performance improvements, mTLS security, improved write throughput and Sentencepiece Encoder.
Photo by Scott Graham on Unsplash
Vespa Product Updates, July 2021
Advances in Vespa features and performance include HTTP/2 feed and query endpoints, ONNX Runtime integration for stateless model inference, and the new open Vespa Factory build and test system.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, May 2021
Advances in features and performance include new int8 and bfloat16 tensor cell types, compact tensor feed format, Approximate Nearest Neighbor using Hamming distance, hash-based attribute dictionaries and case-sensitive attribute search...
Photo by Scott Graham on Unsplash
Vespa Product Updates, March 2021
Advances in Vespa features and performance last month include mass update/delete in /document/v1/, improved memory usage, OR-to-WeakAnd and better full node protection.
Photo by Scott Graham on Unsplash
Vespa Product Updates, January 2021
Advances in Vespa features and performance last month include Automatic Reindexing, Tensor Optimizations and Explainlevel Query Parameter for easier query blueprint tracing.
Photo by Donald Giannatti on Unsplash
Vespa Product Updates, December 2020
Advances in Vespa features and performance include improved tensor ranking performance, Apache ZooKeeper integration, Vespa Python API for researchers and ONNX integration.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, October 2020
Improvement to Vespa feeding APIs
Photo by ThisisEngineering RAEng on Unsplash
Vespa Product Updates, September 2020
Introducing ONNX-Runtime, Hamming Distance Metric, Conditional Update Performance Improvements and Compressed Transaction Log with Synced Ack
Vespa Product Updates, August 2020
Introducing NLP with Transformers, Grafana how-to, Improved GEO Search Support, Query Profile Variants Optimizations, & Build on Debian 10
Vespa Product Updates, June 2020
Announcing support for approximate nearest neighbor vector search which can be combined with filters and text search with state-of-the art performance
Vespa Product Updates, May 2020
The May 2020 update includes Improved Slow Node Tolerance, Multi-Threaded Rank Profile Compilation, Reduced Peak Memory at Startup, Feed Performance Improvements, & Increased Tensor Performance.
Vespa Product Updates, April 2020
The April 2020 update includes Top-K hits, smarter data migration and CloudWatch integration. Contributing to Vespa is now easier with the release of a CentOS 7 dev environment.
Vespa Product Updates, February 2020
Advances in Vespa features and performance in February include LightGBM support, improved tensor performance, benchmarking guide and query builder library
Vespa Product Updates, January 2020
The January 2020 update includes information about new tensor functions, updated sizing guides and various performance improvements.
Vespa Product Updates, December 2019: Improved ONNX support, New rank feature attributeMatch().maxWeight, Free lists for attribute multivalue mapping, faster updates for out-of-sync documents, Zookeeper 3.5.6
In the November Vespa product update, we mentioned Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance and...
Vespa Product Updates, October/November 2019: Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance, and Datadog Monitoring Support
In the September Vespa product update, we mentioned Tensor Float Support, Reduced Memory Use for Text Attributes, Prometheus Monitoring Support, and Query Dispatch Integrated in Container.
Vespa Product Updates, September 2019: Tensor Float Support, Reduced Memory Use for Text Attributes, Prometheus Monitoring Support, and Query Dispatch Integrated in Container
In the August Vespa product update, we mentioned BM25 Rank Feature, Searchable Parent References, Tensor Summary Features, and Metrics Export. Largely developed by Yahoo engineers, Vespa is an open source...
Vespa Product Updates, August 2019: BM25 Rank Feature, Searchable Parent References, Tensor Summary Features, and Metrics Export
In the recent Vespa product update, we mentioned Large Machine Learning Models, Multithreaded Disk Index Fusion, Ideal State Optimizations, and Feeding Improvements. Largely developed by Yahoo engineers, Vespa is an...
Vespa Product Updates, March 2019: Tensor updates, Query tracing and coverage
In last month’s Vespa update, we mentioned Boolean Field Type, Environment Variables, and Advanced Search Core Tuning. Largely developed by Yahoo engineers, Vespa is an open source big data processing...
Vespa Product Updates, February 2019: Boolean Field Type, Environment Variables, and Advanced Search Core Tuning
In last month’s Vespa update, we mentioned Parent/Child, Large File Config Download, and a Simplified Feeding Interface. Largely developed by Yahoo engineers, Vespa is an open source big data processing...
Vespa Product Updates, January 2019: Parent/Child, Large File Config Download, and a Simplified Feeding Interface
In last month’s Vespa update, we mentioned ONNX integration, precise transaction log pruning, grouping on maps, and improvements to streaming search performance. Largely developed by Yahoo engineers, Vespa is an...
Efficient personal search at large scale
Vespa includes a relatively unknown mode which provides personal search at massive scale for a fraction of the cost of alternatives: streaming search. In this article we explain streaming search...
Tag search
Migrating to the Vespa Search Engine
In this post, I will detail the journey at Stanby of how we have addressed the challenges faced by our existing search system through migrating to Vespa.
When you're using vectors you're doing search
Combining scale and quality takes more than vector similarity search
Photo by Scott Graham on Unsplash
Vespa Newsletter, December 2023
Advances in Vespa features and performance include rank score normalization in the global ranking phase, improved feeding using PyVespa, token authentication, and query match debugging!
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, October 2023
Advances in Vespa features and performance include Vespa Cloud Enclave, Lucene Linguistics integration, much faster fuzzy query term matching, and performance and usability improvements.
Photo by Scott Graham on Unsplash
Vespa Newsletter, August 2023
Advances in Vespa features and performance include multilingual models, more control over ANN queries, mapped tensors in queries, and multiple new features in pyvespa and the Vespa CLI.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, July 2023
Advances in Vespa features and performance include Vector Streaming Search, GPU accelerated embeddings, Huggingface models and a solution to MIPS using a nearest neighbor search.
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 Ilya Pavlov on Unsplash
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.
Photo by Scott Graham on Unsplash
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.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, November 2022
Vespa features and performance advances include ANN Pre-Filter Performance, Parent Field Hit-Estimates, Model Training Notebooks, and GCP Support.
Photo by Scott Graham on Unsplash
Vespa Newsletter, October 2022
Advances in Vespa features and performance include a BertBase Embedder / model hub, improved query performance, paged attributes, ARM64 support and term bolding in string arrays.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, September 2022
Advances in Vespa features and performance include rank-phase statistics, detailed rank performance analysis, new query- and trace-applications and a new training video!
Photo by Scott Graham on Unsplash
Vespa Newsletter, June 2022
Advances in Vespa features and performance include ANN with configurable filtering, fuzzy matching, and native embedding support. Also see pyvespa’s new experimental ranking module!
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, April 2022
Advances in Vespa features and performance include tensor and ranking configuration improvements, pyvespa usability features and grouping configuration. Also find new guides for performance and ANN. And a podcast!
Photo by Scott Graham on Unsplash
Vespa Newsletter, January 2022
Advances in Vespa features, performance and operability improvements include: Improved synonym support, faster node recovery, re-balancing and re-indexing, WeakAnd query type and new pyvespa features and sample applications.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, December 2021
Advances in Vespa features and performance include tensor performance improvements, match-features and a Vespa IntelliJ plugin.
Photo by Scott Graham on Unsplash
Vespa Newsletter, November 2021
Advances in Vespa features and performance include improved schema inheritance, Intellij plugin for schemas, Hamming distance in ranking, and performance gains in data dump and application deployment.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, September 2021
Advances in Vespa features and performance include Vespa CLI, nearest neighbor performance improvements, mTLS security, improved write throughput and Sentencepiece Encoder.
Photo by Scott Graham on Unsplash
Vespa Product Updates, July 2021
Advances in Vespa features and performance include HTTP/2 feed and query endpoints, ONNX Runtime integration for stateless model inference, and the new open Vespa Factory build and test system.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, May 2021
Advances in features and performance include new int8 and bfloat16 tensor cell types, compact tensor feed format, Approximate Nearest Neighbor using Hamming distance, hash-based attribute dictionaries and case-sensitive attribute search...
Photo by Scott Graham on Unsplash
Vespa Product Updates, March 2021
Advances in Vespa features and performance last month include mass update/delete in /document/v1/, improved memory usage, OR-to-WeakAnd and better full node protection.
Photo by Scott Graham on Unsplash
Vespa Product Updates, January 2021
Advances in Vespa features and performance last month include Automatic Reindexing, Tensor Optimizations and Explainlevel Query Parameter for easier query blueprint tracing.
Photo by Donald Giannatti on Unsplash
Vespa Product Updates, December 2020
Advances in Vespa features and performance include improved tensor ranking performance, Apache ZooKeeper integration, Vespa Python API for researchers and ONNX integration.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, October 2020
Improvement to Vespa feeding APIs
Photo by ThisisEngineering RAEng on Unsplash
Vespa Product Updates, September 2020
Introducing ONNX-Runtime, Hamming Distance Metric, Conditional Update Performance Improvements and Compressed Transaction Log with Synced Ack
Vespa Product Updates, August 2020
Introducing NLP with Transformers, Grafana how-to, Improved GEO Search Support, Query Profile Variants Optimizations, & Build on Debian 10
Vespa Product Updates, June 2020
Announcing support for approximate nearest neighbor vector search which can be combined with filters and text search with state-of-the art performance
Vespa Product Updates, May 2020
The May 2020 update includes Improved Slow Node Tolerance, Multi-Threaded Rank Profile Compilation, Reduced Peak Memory at Startup, Feed Performance Improvements, & Increased Tensor Performance.
Vespa Product Updates, April 2020
The April 2020 update includes Top-K hits, smarter data migration and CloudWatch integration. Contributing to Vespa is now easier with the release of a CentOS 7 dev environment.
Vespa Product Updates, February 2020
Advances in Vespa features and performance in February include LightGBM support, improved tensor performance, benchmarking guide and query builder library
Vespa Product Updates, January 2020
The January 2020 update includes information about new tensor functions, updated sizing guides and various performance improvements.
Vespa Product Updates, December 2019: Improved ONNX support, New rank feature attributeMatch().maxWeight, Free lists for attribute multivalue mapping, faster updates for out-of-sync documents, Zookeeper 3.5.6
In the November Vespa product update, we mentioned Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance and...
Learning to Rank with Vespa – Getting started with Text Search
Vespa.ai have just published two tutorials to help people to get started with text search applications by building scalable solutions with Vespa. The tutorials were based on the full document...
E-commerce search and recommendation with Vespa.ai
Holiday shopping season is upon us and it’s time for a blog post on E-commerce search and recommendation using Vespa.ai. Vespa.ai is used as the search and recommendation backend at...
Vespa Product Updates, October/November 2019: Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance, and Datadog Monitoring Support
In the September Vespa product update, we mentioned Tensor Float Support, Reduced Memory Use for Text Attributes, Prometheus Monitoring Support, and Query Dispatch Integrated in Container.
Vespa Product Updates, September 2019: Tensor Float Support, Reduced Memory Use for Text Attributes, Prometheus Monitoring Support, and Query Dispatch Integrated in Container
In the August Vespa product update, we mentioned BM25 Rank Feature, Searchable Parent References, Tensor Summary Features, and Metrics Export. Largely developed by Yahoo engineers, Vespa is an open source...
Vespa Product Updates, August 2019: BM25 Rank Feature, Searchable Parent References, Tensor Summary Features, and Metrics Export
In the recent Vespa product update, we mentioned Large Machine Learning Models, Multithreaded Disk Index Fusion, Ideal State Optimizations, and Feeding Improvements. Largely developed by Yahoo engineers, Vespa is an...
Vespa Product Updates, March 2019: Tensor updates, Query tracing and coverage
In last month’s Vespa update, we mentioned Boolean Field Type, Environment Variables, and Advanced Search Core Tuning. Largely developed by Yahoo engineers, Vespa is an open source big data processing...
Vespa Product Updates, February 2019: Boolean Field Type, Environment Variables, and Advanced Search Core Tuning
In last month’s Vespa update, we mentioned Parent/Child, Large File Config Download, and a Simplified Feeding Interface. Largely developed by Yahoo engineers, Vespa is an open source big data processing...
Vespa Product Updates, January 2019: Parent/Child, Large File Config Download, and a Simplified Feeding Interface
In last month’s Vespa update, we mentioned ONNX integration, precise transaction log pruning, grouping on maps, and improvements to streaming search performance. Largely developed by Yahoo engineers, Vespa is an...
Efficient personal search at large scale
Vespa includes a relatively unknown mode which provides personal search at massive scale for a fraction of the cost of alternatives: streaming search. In this article we explain streaming search...
Tag big data
Photo by Scott Graham on Unsplash
Vespa Newsletter, December 2023
Advances in Vespa features and performance include rank score normalization in the global ranking phase, improved feeding using PyVespa, token authentication, and query match debugging!
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, October 2023
Advances in Vespa features and performance include Vespa Cloud Enclave, Lucene Linguistics integration, much faster fuzzy query term matching, and performance and usability improvements.
Photo by Scott Graham on Unsplash
Vespa Newsletter, August 2023
Advances in Vespa features and performance include multilingual models, more control over ANN queries, mapped tensors in queries, and multiple new features in pyvespa and the Vespa CLI.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, July 2023
Advances in Vespa features and performance include Vector Streaming Search, GPU accelerated embeddings, Huggingface models and a solution to MIPS using a nearest neighbor search.
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 Ilya Pavlov on Unsplash
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.
Photo by Scott Graham on Unsplash
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.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, November 2022
Vespa features and performance advances include ANN Pre-Filter Performance, Parent Field Hit-Estimates, Model Training Notebooks, and GCP Support.
Photo by Scott Graham on Unsplash
Vespa Newsletter, October 2022
Advances in Vespa features and performance include a BertBase Embedder / model hub, improved query performance, paged attributes, ARM64 support and term bolding in string arrays.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, September 2022
Advances in Vespa features and performance include rank-phase statistics, detailed rank performance analysis, new query- and trace-applications and a new training video!
Photo by Scott Graham on Unsplash
Vespa Newsletter, June 2022
Advances in Vespa features and performance include ANN with configurable filtering, fuzzy matching, and native embedding support. Also see pyvespa’s new experimental ranking module!
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, April 2022
Advances in Vespa features and performance include tensor and ranking configuration improvements, pyvespa usability features and grouping configuration. Also find new guides for performance and ANN. And a podcast!
Photo by Scott Graham on Unsplash
Vespa Newsletter, January 2022
Advances in Vespa features, performance and operability improvements include: Improved synonym support, faster node recovery, re-balancing and re-indexing, WeakAnd query type and new pyvespa features and sample applications.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, December 2021
Advances in Vespa features and performance include tensor performance improvements, match-features and a Vespa IntelliJ plugin.
Photo by Scott Graham on Unsplash
Vespa Newsletter, November 2021
Advances in Vespa features and performance include improved schema inheritance, Intellij plugin for schemas, Hamming distance in ranking, and performance gains in data dump and application deployment.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, September 2021
Advances in Vespa features and performance include Vespa CLI, nearest neighbor performance improvements, mTLS security, improved write throughput and Sentencepiece Encoder.
Photo by Scott Graham on Unsplash
Vespa Product Updates, July 2021
Advances in Vespa features and performance include HTTP/2 feed and query endpoints, ONNX Runtime integration for stateless model inference, and the new open Vespa Factory build and test system.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, May 2021
Advances in features and performance include new int8 and bfloat16 tensor cell types, compact tensor feed format, Approximate Nearest Neighbor using Hamming distance, hash-based attribute dictionaries and case-sensitive attribute search...
Photo by Scott Graham on Unsplash
Vespa Product Updates, March 2021
Advances in Vespa features and performance last month include mass update/delete in /document/v1/, improved memory usage, OR-to-WeakAnd and better full node protection.
Photo by Scott Graham on Unsplash
Vespa Product Updates, January 2021
Advances in Vespa features and performance last month include Automatic Reindexing, Tensor Optimizations and Explainlevel Query Parameter for easier query blueprint tracing.
Photo by Donald Giannatti on Unsplash
Vespa Product Updates, December 2020
Advances in Vespa features and performance include improved tensor ranking performance, Apache ZooKeeper integration, Vespa Python API for researchers and ONNX integration.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, October 2020
Improvement to Vespa feeding APIs
Photo by ThisisEngineering RAEng on Unsplash
Vespa Product Updates, September 2020
Introducing ONNX-Runtime, Hamming Distance Metric, Conditional Update Performance Improvements and Compressed Transaction Log with Synced Ack
Vespa Product Updates, August 2020
Introducing NLP with Transformers, Grafana how-to, Improved GEO Search Support, Query Profile Variants Optimizations, & Build on Debian 10
Vespa Product Updates, June 2020
Announcing support for approximate nearest neighbor vector search which can be combined with filters and text search with state-of-the art performance
Vespa Product Updates, May 2020
The May 2020 update includes Improved Slow Node Tolerance, Multi-Threaded Rank Profile Compilation, Reduced Peak Memory at Startup, Feed Performance Improvements, & Increased Tensor Performance.
Vespa Product Updates, April 2020
The April 2020 update includes Top-K hits, smarter data migration and CloudWatch integration. Contributing to Vespa is now easier with the release of a CentOS 7 dev environment.
Vespa Product Updates, February 2020
Advances in Vespa features and performance in February include LightGBM support, improved tensor performance, benchmarking guide and query builder library
Vespa Product Updates, January 2020
The January 2020 update includes information about new tensor functions, updated sizing guides and various performance improvements.
Vespa Product Updates, December 2019: Improved ONNX support, New rank feature attributeMatch().maxWeight, Free lists for attribute multivalue mapping, faster updates for out-of-sync documents, Zookeeper 3.5.6
In the November Vespa product update, we mentioned Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance and...
Vespa Product Updates, October/November 2019: Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance, and Datadog Monitoring Support
In the September Vespa product update, we mentioned Tensor Float Support, Reduced Memory Use for Text Attributes, Prometheus Monitoring Support, and Query Dispatch Integrated in Container.
Vespa Product Updates, September 2019: Tensor Float Support, Reduced Memory Use for Text Attributes, Prometheus Monitoring Support, and Query Dispatch Integrated in Container
In the August Vespa product update, we mentioned BM25 Rank Feature, Searchable Parent References, Tensor Summary Features, and Metrics Export. Largely developed by Yahoo engineers, Vespa is an open source...
Vespa Product Updates, August 2019: BM25 Rank Feature, Searchable Parent References, Tensor Summary Features, and Metrics Export
In the recent Vespa product update, we mentioned Large Machine Learning Models, Multithreaded Disk Index Fusion, Ideal State Optimizations, and Feeding Improvements. Largely developed by Yahoo engineers, Vespa is an...
Vespa Product Updates, March 2019: Tensor updates, Query tracing and coverage
In last month’s Vespa update, we mentioned Boolean Field Type, Environment Variables, and Advanced Search Core Tuning. Largely developed by Yahoo engineers, Vespa is an open source big data processing...
Vespa Product Updates, February 2019: Boolean Field Type, Environment Variables, and Advanced Search Core Tuning
In last month’s Vespa update, we mentioned Parent/Child, Large File Config Download, and a Simplified Feeding Interface. Largely developed by Yahoo engineers, Vespa is an open source big data processing...
Serving article comments using reinforcement learning of a neural net
Don’t look at the comments. When you allow users to make comments on your content pages you face the problem that not all of them are worth showing — a difficult problem...
Vespa Product Updates, January 2019: Parent/Child, Large File Config Download, and a Simplified Feeding Interface
In last month’s Vespa update, we mentioned ONNX integration, precise transaction log pruning, grouping on maps, and improvements to streaming search performance. Largely developed by Yahoo engineers, Vespa is an...
Efficient personal search at large scale
Vespa includes a relatively unknown mode which provides personal search at massive scale for a fraction of the cost of alternatives: streaming search. In this article we explain streaming search...
Tag search engines
Photo by Scott Graham on Unsplash
Vespa Newsletter, December 2023
Advances in Vespa features and performance include rank score normalization in the global ranking phase, improved feeding using PyVespa, token authentication, and query match debugging!
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, October 2023
Advances in Vespa features and performance include Vespa Cloud Enclave, Lucene Linguistics integration, much faster fuzzy query term matching, and performance and usability improvements.
Photo by Scott Graham on Unsplash
Vespa Newsletter, August 2023
Advances in Vespa features and performance include multilingual models, more control over ANN queries, mapped tensors in queries, and multiple new features in pyvespa and the Vespa CLI.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, July 2023
Advances in Vespa features and performance include Vector Streaming Search, GPU accelerated embeddings, Huggingface models and a solution to MIPS using a nearest neighbor search.
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 Ilya Pavlov on Unsplash
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.
Photo by Scott Graham on Unsplash
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.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, November 2022
Vespa features and performance advances include ANN Pre-Filter Performance, Parent Field Hit-Estimates, Model Training Notebooks, and GCP Support.
Photo by Scott Graham on Unsplash
Vespa Newsletter, October 2022
Advances in Vespa features and performance include a BertBase Embedder / model hub, improved query performance, paged attributes, ARM64 support and term bolding in string arrays.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, September 2022
Advances in Vespa features and performance include rank-phase statistics, detailed rank performance analysis, new query- and trace-applications and a new training video!
Photo by Scott Graham on Unsplash
Vespa Newsletter, June 2022
Advances in Vespa features and performance include ANN with configurable filtering, fuzzy matching, and native embedding support. Also see pyvespa’s new experimental ranking module!
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, April 2022
Advances in Vespa features and performance include tensor and ranking configuration improvements, pyvespa usability features and grouping configuration. Also find new guides for performance and ANN. And a podcast!
Photo by Scott Graham on Unsplash
Vespa Newsletter, January 2022
Advances in Vespa features, performance and operability improvements include: Improved synonym support, faster node recovery, re-balancing and re-indexing, WeakAnd query type and new pyvespa features and sample applications.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, December 2021
Advances in Vespa features and performance include tensor performance improvements, match-features and a Vespa IntelliJ plugin.
Photo by Scott Graham on Unsplash
Vespa Newsletter, November 2021
Advances in Vespa features and performance include improved schema inheritance, Intellij plugin for schemas, Hamming distance in ranking, and performance gains in data dump and application deployment.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, September 2021
Advances in Vespa features and performance include Vespa CLI, nearest neighbor performance improvements, mTLS security, improved write throughput and Sentencepiece Encoder.
Photo by Scott Graham on Unsplash
Vespa Product Updates, July 2021
Advances in Vespa features and performance include HTTP/2 feed and query endpoints, ONNX Runtime integration for stateless model inference, and the new open Vespa Factory build and test system.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, May 2021
Advances in features and performance include new int8 and bfloat16 tensor cell types, compact tensor feed format, Approximate Nearest Neighbor using Hamming distance, hash-based attribute dictionaries and case-sensitive attribute search...
Photo by Scott Graham on Unsplash
Vespa Product Updates, March 2021
Advances in Vespa features and performance last month include mass update/delete in /document/v1/, improved memory usage, OR-to-WeakAnd and better full node protection.
Photo by Scott Graham on Unsplash
Vespa Product Updates, January 2021
Advances in Vespa features and performance last month include Automatic Reindexing, Tensor Optimizations and Explainlevel Query Parameter for easier query blueprint tracing.
Photo by Donald Giannatti on Unsplash
Vespa Product Updates, December 2020
Advances in Vespa features and performance include improved tensor ranking performance, Apache ZooKeeper integration, Vespa Python API for researchers and ONNX integration.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, October 2020
Improvement to Vespa feeding APIs
Photo by ThisisEngineering RAEng on Unsplash
Vespa Product Updates, September 2020
Introducing ONNX-Runtime, Hamming Distance Metric, Conditional Update Performance Improvements and Compressed Transaction Log with Synced Ack
Vespa Product Updates, August 2020
Introducing NLP with Transformers, Grafana how-to, Improved GEO Search Support, Query Profile Variants Optimizations, & Build on Debian 10
Vespa Product Updates, June 2020
Announcing support for approximate nearest neighbor vector search which can be combined with filters and text search with state-of-the art performance
Vespa Product Updates, May 2020
The May 2020 update includes Improved Slow Node Tolerance, Multi-Threaded Rank Profile Compilation, Reduced Peak Memory at Startup, Feed Performance Improvements, & Increased Tensor Performance.
Vespa Product Updates, April 2020
The April 2020 update includes Top-K hits, smarter data migration and CloudWatch integration. Contributing to Vespa is now easier with the release of a CentOS 7 dev environment.
Vespa Product Updates, February 2020
Advances in Vespa features and performance in February include LightGBM support, improved tensor performance, benchmarking guide and query builder library
Vespa Product Updates, January 2020
The January 2020 update includes information about new tensor functions, updated sizing guides and various performance improvements.
Vespa Product Updates, December 2019: Improved ONNX support, New rank feature attributeMatch().maxWeight, Free lists for attribute multivalue mapping, faster updates for out-of-sync documents, Zookeeper 3.5.6
In the November Vespa product update, we mentioned Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance and...
Vespa Product Updates, October/November 2019: Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance, and Datadog Monitoring Support
In the September Vespa product update, we mentioned Tensor Float Support, Reduced Memory Use for Text Attributes, Prometheus Monitoring Support, and Query Dispatch Integrated in Container.
Vespa Product Updates, September 2019: Tensor Float Support, Reduced Memory Use for Text Attributes, Prometheus Monitoring Support, and Query Dispatch Integrated in Container
In the August Vespa product update, we mentioned BM25 Rank Feature, Searchable Parent References, Tensor Summary Features, and Metrics Export. Largely developed by Yahoo engineers, Vespa is an open source...
Vespa Product Updates, August 2019: BM25 Rank Feature, Searchable Parent References, Tensor Summary Features, and Metrics Export
In the recent Vespa product update, we mentioned Large Machine Learning Models, Multithreaded Disk Index Fusion, Ideal State Optimizations, and Feeding Improvements. Largely developed by Yahoo engineers, Vespa is an...
Vespa Product Updates, March 2019: Tensor updates, Query tracing and coverage
In last month’s Vespa update, we mentioned Boolean Field Type, Environment Variables, and Advanced Search Core Tuning. Largely developed by Yahoo engineers, Vespa is an open source big data processing...
Vespa Product Updates, February 2019: Boolean Field Type, Environment Variables, and Advanced Search Core Tuning
In last month’s Vespa update, we mentioned Parent/Child, Large File Config Download, and a Simplified Feeding Interface. Largely developed by Yahoo engineers, Vespa is an open source big data processing...
Vespa Product Updates, January 2019: Parent/Child, Large File Config Download, and a Simplified Feeding Interface
In last month’s Vespa update, we mentioned ONNX integration, precise transaction log pruning, grouping on maps, and improvements to streaming search performance. Largely developed by Yahoo engineers, Vespa is an...
Efficient personal search at large scale
Vespa includes a relatively unknown mode which provides personal search at massive scale for a fraction of the cost of alternatives: streaming search. In this article we explain streaming search...
Tag vespa vespa7
Vespa 7 is released!
This week we rolled the major version of Vespa over from 6 to 7.
Tag machine learning
Learning to Rank with Vespa – Getting started with Text Search
Vespa.ai have just published two tutorials to help people to get started with text search applications by building scalable solutions with Vespa. The tutorials were based on the full document...
Serving article comments using reinforcement learning of a neural net
Don’t look at the comments. When you allow users to make comments on your content pages you face the problem that not all of them are worth showing — a difficult problem...
Tag recommendation systems
Serving article comments using reinforcement learning of a neural net
Don’t look at the comments. When you allow users to make comments on your content pages you face the problem that not all of them are worth showing — a difficult problem...
Tag reinforcement learning
Serving article comments using reinforcement learning of a neural net
Don’t look at the comments. When you allow users to make comments on your content pages you face the problem that not all of them are worth showing — a difficult problem...
Tag neural networks
Serving article comments using reinforcement learning of a neural net
Don’t look at the comments. When you allow users to make comments on your content pages you face the problem that not all of them are worth showing — a difficult problem...
Tag big data serving
Photo by Scott Graham on Unsplash
Vespa Newsletter, December 2023
Advances in Vespa features and performance include rank score normalization in the global ranking phase, improved feeding using PyVespa, token authentication, and query match debugging!
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, October 2023
Advances in Vespa features and performance include Vespa Cloud Enclave, Lucene Linguistics integration, much faster fuzzy query term matching, and performance and usability improvements.
Photo by Scott Graham on Unsplash
Vespa Newsletter, August 2023
Advances in Vespa features and performance include multilingual models, more control over ANN queries, mapped tensors in queries, and multiple new features in pyvespa and the Vespa CLI.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, July 2023
Advances in Vespa features and performance include Vector Streaming Search, GPU accelerated embeddings, Huggingface models and a solution to MIPS using a nearest neighbor search.
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 Ilya Pavlov on Unsplash
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.
Photo by Scott Graham on Unsplash
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.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, November 2022
Vespa features and performance advances include ANN Pre-Filter Performance, Parent Field Hit-Estimates, Model Training Notebooks, and GCP Support.
Photo by Scott Graham on Unsplash
Vespa Newsletter, October 2022
Advances in Vespa features and performance include a BertBase Embedder / model hub, improved query performance, paged attributes, ARM64 support and term bolding in string arrays.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, September 2022
Advances in Vespa features and performance include rank-phase statistics, detailed rank performance analysis, new query- and trace-applications and a new training video!
Photo by Scott Graham on Unsplash
Vespa Newsletter, June 2022
Advances in Vespa features and performance include ANN with configurable filtering, fuzzy matching, and native embedding support. Also see pyvespa’s new experimental ranking module!
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, April 2022
Advances in Vespa features and performance include tensor and ranking configuration improvements, pyvespa usability features and grouping configuration. Also find new guides for performance and ANN. And a podcast!
Photo by Scott Graham on Unsplash
Vespa Newsletter, January 2022
Advances in Vespa features, performance and operability improvements include: Improved synonym support, faster node recovery, re-balancing and re-indexing, WeakAnd query type and new pyvespa features and sample applications.
Photo by Ilya Pavlov on Unsplash
Vespa Newsletter, December 2021
Advances in Vespa features and performance include tensor performance improvements, match-features and a Vespa IntelliJ plugin.
Photo by Scott Graham on Unsplash
Vespa Newsletter, November 2021
Advances in Vespa features and performance include improved schema inheritance, Intellij plugin for schemas, Hamming distance in ranking, and performance gains in data dump and application deployment.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, September 2021
Advances in Vespa features and performance include Vespa CLI, nearest neighbor performance improvements, mTLS security, improved write throughput and Sentencepiece Encoder.
Photo by Scott Graham on Unsplash
Vespa Product Updates, July 2021
Advances in Vespa features and performance include HTTP/2 feed and query endpoints, ONNX Runtime integration for stateless model inference, and the new open Vespa Factory build and test system.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, May 2021
Advances in features and performance include new int8 and bfloat16 tensor cell types, compact tensor feed format, Approximate Nearest Neighbor using Hamming distance, hash-based attribute dictionaries and case-sensitive attribute search...
Photo by Scott Graham on Unsplash
Vespa Product Updates, March 2021
Advances in Vespa features and performance last month include mass update/delete in /document/v1/, improved memory usage, OR-to-WeakAnd and better full node protection.
Photo by Scott Graham on Unsplash
Vespa Product Updates, January 2021
Advances in Vespa features and performance last month include Automatic Reindexing, Tensor Optimizations and Explainlevel Query Parameter for easier query blueprint tracing.
Photo by Donald Giannatti on Unsplash
Vespa Product Updates, December 2020
Advances in Vespa features and performance include improved tensor ranking performance, Apache ZooKeeper integration, Vespa Python API for researchers and ONNX integration.
Photo by Ilya Pavlov on Unsplash
Vespa Product Updates, October 2020
Improvement to Vespa feeding APIs
Photo by ThisisEngineering RAEng on Unsplash
Vespa Product Updates, September 2020
Introducing ONNX-Runtime, Hamming Distance Metric, Conditional Update Performance Improvements and Compressed Transaction Log with Synced Ack
Vespa Product Updates, August 2020
Introducing NLP with Transformers, Grafana how-to, Improved GEO Search Support, Query Profile Variants Optimizations, & Build on Debian 10
Vespa Product Updates, June 2020
Announcing support for approximate nearest neighbor vector search which can be combined with filters and text search with state-of-the art performance
Vespa Product Updates, May 2020
The May 2020 update includes Improved Slow Node Tolerance, Multi-Threaded Rank Profile Compilation, Reduced Peak Memory at Startup, Feed Performance Improvements, & Increased Tensor Performance.
Vespa Product Updates, April 2020
The April 2020 update includes Top-K hits, smarter data migration and CloudWatch integration. Contributing to Vespa is now easier with the release of a CentOS 7 dev environment.
Vespa Product Updates, February 2020
Advances in Vespa features and performance in February include LightGBM support, improved tensor performance, benchmarking guide and query builder library
Vespa Product Updates, January 2020
The January 2020 update includes information about new tensor functions, updated sizing guides and various performance improvements.
Vespa Product Updates, December 2019: Improved ONNX support, New rank feature attributeMatch().maxWeight, Free lists for attribute multivalue mapping, faster updates for out-of-sync documents, Zookeeper 3.5.6
In the November Vespa product update, we mentioned Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance and...
Vespa Product Updates, October/November 2019: Nearest Neighbor and Tensor Ranking, Optimized JSON Tensor Feed Format, Matched Elements in Complex Multi-value Fields, Large Weighted Set Update Performance, and Datadog Monitoring Support
In the September Vespa product update, we mentioned Tensor Float Support, Reduced Memory Use for Text Attributes, Prometheus Monitoring Support, and Query Dispatch Integrated in Container.
Vespa Product Updates, September 2019: Tensor Float Support, Reduced Memory Use for Text Attributes, Prometheus Monitoring Support, and Query Dispatch Integrated in Container
In the August Vespa product update, we mentioned BM25 Rank Feature, Searchable Parent References, Tensor Summary Features, and Metrics Export. Largely developed by Yahoo engineers, Vespa is an open source...
Vespa Product Updates, August 2019: BM25 Rank Feature, Searchable Parent References, Tensor Summary Features, and Metrics Export
In the recent Vespa product update, we mentioned Large Machine Learning Models, Multithreaded Disk Index Fusion, Ideal State Optimizations, and Feeding Improvements. Largely developed by Yahoo engineers, Vespa is an...
Tag vespa
Learning to Rank with Vespa – Getting started with Text Search
Vespa.ai have just published two tutorials to help people to get started with text search applications by building scalable solutions with Vespa. The tutorials were based on the full document...
E-commerce search and recommendation with Vespa.ai
Holiday shopping season is upon us and it’s time for a blog post on E-commerce search and recommendation using Vespa.ai. Vespa.ai is used as the search and recommendation backend at...
Tag information retrieval
E-commerce search and recommendation with Vespa.ai
Holiday shopping season is upon us and it’s time for a blog post on E-commerce search and recommendation using Vespa.ai. Vespa.ai is used as the search and recommendation backend at...
Tag e commerce business
E-commerce search and recommendation with Vespa.ai
Holiday shopping season is upon us and it’s time for a blog post on E-commerce search and recommendation using Vespa.ai. Vespa.ai is used as the search and recommendation backend at...
Tag holiday shopping online
E-commerce search and recommendation with Vespa.ai
Holiday shopping season is upon us and it’s time for a blog post on E-commerce search and recommendation using Vespa.ai. Vespa.ai is used as the search and recommendation backend at...
Tag tensorflow
Learning to Rank with Vespa – Getting started with Text Search
Vespa.ai have just published two tutorials to help people to get started with text search applications by building scalable solutions with Vespa. The tutorials were based on the full document...
Tag learning to rank
Learning to Rank with Vespa – Getting started with Text Search
Vespa.ai have just published two tutorials to help people to get started with text search applications by building scalable solutions with Vespa. The tutorials were based on the full document...
Tag Search Engines
Vespa.ai and the CORD-19 public API
The Vespa team has been working non-stop to put together the cord19.vespa.ai search app based on the COVID-19 Open Research Dataset (CORD-19) released by the Allen Institute for AI.
Tag Covid 19
Vespa.ai and the CORD-19 public API
The Vespa team has been working non-stop to put together the cord19.vespa.ai search app based on the COVID-19 Open Research Dataset (CORD-19) released by the Allen Institute for AI.
Tag Artificial Intelligence
Vespa.ai and the CORD-19 public API
The Vespa team has been working non-stop to put together the cord19.vespa.ai search app based on the COVID-19 Open Research Dataset (CORD-19) released by the Allen Institute for AI.
Tag Machine Learning
Vespa.ai and the CORD-19 public API
The Vespa team has been working non-stop to put together the cord19.vespa.ai search app based on the COVID-19 Open Research Dataset (CORD-19) released by the Allen Institute for AI.
Tag NLP
Vespa.ai and the CORD-19 public API
The Vespa team has been working non-stop to put together the cord19.vespa.ai search app based on the COVID-19 Open Research Dataset (CORD-19) released by the Allen Institute for AI.
Tag parent child
Parent-child joins and tensors for content recommendation
A real-world application of parent-child joins and tensor functions to model topic popularity for content recommendation.
Tag tensor
Parent-child joins and tensors for content recommendation
A real-world application of parent-child joins and tensor functions to model topic popularity for content recommendation.
Tag vector search
Pre-trained models on Vespa Cloud
Vespa Cloud now provides pre-trained ML models for your applications
Text embedding made simple
Vespa now lets you create a production quality semantic search application from scratch in minutes
Tag semantic search
The Singapore Government Pair Search
The Singaporean government deploys state of the art semantic search
The Singaporean government leverages Vespa to do semantic search in every word ever said in Parliament
Pre-trained models on Vespa Cloud
Vespa Cloud now provides pre-trained ML models for your applications
Text embedding made simple
Vespa now lets you create a production quality semantic search application from scratch in minutes
Tag embeddings
Matryoshka 🤝 Binary vectors: Slash vector search costs with Vespa
Announcing Matryoshka (dimension flexibility) and binary quantization in Vespa and how these features slashes costs.
Photo by Phil Botha on Unsplash
Scaling vector search using Cohere binary embeddings and Vespa
Three comprehensive guides to using the Cohere Embed v3 binary embeddings with Vespa.
Photo by Polina Kuzovkova on Unsplash
Announcing Vespa Long-Context ColBERT
Announcing long-context ColBERT, giving it larger context for scoring and simplifying long-document RAG applications.
Embedding flexibility in Vespa
Why did Vespa score "Exceptional" on Embedding Flexibility in GigaOm's report on Vector Databases?
Photo by Victoire Joncheray on Unsplash
Announcing the Vespa ColBERT embedder
Announcing the native Vespa ColBERT embedder in Vespa, enabling explainable semantic search using token-level vector representations
Photo by Simon Hurry on Unsplash
Exploring the potential of OpenAI Matryoshka 🪆 embeddings with Vespa
Using the "shortening" properties of OpenAI v3 embedding models to greatly reduce latency/cost while retaining near-exact quality
Photo by Rafael Drück on Unsplash
Representing BGE embedding models in Vespa using bfloat16
This post demonstrates how to use recently announced BGE embedding models in Vespa. We evaluate the effectiveness of two BGE variants on the BEIR trec-covid dataset. Finally, we demonstrate how...
Accelerating Transformer-based Embedding Retrieval with Vespa
In this post, we’ll see how to accelerate embedding inference and retrieval with little impact on quality. We’ll take a holistic approach and deep-dive into both aspects of an embedding...
Photo by Bruno Martins on Unsplash
Simplify Search with Multilingual Embedding Models
This blog post presents and shows how to represent a robust multilingual embedding model of the E5 family in Vespa.
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.
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.
Pre-trained models on Vespa Cloud
Vespa Cloud now provides pre-trained ML models for your applications
Text embedding made simple
Vespa now lets you create a production quality semantic search application from scratch in minutes
Tag ANN
Text embedding made simple
Vespa now lets you create a production quality semantic search application from scratch in minutes
Tag HNSW
Text embedding made simple
Vespa now lets you create a production quality semantic search application from scratch in minutes
Tag ML
Pre-trained models on Vespa Cloud
Vespa Cloud now provides pre-trained ML models for your applications
Tag models
Pre-trained models on Vespa Cloud
Vespa Cloud now provides pre-trained ML models for your applications
Tag vespa cloud
Pre-trained models on Vespa Cloud
Vespa Cloud now provides pre-trained ML models for your applications
Tag ARM64
Vespa on ARM64
Vespa is now released as a multiplatform container image, supporting both x86_64 and ARM64.
Tag container image
Vespa on ARM64
Vespa is now released as a multiplatform container image, supporting both x86_64 and ARM64.
Tag GCP
Vespa Cloud on Google Cloud Platform
Vespa Cloud is now available on Google Cloud Platform
Tag Google Cloud Platform
Vespa Cloud on Google Cloud Platform
Vespa Cloud is now available on Google Cloud Platform
Tag rag
Photo by Simon Hurry on Unsplash
Exploring the potential of OpenAI Matryoshka 🪆 embeddings with Vespa
Using the "shortening" properties of OpenAI v3 embedding models to greatly reduce latency/cost while retaining near-exact quality
Photo by Thought Catalog on Unsplash
Turbocharge RAG with LangChain and Vespa Streaming Mode for Sharded Data
A hands-on guide to connect LangChain with Vespa streaming mode to build cost-efficient RAG applications over naturally sharded data.
Photo by Avi Richards on Unsplash
Hands-On RAG guide for personal data with Vespa and LLamaIndex
A hands-on guide to using Vespa streaming mode with PyVespa and LLamaIndex.
Photo by Evgeni Tcherkasski on Unsplash
Yahoo Mail turns to Vespa to do RAG at scale
Vector search in trillions of personal documents can be done cost-effectively with vector streaming search.
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.
Tag vectors
Farfetch: Scaling recommendations with Vespa
E-commerce platform Farfetch explains how they use Vespa to scale their online recommendation system
Marqo chooses Vespa
Vector search experts Marqo choose Vespa as their vector database after benchmarking against Milvus, OpenSearch, Weaviate, Redis, and Qdrant.
Photo by Simon Hurry on Unsplash
Exploring the potential of OpenAI Matryoshka 🪆 embeddings with Vespa
Using the "shortening" properties of OpenAI v3 embedding models to greatly reduce latency/cost while retaining near-exact quality
Photo by Thought Catalog on Unsplash
Turbocharge RAG with LangChain and Vespa Streaming Mode for Sharded Data
A hands-on guide to connect LangChain with Vespa streaming mode to build cost-efficient RAG applications over naturally sharded data.
Photo by Avi Richards on Unsplash
Hands-On RAG guide for personal data with Vespa and LLamaIndex
A hands-on guide to using Vespa streaming mode with PyVespa and LLamaIndex.
Photo by Evgeni Tcherkasski on Unsplash
Yahoo Mail turns to Vespa to do RAG at scale
Vector search in trillions of personal documents can be done cost-effectively with vector streaming search.
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.
Tag streaming
Photo by Thought Catalog on Unsplash
Turbocharge RAG with LangChain and Vespa Streaming Mode for Sharded Data
A hands-on guide to connect LangChain with Vespa streaming mode to build cost-efficient RAG applications over naturally sharded data.
Photo by Avi Richards on Unsplash
Hands-On RAG guide for personal data with Vespa and LLamaIndex
A hands-on guide to using Vespa streaming mode with PyVespa and LLamaIndex.
Photo by Evgeni Tcherkasski on Unsplash
Yahoo Mail turns to Vespa to do RAG at scale
Vector search in trillions of personal documents can be done cost-effectively with vector streaming search.
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.
Tag company
Tag linguistics
Introducing Lucene Linguistics
This post is about an idea that was born at the Berlin Buzzwords 2023 conference and its journey towards the production-ready implementation of the new Apache Lucene-based Vespa Linguistics component....
Tag bm25
Introducing Lucene Linguistics
This post is about an idea that was born at the Berlin Buzzwords 2023 conference and its journey towards the production-ready implementation of the new Apache Lucene-based Vespa Linguistics component....
Tag stemming
Introducing Lucene Linguistics
This post is about an idea that was born at the Berlin Buzzwords 2023 conference and its journey towards the production-ready implementation of the new Apache Lucene-based Vespa Linguistics component....
Tag hybrid search
Photo by Trent Erwin on Unsplash
Redefining Hybrid Search Possibilities with Vespa - part one
This is the first blog post in a series on hybrid search. This first post focuses on efficient hybrid retrieval and representational approaches in IR
Tag mrl
Photo by Simon Hurry on Unsplash
Exploring the potential of OpenAI Matryoshka 🪆 embeddings with Vespa
Using the "shortening" properties of OpenAI v3 embedding models to greatly reduce latency/cost while retaining near-exact quality
Tag vector
When you're using vectors you're doing search
Combining scale and quality takes more than vector similarity search
Tag RAG
The Singapore Government Pair Search
The Singaporean government deploys state of the art semantic search
The Singaporean government leverages Vespa to do semantic search in every word ever said in Parliament
Tag solr
Migrating to the Vespa Search Engine
In this post, I will detail the journey at Stanby of how we have addressed the challenges faced by our existing search system through migrating to Vespa.
Tag elastic
Migrating to the Vespa Search Engine
In this post, I will detail the journey at Stanby of how we have addressed the challenges faced by our existing search system through migrating to Vespa.
Tag elasticsearch
Vinted moves from Elasticsearch to Vespa
With Vespa, Vinted managed to halve the number of servers, slash query latency by 2.5x, indexing latency by 3x, and increase ranking depth by more than 3x.
Migrating to the Vespa Search Engine
In this post, I will detail the journey at Stanby of how we have addressed the challenges faced by our existing search system through migrating to Vespa.
Tag milvus
Marqo chooses Vespa
Vector search experts Marqo choose Vespa as their vector database after benchmarking against Milvus, OpenSearch, Weaviate, Redis, and Qdrant.
Tag opensearch
Marqo chooses Vespa
Vector search experts Marqo choose Vespa as their vector database after benchmarking against Milvus, OpenSearch, Weaviate, Redis, and Qdrant.
Tag weaviate
Marqo chooses Vespa
Vector search experts Marqo choose Vespa as their vector database after benchmarking against Milvus, OpenSearch, Weaviate, Redis, and Qdrant.
Tag redis
Marqo chooses Vespa
Vector search experts Marqo choose Vespa as their vector database after benchmarking against Milvus, OpenSearch, Weaviate, Redis, and Qdrant.
Tag qdrant
Marqo chooses Vespa
Vector search experts Marqo choose Vespa as their vector database after benchmarking against Milvus, OpenSearch, Weaviate, Redis, and Qdrant.
Tag tensors
Farfetch: Scaling recommendations with Vespa
E-commerce platform Farfetch explains how they use Vespa to scale their online recommendation system
Tag recommendation
Navigating in the New AI-Driven E-commerce Landscape
This AI-driven ecommerce evolution is driven by consumer’s increasing demand for personalized experiences, real-time interactions, and seamless omnichannel integration.
How Generative AI Is Changing E-commerce
According to ESG, 41% of retail respondents in a 2024 survey are either already pursuing or planning to pursue AI-powered product recommendations.
Farfetch: Scaling recommendations with Vespa
E-commerce platform Farfetch explains how they use Vespa to scale their online recommendation system
Tag e-commerce
Navigating in the New AI-Driven E-commerce Landscape
This AI-driven ecommerce evolution is driven by consumer’s increasing demand for personalized experiences, real-time interactions, and seamless omnichannel integration.
How Generative AI Is Changing E-commerce
According to ESG, 41% of retail respondents in a 2024 survey are either already pursuing or planning to pursue AI-powered product recommendations.
Farfetch: Scaling recommendations with Vespa
E-commerce platform Farfetch explains how they use Vespa to scale their online recommendation system
Tag personalization
Navigating in the New AI-Driven E-commerce Landscape
This AI-driven ecommerce evolution is driven by consumer’s increasing demand for personalized experiences, real-time interactions, and seamless omnichannel integration.
How Generative AI Is Changing E-commerce
According to ESG, 41% of retail respondents in a 2024 survey are either already pursuing or planning to pursue AI-powered product recommendations.
Tag vespa.ai
Navigating in the New AI-Driven E-commerce Landscape
This AI-driven ecommerce evolution is driven by consumer’s increasing demand for personalized experiences, real-time interactions, and seamless omnichannel integration.
Tag Elasticsearch
A Benchmark for Modernizing Elasticsearch with Vespa
Discover how Vespa outperforms Elasticsearch in query efficiency, scalability, and operational costs, making it a robust choice for modern eCommerce search solutions.
Tag Search Modernization
A Benchmark for Modernizing Elasticsearch with Vespa
Discover how Vespa outperforms Elasticsearch in query efficiency, scalability, and operational costs, making it a robust choice for modern eCommerce search solutions.
Tag Query efficency
A Benchmark for Modernizing Elasticsearch with Vespa
Discover how Vespa outperforms Elasticsearch in query efficiency, scalability, and operational costs, making it a robust choice for modern eCommerce search solutions.
Tag Benchmarking
A Benchmark for Modernizing Elasticsearch with Vespa
Discover how Vespa outperforms Elasticsearch in query efficiency, scalability, and operational costs, making it a robust choice for modern eCommerce search solutions.
Tag eCommerce
A Benchmark for Modernizing Elasticsearch with Vespa
Discover how Vespa outperforms Elasticsearch in query efficiency, scalability, and operational costs, making it a robust choice for modern eCommerce search solutions.
Tag Semantic Search
Vespa for Dummies
A beginner's guide to Vespa, exploring its role in information retrieval and its advantages for enterprise AI applications.
Tag Information Retrieval
Vespa for Dummies
A beginner's guide to Vespa, exploring its role in information retrieval and its advantages for enterprise AI applications.
Tag AI Applications
Vespa for Dummies
A beginner's guide to Vespa, exploring its role in information retrieval and its advantages for enterprise AI applications.
Tag Norway
Why Norway’s Environmental Commitment Reflects in Vespa.ai’s Values
Exploring how Vespa.ai exemplifies Norway's commitment to sustainability through efficient technology.
Tag Vespa.ai
Why Norway’s Environmental Commitment Reflects in Vespa.ai’s Values
Exploring how Vespa.ai exemplifies Norway's commitment to sustainability through efficient technology.
Tag Sustainability
Why Norway’s Environmental Commitment Reflects in Vespa.ai’s Values
Exploring how Vespa.ai exemplifies Norway's commitment to sustainability through efficient technology.
Tag Technology
Why Norway’s Environmental Commitment Reflects in Vespa.ai’s Values
Exploring how Vespa.ai exemplifies Norway's commitment to sustainability through efficient technology.
Tag Environment
Never miss a story from us, subscribe to our newsletter