# ann

Approximate Nearest Neighbor (ANN) search quickly locates data points closest to a query vector.

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## Vespa Newsletter, September 2026

DevFeed: [Vespa Newsletter, September 2026](<https://devfeed.tech/articles/vespa-newsletter-september-2026-12801.md>)

Original publisher: [Read original article](<https://blog.vespa.ai/vespa-newsletter-sept-2026/>)

Author: Bonnie Chase

Published: 2026-09-08T00:00:00Z

Content type: news

Language: en

Sources: [Vespa Blog](<https://devfeed.tech/sources/vespa-blog.md>)

Topics: [ann](<https://devfeed.tech/topics/ann.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Provisioning](<https://devfeed.tech/topics/provisioning.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [algorithm](<https://devfeed.tech/tags/algorithm.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [features](<https://devfeed.tech/tags/features.md>), [graph](<https://devfeed.tech/tags/graph.md>), [latency](<https://devfeed.tech/tags/latency.md>), [newsletter](<https://devfeed.tech/tags/newsletter.md>), [product](<https://devfeed.tech/tags/product.md>), [provisioning](<https://devfeed.tech/tags/provisioning.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [september-2026](<https://devfeed.tech/tags/september-2026.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>), [updates](<https://devfeed.tech/tags/updates.md>)

### AI overview

The September 2026 Vespa newsletter announces updates including time-constrained ANN search, sub-query ranking support, flexible provisioning, new rank features, and telemetry export. It also introduces Vespa.ai Live, an in-person community meetup focused on retrieval and ranking systems.

### Source excerpt

Advances in Vespa include time-constrained ANN search, sub-query ranking support, flexible provisioning, new rank features and telemetry export

## Benchmarking vector indexes

DevFeed: [Benchmarking vector indexes](<https://devfeed.tech/articles/benchmarking-vector-indexes-14092.md>)

Original publisher: [Read original article](<https://www.percona.com/blog/benchmarking-vector-indexes/>)

Author: Evgeniy Patlan

Published: 2026-08-27T13:35:32Z

Content type: article

Language: en

Sources: [Blog - Percona](<https://devfeed.tech/sources/blog-percona.md>)

Topics: [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [Ground truth / benchmark quality](<https://devfeed.tech/topics/ground-truth-benchmark-quality.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [ann](<https://devfeed.tech/topics/ann.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Sorting](<https://devfeed.tech/topics/sorting.md>)

Tags: [ai-vector](<https://devfeed.tech/tags/ai-vector.md>), [ann](<https://devfeed.tech/tags/ann.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [blog](<https://devfeed.tech/tags/blog.md>), [blog-post](<https://devfeed.tech/tags/blog-post.md>), [data](<https://devfeed.tech/tags/data.md>), [database-performance](<https://devfeed.tech/tags/database-performance.md>), [database-trends](<https://devfeed.tech/tags/database-trends.md>), [databases](<https://devfeed.tech/tags/databases.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [index](<https://devfeed.tech/tags/index.md>), [measurements](<https://devfeed.tech/tags/measurements.md>), [model](<https://devfeed.tech/tags/model.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [percona](<https://devfeed.tech/tags/percona.md>), [points](<https://devfeed.tech/tags/points.md>), [report](<https://devfeed.tech/tags/report.md>), [run](<https://devfeed.tech/tags/run.md>), [search](<https://devfeed.tech/tags/search.md>), [sorting](<https://devfeed.tech/tags/sorting.md>), [vector-search](<https://devfeed.tech/tags/vector-search.md>), [vectorsearch](<https://devfeed.tech/tags/vectorsearch.md>)

### AI overview

This article explains how Percona's vector-bench measures vector indexes by building database engines from pinned versions, running them in identical containers with the same data and hardware, and comparing consistent measurements. It describes embeddings, exact search, approximate nearest neighbour search, and ground truth as the brute-force reference needed to evaluate index accuracy.

### Source excerpt

Nearly every database has vector search now, and every one of them has a blog post with a big number in it. Almost none of those numbers can be checked, because the thing that makes them meaningful is usually missing. We built a vector-bench to stop guessing. You name the engines you want, build them ... Continued The post Benchmarking vector indexes appeared first on Percona.

## Vector search database: news & 2026 guide

DevFeed: [Vector search database: news & 2026 guide](<https://devfeed.tech/articles/vector-search-database-news-2026-guide-4860.md>)

Original publisher: [Read original article](<https://redis.io/blog/vector-search-database-news-2026-guide/>)

Author: Simran Regmi

Published: 2026-08-13T00:00:00Z

Content type: tutorial

Language: en

Sources: [Redis Blog](<https://devfeed.tech/sources/redis-blog.md>)

Topics: [Database](<https://devfeed.tech/topics/database.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [ann](<https://devfeed.tech/topics/ann.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>)

Tags: [ann](<https://devfeed.tech/tags/ann.md>), [database](<https://devfeed.tech/tags/database.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [guide](<https://devfeed.tech/tags/guide.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [search](<https://devfeed.tech/tags/search.md>), [tech-de](<https://devfeed.tech/tags/tech-de.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

This guide explains how vector search databases store embeddings and retrieve semantically similar items for LLM-powered applications. It covers vector spaces, dimensionality, similarity measures, traditional indexes, the curse of dimensionality, and approximate nearest neighbor search, including HNSW.

### Source excerpt

If you've built anything on top of an LLM in the past couple of years, you may have hit the wall many builders hit: the model writes fluently but has no view into your data. A vector search database helps close that gap. It stores vector embeddings an...

## The space complexity of vector indexes in LibSQL

DevFeed: [The space complexity of vector indexes in LibSQL](<https://devfeed.tech/articles/the-space-complexity-of-vector-indexes-in-libsql-6047.md>)

Original publisher: [Read original article](<https://turso.tech/blog/the-space-complexity-of-vector-indexes-in-libsql>)

Author: Volodymyr Pavlyshyn

Published: 2024-10-03T00:00:00Z

Content type: article

Language: en

Sources: [Turso Blog](<https://devfeed.tech/sources/turso-blog.md>)

Topics: [libSQL](<https://devfeed.tech/topics/libsql.md>), [ann](<https://devfeed.tech/topics/ann.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Database](<https://devfeed.tech/topics/database.md>), [Latency](<https://devfeed.tech/topics/latency.md>)

Tags: [ann](<https://devfeed.tech/tags/ann.md>), [compression](<https://devfeed.tech/tags/compression.md>), [env-file-security](<https://devfeed.tech/tags/env-file-security.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [latency](<https://devfeed.tech/tags/latency.md>), [libsql](<https://devfeed.tech/tags/libsql.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [storage](<https://devfeed.tech/tags/storage.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

This article examines the storage complexity of libSQL vector indexes based on the graph-based FreshDiskANN algorithm. It explains how graph storage and neighbor vectors increased a database from 3.5 MB to 50 MB, then discusses compression and max-neighbor settings for reducing space while balancing search speed and accuracy.

### Source excerpt

How Kin shrunk libSQL's LM-DiskANN vector index storage on mobile by tuning compress_neighbors and max_neighbors, trading a little recall for big space wins.

## Approximate nearest neighbor search with DiskANN in libSQL

DevFeed: [Approximate nearest neighbor search with DiskANN in libSQL](<https://devfeed.tech/articles/approximate-nearest-neighbor-search-with-diskann-in-libsql-5892.md>)

Original publisher: [Read original article](<https://turso.tech/blog/approximate-nearest-neighbor-search-with-diskann-in-libsql>)

Author: Pekka Enberg

Published: 2024-07-26T00:00:00Z

Content type: article

Language: en

Sources: [Turso Blog](<https://devfeed.tech/sources/turso-blog.md>)

Topics: [libSQL](<https://devfeed.tech/topics/libsql.md>), [ann](<https://devfeed.tech/topics/ann.md>), [SQLite](<https://devfeed.tech/topics/sqlite.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [ann](<https://devfeed.tech/tags/ann.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [libsql](<https://devfeed.tech/tags/libsql.md>), [performance](<https://devfeed.tech/tags/performance.md>), [rag](<https://devfeed.tech/tags/rag.md>), [sql](<https://devfeed.tech/tags/sql.md>), [sqlite](<https://devfeed.tech/tags/sqlite.md>), [vector](<https://devfeed.tech/tags/vector.md>), [vector-search](<https://devfeed.tech/tags/vector-search.md>)

### AI overview

This article explains how libSQL implements approximate nearest neighbor search for large embedding datasets using the low-memory LM-DiskANN algorithm. It describes the trade-off between exactness and speed, graph-based search, vector indexing in SQLite, cosine distance, and the integration between SQLite's code generator and the DiskANN index.

### Source excerpt

Learn how to use retrieval augmented generation to improve vector search results with SQLite.

## pgvector: 30x Faster Index Build for your Vector Embeddings

DevFeed: [pgvector: 30x Faster Index Build for your Vector Embeddings](<https://devfeed.tech/articles/pgvector-30x-faster-index-build-for-your-vector-embeddings-5720.md>)

Original publisher: [Read original article](<https://neon.com/blog/pgvector-30x-faster-index-build-for-your-vector-embeddings>)

Author: Raouf Chebri

Published: 2024-02-07T15:43:47Z

Content type: article

Language: en

Sources: [Blog -- Neon Docs](<https://devfeed.tech/sources/blog-neon-docs.md>)

Topics: [Databases](<https://devfeed.tech/topics/databases.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [ann](<https://devfeed.tech/topics/ann.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [API](<https://devfeed.tech/topics/api.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ann](<https://devfeed.tech/tags/ann.md>), [api](<https://devfeed.tech/tags/api.md>), [database](<https://devfeed.tech/tags/database.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [llm](<https://devfeed.tech/tags/llm.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [product](<https://devfeed.tech/tags/product.md>), [rag](<https://devfeed.tech/tags/rag.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

Neon describes how parallel HNSW index builds in pgvector can reduce vector index build time by 30x. The article explains how Neon's elastic, separate-storage-and-compute architecture can allocate extra resources during indexing and scale them down afterward for AI applications using vector search, semantic search, and Retrieval Augmented Generation.

### Source excerpt

We are Neon, the serverless Postgres. We power thousands of AI apps with the pgvector extension and separate storage and compute enabling your database resources to scale independently. In this article, Raouf explains how you can use Neon's elasticity, and parallel HNSW index bui...

## Optimizing vector search performance with pgvector

DevFeed: [Optimizing vector search performance with pgvector](<https://devfeed.tech/articles/optimizing-vector-search-performance-with-pgvector-5704.md>)

Original publisher: [Read original article](<https://neon.com/blog/optimizing-vector-search-performance-with-pgvector>)

Author: Raouf Chebri

Published: 2023-06-28T16:01:07Z

Content type: article

Language: en

Sources: [Blog -- Neon Docs](<https://devfeed.tech/sources/blog-neon-docs.md>)

Topics: [AI search](<https://devfeed.tech/topics/ai-search.md>), [ann](<https://devfeed.tech/topics/ann.md>), [Database](<https://devfeed.tech/topics/database.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ann](<https://devfeed.tech/tags/ann.md>), [community](<https://devfeed.tech/tags/community.md>), [performance](<https://devfeed.tech/tags/performance.md>), [pgvector](<https://devfeed.tech/tags/pgvector.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [product](<https://devfeed.tech/tags/product.md>), [search](<https://devfeed.tech/tags/search.md>), [vector](<https://devfeed.tech/tags/vector.md>), [vector-search](<https://devfeed.tech/tags/vector-search.md>)

### AI overview

This article explains how to optimize vector similarity search with pgvector in Postgres. It compares exact sequential scans with approximate nearest neighbor search using the ivfflat index, discusses performance as datasets grow, and introduces the Lists and Probes parameters that affect search speed and recall.

### Source excerpt

According to the StackOverflow Survey 2023, nearly half of professional developers use Postgres. It's natural then that the Postgres extension for vector similarity search, pgvector, is one of the most popular options for prototyping AI-powered applications. But how to properly u...