# bm25

Published articles for bm25.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Introducing Lead: TIN-compatible full-text search for CI

DevFeed: [Introducing Lead: TIN-compatible full-text search for CI](<https://devfeed.tech/articles/introducing-lead-tin-compatible-full-text-search-for-ci-42160.md>)

Original publisher: [Read original article](<https://planetscale.com/blog/introducing-lead>)

Author: Eric Ridge

Published: 2026-09-17T00:00:00Z

Content type: release

Language: en

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

Topics: [ci](<https://devfeed.tech/topics/ci.md>), [Development](<https://devfeed.tech/topics/development.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [SQL](<https://devfeed.tech/topics/sql.md>)

Tags: [3](<https://devfeed.tech/tags/3.md>), [acid](<https://devfeed.tech/tags/acid.md>), [ansi](<https://devfeed.tech/tags/ansi.md>), [bm25](<https://devfeed.tech/tags/bm25.md>), [ci](<https://devfeed.tech/tags/ci.md>), [concurrent](<https://devfeed.tech/tags/concurrent.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [extension](<https://devfeed.tech/tags/extension.md>), [full-text-search](<https://devfeed.tech/tags/full-text-search.md>), [html](<https://devfeed.tech/tags/html.md>), [lead](<https://devfeed.tech/tags/lead.md>), [make](<https://devfeed.tech/tags/make.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [search](<https://devfeed.tech/tags/search.md>), [sql](<https://devfeed.tech/tags/sql.md>), [staging](<https://devfeed.tech/tags/staging.md>), [tests](<https://devfeed.tech/tags/tests.md>), [tokenizers](<https://devfeed.tech/tags/tokenizers.md>), [transactions](<https://devfeed.tech/tags/transactions.md>), [version](<https://devfeed.tech/tags/version.md>)

### AI overview

Lead is a TIN-compatible PostgreSQL full-text search extension designed for CI, development, and staging. It preserves TIN's search features and correctness but uses full table scans, making it much slower and suitable for small test datasets rather than production workloads.

### Source excerpt

Lead is a feature-equivalent version of TIN you can run in CI

## Introducing TIN: full-text search for Postgres

DevFeed: [Introducing TIN: full-text search for Postgres](<https://devfeed.tech/articles/introducing-tin-full-text-search-for-postgres-31551.md>)

Original publisher: [Read original article](<https://planetscale.com/blog/introducing-tin>)

Author: Patrick Reynolds

Published: 2026-09-16T00:00:00Z

Content type: release

Language: en

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

Topics: [Databases](<https://devfeed.tech/topics/databases.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>)

Tags: [backups](<https://devfeed.tech/tags/backups.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [bm25](<https://devfeed.tech/tags/bm25.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [full-text-search](<https://devfeed.tech/tags/full-text-search.md>), [index](<https://devfeed.tech/tags/index.md>), [performance](<https://devfeed.tech/tags/performance.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [reddit](<https://devfeed.tech/tags/reddit.md>), [replication](<https://devfeed.tech/tags/replication.md>), [search](<https://devfeed.tech/tags/search.md>), [text](<https://devfeed.tech/tags/text.md>), [wikipedia](<https://devfeed.tech/tags/wikipedia.md>)

### AI overview

PlanetScale announces TIN, a full-text search extension for Postgres and Neki databases. The article describes supported query and matching features, transaction and update behavior, and benchmark workloads and corpora used to assess performance.

### Source excerpt

TIN is a fast, full-featured, full-text search index for Postgres

## Your agent wants to search like a 2010 quant

DevFeed: [Your agent wants to search like a 2010 quant](<https://devfeed.tech/articles/your-agent-wants-to-search-like-a-2010-quant-12802.md>)

Original publisher: [Read original article](<https://blog.vespa.ai/your-agent-wants-to-search-like-a-2010-quant/>)

Author: Jon Bratseth

Published: 2026-07-07T00:00:00Z

Content type: opinion

Language: en

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

Topics: [AI search](<https://devfeed.tech/topics/ai-search.md>), [information retrieval](<https://devfeed.tech/topics/information-retrieval.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Google Search](<https://devfeed.tech/topics/google-search.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [bm25](<https://devfeed.tech/tags/bm25.md>), [genai](<https://devfeed.tech/tags/genai.md>), [google-search](<https://devfeed.tech/tags/google-search.md>), [hybrid-search](<https://devfeed.tech/tags/hybrid-search.md>), [information-retrieval](<https://devfeed.tech/tags/information-retrieval.md>), [rag](<https://devfeed.tech/tags/rag.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [search](<https://devfeed.tech/tags/search.md>)

### AI overview

The article argues that AI agents should retrieve information with more control and sophistication than ordinary human search users. It describes a progression from vector retrieval to hybrid search using methods such as BM25 and machine-learned ranking, and presents search as code as a possible next stage.

### Source excerpt

The idea of empowering AI agents to retrieve information like a professional is going mainstream.

## Re-autoresearching MSMARCO BM25, on Vespa

DevFeed: [Re-autoresearching MSMARCO BM25, on Vespa](<https://devfeed.tech/articles/re-autoresearching-msmarco-bm25-on-vespa-12796.md>)

Original publisher: [Read original article](<https://blog.vespa.ai/re-autoresearching-msmarco-bm25-on-vespa/>)

Author: andreer thomas

Published: 2026-05-29T00:00:00Z

Content type: article

Language: en

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

Topics: [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Python](<https://devfeed.tech/topics/python.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [generalization in machine learning](<https://devfeed.tech/topics/generalization-in-machine-learning.md>), [pandas](<https://devfeed.tech/topics/pandas.md>), [Google Search](<https://devfeed.tech/topics/google-search.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [bm25](<https://devfeed.tech/tags/bm25.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [experiment](<https://devfeed.tech/tags/experiment.md>), [google-search](<https://devfeed.tech/tags/google-search.md>), [information-retrieval](<https://devfeed.tech/tags/information-retrieval.md>), [openai](<https://devfeed.tech/tags/openai.md>), [pandas](<https://devfeed.tech/tags/pandas.md>), [python](<https://devfeed.tech/tags/python.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [zero-shot](<https://devfeed.tech/tags/zero-shot.md>)

### AI overview

This article reproduces an MSMARCO BM25 autoresearch experiment in Vespa. It compares LLM-driven Python reranking with an approach restricted to existing Vespa rank features and reports a comparable improvement on a 650,000-passage subset, with better generalization to the full dataset.

### Source excerpt

BM25 is having a moment. We reproduce Doug Turnbull's MSMARCO autoresearch experiment in Vespa and get a comparable MRR@10 lift from existing rank features -- with twice the generalization to full MSMARCO.

## Training and Finetuning Sparse Embedding Models with Sentence Transformers

DevFeed: [Training and Finetuning Sparse Embedding Models with Sentence Transformers](<https://devfeed.tech/articles/training-and-finetuning-sparse-embedding-models-with-sentence-transformers-7530.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/train-sparse-encoder>)

Author: Tom Aarsen; Arthur BRESNU

Published: 2025-07-01T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [sentence-transformers](<https://devfeed.tech/topics/sentence-transformers.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [bm25](<https://devfeed.tech/tags/bm25.md>), [classification](<https://devfeed.tech/tags/classification.md>), [community](<https://devfeed.tech/tags/community.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [examples](<https://devfeed.tech/tags/examples.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [models](<https://devfeed.tech/tags/models.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [search](<https://devfeed.tech/tags/search.md>), [sentence-transformers](<https://devfeed.tech/tags/sentence-transformers.md>), [training](<https://devfeed.tech/tags/training.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

This tutorial explains how to fine-tune sparse embedding models with Sentence Transformers. It covers the required components, pretrained sparse encoders available through the Hugging Face Hub, the distinction between dense and sparse embeddings, interpretability through vocabulary tokens, and neural query/document expansion compared with BM25.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## pg\_search is Available on Neon

DevFeed: [pg\_search is Available on Neon](<https://devfeed.tech/articles/pg-search-is-available-on-neon-5719.md>)

Original publisher: [Read original article](<https://neon.com/blog/pgsearch-on-neon>)

Author: Bryan Clark

Published: 2025-03-18T14:18:47Z

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>), [SQL](<https://devfeed.tech/topics/sql.md>), [elasticsearch](<https://devfeed.tech/topics/elasticsearch.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Rust](<https://devfeed.tech/topics/rust.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [analytics](<https://devfeed.tech/tags/analytics.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [bm25](<https://devfeed.tech/tags/bm25.md>), [company](<https://devfeed.tech/tags/company.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [elasticsearch](<https://devfeed.tech/tags/elasticsearch.md>), [free](<https://devfeed.tech/tags/free.md>), [performance](<https://devfeed.tech/tags/performance.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [rust](<https://devfeed.tech/tags/rust.md>), [search](<https://devfeed.tech/tags/search.md>), [speed](<https://devfeed.tech/tags/speed.md>), [sql](<https://devfeed.tech/tags/sql.md>)

### AI overview

Neon has made ParadeDB's pg_search extension available to its users. The Rust-based PostgreSQL extension provides Elasticsearch-style full-text search features, including BM25 ranking, typo tolerance, prefix search, highlighted matches, and faceted search, without external services. A benchmark on 10 million rows reports queries up to 1,000 times faster than a tuned PostgreSQL GIN-based setup.

### Source excerpt

We've teamed up with ParadeDB to bring pg_search to all Neon users, making full-text search in Postgres faster and more powerful. Get Elastic-level speed within Neon--perfect for search-heavy apps, analytics, and filtering large datasets. Try it on the Free Plan. Elasticsearch Pow...