A simple term frequency lib
A super simple in memory search index based on Term Frequency–Inverse Document Frequency using the awesome natural module.
Watches a Twitter stream for hashtags and adjusts a light from hot (red) to blue (cool) depending on the difference between the near term frequency of this term versus its long term frequency. One of my favorite demos of a Nitrogen application.
RPC configuration for Frequency for use with Polkadotjs API
Quadrat analysis lays a set of equal-size areas(quadrat) over the study area and counts the number of features in each quadrat and creates a frequency table.
Automated stream rotation useful for log files
Creates a term vector from tokenized text.
Display images in terminals using the iTerm inline image protocol
A transport for winston which logs to a rotating file each day.
A module which will endeavor to guess your terminal's level of color support.
Public logs API for OpenTelemetry
Set for RDF/JS Terms
<h1> Term frequency–inverse document frequency in JavaScrip </h1> <p style="color:rgb(233,99,99);"> WARNNING: This library contains modern es6 features, its intended to be used with webpack and babel or another module bundler and es7 to es5 compiler </p>
This package provides support for the [RedisBloom](https://redis.io/docs/data-types/probabilistic/) module, which adds additional probabilistic data structures to Redis.
A TypeScript/JavaScript implementation of the RDF/JS data factory.
Map for RDF/JS Terms keys
Fast and tiny fuzzy-search utility
A lightweight, bucket-based rate limiter for JavaScript that controls request frequency with minimal overhead.
Resolve Node.js version aliases like 'latest', 'lts' or 'erbium'
List of CSS frequency units.
Zep: Fast, scalable building blocks for production LLM apps
Software related dictionaries for cspell.
A term-xsd-to-boolean function-factory actor
A term-floor function-factory actor
BM25 reranker for RAG: in-memory term-frequency reranking against a small candidate set. Stateless, zero deps.
Oxios Agent OS — Agent Operating System powered by oxi-sdk
Unified intelligence layer — knowledge graphs, adaptive prompting, RAG, spectral math, and code analysis for the Brainwires Agent Framework
Knowledge layer — knowledge graphs, BKS/PKS, brain client, entity extraction for the Brainwires Agent Framework. RAG / spectral / code-analysis live in `brainwires-rag`; prompting lives in `brainwires-prompting`.
Deterministic context selection engine for AI agents and LLMs
Interactive Graph Structure Utility
A transactional relational-graph-vector database using Datalog — a maintained fork of CozoDB, tuned as a substrate for agentic memory
A high-speed, blocking, information retrieval API for rust
A document vector search with flexible matrix transforms. Currently supports Latent semantic analysis and Term frequency - inverse document frequency
A document vector search with flexible matrix transforms. Currently supports Latent semantic analysis and Term frequency - inverse document frequency
Term Frequency - Inverse Document Frequency
A document vector search with flexible matrix transforms. Currently supports Latent semantic analysis and Term frequency - inverse document frequency
Term Frequency - Inverse Document Frequency
Simplified tokenization, stemming, and term-frequency map indexes
Calculate TF-IDF out of a text, resulting in a hash with term as key, frequency as value. Sorry for taking the convenient name for myself! See examples/demo_tf.rb for usage
Jekyll plugin to show related posts based on the content, tags, and categories. The similarity is calculated using TF-IDF(term frequency-inverted document frequency). Since tags and categories are use-defined values, those are considered with higher weights than a content while calculating.
Jekyll plugin to show related posts based on the content, tags, and categories. The similarity is calculated using TF-IDF(term frequency-inverted document frequency). Since tags and categories are use-defined values, those are considered with higher weights than a content while calculating.
Jekyll plugin to show related posts based on the content, tags, and categories. The similarity is calculated using TF-IDF(term frequency-inverted document frequency). Since tags and categories are use-defined values, those are considered with higher weights than a content while calculating.
In information retrieval, tf–idf or TFIDF, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in searches of information retrieval, text mining, and user modeling. The tf–idf value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word, which helps to adjust for the fact that some words appear more frequently in general.