Results for indexed-vector

A Rust library that implements vector container that can be indexed with specified function.

Abstraction for indexing and searching vectors

gromit uses Redis and OpenAI embeddings to index your documentation

vapey uses Redis and OpenAI embeddings to index your documentation

Interface to control vector databases settings (like indexes, collections, etc).

Ruby client library which includes index and vector operations to upload embeddings into Pinecone and do similarity searches on them.

A Ruby library for text classification featuring Naive Bayes, LSI (Latent Semantic Indexing), Logistic Regression, and k-Nearest Neighbors classifiers. Includes TF-IDF vectorization, streaming/incremental training, pluggable persistence backends, thread safety, and a native C extension for fast LSI operations.

Crowsad was conceptualized as a way of blending Duck Duck Go with Dangling Modifier generation so as to learn new words overs time, rather than having to index everything in one go. It uses four input vectors and one output vector. Github version is old version. Credit Andrew Jones for Duck Duck Go.

Ragnar is a high-performance RAG system that leverages Rust libraries through Ruby bindings for embeddings, vector search, and topic modeling. It provides a complete CLI for indexing documents and querying with LLMs.

MCP server that indexes codebases using AST-aware chunking and vector embeddings, providing semantic search for Claude Code and other MCP clients.

Add vector search to your Ruby apps without external services. zvec provides native bindings to Alibaba's high-performance C++ vector database via Rice, supporting HNSW, IVF, and flat indexes with multiple distance metrics. Build semantic search, recommendations, RAG pipelines, and similarity matching with pure Ruby — no HTTP APIs, no infrastructure, no latency overhead.

LEANN (Lightweight Embedding-Aware Neural Neighbor) is a Ruby gem for building and searching vector indexes with minimal storage. It provides semantic search and RAG capabilities with a beautiful, simple API. Supports multiple embedding providers: RubyLLM, OpenAI, Ollama, and FastEmbed.

Proper related posts plugin for Jekyll - uses document correlation matrix on TF-IDF (optionally with Latent Semantic Indexing). Each document is tokenized and stemmed, every word found is treated as keyword for analysis (except for some stop words). TF-IDF matrix for the whole site is calculated (including extra provided weights), then if given accuraccy is lower than 1.0, LSI algorithm is used to compute new simplified vector space. Document correlation matrix is created using dot product of the matrix and its transpose. For each of the post' related documents are inserted into priority queue (sorted by score from document correlation matrix), assuming the score is greater than minimal required score. Selected few bests related posts are retrieven from the queue. Liquid template for each post is rendered and <related-posts /> is replaced with the outcomes of algorithm.

No description provided.

No description provided.