Elasticsearch integrations for ActiveModel/Record and Ruby on Rails
Principalele funcționalități ale elastic/elasticsearch-rails sunt: Full Text Search and Retrieval, Search Engines.
Alternativele open-source pentru elastic/elasticsearch-rails includ: sunspot/sunspot — Solr-powered search for Ruby objects. brexhq/prompt-engineering — This project is a comprehensive guide and framework for large language model prompt engineering. It provides a… xlang-ai/instructor-embedding — Instructor-embedding is a natural language processing framework designed to transform unstructured text into… apache/lucene-solr — This project is a full text search engine and enterprise search infrastructure designed for indexing and retrieving… blevesearch/bleve — Bleve is a search indexing engine library written in Go, designed to provide full-text search and document retrieval… bleroy/lunr-core — Lunr-core is a small, full text search library for use in small applications. It's a .NET port of LUNR.js.
This project is a comprehensive guide and framework for large language model prompt engineering. It provides a collection of techniques and patterns for optimizing model responses through structured system prompts, context management, and a variety of implementation patterns. The project focuses on several specialized domains, including the creation of autonomous agents through reasoning loops and the implementation of retrieval augmented generation to inject semantic context into prompts. It also provides methods for enforcing structured outputs in serialization formats like JSON or YAML for
Instructor-embedding is a natural language processing framework designed to transform unstructured text into high-dimensional numerical vectors. By utilizing a transformer-based encoder architecture, the system facilitates semantic retrieval, data classification, and similarity analysis across large datasets. The framework distinguishes itself through instruction-conditioned vector projection, which incorporates natural language instructions directly into the embedding process to improve performance for specific tasks without requiring additional training. It functions as a contrastive learni
This project is a full text search engine and enterprise search infrastructure designed for indexing and retrieving large sets of documents. It provides a comprehensive framework for information discovery using ranked results and linguistic analysis. The system integrates high-dimensional vector similarity search for semantic retrieval alongside traditional full-text capabilities. It distinguishes itself through support for geospatial data retrieval, multilingual text processing, and a search suggestion workflow that includes typo-tolerant query completion and spellchecking. The platform cov
