elasticelasticsearch-ruby
1,977 estrellas616 forksRubyApache-2.05 vistaswww.elastic.co/docs/reference/elasticsearch/clients/ruby
Ruby integrations for Elasticsearch
Las características principales de elastic/elasticsearch-ruby son: Full Text Search and Retrieval, Search, Elasticsearch.
Las alternativas de código abierto para elastic/elasticsearch-ruby incluyen: sunspot/sunspot — Solr-powered search for Ruby objects. pat/thinking-sphinx — Sphinx/Manticore plugin for ActiveRecord/Rails. 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… django-haystack/django-haystack — Django Haystack is a search abstraction layer and framework for integrating full-text search into Django applications.… ankane/searchkick — Searchkick is an integration library and wrapper that connects application models to search engines such as…
Sphinx/Manticore plugin for ActiveRecord/Rails
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
