Search abstraction over different search engines written in PHP. Currently implemented Elasticsearch, Opensearch, Algolia, Meilisearch, RediSearch, Solr, Typesense. Documentation: https://schranz-search.github.io/schranz-search/

An adapter to support RediSearch in schranz-search/seal search abstraction. Part of the https://github.com/schranz-search/schranz-search project. READ-ONLY Repository

A demo exploring realistic data from mobile phone users across India, using RediSearch and RedisJSON modules of Redis Enterprise

Learn Redis Enterprise and Redis Stack with simple Google Colab notebooks

Full Text Search Benchmark, a tool for comparing and evaluating full-text search engines.

Django package that provides auto indexing and searching capabilities for Django model instances using RediSearch.

Documentation for my Medium article on Exploring the Power Duo: Redisearch 2 and RedisJSON with Golang

Go client for RediSearch

A simple REST service exposing some RedisSearch functionality to support simple self-hosted site search functionality.

A RediSearch tutorial to learn the basis of RediSearch module, with sample application in Java, Python and Node.js

PHP client library for RediSearch.

Java client for Redis Modules

基于spring cloud 和 fastdfs 的分布式图片上传服务.

RediSearch demo with beer

Pure-Crystal Redis client, supporting clustering, RedisJSON, RediSearch, RedisGraph, and RedisTimeSeries

Explore cutting-edge Redis capabilities for Vector Similarity Search, Hybrid Search (Vector Similarity + Meta Search), Semantic Caching, and an advanced RAG model integrated with a Language Model (LLM) Chatbot. Unlock the full potential of Redis as a vector database with this comprehensive showcase of powerful features.

Real-time Indexed SQL Queries for Redis

Ruby wrapper around RediSearch that can integrate with Rails

PHP Client for RediSearch

Utilizing Redis Vector Similarity Search, this demo project streamlines expense categorization from bank transactions. The approach harnesses pre-trained models, sidestepping the need for finetuning. This ensures efficient, accurate expense categorization without complex model adjustments