Searchkick

It lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with it pretty much as with a database server.

Search the world's information, including webpages, images, videos and more. Google has many special features to help you find exactly what you're looking for.

It uses the tools you use to make application building a snap. It is built on the battle-tested Apache Zookeeper, it makes it easy to scale up and down.

Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.

It builds completely static HTML sites that you can host on GitHub pages, Amazon S3, or anywhere else you choose. There's a stack of good looking themes available. The built-in dev-server allows you to preview your documentation as you're writing it. It will even auto-reload and refresh your browser whenever you save your changes.

It is an open-source Vector Search Engine and Vector Database written in Rust. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more.

An open-source, high-performance, distributed SQL database built for resilience and scale. Re-uses the upper half of PostgreSQL to offer advanced RDBMS features, architected to be fully distributed like Google Spanner.

It is an open-source vector search engine. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.

We help your website visitors find what they are looking for. AddSearch is a lightning fast, accurate and customizable site search engine with a Search API. AddSearch works on all devices and is easy to install, customize and tweak.

It is a C++ based full-text search engine including similarity ranking capabilities natively integrated into ArangoDB. It allows users to combine two information retrieval techniques: boolean and generalized ranking retrieval. Search results “approved” by the boolean model can be ranked by relevance to the respective query using the Vector Space Model in conjunction with BM25 or TFIDF weighting schemes.

It organizes your search results into topics. With an instant overview of what's available, you will quickly find what you're looking for.

A fast, lightweight and schema-less search backend. It ingests search texts and identifier tuples that can then be queried against in microseconds.

It is a search engine that does full text indexing. It is a lightweight alternative to Elasticsearch and runs in less than 100 MB of RAM. It uses bluge as the underlying indexing library. It is very simple and easy to operate as opposed to Elasticsearch which requires a couple dozen knobs to understand and tune.

It is an embeddable super fast full text search engine. It can be embedded into MySQL. Mroonga is a storage engine that is based on it.

It is an end-to-end vector search engine. Vector generation, storage, and retrieval are handled out of the box through a single API. No need to bring your own embeddings.

It is a powerful agent-first search engine that enables you to run a webscale search engine locally or to connect via remote API. It's ideal for both Large Language Models (LLMs) and human users.