Work in progress!

The engine is currently under heavy development and the documentation may be slightly out of date. As the API stabilizes, both the content and scope of the present documentation will increase. For any inquiries, bugs or feature requests, be sure to contact the developers or file an issue.


Garamond is a small, flexible neural and data search engine. Search functionality available through API method calls from Julia code or through communication with a Garamond search server.


The Garamond repository can be downloaded through git:

$ git clone

or from inside Julia. Entering the Pkg mode with ] and writing:


downloads the master branch of the repository and adds Garamond to the current active environment.


Architecturally, the engine employs an analytical database and a series of search indexes to perform the search. The database acts as data source to create the indexes (searcher objects), for building responses for clients and as input source for the ranking and recommendation operations. Incoming search/recommendation/ranking requests are parsed to an internal request format and sent to the database and searchers, depending on the request. Each searcher has its own characteristics i.e. ways of embedding documents, searching through the vectors. Search results from searchers can be combined in a variety of ways. The results are then filtered, aggregated and a response is built and sent to the client. Any number of searcher objects can be created and each of these can re-use data embedders i.e. objects that transform data into numerical vectors. Data can be either indexed while the engine is running online i.e. pushed into all indexes plus database or, by fully reloading and reindexing. The engine supports runtime compilation of custom data loaders, parsers, rankers and recommenders. These can be thin clients that connect i.e. through TCP sockets, HTTP, to external systems that perform the actual workload, provided that the internal client APIs are implemented.

Main features

Longer term plans