OpenK9 is a powerful modular Open Source Cognitive Enterprise Search System, enhanced through AI and Machine Learning. Using state-of-art Information Retrieval and Machine Learning algorithms, OpenK9 is able to enrich and index data from different data sources.
How It Works
The system is composed by several modules that make up a data processing pipeline.
- Data Source plugins are called from a scheduler, and they are responsible for fetching new data and put it into the Queue;
- A MQTT Queue handles ingestion backpressure, splitting the processing workload into a pool of workers for enrichment;
- Several Enrich plugins process the data from the queue and extract info using AI;
- The enriched document are then saved and indexed into a Search Index;
- The entities found by the enrich pipeline are also persisted into a Knowledge Graph;
- A Query Parser is able to convert complex queries into the language used to query the Search Index and the Knowledge Graph.
OpenK9 is extremely modular and easily extensible. A new data source can be connected quickly and efficiently.
Furthermore, OpenK9 is easily manageable: through the administration panel, it is very easy to create and configure a new search environment for a new data source.
Once the data has been indexed we provide several ways to search into it.
- Standalone Search App: we provide a standalone ready to use frontend to search into data;
- Reusable UI Components: we provide several React components to be embedded into your application, that allow interacting with OpenK9 Search;
- Headless API: OpenK9 exposes several REST endpoints that allow you to to search into entities and data using HTTP.