Vespa allows developers to extend functionality to meet customization needs in the following ways:
- Customizing Java Components: Write subclasses of Searcher (intervening in the query process) or Ranker (defining the sorting logic), referring to the officialdevelopment documentationThe
- Integration of external models: Deploy ONNX or TensorFlow models that are called during the sequencing phase (e.g., CTR prediction models).
- Plug-in configuration: Override the default behavior by modifying the configuration (e.g., adjusting the HNSW index parameter).
Specific steps::
- Compile the extension code using Maven to generate JAR packages.
- pass (a bill or inspection etc)
vespa deploy --application my-custom-appDeploy to instances. - Referencing new components in a query, such as specifying a custom ranking model:
{ "ranking": "my_custom_ranker" }
caveat: Extensions need to ensure compatibility with the Vespa version, it is recommended to regularly synchronize with the open source repository updates. For complex requirements, real-time data pipeline can be realized in combination with Vespa's streaming processing API.
This answer comes from the articleVespa.ai: an open source platform for building efficient AI search and recommendation systemsThe
May not be reproduced without permission:AI productivity tools " How can developers extend the functionality of Vespa.ai? What are some specific ways?
