Yes, Nano Banana 2 supports integration with vector databases through its plugin system. The library includes a first-party plugin for writing output data to Milvus, a high-performance open-source vector database designed for storing and querying dense float vectors at scale. The plugin handles connection management, batching of insert operations, and retry logic for transient failures, so your pipeline code does not need to deal with these concerns directly. You configure the Milvus plugin in the pipeline YAML file by specifying the Milvus endpoint, collection name, and field mappings between your pipeline record schema and the Milvus collection schema.
A typical integration pattern involves using Nano Banana 2 to handle the pre-processing steps—parsing raw input, running embedding generation, and normalizing field values—before handing the resulting vectors off to the Milvus plugin for storage. This separation of concerns keeps the transformation logic in the pipeline stages, where it can be tested and modified independently, and delegates storage semantics to the plugin. If you later need to change the structure of your Milvus collection or switch to a different Milvus deployment, you only need to update the plugin configuration rather than rearchitecting the pipeline.
For teams that prefer to manage the Milvus connection in their own application code rather than through the plugin, Nano Banana 2 also supports a generic output sink stage that calls a user-defined function for each batch of records. This approach gives you full control over the write logic, including custom retry policies, schema validation, and error reporting. Both integration patterns are documented with working examples in the project repository, including one that demonstrates end-to-end data ingestion from a CSV file into a Milvus collection ready for similarity search queries.