- Ease of use, with builders in a position to get began by way of an API enabling speedy prototyping and experimentation.
- Managed orchestration, to deal with knowledge retrieval and LLM integration.
- Customization and open supply assist, with builders in a position to select from parsing, chunking, annotation, embedding, vector storage, and open supply fashions. Builders can also customise their very own parts.
- Integration flexibility, to hook up with varied vector databases akin to Pinecone and Weaviate, or use Vertex AI Search.
Within the introductory weblog publish, Google cited business use instances for Vertex AI RAG Engine in monetary providers, well being care, and authorized. The publish additionally supplied hyperlinks to sources together with a getting began pocket book, instance integrations with Vertex AI Vector Search, Vertex AI Function Retailer, Pinecone, and Weaviate, and a information to hyperparameter tuning for retrieval with RAG Engine.