- Ease of use, with builders capable of get began through an API enabling speedy prototyping and experimentation.
- Managed orchestration, to deal with knowledge retrieval and LLM integration.
- Customization and open supply help, with builders ready to select from parsing, chunking, annotation, embedding, vector storage, and open supply fashions. Builders can also customise their very own elements.
- Integration flexibility, to connect with varied vector databases reminiscent of Pinecone and Weaviate, or use Vertex AI Search.
Within the introductory weblog publish, Google cited trade use circumstances for Vertex AI RAG Engine in monetary companies, well being care, and authorized. The publish additionally offered hyperlinks to assets together with a getting began pocket book, instance integrations with Vertex AI Vector Search, Vertex AI Characteristic Retailer, Pinecone, and Weaviate, and a information to hyperparameter tuning for retrieval with RAG Engine.
