- Ease of use, with developers able to get started via an API enabling rapid prototyping and experimentation.
- Managed orchestration, to handle data retrieval and LLM integration.
- Customization and open source support, with developers able to choose from parsing, chunking, annotation, embedding, vector storage, and open source models. Developers also can customize their own components.
- Integration flexibility, to connect to various vector databases such as Pinecone and Weaviate, or use Vertex AI Search.
In the introductory blog post, Google cited industry use cases for Vertex AI RAG Engine in financial services, health care, and legal. The post also provided links to resources including a getting started notebook, example integrations with Vertex AI Vector Search, Vertex AI Feature Store, Pinecone, and Weaviate, and a guide to hyperparameter tuning for retrieval with RAG Engine.
