Google Gemini

## What embedding models does Google offer for RAG applications on Vertex AI?

## Overview Google provides specialized embedding models including text-embedding-005 and gemini-embedding-001 for RAG implementations. ## Key Features These models power the Vertex AI RAG Engine and File Search Tool for semantic search capabilities. ## Technical Specifications The embedding models support configurable dimensionality and task-specific optimization. ## How It Works Documents are chunked, embedded, and stored in a vector index for retrieval during inference. ## Use Cases ## Limitations and Requirements Embedding quality affects RAG retrieval accuracy. Custom fine-tuning may be needed for specialized domains. ## Comparison to Alternatives ## Summary In conclusion, Google's embedding models provide a solid foundation for enterprise RAG applications on Vertex AI.