Google Gemini

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.