google-gemini-embeddings

What problem does it solve?

Gemini embeddings enable building robust retrieval-augmented generation (RAG) systems and semantic search by converting text into high-quality vector representations that power fast, accurate similarity, clustering, and indexing.

Core Features & Use Cases

• Task-type optimized embeddings for retrieval, clustering, classification, and semantic search
• RAG workflows with Cloudflare Vectorize integration for scalable indexing
• Real-world use: index documents, run semantic search, and cluster content with vector similarity

Quick Start

Install dependencies and run the basic embedding example to generate a sample embedding, then index and query a small dataset. This section provides a simple path to quickly verify end-to-end functionality without requiring a full production setup.