gemini-embed

To generate vector embeddings for RAG, you provide text to the Gemini API endpoint using the gemini-embedding-001 model. The skill converts individual strings or batches of documents into numeric vectors for semantic search and retrieval.

Yes, you need a GOOGLE_API_KEY environment variable set. This key authenticates your requests to the Gemini embedding endpoint to successfully generate vectors for your semantic search tasks.

Yes, you can process batch embeddings for document classification and clustering. The skill supports generating vector representations for multiple text inputs simultaneously to optimize throughput across large document collections.

The best way to embed text into Gemini vectors for fast retrieval is using the gemini-embedding-001 model. You embed all documents once, store the resulting vectors, and run user queries against the vector store.

Yes, Gemini embedding supports both single string and batch text processing. You can generate embeddings for an individual string like a test query or process a batch of documents to optimize throughput.

Use the gemini-embedding-001 model for semantic similarity search because it provides a scalable way to convert text into numeric embeddings. This enables efficient similarity matching, classification, and retrieval across large text collections.