Dimensions option for Embeddings Google Vertex and Embeddings Google Gemini nodes

Feature Requests
1

The idea is:

Add a dimensions option for Embeddings Google Vertex and Embeddings Google Gemini nodes, just as is available today for the Embeddings OpenAI and Embeddings Azure OpenAI nodes.

My use case:

My vector store is a higher dimension than the default resulting in: “Error inserting: expected 1536 dimensions, not 3072 400 Bad Request”.

I think it would be beneficial to add this because:

This would allow n8n AI users to leverage the full capabilities of the highest ranking embedding models, where gemini-embedding-001 has been at the top for sometime now. More, open-source stores such as pgvector and providers like Supabase support making use of these high dimension vectors. The result would be improved RAG’s powered by n8n.

2

Here are two quickly put together community nodes that I’m testing out to fill this need. Both are based off the official nodes, just with the options not currently included.

I’ve added both and am generating 3072 dimension vectors. YMMV, feedback welcome.

1 Like