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Embeddings Google Gemini#

Use the Embeddings Google Gemini node to generate embeddings for a given text.

On this page, you'll find the node parameters for the Embeddings Google Gemini node, and links to more resources.

Credentials

You can find authentication information for this node here.

Parameter resolution in sub-nodes

Sub-nodes behave differently to other nodes when processing multiple items using an expression.

Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five name values, the expression {{ $json.name }} resolves to each name in turn.

In sub-nodes, the expression always resolves to the first item. For example, given an input of five name values, the expression {{ $json.name }} always resolves to the first name.

Node parameters#

Model: the model to use to generate the embedding. Learn more about available models in Google Gemini's models documentation.

Templates and examples#

Ask questions about a PDF using AI

by David Roberts

View template details
Chat with PDF docs using AI (quoting sources)

by David Roberts

View template details
AI Crew to Automate Fundamental Stock Analysis - Q&A Workflow

by Derek Cheung

View template details
Browse Embeddings Google Gemini integration templates, or search all templates

Refer to Langchain's Google Generative AI embeddings documentation for more information about the service.

View n8n's Advanced AI documentation.

  • completion: Completions are the responses generated by a model like GPT.
  • hallucinations: Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
  • vector database: A vector database stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
  • vector store: A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.