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Google Gemini is an LLM plugin. It takes the transcribed conversation and generates the reply.

Setup

Set your Google API key in the worker environment. Generate a key from the Google AI Studio:

Usage

Import the plugin and pass it to the pipeline’s llm slot.

Vertex AI

By default the plugin calls the public Gemini API with GOOGLE_API_KEY. To run Gemini through Vertex AI instead, enable the Vertex backend and supply your Google Cloud project and location. Authenticate with a service account. Set GOOGLE_APPLICATION_CREDENTIALS:
location defaults to us-central1 in all SDKs. In Python, project_id is required when vertexai=True and must be passed explicitly. Node JS carries the same two settings inside vertexai_config.

Configuration Options

Constructor parameters for GoogleLLM. The Python and Node JS SDKs share these field names.

Core

Generation knobs

Vertex AI

Safety

Extended thinking

For voice, short replies feel best. A zero or small thinking budget and a concise system instruction keep latency down.

Import paths