Google Gemini Interactions LLM

Overview

GoogleInteractionsLLMService provides integration with Google’s Gemini models through the Interactions API. It takes the same settings as GoogleLLMService, so switching between the two is a change of class, and a conversation can move between them mid-call, including one with function calls. Each request carries the full context with store=False, so Google keeps no copy of the conversation.

Example Implementation

Complete example with function calling

Gemini Documentation

Official Google Gemini API documentation and features

Installation

uv add "pipecat-ai[google]"

The service requires google-genai >= 2.0.0.

Prerequisites

Required Environment Variables

GOOGLE_API_KEY: Your Google Gemini API key for authentication

Configuration

from pipecat.services.google.interactions.llm import GoogleInteractionsLLMService

str

required

Google AI API key for authentication.

GoogleInteractionsLLMService.Settings

default:"None"

Runtime-configurable model settings. See Settings below.

list[dict[str, Any]]

default:"None"

Tools to offer the model when the context carries none, in Gemini’s generateContent format or the Interactions API’s. Built-in Gemini tools are passed here.

dict[str, Any]

default:"None"

Configuration for tool usage, in Gemini’s generateContent format (function_calling_config).

HttpOptions

default:"None"

HTTP options for the Google API client. The API version defaults to v1beta unless set here.

float | None

default:"20.0"

How long to wait for the next event of a streamed response before giving up on it. This is a gap between events, not a limit on how long a response may take overall. Set to None to wait indefinitely.

float | None

default:"5.0"

How long to wait for the first event before giving up on the request and re-issuing it, when retry_on_timeout is set.

bool

default:"False"

Whether to re-issue the request once if the first event doesn’t arrive within retry_timeout_secs.

Settings

Runtime-configurable settings passed via the settings constructor argument using GoogleInteractionsLLMService.Settings(...). These can be updated mid-conversation with LLMUpdateSettingsFrame. See Service Settings for details. The fields are those of GoogleLLMService.Settings; see its Settings table for types and descriptions. The defaults match, except where noted below.

Parameter	Default	Behavior in this service
`top_k`	`None`	Ignored with a warning; the Interactions API has no such field.
`frequency_penalty`	`None`	Ignored with a warning; the Interactions API has no such field.
`presence_penalty`	`None`	Ignored with a warning; the Interactions API has no such field.
`thinking`	`None`	Only `thinking_level` and `include_thoughts` apply. A `thinking_budget` is ignored with a warning. If not provided, Pipecat sends the lowest thinking level for the model.
`safety_settings`	`None`	Content safety filters, as a list of `SafetySetting`. Categories left unspecified keep the Gemini API defaults.

Usage

Basic Setup

import os

from pipecat.services.google.interactions.llm import GoogleInteractionsLLMService

llm = GoogleInteractionsLLMService(
    api_key=os.getenv("GOOGLE_API_KEY"),
    settings=GoogleInteractionsLLMService.Settings(
        system_instruction="You are a helpful assistant.",
    ),
)

With Thinking Level

llm = GoogleInteractionsLLMService(
    api_key=os.getenv("GOOGLE_API_KEY"),
    settings=GoogleInteractionsLLMService.Settings(
        thinking=GoogleInteractionsLLMService.ThinkingConfig(
            thinking_level="high",
            include_thoughts=True,
        ),
    ),
)

Notes

Supported features: Streaming text, function calls (parallel and sequential), thought summaries, images, audio and documents, and token usage metrics. run_inference() accepts a response_schema to enforce the structure of the reply.
Empty responses: A completed response with no text and no function call logs a warning and pushes a non-fatal ErrorFrame, so the application can react instead of the bot going silent.

Event Handlers

GoogleInteractionsLLMService supports the following event handlers, inherited from LLMService:

Event	Description
`on_completion_timeout`	Called when an LLM completion request times out
`on_function_calls_started`	Called when function calls are received and execution is about to start
`on_function_calls_cancelled`	Called after one or more in-progress function calls are cancelled

Get Started

Standards

Pipecat Server

Client SDKs

Pipecat Flows

Pipecat Cloud

CLI

Pipecat Context Hub

Google Gemini Interactions LLM

Overview

Example Implementation

Gemini Documentation

Installation

Prerequisites

Required Environment Variables

Configuration

Settings

Usage

Basic Setup

With Thinking Level

Notes

Event Handlers

​Overview

Example Implementation

Gemini Documentation

​Installation

​Prerequisites

​Required Environment Variables

​Configuration

​Settings

​Usage

​Basic Setup

​With Thinking Level

​Notes

​Event Handlers

Overview

Installation

Prerequisites

Required Environment Variables

Configuration

Settings

Usage

Basic Setup

With Thinking Level

Notes

Event Handlers