GoogleAIGeminiGenerator

Generates text using multimodal Gemini models through Google AI Studio.#### Multimodal example

Basic Information

• Type: haystack_integrations.components.generators.google_ai.gemini.GoogleAIGeminiGenerator

Inputs

Parameter	Type	Default	Description
parts	Variadic[Union[str, ByteStream, Part]]		A heterogeneous list of strings, ByteStream or Part objects.
streaming_callback	Optional[Callable[[StreamingChunk], None]]	None	A callback function that is called when a new token is received from the stream.

Outputs

Parameter	Type	Default	Description
replies	List[str]		A dictionary containing the following key: - replies: A list of strings containing the generated responses.

Overview

Work in Progress

Bear with us while we're working on adding pipeline examples and most common components connections.

Generates text using multimodal Gemini models through Google AI Studio.#### Multimodal example

import requests
from haystack.utils import Secret
from haystack.dataclasses.byte_stream import ByteStream
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiGenerator

BASE_URL = (
    "https://raw.githubusercontent.com/deepset-ai/haystack-core-integrations"
    "/main/integrations/google_ai/example_assets"
)

URLS = [
    f"{BASE_URL}/robot1.jpg",
    f"{BASE_URL}/robot2.jpg",
    f"{BASE_URL}/robot3.jpg",
    f"{BASE_URL}/robot4.jpg"
]
images = [
    ByteStream(data=requests.get(url).content, mime_type="image/jpeg")
    for url in URLS
]

gemini = GoogleAIGeminiGenerator(model="gemini-2.0-flash", api_key=Secret.from_token("<MY_API_KEY>"))
result = gemini.run(parts = ["What can you tell me about this robots?", *images])
for answer in result["replies"]:
    print(answer)

Usage Example

components:
  GoogleAIGeminiGenerator:
    type: google_ai.src.haystack_integrations.components.generators.google_ai.gemini.GoogleAIGeminiGenerator
    init_parameters:

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_key	Secret	Secret.from_env_var('GOOGLE_API_KEY')	Google AI Studio API key.
model	str	gemini-2.0-flash	Name of the model to use. For available models, see https://ai.google.dev/gemini-api/docs/models/gemini
generation_config	Optional[Union[GenerationConfig, Dict[str, Any]]]	None	The generation configuration to use. This can either be a GenerationConfig object or a dictionary of parameters. For available parameters, see the GenerationConfig API reference.
safety_settings	Optional[Dict[HarmCategory, HarmBlockThreshold]]	None	The safety settings to use. A dictionary with HarmCategory as keys and HarmBlockThreshold as values. For more information, see the API reference
streaming_callback	Optional[Callable[[StreamingChunk], None]]	None	A callback function that is called when a new token is received from the stream. The callback function accepts StreamingChunk as an argument.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
parts	Variadic[Union[str, ByteStream, Part]]		A heterogeneous list of strings, ByteStream or Part objects.
streaming_callback	Optional[Callable[[StreamingChunk], None]]	None	A callback function that is called when a new token is received from the stream.