VertexAIGeminiGenerator

VertexAIGeminiGenerator enables text generation using Google Gemini models.

Basic Information

• Type: haystack_integrations.components.generators.google_vertex.gemini.VertexAIGeminiGenerator

Inputs

Parameter	Type	Default	Description
parts	Variadic[Union[str, ByteStream, Part]]		Prompt for the model.
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 with the following keys: - replies: A list of generated content.

Overview

Work in Progress

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

VertexAIGeminiGenerator enables text generation using Google Gemini models.

Usage example:

from haystack_integrations.components.generators.google_vertex import VertexAIGeminiGenerator

gemini = VertexAIGeminiGenerator()
result = gemini.run(parts = ["What is the most interesting thing you know?"])
for answer in result["replies"]:
    print(answer)

>>> 1. **The Origin of Life:** How and where did life begin? The answers to this ...
>>> 2. **The Unseen Universe:** The vast majority of the universe is ...
>>> 3. **Quantum Entanglement:** This eerie phenomenon in quantum mechanics allows ...
>>> 4. **Time Dilation:** Einstein's theory of relativity revealed that time can ...
>>> 5. **The Fermi Paradox:** Despite the vastness of the universe and the ...
>>> 6. **Biological Evolution:** The idea that life evolves over time through natural ...
>>> 7. **Neuroplasticity:** The brain's ability to adapt and change throughout life, ...
>>> 8. **The Goldilocks Zone:** The concept of the habitable zone, or the Goldilocks zone, ...
>>> 9. **String Theory:** This theoretical framework in physics aims to unify all ...
>>> 10. **Consciousness:** The nature of human consciousness and how it arises ...

Usage Example

components:
  VertexAIGeminiGenerator:
    type: google_vertex.src.haystack_integrations.components.generators.google_vertex.gemini.VertexAIGeminiGenerator
    init_parameters:

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
project_id	Optional[str]	None	ID of the GCP project to use. By default, it is set during Google Cloud authentication.
model	str	gemini-2.0-flash	Name of the model to use. For available models, see https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models.
location	Optional[str]	None	The default location to use when making API calls, if not set uses us-central-1.
generation_config	Optional[Union[GenerationConfig, Dict[str, Any]]]	None	The generation config to use. Can either be a GenerationConfig object or a dictionary of parameters. Accepted fields are: - temperature - top_p - top_k - candidate_count - max_output_tokens - stop_sequences
safety_settings	Optional[Dict[HarmCategory, HarmBlockThreshold]]	None	The safety settings to use. See the documentation for HarmBlockThreshold and HarmCategory for more details.
system_instruction	Optional[Union[str, ByteStream, Part]]	None	Default system instruction to use for generating content.
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]]		Prompt for the model.
streaming_callback	Optional[Callable[[StreamingChunk], None]]	None	A callback function that is called when a new token is received from the stream.