---
title: "VertexAIGeminiGenerator"
id: vertexaigeminigenerator
slug: "/vertexaigeminigenerator"
description: "`VertexAIGeminiGenerator` enables text generation using Google Gemini models."
---

# VertexAIGeminiGenerator

`VertexAIGeminiGenerator` enables text generation using Google Gemini models.

:::warning Deprecation Notice

This integration uses the deprecated google-generativeai SDK, which will lose support after August 2025.

We recommend switching to the new [GoogleGenAIChatGenerator](googlegenaichatgenerator.mdx) integration instead.
:::

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [`PromptBuilder`](../builders/promptbuilder.mdx)                                                  |
| **Mandatory run variables**            | `parts`: A variadic list containing a mix of images, audio, video, and text to prompt Gemini    |
| **Output variables**                   | `replies`: A list of strings or dictionaries with all the replies generated by the model        |
| **API reference**                      | [Google Vertex](/reference/integrations-google-vertex)                                                 |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_vertex |

</div>

`VertexAIGeminiGenerator` supports `gemini-1.5-pro` and `gemini-1.5-flash`/  `gemini-2.0-flash` models. Note that [Google recommends upgrading](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions) from `gemini-1.5-pro` to `gemini-2.0-flash`.

For details on available models, see https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models.

:::info
To explore the full capabilities of Gemini check out this [article](https://haystack.deepset.ai/blog/gemini-models-with-google-vertex-for-haystack) and the related [Colab notebook](https://colab.research.google.com/drive/10SdXvH2ATSzqzA3OOmTM8KzD5ZdH_Q6Z?usp=sharing).
:::

### Parameters Overview

`VertexAIGeminiGenerator` uses Google Cloud Application Default Credentials (ADCs) for authentication. For more information on how to set up ADCs, see the [official documentation](https://cloud.google.com/docs/authentication/provide-credentials-adc).

Keep in mind that it’s essential to use an account that has access to a project authorized to use Google Vertex AI endpoints.

You can find your project ID in the [GCP resource manager](https://console.cloud.google.com/cloud-resource-manager) or locally by running `gcloud projects list` in your terminal. For more info on the gcloud CLI, see its [official documentation](https://cloud.google.com/cli).

### Streaming

This Generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) the tokens from the LLM directly in output. To do so, pass a function to the `streaming_callback` init parameter.

## Usage

You should install `google-vertex-haystack` package to use the  `VertexAIGeminiGenerator`:

```shell
pip install google-vertex-haystack
```

### On its own

Basic usage:

```python
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 question are still shrouded in mystery, but scientists continuously uncover new insights into the remarkable story of our planet's earliest forms of life.
>>> 2. **The Unseen Universe:** The vast majority of the universe is comprised of matter and energy that we cannot directly observe. Dark matter and dark energy make up over 95% of the universe, yet we still don't fully understand their properties or how they influence the cosmos.
>>> 3. **Quantum Entanglement:** This eerie phenomenon in quantum mechanics allows two particles to become so intertwined that they share the same fate, regardless of how far apart they are. This has mind-bending implications for our understanding of reality and could potentially lead to advancements in communication and computing.
>>> 4. **Time Dilation:** Einstein's theory of relativity revealed that time can pass at different rates for different observers. Astronauts traveling at high speeds, for example, experience time dilation relative to people on Earth. This phenomenon could have significant implications for future space travel.
>>> 5. **The Fermi Paradox:** Despite the vastness of the universe and the abundance of potential life-supporting planets, we have yet to find any concrete evidence of extraterrestrial life. This contradiction between scientific expectations and observational reality is known as the Fermi Paradox and remains one of the most intriguing mysteries in modern science.
>>> 6. **Biological Evolution:** The idea that life evolves over time through natural selection is one of the most profound and transformative scientific discoveries. It explains the diversity of life on Earth and provides insights into our own origins and the interconnectedness of all living things.
>>> 7. **Neuroplasticity:** The brain's ability to adapt and change throughout life, known as neuroplasticity, is a remarkable phenomenon that has important implications for learning, memory, and recovery from brain injuries.
>>> 8. **The Goldilocks Zone:** The concept of the habitable zone, or the Goldilocks zone, refers to the range of distances from a star within which liquid water can exist on a planet's surface. This zone is critical for the potential existence of life as we know it and has been used to guide the search for exoplanets that could support life.
>>> 9. **String Theory:** This theoretical framework in physics aims to unify all the fundamental forces of nature into a single coherent theory. It suggests that the universe has extra dimensions beyond the familiar three spatial dimensions and time.
>>> 10. **Consciousness:** The nature of human consciousness and how it arises from the brain's physical processes remain one of the most profound and elusive mysteries in science. Understanding consciousness is crucial for unraveling the complexities of the human mind and our place in the universe.
```

Advanced usage, multi-modal prompting:

```python
import requests
from haystack.dataclasses.byte_stream import ByteStream
from haystack_integrations.components.generators.google_vertex import VertexAIGeminiGenerator

URLS = [
    "https://raw.githubusercontent.com/silvanocerza/robots/main/robot1.jpg",
    "https://raw.githubusercontent.com/silvanocerza/robots/main/robot2.jpg",
    "https://raw.githubusercontent.com/silvanocerza/robots/main/robot3.jpg",
    "https://raw.githubusercontent.com/silvanocerza/robots/main/robot4.jpg"
]
images = [
    ByteStream(data=requests.get(url).content, mime_type="image/jpeg")
    for url in URLS
]

gemini = VertexAIGeminiGenerator()
result = gemini.run(parts = ["What can you tell me about this robots?", *images])
for answer in result["replies"]:
    print(answer)
>>> The first image is of C-3PO and R2-D2 from the Star Wars franchise. C-3PO is a protocol droid, while R2-D2 is an astromech droid. They are both loyal companions to the heroes of the Star Wars saga.
>>> The second image is of Maria from the 1927 film Metropolis. Maria is a robot who is created to be the perfect woman. She is beautiful, intelligent, and obedient. However, she is also soulless and lacks any real emotions.
>>> The third image is of Gort from the 1951 film The Day the Earth Stood Still. Gort is a robot who is sent to Earth to warn humanity about the dangers of nuclear war. He is a powerful and intelligent robot, but he is also compassionate and understanding.
>>> The fourth image is of Marvin from the 1977 film The Hitchhiker's Guide to the Galaxy. Marvin is a robot who is depressed and pessimistic. He is constantly complaining about everything, but he is also very intelligent and has a dry sense of humor.
```

### In a pipeline

In a RAG pipeline:

```python
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.builders import PromptBuilder
from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.generators.google_vertex import VertexAIGeminiGenerator

docstore = InMemoryDocumentStore()
docstore.write_documents([Document(content="Rome is the capital of Italy"), Document(content="Paris is the capital of France")])

query = "What is the capital of France?"

template = """
Given the following information, answer the question.

Context:
{% for document in documents %}
    {{ document.content }}
{% endfor %}

Question: {{ query }}?
"""
pipe = Pipeline()

pipe.add_component("retriever", InMemoryBM25Retriever(document_store=docstore))
pipe.add_component("prompt_builder", PromptBuilder(template=template))
pipe.add_component("gemini", VertexAIGeminiGenerator())
pipe.connect("retriever", "prompt_builder.documents")
pipe.connect("prompt_builder", "gemini")

res=pipe.run({
    "prompt_builder": {
        "query": query
    },
    "retriever": {
        "query": query
    }
})

print(res)
```

## Additional References

🧑‍🍳 Cookbook: [Function Calling and Multimodal QA with Gemini](https://haystack.deepset.ai/cookbook/vertexai-gemini-examples)
