GoogleAIGeminiGenerator

This component enables text generation using the Google Gemini models.

Deprecation Notice

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

We recommend switching to the new GoogleGenAIChatGenerator integration instead.

Most common position in a pipeline	After a PromptBuilder
Mandatory init variables	"api_key": A Google AI Studio API key. Can be set with GOOGLE_API_KEY env var.
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 AI
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_ai

GoogleAIGeminiGenerator supports gemini-2.5-pro-exp-03-25, gemini-2.0-flash, gemini-1.5-pro, and gemini-1.5-flash models.

For available models, see https://ai.google.dev/gemini-api/docs/models/gemini.

Parameters Overview

GoogleAIGeminiGenerator uses a Google AI Studio API key for authentication. You can write this key in an api_key parameter or as a GOOGLE_API_KEY environment variable (recommended).

To get an API key, visit the Google AI Studio website.

Streaming

This Generator supports streaming the tokens from the LLM directly in output. To do so, pass a function to the streaming_callback init parameter.

Usage

Start by installing the google-ai-haystack package to use the GoogleAIGeminiGenerator:

shell

pip install google-ai-haystack

On its own

Basic usage:

python

import os
from haystack_integrations.components.generators.google_ai import GoogleAIGeminiGenerator

os.environ["GOOGLE_API_KEY"] = "<MY_API_KEY>"

gemini = GoogleAIGeminiGenerator(model="gemini-1.5-pro")
res = gemini.run(parts = ["What is the most interesting thing you know?"])
for answer in res["replies"]:
    print(answer)

This is a more advanced usage that also uses text and images as input:

python

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

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
]

os.environ["GOOGLE_API_KEY"] = "<MY_API_KEY>"

gemini = GoogleAIGeminiGenerator(model="gemini-1.5-pro")
result = gemini.run(parts = ["What can you tell me about this robots?", *images])
for answer in result["replies"]:
    print(answer)

In a pipeline

In a RAG pipeline:

python

import os
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_ai import GoogleAIGeminiGenerator

os.environ["GOOGLE_API_KEY"] = "<MY_API_KEY>"

docstore = InMemoryDocumentStore()

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

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

Question: What's the official language of {{ country }}?
"""
pipe = Pipeline()

pipe.add_component("retriever", InMemoryBM25Retriever(document_store=docstore))
pipe.add_component("prompt_builder", PromptBuilder(template=template))
pipe.add_component("gemini", GoogleAIGeminiGenerator(model="gemini-pro"))
pipe.connect("retriever", "prompt_builder.documents")
pipe.connect("prompt_builder", "gemini")

pipe.run({
    "prompt_builder": {
        "country": "France"
    }
})