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Pixeltable’s Gemini integration enables you to access the Gemini LLM via the Google Gemini API.

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Prerequisites

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Important notes

  • Google AI Studio usage may incur costs based on your plan.
  • Be mindful of sensitive data and consider security measures when integrating with external services.
First you’ll need to install required libraries and enter a Gemini API key obtained via Google AI Studio. 
%pip install -qU pixeltable google-genai

import os
import getpass

if 'GEMINI_API_KEY' not in os.environ:
    os.environ['GEMINI_API_KEY'] = getpass.getpass('Google AI Studio API Key:')
Now let’s create a Pixeltable directory to hold the tables for our demo. 
import pixeltable as pxt

# Remove the 'gemini_demo' directory and its contents, if it exists
pxt.drop_dir('gemini_demo', force=True)
pxt.create_dir('gemini_demo')

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Generate content

Create a Table: In Pixeltable, create a table with columns to represent your input data and the columns where you want to store the results from Gemini. 
from google.genai.types import GenerateContentConfigDict
from pixeltable.functions import gemini

# Create a table in Pixeltable and pick a model hosted on Google AI Studio with some parameters

t = pxt.create_table('gemini_demo.text', {'input': pxt.String})

config = GenerateContentConfigDict(
    stop_sequences=['\n'],
    max_output_tokens=300,
    temperature=1.0,
    top_p=0.95,
    top_k=40,
)
t.add_computed_column(output=gemini.generate_content(
    t.input,
    model='gemini-2.0-flash',
    config=config
))

# Ask Gemini to generate some content based on the input
t.insert([
    {'input': 'Write a story about a magic backpack.'},
    {'input': 'Tell me a science joke.'}
])

# Parse the response into a new column
t.add_computed_column(response=t.output['candidates'][0]['content']['parts'][0]['text'])
t.select(t.input, t.response).head()

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Generate images with Imagen

from google.genai.types import GenerateImagesConfigDict

images_t = pxt.create_table('gemini_demo.images', {'prompt': pxt.String})

config = GenerateImagesConfigDict(aspect_ratio='16:9')
images_t.add_computed_column(generated_image=gemini.generate_images(
    images_t.prompt,
    model='imagen-4.0-generate-001',
    config=config
))

images_t.insert([{'prompt': 'A friendly dinosaur playing tennis in a cornfield'}])

images_t.head()

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Generate video with Veo

videos_t = pxt.create_table('gemini_demo.videos', {'prompt': pxt.String})

videos_t.add_computed_column(generated_video=gemini.generate_videos(
    videos_t.prompt,
    model='veo-2.0-generate-001',
))

videos_t.insert([{'prompt': 'A giant pixel floating over the open ocean in a sea of data'}])

videos_t.head()

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Generate Video from an existing Image

We’ll add an additional computed column to our existing images_t to animate the generated images. 
images_t.add_computed_column(generated_video=gemini.generate_videos(
    image=images_t.generated_image,
    model='veo-2.0-generate-001',
))

images_t.head()

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Learn more

To learn more about advanced techniques like RAG operations in Pixeltable, check out the RAG Operations in Pixeltable tutorial. If you have any questions, don’t hesitate to reach out.
Last modified on December 13, 2025