Google Gemini is a multimodal AI by DeepMind that processes text, audio, images, and more.
Generates content from text input using the Google Gemini API. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Generates content from both text and image input using the Gemini API. See the documentation
The Google Gemini API is a cutting-edge tool from Google that enables developers to leverage AI models like Imagen and MusicLM to create and manipulate images and music based on textual descriptions. With Pipedream, you can harness this API to automate workflows that integrate AI-generated content into a variety of applications, from generating visuals for social media posts to composing background music for videos. Pipedream's serverless platform allows you to connect Google Gemini API with other apps to create complex, event-driven workflows without managing infrastructure.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
google_gemini: {
type: "app",
app: "google_gemini",
}
},
async run({steps, $}) {
const data = `{{your_promptt}}`;
//E.g. {"contents":[{"parts":[{"text":"Write a story about a magic backpack"}]}]}
return await axios($, {
method: "POST",
url: `https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent`,
headers: {
"Content-Type": "application/json",
},
params: {
key: `${this.google_gemini.$auth.api_key}`,
},
data
})
},
})
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt
file or running pip
.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}