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
Run any Go code and use any Go package available with a simple import. Refer to the Pipedream Go 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
})
},
})
You can execute custom Go scripts on-demand or in response to various triggers and integrate with thousands of apps supported by Pipedream. Writing with Go on Pipedream enables backend operations like data processing, automation, or invoking other APIs, all within the Pipedream ecosystem. By leveraging Go's performance and efficiency, you can design powerful and fast workflows to streamline complex tasks.
package main
import (
"fmt"
pd "github.com/PipedreamHQ/pipedream-go"
)
func main() {
// Access previous step data using pd.Steps
fmt.Println(pd.Steps)
// Export data using pd.Export
data := make(map[string]interface{})
data["name"] = "Luke"
pd.Export("data", data)
}