Endpoint#

Endpoint
POST https://api.xpluse.plus/gemini/v1beta/models/{model}:generateContent
POST https://api.xpluse.plus/gemini/v1beta/models/{Models}:streamGenerateContent

Call Google Gemini models through the Gemini native API. DataMind AI is compatible with the Google GenAI SDK and supports function calling, code execution, Grounding, and image generation/editing.

Authentication#

Header
x-goog-api-key: <your DATAMIND_API_KEY>

Basic request#

cURL
curl "https://api.xpluse.plus/gemini/v1beta/models/google/gemini-3.1-flash-lite-preview:generateContent" \
  -H "x-goog-api-key: $DATAMIND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{"parts": [{"text": "Implement a simple web server in Python"}]}]
  }' 
Python
from google import genai

client = genai.Client(
    api_key="<your DATAMIND_API_KEY>",
    http_options={"api_version": "v1beta", "base_url": "https://api.xpluse.plus/gemini"}
)

response = client.models.generate_content(
    model="google/gemini-3.1-flash-lite-preview",
    contents="Implement a simple web server in Python"
)

print(response.text)
TypeScript
import { GoogleGenAI } from '@google/genai'

const ai = new GoogleGenAI({
  apiKey: '<your DATAMIND_API_KEY>',
  httpOptions: { apiVersion: 'v1beta', baseUrl: 'https://api.xpluse.plus/gemini' }
})

const response = await ai.models.generateContent({
  model: 'google/gemini-3.1-flash-lite-preview',
  contents: 'Implement a simple web server in Python'
})

console.log(response.text)

Streaming response#

Python
response = client.models.generate_content_stream(
    model="google/gemini-3.1-flash-lite-preview",
    contents="Write an article about AI"
)

for chunk in response:
    print(chunk.text, end="", flush=True)

Multimodal input#

Gemini natively supports multimodal input, including images, audio, and video:

Python
import base64

with open("photo.jpg", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()

response = client.models.generate_content(
    model="google/gemini-3.1-flash-lite-preview",
    contents=[
        {"text": "Describe the contents of this image"},
        {"inline_data": {"mime_type": "image/jpeg", "data": image_data}}
    ]
)

Response example#

JSON
{
  "candidates": [{
    "content": {"role": "model", "parts": [{"text": "..."}]},
    "finishReason": "STOP"
  }],
  "ModelsVersion": "google/gemini-3.1-flash-lite-preview",
  "usageMetadata": {"promptTokenCount": 12, "candidatesTokenCount": 256, "totalTokenCount": 268}
}

Supported models#

ModelDescription
google/gemini-3.1-pro-previewStrongest reasoning
google/gemini-3-pro-previewBalanced performance
google/gemini-3.1-flash-lite-previewFast and cost-effective
google/gemini-3.1-flash-image-previewImage generation and editing

Last updated on June 22, 2026