Gemini Embedding 2 Serverless API

 1# pip install "segmind>=1.1.0"
 2# export SEGMIND_API_KEY="YOUR_API_KEY"
 3import segmind
 4
 5# Async (v2): submit to the queue and block until COMPLETED.
 6# run() returns the final result dict (600s deadline, 1.0s poll by default).
 7result = segmind.run(
 8    "gemini-embedding-2",
 9    input="Segmind provides fast and affordable AI model APIs for image generation, video creation, and more.",
10    task_type="RETRIEVAL_DOCUMENT",
11    output_dimensionality=768,
12)
13print(result["status"])                      # COMPLETED
14print(result.get("output"))                  # model output (e.g. media URL)
15print(result["metrics"]["inference_time"])   # server compute seconds
16
17# --- Or submit + poll manually (track request_id, control the cadence) ---
18from segmind import SegmindClient, InferenceFailed, InferenceTimeout
19
20client = SegmindClient()                      # reads SEGMIND_API_KEY
21payload = {
22    "input": "Segmind provides fast and affordable AI model APIs for image generation, video creation, and more.",
23    "task_type": "RETRIEVAL_DOCUMENT",
24    "output_dimensionality": 768,
25}
26job = client.submit_async("gemini-embedding-2", **payload)
27print(job.request_id)                         # available immediately
28try:
29    result = job.wait(timeout=600, interval=1.0)
30except InferenceTimeout as e:
31    print("still running:", e.request_id)
32except InferenceFailed as e:
33    print("failed:", e.detail)