Alibaba Cloud Model Studio:API d'embedding multimodal

Singapore

China (Beijing)

Embedding d'image

import dashscope
import json
from http import HTTPStatus
# Replace with your image URL.
image = "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"
input = [{'image': image}]
# Call the model API.
resp = dashscope.MultiModalEmbedding.call(
    model="tongyi-embedding-vision-plus",
    input=input
)

if resp.status_code == HTTPStatus.OK:
    result = {
        "status_code": resp.status_code,
        "request_id": getattr(resp, "request_id", ""),
        "code": getattr(resp, "code", ""),
        "message": getattr(resp, "message", ""),
        "output": resp.output,
        "usage": resp.usage
    }
    print(json.dumps(result, ensure_ascii=False, indent=4))

import dashscope
import base64
import json
from http import HTTPStatus
# Read the image and convert it to Base64. Replace xxx.png with your image file.
image_path = "xxx.png"
with open(image_path, "rb") as image_file:
    # Read the file and convert it to Base64.
    base64_image = base64.b64encode(image_file.read()).decode('utf-8')
# Set the image format.
image_format = "png"  # Change this to your image's format (e.g., jpg, bmp).
image_data = f"data:image/{image_format};base64,{base64_image}"
# Input data
input = [{'image': image_data}]

# Call the model API.
resp = dashscope.MultiModalEmbedding.call(
    model="tongyi-embedding-vision-plus",
    input=input
)
if resp.status_code == HTTPStatus.OK:
    result = {
        "status_code": resp.status_code,
        "request_id": getattr(resp, "request_id", ""),
        "code": getattr(resp, "code", ""),
        "message": getattr(resp, "message", ""),
        "output": resp.output,
        "usage": resp.usage
    }
    print(json.dumps(result, ensure_ascii=False, indent=4))

Embedding vidéo

Actuellement, le modèle prend en charge l'entrée vidéo uniquement via URL. Les fichiers vidéo locaux ne sont pas pris en charge.

import dashscope
import json
from http import HTTPStatus
# Replace with your video URL.
video = "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250107/lbcemt/new+video.mp4"
input = [{'video': video}]
# Call the model API.
resp = dashscope.MultiModalEmbedding.call(
    model="tongyi-embedding-vision-plus",
    input=input
)

if resp.status_code == HTTPStatus.OK:
    result = {
        "status_code": resp.status_code,
        "request_id": getattr(resp, "request_id", ""),
        "code": getattr(resp, "code", ""),
        "message": getattr(resp, "message", ""),
        "output": resp.output,
        "usage": resp.usage
    }
    print(json.dumps(result, ensure_ascii=False, indent=4))

Embedding de texte

import dashscope
import json
from http import HTTPStatus

text = "General multimodal representation model example"
input = [{'text': text}]
# Call the model API.
resp = dashscope.MultiModalEmbedding.call(
    model="tongyi-embedding-vision-plus",
    input=input
)

if resp.status_code == HTTPStatus.OK:
    result = {
        "status_code": resp.status_code,
        "request_id": getattr(resp, "request_id", ""),
        "code": getattr(resp, "code", ""),
        "message": getattr(resp, "message", ""),
        "output": resp.output,
        "usage": resp.usage
    }
    print(json.dumps(result, ensure_ascii=False, indent=4))

Embedding fusionné

import dashscope
import json
import os
from http import HTTPStatus

# Fuses text, image, and video into a single fused embedding.
# Ideal for use cases like cross-modal retrieval and image search.
text = "This is a test text for generating a multimodal fused embedding."
image = "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"
video = "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250107/lbcemt/new+video.mp4"

# Input includes text, image, and video. Set enable_fusion=True to generate a fused embedding.
input_data = [
    {"text": text},
    {"image": image},
    {"video": video}
]

resp = dashscope.MultiModalEmbedding.call(
    # If the environment variable is not set, provide your Model Studio API key, e.g., api_key="sk-xxx".
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen3-vl-embedding",
    input=input_data,
    enable_fusion=True,
    # Optional: Specify the embedding dimension. Valid values: 2560, 2048, 1536, 1024, 768, 512, and 256. Default: 2560.
    # parameters={"dimension": 1024}
)

print(json.dumps(resp, ensure_ascii=False, indent=4))

Embedding fusionné multi-images

Utilisez qwen3-vl-embedding pour fusionner plusieurs images et du texte en un seul embedding. Pour fusionner plusieurs images, passez plusieurs éléments image. Cette approche est idéale pour la recherche sémantique utilisant des images produits sous plusieurs angles accompagnées d'une description textuelle.

import dashscope
import json
import os
from http import HTTPStatus

# Fuses multiple product images and a description into a single embedding.
# Ideal for comprehensive semantic retrieval using multi-angle product images and a text description.
text = "White sports shoes, lightweight and breathable, suitable for running and daily wear."
image1 = "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"
image2 = "https://img.alicdn.com/imgextra/i3/O1CN01rdstgY1uiZWt8gqSL_!!6000000006071-0-tps-1970-356.jpg"

# Pass multiple image items and set enable_fusion=True to fuse all inputs into a single embedding.
input_data = [
    {"text": text},
    {"image": image1},
    {"image": image2}
]

resp = dashscope.MultiModalEmbedding.call(
    # If the environment variable is not set, provide your Model Studio API key, e.g., api_key="sk-xxx".
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen3-vl-embedding",
    input=input_data,
    enable_fusion=True
)

print(json.dumps(resp, ensure_ascii=False, indent=4))

Version snapshot 2026-03-06

Cet exemple montre comment utiliser le modèle tongyi-embedding-vision-plus-2026-03-06 ainsi que ses paramètres res_level (résolution) et max_video_frames (frames vidéo). Basé sur la fondation Qwen3, ce modèle prend en charge plus de 30 langues et génère à la fois des embeddings indépendants et fusionnés.

import dashscope
import json
import os
from http import HTTPStatus

# Demonstrates using the res_level (resolution) and max_video_frames (video frames) parameters.
image = "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"
video = "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250107/lbcemt/new+video.mp4"
text = "This is a visual multimodal representation model."

input_data = [
    {"text": text},
    {"image": image},
    {"video": video}
]

resp = dashscope.MultiModalEmbedding.call(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="tongyi-embedding-vision-plus-2026-03-06",
    input=input_data,
    dimension=1152,      # Valid values: 1152, 1024, 512, 256, 128, 64
    res_level=1,         # Resolution level: 0, 1, 2, or 3. The default is 1.
    max_video_frames=64  # Maximum number of sampled video frames. Default: 8. Maximum: 64.
)

if resp.status_code == HTTPStatus.OK:
    result = {
        "status_code": resp.status_code,
        "request_id": getattr(resp, "request_id", ""),
        "output": resp.output,
        "usage": resp.usage
    }
    print(json.dumps(result, ensure_ascii=False, indent=4))

Pour générer un embedding fusionné avec la version 2026-03-06, placez le texte, l'image et la vidéo dans le même objet de contenu. Le modèle fusionne alors toutes les entrées en un seul embedding de type fused.

import dashscope
import json
import os
from http import HTTPStatus

# To create a fused embedding, place text and image in the same content object.
# The model fuses all inputs into a single embedding of type `fused`.
text = "White sports shoes, lightweight and breathable, suitable for running and daily wear."
image = "https://dashscope.oss-cn-beijing.aliyuncs.com/images/256_1.png"

input_data = [
    {"text": text, "image": image}
]

resp = dashscope.MultiModalEmbedding.call(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="tongyi-embedding-vision-plus-2026-03-06",
    input=input_data,
    dimension=1152
)

if resp.status_code == HTTPStatus.OK:
    result = {
        "status_code": resp.status_code,
        "request_id": getattr(resp, "request_id", ""),
        "output": resp.output,
        "usage": resp.usage
    }
    print(json.dumps(result, ensure_ascii=False, indent=4))