Category: provider

Model Studio Multimodal Embedding

Validation

mkdir -p output/aliyun-qwen-multimodal-embedding
python -m py_compile skills/ai/search/aliyun-qwen-multimodal-embedding/scripts/prepare_multimodal_embedding_request.py && echo "py_compile_ok" > output/aliyun-qwen-multimodal-embedding/validate.txt

Pass criteria: command exits 0 and output/aliyun-qwen-multimodal-embedding/validate.txt is generated.

Output And Evidence

• Save normalized request payloads, selected dimensions, and sample input references under output/aliyun-qwen-multimodal-embedding/.
• Record the exact model, modality mix, and output vector dimension for reproducibility.

Use this skill when the task needs text, image, or video embeddings from Model Studio for retrieval or similarity workflows.

Critical model names

Use one of these exact model strings as needed:

• qwen3-vl-embedding
• qwen2.5-vl-embedding
• tongyi-embedding-vision-plus-2026-03-06

Selection guidance:

• Prefer qwen3-vl-embedding for the newest multimodal embedding path.
• Use qwen2.5-vl-embedding when you need compatibility with an older deployed pipeline.

Prerequisites

• Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.
• Pair this skill with a vector store such as DashVector, OpenSearch, or Milvus when building retrieval systems.

Normalized interface (embedding.multimodal)

Request

• model (string, optional): default qwen3-vl-embedding
• texts (array, optional)
• images (array, optional): public URLs or local paths uploaded by your client layer
• videos (array, optional): public URLs where supported
• dimension (int, optional): e.g. 2560, 2048, 1536, 1024, 768, 512, 256 for qwen3-vl-embedding

Response

• embeddings (array