by 运行时依赖
版本
- Initial release of aliyun-qwen-multimodal-embedding skill. - Supports generation of multimodal embeddings (text, image, video) using Alibaba Cloud Model Studio models for retrieval, search, clustering, or offline vectorization. - Provides normalized embedding.multimodal interface with customizable model, input types, and output dimensions. - Includes validation and reproducibility steps, plus guidance for pairing with vector stores. - Documents exact supported model names and selection guidance.
安装命令
点击复制技能文档
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-embeddingqwen2.5-vl-embeddingtongyi-embedding-vision-plus-2026-03-06
Selection guidance:
- Prefer
qwen3-vl-embeddingfor the newest multimodal embedding path. - Use
qwen2.5-vl-embeddingwhen you need compatibility with an older deployed pipeline.
Prerequisites
- Set
DASHSCOPE_API_KEYin your environment, or adddashscope_api_keyto~/.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): defaultqwen3-vl-embeddingtexts(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.2560,2048,1536,1024,768,512,256forqwen3-vl-embedding
Response
embeddings(arraydimension(int)usage(object, optional)
Quick start
python skills/ai/search/aliyun-qwen-multimodal-embedding/scripts/prepare_multimodal_embedding_request.py \
--text "A cat sitting on a red chair" \
--image "https://example.com/cat.jpg" \
--dimension 1024
Operational guidance
- Keep
input.contentsas an array; malformed shapes are a common 400 cause. - Pin the output dimension to match your index schema before writing vectors.
- Use the same model and dimension across one vector index to avoid mixed-vector incompatibility.
- For large image or video batches, stage files in object storage and reference stable URLs.
Output location
- Default output:
output/aliyun-qwen-multimodal-embedding/request.json - Override base dir with
OUTPUT_DIR.
References
references/sources.md