Alibaba Cloud AI Search Multimodal Embedding

Use when multimodal embeddings are needed from Alibaba Cloud Model Studio models such as qwen3-vl-embedding for image, video, and text retrieval, cross-mod...

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Model Studio Multimodal Embedding

Validation

bash

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)
• dimension (int)
• usage (object, optional)

Quick start

bash

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.contents as 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