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aliyun-qwen-multimodal-embedding

aliyun-qwen-multimodal-embedding

github.com/cinienceData & databases39657 installsUpdated 11w ago
Repository

About aliyun-qwen-multimodal-embedding

A skill for preparing Model Studio requests to generate text, image, or video embeddings. It supports multimodal retrieval workflows and records model and vector-dimension details for reproducibility.

Key features

  1. 01Prepare multimodal embedding requests
  2. 02Accept text, image, and video inputs
  3. 03Select model and vector dimension
  4. 04Record request details for reproducibility

Use cases

  1. 01Create embeddings for multimodal retrieval
  2. 02Prepare vectors for a consistent vector index
  3. 03Generate embeddings from text and image inputs

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/cinience/alicloud-skills --skill aliyun-qwen-multimodal-embedding

After installing, type/aliyun-qwen-multimodal-embeddingin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

agents/
openai.yaml
references/
sources.md
scripts/
prepare_multimodal_embedding_request.py

Details

From the project README

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)
  • dimension (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.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

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