
Aliyun Qwen Text Embedding
- 50 installs
- 396 repo stars
- Updated July 18, 2026
- cinience/alicloud-skills
Generate text embeddings with Alibaba Cloud Model Studio models for semantic search, RAG, clustering, and offline vectorization pipelines.
About
Uses Model Studio text embedding models to produce vectors for semantic search, RAG, and clustering. A developer uses it to embed text for retrieval and offline vectorization pipelines.
- text-embedding-v4/v3 and qwen3-embedding variants
- Targets semantic search, RAG, and clustering
Aliyun Qwen Text Embedding by the numbers
- 50 all-time installs (skills.sh)
- Ranked #7,298 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/cinience/alicloud-skills --skill aliyun-qwen-text-embeddingAdd your badge
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| Installs | 50 |
|---|---|
| repo stars | ★ 396 |
| Last updated | July 18, 2026 |
| Repository | cinience/alicloud-skills ↗ |
What it does
Generate text embeddings with Alibaba Cloud Model Studio models for semantic search, RAG, clustering, and offline vectorization pipelines.
Files
Category: provider
Model Studio Text Embedding
Validation
mkdir -p output/aliyun-qwen-text-embedding
python -m py_compile skills/ai/search/aliyun-qwen-text-embedding/scripts/prepare_embedding_request.py && echo "py_compile_ok" > output/aliyun-qwen-text-embedding/validate.txtPass criteria: command exits 0 and output/aliyun-qwen-text-embedding/validate.txt is generated.
Critical model names
Use one of these exact model strings as needed:
text-embedding-v4text-embedding-v3text-embedding-v2text-embedding-v1qwen3-embedding-8bqwen3-embedding-4bqwen3-embedding-0.6b
Quick start
python skills/ai/search/aliyun-qwen-text-embedding/scripts/prepare_embedding_request.py \
--text "Alibaba Cloud Model Studio" \
--output output/aliyun-qwen-text-embedding/request.jsonNotes
- Pair this skill with
skills/ai/search/aliyun-dashvector-search/or other vector-store skills. - For image or multimodal embeddings, add dedicated multimodal embedding coverage separately.
References
references/sources.md
interface:
display_name: "Alibaba Cloud AI Search Text Embedding"
short_description: "Text embeddings from Model Studio embedding models"
default_prompt: "Use $aliyun-qwen-text-embedding to complete this ai/search embedding task on Alibaba Cloud."
Sources
- https://help.aliyun.com/zh/model-studio/embedding
- https://help.aliyun.com/zh/model-studio/newly-released-models
#!/usr/bin/env python3
"""Prepare a minimal request payload for Model Studio text embedding."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--text", default="Alibaba Cloud Model Studio")
parser.add_argument("--model", default="text-embedding-v4")
parser.add_argument(
"--output",
default="output/aliyun-qwen-text-embedding/request.json",
)
args = parser.parse_args()
payload = {
"model": args.model,
"input": {
"texts": [args.text],
},
}
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
print(output)
if __name__ == "__main__":
main()