
Aliyun Qwen Rerank
- 51 installs
- 396 repo stars
- Updated July 18, 2026
- cinience/alicloud-skills
Rerank search candidates with Alibaba Cloud Model Studio rerank models for hybrid retrieval, top-k refinement, and multilingual relevance sorting.
About
Uses Model Studio rerank models to reorder retrieved candidates by relevance for hybrid search and RAG. A developer uses it to refine top-k results after an initial retrieval step.
- gte-rerank-v2, gte-multilingual, and qwen3-reranker variants
- Targets hybrid retrieval and multilingual relevance sorting
Aliyun Qwen Rerank by the numbers
- 51 all-time installs (skills.sh)
- Ranked #7,219 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 51 |
|---|---|
| repo stars | ★ 396 |
| Last updated | July 18, 2026 |
| Repository | cinience/alicloud-skills ↗ |
What it does
Rerank search candidates with Alibaba Cloud Model Studio rerank models for hybrid retrieval, top-k refinement, and multilingual relevance sorting.
Files
Category: provider
Model Studio Rerank
Validation
mkdir -p output/aliyun-qwen-rerank
python -m py_compile skills/ai/search/aliyun-qwen-rerank/scripts/prepare_rerank_request.py && echo "py_compile_ok" > output/aliyun-qwen-rerank/validate.txtPass criteria: command exits 0 and output/aliyun-qwen-rerank/validate.txt is generated.
Critical model names
Use one of these exact model strings:
gte-rerank-v2gte-rerankgte-multilingual-rerankqwen3-reranker-8bqwen3-reranker-4bqwen3-reranker-0.6b
Quick start
python skills/ai/search/aliyun-qwen-rerank/scripts/prepare_rerank_request.py \
--query "cloud vector database" \
--output output/aliyun-qwen-rerank/request.jsonNotes
- Use after embedding/vector retrieval to reorder candidates.
- Prefer multilingual rerankers when query/document languages differ.
References
references/sources.md
interface:
display_name: "Alibaba Cloud AI Search Rerank"
short_description: "Candidate reranking with Model Studio rerank models"
default_prompt: "Use $aliyun-qwen-rerank to complete this ai/search rerank 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 rerank."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--query", default="cloud vector database")
parser.add_argument("--model", default="gte-rerank-v2")
parser.add_argument(
"--output",
default="output/aliyun-qwen-rerank/request.json",
)
args = parser.parse_args()
payload = {
"model": args.model,
"input": {
"query": args.query,
"documents": [
"Alibaba Cloud DashVector supports vector retrieval.",
"This paragraph is unrelated to vector search.",
],
},
}
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()