Qwen-Embedding
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Embedding
Overview
Embedding
The General Text Vector V4 version is a multi-language text vector model developed by the Tongyi Lab based on Qwen3. Compared to the V3 version, it significantly improves performance in text retrieval, clustering, and classification tasks. It achieves a 15% to 40% improvement in evaluation tasks such as MTEB multilingual, Chinese-English, and code retrieval. Additionally, it supports user-defined vector dimensions ranging from 64 to 2048.
Input
Text
Output
Text
Features
Prefix Completion
Function Calling
Cache
Structured Outputs
Batches
Web Search
Pricing
- Text Input$0.07Per 1M tokens
Rate Limits
- RPMRequests Per Minute1.80K
- TPMTokens Per Minute1M
API Reference
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import dashscope
from http import HTTPStatus
input_texts = "The quality of the clothes is excellent, very beautiful. It was worth the long wait. I like it and will come back to buy here again"
resp = dashscope.TextEmbedding.call(
model="text-embedding-v4",
input=input_texts
)
print(resp)