Qwen-Embedding

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Overview

The general text vectorization model is a multilingual unified text vectorization model developed by Tongyi Lab based on the large language model (LLM) foundation. This model is designed for multiple mainstream languages worldwide and provides advanced vectorization services to help developers convert text data into high-quality vector data.

Input

Text

Output

Text

Features

Prefix Completion

Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docs

Function Calling

Use function calling to connect large language models with external tools and systems.View docs

Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docs

Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.View docs

Batches

Asynchronously process requests in batches to reduce costs.View docs

Web Search

Enable web search so the model can answer with real-time retrieved data.View docs

Fine-tuning

Train models on sample data to better adapt them to specific tasks.View docs

Pricing

Embedding(Batch File)
$0.035Per 1M tokens
Text Input
$0.07Per 1M tokens

Rate Limits

TPMTokens Per Minute
24M
RPMRequests Per Minute
6K

API Reference

Call API

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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-v3",
input=input_texts
)
print(resp)