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Completions API for Qwen models
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The Completions API is designed for text completion scenarios, such as code completion and content continuation.
Note
This document is specific to the Beijing region in the Chinese mainland. You must use an API key from the China (Beijing) region.
Supported models
The following Qwen Coder models are supported:
qwen-coder-turbo
Prerequisites
Obtain an API key and configure it as an environment variable. If you use the OpenAI software development kit (SDK), install the SDK.
Getting started
You can use the Completions API for text completion. Two text completion scenarios are supported:
Generate content that follows a given prefix.
Generate content between a given prefix and suffix.
Generating content that precedes a given suffix is not supported.
Quick start
Pass information such as a function name, input parameters, and usage instructions in the prefix. The Completions API returns the generated code.
The prompt template is:
plaintext
<|fim_prefix|>{prefix_content}<|fim_suffix|>In the template, {prefix_content} is the prefix information that you pass.
Python
Node.js
curl
Python
python
import os
from openai import OpenAI
client = OpenAI(
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
api_key=os.getenv("DASHSCOPE_API_KEY")
)
completion = client.completions.create(
model="qwen-coder-turbo",
prompt="<|fim_prefix|>Write a Python quick sort function, def quick_sort(arr):<|fim_suffix|>",
)
print(completion.choices[0].text)Node.js
nodejs
import OpenAI from "openai";
const openai = new OpenAI(
{
// If you have not configured environment variables, replace the following line with your Model Studio API key: apiKey: "sk-xxx",
apiKey: process.env.DASHSCOPE_API_KEY,
baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1"
}
);
async function main() {
const completion = await openai.completions.create({
model: "qwen-coder-turbo",
prompt: "<|fim_prefix|>Write a Python quick sort function, def quick_sort(arr):<|fim_suffix|>",
});
console.log(completion.choices[0].text)
}
main();curl
curl
curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-coder-turbo",
"prompt": "<|fim_prefix|>Write a Python quick sort function, def quick_sort(arr):<|fim_suffix|>"
}'Generate content between a prefix and a suffix
The Completions API can generate content between a given prefix and suffix. Pass information such as a function name, input parameters, and usage instructions in the prefix. Pass information such as the function's return parameters in the suffix. The Completions API returns the generated code.
The prompt template is:
plaintext
<|fim_prefix|>{prefix_content}<|fim_suffix|>{suffix_content}<|fim_middle|>In the template, {prefix_content} is the prefix information and {suffix_content} is the suffix information.
Python
Node.js
curl
Python
python
import os
from openai import OpenAI
client = OpenAI(
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
api_key=os.getenv("DASHSCOPE_API_KEY")
)
prefix_content = f"""def reverse_words_with_special_chars(s):
'''
Reverse each word in the string (preserving the position of non-alphabetic characters) and maintain the word order.
Example:
reverse_words_with_special_chars("Hello, world!") -> "olleH, dlrow!"
Parameters:
s (str): The input string, which may contain punctuation.
Returns:
str: The processed string, where words are reversed but non-alphabetic characters remain in their original positions.
'''
"""
suffix_content = "return result"
completion = client.completions.create(
model="qwen-coder-turbo",
prompt=f"<|fim_prefix|>{prefix_content}<|fim_suffix|>{suffix_content}<|fim_middle|>",
)
print(completion.choices[0].text)Node.js
nodejs
import OpenAI from 'openai';
const client = new OpenAI({
baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1",
apiKey: process.env.DASHSCOPE_API_KEY
});
const prefixContent = `def reverse_words_with_special_chars(s):
'''
Reverse each word in the string (preserving the position of non-alphabetic characters) and maintain the word order.
Example:
reverse_words_with_special_chars("Hello, world!") -> "olleH, dlrow!"
Parameters:
s (str): The input string, which may contain punctuation.
Returns:
str: The processed string, where words are reversed but non-alphabetic characters remain in their original positions.
'''
`;
const suffixContent = "return result";
async function main() {
const completion = await client.completions.create({
model: "qwen-coder-turbo",
prompt: `<|fim_prefix|>${prefixContent}<|fim_suffix|>${suffixContent}<|fim_middle|>`
});
console.log(completion.choices[0].text);
}
main();curl
curl
curl -X POST https://dashscope.aliyuncs.com/compatible-mode/v1/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-coder-turbo",
"prompt": "<|fim_prefix|>def reverse_words_with_special_chars(s):\n\"\"\"\nReverse each word in the string (preserving the position of non-alphabetic characters) and maintain the word order.\n Example:\n reverse_words_with_special_chars(\"Hello, world!\") -> \"olleH, dlrow!\"\n Parameters:\n s (str): The input string, which may contain punctuation.\n Returns:\n str: The processed string, where words are reversed but non-alphabetic characters remain in their original positions.\n\"\"\"\n<|fim_suffix|>return result<|fim_middle|>"
}'Input and output parameters
Input parameters
| Parameter | Type | Required | Description |
|---|
| Parameter | Type | Required | Description |
| model | string | Yes | The name of the model to call. |
| prompt | string | Yes | The prompt for which to generate completions. |
| max_tokens | integer | No | The maximum number of tokens to return in the request. > max_tokens does not affect the generation process of the Large Language Model (LLM). If the number of tokens generated by the model exceeds max_tokens, the returned content is truncated. |
| temperature | float | No | The sampling temperature, which controls the diversity of the generated text. A higher temperature results in more diverse text, while a lower temperature results in more deterministic text. Valid values: [0, 2.0). Because both temperature and top_p control text diversity, set only one of them. |
| top_p | float | No | The probability threshold for nucleus sampling, which controls the diversity of the generated text. A higher top_p results in more diverse text, while a lower top_p results in more deterministic text. Valid values: (0, 1.0]. Because both temperature and top_p control text diversity, set only one of them. |
| stream | boolean | No | Specifies whether to stream the response. Valid values: - false (default): Returns the result after all content is generated. - true: Streams the output as it is generated. A chunk is output immediately after a part of the content is generated. |
| stream_options | object | No | When streaming output is enabled, set this parameter to {"include_usage": true} to display the number of tokens used in the last line of the output. |
| stop | string or array | No | The model automatically stops generating text when it is about to include the string or token_id specified in the stop parameter. You can pass sensitive words in the stop parameter to control the model's output. |
| seed | integer | No | Setting the seed parameter makes the text generation process more deterministic. This is typically used to ensure that the model produces consistent results for each run. If you pass the same seed value for each model call and keep other parameters unchanged, the model returns the same result as much as possible. The value must be in the range of 0 to 231−1. |
| presence_penalty | float | No | Controls the degree of content repetition when the model generates text. The value must be in the range of [-2.0, 2.0]. A positive value reduces repetition, and a negative value increases repetition. |
Output parameters
| Parameter | Type | Description |
|---|
| Parameter | Type | Description |
| id | string | The unique identifier for the call. |
| choices | array | An array of model-generated content. |
| choices[0].text | string | The content generated by the request. |
| choices[0].finish_reason | string | The reason why the model stopped generating content. |
| choices[0].index | integer | The index of the current element in the array. The value is fixed at 0. |
| choices[0].logprobs | object | This is currently fixed at null. |
| created | integer | The time when the request was created. |
| model | string | The name of the model used for the request. |
| system_fingerprint | string | This parameter is currently fixed at null. |
| object | string | The object type. This is always "text_completion". |
| usage | object | The usage statistics for the request. |
| usage.prompt_tokens | integer | The number of tokens in the prompt. |
| usage.completion_tokens | integer | The number of tokens in choices[0].text. |
| usage.total_tokens | integer | The sum of usage.prompt_tokens and usage.completion_tokens. |
Error codes
If the model call fails and returns an error message, see Error codes for resolution.
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Supported models
Prerequisites
Getting started
Quick start
Generate content between a prefix and a suffix
Input and output parameters
Input parameters
Output parameters
Error codes
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