qwen-api 1.4.16

Unofficial Qwen API Client

qwen-api

Unofficial Python SDK for accessing Qwen AI API.

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✨ Features

• Prompt AI with various Qwen models
  Supports multiple models including: qwen-max-latest, qwen-plus-latest, qwq-32b, qwen-turbo-latest, qwen2.5-omni-7b, qvq-72b-preview-0310, qwen2.5-vl-32b-instruct, qwen2.5-14b-instruct-1m, qwen2.5-coder-32b-instruct, and qwen2.5-72b-instruct.

• Streaming Response
  Get token-by-token output in real-time for interactive applications.

• Synchronous & Asynchronous Support
  Seamless integration for both sync and async workflows with the same intuitive API.

• Web Search Integration
  Enhance responses with real-time information using web search capabilities.

• File Upload Support
  Upload files (including images) to the Qwen API for processing and analysis.

• Advanced Reasoning
  Suitable for complex tasks requiring multi-hop reasoning and deep thinking capabilities.

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📦 Installation

To install the package, use:

pip install qwen-api

🚀 Usage

Basic Usage

from qwen_api.client import Qwen
from qwen_api.types.chat import ChatMessage

# Create a client instance
client = Qwen()

# Create a chat message
messages = [
   ChatMessage(
      role="user",
      content="what is LLM?",
      web_search=True,
      thinking=False,
   )
]

# Get a response from the API
response = client.chat.create(
   messages=messages,
   model="qwen-max-latest",
)

# Print the response
print(response)

File Upload Example

Here's how to upload a file and include it in a chat request:

from qwen_api import Qwen
from qwen_api.core.exceptions import QwenAPIError
from qwen_api.core.types.chat import ChatMessage, TextBlock, ImageBlock


def main():
    client = Qwen(logging_level="DEBUG")

    try:
        # Upload an image file
        getdataImage  = client.chat.upload_file(
            file_path="tes_image.png"
        )

        # Create a chat message with both text and image content
        messages = [ChatMessage(
            role="user",
            web_search=False,
            thinking=False,
            blocks=[
                TextBlock(
                    block_type="text",
                    text="What's in this image?"
                ),
                ImageBlock(
                    block_type="image",
                    url=getdataImage   .file_url,
                    image_mimetype=getdataImage.image_mimetype
                )
            ]
        )]

        # Get a streaming response
        response = client.chat.create(
            messages=messages,
            model="qwen-max-latest",
            stream=True,
        )

        # Process the stream
        for chunk in response:
            delta = chunk.choices[0].delta
            if 'extra' in delta and 'web_search_info' in delta.extra:
                print("\nSearch results:", delta.extra.web_search_info)
                print()

            print(delta.content, end="", flush=True)

    except QwenAPIError as e:
        print(f"Error: {str(e)}")


if __name__ == "__main__":
    main()

Async Usage

import asyncio
from qwen_api.client import Qwen
from qwen_api.types.chat import ChatMessage

async def main():
    # Create a client instance
    client = Qwen()

    # Create a chat message
    messages = [
        ChatMessage(
            role="user",
            content="what is LLM?",
            web_search=True,
            thinking=False,
        )
    ]

    # Get a response from the API
    response = await client.chat.acreate(
        messages=messages,
        model="qwen-max-latest",
    )

    # Print the response
    print(response)

asyncio.run(main())

Asynchronous File Upload Example

Here's how to perform file upload asynchronously:

import asyncio
from qwen_api import Qwen
from qwen_api.core.exceptions import QwenAPIError
from qwen_api.core.types.chat import ChatMessage, TextBlock, ImageBlock


async def main():
    client = Qwen()

    try:
        # Upload an image file asynchronously
        getdataImage  = await client.chat.async_upload_file(
            file_path="tes_image.png"
        )

        # Create a chat message with both text and image content
        messages = [ChatMessage(
            role="user",
            web_search=False,
            thinking=False,
            blocks=[
                TextBlock(
                    block_type="text",
                    text="What's in this image?"
                ),
                ImageBlock(
                    block_type="image",
                    url=getdataImage   .file_url,
                    image_mimetype=getdataImage
                )
            ]
        )]

        # Get a streaming response
        response = await client.chat.acreate(
            messages=messages,
            model="qwen-max-latest",
            stream=True,
        )

        # Process the stream
        async for chunk in response:
            delta = chunk.choices[0].delta
            if 'extra' in delta and 'web_search_info' in delta.extra:
                print("\nSearch results:", delta.extra.web_search_info)
                print()

            print(delta.content, end="", flush=True)

    except QwenAPIError as e:
        print(f"Error: {str(e)}")


if __name__ == "__main__":
    asyncio.run(main())

Output:

choices=Choice(message=Message(role='assistant', content='A Large Language Model (LLM) is a type of artificial intelligence model that utilizes machine learning techniques to understand and generate human language [[2]]. It is designed for natural language processing tasks such as language generation [[1]]. LLMs are highly effective at generating the most plausible text in response to an input, which is the primary task they were built for [[5]]. These models are trained on vast datasets and consist of very large deep learning models that are pre-trained on extensive amounts of data [[4]]. Additionally, LLMs are a subset of generative AI that focuses specifically on generating text [[6]].'), extra=Extra(web_search_info=[WebSearchInfo(url='https://en.wikipedia.org/wiki/Large_language_model', title='Large language model - Wikipedia', snippet='A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.', hostname=None, hostlogo=None, date=''), WebSearchInfo(url='https://www.redhat.com/en/topics/ai/what-are-large-language-models', title='What are large language models? - Red Hat', snippet='A large language model (LLM) is a type of artificial intelligence model that utilizes machine learning techniques to understand and generate human language.', hostname='红帽', hostlogo='https://img.alicdn.com/imgextra/i2/O1CN01fvSs6e1d0HjVt2Buc_!!6000000003673-73-tps-48-48.ico', date=' (2023-09-26)'), WebSearchInfo(url='https://www.sap.com/resources/what-is-large-language-model', title='What is a large language model (LLM)? - SAP', snippet='A large language model (LLM) is a type of artificial intelligence (AI) that excels at processing, understanding, and generating human language.', hostname='思爱普SAP', hostlogo='https://img.alicdn.com/imgextra/i2/O1CN01egAMx022rHxuPkTZz_!!6000000007173-73-tps-48-48.ico', date=' (2024-07-01)'), WebSearchInfo(url='https://aws.amazon.com/what-is/large-language-model/', title='What is LLM? - Large Language Models Explained - AWS', snippet='Large language models, also known as LLMs, are very large deep learning models that are pre-trained on vast amounts of data. The underlying transformer is a', hostname='亚马逊', hostlogo='https://img.alicdn.com/imgextra/i4/O1CN01WOsM1L1YEPsOe7ywI_!!6000000003027-73-tps-48-48.ico', date=''), WebSearchInfo(url='https://developers.google.com/machine-learning/resources/intro-llms', title='Introduction to Large Language Models | Machine Learning', snippet='LLMs are highly effective at the task they were built for, which is generating the most plausible text in response to an input. They are even', hostname=None, hostlogo=None, date=' (2024-09-06)'), WebSearchInfo(url='https://medium.com/@meenn396/differences-between-llm-deep-learning-machine-learning-and-ai-3c7eb1c87ef8', title='Differences between LLM, Deep learning, Machine learning, and AI', snippet='A Large Language Model (LLM) is a subset of generative AI that focuses on generating text. The LLM is trained on a vast dataset and consists of', hostname=None, hostlogo=None, date=' (2024-09-30)'), WebSearchInfo(url='https://maddevs.io/glossary/large-language-model/', title='What Is a Large Language Model (LLM) | Machine Learing Glossary', snippet='A Large Language Model (LLM) is an AI system that understands and generates human language by analyzing vast amounts of text data. LLMs and Generative', hostname=None, hostlogo=None, date=''), WebSearchInfo(url='https://medium.com/@marketing_novita.ai/ml-vs-llm-what-is-the-difference-between-machine-learning-and-large-language-model-1d2ffa8756a6', title='ML vs LLM: What is the difference between Machine Learning and ', snippet="Initially, it's essential to recognize that Large Language Models (LLMs) are a subset of Machine Learning (ML). Machine Learning encompasses a", hostname=None, hostlogo=None, date=' (2024-05-08)'), WebSearchInfo(url='https://medium.com/@siladityaghosh/ai-machine-learning-llm-and-nlp-d09ae7b65582', title='AI, Machine Learning, LLM, and NLP | by Siladitya Ghosh - Medium', snippet='Large Language Models (LLM):. Definition: LLM involves training models on vast datasets to comprehend and generate human-like text, facilitating', hostname=None, hostlogo=None, date=' (2024-01-08)'), WebSearchInfo(url='https://github.com/Hannibal046/Awesome-LLM', title='Awesome-LLM: a curated list of Large Language Model - GitHub', snippet='Here is a curated list of papers about large language models, especially relating to ChatGPT. It also contains frameworks for LLM training, tools to deploy LLM', hostname='GitHub', hostlogo='https://img.alicdn.com/imgextra/i1/O1CN01Pzz5rH1SIBQeVFb7w_!!6000000002223-55-tps-32-32.svg', date='')]))

Streaming

# Create a client instance
client = Qwen()

# Create a chat message
messages = [
   ChatMessage(
      role="user",
      content="what is LLM?",
      web_search=True,
      thinking=False,
   )
]

# Get a streaming response from the API
response = client.chat.create(
   messages=messages,
   model="qwen-max-latest",
   stream=True,
)

# Process the stream
for chunk in response:
   print(chunk.model_dump())

Output:

{'choices': [{'delta': {'role': 'assistant', 'content': '', 'name': '', 'function_call': {'name': 'web_search', 'arguments': ''}, 'extra': None}}]}
{'choices': [{'delta': {'role': 'function', 'content': '', 'name': 'web_search', 'function_call': None, 'extra': {'web_search_info': [{'url': 'https://en.wikipedia.org/wiki/Large_language_model', 'title': 'Large language model - Wikipedia', 'snippet': 'A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.', 'hostname': None, 'hostlogo': None, 'date': ''}, {'url': 'https://www.redhat.com/en/topics/ai/what-are-large-language-models', 'title': 'What are large language models? - Red Hat', 'snippet': 'A large language model (LLM) is a type of artificial intelligence model that utilizes machine learning techniques to understand and generate human language.', 'hostname': '红帽', 'hostlogo': 'https://img.alicdn.com/imgextra/i2/O1CN01fvSs6e1d0HjVt2Buc_!!6000000003673-73-tps-48-48.ico', 'date': ' (2023-09-26)'}, {'url': 'https://www.sap.com/resources/what-is-large-language-model', 'title': 'What is a large language model (LLM)? - SAP', 'snippet': 'A large language model (LLM) is a type of artificial intelligence (AI) that excels at processing, understanding, and generating human language.', 'hostname': '思爱普SAP', 'hostlogo': 'https://img.alicdn.com/imgextra/i2/O1CN01egAMx022rHxuPkTZz_!!6000000007173-73-tps-48-48.ico', 'date': ' (2024-07-01)'}, {'url': 'https://aws.amazon.com/what-is/large-language-model/', 'title': 'What is LLM? - Large Language Models Explained - AWS', 'snippet': 'Large language models, also known as LLMs, are very large deep learning models that are pre-trained on vast amounts of data. The underlying transformer is a', 'hostname': '亚马逊', 'hostlogo': 'https://img.alicdn.com/imgextra/i4/O1CN01WOsM1L1YEPsOe7ywI_!!6000000003027-73-tps-48-48.ico', 'date': ''}, {'url': 'https://developers.google.com/machine-learning/resources/intro-llms', 'title': 'Introduction to Large Language Models | Machine Learning', 'snippet': 'LLMs are highly effective at the task they were built for, which is generating the most plausible text in response to an input. They are even', 'hostname': None, 'hostlogo': None, 'date': ' (2024-09-06)'}, {'url': 'https://medium.com/@meenn396/differences-between-llm-deep-learning-machine-learning-and-ai-3c7eb1c87ef8', 'title': 'Differences between LLM, Deep learning, Machine learning, and AI', 'snippet': 'A Large Language Model (LLM) is a subset of generative AI that focuses on generating text. The LLM is trained on a vast dataset and consists of', 'hostname': None, 'hostlogo': None, 'date': ' (2024-09-30)'}, {'url': 'https://maddevs.io/glossary/large-language-model/', 'title': 'What Is a Large Language Model (LLM) | Machine Learing Glossary', 'snippet': 'A Large Language Model (LLM) is an AI system that understands and generates human language by analyzing vast amounts of text data. LLMs and Generative', 'hostname': None, 'hostlogo': None, 'date': ''}, {'url': 'https://medium.com/@marketing_novita.ai/ml-vs-llm-what-is-the-difference-between-machine-learning-and-large-language-model-1d2ffa8756a6', 'title': 'ML vs LLM: What is the difference between Machine Learning and ', 'snippet': "Initially, it's essential to recognize that Large Language Models (LLMs) are a subset of Machine Learning (ML). Machine Learning encompasses a", 'hostname': None, 'hostlogo': None, 'date': ' (2024-05-08)'}, {'url': 'https://medium.com/@siladityaghosh/ai-machine-learning-llm-and-nlp-d09ae7b65582', 'title': 'AI, Machine Learning, LLM, and NLP | by Siladitya Ghosh - Medium', 'snippet': 'Large Language Models (LLM):. Definition: LLM involves training models on vast datasets to comprehend and generate human-like text, facilitating', 'hostname': None, 'hostlogo': None, 'date': ' (2024-01-08)'}, {'url': 'https://github.com/Hannibal046/Awesome-LLM', 'title': 'Awesome-LLM: a curated list of Large Language Model - GitHub', 'snippet': 'Here is a curated list of papers about large language models, especially relating to ChatGPT. It also contains frameworks for LLM training, tools to deploy LLM', 'hostname': 'GitHub', 'hostlogo': 'https://img.alicdn.com/imgextra/i1/O1CN01Pzz5rH1SIBQeVFb7w_!!6000000002223-55-tps-32-32.svg', 'date': '')]))

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📂 Documentation

For complete documentation, visit the documentation file.

---

⚙️ Environment Setup

To use qwen-api, you need to obtain your AUTH TOKEN and COOKIE from https://chat.qwen.ai. Follow these steps:

To use Alibaba Cloud OSS functionality, you'll also need to ensure you have the SDK installed:

poetry add oss2

1. Sign Up or Log In Visit https://chat.qwen.ai and sign up or log in to your account.

2. Open Developer Tools

   • Right-click anywhere on the page and select Inspect, or
   • Use the shortcut: Ctrl+Shift+I (Windows/Linux) or Cmd+Option+I (Mac)
   • Navigate to the Network tab

3. Send a Message Go back to https://chat.qwen.ai and send a message in the chat.

4. Find the completions Request In the Network tab, filter by Fetch/XHR and locate a request named completions.

5. Copy the Authorization Token and Cookie

   • Click the completions request and go to the Headers tab.
   • Look for the Authorization header that starts with Bearer, and copy only the token part (without the word "Bearer"). Example:

     Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...
     

   • Scroll down and find the Cookie header. Copy the entire value. Example (partial):

     Cookie: cna=lyp6INOXADYCAbb9MozTsTcp; cnaui=83a0f88d-86d8-...; token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...
     

6. Save in .env File Create a .env file in the root directory of your project and paste the following:

   QWEN_AUTH_TOKEN=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...  # no "Bearer"
   QWEN_COOKIE="cna=lyp6INOXADYCA...; cnaui=83a0f88d-86d8-...; token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
   

⚠️ Note:

• Never share your token or cookie publicly.
• Tokens and cookies may expire. If authentication fails, repeat the steps above to obtain a new one.

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📂 Examples

Check the examples/ folder for more advanced usage, including:

• Basic Usage: Simple synchronous and asynchronous examples for getting started
• Streaming: Examples demonstrating real-time response processing
• File Upload: Demonstrations of file upload capabilities, including image processing
• LlamaIndex Integration: Advanced examples using LlamaIndex framework

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📃 License

This project uses the MIT License:

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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🙋‍♂️ Contributing

We welcome contributions! Here's how to contribute:

1. Fork the project
2. Create your feature branch (git checkout -b feature/feature-name)
3. Commit your changes (git commit -m 'Add new feature')
4. Push to the branch (git push origin feature/feature-name)
5. Open a Pull Request