qwen-chat 1.0.4

Unofficial Qwen API Client, Chat Wrapper, and LlamaIndex adapter

qwen-chat 🚀

An unofficial, feature-rich Python SDK and client wrapper for the Qwen AI Web API. Enjoy seamless access to advanced models like qwen3.6-plus with support for streaming, multi-turn conversations, system prompts, web search, deep reasoning, automatic local image uploads, and LlamaIndex integration.

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✨ Features

• 🧠 Complete Model Support
  Interact with Qwen's latest web models including qwen3.6-plus, qwen-max-latest, qwq-32b, qwen2.5-omni-7b, and specialized vision/coder versions.

• 💬 Multi-turn Chat Sessions
  Have back-and-forth conversations that maintain context across messages — just like chatting in the Qwen web UI. Build interactive terminal chatbots or conversational agents easily.

• ⚡ Synchronous & Asynchronous Clients
  Whether you're building a script or a highly concurrent async web server, qwen-chat has you covered with native create() and acreate() workflows.

• 🌊 Real-time SSE Streaming (On/Off)
  Toggle stream=True or stream=False per request. Stream token-by-token responses directly to your UI or console, or get the full response at once.

• 🎭 System & Assistant Roles
  Use system role messages to define the AI's personality, behavior, and instructions. Chain assistant and user roles for few-shot prompting and context injection.

• 📸 Automatic Local Image Uploads
  Pass a local file path or raw bytes to an ImageBlock. The client automatically fetches STS tokens and uploads the image to Alibaba Cloud OSS under the hood — no boilerplate required!

• 🔍 Integrated Web Search
  Toggle real-time search on or off per message to query live web data and receive detailed citations alongside responses.

• 💡 Thinking & Reasoning Controls
  Adjust thinking budget settings to enable advanced reasoning capabilities on complex tasks.

• 🦙 LlamaIndex Adapter
  Use Qwen directly inside LlamaIndex with QwenLlamaIndex from the same qwen_chat package. No separate qwen-api or qwen-llamaindex package is required.

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📦 Installation

Install qwen-chat via pip:

pip install qwen-chat

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⚙️ Environment Setup

To authenticate requests, extract your Authorization bearer token from the Qwen Web UI:

1. Go to https://chat.qwen.ai and log in.
2. Open developer tools (F12 or Ctrl+Shift+I / Cmd+Option+I) and navigate to the Network tab.
3. Send any message in the chat interface.
4. Locate the completions request (filter by Fetch/XHR).
5. Click on the request and go to the Headers tab. Copy the value of the Authorization header without the word "Bearer " (just copy the token starting with eyJ...).
6. Save this value in a .env file in the root of your project:

QWEN_AUTH_TOKEN=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...

Note: You do not need to set up cookies. The package handles cookies automatically.

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🚀 Usage Examples

1. Basic Text Completion

The simplest way to get a response from Qwen:

from qwen_chat import Qwen
from qwen_chat.core.types.chat import ChatMessage

client = Qwen()

messages = [
    ChatMessage(role="user", content="What is Python?")
]

response = client.chat.create(messages=messages, model="qwen3.6-plus")
print("🤖", response.choices.message.content)

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LlamaIndex Usage

Use Qwen as a LlamaIndex LLM from the same qwen-chat package:

from qwen_chat import QwenLlamaIndex

llm = QwenLlamaIndex(model="qwen3.6-plus")

response = llm.complete("Reply with only: ok")
print(response.text)

Chat example:

from llama_index.core.base.llms.types import ChatMessage
from qwen_chat import QwenLlamaIndex

llm = QwenLlamaIndex(model="qwen3.6-plus")

response = llm.chat([
    ChatMessage(role="user", content="What is LlamaIndex?")
])

print(response.message.content)

Streaming example:

from qwen_chat import QwenLlamaIndex

llm = QwenLlamaIndex(model="qwen3.6-plus")

for chunk in llm.stream_complete("Write one sentence about Python."):
    print(chunk.delta, end="", flush=True)

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2. Streaming On / Off

Stream OFF (get full response at once):

from qwen_chat import Qwen
from qwen_chat.core.types.chat import ChatMessage

client = Qwen()
messages = [ChatMessage(role="user", content="Tell me a short joke.")]

# stream=False (default) — returns complete response
response = client.chat.create(messages=messages, model="qwen3.6-plus", stream=False)
print("🤖", response.choices.message.content)

Stream ON (token-by-token output):

from qwen_chat import Qwen
from qwen_chat.core.types.chat import ChatMessage

client = Qwen()
messages = [ChatMessage(role="user", content="Write a poem about the ocean.")]

# stream=True — yields chunks in real-time
stream = client.chat.create(messages=messages, model="qwen3.6-plus", stream=True)

print("🤖 ", end="")
for chunk in stream:
    print(chunk.choices[0].delta.content, end="", flush=True)
print()

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3. System Role & Assistant Role (Custom Personality)

Use the system role to define how the AI behaves. Use assistant role for few-shot examples:

from qwen_chat import Qwen
from qwen_chat.core.types.chat import ChatMessage

client = Qwen()

messages = [
    # System prompt — defines AI personality and behavior
    ChatMessage(
        role="system",
        content="You are a friendly pirate captain. Always respond in pirate speak with lots of 'Arrr!' and nautical references."
    ),
    # User message
    ChatMessage(
        role="user",
        content="What's the weather like today?"
    )
]

response = client.chat.create(messages=messages, model="qwen3.6-plus")
print("🏴‍☠️", response.choices.message.content)

Few-shot prompting with assistant role:

from qwen_chat import Qwen
from qwen_chat.core.types.chat import ChatMessage

client = Qwen()

messages = [
    ChatMessage(role="system", content="You are a helpful translator. Translate English to Bengali."),
    # Few-shot example
    ChatMessage(role="user", content="Hello"),
    ChatMessage(role="assistant", content="হ্যালো"),
    ChatMessage(role="user", content="How are you?"),
    ChatMessage(role="assistant", content="আপনি কেমন আছেন?"),
    # Actual request
    ChatMessage(role="user", content="I love programming")
]

response = client.chat.create(messages=messages, model="qwen3.6-plus")
print("🤖", response.choices.message.content)

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4. Interactive Terminal Chat (Multi-turn Conversation)

Build a fully interactive terminal chatbot that maintains conversation context across multiple turns — just like the Qwen web UI.

How it works: The Qwen API remembers your conversation server-side via chat_id and parent_id. You only need to send the new message each turn — the AI already knows the full conversation history.

from qwen_chat import Qwen
from qwen_chat.core.types.chat import ChatMessage

def main():
    client = Qwen()
    print("💬 Qwen Chat Terminal")
    print("Type 'exit' to quit\n")

    # Optional: send a system prompt as the very first message
    system_msg = [
        ChatMessage(
            role="system",
            content="You are a helpful and friendly AI assistant."
        )
    ]
    client.chat.create(messages=system_msg, model="qwen3.6-plus")

    while True:
        try:
            user_input = input("🧑 You: ").strip()
        except (EOFError, KeyboardInterrupt):
            print("\nBye! 👋")
            break

        if not user_input or user_input.lower() == "exit":
            print("Bye! 👋")
            break

        # Send only the new user message — the API remembers the conversation
        messages = [ChatMessage(role="user", content=user_input)]

        response = client.chat.create(
            messages=messages,
            model="qwen3.6-plus",
            temperature=0.7
        )

        print(f"🤖 AI: {response.choices.message.content}\n")

if __name__ == "__main__":
    main()

Interactive chat with streaming output:

from qwen_chat import Qwen
from qwen_chat.core.types.chat import ChatMessage

def main():
    client = Qwen()
    print("💬 Qwen Streaming Chat")
    print("Type 'exit' to quit\n")

    while True:
        try:
            user_input = input("🧑 You: ").strip()
        except (EOFError, KeyboardInterrupt):
            print("\nBye! 👋")
            break

        if not user_input or user_input.lower() == "exit":
            print("Bye! 👋")
            break

        # Send only the new message each turn
        messages = [ChatMessage(role="user", content=user_input)]

        stream = client.chat.create(
            messages=messages,
            model="qwen3.6-plus",
            stream=True
        )

        print("🤖 AI: ", end="")
        for chunk in stream:
            text = chunk.choices[0].delta.content
            print(text, end="", flush=True)
        print("\n")

if __name__ == "__main__":
    main()

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5. Web Search with Citations

from qwen_chat import Qwen
from qwen_chat.core.types.chat import ChatMessage

client = Qwen()

messages = [
    ChatMessage(
        role="user",
        content="What are the latest developments in AI?",
        web_search=True
    )
]

stream = client.chat.create(messages=messages, model="qwen3.6-plus", stream=True)

for chunk in stream:
    delta = chunk.choices[0].delta
    if delta.extra and delta.extra.web_search_info:
        print("\n🔍 Sources:")
        for source in delta.extra.web_search_info:
            print(f"  • {source.title}: {source.url}")
        print()
    print(delta.content, end="", flush=True)
print()

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6. Automatic Local Image Upload (Vision)

Just pass your local image path directly inside ImageBlock. The client handles the secure upload automatically:

from qwen_chat import Qwen
from qwen_chat.core.types.chat import ChatMessage, TextBlock, ImageBlock

client = Qwen()

messages = [
    ChatMessage(
        role="user",
        blocks=[
            TextBlock(text="What's in this image? Describe it in detail."),
            ImageBlock(path="photo.jpg")  # Auto-uploaded to Alibaba OSS!
        ]
    )
]

response = client.chat.create(messages=messages, model="qwen3.6-plus")
print("🤖", response.choices.message.content)

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7. Async Usage

import asyncio
from qwen_chat import Qwen
from qwen_chat.core.types.chat import ChatMessage

async def main():
    client = Qwen()
    messages = [ChatMessage(role="user", content="Explain quantum computing in one sentence.")]
    
    response = await client.chat.acreate(messages=messages, model="qwen3.6-plus")
    print("🤖", response.choices.message.content)

asyncio.run(main())

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8. Thinking & Reasoning Mode

Enable deep thinking for complex tasks:

from qwen_chat import Qwen
from qwen_chat.core.types.chat import ChatMessage

client = Qwen()

messages = [
    ChatMessage(
        role="user",
        content="Solve this step by step: If a train travels 120km in 2 hours, then stops for 30 minutes, then travels 90km in 1.5 hours, what is the average speed for the entire journey?",
        thinking=True,
        thinking_budget=4096
    )
]

response = client.chat.create(messages=messages, model="qwq-32b")
print("🤖", response.choices.message.content)

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🙋‍♂️ Contributing

Contributions are welcome! Here's how:

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

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📃 License

This project is licensed under the MIT License.

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📞 Contact & Support

For queries, support, or custom integrations, feel free to reach out!

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Made with ❤️ by Shahadat Hassan