Qwen Text Chat — Execution Guide

Fallback paths when the bundled script (Path 1) fails or is unavailable.

Path 0 · Environment Fix

When the script fails due to environment issues (not API errors):

1. python3 not found: Try python --version or py -3 --version. Use whichever returns 3.9+. If none work, help the user install Python 3.9+ from https://www.python.org/downloads/. 2. Version too low (Python 3.9+ required or SyntaxError): Install Python 3.9+ alongside existing Python, then use python3.9 or python3.11 explicitly. 3. SSL errors (CERTIFICATE_VERIFY_FAILED): On macOS, run /Applications/Python\ 3.x/Install\ Certificates.command. On Linux/Windows, set SSL_CERT_FILE to point to your CA bundle. 4. Proxy: Set HTTPS_PROXY=http://proxy:port before running the script.

After fixing, retry the script (Path 1). If the environment is unfixable, fall through to Path 2 (curl) below — curl is available on most systems without Python.

Path 2 · Direct API Call (curl)

Non-streaming — single request, full response:

curl -sS -X POST "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions" \
  -H "Authorization: Bearer $DASHSCOPE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.6-plus",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello!"}
    ]
  }'

Response: Extract the generated text from choices[0].message.content:

{
  "choices": [{"message": {"role": "assistant", "content": "Hello! How can I help you?"}}],
  "usage": {"prompt_tokens": 20, "completion_tokens": 8, "total_tokens": 28}
}

Streaming — tokens arrive incrementally, recommended for interactive use:

curl -sS --no-buffer -X POST "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions" \
  -H "Authorization: Bearer $DASHSCOPE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.6-plus",
    "messages": [{"role": "user", "content": "Write a haiku."}],
    "stream": true
  }'

Each SSE chunk contains choices[0].delta.content with partial text.

Region endpoints (replace base URL as needed):

| Region | Base URL | |--------|----------| | Beijing (default) | https://dashscope.aliyuncs.com/compatible-mode/v1 |

Paths 3–5 · Fallback Cascade

When agent-executed paths (1–2) fail or shell is restricted:

Path 3 — Generate Python script: Read scripts/text.py to understand the API logic. Write a self-contained Python script (stdlib urllib or OpenAI SDK) tailored to the user's task. Present it for the user to save and run. Use os.environ["DASHSCOPE_API_KEY"] — never hardcode or expose the key.

OpenAI SDK example:

from openai import OpenAI
import os
client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
response = client.chat.completions.create(
    model="qwen3.6-plus",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
)
print(response.choices[0].message.content)

Requires pip install openai>=1.55.0 — use a venv if dependency conflicts occur: python3 -m venv .venv && source .venv/bin/activate && pip install openai>=1.55.0.

Path 4 — Generate curl commands: Customize the curl templates from Path 2 with the user's specific parameters. Present as ready-to-copy commands.

Path 5 — Autonomous resolution: Read scripts/text.py source and references/*.md to understand the full API contract. Reason about alternative approaches and implement.

Function Calling

Pass tools with function definitions:

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string", "description": "City name"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]
response = client.chat.completions.create(
    model="qwen3.6-plus",
    messages=[{"role": "user", "content": "What is the weather in Beijing?"}],
    tools=tools,
)
Check response.choices[0].message.tool_calls for function invocations

Thinking Mode

Qwen3.6/Qwen3.5 models support enable_thinking for extended reasoning. When enabled, the model may return thinking content before the final answer. Do not enable by default — only set enable_thinking: true when the user explicitly asks for deep thinking, step-by-step reasoning, or chain-of-thought. Keeping it off improves response speed for simple or conversational requests.

response = client.chat.completions.create(
    model="qwen3.6-plus",
    messages=[{"role": "user", "content": "Solve: 17 * 23 step by step."}],
    extra_body={"enable_thinking": True},
)

Via curl, add "enable_thinking": true to the request body.