Deep research (Qwen-Deep-Research)

import os
import dashscope

# China North 2 (Beijing). This model is available only in this region.
# API keys are region-specific. See https://docs.qwencloud.com/developer-guides/getting-started/regions
dashscope.base_http_api_url = "https://cn-beijing.maas.qwencloudapi.com/api/v1"

API_KEY = os.getenv("DASHSCOPE_API_KEY")


def call_deep_research_model(messages, step_name):
  print(f"\n=== {step_name} ===")
  current_phase = None
  phase_content = ""
  references = []
  research_goal = ""
  web_sites = []

  responses = dashscope.Generation.call(
    api_key=API_KEY,
    model="qwen-deep-research",
    messages=messages,
    # Only streaming output is supported.
    stream=True,
  )

  for response in responses:
    if hasattr(response, "status_code") and response.status_code != 200:
      print(f"HTTP status: {response.status_code}, code: {response.code}, message: {response.message}")
      break

    message = response.output.get("message", {})
    phase = message.get("phase")
    content = message.get("content", "")
    status = message.get("status")
    extra = message.get("extra", {})

    # A new phase started.
    if phase != current_phase:
      if current_phase and phase_content:
        print(f"\n{current_phase} phase completed")
      current_phase = phase
      phase_content = ""
      print(f"\nEntering {phase} phase")

    # References are delivered in the answer phase.
    if phase == "answer":
      new_references = extra.get("deep_research", {}).get("references")
      if new_references and new_references != references:
        references = new_references
        print(f"\nReferences ({len(references)}):")
        for i, ref in enumerate(references, 1):
          print(f"  {i}. {ref.get('title')} - {ref.get('url')}")

    # Web search progress is delivered in the WebResearch phase.
    if phase == "WebResearch":
      research = extra.get("deep_research", {}).get("research", {})
      if status in ("streamingQueries", "streamingThinking") and research.get("researchGoal"):
        research_goal += research["researchGoal"]
        print(f"\nResearch goal: {research_goal}", end="", flush=True)
      elif status == "streamingWebResult" and research.get("webSites"):
        sites = research["webSites"]
        if sites != web_sites:
          web_sites = sites
          print(f"\nFound {len(sites)} relevant websites:")
          for i, site in enumerate(sites, 1):
            print(f"  {i}. {site.get('title')} - {site.get('url')}")

    if content:
      phase_content += content
      print(content, end="", flush=True)

    if status == "finished":
      usage = response.usage or {}
      print(f"\n\nTokens: {usage.get('input_tokens', 0)} input, {usage.get('output_tokens', 0)} output")

    # KeepAlive only keeps the connection open between long-running steps.
    if phase == "KeepAlive":
      continue

  return phase_content


def main():
  # Step 1: the model asks clarifying questions about the research scope.
  messages = [{"role": "user", "content": "Research the application of AI in education"}]
  step1_content = call_deep_research_model(messages, "Step 1: Clarifying questions")

  # Step 2: answer the clarifying questions and start the research.
  messages = [
    {"role": "user", "content": "Research the application of AI in education"},
    {"role": "assistant", "content": step1_content},
    {"role": "user", "content": "I mainly focus on personalized learning and intelligent assessment"},
  ]
  call_deep_research_model(messages, "Step 2: Deep research")


if __name__ == "__main__":
  main()

import os
import dashscope

# China North 2 (Beijing). This model is available only in this region.
# API keys are region-specific. See https://docs.qwencloud.com/developer-guides/getting-started/regions
dashscope.base_http_api_url = "https://cn-beijing.maas.qwencloudapi.com/api/v1"

messages = [
  {
    "role": "user",
    "content": [
      {"image": "https://example.com/chart.png"},
      {"text": "Analyze the data trends in this chart and research the key findings in depth"}
    ]
  }
]

responses = dashscope.Generation.call(
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  model="qwen-deep-research",
  messages=messages,
  stream=True,
)

for response in responses:
  content = response.output.get("message", {}).get("content", "")
  if content:
    print(content, end="", flush=True)