69 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			69 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
| import json  # Import the json module
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| 
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| from qwen_agent.agents import Assistant
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| 
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| # Define LLM
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| llm_cfg = {
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|     'model': 'qwen3:0.6B',
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| 
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|     # Use the endpoint provided by Alibaba Model Studio:
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|     # 'model_type': 'qwen_dashscope',
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|     # 'api_key': os.getenv('DASHSCOPE_API_KEY'),
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| 
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|     # Use a custom endpoint compatible with OpenAI API:
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|     'model_server': 'http://localhost:11434/v1',  # api_base
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|     'api_key': 'EMPTY',
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| 
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|     # Other parameters:
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|     # 'generate_cfg': {
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|     #         # Add: When the response content is `<think>this is the thought</think>this is the answer;
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|     #         # Do not add: When the response has been separated by reasoning_content and content.
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|     #         'thought_in_content': True,
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|     #     },
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| }
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| 
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| # Define Tools
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| tools = [
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|     {'mcpServers': {  # You can specify the MCP configuration file
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|         'time': {
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|             'command': 'uvx',
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|             'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
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|         },
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|         "fetch": {
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|             "command": "uvx",
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|             "args": ["mcp-server-fetch"]
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|         },
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|         "ddg-search": {
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|             "command": "uvx",
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|             "args": ["duckduckgo-mcp-server"]
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|         }
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|     }
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|     },
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|     'code_interpreter',  # Built-in tools
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| ]
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| 
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| # Define Agent
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| bot = Assistant(llm=llm_cfg, function_list=tools)
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| 
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| # Streaming generation
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| messages = [{'role': 'user',
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|              'content': 'Write a 500 word blog post about the latest qwen 3 model. Use the search tool, and fetch the top 3 articles before you write the post. Write in a casual, but factual style - no hyperbole. Provide references to the webpages in the output.'}]
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| 
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| final_responses = None
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| # Consider adding error handling around bot.run
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| try:
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|     for responses in bot.run(messages=messages, enable_thinking=True, max_tokens=30000):
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|         print(".", end="", flush=True)
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|         final_responses = responses.pop()
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| except Exception as e:
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|     print(f"An error occurred during agent execution: {e}")
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| 
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| # Pretty-print the final response object
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| if final_responses:
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|     print("--- Full Response Object ---")
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|     print(json.dumps(final_responses, indent=2))  # Use indent=2 (or 4) for pretty printing
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|     print("\n--- Extracted Content ---")
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|     print(final_responses.get('content', 'No content found in response.'))  # Use .get for safer access
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| else:
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|     print("No final response received from the agent.")
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