69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
import json # Import the json module
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from qwen_agent.agents import Assistant
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# Define LLM
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llm_cfg = {
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# 'model': 'hf.co/unsloth/Qwen3-30B-A3B-GGUF:Q5_K_M',
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'model': 'qwen3:32b',
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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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# 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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# 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=Europe/London']
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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": "npx",
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"args": ["-y", "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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# Define Agent
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bot = Assistant(llm=llm_cfg, function_list=tools)
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# Streaming generation
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messages = [{'role': 'user',
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'content':
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""""
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- ***Research** Kagi search, privacy and company investors and financials.
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-- **Analyze** Recent developments around Kagi.
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-- **Answer** Is it a company that I can trust with my money and data?"""}]
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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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# 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.") |