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27 lines
1.3 KiB
27 lines
1.3 KiB
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class ReflectionTuner:
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def __init__(self, agent, reflection_steps=3):
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self.agent = agent
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self.reflection_steps = reflection_steps
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def reflect_and_tune(self, initial_task):
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response = self.agent.run(initial_task)
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for step in range(self.reflection_steps):
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# Analyzing the response and adjusting based on findings
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feedback = self.analyze_response(response)
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if feedback:
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print(f"Reflection step {step + 1}: Adjusting response based on feedback.")
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response = self.agent.run(feedback) # Rerun with adjusted task or prompt
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else:
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print(f"No further tuning required at step {step + 1}. Final response achieved.")
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break
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return response
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def analyze_response(self, response):
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# Basic logic to analyze the response quality and determine next steps
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if "error" in response.lower() or "incomplete" in response.lower():
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return "Please refine the explanation and address missing points."
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elif "unclear" in response.lower() or "vague" in response.lower():
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return "Provide a more detailed and specific analysis."
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return None # No adjustment required if response is satisfactory
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