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| # © 2025 Elena Marziali — Code released under Apache 2.0 license. | |
| # See LICENSE in the repository for details. | |
| # Removal of this copyright is prohibited. | |
| # This function simulates an intentional decision-making process by the AI agent. | |
| # It analyzes the proposed action in relation to the goal, available alternatives, and context. | |
| # Metacognition functions that adapt to the system | |
| def execute_intentional_choice(action, goal, alternatives, context): | |
| ai_explanation = choice_with_intention(action, goal, alternatives, context) | |
| explanation_content = getattr(ai_explanation, "content", str(ai_explanation)).strip() | |
| intentional_log.append({ | |
| "action": action, | |
| "reason": explanation_content, | |
| "impact": f"Expected outcome for goal: {goal}", | |
| "timestamp": datetime.datetime.utcnow().isoformat() | |
| }) | |
| return explanation_content | |
| # Generates a response with intentionality by combining reasoning, AI response, and extracted text | |
| def generate_response_with_intention(prompt, action, goal, alternatives, context): | |
| reasoning = execute_intentional_choice(action, goal, alternatives, context) | |
| ai_response = llm.invoke(prompt) | |
| response_text = getattr(ai_response, "content", str(ai_response)).strip() | |
| return f"{response_text}\n\n*Agent's intentional explanation:*\n{reasoning}" |