45 lines
1.9 KiB
Python
45 lines
1.9 KiB
Python
from rule_engine import RuleEngine, Rule
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class ContinuousLearning:
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def __init__(self, db_name):
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# Initialize RuleEngine
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self.rule_engine = RuleEngine(db_name)
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def add_initial_rules(self, rules):
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# Add initial rules to the rule engine
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for rule in rules:
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rule_obj = Rule(rule[0], rule[1]) # Create Rule object from input tuple
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self.rule_engine.add_rule(rule_obj)
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def handle_user_input(self, user_input):
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# Match user input against rules
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matched_rule = self.rule_engine.match_rule(user_input)
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if matched_rule:
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# Retrieve output pattern from matched rule
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response = matched_rule.output_pattern
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else:
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# No match found, generate default response
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response = "I'm sorry, I didn't understand that."
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return response
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def capture_user_feedback(self, user_input, user_feedback):
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# Update rule based on user feedback
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# This is the continuous learning part, where the rule engine is updated with new data
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# based on user feedback to improve its responses
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matched_rule = self.rule_engine.match_rule(user_input)
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if matched_rule:
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matched_rule.output_pattern = user_feedback # Update output pattern of matched rule
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self.rule_engine.update_rule(user_input, matched_rule.output_pattern)
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# Other methods for dynamic rule system functionality can be added here
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def process_message(self, user_input, user_feedback=None):
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# Implement message processing logic here
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# For example, you can call handle_user_input() to handle the user input
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# and return the response generated from the matched rule's output pattern.
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if user_feedback:
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self.capture_user_feedback(user_input, user_feedback)
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response = self.handle_user_input(user_input)
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return response |