init

.gitignore

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+models
+__pycache__
+data/*.dat

Makefile

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+help:
+	@echo "    train-nlu"
+	@echo "        Train the natural language understanding using Rasa NLU."
+	@echo "    train-core"
+	@echo "        Train a dialogue model using Rasa core."
+	@echo "    run"
+	@echo "        Runs the bot on the command line."
+
+run:
+	python bot.py run
+
+train-nlu:
+	python bot.py train-nlu
+
+train-core:
+	python bot.py train-dialogue

bot.py

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+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import argparse
+import logging
+import warnings
+
+from policy import MobilePolicy
+from rasa_core import utils
+from rasa_core.actions import Action
+from rasa_core.agent import Agent
+from rasa_core.channels.console import ConsoleInputChannel
+from rasa_core.interpreter import RasaNLUInterpreter
+from rasa_core.policies.memoization import MemoizationPolicy
+
+logger = logging.getLogger(__name__)
+
+support_search = ["消费", "流量"]
+
+def extract_item(item):
+    if item is None:
+        return None
+    for name in support_search:
+        if name in item:
+            return name
+    return None
+
+
+class ActionSearchConsume(Action):
+    def name(self):
+        return 'action_search_consume'
+
+    def run(self, dispatcher, tracker, domain):
+        item = tracker.get_slot("item")
+        item = extract_item(item)
+        if item is None:
+            dispatcher.utter_message("您好，我现在只会查话费和流量")
+            dispatcher.utter_message("你可以这样问我：“帮我查话费”")
+            return []
+
+        time = tracker.get_slot("time")
+        if time is None:
+            dispatcher.utter_message("您想查询哪个月的消费？")
+            return []
+        # query database here using item and time as key. but you may normalize time format first.
+        dispatcher.utter_message("好，请稍等")
+        if item == "流量":
+            dispatcher.utter_message(
+                "您好，您{}共使用{}二百八十兆，剩余三十兆。".format(time, item))
+        else:
+            dispatcher.utter_message("您好，您{}共消费二十八元。".format(time))
+        return []
+
+
+def train_dialogue(domain_file="mobile_domain.yml",
+                   model_path="models/dialogue",
+                   training_data_file="data/mobile_story.md"):
+    agent = Agent(domain_file,
+                  policies=[MemoizationPolicy(max_history=3),
+                            MobilePolicy()])
+
+    training_data = agent.load_data(training_data_file)
+    agent.train(
+            training_data,
+            epochs=400,
+            batch_size=100,
+            validation_split=0.2
+    )
+
+    agent.persist(model_path)
+    return agent
+
+
+def train_nlu():
+    from rasa_nlu.training_data import load_data
+    from rasa_nlu import config
+    from rasa_nlu.model import Trainer
+
+    training_data = load_data('data/mobile_nlu_data.json')
+    trainer = Trainer(config.load("mobile_nlu_model_config.yml"))
+    trainer.train(training_data)
+    model_directory = trainer.persist('models/',
+                                      project_name="nlu",
+                                      fixed_model_name="current")
+
+    return model_directory
+
+
+def run(serve_forever=True):
+    interpreter = RasaNLUInterpreter("models/nlu/current")
+    agent = Agent.load("models/dialogue", interpreter=interpreter)
+
+    if serve_forever:
+        agent.handle_channel(ConsoleInputChannel())
+    return agent
+
+
+if __name__ == '__main__':
+    utils.configure_colored_logging(loglevel="INFO")
+
+    parser = argparse.ArgumentParser(
+            description='starts the bot')
+
+    parser.add_argument(
+            'task',
+            choices=["train-nlu", "train-dialogue", "run"],
+            help="what the bot should do - e.g. run or train?")
+    task = parser.parse_args().task
+
+    # decide what to do based on first parameter of the script
+    if task == "train-nlu":
+        train_nlu()
+    elif task == "train-dialogue":
+        train_dialogue()
+    elif task == "run":
+        run()