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Thank you for sharing. I am running my vocie bot with RASA-CALM but taht is very slow . Even for single greet,it takes 7-8 seconds as it hits LLM mutliple times, .Pelase see logs below. To avoid that I used both NLUCommandAdapter and LLMCommandGenerator and provided NLU training data for spectific intetns but that also did not help.
2024-03-25 18:41:20 INFO root - Rasa server is up and running. 2024-03-25 18:41:20 INFO root - Enabling coroutine debugging. Loop id 140248276217872. Bot loaded. Type a message and press enter (use ‘/stop’ to exit): Your input → Howdy 2024-03-25 18:56:12 DEBUG rasa.core.lock_store - Issuing ticket for conversation ‘8f52c34f7281409e8286f6b92ee36efb’. 2024-03-25 18:56:12 DEBUG rasa.core.lock_store - Acquiring lock for conversation ‘8f52c34f7281409e8286f6b92ee36efb’. 2024-03-25 18:56:12 DEBUG rasa.core.lock_store - Acquired lock for conversation ‘8f52c34f7281409e8286f6b92ee36efb’. 2024-03-25 18:56:12 DEBUG rasa.core.tracker_store - Could not find tracker for conversation ID ‘8f52c34f7281409e8286f6b92ee36efb’. 2024-03-25 18:56:12 DEBUG rasa.core.tracker_store - No event broker configured. Skipping streaming events. 2024-03-25 18:56:12 DEBUG rasa.core.processor - Starting a new session for conversation ID ‘8f52c34f7281409e8286f6b92ee36efb’. 2024-03-25 18:56:12 DEBUG rasa.core.processor - [debug ] processor.actions.policy_prediction action_name=action_session_start policy_name=None prediction_events= 2024-03-25 18:56:12 DEBUG rasa.core.processor - [debug ] processor.actions.log action_name=action_session_start rasa_events=[SessionStarted(type_name: session_started), ActionExecuted(action: action_listen, policy: None, confidence: None)] 2024-03-25 18:56:12 DEBUG rasa.core.processor - [debug ] processor.slots.log slots={‘reminder_callback_pending’: False} 2024-03-25 18:56:12 DEBUG rasa.engine.runner.dask - Running graph with inputs: {‘message’: [UserMessage(text: Howdy, sender_id: 8f52c34f7281409e8286f6b92ee36efb)], ‘tracker’: DialogueStateTracker(sender_id: 8f52c34f7281409e8286f6b92ee36efb)}, targets: [‘run_RegexMessageHandler’] and ExecutionContext(model_id=‘3bf778a5905a49d7a6fc7af246dd986e’, should_add_diagnostic_data=False, is_finetuning=False, node_name=None). 2024-03-25 18:56:12 DEBUG rasa.engine.graph - [debug ] graph.node.running_component clazz=NLUMessageConverter fn=convert_user_message node_name=nlu_message_converter 2024-03-25 18:56:12 DEBUG rasa.engine.graph - [debug ] graph.node.running_component clazz=WhitespaceTokenizer fn=process node_name=run_WhitespaceTokenizer0 2024-03-25 18:56:12 DEBUG rasa.engine.graph - [debug ] graph.node.running_component clazz=LexicalSyntacticFeaturizer fn=process node_name=run_LexicalSyntacticFeaturizer1 2024-03-25 18:56:12 DEBUG rasa.engine.graph - [debug ] graph.node.running_component clazz=CountVectorsFeaturizer fn=process node_name=run_CountVectorsFeaturizer2 2024-03-25 18:56:12 DEBUG rasa.engine.graph - [debug ] graph.node.running_component clazz=DIETClassifier fn=process node_name=run_DIETClassifier3 2024-03-25 18:56:12 DEBUG rasa.engine.graph - [debug ] graph.node.running_component clazz=EntitySynonymMapper fn=process node_name=run_EntitySynonymMapper4 2024-03-25 18:56:12 DEBUG rasa.engine.graph - [debug ] graph.node.running_component clazz=FlowsProvider fn=provide_inference node_name=flows_provider 2024-03-25 18:56:12 DEBUG rasa.engine.graph - [debug ] graph.node.running_component clazz=LLMCommandGenerator fn=process node_name=run_LLMCommandGenerator5 2024-03-25 18:56:12 DEBUG rasa.utils.log_utils - [debug ] llm_command_generator.predict_commands.prompt_rendered prompt=Your task is to analyze the current conversation context and generate a list of actions to start new business processes that we call flows, to extract slots, or respond to small talk and knowledge requests.
These are the flows that can be started, with their description and slots:
hello_world: greet
read_news_flow: news
answer_to_question: ask any questions
read_weather_updates: weather