Integration of Rasa into the code

Dear Rasa Team,

I am currently trying to integrate Rasa into my python code (python 3.7, Ubuntu 18.04, Rasa 1.10.12) and here is a demo snippet of my code:

from rasa.core.policies.memoization import MemoizationPolicy
from rasa.core.policies.form_policy import FormPolicy
from rasa.core.policies.fallback    import FallbackPolicy
from rasa.core.policies.ted_policy  import TEDPolicy

from rasa.core.agent       import Agent
from rasa.core.interpreter import RasaNLUInterpreter

from rasa.utils.endpoints import EndpointConfig

memoization = MemoizationPolicy(
                            max_history = 5
)
form_policy = FormPolicy()
fallback    = FallbackPolicy(
                            fallback_action_name = "utter_default",
                            core_threshold       = 0.1,
                            nlu_threshold        = 0.1,
                            ambiguity_threshold  = 0.1
)
ted_policy = TEDPolicy(
                    max_history = 5,
                    epochs = 100
)

interpreter = RasaNLUInterpreter("models/20200901-123145/nlu")

domain="models/20200901-123145/core/domain.yml"

action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
agent = Agent(domain=domain, policies=[memoization, form_policy, fallback, ted_policy])
agent.load(model_path="models/20200901-123145", action_endpoint=action_endpoint) #, interpreter=interpreter)

# -------------------------------------------------------------------------------------------------

## Start the text-to-intent extraction
def parse_to_rasa(text):
    print("Received text: " + text)
    rasa_response = asyncio.run(agent.handle_text(text_message=text))
    rasa_response = rasa_response[0]
    rasa_response = str(rasa_response['text']) 
    print(rasa_response)

if __name__ == '__main__':
    parse_to_rasa("Speichere 4 Liter Fanta")

The output is:

python3.7 rasa_code_integration_tester.py 
2020-10-13 17:21:41.449871: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory
2020-10-13 17:21:41.449960: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory
2020-10-13 17:21:41.449978: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2020-10-13 17:21:41.972795: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory
2020-10-13 17:21:41.972816: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2020-10-13 17:21:42.474931: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2020-10-13 17:21:42.474952: E tensorflow/stream_executor/cuda/cuda_driver.cc:351] failed call to cuInit: UNKNOWN ERROR (303)
2020-10-13 17:21:42.474965: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ilia-ThinkPad-T480s): /proc/driver/nvidia/version does not exist
2020-10-13 17:21:42.475095: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-10-13 17:21:42.498087: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1999965000 Hz
2020-10-13 17:21:42.498463: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5b9ed50 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-10-13 17:21:42.498487: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
WARNING:tensorflow:From /home/ilia/Claire_code/E66-Terminal/src/text-to-intent/.rasa-venv/lib/python3.7/site-packages/tensorflow_core/python/ops/array_grad.py:563: _EagerTensorBase.cpu (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.identity instead.
2020-10-13 17:21:43.205027: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled
2020-10-13 17:21:47.153158: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled
2020-10-13 17:21:47.949516: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled
Received text: Speichere 4 Liter Heparin
/home/ilia/Claire_code/E66-Terminal/src/text-to-intent/.rasa-venv/lib/python3.7/site-packages/rasa/utils/common.py:363: UserWarning: Interpreter parsed an intent 'Speichere 4 Liter Heparin' which is not defined in the domain. Please make sure all intents are listed in the domain.
  More info at https://rasa.com/docs/rasa/core/domains/
Tut mir leid, ich habe es leider nicht verstanden.

I have Rasa custom actions server running in the background (rasa run actions) and Rasa duckling server (sudo docker run -p 8000:8000 rasa/duckling). My Rasa chatbot works well if I run it from the terminal with “rasa shell”.

What am I missing? What am I doing wrong?

Many thanks in advance.

Best regards, Ilia

P.S. As you can probably see, I have set the configuration policy for the core, but I don’t know where to set the pipeline properties inside my code. When using Rasa shell bot, I have set both the pipeline properties and the core policies in the config.yml file.

What do your domain and nlu files look like for this intent? Can you enable debug logging.

Hi Stephens,

thank you for your reply. Here are my domain and nlu files together with the output from the debug logging enabled when using the shell bot:

DOMAIN:

session_config:
  session_expiration_time: 0 # value in minutes, 0 means infinitely long
  carry_over_slots_to_new_session: true # set to false to forget slots between sessions

intents:
  - drinks_documentation
  - drinks_summary
 
entities:
  - drink_name
  - amount
  - measurmentUnit

slots:
  drink_name:
    type: unfeaturized
  amount:
    type: unfeaturized
  measurmentUnit:
    type: unfeaturized
  requested_slot:
    type: unfeaturized

forms:
 - drinks_saved_form

actions:
 - show_drinks_summary

responses:
  utter_default:
  - text: "Tut mir leid, ich habe es leider nicht verstanden."
  utter_drink_name:
  - text: "Welchen Getränk soll ich abspeichern?"
  utter_ask_amount:
  - text: "Wie viel von dem Getränk soll ich speichern?"
  utter_ask_measurmentUnit:
  - text: "Was ist die Einheit?"

NLU:

## intent:drinks_documentation
- [50](amount) [liter](measurmentUnit) von [Fanta](drink_name)
- Speichere    [50](amount) [liter](measurmentUnit) von [Fanta](drink_name)
- Dokumentiere [40](amount) [liter](measurmentUnit) von [Fanta](drink_name)
- Fasse auf    [100](amount) [liter](measurmentUnit) von [Fanta](drink_name)
- Vermerke     [10](amount) [liter](measurmentUnit) von [Fanta](drink_name)
- Nehme auf    [2](amount) [liter](measurmentUnit) von [Fanta](drink_name)

- [50](amount) [mililiter](measurmentUnit) von [Cola](drink_name)
- speichere    [50](amount) [mililiter](measurmentUnit) von [Cola](drink_name)
- dokumentiere [40](amount) [mililiter](measurmentUnit) von [Cola](drink_name)
- fasse auf    [100](amount) [mililiter](measurmentUnit) von [Cola](drink_name)
- vermerke     [10](amount) [mililiter](measurmentUnit) von [Cola](drink_name)
- nehme auf    [2](amount) [mililiter](measurmentUnit) von [Cola](drink_name)

## intent:drinks_summary
- Welche Getränke gibt es?
- Zeig mir bitte die Getränkeliste an
- Die verfügbaren Getränke an

## lookup:measurmentUnit
  data/lookups/measurmentUnits.txt

## lookup:drink_name 
  data/lookups/drinkNames.txt

SHELL OUTPUT DEBUG LOGGING:

rasa shell --debug
2020-10-15 14:50:26 DEBUG    rasa.model  - Extracted model to '/tmp/tmp1xrc82_c'.
2020-10-15 14:50:26 DEBUG    rasa.cli.utils  - Parameter 'endpoints' not set. Using default location 'endpoints.yml' instead.
2020-10-15 14:50:26 DEBUG    rasa.cli.utils  - Parameter 'credentials' not set. Using default location 'credentials.yml' instead.
2020-10-15 14:50:26 DEBUG    rasa.model  - Extracted model to '/tmp/tmp86rr56f5'.
2020-10-15 14:50:27 INFO     root  - Connecting to channel 'cmdline' which was specified by the '--connector' argument. Any other channels will be ignored. To connect to all given channels, omit the '--connector' argument.
2020-10-15 14:50:27 DEBUG    sanic.root  - CORS: Configuring CORS with resources: {'/*': {'origins': [''], 'methods': 'DELETE, GET, HEAD, OPTIONS, PATCH, POST, PUT', 'allow_headers': ['.*'], 'expose_headers': 'filename', 'supports_credentials': True, 'max_age': None, 'send_wildcard': False, 'automatic_options': True, 'vary_header': True, 'resources': {'/*': {'origins': ''}}, 'intercept_exceptions': True, 'always_send': True}}
2020-10-15 14:50:27 DEBUG    rasa.core.utils  - Available web server routes: 
/webhooks/rest                                     GET                            custom_webhook_CmdlineInput.health
/webhooks/rest/webhook                             POST                           custom_webhook_CmdlineInput.receive
/                                                  GET                            hello
2020-10-15 14:50:27 INFO     root  - Starting Rasa server on http://localhost:5005
2020-10-15 14:50:27 DEBUG    rasa.core.utils  - Using the default number of Sanic workers (1).
2020-10-15 14:50:27 INFO     root  - Enabling coroutine debugging. Loop id 96674688.
2020-10-15 14:50:27 DEBUG    rasa.model  - Extracted model to '/tmp/tmpltw_e4xb'.
2020-10-15 14:50:27 DEBUG    rasa.utils.tensorflow.models  - Loading the model ...
2020-10-15 14:50:27.578891: E tensorflow/stream_executor/cuda/cuda_driver.cc:351] failed call to cuInit: UNKNOWN ERROR (303)
2020-10-15 14:50:28 DEBUG    rasa.utils.tensorflow.models  - Finished loading the model.
2020-10-15 14:50:28 DEBUG    rasa.utils.tensorflow.models  - Building tensorflow prediction graph...
2020-10-15 14:50:31 DEBUG    rasa.utils.tensorflow.models  - Finished building tensorflow prediction graph.
2020-10-15 14:50:31 DEBUG    rasa.nlu.classifiers.diet_classifier  - Failed to load model for 'ResponseSelector'. Maybe you did not provide enough training data and no model was trained or the path '/tmp/tmpltw_e4xb/nlu' doesn't exist?
2020-10-15 14:50:31 DEBUG    rasa.core.tracker_store  - Connected to InMemoryTrackerStore.
2020-10-15 14:50:31 DEBUG    rasa.core.lock_store  - Connected to lock store 'InMemoryLockStore'.
2020-10-15 14:50:32 DEBUG    rasa.model  - Extracted model to '/tmp/tmpwdv2447a'.
2020-10-15 14:50:32 DEBUG    pykwalify.compat  - Using yaml library: /home/ilia/Claire_code/E66-Terminal/src/text-to-intent/.rasa-venv/lib/python3.7/site-packages/ruamel/yaml/__init__.py
2020-10-15 14:50:32 DEBUG    rasa.utils.tensorflow.models  - Loading the model ...
2020-10-15 14:50:32 DEBUG    rasa.utils.tensorflow.models  - Finished loading the model.
2020-10-15 14:50:32 DEBUG    rasa.utils.tensorflow.models  - Building tensorflow prediction graph...
2020-10-15 14:50:32 DEBUG    rasa.utils.tensorflow.models  - Finished building tensorflow prediction graph.
2020-10-15 14:50:32 INFO     rasa.core.policies.ensemble  - MappingPolicy not included in policy ensemble. Default intents 'restart and back will not trigger actions 'action_restart' and 'action_back'.
2020-10-15 14:50:32 DEBUG    rasa.core.nlg.generator  - Instantiated NLG to 'TemplatedNaturalLanguageGenerator'.
2020-10-15 14:50:32 INFO     root  - Rasa server is up and running.
Bot loaded. Type a message and press enter (use '/stop' to exit): 
Your input ->  Speichere 4 Liter Fanta                                                                                                                                                                             
2020-10-15 14:50:35 DEBUG    rasa.core.lock_store  - Issuing ticket for conversation '87c7fc96cdf84e9a8e27b2704330cf47'.
2020-10-15 14:50:35 DEBUG    rasa.core.lock_store  - Acquiring lock for conversation '87c7fc96cdf84e9a8e27b2704330cf47'.
2020-10-15 14:50:35 DEBUG    rasa.core.lock_store  - Acquired lock for conversation '87c7fc96cdf84e9a8e27b2704330cf47'.
2020-10-15 14:50:35 DEBUG    rasa.core.tracker_store  - Creating a new tracker for id '87c7fc96cdf84e9a8e27b2704330cf47'.
2020-10-15 14:50:35 DEBUG    rasa.core.processor  - Starting a new session for conversation ID '87c7fc96cdf84e9a8e27b2704330cf47'.
2020-10-15 14:50:35 DEBUG    rasa.core.processor  - Action 'action_session_start' ended with events '[<rasa.core.events.SessionStarted object at 0x7f2ac01c6dd0>, <rasa.core.events.ActionExecuted object at 0x7f2ac047ab10>]'.
2020-10-15 14:50:35 DEBUG    rasa.core.processor  - Current slot values: 
	amount: None
	drink_name: None
	measurmentUnit: None
	requested_slot: None
2020-10-15 14:50:35 DEBUG    rasa.nlu.classifiers.diet_classifier  - There is no trained model for 'ResponseSelector': The component is either not trained or didn't receive enough training data.
2020-10-15 14:50:35 DEBUG    rasa.nlu.selectors.response_selector  - Adding following selector key to message property: default
2020-10-15 14:50:35 DEBUG    rasa.core.processor  - Received user message 'Speichere 4 Liter Fanta' with intent '{'name': 'drinks_documentation', 'confidence': 0.9995071887969971}' and entities '[{'entity': 'amount', 'start': 10, 'end': 11, 'value': '4', 'extractor': 'DIETClassifier'}, {'entity': 'measurmentUnit', 'start': 12, 'end': 17, 'value': 'Liter', 'extractor': 'DIETClassifier'}, {'entity': 'drink_name', 'start': 18, 'end': 23, 'value': 'Fanta', 'extractor': 'DIETClassifier'}]'
2020-10-15 14:50:35 DEBUG    rasa.core.processor  - Current slot values: 
	amount: 4
	drink_name: Fanta
	measurmentUnit: Liter
	requested_slot: None
2020-10-15 14:50:35 DEBUG    rasa.core.processor  - Logged UserUtterance - tracker now has 7 events.
2020-10-15 14:50:35 DEBUG    rasa.core.policies.memoization  - Current tracker state [None, None, None, {}, {'entity_amount': 1.0, 'prev_action_listen': 1.0, 'entity_measurmentUnit': 1.0, 'intent_drinks_documentation': 1.0, 'entity_drink_name': 1.0}]
2020-10-15 14:50:35 DEBUG    rasa.core.policies.memoization  - There is no memorised next action
2020-10-15 14:50:35 DEBUG    rasa.core.policies.form_policy  - There is no active form
2020-10-15 14:50:35 DEBUG    rasa.core.policies.fallback  - NLU confidence threshold met, confidence of fallback action set to core threshold (0.1).
2020-10-15 14:50:35 DEBUG    rasa.core.policies.ensemble  - Predicted next action using policy_3_TEDPolicy
2020-10-15 14:50:35 DEBUG    rasa.core.processor  - Predicted next action 'drinks_saved_form' with confidence 0.61.
2020-10-15 14:50:35 DEBUG    rasa.core.actions.action  - Calling action endpoint to run action 'drinks_saved_form'.
2020-10-15 14:50:35 DEBUG    rasa.core.processor  - Action 'drinks_saved_form' ended with events '[BotUttered('Ich habe 4 Liter Fanta gespeichert', {"elements": null, "quick_replies": null, "buttons": null, "attachment": null, "image": null, "custom": null}, {}, 1602766235.7694142), <rasa.core.events.Form object at 0x7f2ac0533710>, <rasa.core.events.SlotSet object at 0x7f2ac016d110>, <rasa.core.events.SlotSet object at 0x7f2ac010be90>, <rasa.core.events.SlotSet object at 0x7f2ac00eccd0>, <rasa.core.events.SlotSet object at 0x7f2ac00ecc50>, <rasa.core.events.SlotSet object at 0x7f2ac00ece10>, <rasa.core.events.SlotSet object at 0x7f2ac00ecdd0>, <rasa.core.events.SlotSet object at 0x7f2ac00ecd90>, <rasa.core.events.SlotSet object at 0x7f2ac00ece90>, <rasa.core.events.SlotSet object at 0x7f2ac00ecd10>, <rasa.core.events.SlotSet object at 0x7f2ac00ece50>, <rasa.core.events.Form object at 0x7f2ac00ecfd0>, <rasa.core.events.SlotSet object at 0x7f2ac00ecf90>]'.
2020-10-15 14:50:35 DEBUG    rasa.core.processor  - Current slot values: 
	amount: None
	drink_name: None
	measurmentUnit: None
	requested_slot: None
2020-10-15 14:50:35 DEBUG    rasa.core.policies.memoization  - Current tracker state [None, None, {}, {'entity_amount': 1.0, 'prev_action_listen': 1.0, 'entity_measurmentUnit': 1.0, 'intent_drinks_documentation': 1.0, 'entity_drink_name': 1.0}, {'entity_amount': 1.0, 'prev_drinks_saved_form': 1.0, 'entity_measurmentUnit': 1.0, 'intent_drinks_documentation': 1.0, 'entity_drink_name': 1.0}]
2020-10-15 14:50:35 DEBUG    rasa.core.policies.memoization  - There is no memorised next action
2020-10-15 14:50:35 DEBUG    rasa.core.policies.form_policy  - There is no active form
2020-10-15 14:50:35 DEBUG    rasa.core.policies.fallback  - NLU confidence threshold met, confidence of fallback action set to core threshold (0.1).
2020-10-15 14:50:35 DEBUG    rasa.core.policies.ensemble  - Predicted next action using policy_3_TEDPolicy
2020-10-15 14:50:35 DEBUG    rasa.core.processor  - Predicted next action 'action_listen' with confidence 0.95.
2020-10-15 14:50:35 DEBUG    rasa.core.processor  - Action 'action_listen' ended with events '[]'.
2020-10-15 14:50:35 DEBUG    rasa.core.lock_store  - Deleted lock for conversation '87c7fc96cdf84e9a8e27b2704330cf47'.
Ich habe 4 Liter Fanta gespeichert
Your input ->                                                                                                                                                                                                      

As you can see, interacting with the chatbot through shell works fine. Many thanks for your attention!

Best regards, Ilia

Hi, since my last feedback I have made some updates and I had a slight progress, namely, I don’t get the warning anymore:

/home/ilia/Claire_code/E66-Terminal/src/text-to-intent/.rasa-venv/lib/python3.7/site-packages/rasa/utils/common.py:363: UserWarning: Interpreter parsed an intent 'Speichere 4 Liter Heparin' which is not defined in the domain. Please make sure all intents are listed in the domain.
 More info at https://rasa.com/docs/rasa/core/domains/

Here is how my code looks like now:

import asyncio

from rasa.core.policies.memoization    import MemoizationPolicy
from rasa.core.policies.form_policy    import FormPolicy
from rasa.core.policies.fallback       import FallbackPolicy
from rasa.core.policies.ted_policy     import TEDPolicy

from rasa.core.domain        import Domain
from rasa.core.agent         import Agent
from rasa.core.interpreter   import RasaNLUInterpreter
from rasa.utils.endpoints    import EndpointConfig

class IntegrationWithAgent:
 
    memoization = MemoizationPolicy()
    memoization.load("models/20201015-140029/core/policy_0_MemoizationPolicy")

    form_policy = FormPolicy()
    form_policy.load("models/20201015-140029/core/policy_1_FormPolicy")

    fallback = FallbackPolicy()
    fallback.load("models/20201015-140029/core/policy_2_FallbackPolicy")

    ted_policy = TEDPolicy()
    ted_policy.load("models/20201015-140029/core/policy_3_TEDPolicy")

    domain=Domain.load("models/20201015-140029/core/domain.yml")
    domain.load_specification("models/20201015-140029/core/")
    
    interpreter = RasaNLUInterpreter("models/20201015-140029/nlu")    
    model_server    = EndpointConfig(url="http://localhost:5005/")
    action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")

    agent = Agent(
                  domain=domain, 
                  policies=[memoization, form_policy, fallback, ted_policy], 
                  interpreter=interpreter, 
                  action_endpoint=action_endpoint, 
                  model_server=model_server
                )

    # -------------------------------------------------------------------------------------------------

    ## Start the text-to-intent extraction
    def parse_to_rasa(self, text):
        print("Received text: " + text)
        print("All components are ready: " + str(self.agent.is_ready()))
        
        message_output = asyncio.run(self.agent.parse_message_using_nlu_interpreter(text, None))
        print(message_output['text'])
        print(message_output['intent'])
        print(message_output['intent_ranking'])
        print(message_output['entities'])
        
        rasa_response = asyncio.run(self.agent.handle_text(text_message=text))
        rasa_response = rasa_response[0]
        rasa_response = str(rasa_response['text']) 
        print(rasa_response)

if __name__ == '__main__':

    intAgent = IntegrationWithAgent()
    intAgent.parse_to_rasa("Speichere 4 Liter Fanta")
    

Also, I have went through dependencies in agent.py, processor.py, trackers.py, tracker_store.py, interpreter.py, utils.py, policies folder and others. While going through the code I have also set two print out functions in processor.py:

(1) After line 789:

print(followup_action)

(2) After line 300:

for i in action_confidences:
            print(i)

The output is:

python3.7 rasa_code_integration_tester.py 
2020-10-27 17:20:15.356107: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory
2020-10-27 17:20:15.356177: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory
2020-10-27 17:20:15.356188: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2020-10-27 17:20:15.889315: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory
2020-10-27 17:20:15.889335: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2020-10-27 17:20:16.490348: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2020-10-27 17:20:16.490369: E tensorflow/stream_executor/cuda/cuda_driver.cc:351] failed call to cuInit: UNKNOWN ERROR (303)
2020-10-27 17:20:16.490382: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (ilia-ThinkPad-T480s): /proc/driver/nvidia/version does not exist
2020-10-27 17:20:16.490514: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-10-27 17:20:16.513537: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1999965000 Hz
2020-10-27 17:20:16.513908: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5f43910 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-10-27 17:20:16.513936: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-10-27 17:20:16.716748: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled
WARNING:tensorflow:From /home/ilia/Claire_code/E66-Terminal/src/text-to-intent/.rasa-venv/lib/python3.7/site-packages/tensorflow_core/python/ops/array_grad.py:563: _EagerTensorBase.cpu (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.identity instead.
2020-10-27 17:20:19.210123: W tensorflow/core/kernels/data/generator_dataset_op.cc:103] Error occurred when finalizing GeneratorDataset iterator: Cancelled: Operation was cancelled
Received text: Speichere 4 Liter Fanta
All components are ready: True
Speichere 4 Liter Fanta
{'name': 'drinks_documentation', 'confidence': 0.9995071887969971}
[{'name': 'drinks_documentation', 'confidence': 0.9995071887969971}, {'name': 'drinks_summary', 'confidence': 0.0004928417038172483}]
[{'entity': 'amount', 'start': 10, 'end': 11, 'value': '4', 'extractor': 'DIETClassifier'}, {'entity': 'measurmentUnit', 'start': 12, 'end': 17, 'value': 'Liter', 'extractor': 'DIETClassifier'}, {'entity': 'drink_name', 'start': 18, 'end': 23, 'value': 'Fanta', 'extractor': 'DIETClassifier'}]
None
0.0
0.0
0.0
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
action_listen
1.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Tut mir leid, ich habe es leider nicht verstanden.

As you can see, the intent itself is recognized well, but the prediction values for all actions are zero. What did I miss? Where do I have to set the prediction values for my actions, because I am referencing the paths to the already trained policies?

Best regards,
Ilia