| PolarSPARC |
Hands-on Primer on Rasa - Part 2
| Bhaskar S | 11/10/2024 |
Overview
Previously in Part-1 of this series, we covered the setup, testing of the deployed chatbot instance, the core concepts, and the important files of the Rasa chatbot framework.
In second part of this primer, we will proceed to deploy a simple custom Linux commands' helper chatbot which will allow users to get help on which linux command to execute for a specific task.
Note that this simple linux commands' helper chatbot will only be able to help with only 3 commands for simplicity.
Hands-on with Rasa
First, the file config.yml is used to define the behavior of Rasa NLU and Rasa Core sub-systems.
For Rasa NLU, the file defines the NLU pipeline, which is a sequence of NLP stages that all user input text have to traverse through, such as, tokenization, featurization, classifiers, entity extraction, etc
For Rasa Core, the file defines the policies for the chatbot to decide what to do next once the user intent has been determined.
We will change the contents of the file as shown below:
# Configuration for Rasa NLU.
# https://rasa.com/docs/rasa-pro/nlu-based-assistants/components/
language: en
pipeline:
# # See https://rasa.com/docs/rasa-pro/nlu-based-assistants/model-configuration for more information.
- name: WhitespaceTokenizer
- name: RegexFeaturizer
- name: LexicalSyntacticFeaturizer
- name: CountVectorsFeaturizer
- name: CountVectorsFeaturizer
analyzer: char_wb
min_ngram: 1
max_ngram: 4
- name: DIETClassifier
epochs: 100
constrain_similarities: true
- name: EntitySynonymMapper
- name: ResponseSelector
epochs: 100
constrain_similarities: true
- name: FallbackClassifier
threshold: 0.3
ambiguity_threshold: 0.1
# Configuration for Rasa Core.
# https://rasa.com/docs/rasa-pro/concepts/policies/policy-overview/
policies:
# # See https://rasa.com/docs/rasa-pro/concepts/policies/policy-overview for more information.
- name: MemoizationPolicy
- name: RulePolicy
- name: UnexpecTEDIntentPolicy
max_history: 5
epochs: 100
- name: TEDPolicy
max_history: 5
epochs: 100
constrain_similarities: true
Next, the file domain.yml is used to define the universe of the Rasa chatbot, such as, all the intents identified by the Rasa NLU sub-system and all the response the chatbot sends to the user based on the intent.
We will change the contents of the file as shown below:
version: "3.1" intents: - greet - goodbye - linux_du_command - linux_ping_command - linux_uname_command responses: utter_greet: - text: "Howdy!" utter_ready_to_help: - text: "What linux command would you need help with?" utter_du_command: - text: "Use the command 'du -s -m <directory>' to find the size of the directory and its contents" utter_ping_command: - text: "Use the command 'ping -c 3 <host>' to check if the host is reachable" utter_uname_command: - text: "Use the command 'uname -a' to get information about the system" utter_goodbye: - text: "Bye" session_config: session_expiration_time: 60 carry_over_slots_to_new_session: true
Moving along, the file data/nlu.yml is used to define the training samples of all the user intents, along with examples of the utterances corresponding to the intents, for the Rasa chatbot to be trained on.
We will change the contents of the file as shown below:
version: "3.1"
nlu:
- intent: greet
examples: |
- hey
- hello
- hi
- hello there
- hey there
- intent: goodbye
examples: |
- good bye
- bye
- goodbye
- bye bye
- see you later
- intent: linux_du_command
examples: |
- command to find the size of a directory and its contents
- size of a directory and its contents
- size of a directory
- intent: linux_ping_command
examples: |
- command to check if host is reachable
- check if host is reachable
- check host is reachable
- intent: linux_uname_command
examples: |
- command to get information about a system
- get information about a system
- information about a host
Next stop, the file data/rules.yml is used to define the training data for all conversations that always follow a pre-defined set of steps, which the Rasa chatbot can be trained on.
We will change the contents of the file as shown below:
version: "3.1" rules: - rule: Say goodbye anytime the user says goodbye steps: - intent: goodbye - action: utter_goodbye
Finally, the file data/stories.yml is used to define the training data for all the dialog scenarios, which represent the users intent to the appropriate Rasa chatbot response.
We will change the contents of the file as shown below:
version: "3.1" stories: - story: greet path steps: - intent: greet - action: utter_greet - action: utter_ready_to_help - story: du command path steps: - intent: linux_du_command - action: utter_du_command - action: utter_goodbye - story: ping command path steps: - intent: linux_ping_command - action: utter_ping_command - action: utter_goodbye - story: uname command path steps: - intent: linux_uname_command - action: utter_uname_command - action: utter_goodbye
Make sure *NOT* to include the same steps in both the rules and stories - it will lead to a conflict error during training !!!
Now, it is time to train our custom linux commands' helper chatbot model.
Before we kick the training process, execute the following command in the terminal window:
$ ls -l $HOME/.rasa/models
The following should be the typical output:
total 24416 drwxr-xr-x 2 polarsparc polarsparc 4096 Nov 2 14:17 . drwxrwxr-x 10 polarsparc polarsparc 4096 Nov 9 19:01 .. -rw-r--r-- 1 polarsparc polarsparc 24992178 Nov 2 14:17 20241102-181642-avocado-bright.tar.gz
To start the custom linux commands' helper chatbot training, execute the following command in the terminal window:
$ docker run --rm --name rasa-pro -u $(id -u $USER):$(id -g $USER) -v $HOME/.rasa/:/app -e RASA_PRO_LICENSE=${RASA_PRO_LICENSE} rasa/rasa-pro:3.10.8 train --verbose
The following should be the typical trimmed output:
[ ... SNIP ... ]
2024-11-10 00:12:57 INFO rasa.cli.train - {"event_info": "Started validating domain and training data...", "event": "cli.train.run_training", "level": "info"}
[ ... SNIP ... ]
2024-11-10 00:13:02 INFO rasa.validator - {"event_info": "Validating intents...", "event": "validator.verify_intents_in_stories.start", "level": "info"}
2024-11-10 00:13:02 INFO rasa.validator - {"event_info": "Validating uniqueness of intents and stories...", "event": "validator.verify_example_repetition_in_intents.start", "level": "info"}
2024-11-10 00:13:02 INFO rasa.validator - {"event_info": "Story structure validation...", "event": "validator.verify_story_structure.start", "level": "info"}
Processed story blocks: 100%|||||||||||| 4/4 [00:00<00:00, 3710.95it/s, # trackers=1]
2024-11-10 00:13:02 INFO rasa.core.training.story_conflict - Considering all preceding turns for conflict analysis.
2024-11-10 00:13:02 INFO rasa.validator - {"event_info": "No story structure conflicts found.", "event": "validator.verify_story_structure.no_conflicts", "level": "info"}
2024-11-10 00:13:02 INFO rasa.validator - {"event": "validation.flows.started", "level": "info"}
2024-11-10 00:13:02 WARNING rasa.validator - {"event_info": "No flows were found in the data files. Will not proceed with flow validation.", "event": "validator.verify_flows", "level": "warning"}
Processed story blocks: 100%|||||||||||| 4/4 [00:00<00:00, 4650.00it/s, # trackers=1]
[ ... SNIP ... ]
Processed story blocks: 100%|||||||||||| 4/4 [00:00<00:00, 112.36it/s, # trackers=50]
Processed rules: 100%|||||||||||| 1/1 [00:00<00:00, 4573.94it/s, # trackers=1]
2024-11-10 00:13:17 INFO rasa.engine.training.hooks - Starting to train component 'MemoizationPolicy'.
Processed trackers: 100%|||||||||||| 4/4 [00:00<00:00, 5064.06it/s, # action=13]
Processed actions: 13it [00:00, 20269.87it/s, # examples=13]
2024-11-10 00:13:18 INFO rasa.engine.training.hooks - Finished training component 'MemoizationPolicy'.
2024-11-10 00:13:18 INFO rasa.engine.training.hooks - Starting to train component 'RulePolicy'.
Processed trackers: 100%|||||||||||| 1/1 [00:00<00:00, 2157.56it/s, # action=3]
Processed actions: 3it [00:00, 29127.11it/s, # examples=2]
[ ... SNIP ... ]
Processed trackers: 100%|||||||||||| 5/5 [00:00<00:00, 3371.09it/s]
2024-11-10 00:13:18 INFO rasa.engine.training.hooks - Finished training component 'RulePolicy'.
2024-11-10 00:13:18 INFO rasa.engine.training.hooks - Starting to train component 'TEDPolicy'.
Processed trackers: 100%|||||||||||| 244/244 [00:00<00:00, 5045.48it/s, # action=125]
Epochs: 100%|||||||||||| 100/100 [00:12<00:00, 7.86it/s, t_loss=0.338, loss=0.16, acc=1]
2024-11-10 00:13:31 INFO rasa.engine.training.hooks - Finished training component 'TEDPolicy'.
2024-11-10 00:13:31 INFO rasa.engine.training.hooks - Starting to train component 'UnexpecTEDIntentPolicy'.
[ ... SNIP ... ]
Processed trackers: 100%|||||||||||| 244/244 [00:00<00:00, 9155.98it/s, # intent=21]
Epochs: 100%|||||||||||| 100/100 [00:07<00:00, 13.19it/s, t_loss=0.106, loss=0.00145, acc=1]
2024-11-10 00:13:40 INFO rasa.engine.training.hooks - Finished training component 'UnexpecTEDIntentPolicy'.
2024-11-10 00:13:41 INFO rasa.engine.training.hooks - Restored component 'CountVectorsFeaturizer' from cache.
2024-11-10 00:13:41 INFO rasa.engine.training.hooks - Restored component 'CountVectorsFeaturizer' from cache.
2024-11-10 00:13:41 INFO rasa.engine.training.hooks - Restored component 'DIETClassifier' from cache.
2024-11-10 00:13:41 INFO rasa.engine.training.hooks - Restored component 'EntitySynonymMapper' from cache.
2024-11-10 00:13:41 INFO rasa.engine.training.hooks - Restored component 'LexicalSyntacticFeaturizer' from cache.
2024-11-10 00:13:41 INFO rasa.engine.training.hooks - Restored component 'RegexFeaturizer' from cache.
2024-11-10 00:13:41 INFO rasa.engine.training.hooks - Restored component 'ResponseSelector' from cache.
2024-11-10 00:13:44 INFO rasa.model_training - {"event_info": "Your Rasa model is trained and saved at 'models/20241110-001312-approximate-beech.tar.gz'.", "event": "model_training.train.finished_training", "level": "info"}
The training process has completed and one more time execute the following command in the terminal window:
$ ls -l $HOME/.rasa/models
The following should be the typical output:
total 47680 drwxr-xr-x 2 polarsparc polarsparc 4096 Nov 9 19:13 . drwxrwxr-x 10 polarsparc polarsparc 4096 Nov 9 19:08 .. -rw-r--r-- 1 polarsparc polarsparc 24992178 Nov 2 14:17 20241102-181642-avocado-bright.tar.gz -rw-r--r-- 1 polarsparc polarsparc 23820630 Nov 9 19:13 20241110-001312-approximate-beech.tar.gz
Notice that a new chatbot model 20241110-001312-approximate-beech.tar.gz has been created.
To test the custom linux commands' helper chatbot, execute the following command in the terminal window:
$ docker run -it --rm --name rasa-pro -u $(id -u $USER):$(id -g $USER) -v $HOME/.rasa/:/app -e RASA_PRO_LICENSE=${RASA_PRO_LICENSE} rasa/rasa-pro:3.10.8 shell
The following should be the typical trimmed output:
[ ... SNIP ... ] 2024-11-10 00:19:15 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. 2024-11-10 00:19:15 INFO root - Starting Rasa server on http://0.0.0.0:5005 2024-11-10 00:19:21 INFO rasa.core.processor - Loading model models/20241110-001312-approximate-beech.tar.gz... [ ... SNIP ... ] 2024-11-10 00:19:41 INFO root - Rasa server is up and running. Bot loaded. Type a message and press enter (use '/stop' to exit): Your input ->
Notice that the custom linux commands' helper chatbot is waiting for user input on the prompt Your input ->.
Type howdy at the chatbot prompt.
The following should be the typical output:
Howdy! What linux command would you need help with?
Next, type check host reachable at the chatbot prompt.
The following should be the typical output:
Use the command 'ping -c 3 <host>' to check if the host is reachable Bye
Next, type info about system at the chatbot prompt.
The following should be the typical output:
Use the command 'uname -a' to get information about the system Bye
Finally, type /stop at the chatbot prompt and the chatbot will exit.
BINGO - we have successfully tested our custom linux commands' helper chatbot !!!
Given that we have covered the creation and demonstration of a custom linux commands' helper chatbot, we conclude Part-2 of this series !!!
References