This demo showcases a chatbot built with Rasa's LLM-native approach: CALM.

This project is released under the Rasa's Early Release Software Access Terms.

The demo bot's business logic is implemented as a set of flows, rules, and stories, which are organized into three main skill groups: Contacts, Transactions, and Others/Misc.

The skill groups Contacts and Transactions are implemented using CALM, e.g. are defined in flows. The skill group Others/Misc is implemented via the nlu-based approach. Coexistence allows you to run a single assistant that uses both the Conversational AI with Language Models (CALM) approach and an NLU-based system in parallel.

Each flow consists of a yaml file and a domain definition, which includes actions, slots, and bot ressponses.

Additionaly, the bot can showcase the enterprise search capability based on the SQUAD dataset.

The table below shows all the skills implemented in the bot:

Rasa ships with a default behavior in CALM for every conversation repair case which is handled through a default pattern flow. In addition to its core functionality, the demo bot also includes an examples of pattern overriding in data/flows/patterns.yml.

This section guides you through the steps to get your Rasa bot up and running. We've provided simple make commands for a quick setup, as well as the underlying Rasa commands for a deeper understanding. Follow these steps to set up the environment, train your bot, launch the action server, start interactive sessions, and run end-to-end tests.

Prerequisites:

• rasa pro license
• python (3.10.12), e.g. using pyenv pyenv install 3.10.12
• Some flows require to set up and run Duckling server The easiest option is to spin up a docker container using docker run -p 8000:8000 rasa/duckling. Alternatively, you can use the make run-duckling command locally. This runs automatically only when you use the make run command, before it launches the Inspector app.

After you cloned the repository, follow these installation steps:

1. Locate to the cloned repo:

   cd rasa-calm-demo
   

2. Set the python environment with pyenv or any other tool that gets you the right python version

   pyenv local 3.10.12
   

3. Install the dependencies with pip

   pip install uv
   uv pip install rasa-pro --extra-index-url=https://europe-west3-python.pkg.dev/rasa-releases/rasa-pro-python/simple/
   

4. Create an environment file .env in the root of the project with the following content:

   RASA_PRO_LICENSE=<your rasa pro license key>
   OPENAI_API_KEY=<your openai api key>
   RASA_DUCKLING_HTTP_URL=<url to the duckling server>

5. If using qdrant for extractive search:

   • Setup a local docker instance of Qdrant

     docker pull qdrant/qdrant
     docker run -p 6333:6333 -p 6334:6334 \
        -v $(pwd)/qdrant_storage:/qdrant/storage:z \
        qdrant/qdrant
     

   • Upload data to Qdrant
     • In your virtual environment where Rasa Pro is installed, also install these dependencies:

       pip install uv
       uv pip install -r qdrant-requirements.txt
       

     • Ingest documents from SQUAD dataset (modify the script if qdrant isn't running locally!)

       python scripts/load-data-to-qdrant.py
       

   You can toggle parameter use_generative_llm in config.yml to change the behavior. The answer is selected from the first search result -> metadata -> answer key

You can use a custom component for Information Retrieval by defining the custom component class name in the config as follows:

policies:
- name: FlowPolicy
- name: EnterpriseSearchPolicy
vector_store:
   type: "addons.qdrant.Qdrant_Store"

This configuration refers to addons/qdrant.py file and the class Qdrant_Store. This class is also an example that information retrievers can use a custom query, note that in search() function the query is rewritten using the chat transcript by prepare_search_query function.

Check config/config.yml to make sure the configuration is appropriate before you train and run the bot. There are some alternative configurations available in the config folder. Theses can be used via the appropriate make command during training.

To train a model use make command for simplicity:

make rasa-train

which is a shortcut for:

rasa train -c config/config.yml -d domain --data data

Alternative configurations can be accessed for Multistep command generation:

make rasa-train-multistep

or for Enterprise search with qdrant for extractive search:

make rasa-train-qdrant

The trained model is stored in models directory located in the project root.

Before interacting with your assistant, start the action server to enable the assistant to perform custom actions located in the actions directory. Start the action server with the make command:

make rasa-actions

which is a shortcut for:

rasa run actions

Once the action server is started, you have two options to interact with your trained assistant:

1. GUI-based interaction using rasa inspector:

rasa inspect --debug

2. CLI-based interaction using rasa shell:

rasa shell --debug

The demo bot comes with a set of e2e tests, categorized into two primary groups: failing, and passing. These tests are organized not per individual flow but according to CALM functionalities.

You have the flexibility to run either all tests, only the passing tests, only the failing tests, or a single specific test.

To run all the tests you can use the make command:

make rasa-test

or

rasa test e2e e2e_tests

To run passing/failing/flaky tests you can use the make command:

make rasa-test-passing

make rasa-test-failing

make rasa-test-flaky

or

run rasa test e2e e2e_tests/passing

run rasa test e2e e2e_tests/failing

run rasa test e2e e2e_tests/flaky

To run a single test with make command, you need to provide the path to a target test in an environment variable target:

export target=e2e_tests/path/to/a/target/test.yml

and then run:

make rasa-test-one

or

rasa test e2e e2e/tests/path/to/a/target/test.yml

To run only the tests which are relevant to multistep command generation, you can use the make command:

make rasa-test-multistep

or

run rasa test e2e e2e_tests/multistep

To enable the feature, please set the environment variable RASA_PRO_BETA_E2E_ASSERTIONS to true in your testing environment.

export RASA_PRO_BETA_E2E_ASSERTIONS=true

To run all the tests:

rasa test e2e e2e_tests_with_assertions

To run passing/failing/flaky tests:

rasa test e2e e2e_tests_with_assertions/passing

rasa test e2e e2e_tests_with_assertions/failing

rasa test e2e e2e_tests_with_assertions/flaky

To run a single test , provide the path to a target test:

rasa test e2e e2e_tests_with_assertions/tests/path/to/a/target/test.yml