Rasa CALM Demo

rasa-calm-demo is a chatbot built with Rasa's LLM-native approach: CALM. The bot is used during the QA process of Rasa to test the CALM features and capabilities.

Caution

Please note that the demo bot is an evolving platform. The flows currently implemented in the demo bot are designed to showcase different features and capabilities of the CALM bot. The functionality of each flow may vary, reflecting CALM's current stage of development.

Important

rasa-calm-demo is not intended to show best practices for building a production-ready bot.

Note

This demo bot is currently compatible with 3.14.0.

Terms of Use

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

Demo Bot

The demo bot’s business logic is structured using Rasa’s flows, rules, and stories, with functionality organized into several main skill groups. Some skill groups are implemented with the Conversational AI with Language Models (CALM) approach, defined through flows, while others use the NLU-based method based on training data. The coexistence feature enables both CALM and NLU-based systems to operate within a single assistant, providing flexible support for a range of conversational experiences. Each flow comprises a yaml file and an associated domain definition, detailing actions, slots, and bot responses. The demo bot also demonstrates enterprise search capabilities, such as answering questions using the SQUAD dataset. Additionally, the bot supports comprehensive conversation repair via Rasa’s default patterns, and provides examples of extending or overriding these patterns within the project.

Running the project

This section guides you through the steps to get rasa-calm-demo 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.

Installation

Important

To build, run, and explore the bot's features, you need Rasa Pro license. You also need access to the rasa-pro Python package. For installation instructions please refer our documentation here.

Note

If you want to check out the state of the demo bot compatible with earlier Rasa versions, please check out the corresponding branch in the repository (e.g. for Rasa 3.9.x: 3.9.x).

Prerequisites:

• Rasa Pro license
• Python - supported Python versions: >=3.10.0,<3.14. For Python 3.10.12 for example, you can use pyenv: pyenv install 3.10.12
• 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 virtualenv 3.10.12 rasa-calm-demo-python-3.10.12  # create a virtual env
   pyenv activate rasa-calm-demo-python-3.10.12  # activate your virtual env
   pyenv local rasa-calm-demo-python-3.10.12  # set auto-activation
   

3. Install the dependencies with pip

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

   The above command installs the rasa pro package, including the nlu extra that includes the optional dependencies for NLU components.
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>

Configuration

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.

Training the bot

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

Please note: The tensorflow-metal ("^1.2.0") GPU acceleration plugin is automatically included for Apple Silicon Mac users with Python 3.9-3.11 when installing with the nlu or full extras. Due to compatibility issues with TensorFlow 2.19+, you may need to uninstall this package if you encounter import errors when training the bot on your particular hardware.

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

Starting the assistant

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

Running E2E tests

The demo bot comes with a set of end-to-end (E2E) tests. You have the flexibility to run either all 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/tests_for_default_config

---

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

Using Enterprise Search with Qdrant

To use the Enterprise Search capabilities with Qdrant, follow these steps:

1. 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
   

2. 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
     

3. 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

Custom Information Retriever

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.