Rasa Integration

Overview

Rasa is a comprehensive open-source framework for building contextual AI assistants and chatbots. It provides the infrastructure for developing sophisticated conversational experiences that can be deployed across multiple channels.

Key Features

Contextual Understanding: Maintains conversation state and user context
Intent Classification: Accurately identifies user intents
Entity Extraction: Recognizes and extracts key information
Custom Actions: Execute backend operations based on user inputs
Multi-Channel Support: Deploy across websites, messaging apps, and voice interfaces

Use Cases

Customer service automation with contextual understanding
Internal enterprise assistants with secure data access
Multi-turn conversational interfaces for complex tasks
Voice-enabled assistants for hands-free operation

Note: Custom LLM integration is available only in Rasa Pro.

Setup Instructions

Install Rasa and Create Project
Terminal window
```
# Install Rasa
pip install rasa

# Create a new Rasa project
rasa init
```
For more detailed setup instructions, refer to the Rasa documentation.
Install Rasa Pro (required for custom LLM integration)

Visit then Rasa Pro page to obtain a license. You will be prompted to fill out a form and will receive license information and installation instructions via email.

Add the Pro repository to your pip configuration.
Terminal window
```
pip install rasa-pro
```

Configure config.yml

Navigate to your project directory and locate the config.yml file (for more details, refer to the Rasa configuration documentation).

Edit the config.yml to include the LLM configuration:

recipe: default.v1
language: en
pipeline:
  - name: WhitespaceTokenizer
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
  - name: CountVectorsFeaturizer
    analyzer: char_wb
    min_ngram: 1
    max_ngram: 4
  - name: RasaLLM
    model_name: "Deepseek" # Custom relaxAI model name
    base_url: "https://api.relax.ai/v1/"
    llm_parameters:
      model: "DeepSeek-R1-0528"
    # Add your API key in credentials.yml, not here

policies:
  - name: MemoizationPolicy
  - name: RulePolicy
  - name: TEDPolicy
    max_history: 5
    epochs: 100

Configure credentials.yml file
- In the same project directory, locate or create the credentials.yml file.
- Add your relaxAI API key to this file:
```
custom_llm:
  url: "https://api.relax.ai/v1/chat/completions"
  api_key: "RELAX_API_KEY"
```

Create Custom Action Handler (optional)

Navigate to the actions directory in your project and edit the actions.py file.

Add a custom action class that uses the relaxAI API for specific tasks

from rasa_sdk import Action
from rasa_sdk.events import SlotSet
import requests

class ActionQueryrelaxAI(Action):
    def name(self):
        return "action_query_relaxai"

    def run(self, dispatcher, tracker, domain):
        # Custom code to call relaxAI API
        # ...
        return []

Train Your Model

Return to your terminal in the project directory and run the training command:
Terminal window
```
rasa train
```
- Training progress will be displayed in the terminal
Start Action Server (if using custom actions)

Open a new terminal window and navigate to your project directory
- Run the actions server:
Terminal window
```
rasa run actions
```
Test Your Setup
- In your original terminal window, run:
Terminal window
```
rasa shell
```
- Test your bot with sample conversations.

You can switch between available relaxAI models by changing the model parameter in config.yml:

DeepSeek-R1-0528
Llama-4-Maverick-17B-128E