Welcome to the world of RasaGPT, the innovative headless LLM chatbot platform that combines the strengths of Rasa and Langchain. In this guide, we will walk you through setting up RasaGPT and getting it ready for action in no time. Let’s dive in!

What is RasaGPT?

RasaGPT is designed to take the heavy lifting out of building chatbots. It provides a boilerplate for integrating Rasa with Telegram, leveraging an LLM library such as Langchain for efficient indexing, retrieval, and context injection. Think of it as a ready-to-go salad kit—you just need to toss in your favorite dressing (i.e., your customizations) and serve!

Getting Started

Before jumping in, ensure that you have the necessary dependencies installed:

• Python 3.9
• Docker and Docker Compose
• OpenAI API key
• Telegram bot credentials
• Ngrok auth token
• Make
• SQLModel

1. Clone the Code

Start by getting the RasaGPT code onto your machine:

git clone https://github.com/paulpierre/RasaGPT.git
cd RasaGPT

2. Setup the Environment

Create a .env file and fill it with your credentials:

cp .env-example .env
# Edit your .env file with all the necessary credentials

3. Install the Application

Run the following command to install all necessary components:

make install

If you wish to make adjustments or troubleshoot, type make for options.

Understanding the Code Through an Analogy

A good way to understand RasaGPT’s setup is to think of it like building a Lego castle:

• Rasa as the Foundation: Just like a solid base is crucial for a Lego castle, Rasa provides the core framework for handling conversation flow.
• Langchain as the Towers: Towers give structure and height, similar to how Langchain helps elevate the functionality of Rasa with enhanced indexing and retrieval capabilities.
• Docker as the Encasing: The Docker container wraps your castle, protecting it and allowing you to transport it anywhere while maintaining its integrity.

Features of RasaGPT

RasaGPT offers a range of compelling features, such as:

• LLM learns from arbitrary corpus data using Langchain
• Document upload and training via FastAPI
• Automatic re-training on document upload
• Swagger documentation for API access
• Flexibility for integrating with multiple platforms like Telegram, Slack, and more

Troubleshooting

If you encounter issues, consider the following:

• Check your Docker container logs by navigating to http://localhost:9999
• For Ngrok issues, ensure the webhook URL matches between Ngrok and Telegram. Use the following command:

curl -sS https://api.telegram.org/bot/getWebhookInfo | json_pp

• If the URLs do not match, restart the services with make restart

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Next Steps

Now that you have a basic understanding of how to set up RasaGPT and troubleshoot issues, you can start experimenting with its various features. Once you’ve got everything working smoothly, test out your bot by chatting with it on Telegram.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.