Overview

RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. It is boilerplate and a reference implementation of Rasa and Telegram utilizing an LLM library like Langchain for indexing, retrieval and context injection.

• Resources: https://rasagpt.dev
• 🧑‍ Github: https://github.com/paulpierre/RasaGPT
• 🧙 Author: @paulpierre

What is Rasa?

In their own words:

Rasa is an open source (Python) machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

In my words:

Rasa is a very popular (dare I say de facto?) and easy-enough to use chatbot framework with built in NLU ML pipelines that are obsolete and a conceptual starting point for a reimagined chatbot framework in a world of LLMs.

‍️ Why RasaGPT?

RasaGPT works out of the box. A lot of the implementing headaches were sorted out so you don’t have to, including:

• Creating your own proprietary bot end-point using FastAPI, document upload and “training” 'pipeline included
• How to integrate Langchain/LlamaIndex and Rasa
• Library conflicts with LLM libraries and passing metadata
• Dockerized support on MacOS for running Rasa
• Reverse proxy with chatbots via ngrok
• Implementing pgvector with your own custom schema instead of using Langchain’s highly opinionated PGVector class
• Adding multi-tenancy (Rasa doesn't natively support this), sessions and metadata between Rasa and your own backend / application

The backstory is familiar. A friend came to me with a problem. I scoured Google and Github for a decent reference implementation of LLM’s integrated with Rasa but came up empty-handed. I figured this to be a great opportunity to satiate my curiosity and 2 days later I had a proof of concept, and a week later this is what I came up with.