[riter]

thanks for asking.

this implementation leverages Rasa and stands up a FastAPI server where it receives the user response webhook first and gets processed by (or bypasses) Rasa.

The LLM queries a set of documents indexed by Langchain. Dummy data has been included (Pepe Corp.)

Rasa has support for a "fallback" mechanism whereby if a user's response scores low on your pre-configured Rasa intents (like Greet) you can have it route directly to the LLM as well. But for now RasaGPT capture and routes the Telegram response to the FastAPI webhook endpoint.

the LLM itself and prompts I configured provides a boolean on whether the response should be escalated to a human or not, based on LLM+Langchain not knowing the answer to the user's query from the indexed documents.

I hope that answers your question, if not happy to follow-up!