📄️ Create embedding for your data via the vector DB
There are two ways to deal with the hallucination from LLM's answer. One is to fine-tune a new model with your domain knowledge. The other one is to build a RAG-based (Retrieval-Augmented Generation) LLM agent via a vector DB. Both solutions can solve the challenge. However, fine-tuning a model requires GPU, which is more expensive. In this series of tutorials, I will walk you through how to build a RAG-based LLM agent.
📄️ Create a Discord bot with your embeddings
In the last tutorial, we have created embeddings for your knowledge base and named your embeddings. I will walk through how to build a RAG-based Discord bot based on the embeddings we created.
📄️ Create a web chatbot with your embeddings
In the last tutorial, we have created embeddings for your knowledge base and named your embeddings. I will walk through how to build a RAG-based web chatbot based on the embeddings we created.