Build a RAG Chatbot From Scratch in About 40 Lines of Python

1. 1Import necessary libraries like transformers and torch
2. 2Load a pre-trained language model and tokenizer
3. 3Define a custom dataset class for your chatbot's knowledge base
4. 4Implement a retrieval mechanism to fetch relevant information
5. 5Build a RAG model by combining the retrieval mechanism with the language model
6. 6Train and fine-tune the RAG model on your dataset

NLP engineers and conversational AI developers can benefit from this tutorial to improve their chatbot's performance and accuracy, while product managers can use this to enhance customer experience

💡 RAG chatbots can reduce the likelihood of large language models providing confidently incorrect information by retrieving relevant context from a knowledge base

🤖 Build a RAG chatbot in 40 lines of Python! 🚀 Improve conversational AI accuracy with retrieval-augmented generators