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Open Source Generative AI in Question-Answering (NLP) using Python

Generative AI and LLMs

Generative question-answering focuses on the generation of multi-sentence answers to open-ended questions. It usually works by searching massive document stores for relevant information and then using it to generate answers synthetically. This tutorial demonstrates how to build a question-answering system using generative AI. 🌲 Pinecone example: https://github.com/pinecone-io/examples/blob/master/learn/search/question-answering/abstractive-question-answering.ipynb 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/nlp-transformers 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 What is generative AI and Q&A? 01:02 Generative question-answering architecture 04:36 Getting code and prerequisites 05:06 Data preprocessing 07:41 Embedding and indexing text 13:50 BART text generation model 14:52 Querying with generative question-answering 17:45 Asking questions and getting results 21:29 Final notes

About the course

The Generative AI and Large Language Models (LLMs) course covers everything you need to know about: - Generative AI - Large Language Models (LLMs) - OpenAI, Cohere, Hugging Face - Managed vs. Open Source - LLM Libraries like LangChain and GPT Index - Long-term memory and retrieval-augmentation And more to come...