Linkedin Learning – Creating a Chat Tool Using OpenAI Models and Pinecone
English | Tutorial | Size: 213.82 MB
Embeddings and vector databases allow developers to create tools that can retrieve knowledge from custom documents and use it to form more accurate and dynamic conversations. But while cutting-edge AI models like ChatGPT can generate useful conversational responses to many different kinds of queries, the replies are always limited to the data that was available when the model was last trained.
In this course, instructor Guil Hernandez offers an overview of text embeddings, vector databases, and retrieval-augmented generation (RAG) to elevate and optimize your AI learning journey. Along the way, test out your new skills in the exercise challenges provided at the end of each section.
Learning Objectives:
• Discover embeddings in the context of generative AI.
• Set up and store embeddings in a vector database.
• Utilize best practices related to retrieval-augmented generation (RAG).
• Generate conversational responses from data retrieved from a vector database using OpenAI models and Pinecone.
RAPIDGATOR:
rapidgator.net/file/751d8ec19aa1bb7ef2ee57f08f9305cb/LinkedIn_Learning_-_Creating_a_Chat_Tool_Using_OpenAI_Models_and_Pinecone.rar.html