In this video, we will take a deep dive into the World of Embeddings and understand how to use them in RAG pipeline in Llama-index. First, we will understand the concept and then will look at home to use different embeddings including OpenAI Embedding, Open source embedding (BGE, and instructor embeddings) in llama-index. We will also benchmark their speed.
CONNECT:
☕ Buy me a Coffee: ko-fi.com/promptengineering
|🔴 Support my work on Patreon: Patreon.com/PromptEngineering
🦾 Discord: / discord
▶️️ Subscribe: www.youtube.com/@engineerprom...
📧 Business Contact: engineerprompt@gmail.com
💼Consulting: calendly.com/engineerprompt/c...
LINKS:
Google Colab: tinyurl.com/mr2mf65n
llama-Index RAG: • Talk to Your Documents...
How to chunk Documents: • LangChain: How to Prop...
llama-Index Github: github.com/jerryjliu/llama_index
TIMESTAMPS:
[00:00] Intro
[01:21] What are Embeddings
[03:58] How they Work!
[05:54] Custom Embeddings
[08:30] OpenAI Embeddings
[09:33] Open-Source Embeddings
[10:45] BGE Embeddings
[11:42] Instructor Embeddings
[11:57] Speed Benchmarking
Негізгі бет Ғылым және технология Understanding Embeddings in RAG and How to use them - Llama-Index
Пікірлер: 53