7. Embeddings in Depth - Part of the Ollama Course

Video Description

Dive into the world of embeddings and their crucial role in modern AI applications, particularly in enhancing search capabilities and information retrieval. This video, part of our comprehensive Ollama course, explains: - What embeddings are and how they differ from traditional text-matching searches - The importance of embeddings in Retrieval Augmented Generation (RAG) - How to create and use embeddings with Ollama's API - A practical comparison of different embedding models, including: - nomic-embed-text - mxbai-embed-large - all-minilm - snowflake-arctic-embed - bge-m3 - bge-large - llama3.1 We'll demonstrate real-world applications, discuss performance considerations, and explore the nuances of working with embeddings. Whether you're new to AI or looking to deepen your understanding, this video provides valuable insights into this powerful technology. Join us as we uncover how embeddings are transforming the way we interact with and retrieve information in the age of AI. #AI #MachineLearning #Embeddings #Ollama #InformationRetrieval My Links 🔗 👉🏻 Subscribe (free): https://www.youtube.com/technovangelist 👉🏻 Join and Support: https://www.youtube.com/channel/UCHaF9kM2wn8C3CLRwLkC2GQ/join 👉🏻 Newsletter: https://technovangelist.substack.com/subscribe 👉🏻 Twitter: https://www.twitter.com/technovangelist 👉🏻 Discord: https://discord.gg/uS4gJMCRH2 👉🏻 Patreon: https://patreon.com/technovangelist 👉🏻 Instagram: https://www.instagram.com/technovangelist/ 👉🏻 Threads: https://www.threads.net/@technovangelist?xmt=AQGzoMzVWwEq8qrkEGV8xEpbZ1FIcTl8Dhx9VpF1bkSBQp4 👉🏻 LinkedIn: https://www.linkedin.com/in/technovangelist/ 👉🏻 All Source Code: https://github.com/technovangelist/videoprojects Want to sponsor this channel? Let me know what your plans are here: https://www.technovangelist.com/sponsor 00:00 - Start 00:36 - There is another way 00:47 - Welcome to the course 01:25 - How do embeddings fit in 01:45 - What does the actual embedding 02:12 - Dimensions 02:39 - Similarity Search 03:39 - How to create the embedding 03:58 - The 3 endpoints 04:43 - The right endpoint to use 05:25 - Python and JS/TS libraries 05:54 - Let's look at a simple example 06:09 - The sample I used to embed 06:50 - Which is faster 07:34 - Let's look at the answers 08:55 - Where to find the example code 09:08 - Some of the variables to play with 09:32 - Frustrations